Crop Physiology Case Histories for Major Crops 0128191945, 9780128191941

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Crop Physiology

Case Histories for Major Crops

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Crop Physiology

Case Histories for Major Crops

Edited by

Victor O. Sadras

South Australian R&D Institute, and The University of Adelaide, Adelaide, SA, Australia

Daniel F. Calderini

Institute of Plant Production and Protection, Universidad Austral de Chile, Valdivia, Chile

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2021 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN 978-0-12-819194-1

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Contents Contributors xiii Preface xvii Acknowledgements xxi

1. Maize María E. Otegui, Alfredo G. Cirilo, Sergio A. Uhart, and Fernando H. Andrade 1 Introduction 3 1.1 Global trends 3 1.2 Main production areas 3 1.3 Maize in rotations: Suitability of previous and consequences for following crops 5 1.4 Multiple cropping 6 2 Crop structure, morphology, and development 6 2.1 Main phenological events 6 2.2 Genotypic and environmental drivers of maize development 8 3 Growth and resources 10 3.1 Capture and efficiency in the use of radiation 10 3.2 Capture and efficiency in the use of water 12 3.3 Capture and efficiency in the use of nutrients 17 4 Grain yield and quality 24 4.1 Kernel number 25 4.2 Kernel weight 26 4.3 Biomass partitioning 27 4.4 Grain quality 28 5 Concluding remarks: Challenges and opportunities 29 References 30

2. Rice Shu Fukai and Len J. Wade 1 Introduction 45 1.1 Global significance of rice 45 1.2 Rice ecosystem classification with emphasis on water availability 46 1.3 Crop management 47

2 Crop structure, morphology, and development 48 2.1 Germination and seedling emergence 48 2.2 Phenological development 50 2.3 Shoot development and growth 52 2.4 Rood development and growth 53 3 Growth and resources 54 3.1 Capture and efficiency in the use of radiation 54 3.2 Capture and efficiency in the use of water 56 3.3 Capture and efficiency in the use of nutrients 59 4 Yield and quality 65 4.1 Sink–source relations 65 4.2 Response to abiotic factors 71 4.3 Crop management for yield and quality 79 4.4 Mechanisation 83 5 Concluding remarks: Challenges and opportunities 84 5.1 Adaptation mechanisms to reduced water input in irrigated system 84 5.2 Adaptation mechanisms for drought avoidance in rainfed lowland rice 85 5.3 Adaptation mechanism for mechanised rice farming 85 5.4 Factors determining grain set 85 5.5 Enhancing yield potential 85 5.6 Head rice yield 86 Acknowledgement 86 References 86

3. Wheat Gustavo A. Slafer, Roxana Savin, Dante Pinochet, and Daniel F. Calderini 1 Introduction 99 1.1 Wheat origin, production, and yield 99 1.2 Trends in production, area, and yield 99 2 Crop structure, morphology, and development 101 2.1 Yield determination 101 2.2 Crop phenology 107 v

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3 Capture and efficiency in the use of resources 118 3.1 Capture and use efficiency of radiation 118 3.2 Capture and efficiency in the use of water 122 3.3 Capture and efficiency in the use of nutrients 126 4 Yield responsiveness to management and breeding 131 4.1 Yield responsiveness to management and breeding 131 5 Quality 139 5.1 Grain quality traits 139 5.2 Grain proteins, nutrients, fibre, and healthy traits 141 5.3 Sensitivity of grain quality traits to environmental stresses 141 5.4 Grain quality and crop management 142 6 Concluding remarks: Challenges and opportunities 144 References 145

4. Barley Daniel J. Miralles, L. Gabriela Abeledo, Santiago Alvarez Prado, Karine Chenu, Román A. Serrago, and Roxana Savin 1 Introduction 165 1.1 Global trends in harvested area and yield 165 2 Crop structure, morphology, and development 165 2.1 Differentiation of vegetative and reproductive organs 166 2.2 Dynamics of initiation and appearance of vegetative and reproductive organs 167 2.3 Genotypic and environmental drivers of barley development 170 3 Growth and resources 172 3.1 Capture and efficiency in the use of radiation 172 3.2 Capture and efficiency in the use of water 174 3.3 Capture and efficiency in the use of nutrients 179 3.4 Requirement of other nutrients 182 4 Grain yield and quality 183 4.1 Grain number and the critical period 183 4.2 Grain filling 184 4.3 Barley uses and grain quality 184 4.4 Environmental factors altering quality 186 4.5 Genetic factors determining malting and brewery quality 187

5 Concluding remarks: Challenges and opportunities 188 References 188

5. Sorghum Andrew Borrell, Erik van Oosterom, Barbara George-Jaeggli, Daniel Rodriguez, Joe Eyre, David J. Jordan, Emma Mace, Vijaya Singh, Vincent Vadez, Mike Bell, Ian Godwin, Alan Cruickshank, Yongfu Tao, and Graeme Hammer 1 Introduction 197 1.1 Agronomic context 197 2 Crop structure, morphology, and development 199 2.1 Crop phenology 199 2.2 Adaptation to environmental conditions 200 3 Growth and resources 200 3.1 Capture and efficiency in the use of radiation 200 3.2 Capture and efficiency in the use of water 204 3.3 Capture and efficiency in the use of nutrients 206 4 Yield and quality 210 4.1 Grain number and size 210 4.2 Grain quality 211 4.3 Crop stresses and effects on grain yield determination 212 5 Concluding remarks: Challenges and opportunities 213 5.1 Challenges and opportunities 213 5.2 Research priorities 214 References 214

6. Oat Bao-Luo Ma, Zhiming Zheng, and Changzhong Ren 1 Introduction 223 1.1 Production and nutrition 223 1.2 Agronomic roles of oat in farming systems 225 2 Crop structure, morphology, and development 226 2.1 Phenology and critical growth stages 226 2.2 Genotypic differences 227 2.3 Environmental effect 228 2.4 Manipulation of plant development 228 3 Growth and resources 228 3.1 Capture and efficiency in the use of radiation 228

Contents  vii

3.2 Capture and efficiency in the use of water 233 3.3 Capture and efficiency in the use of nutrients 235 4 Grain yield and quality 238 4.1 Grain yield and yield components 238 4.2 Grain quality 240 4.3 Forage quality 241 5 Concluding remarks: Challenges and opportunities 241 References 242

7. Quinoa H. Daniel Bertero 1 Introduction 251 2 Crop structure, morphology, and development 252 2.1 Seed germination and conservation 252 2.2 Phasic development 252 3 Growth and resources 254 3.1 Capture and efficiency in the use of radiation 254 3.2 Capture and efficiency in the use of water 256 3.3 Capture and efficiency in the use of nutrients 260 4 Yield and quality 268 4.1 Critical periods of yield determination 268 4.2 Dry matter and numeric yield components 269 4.3 Grain weight 269 4.4 Reproductive partitioning and limitations to grain yield 270 4.5 Other stresses and interactions between stresses 271 5 Grain quality 271 6 Concluding remarks: Challenges and opportunities 273 References 273

8. Soybean Patricio Grassini, Nicolas Cafaro La Menza, Juan I. Rattalino Edreira, Juan Pablo Monzón, Fatima A. Tenorio, and James E. Specht 1 Introduction 283 2 Crop structure, morphology, and development 288 3 Growth and resources 293 3.1 Capture and efficiency in the use of radiation 294 3.2 Capture and efficiency in the use of water 296

3.3 Capture and efficiency in the use of nitrogen 299 3.4 Dry matter and nitrogen partitioning 301 3.5 Other nutrients 302 4 Yield and quality 303 4.1 Yield potential and yield gaps 303 4.2 Seed quality 304 5 Concluding remarks: Challenges and opportunities 307 Acknowledgements 307 References 308

9. Field pea Lachlan Lake, Lydie Guilioni, Bob French, and Victor O. Sadras 1 Introduction 321 1.1 Origin and agronomy 321 1.2 Pests and diseases 322 2 Crop structure, morphology, and development 323 2.1 Seed and plant characteristics 323 2.2 Phenology 324 2.3 Critical period 326 3 Growth and resources 326 3.1 Capture and efficiency in the use of radiation 326 3.2 Capture and efficiency of use of water 328 3.3 Capture and efficiency in the use of nutrients 329 4 Yield and quality 331 4.1 Grain number and weight 331 4.2 Biomass and harvest index 333 4.3 Yield and quality trade-offs 334 5 Concluding remarks: Challenges and opportunities 334 Acknowledgements 334 References 334

10. Chickpea Vincent Vadez, Amir Hajjarpoor, Lijalem Balcha Korbu, Majid Alimagham, Raju Pushpavalli, Maria Laura Ramirez, Junichi Kashiwagi, Jana Kholova, Neil C. Turner, and Victor O. Sadras 1 Introduction and agronomic context 343 1.1 Origin and ecology 343 1.2 The role of chickpea in farming systems 343 2 Crop structure, morphology, and development 345 3 Growth and resources 345 3.1 Capture and efficiency in the use of radiation 345

viii  Contents

3.2 Capture and efficiency in the use of water 348 3.3 Capture and efficiency in the use of nutrients 350 4 Yield and quality 351 4.1 Yield and its components 351 4.2 Seed quality 352 5 Concluding remarks: Challenges and opportunities 353 References 353

11. Peanut Rao Rachaputi, Yashvir S. Chauhan, and Graeme C. Wright 1 Introduction 361 1.1 Area, production, and yield 361 2 Crop structure, morphology, and development 361 2.1 Sowing to emergence 362 2.2 Emergence to flowering 363 2.3 Flowering to maturity 363 2.4 Combining sowing date and genotype to match growing environment 365 3 Growth and resources 366 3.1 Capture and efficiency in the use of radiation 366 3.2 Capture and efficiency in the use of water 367 4 Capture and efficiency in the use of nutrients 368 4.1 Nitrogen 368 4.2 Calcium 369 4.3 Phosphorus 370 4.4 Zinc 370 5 Grain yield and quality 370 5.1 Grain yield 370 5.2 Seed quality 372 5.3 Trade-offs between yield and quality traits 375 6 Concluding remarks: Challenges and opportunities 375 Acknowledgements 376 References 376

12. Common bean Millicent R. Smith and Idupulapati M. Rao 1 Introduction 385 1.1 Climatic zones 385 1.2 Major growing regions 385 1.3 Role in farming systems 386 1.4 Implications of climate change 387 2 Crop structure, morphology and development 389 2.1 Morphological variation 389

2.2 Taxonomy and gene pools 389 2.3 Phenological development 390 2.4 Determinancy and growth habit 390 2.5 Critical stages of crop development 391 2.6 Strategies for adaptation to climate change 392 3 Growth and resources 392 3.1 Capture and efficiency in the use of radiation 392 3.2 Capture and efficiency in the use of water 394 3.3 Capture and efficiency in the use of nutrients 396 4 Yield and quality 398 4.1 Yield and related traits 398 4.2 Nutritional quality 400 5 Concluding remarks: Challenges and opportunities 400 Acknowledgements 401 References 401

13. Lentil Akanksha Sehgal, Kumari Sita, Abdul Rehman, Muhammad Farooq, Shiv Kumar, Rashmi Yadav, Harsh Nayyar, Sarvjeet Singh, and Kadambot H.M. Siddique 1 Introduction 409 2 Crop structure, morphology, and development 410 2.1 Crop structure: height and branching 410 2.2 Phenological development 411 2.3 Development and adaptation to stress 412 3 Growth and resources 415 3.1 Capture and efficiency in the use of radiation 415 3.2 Capture and efficiency in the use of water 415 3.3 Capture and efficiency in the use of nutrients 417 4 Yield and quality 418 4.1 Reproductive development 418 5 Concluding remarks: Challenges and opportunities 421 References 421

14. Lupin Alejandro del Pozo and Mario Mera 1 Introduction 431 2 Crop structure, morphology, and development 432 2.1 Crop development 433 2.2 Branching patterns 434

Contents  ix

2.3 Use of restricted branching in lupin breeding 436 2.4 Dwarfism 437 3 Growth and resources 438 3.1 Capture and efficiency in the use of radiation 438 3.2 Capture and efficiency of use of water 440 3.3 Capture and efficiency of use of nutrients 441 4 Yield and quality 443 4.1 Yield 443 4.2 Yield components 443 4.3 Pod wall 444 4.4 Grain protein 444 5 Concluding remarks: Challenges and opportunities 445 Acknowledgement 445 References 445

15. Faba bean M. Inés Mínguez and Diego Rubiales 1 Introduction 453 1.1 Origin of the crop 453 1.2 Cropping environment and production 454 2 Crop structure, morphology, and development 455 2.1 Crop structure 455 2.2 Vegetative and reproductive responses 458 2.3 Quantifying phenological development 459 3 Growth and resources 461 3.1 Capture and efficiency in the use of radiation 461 3.2 Capture and efficiency in the use of water 464 3.3 Capture and efficiency in the use of nutrients 466 4 Yield and quality 469 4.1 Crop yield 469 4.2 Yield components 470 4.3 Indeterminate, determinate, and semideterminate cultivars 471 4.4 Nutritional issues and grain quality 472 4.5 Role of faba bean in cropping system productivity 472 4.6 Biotic stresses 473 5 Concluding remarks: Challenges and opportunities 474 5.1 Maintaining yield gain 475 5.2 Optimal cultivar design 475 5.3 Intercropping 475 5.4 Coordination of faba bean research 476 Acknowledgements 477 References 477

16. Sunflower Philippe Debaeke and Natalia G. Izquierdo 1 Introduction 483 1.1 The role of sunflower crop in farming systems 483 1.2 Sunflower-based cropping systems 484 1.3 Implications of climate change for sunflower cropping 484 1.4 Sunflower crop physiology research since the 1980s 485 2 Crop structure, morphology and development 485 2.1 Growth stages and phenophases 485 2.2 Drivers of crop phenology and development 487 2.3 Manipulation of crop development to match critical periods and environments 487 3 Growth and resources 488 3.1 Capture and efficiency in the use of radiation 488 3.2 Capture and efficiency in the use of water 492 3.3 Capture and efficiency in the use of nutrients 495 4 Yield 497 4.1 Frameworks of yield elaboration 497 4.2 Allocation of dry matter 497 4.3 Components of grain yield 498 5 Grain and oil quality 500 5.1 Physiology of oil accumulation 500 5.2 Factors affecting oil concentration effects on oil concentration 501 5.3 Sunflower oil quality 502 6 Concluding remarks: Challenges and opportunities 504 References 505

17. Canola John A. Kirkegaard, Julianne M. Lilley, Peter M. Berry, and Deborah P. Rondanini 1 Introduction 519 1.1 Origin, development and uses 519 1.2 Global production systems 519 1.3 Canola cropping systems 523 1.4 Agronomic implications of predicted climate change 524 2 Crop structure, morphology and development 526 2.1 Phenological stages 526 2.2 Impact of development on yield potential and adaptive management 527

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2.3 Matching sowing date with varietal phenology in diverse environments 529 3 Growth and resources 530 3.1 Capture and efficiency in the use of radiation 530 3.2 Capture and efficiency in the use of water 533 3.3 Capture and efficiency in the use of nutrients 535 4 Yield and quality 536 4.1 Allocation of dry matter 536 4.2 Yield components 538 4.3 Seed quality—Oil and protein 540 5 Concluding remarks: Challenges and opportunities 542 References 543

18. Potato X. Carolina Lizana, Patricio Sandaña, Anita Behn, Andrea Ávila-Valdés, David A. Ramírez, Rogério P. Soratto, and Hugo Campos 1 Introduction 551 1.1 Potato production systems 551 1.2 Climate change 553 2 Crop structure, morphology, and development 554 2.1 Drivers of potato development 556 3 Growth and resources 559 3.1 Capture and efficiency in the use of radiation 559 3.2 Capture and efficiency in the use of water 562 3.3 Capture and efficiency in the use of nutrients 565 4 Yield and quality 574 4.1 Yield 574 4.2 Quality 575 5 Conclusion: Challenges and opportunities 577 References 578

19. Cassava James H. Cock and David J. Connor 1 Introduction 589 1.1 Origin of the crop 589 1.2 Production environment 589 1.3 Cassava production 590 1.4 Role in the rural economy 591 1.5 Cassava in cropping systems 591 2 Crop structure, morphology and development 592 2.1 Crop structure 592 2.2 Stem cuttings 592

2.3 Flower induction and branching 592 2.4 Production of nodal units 593 2.5 Leaves 594 2.6 Stomates 595 2.7 Tuber formation and growth 596 3 Growth and resources 597 3.1 Capture and efficiency in use of radiation 597 3.2 Capture and efficiency in use of water 600 3.3 Capture and efficiency in use of nutrients 606 4 Yield and quality 612 4.1 Yield formation in cassava 613 4.2 Optimal design for high yield and stability 614 4.3 Yield progress 615 4.4 Potential yield and yield-gap analysis 616 4.5 Nutrient management for sustainable yield 619 4.6 Yield and production prospects under climate change 621 5 Concluding remarks: Challenges and opportunities 625 5.1 A two-part future 625 5.2 Reduced production costs and greater labour productivity 625 5.3 Super high-yielding cultivars for favourable areas 626 5.4 High-yielding cultivars for droughtprone areas 626 5.5 General considerations for field research 627 5.6 Conceptualising knowledge 628 5.7 Closing comment 628 References 628

20. Sugar beet Christa M. Hoffmann, Heinz-Josef Koch, and Bernward Märländer 1 Introduction 635 1.1 Commodity sugar 635 1.2 History 635 1.3 The crop 636 1.4 Breeding—G × E effect 637 1.5 Seed production 637 1.6 Cultivation and management 638 1.7 Winter beet cultivation 639 1.8 Growers’ management—G × E × M effect 639 2 Crop structure, morphology, and development 640 2.1 Emergence 640 2.2 Bolting 641 2.3 Leaf and canopy formation 641 2.4 Storage root development 642

Contents  xi

2.5 2.6 2.7 2.8 2.9

Cambium ring formation 644 Sugar storage 645 Assimilate partitioning 645 Limitations: Regulation of partitioning 648 Implications of a sink limitation for breeding and management 648 2.10 Genotype by environment interactions 649 2.11 Temperature stress 650 2.12 Manipulation of crop development as an adaptation to climate change 650 3 Growth and resources 651 3.1 Capture and efficiency in the use of radiation 651 3.2 Capture and efficiency in the use of water 652 3.3 Capture and efficiency in the use of nutrients 655 4 Yield and quality 660 4.1 Yield and quality traits 660 4.2 Impact on quality 661 4.3 Sugar beet yield types 661 4.4 Improvements through breeding 661 4.5 Sugar beet storage 663 5 Concluding remarks: Challenges and opportunities 663 Author contributions 664 References 664

21. Sugarcane Abraham Singels, Phillip Jackson, and Geoff Inman-Bamber 1 Introduction 675 2 Crop structure, morphology, and development 677 2.1 Germination 677 2.2 Tillering 678 2.3 Stalk elongation 679 2.4 Flowering and seed formation 679 2.5 Maturation 680

3 Growth and resources 680 3.1 Capture and efficiency in the use of radiation 680 3.2 Capture and efficiency in the use of water 688 3.3 Capture and efficiency in the use of nutrients 693 4 Yield and quality 697 4.1 Whole plant biomass partitioning 699 4.2 Internode sucrose accumulation 700 4.3 Agronomic management to maximise sucrose yields 701 4.4 Breeding for high sucrose yields 701 4.5 Climate change impacts on yield 703 5 Concluding remarks: Challenges and opportunities 703 References 705

22. Cotton John Snider, Mike Bange, and Jim Heitholt 1 Introduction 715 2 Crop structure, morphology, and development 716 2.1 Developmental phases 716 2.2 General considerations 721 3 Growth and resources 721 3.1 Capture and efficiency in the use of radiation 721 3.2 Water use efficiency 728 3.3 Capture and efficiency in the use of nutrients 730 4 Yield and quality 733 4.1 Genotype 733 4.2 Production environment 734 5 Concluding remarks: Challenges and opportunities 739 References 740 Index 747

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Contributors Numbers in parentheses indicate the pages on which the authors’ ­contributions begin.

L. Gabriela Abeledo  (165), Department of Plant Production, School of Agriculture, University of Buenos Aires, Buenos Aires; CONICET, Buenos Aires, Argentina Majid Alimagham  (343), Institute for Research and Development (IRD), UMR DIADE, University of Montpellier, Montpellier, France Fernando H. Andrade  (3), INTA-National University of Mar del Plata and CONICET, Balcarce, Buenos Aires, Argentina Andrea Ávila-Valdés  (551), Graduate School, Faculty of Agricultural Sciences, Austral University of Chile, Campus Isla Teja; Research Center in Volcanic Soils, Austral University of Chile, Valdivia, Chile Mike Bange  (715), Grains Research and Development Corporation, Toowoomba, QLD, Australia Anita Behn  (551), Institute of Plant Production and Protection, Austral University of Chile, Valdivia, Chile Mike Bell  (197), University of Queensland, School of Agriculture and Food Sciences, Gatton, QLD, Australia Peter M. Berry  (519), ADAS, High Mowthorpe, North Yorkshire, United Kingdom H. Daniel Bertero (251), Department of Plant Production, School of Agriculture, University of Buenos Aires; IFEVA-CONICET, Buenos Aires, Argentina Andrew Borrell  (197), University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Warwick, QLD, Australia

X. Carolina Lizana (551), Institute of Plant Production and Protection; Research Center in Volcanic Soils, Austral University of Chile, Valdivia, Chile Yashvir S. Chauhan  (361), Department of Agriculture and Fisheries, Kingaroy, QLD, Australia, Queensland Alliance for Agriculture and Food Innovation (QAAFI), University of Queensland, Australia Karine Chenu (165), University of Queensland, Brisbane, QLD, Australia Alfredo G. Cirilo  (3), Department of Crop Production and Environmental Management, INTA Experimental Station, Pergamino, Argentina James H. Cock (589), Emeritus, The International Center for Tropical Agriculture (CIAT), Palmira, Colombia David J. Connor  (589), Department of Agriculture and Food, The University of Melbourne, Melbourne, VIC, Australia Alan Cruickshank  (197), Agri-Science Queensland, Department of Agriculture and Fisheries, Warwick, QLD, Australia Philippe Debaeke (483), INRAE (National Research Institute for Agriculture, Food and Environment), Université de Toulouse, UMR AGIR, Castanet-Tolosan, France Alejandro del Pozo (431), Plant Breeding and Phenomics Center, Faculty of Agricultural Science, University of Talca, Talca, Chile Joe Eyre  (197), University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Gatton, QLD, Australia

Daniel F. Calderini (99), Institute of Plant Production and Protection, Universidad Austral de Chile, Valdivia, Chile

Muhammad Farooq (409), Department of Crop Sciences, College of Agricultural and Marine Sciences, Sultan Qaboos University, Muscat, Oman; Department of Agronomy, University of Agriculture, Faisalabad, Pakistan; The UWA Institute of Agriculture and School of Agriculture & Environment, The University of Western Australia, Perth, WA, Australia

Hugo Campos  (551), International Potato Center, Lima, Peru

Bob French (321), Department of Primary Industries and Regional Development, Merredin, WA, Australia

Nicolas Cafaro La Menza (283), Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States

xiii

xiv   Contributors

Shu Fukai  (45), University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD, Australia

Lijalem Balcha Korbu  (343), Ethiopian Institute of Agricultural Research (EIAR), Debre Zeit Research Center, Debre Zeit, Ethiopia

Barbara George-Jaeggli (197), University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Agri-Science Queensland, Department of Agriculture and Fisheries, Warwick, QLD, Australia

Shiv Kumar  (409), International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco

Ian Godwin (197), University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD, Australia Patricio Grassini  (283), Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States Lydie Guilioni (321), L’Institut Agro, Montpellier SupAgro, Department of Biology and Ecology, Montpellier, France Amir Hajjarpoor  (343), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Crop Physiology Laboratory, Patancheru, Telangana, India Graeme Hammer  (197), University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD, Australia Jim Heitholt  (715), Department of Plant Sciences, University of Wyoming, Powell, WY, United States Christa M. Hoffmann  (635), Institute of Sugar Beet Research, Göttingen, Germany Geoff Inman-Bamber  (675), College of Science, Technology and Engineering, James Cook University, Townsville, QLD, Australia Natalia G. Izquierdo  (483), CONICET - IIDEAGROS, Facultad de Ciencias Agrarias, Universidad Nacional de Mar del Plata, Balcarce, Argentina Phillip Jackson  (675), CSIRO Agriculture and Food, Australian Tropical Science Innovation Precinct, James Cook University, Townsville, QLD, Australia David J. Jordan  (197), University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Warwick, QLD, Australia Junichi Kashiwagi  (343), Crop Science Lab., Research Faculty of Agriculture, Hokkaido University, Sapporo, Japan Jana Kholova (343), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Crop Physiology Laboratory, Patancheru, Telangana, India John A. Kirkegaard (519), CSIRO Agriculture and Food, Canberra, ACT, Australia Heinz-Josef Koch (635), Institute of Sugar Beet Research, Göttingen, Germany

Lachlan Lake (321), South Australian R&D Institute, and The University of Adelaide, Adelaide, SA, Australia Julianne M. Lilley  (519), CSIRO Agriculture and Food, Canberra, ACT, Australia Bao-Luo Ma  (223), Agriculture and Agri-Food Canada, Ottawa Research and Development Centre (ORDC), Ottawa, ON, Canada Emma Mace (197), University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Agri-Science Queensland, Department of Agriculture and Fisheries, Warwick, QLD, Australia Bernward Märländer  (635), Institute of Sugar Beet Research, Göttingen, Germany Mario Mera (431), Department of Agricultural Production, Faculty of Agricultural and Forestry Sciences, University La Frontera, Temuco, Chile M. Inés Mínguez  (453), Department of Agricultural Production, School of Agricultural Engineering, Food Technology and Biosystems, and Research Centre for the Management of Agricultural and Environmental Risks; Technical University of Madrid (UPM), Madrid, Spain Daniel J. Miralles (165), Department of Plant Production, School of Agriculture; University of Buenos Aires and IFEVA-CONICET, Buenos Aires, Argentina Juan Pablo Monzón (283), Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States; CONICET, Balcarce, Buenos Aires, Argentina Harsh Nayyar  (409), Department of Botany, Panjab University, Chandigarh, India Erik van Oosterom  (197), University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD, Australia María E. Otegui  (3), Department of Plant Production, School of Agriculture, CONICET at INTA Pergamino Experimental Station and University of Buenos Aires, Buenos Aires, Argentina Dante Pinochet (99), Institute of Agricultural Engineering and Soil Science, Universidad Austral de Chile, Valdivia, Chile Santiago Alvarez Prado  (165), Department of Plant Production, School of Agriculture, University of Buenos Aires, Buenos Aires; CONICET, Buenos Aires; IFEVA, Buenos Aires, Argentina

Contributors    xv

Raju Pushpavalli  (343), CSIRO Agriculture and Food, Floreat, WA, Australia Rao Rachaputi (361), Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Gatton, Australia David A. Ramirez  (551), International Potato Center, Lima, Peru Maria Laura Ramirez  (343), Mycology and Mycotoxicology Research Institute (UNRC-CONICET), Río Cuarto, Córdoba, Argentina Idupulapati M. Rao  (385), International Center for Tropical Agriculture (CIAT), Cali, Colombia Juan I. Rattalino Edreira (283), Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States Abdul Rehman  (409), Department of Crop Sciences and Biotechnology, Dankook University, Cheonan-si, Korea Changzhong Ren (223), Baicheng Academy of Agricultural Sciences, Baicheng, China Daniel Rodriguez  (197), University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Gatton, QLD, Australia Deborah P. Rondanini  (519), Universidad de Buenos Aires, Buenos Aires, Argentina Diego Rubiales (453), Institute for Sustainable Agriculture, CSIC, Córdoba, Spain Victor O. Sadras (321, 343), South Australian R&D Institute, and The University of Adelaide, Adelaide, SA, Australia Patricio Sandaña (551), Institute of Plant Production and Protection; Research Center in Volcanic Soils, Austral University of Chile, Valdivia, Chile Roxana Savin  (99, 165), Department of Crop and Forest Sciences, University of Lleida—AGROTECNIO Center, Lleida, Spain Akanksha Sehgal  (409), Department of Plant and Soil Science, Mississippi State University, Starkville, MS, United States Román A. Serrago (165), Department of Plant Production, School of Agriculture, University of Buenos Aires, Buenos Aires; CONICET, Buenos Aires, Argentina Kadambot H.M. Siddique  (409), The UWA Institute of Agriculture and School of Agriculture & Environment, The University of Western Australia, Perth, WA, Australia Abraham Singels (675), South African Sugarcane Research Institute, Mount Edgecombe; Department of Plant and Soil Sciences, University of Pretoria, Pretoria, South Africa Sarvjeet Singh  (409), Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, India

Vijaya Singh (197), University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD, Australia Kumari Sita  (409), Institute of Himalayan Bioresource Technology (IHBT), Palampur, India Gustavo A. Slafer  (99), ICREA, Catalonian Institution for Research and Advanced Studies, and Department of Crop and Forest Sciences, University of Lleida— AGROTECNIO Center, Lleida, Spain Millicent R. Smith  (385), School of Agriculture and Food Sciences, University of Queensland; Queensland Alliance for Agriculture and Food Innovation (QAAFI), Gatton, QLD, Australia John Snider (715), Department of Crop and Soil Sciences, University of Georgia, Tifton, GA, United States Rogério P. Soratto (551), College of Agricultural Sciences and Center of Tropical Roots and Starches, São Paulo State University (UNESP), Botucatu, SP, Brazil James E. Specht  (283), Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States Yongfu Tao (197), University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Warwick, QLD, Australia Fatima A. Tenorio  (283), Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States Neil C. Turner  (343), School of Agriculture and Environment, Faculty of Science, The University of Western Australia, Perth, WA, Australia Sergio A. Uhart  (3), Faculty of Agronomy, National University of the Northeast, Santiago del Estero, Argentina Vincent Vadez  (197, 343), Institute for Research and Development (IRD), UMR DIADE, University of Montpellier, Montpellier, France; International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Crop Physiology Laboratory, Patancheru, Telangana, India Len J. Wade  (45), University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD, Australia Graeme C. Wright  (361), Peanut Company of Australia, Kingaroy, Queensland, Australia Rashmi Yadav  (409), ICAR-National Bureau of Plant Genetic Resources, New Delhi, India Zhiming Zheng (223), Agriculture and Agri-Food Canada, Ottawa Research and Development Centre (ORDC), Ottawa, ON, Canada

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Preface In 2009 we published the first edition of Crop Physiology: Applications for Genetic Improvement and Agronomy, with a ­second edition in 2015. The premise of these books was that farmers rely primarily on two technological outputs: varieties and practices. Plant breeding and agronomy are, therefore, the two technologies with immediate application. Hence crop physiology is most valuable when it engages meaningfully with breeding and agronomy. This remains the premise of this book. Our previous books were structured around processes—development, the carbon, water, and nitrogen economies of crops, and the formation of yield in the context of cropping systems. In Hall (2018) updated his crop physiology book with a focus on processes in a timely context of climate change. With at least three process-based books between 2009 and 2018, we felt it was time for an alternative, albeit not new, perspective. In this book, we follow Evans (1975) scheme of revisiting physiological processes for individual crops. Chapters open with a crop-specific agronomic context and deal with development, the carbon, water, and nitrogen economies of individual crops, the formation of yield, and aspects of quality. The first group of chapters deals with cereals—maize, rice, wheat, barley, sorghum, and oat. Maize, rice, and wheat provide most of the energy and a good part of protein to human diets worldwide, and improvements in the yield of these crops are the core of food security for the foreseeable future. Barley and sorghum are mostly feed grains supporting the increasing demand for animal products in increasingly wealthy segments of some societies. Wheat, barley, and oat have a similar critical period when grain number, and in turn yield, is more vulnerable to stress (Fig. 1). Oat has become a niche crop after a peak in the early 20th century when mechanical engines replaced horses in transport and industries, including agriculture (Fig. 2); this highlights the disruptive nature of any meaningful technology. In common with quinoa, there is a potential for larger acreages of oat motivated by health and nutrition drivers, but the future of both crops is uncertain beyond their present niche. Physiologically, maize and sorghum share a C4 metabolism and are particularly adapted to warmer environments. However, maize and sorghum differ in the reproductive biology; owing to its lower apical dominance, tillering makes sorghum more comparable to rice and wheat. The second group of chapters deals with grain legumes. Soybean stands out as the world’s largest source of vegetal protein (Fig. 3a) and is critical to food security together with the three big cereals and potato. Common aspects of crop physiology in grain legumes include a typical critical period for yield response to stress (Fig. 1). In comparison to cereals, the critical window is delayed towards pod set and grain fill in legumes. Two main reasons underlie the difference in the critical period between small-grain cereals and grain legumes. Firstly, flowering is a convergent processes of development across stems from different categories in cereals. In comparison, flowering in pulses refers to the first flower on the main branch. The second reason, linked with the previous, is the contrasting flowering biology between determinate cereals and the extended flowering period of semi-determinate or indeterminate crops like grain legumes and canola. The resulting approximate 40 days difference in the timing of maximum grain yield sensitivity to stress supports a genuine physiological contrast between grain legumes and cereals with agronomic implications. Other common aspects are nitrogen fixation and implications for the role of legumes in cropping systems and qualitative (determinate vs. indeterminate nodules) and quantitative idiosyncratic features (e.g. degree of inhibition of nitrogen fixation by soil mineral nitrogen). The pulses are increasingly important but remain minor crops (Fig. 3b). As expected from the magnitude of the crop production, research efforts lag in other grain legumes relative to soybean but with no apparent proportionality, whereas current production of soybean is 8 times larger than the production of peanut and 20 times larger than the production of chickpea; a coarse measure of research output is only 5 times larger (Fig. 3c). Quantifying the gap between actual production and research aimed at improved production warrants investigation. Peanut sets pods underground. Because pods do not transpire, they do not receive xylem-transported Ca from the roots but absorb Ca directly from the surrounding soil through mass flow. These physiological traits have unique implications for calcium management in peanut. Sunflower, canola, and cotton feature oil-reach seeds with physiological implications (e.g. lower radiation use efficiency than seed crops with starchy grain), some similarities (e.g. influence of temperature on fatty acid composition in seed oil

xvii

xviii  Preface

FIG. 1  Annual grain crops have species-specific critical windows for yield determination when grain set is more sensitive to stress. Circles represent small-grain cereals (wheat, barley, and oat), and squares are for pulses and canola. As an example of a non-grain crop for comparison, triangles are for potato. Summary of small-grain cereals and pulses is from Sadras, V.O., Dreccer, M.F., 2015. Adaptation of wheat, barley, canola, field pea and chickpea to the thermal environments of Australia. Crop Past. Sci. 66, 1137–1150; and potato from Sandaña and Lizana, data not published.

Oat acreage in US (thousands of acres)

700

Ford Motor Company funded Ford T released First moving assembly line

600 500 400 300 200 100 0

1860 1880 1900 1920 1940 1960 1980 2000 2020

Year FIG.  2  An example of technological disruption: time-trend of oat acreage in the USA in relation to milestones in the automobile industry. Sources: FAOSTAT and Wikipedia.

reserves), and contrasting seasonality and roles in cropping systems. Sunflower acreage and research effort is decreasing in contrast to the worldwide expansion of canola. Potato, cassava, sugar beet, and sugar cane are grown for carbohydrate-rich vegetative organs. They share a lack of (or tenuous) critical period in comparison to a marked sensitive stage in seed crops (Fig. 1). The potential decoupling of growth and reproduction is therefore not an issue in these species, but some physiologically interesting and agronomically important trade-offs require attention, e.g. between expansive growth and storage of sucrose in sugar cane. In contrast to many other plant species, which exclude sodium, sugar beet is a halophyte that absorbs and utilises it and can respond positively to sodium fertilisation. Victor O. Sadras Daniel F. Calderini

Preface  xix

FIG. 3  Time trend of (a) global production of soybean, (b) global production of common bean, peanut, and chickpea, and (c) scientific papers on chickpea and peanut. (b) and (c) are soybean-to-other-crop ratio. Ratios in (c) are calculated from number of papers per decade with ‘crop’ and ‘yield’ in title from Web of Science, where ‘crop’ is ‘soybean’, ‘chickpea’, and ‘peanut or groundnut’. Sources: (a, b) FAOSTAT.

References Evans, L.T., 1975. Crop Physiology: Some Case Histories. Cambridge University Press, Cambridge. 374 pp. Hall, A.E., 2018. Crop Responses to Environment. Adapting to Global Climate Change. CRC Press, Boca Raton.

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Acknowledgements Our most sincere appreciation to the authors for their expertise, time, and understanding. We thank our host organisations, the South Australian Research and Development Institute, and Universidad Austral de Chile, for their support with this project. We thank Elsevier and Academic Press teams Nancy Maragioglio, Rafael Trombaco, Kavitha Balasundaram, and Paul Prasad for their professional support. We thank Victoria Abarzúa and Gabriela Carrasco-Puga for logistics and revision of references. We thank the publishers who permitted reproduction of previously published material. To Ana and Magda for their love, support, and patience. The editors

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Image source: Authors

Chapter 1

Maize María E. Oteguia, Alfredo G. Cirilob, Sergio A. Uhartc, and Fernando H. Andraded a

Department of Plant Production, School of Agriculture, CONICET at INTA Pergamino Experimental Station and University of Buenos Aires, Buenos Aires, Argentina, bDepartment of Crop Production and Environmental Management, INTA Experimental Station, Pergamino, Argentina, cFaculty of Agronomy, National University of the Northeast, Santiago del Estero, Argentina, dINTA-National University of Mar del Plata and CONICET, Balcarce, Buenos Aires, Argentina

1 Introduction 1.1  Global trends Native to the New World, maize is the main staple for a large part of humankind, being especially important for several Latin American and African countries. In these countries, maize can be consumed as porridge (such as grits, polenta, or ugali), popcorn, roasted kernels, vegetable (fresh, frozen, or canned sweet maize), flour, or meal (bread, tortillas, chips, extruded snacks, etc.). Worldwide, its grains are used to produce ethanol (for beverages or as a fuel source), cooking oil, and starch. Grains and by-products are also used as animal feed, whereas its biomass is an energy source and is used as silage. Despite its broad distribution across all continents (Fig. 1.1a left), four of the top ten maize producing countries (Fig. 1.1b left) are in the Americas, where it covers the largest area worldwide. Maize is well-adapted around the world and returns high yields (Fig. 1.1c left). Low technological development sets a limit to crop yield in otherwise suitable environments (e.g. Africa). Land cropped to maize increased at a rate of close to 1 Mha y− 1 between 1961 and 2005 (Fig. 1.1a right) and increased to over 4 Mha y− 1 afterwards. A good part of the change has been driven by crop substitution, such as rice by maize in China. Variations in area produced similar variations in overall production (Fig.  1.1b right), with rates that increased from 10.5 to 34 M t y− 1 in almost the same period (breakpoint in 2003). Contrarily, global grain yield (GY) increased at a constant rate of 66 kg ha− 1 y− 1 for the whole period. Not surprisingly (Hall and Richards, 2013), outstanding breeding events such as the delivery of Bt corn to market in 1996 did not produce equivalent landmarks in maize production during the evaluated period. Climate extremes, such as drought or heat stress, can lead to harvest failures and threaten the livelihood of agricultural producers and the food security of communities. Improving the understanding of their impacts on maize GY is crucial to enhance the resilience of the global food system. Climate factors, including mean climate and climate extremes, explain 16%– 39% of the variance of yield anomalies (YA), with 10%–31% of the explained variance attributable to climate conditions (Fig. 1.2). YA related more closely with temperature extremes than with precipitation-related factors (Vogel et al., 2019). The forecast for future scenarios is a loss of climatic suitability for maize in Sub-Saharan Africa and Latin America regions but accompanied by an expansion in the northern hemisphere, particularly in Europe. The relative change in climatically suitable areas for future maize production was estimated for the top five producers. Production in 2050 is expected to increase 8% for the USA and 4% for China, and to decrease 5% for Brazil, 2% for Argentina, and 11% for México. The incidence of low temperature and waterlogging, presently common in Europe and Asia, is projected to diminish, whereas heat stress in Africa and drought stress in South America are projected to increase (Ramirez-Cabral et al., 2017).

1.2  Main production areas The USA is the world leader in annual maize production, with 38.9 Mha (Fig. 1.1a) that produce 392 Mt (Fig. 1.1b). Twenty percent of the production is exported. After the introduction of teosinte in the USA territory thousands of years ago, many native communities adopted it as a staple crop as far north as Canada, reaching a production of ca. 13.9 M t y− 1. First European settlers to arrive to Western USA learned maize cultivation from native Americans and spread the crop eastwards. Presently, maize is produced in a variable proportion in most USA states, Iowa being the leader in total production and GY (Fischer and Edmeades, 2010), closely followed by Illinois and Nebraska. Crop Physiology: Case Histories for Major Crops. https://doi.org/10.1016/B978-0-12-819194-1.00001-3 Copyright © 2021 Elsevier Inc. All rights reserved.

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4  Crop Physiology: Case Histories for Major Crops

200

Production (M t)

(b)

160

1000 750

10.5 M t y–1

500

2003

250

4000 66 kg ha–1 y–1 2000

³12 t ha–1

0

34 M t y–1

6000 Yield (kg ha–1)

(c)

2005

120

³335 M t

0

0.88 M ha y–1

140

100 1250

³37 M ha

0

4.36 M ha y–1

180

Area (M ha)

(a)

0 1960

1980 Year 2000

2020

60 50

Mean

SD

8.5

40 30 20

4.9

45 11.3

26.5

10

(a)

0

North America

Asia

8.6

12.6

8.8

Europe

South America

6.7

7

Africa

Regional Production Shares (%)

Regional Production Shares (%)

FIG. 1.1  Left panels: world distribution (averaged for the 2008–17 period) of (a) area cropped to maize, (b) production, and (c) GY. Right panels: evolution of annual records of each variable for the 1961–2017 period. Lines represent (a and b) bilinear and (c) linear fitted models. The corresponding breakpoints (vertical arrows in a and b) and slopes (all variables) are indicated. Based on FAO, 2019. Records accessed on 1st Aug 2019, http://www.fao. org/faostat/en/#data/QC.

YA

140 120

7.2

100

38.5

(b)

4.8

MCC

37.6

32.5

80

MCC+EE

22.4

6.4

16.3

6.5

10

60 40

82.3

71.3

79.7

Asia

Europe

65.8

69

South America

Africa

20 0

North America

FIG. 1.2  (a) Mean regional production shares per continent during 1990–2008 and standard deviation (SD) of production anomalies (after detrending) relative to mean production and (b) Explained variance of production shares accounted for yield anomalies (YA), mean climate conditions plus extreme events (MCC + EE), and mean climate conditions (MCC). Adapted from Vogel, E., Donat, M.G., Alexander, L.V, Meinshausen, M., Ray, D.K., Karoly, D., Meinshausen, N., Frieler, K., 2019. The effects of climate extremes on global agricultural yields. Environ. Res. Lett. 14, 054010, https://doi.org/10.1088/1748-9326/ab154b.

China is the second global maize producer (Fig. 1.1b). Over the past few years, maize has become the main crop of the country, both in production (257 M t y− 1) and cultivated land (42.4 Mha− 1). This change is attributed to the increasing local demand for livestock feed (i.e. animal protein). A greater proportion of urban, wealthier population has driven consumption of meat products per capita to increase at a rate of 1.38 kg y− 1 since 2011. Over the past 25 years, maize production has increased 125%, while rice production has experienced a modest 7% increase. While in the 1940s, two-thirds of the Chinese maize crop production were used for human consumption, presently, 60% is used as animal feed, 10% for direct human consumption, and the remaining for industry. Brazil is the third world maize producer, with nearly 94.5 M t annually and 17.3 Mha. This country is consolidating as the second main exporter. The production comes from two contrasting systems. One is called safra (full season), characterised by sowing starting early in August up to October–November. The other is called safrinha that presently occupies

Maize Chapter | 1  5

two-thirds of total area cropped to maize. The safrinha corresponds to late-sown maize (starting in January up to February), usually rotated with soybean to avoid fast soil nitrogen (N) decay. It predominates in the Mato Grosso states at the CentreWest of the country, which have displaced the traditional maize producing area located in the south–south east. The fourth maize producer is Argentina, accounting for approximately 6.4 Mha and reaching 51 M t y− 1 in 2018–19. Argentina is presently the third exporter, close to Brazil. Like in the neighbour country, there are two main sowing dates: the early one that ranges between late August and early November and the late one from December to end of January. Delayed sowing became economically feasible with the introduction of Bt maize in 1997 (Otegui et al., 2002) and gained fast adoption after the severe drought of 2008–09. It presently occupies 45%–55% of the total area. Córdoba, in the geographical centre of the country, is presently the main maize producer province of Argentina. The EU produces 61 M t of maize per year. France, chiefly in the south, leads maize production within the EU. The Aquitania region delivers 21% of overall France production, which is presently 14 M t y− 1 in 1.7 Mha. The crop is generally sown between April and May and is harvested between September and November. Owing to low local consumption, most of the French production is exported. Other important maize producers in the EU are Romania, Hungary, Spain, Germany, and Poland. Ukraine, along with Russia, is the principal maize producer in the Black Sea region. Ukraine has vast sectors of black soils known as chernozems, which are among the most fertile in the world. Ukrainian growers produce 32 M t of maize annually in 4.3 Mha. In 2018–19, Ukraine accounted for 16% of global maize exports, becoming the fourth exporter. India produces 28 M t of maize annually in 9.2 Mha. The crop is primarily grown in the northern states, including Uttar Pradesh, Bihar, Himachal Pradesh, and Rajasthan. Uttar Pradesh and Bihar account for nearly 16% and 14% of the country’s maize production, respectively. The crop is usually sown at the beginning of the rainy season, between mid-May and July, and is harvested between November and January. Maize is the most important crop grown in Mexico, with almost 60% of the country’s cropland from sea level up to 2600 masl. Occident, Bajio, and Sinaloa regions contribute with nearly 60% of total production. Climate allows for two harvests per year, which in 2018–19 produced 27 M t in an area of 7.4 Mha. The first sowing takes place in spring–summer (April–July) and accounts for 75% of the annual production. The remaining 25% corresponds to the second one, which takes place during autumn–winter and is conducted under irrigation. Mexico has no export surplus but presently meets the internal demand of white maize for human consumption plus a small part for animal feed. However, it still needs to import 14 M t of maize for its growing livestock sector. Indonesia is the leading maize producer (26 M t in 5.3 Mha) in the ASEAN Economic Community, followed by the Philippines (7.5 M t) and Vietnam (5.2 M t). Indonesia needs to import maize, primarily for livestock. The main constraint to Indonesian production is the lack of enough agricultural land, which is balanced with forested lands. South Africa produced 16 M t of maize in 2.6 Mha during 2018–19. It is one of the main African producers together with Nigeria, Egypt, and Ethiopia (10 M, 8 M, and 7.8 M t, respectively). The crop is cultivated primarily in the north and north-east of the country. The South African provinces of Guateng, north-west, Mpumalanga, and Orange Free generate the highest maize GYs of the country. This crop is sown between September and December and is harvested between April and June. The rest of the world (141 countries) accounts for the production of the remaining 140 M t to complete 1147 M t in 2018 (based on FAO, 2019).

1.3  Maize in rotations: Suitability of previous and consequences for following crops Being a C4 plant, a high proportion of maize in the cropping sequence assures large biomass productivity and high water and radiation use efficiencies to the crop system (Caviglia et al., 2013). Maize has positive effects on soil carbon (C) ­balance and soil physical properties (e.g. infiltration, stability of aggregates) compared to other crops because it produces a high amount of stubble with a high C/N ratio. The effect of maize on soil carbon content and on the soil carbon content equilibrium depends on management practices and GY as well as on soil and climate. The annual soil carbon balance is positively related to maize GY and negatively associated with soil carbon content. For maize GYs higher than 10 t ha− 1 in the US Corn Belt, soil carbon balance was negatively associated with soil carbon percent, and the balance was positive at soil carbon percent below 3% (Lucas et al., 1977). For the south-east of the Pampas region of Argentina, crop sequences with maize caused smaller reductions in soil organic matter along the years when compared with rotations without maize (Studdert and Echeverría, 2002). The most relevant short-term effects of the preceding crop in the rotation are related to biotic adversities and the availability of water and nutrients. Maize reduces N availability for the next crop because of its high nutrient demand and high total N harvested with the grain, which leaves a large amount of residues with a high C/N ratio. Accordingly,

6  Crop Physiology: Case Histories for Major Crops

GY of non-fertilised wheat following maize is penalised and has a high response to N fertilisation when compared with wheat following other crops (Echeverría et al., 1992). Hence, a nitrogen (N) fixing crop or a crop with low N requirement is convenient after maize (Domínguez et al., 2001). Additionally, where water recharge of the soil profile during fallow and after sowing is limited and rainfall during the growing season is insufficient, the effect of the preceding crop on water availability becomes relevant. In this sense, maize usually consumes more water than the other summer crops (Della Maggiora et al., 2002). Growing maize in consecutive seasons reduces GY (Berzsenyi et al., 2000). Repeating maize in the cropping sequence reduces its productivity when compared with maize in rotation after pasture or soybean (Domínguez et al., 2001; Farmaha et al., 2016; Sindelar et al., 2015). This positive rotation effect is usually because of the increased N supply to maize following soybean or pasture (Domínguez et al., 2001). Fertiliser, however, does not fully compensate the described positive crop rotation effect, which could be also attributed to (1) improvements in soil water availability, soil structure, and soil microbial activity, (2) reductions in weed, insect, and disease incidence, and (3) absence of phytotoxic compounds and/or presence of growth-promoting substances originated from crop residues (Bullock, 1992; Karlen et al., 1994). Nevertheless, in semi-arid regions like those in the north of Argentina, the rotation soybean-maize requires a minimum of 50% of maize to adequately supply carbon to the soil through residues and roots, improving water capture efficiency, soil physical characteristics, and tolerance to erosion (Martini and Angeli, 2017).

1.4  Multiple cropping An option to intensify the use of agricultural land consists of sowing two or more crops per season as double crops, relay crops, or intercrops (Caviglia et al., 2004; Coll et al., 2012; Neto et al., 2010). These intensified systems have been implemented in many regions worldwide (Fischer et  al., 2014). They increase annual land productivity and water, radiation, and nutrient productivity, mainly through improved resource capture (Andrade et al., 2015; Caviglia and Andrade, 2010). The increase in resource capture results in (1) reduced surface run-off, percolation, erosion, water contamination, and water table level and (2) increased stubble inputs, soil carbon content, soil protection, and better weed control. Including maize in intensified systems also ensures high resource use efficiency (Caviglia and Andrade, 2010) and biomass production (Monzon et al., 2014), which contribute to maintain soil carbon balance (Oelbermann and Echarte, 2011). Examples of intensified systems that include maize are (1) winter wheat–summer maize double cropping in the North China Plain, which provides about 33% of the nation’s maize production (Wang et al., 2012; Yu et al., 2006), (2) maize as a second crop after soybean harvest (safrinha) in Mato Grosso and Parana, Brazil, with more than 3.3 Mha in 2013, (3) maize grown as double crop after field pea or other winter crop as an alternative to the widespread wheat/soybean double crop in Argentina (Andrade et al., 2017; Mercau and Otegui, 2015), and (4) soybean as double crop after maize harvest or as relay intercrop from 40 to 60 days after maize sowing (Monzon et al., 2014). Xu et al. (2020) estimated the worldwide average land equivalent ratio of the maize/soybean intercropping in 1.32 ± 0.02, underscoring the high potential of intercropping over sole crops. The productivity of such intensified option depends on the frost-free period, temperature regime, and water budget of each environment (Andrade and Satorre, 2015; Caviglia et al., 2013, 2019; Monzon et al., 2014; Van Opstal et al., 2011). The length of the growing season may limit the adoption of these alternatives in several regions. Accordingly, the increase in annual land productivity for maize–soybean double crop and relay intercrop was directly associated with the length of the growing season (Monzon et al., 2014).

2  Crop structure, morphology, and development 2.1  Main phenological events Ritchie et al. (1986) summarised visual maize development in the most widely used phenological scale (Fig. 1.3a). The VT-R1 period (VT: tasselling; R1: silking) splits the crop between vegetative (Vn, with n representative of leaf number) and reproductive (Rn, with n spanning from visible silks in R1 up to physiological maturity in R6) phases. Shoot meristems, however, follow a microscopic differentiation pattern that was described and illustrated in detail for the first time by Bonnett (1940, 1948). This pattern includes (1) floral initiation (FI) of the main stem apex (Fig. 1.3a), which represents the shift from leaf primordia differentiation to male differentiation (FIM) and (2) FI at axillary meristems (Fig. 1.3a), which represents the shift from husks differentiation to female differentiation (FIF). The former will produce the panicle (described as tassel) responsible for pollen production. The latter will produce the spikes (described as ears), which will bear the ovaries (Fig. 1.3b). The FIM sets total leaf number (TLN) in the main stem and usually takes place when the number of visible leaf tips is one-third of TLN, reflecting the tight coordination between the number of leaves initiated by the apical meristem and

TLN

LAIMAX

KNP

KW & GY

Leaf area index .........

4.5

Photoperiod

300

3.0

200

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1.5

FIMFIF 0

ML BL milky Start ear Kernel Active Phisiological Silking elongation Harvest set Grain Filling Maturity Critical period

Inducible phase Juvenile phase 300

7-10

30-40

S VE

V6

(a)

700

900

V8

V14

VT-R1

1750

Cd

140-170

DAS Scale

1100

70-90 R2

R6

100

Reproductive Stages (R)

Vegetative Stages (V)

6

1

5

2

3

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Temperature

4

1 2

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5

6

FIG. 1.3  (a) Schematic representation of the maize growth cycle. The x axis includes the scale proposed by Ritchie et al. (1986) together with cycle duration in thermal time (TT, °Cd) and in days after sowing (DAS) and main developmental events and phases. The span of the horizontal arrows represents (1) the period of crop response to temperature and photoperiod, and (2) the extension of the juvenile phase, the inducible phase, and the critical period for kernel number per plant (KNP) determination. Vertical arrows indicate the time of TLN and maximum leaf area index (LAIMAX) establishment, as well as of KNP, individual kernel weight (KW), and grain yield (GY) establishment. Data of cycle duration, leaf area index (LAI; m2 of green leaves per m2 of soil), and KW are representative of temperate hybrids with a TLN of ≈ 20 and a relative maturity (RM) of ≈ 120. Images of just extruded silks and a mature kernel with the characteristic black layer (BL) at the placental region are representative of the silking and physiological maturity stages, respectively. The latter is preceded by a schematic representation of a kernel at the half-milk stage (90%–95% of final KW), with the milk line (ML) separating the starchy from the milky endosperm. FIM: male floral initiation; FIF: female floral initiation. (b) Illustrative images of ear development between the start of active ear elongation (≈ V14) and silking (R1). In the example, the ear length increases from ≈ 1.5 cm in (1) to ≈ 8.5 cm in (6), depending upon the genotype and growing conditions. At the start of active ear growth, spikelets at the base of the ear have completely developed florets with silks of less than 1 mm (circle in 1). These silks will reach 14.5 cm at silking (the bottommost red arrow in 6). Floret development and silk elongation continues acropetally along the ear from (1) to (6). Active silk elongation can be observed in florets at the middle of the ear during 5–7 days before silking (4), when silks at bottommost positions are 4 cm long and have reached the tip of the ear. The bottommost ovaries will be 10 cm long during 1–2 days before silking (5), whereas those from the middle of the ear will reach silking on the same date with silks of only ≈ 10 cm length (6). Almost all florets along the ear will reach complete floret development at silking. Pictures obtained by M.E. Otegui on maize crops grown a Pergamino (33° 56′ S, 60° 33′ W), Argentina. Reference scale is in mm.

8  Crop Physiology: Case Histories for Major Crops

the number of visible leaves up to FIM (Padilla and Otegui, 2005). This coordination holds across maize hybrids of different maturity groups, with TLNs between 15 (short maturity) and 25 (long maturity). There are no axillary buds in the topmost leaves between the apical meristem and apical ear bud because apical dominance arrests the acropetal differentiation of axillary buds at the time of FIM (Lejeune and Bernier, 1996). Therefore, FIF is delayed with respect to FIM (Otegui and Melón, 1997), and it occurs first at the last differentiated, topmost axillary bud in the so-called ear-leaf. The ear leaf is among the largest leaves of the plant and is located at approximately one-third of the TLN counting downwards along the stem (Dwyer et al., 1992). This proportion, however, is under genetic control and consequently, may vary across genotypes (Li et al., 2015), with the concomitant effect on leaf area distribution above and below the ear. The FIF continues downwards, which explains the existence of more than one grain-bearing ear per plant (i.e. prolificacy > 1) depending upon the genotype (i.e. prolific vs. non-prolific), the resource availability per plant, and their interaction (Otegui, 1995; Tetio-Kagho and Gardner, 1988a). Buds at the lowermost nodes do not elongate and do not become reproductive. These buds may develop into tillers, particularly for some hybrids grown at very low stand densities in arid environments (Kapanigowda et al., 2010). From FI onwards, reproductive buds undergo the developmental processes that shape the characteristic morphology of maize tassel and ears (Bonnett, 1966). The final results of these processes are the events of anthesis (i.e. anthers dehiscence and pollen shed by tassels) and silking (silks extrusion from the husk by one or more ears per plant). At this stage, all differentiated leaves have completed their expansion and the crops have reached its maximum leaf area (Fig. 1.3a), whereas most florets along the ear of maize are totally developed and have started silk elongation (Fig. 1.3b). Contrary to other cereal crops as wheat, floret development per se does not represent a constraint to maize kernel set in most field conditions (Otegui et al., 1995a, 1995b; Otegui, 1997; Otegui and Melón, 1997; Rattalino Edreira et al., 2011), but environmental constraints that cause a delay in ear morphogenesis may affect the silking pattern (Rossini et al., 2012). A slight protandry (i.e. anthesis anticipation respect to silking of the topmost ear) is common in maize, producing an anthesis-silking interval (ASI) of a few days in standard growing conditions. This effect of apical dominance is enhanced when crops are exposed to stressful environments around flowering, increasing the ASI (Bolaños and Edmeades, 1993; D’Andrea et al., 2009; Edmeades et al., 1993; Hall et al., 1982), and reducing the total number of exposed silks (Debruin et al., 2018; Messina et al., 2019; Rossini et al., 2020) with the concomitant negative effect on kernel set. In contrast, some genotypes may express protogyny, i.e. anticipation of silking with respect to anthesis (Haskell, 1953), particularly when grown under no abiotic/biotic constraint at low stand density (Borrás et al., 2009). This condition usually improves pollination synchrony within and between ears (Uribelarrea et al., 2002), promoting increased prolificacy and kernel set (Cárcova et al., 2000). There is an important variation for prolificacy expression in maize (De Leon and Coors, 2002), with no clear benefit between prolific and non-prolific hybrids in many cropping conditions (Otegui, 1995). The exception to this trend is in drought-prone environments for which reduced stand density is recommended, where prolific hybrids have higher GY stability across years (Ross et al., 2020). Ovary fertilisation along the ear proceeds between the start of silking (R1) and the start of active kernel growth of the early-fertilised ovaries (R2), which usually represents a limit to successful kernel set among the late-fertilised ones (Cárcova and Otegui, 2007). Early kernel growth is chiefly linked to water influx (Borrás et al., 2003b), whereas active biomass accumulation starts at R2 and continues up to R6 (Fig. 1.3a). Biomass accumulates steadily in kernels along this period in parallel with a steady decline in kernel water content. Maximum kernel weight (KW) is reached at physiological maturity (Fig. 1.3a), when a black layer of necrotic tissue is evident at the placental region of the kernel (Daynard and Duncan, 1969) and its water content has dropped to ≈ 30%–40% (Borrás et al., 2004; Gambín et al., 2007). A good surrogate to anticipate R6 is to follow the ML development (Fig. 1.3a). This line represents the interface between the solid (starchy) and liquid (milky) matrices of the maturing endosperm (Afuakwa and Crookston, 1984) and progresses from the crown to the base of the kernel. A 50% ML (or half-milk stage) corresponds to ≈ 40% kernel moisture and ≈ 90%–95% of final GY, whereas there is no milk remaining when the black layer becomes evident. Anticipation of R6 is particularly important in the production of maize silage (Ma et al., 2006).

2.2  Genotypic and environmental drivers of maize development 2.2.1 Temperature For a given genotype, the duration in days of the above-described cycle is primarily modified by temperature (Fig. 1.3a), which affects the extension of all developmental stages in most evaluated environments (Capristo et al., 2007; Kumudini et al., 2014; Shim et al., 2017; Tsimba et al., 2013). In the absence of photoperiodic effects and abiotic constraints, such as water or N deficiencies, maize developmental rate during the vegetative period (i.e. pre-VT) increases between a base temperature (Tb) of ≈ 8–10 °C and an optimum temperature (Top) of ≈ 30–35 °C, whereas temperatures > Top reduce the developmental rate (Cicchino et al., 2010a; Gilmore and Rogers, 1958; Kiniry, 1991). References for Tb and Top are less

Maize Chapter | 1  9

accurate for the grain-filling period (Kumudini et al., 2014), being 8 °C in CERES-Maize model (Kiniry, 1991) but assumed as 0 °C by other authors (Sinclair et al., 1990; Tsimba et al., 2013). Within Tb and Top, the TT (in °Cd) requirement for completing a given phase is represented by the inverse of the slope in the model fitted to the response of the developmental rate to temperature; i.e. it is a genotype-dependent constant (Ellis et al., 1992). On the basis of the TT concept, several models have been developed to predict cycle duration that are used in crop simulation models such as CERES-Maize (Jones and Kiniry, 1986) and APSIM (Keating et al., 2003). These models differ in cardinal temperatures and the associated functions for computations. Therefore TT requirements reported in the literature must be taken with care for comparisons because they may vary depending upon the approach (Dwyer et al., 1999a; Kumudini et al., 2014). Predictions of these models usually focus on TT to anthesis, silking, and physiological maturity and do not include the period of kernel desiccation up to commercial harvest maturity. The only methods that include differences in kernel desiccation rate are the ones developed by FAO and the comparative RM or simply RM. Originally (Jugenheimer, 1958), the former used a kernel moisture content of 34%–36% as reference, which dropped to less than 20% in the 1990s (Marton et al., 2008). The latter is an adaptation of the originally known as Minnesota Relative Maturity Rating (MRMR) because of its origin (Peterson and Hicks, 1973), and it is the most widely used all across the Americas. The RM method ranks hybrids comparatively so that two RM units represent a 1% difference in kernel moisture at harvest. As a general reference, hybrids that range between 1014 and 1453°Cd for the whole cycle and represent RMs of 75–110 or FAO of 100–500 are recommended for latitudes between 48º and 39ºN, respectively, in Canada and the USA (Dwyer et al., 1999a; Troyer, 2001). The TT requirement rises to 1200–1700°Cd for the whole cycle (Fig. 1.3a) among hybrids recommended for mid-latitude environments (30–38ºS) as those representative of the Pampa region in Argentina (Capristo et al., 2007; Otegui et al., 1996), which correspond to RMs of 110–128 or FAO 500–600 (Di Matteo et al., 2016; Luque et al., 2006; Troyer, 2001). TT requirements (> 1500°Cd) and RMs (125–140) and FAO rankings (600–800) increase for subtropical and tropical conditions as those representative of Brazil (Bergamaschi et al., 2013; Tojo Soler et al., 2005) or India (Thimme Gowda et al., 2013). Despite this conceptual framework, TT requirements reported in literature must be taken with care for comparisons because they may vary widely depending upon the approach used for computation (Dwyer et al., 1999a, 1999b; Kumudini et al., 2014). Expansion of all differentiated leaves takes place between crop emergence (VE) and tasselling-anthesis (Fig. 1.3a) at a relatively constant phyllochron of usually 37–42°Cd leaf− 1 among reported hybrids (Hesketh and Warrington, 1989) but may extend up to 75°Cd leaf− 1 among hybrids in the CERES-Maize database of DSSAT (Hoogenboom et al., 2017) and between 33 and 62°Cd leaf− 1 among inbreds (Giauffret et al., 1995). The TT requirement for leaf primordia differentiation at the apical meristem (i.e. plastochron) may range between 24.3 and 36.4°Cd leaf− 1, with Tb between 4.0 and 8.1 °C, depending upon the genotype and based on soil temperature at 5-cm depth (Padilla and Otegui, 2005).

2.2.2 Photoperiod As a short-day plant (i.e. highest developmental rate under short photoperiod), the TLN may vary depending upon (1) the photoperiod during the inducible phase and (2) the degree of photoperiod sensitivity. Two sub-phases can be recognised during the vegetative phase of maize apical meristem, during which only leaf primordia are differentiated. The first one is a juvenile (i.e. photoperiod insensitive) phase, which is followed by an inducible (i.e. photoperiod sensitive) phase (Fig. 1.3a). The latter is subsequently followed by a realisation phase, when the meristem has already initiated reproductive differentiation and is no longer sensitive to photoperiod (Kiniry et al., 1983b). The FIM sets the limit between the inducible and realisation phases, and it is usually evident as an elongation of the apical meristem (Bonnett, 1966; Stevens et al., 1986). No similar morphological change identifies the transition between the juvenile and the sensitive phases of the meristem. Detection of a juvenile period in three photoperiod-sensitive hybrids was possible by exposing plants to alternating short and long photoperiods at different intervals during the cycle and recording tassel emergence date (Kiniry et al., 1983b). These experiments in controlled conditions allowed the detection of a juvenile phase that ended 4–8 days before FIM when plants were grown at short photoperiods (i.e. highly inducible). Therefore the inducible phase lasts 4–8 days under optimum, short photoperiods and is expected to be ≥ 4–8 days for photoperiod-sensitive genotypes grown at photoperiods longer than a threshold. Subsequent experiments (Kiniry et al., 1983a) in controlled conditions allowed the estimation of the main parameters (Major, 1980) of maize developmental response to photoperiod (BVP: basic vegetative phase, sum of the juvenile and inducible phases under optimum photoperiods; TP: threshold photoperiod; PS: photoperiod sensitivity) for hybrids of different maturity groups. These parameters ranged between 139 and 344°Cd for BVP, 10 and 13.5 h for TP, and 0 and 36°Cd h− 1 for PS. The BVP (equivalent to earliness per se presently used in wheat, see Chapter 3: Wheat, Section 2.2) was the shortest for the early-maturity group (≤ 139°Cd), and longest for the late-maturity group (≥ 251°Cd). Duration of the juvenile phase presently documented at the CERES-Maize database of DSSAT range between 110 and 458°Cd, whereas photoperiod sensitivity ranges between 0 and 5 d h− 1 (Hoogenboom et al., 2017). The use of days for the inducible phase

10  Crop Physiology: Case Histories for Major Crops

a­ llows for a variable TT duration of this phase, which may or may not modify TLN depending upon temperature. This subtle change is key to capture genotype × environment effects that are usually evident as a variation in the TLN in field conditions because of variable temperature regimes (Tollenaar and Hunter, 1983). Such effects are commonly registered even for the same hybrid sown in the same site and sowing date across years (Cirilo and Andrade, 1994a; Otegui et al., 1995b). Knowledge of the previously described genotype × temperature × photoperiod interactions is critical for matching genotype and environment under two main premises. Firstly, total cycle duration must fit within the length of the growing season and must avoid excessively low temperatures at early and late growth stages. These situations are frequent in exceptionally early (Otegui et al., 1996) and late sowings (Bonelli et al., 2016; Mercau and Otegui, 2015). In extremis, early frosts may cause seedling mortality and reduce stand density and uniformity because maize cannot compensate for plant losses. Uneven stands are particularly critical in maize (Liu et al., 2004) because they promote hierarchies among plants (i.e. dominant and dominated individuals) with negative effects on the final number of kernels m− 2 and GY (Maddonni and Otegui, 2004). Late frost may prematurely arrest grain filling with direct penalisation of GY (Baum et al., 2019; Bonelli et al., 2016; Mercau and Otegui, 2015). Such penalisation may also take place with excessively low irradiance late in the cycle, which reduces plant growth per set kernel (Cirilo and Andrade, 1996; Maddonni et al., 1998), may hasten reserve deployment (Jones and Simmons, 1983; Uhart and Andrade, 1991) and may shorten the grain-filling period and increase grain moisture at physiological maturity (Sala et al., 2007). Secondly, sowing date and genotype selection should pursuit the concurrence of the critical period for kernel set with the optimum conditions for crop growth (Section 4.1) in order to (1) avoid negative effects of the environment on the ASI (in days), which affects kernel set (Bolaños and Edmeades, 1993; Hall et al., 1982), and (2) maximise the duration of the critical period for kernel set (Otegui and Bonhomme, 1998) and crop growth rate (CGR) during the critical period (Andrade et al., 1999) for maximising the number of kernels m− 2 (Section 4.1).

3  Growth and resources 3.1  Capture and efficiency in the use of radiation 3.1.1  Canopy size and light interception The total shoot biomass (BT) produced in a period of n days can be expressed as the cumulative daily product of incident photosynthetically active radiation (IPAR), the efficiency of the canopy for intercepting IPAR (ei), and the radiation use efficiency (RUE) (Eq. 1.1).



   IPAR  e  RUE  , with IPARi  IPAR  e

BT g m 2 

n

0

i

i

(1.1)

During early growth, maize canopies invest a large proportion of resources (photoassimilates and nutrients) in leaves, which in turn promotes ei. From seedling emergence (VE) to flowering (VT-R1), the canopy will develop a leaf area three to five times greater than the land area it covers, a relationship known as LAI (Fig. 1.3a). In the absence of plant stress, CGR increases with increasing ei, peaking at ei = 0.95. The LAI required to achieve this ei is the critical LAI (LAIC). In maize, a typical LAIC is 3–4. Differences in LAIC can be related to contrasting leaf habit (e.g. erectophile or planophile) between hybrids (Maddonni and Otegui, 1996) and sowing patterns (Maddonni et al., 2001, 2006). Then, ei is an exponential function of LAI, with a curvature that depends upon an extinction coefficient k (Eq. 1.2) that usually ranges between 0.4 and 0.6 for maize. e i  1  exp

 k  LAI 

(1.2)

Leaf area is a function of leaf area growth and senescence. Leaf area growth depends on leaf number and size. Leaf number is mainly regulated by the genotype (Dwyer et al., 1992) and by the environmental conditions, depending on the crop response to photoperiod and temperature (Section 2.2). Leaf area per plant may be greater under long photoperiods that favour more leaves in sensitive hybrids (Bonhomme et al., 1991; Kiniry et al., 1983b). Individual leaf development and expansion, however, are mainly driven by temperature (Section 2.2; Ritchie and Nesmith, 1991). Leaf appearance rate increases between 8 and 34 °C and decreases at higher temperature (Kiniry, 1991). Leaf expansion rate (LER) is a direct function of temperature (greatest between 22 and 32 °C; Ritchie and Nesmith, 1991), whereas duration of the expansion period is inversely related to temperature (Hay and Walker, 1989). Owing to total or partial compensation between these two processes, the canopy achieves maximum leaf area when the average daily temperature is 21 °C (Hardacre and Turnbull, 1986; Reid et al., 1990; Wilson et al., 1973). A consequence of these characteristics is the

Maize Chapter | 1  11

increase in LAI in response to the delay in sowing date registered for spring sowings among temperate hybrids grown at the same stand density under no abiotic constraint (Maddonni and Otegui, 1996). LER is highly sensitive to water and nutrient deficits, as discussed in Sections 3.2.3 and 3.3.2. From crop emergence to anthesis, generation and expansion of leaves dominate leaf area dynamics, with little contribution of senescence except in extreme situations (e.g. high plant population density and shortage of N). Early vigour and maintenance of active leaf area ensure high capture of radiation and thus high biomass production. Maximum green leaf area is attained at anthesis (Fig. 1.3a). Afterwards, changes in leaf area are a direct function of the senescence rate (Borrás et al., 2003a). Leaf senescence is the series of biochemical and physiological events comprising the final stage of development, from the fully expanded state until leaf death. Leaf senescence is genetically determined and modulated by environmental factors (Thomas and Ougham, 2015). For instance, maize expressing the sunflower transcription factor HaHB11 exhibits improved GY chiefly through delayed leaf senescence (Raineri et  al., 2019). Key environmental factors accelerating senescence include low radiation, low red:far-red ratio, water and nutrient deficiencies, vascular and leaf diseases, and unbalanced source–sink ratio (Borrás et al., 2003a; Rajcan and Tollenaar, 1999; Sadras et al., 2000).

3.1.2  Radiation-use efficiency and its response to environmental factors Maize average RUE during the season is higher than for other summer grain crops such as soybean and sunflower (Andrade et al., 2005). For maize growing under adequate conditions, RUE is between 2 and 4 g MJ− 1 (Andrade et al., 1992; Hao et al., 2016; Lindquist et al., 2005; Otegui et al., 1995b; Westgate et al., 1997), when obtained from spot measurements at midday on sunny days and expressed on shoot biomass and a photosynthetically active radiation (PAR) basis. The superior RUE of maize results from (1) its C4 metabolism (Hesketh, 1963), with leaf photosynthetic rate 30%–40% higher than C3 species such as soybean, (2) its lower extinction coefficient (Eq. 1.2) that allows a more uniform distribution of the incoming radiation within the crop canopy, and (3) the low energy cost of carbohydrate-rich plant tissues (cellulose during vegetative growth and starch during grain filling) compared to protein and fat-rich tissues (Varlet-Grancher et al., 1982). Sinclair and Muchow (1999) and Stöckle and Kemanian (2009) reviewed the sources of variation in RUE for different species. Low temperatures (Andrade et al., 1993; Westgate et al., 1997), water deficits (Muchow, 1989a), and nutrient deficiencies (Muchow and Davis, 1988; Uhart and Andrade, 1995a) reduce RUE. In most crop environments, low temperatures during early vegetative growth in early sowing and during grain filling in late sowing reduce RUE (Cirilo et al., 1992; Cirilo and Andrade, 1994a; M.E. Otegui et al., 1995b; Wilson et al., 1995)(Cirilo et al., 1992; Cirilo and Andrade, 1994a; Wilson et al., 1995). The effect of water and nutrient deficiencies on crop biomass production, however, is largely accounted for by the reduction in the amount of radiation intercepted by the crop because the decline in RUE is generally less important (Boyer, 1970; Gifford et al., 1984; Uhart and Andrade, 1995a). This is the consequence of leaf expansion being much more sensitive to water and nutrient deficits than photosynthetic rate per unit leaf area (Muller et al., 2011; Sadras and Milroy, 1996; Salah and Tardieu, 1997). Reductions in RUE as a result of water deficits are explained by stomatal or non-stomatal factors (Farquhar and Sharkey, 1982), according to the direction of the changes in CO2 concentration in the stomatal cavity (decreases or increases, respectively). High source–sink ratio during grain filling can reduce RUE (Borrás and Otegui, 2001; Rajcan and Tollenaar, 1999), possibly because of photosynthetic feedback inhibition.

3.1.3  Crop growth rate and growth duration in response to management practices As it was already indicated, CGR is a function of IPARi and RUE, which depend on temperature and on the water and nutrient status of the crop. In turn, growth duration is determined by factors controlling phenological development, mainly temperature (Section 2.2). Rate of growth and growth duration are integrated into conceptual variables largely correlated with total biomass accumulation, i.e. photothermal quotient and growth per unit TT (Andrade et al., 1999; Fischer, 1985). Maize canopies, as in all annual species, do not profit from all the IPAR during the growing season. The proportion intercepted generally ranges between 59% and 79% of total IPAR (Otegui et al., 1995b). This limitation can be overcome in part by (1) early sowing, which does not improve the proportion of IPAR that is intercepted along the season but improves the total amount of light intercepted by the crop (Bonelli et al., 2016; Cirilo and Andrade, 1994a; Otegui et al., 1995b); (2) long season hybrids (Otegui et al., 1995b); (3) increasing plant population density (Westgate et al., 1997), and (4) reducing row spacing (Andrade et al., 2002a; Maddonni et al., 2006). These practices increase IPARi because they promote rapid canopy closure and/or increase the amount of IPAR. The benefits of early ground cover do not translate into increased GY if they do not improve ei at the critical stages of GY determination (Maddonni et al., 2006; Westgate et al., 1997; Section 4.1). Delaying sowing hastens vegetative growth and development because of high temperatures. Vegetative growth, however, is accelerated to a greater extent, so late-sown plants are generally larger than those sown early (Cirilo and Andrade, 1994a; Knapp and Reid, 1981; Maddonni and Otegui, 1996). Under these conditions, crops achieve maximal light i­nterception

12  Crop Physiology: Case Histories for Major Crops

in a shorter period from emergence (Bonhomme et al., 1994; Cirilo and Andrade, 1994a; Maddonni and Otegui, 1996). However, shortening of the growing cycle in late sowings decreases the total amount of radiation intercepted by the crop and, thus crop dry matter at harvest (Cirilo and Andrade, 1994a; Otegui et al., 1995b; Srivastava et al., 2018). Delays in sowing result in deterioration of some environmental conditions (i.e. reduced incident radiation and reduced RUE related to reduced temperatures) during the critical period for grain number determination and mostly during grain filling (Bonelli et al., 2016; Cirilo and Andrade, 1994b; Tsimba et al., 2013; van Roekel and Coulter, 2011), as analysed in Section 4. Plant density is the practice with the greatest impact on LAI and hence on light interception of maize canopies (Overman and Scholtz, 2011). LAI decreases markedly in response to reductions in plant density (Cox, 1996; Maddonni et al., 2001; Tetio-Kagho and Gardner, 1988b) because leaf area per plant does not vary much when resources per plant increase (Andrade et al., 2005). This lack of vegetative plasticity in maize is the consequence of a very stable leaf size, a nearly constant leaf number (Vega et al., 2000), and a low capacity for tillering (Doebley et al., 1997). Thus radiation interception in maize is highly responsive to plant density (van Roekel and Coulter, 2011). This decrease in ei with reduced plant densities contrasts with the response of other crops. Decreasing plant density results in large reductions in IPARi at the critical period for grain number determination in maize, which results in reduced CGR at flowering and, in turn, in lower number of grains per unit area (Andrade et al., 1999). Decreasing row spacing at equal plant densities leads to more equidistant plant distribution, hence reducing plant-toplant competition for water, nutrients, and light, and increases intercepted radiation and biomass (Barbieri et al., 2008, 2012, 2013; Bullock et al., 1988). It also reduces the LAI required to intercept 95% of the incident radiation because of a higher light extinction coefficient (Flénet et al., 1996; Riahinia and Dehdashti, 2008). In the absence of significant water and nutrient deficiencies, however, the benefits of decreasing row spacing are variable. Some researchers report GY increases (Bullock et al., 1988; Olson and Sander, 1988; Scarsbrook and Doss, 1973), while others do not (Ottman and Welch, 1989; Westgate et al., 1997; van Roekel and Coulter, 2012). GY responses to decreased distance between rows are inversely proportional to ei achieved with the wide row control treatment during the critical period for grain number determination (Andrade et al., 2002a). Thus GY increase in response to narrow rows is closely related to the improvement in ei during the critical period for grain set. Full light interception can probably not be achieved when (1) short-season and/or erect-leaf cultivars are grown (Bavec and Bavec, 2002); (2) plants are defoliated by frost, hail or insects; or (3) plants are subjected to water or nutrient stress at vegetative stages (Barbieri et al., 2000). Because drought or nutrient deficiencies during vegetative periods limit leaf area expansion (D’Andrea et al., 2006; Salah and Tardieu, 1997; Uhart and Andrade, 1995a), they would increase the probability of response to reduced row spacing. Early sowing in maize could also increase the response to reductions in row spacing because this practice leads to smaller plants with fewer leaves (Duncan et al., 1973; Maddonni and Otegui, 1996) (Section 2.2). The length of the growing cycle is critical in matching genotype and environment (Capristo et al., 2007; Wilkens et al., 2015). In general, the longer the growing season, the longer the maturity group of adapted cultivars (Section 2.2). At low latitudes, temperature and radiation do not vary much along the year, and long-season hybrids are generally more suitable because they compensate reductions in cycle duration because of high mean temperature with more leaves (Section 2.2); this phenotype enables to capture more incoming radiation than short-maturity hybrids in those environments (Lafitte and Edmeades, 1997). Contrarily, at high latitudes, radiation and temperature decrease markedly during grain filling (Maddonni et al., 1998). Therefore, GY usually decreases when sowing is delayed and hybrid maturity class is increased above the limit set to total cycle duration by the frost-free period (Baum et al., 2019). A short-season hybrid with low leaf area per plant and low vegetative plasticity may not achieve full light interception at the critical stages (Eq. 1.2) and therefore is more likely to benefit from higher plant density and reductions in row spacing than a long-season cultivar (Assefa et al., 2016; Lindsey and Thomison, 2016; Sarlangue et al., 2007). The detrimental effects of delayed sowing in maize are, in general, more pronounced in long-season hybrids. These hybrids benefit most from early sowings and show the largest reductions in GY in response to delayed sowing (Olson and Sander, 1988; Tsimba et al., 2013). The benefit of late-sown early-maturity hybrids depend on the magnitude of the delay and the potential length of the growing season (Baum et al., 2019; Lauer et al., 1999).

3.2  Capture and efficiency in the use of water 3.2.1  Environmental patterns of water supply and demand Within the broad range of temperatures and frost-free periods for maize outlined in Section 1, crops are mostly grown where rainfall exceeds the 250 mm y− 1, with no rainfed commercial production and with rainfall below 150 mm during the

Maize Chapter | 1  13

warm season (Shaw, 1988). This distribution is linked to the high sensitivity of maize GY to water deficits. For instance, Meng et al. (2016) estimated a decline of 0.17% mm− 1 in relative GY (i.e. quotient between rainfed and irrigated GY) when rainfall dropped below 462 mm in the Chinese maize belt during the growing season, with no GY record for rainfall ≤ 240 mm. Globally, Rattalino Edreira et al. (2018) estimated a marked decline (64%) in rainfed water productivity (i.e. GY per unit of potential crop evapotranspiration under water-limited conditions) when average evaporative demand increased from 3 to 7 mm d− 1 or when the fraction of soil evaporation to potential rainfed crop evapotranspiration increased from less than 20% to more than 40%. Both low evaporative demand and low soil evaporation usually corresponded to humid, cool high-latitude environments typically represented by European countries and the north-central USA. In contrast, high values corresponded to arid, warm low-latitude environments well represented by sub-Saharan Africa and the western US corn belt. It has been well documented that maize GY reductions are particularly pronounced when water deficits take place around flowering (Claassen and Shaw, 1970; Hall et al., 1982). In these conditions, GY can be more affected than biomass production, bringing a marked decline in harvest index (HI, grain to total biomass ratio) and water productivity (Sinclair et al., 1990). Despite these characteristics, Rattalino Edreira et al. (2018) estimated that maize global GY gaps because of non-water limitations (i.e. the difference between potential water-limited GY and on-farm GY) could be equivalent to those produced by water scarcity (i.e. the difference between potential GY and potential water-limited GY), even in climatic zones traditionally considered as highly water-limited as the sub-Saharan Africa. This paradigm shift is expected to modify, at least partially, systems analysis of crop management to improve water productivity.

3.2.2  Root expansion and senescence, root size, architecture, and functionality Typical for gramineous plants, maize main root system corresponds to nodal or crown roots that arise from basal nodes (i.e. older) (Gregory, 2006). These nodes do not elongate and produce the adventitious root system that comprises multiple root axes and their laterals. The system is of fibrous, finely distributed appearance. Additional aerial roots from higher nodes may grow into the soil and contribute to water and nutrient uptake and plant anchorage. McCully (1999) summarised the characteristics of field-grown maize roots, indicating that first-order laterals are short (less than 3 cm) when compared with lab-grown roots, most of them reach final length in less than 2.5 days, and they usually persist along the whole cycle. In this system, however, maturation for adequate water transfer does not take place until 15–40 cm from the root tip for large xylem vessels, whereas for small vessels, this distance is reduced to 4–9 cm and for the very narrow protoxylem to about 1–2 cm. Tips of lateral roots have early and rapid senescence, which progresses towards its older, basal part (Fusseder, 1987). Although only one-third of these roots produce second-order laterals, overall laterals represent × 30 the length of the axial roots. In this system, root hairs are key to water and nutrient uptake, and their life-span ranges between a few days and 1–3 weeks (Fusseder, 1987), depending upon the method used for the analysis (cytoplasmic intactness or nuclear staining, respectively). The profuse maize root system develops primarily between sowing and R2 (McCully, 1999), when it reaches its maximum depth (Fig. 1.4a). For soils with no permanent limitation to root growth, the evolution of root depth follows a general sigmoid pattern (Fig. 1.4a). Higher soil bulk density (e.g. silty clay loam soils of the Argentine Pampas respect to loam soils of Iowa) and reduced temperature (high latitude with respect to low latitude) may delay maize root penetration across soil layers, modifying the root front velocity (RFV; Fig. 1.4a). Estimated maximum RFVs for maize ranged between 2.4 and 3.4 cm d− 1 and were reached between 43 and 56 days after sowing in the evaluated temperate environments. In a given environment, hybrids with extended cycle duration (i.e. high RM) are expected to have deeper roots than their short-cycle counterparts, whereas those with acute root angles are expected to have enhanced RFV with respect to those with wide angles (Hammer et al., 2009). Maximum root depth (Fig. 1.4b) and root abundance at each soil layer (Fig. 1.4c) vary with growing conditions. For instance, maize rooting depth can reach 2.25 m on loam soils (Typic haplustols) when exposed to terminal drought in a mid-latitude environment (Dardanelli et al., 1997). In contrast, no water uptake was detected below 1.65 m under similar drought conditions when crops were growing on silty clay loams (Typic argiudols) of the same region (Fig. 1.4b). Roots can traverse dense layers when layers are wet or bypass them through cracks when soils dry (Dardanelli et al., 2004). However, root proliferation within a soil layer is severely affected by increased bulk density (Fig. 1.4c), which promotes root clumping and the concomitant negative effect on water extraction (Fig. 1.4b and d). This restriction may also compromise root proliferation in subsequent soil layers (Dardanelli et al., 2004), which may not reach the critical root density for maximum water extraction rate, reducing the actual amount of extractable soil water in deep soil layers (Carretero et al., 2014). Similarly, management practices with a negative effect on soil structure also affect maize root proliferation, as observed for long respect to short cropping periods in a rotation (Cárcova et al., 2000) and for soil densification related to tillage (Díaz-Zorita et al., 2002; Taboada and Alvarez, 2008).

14  Crop Physiology: Case Histories for Major Crops

Relative PASW Argentina Corn belt (33°33’S, 60°20’W) Argentina temperate cool (37°47’S, 58°18’W) US Corn belt (42°01’N, 93°46’W)

4

0.25

0.50

0.75

1.00

–25

Root front velocity (cm day–1)

3 –50 2 1

–75

Clayey layer for squares

–100

20

0 25 50 75 100 125 150 175 200

40

60

80

100 DAS

–125 –150 –175 –200 –225

(a) 0.0

Soil depth (cm)

Soil depth (cm)

0.00 0

0

20

Root abundance 40 60

(b) 80

100 0

Water uptake (%) 20

60

–0.1 –0.2 –90

Soil depth (cm)

–0.3

Clayey layer

–0.4 –0.5

–180

Well-watered

–0.6 –0.7 –0.8 –0.9 –1.0

(c)

FAO Arenosol

USDA Entisol

Planosol

Argialbol

Vertisol

Vertisol

Luvisol

Alfisol

–90

Water deficit –180

(d)

FIG. 1.4  Maize root system: expansion, size, and functionality. (a) Evolution pattern of the root front of maize crops growing in different environments (lower panel) and derived RFV (upper panel). The former was estimated from depletion of soil water (Argentina) or in situ measurements (USA). DAS: days after sowing. (b) Maximum rooting depth based on relative plant available soil water (PASW) at physiological maturity (dashed line) of maize crops grown on contrasting soil types (circles: Haplustol soil; squares: Typic argiudol soil) in the Pampas of Argentina. The solid lines correspond to initial soil water content (0: permanent wilting point; 1: field capacity). (c) Root abundance (as proportion of in situ evaluated soil blocks with at least one visible root) at R1 of one maize hybrid grown on different soils of central France. Vertical dashed lines indicate the presence of massive structures (arenosols and planosols) or shrinkage cracks (vertisols). (d) Proportion of total water uptake from different soil layers during the silking-20 days (solid lines) and silking + 20 days (dashed lines) periods of maize crops exposed to contrasting water regimes. a: Adapted from Cárcova, J., Maddonni, G.A., Ghersa, C.M., 2000. Long-Term Cropping Effects on Maize on Maize: Crop Evapotranspiration and Grain Yield. Agron. J. 92, 1256–1265. https://doi.org/10.2134/ agronj2000.9261256x; Otegui, M.E., 1992. Incidencia de una sequía alrededor de antesis en el cultivo de maíz. Consumo de agua, producción de materia seca y determinación del rendimiento. Tesis MSc. Facultad de Ciencias Agrarias, Universidad Nacional de Mar del Planta; Ordóñez, R.A., Castellano, M.J., Hatfield, J.L., Helmers, M.J., Licht, M.A., Liebman, M., Dietzel, R., Martinez-Feria, R., Iqbal, J., Puntel, L.A., Córdova, S.C., Togliatti, K., Wright, E.E., Archontoulis, S. V., 2018. Maize and soybean root front velocity and maximum depth in Iowa, USA. F. Crop. Res. 215, 122–131. https:// doi.org/10.1016/j.fcr.2017.09.003; b: Adapted from Dardanelli, J.L., Bachmeier, O.A., Sereno, R., Gil, R., 1997. Rooting depth and soil water extraction patterns of different crops in a silty loam haplustoll. F. Crop. Res. 54, 29–38. https://doi.org/10.1016/S0378-4290(97)00017-8; Otegui, unpublished; c: Adapted from Nicoullaud, B., King, D., Tardieu, F., 1994. Vertical distribution of maize roots in relation to permanent soil characteristics. Plant Soil 159, 245–254. https://doi.org/10.1007/BF00009287; d: Adapted from Otegui, M.E., Andrade, F.H., Suero, E.E., 1995a. Growth, water use, and kernel abortion of maize subjected to drought at silking. F. Crop. Res. 40, 87–94. https://doi.org/10.1016/0378-4290(94)00093-R.

Maize Chapter | 1  15

3.2.3  Crop water use and canopy conductance as related to canopy architecture, stomatal conductance, and canopy-atmosphere coupling Common to all crops, biomass production (Eq. 1.1) and the amount of water transpired by maize canopies are tightly related to the amount of solar radiation (SR) intercepted along the cycle (McNaughton and Jarvis, 1991), and both processes depend upon leaf differentiation (Section 2) and expansion (Fig. 1.5). Crop capacity to intercept light (Eq. 1.2) depends chiefly on a high leaf elongation rate (LER) to achieve the critical LAI (LAIC; Section 3.1.1) at the start of the critical period for kernel set (Section 4.1; Fig. 1.5d and e). In these conditions, LER depends on temperature in the 10–35 °C range 1.2

0.8 0.6

50%

0.4 Irriqated Control Water Deficit 1 Water Deficit 2

0.2 0.0

0

30

60

DAS

90

120

150

0.6

0

Y1(MPa) –0.6

0.4 0.2 0.0

60

0

30

60

DAS

90

120

150

90

120

150

7

5 4 3 2

0

30

60

DAS

90

120

0

150

(d) 1000 900 800 700 600 500 400 300 200 100 0

DAS

Free Water Reference

Transpired Water (mm)

Light Interception Efficiency

(e)

30

1

(c) 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

0

6 Leaf Area Index

0.8

(b) LER (mm h–1)

Sl on Expansion Processes

(a) 1.0

Daily ETc/PET

FTSW

1.0

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

0

30

60

90 DAS

120

150

(f)

0

500 1000 1500 2000 2500 Accumulated Intercepted SR (MJ m–2)

FIG. 1.5  Maize crop water use. DSSAT 4.7.0.0 (Hoogenboom et al., 2017) simulated results of processes conducive to the amount of water transpired by crops grown under contrasting water regimes in Spain (based on Cavero et al., 2000). DAS: days after sowing. (a) Water regimes and their effects on the fraction of transpirable soil water (FTSW), (b) crop (ETc) to potential (PET) evapotranspiration ratio, (c) degree of water stress (SI: stress index) on tissue expansion processes, and (d) LAI along the cycle. Effects on LAI summarise the response of LER to pre-dawn leaf water potential (Ψ) (c inset; Chenu et al., 2008). (e) Large effects on LAI did not affect to the same extent maximum light interception efficiency (ei; Eq. 1.1) of stressed crops but (f) did affect their capacity to hold it along the reproductive phases, with the concomitant variation in the amount of SR intercepted by canopies that drive plants transpiration. Short black arrows in (a–e) indicate silking date on ca. 80 DAS. Squares in (d) correspond to observed LAI at silking and maturity. Long vertical arrows in (d) and (e) indicate the date when ‘control’ and ‘water deficit 1’ plots reached ≈ 95% ei, which corresponded to LAI ≅ 4.5 (horizontal arrow in d). Plots under water deficit 2 reached a maximum LAI of 3.45 and ei = 0.87.

16  Crop Physiology: Case Histories for Major Crops

(Sadok et al., 2007) and varies with leaf position along the stem (Andrieu et al., 2006; Dwyer et al., 1992). Therefore and provided all other variables remain uniform (e.g. VPD, mineral nutrition, stand density), the effects of soil water deficits (Fig. 1.5a and c) on maize LER (Fig. 1.5c, inset; Tanguilig et al., 1987) and LAI (Fig. 1.5d; Cavero et al., 2000) are directly driven by fluctuations in the soil–leaf water potential continuum that affect cell turgor (Boyer, 1970; Reymond et al., 2003) and plant hydraulic conductivity. These fluctuations depend primarily upon the crop water regime (Fig. 1.5a) and can be influenced by management practices (row spacing, tillage system, soil mulching) that affect the partitioning of crop evapotranspiration (ETc) between transpiration (T) and soil evaporation (Es) (Al-kaisi et al., 1989; Villalobos and Fereres, 1990). Reductions in intercepted SR because of water shortages will depend upon the extent of effects driven by LAI reductions on ei (Fig. 1.5e), which include the pre-silking period of canopy expansion and canopy senescence (Fig. 1.5d). Water deficit reducing LAI reduces both cumulative intercepted SR (Muchow, 1989a) and transpiration (Muchow and Sinclair, 1991; Tanguilig et al., 1987). The relative response, however, differs among water regimes (Fig. 1.5f), being larger for water loss (up to 57% in the example) than for SR captured (up to 32% in the example). This uneven response is because of the additional reduction of transpiration in response to the decline in canopy conductance (gc) in drying soils. As an isohydric species (Tardieu and Simonneau, 1998), maize tends to keep a high leaf water potential (Ψ) by an efficient stomata control through hydraulic and non-hydraulic signals. The latter is mediated by xylem abscisic acid (ABA) (Bahrun et al., 2002), for which there is still some controversy about its origin in the plant (Christmann et al., 2007; Jackson, 1997). Main effects of increased xylem ABA on growth of maize plants exposed to water deficits are related to stomatal control (Tardieu et al., 2010). Increased ABA reduces leaf expansion (Cramer and Quarrie, 2002) and enhances stomatal closure, both conducive to reduced transpiration. Stomatal closure reduces gc, which maintains the plant water status (i.e. prevents a large drop inΨ) and improves plant hydraulic conductivity. Moreover, ABA effects on overall improved water status seem partially mediated by enhanced aquaporins activity, as reported by Parent et al. (2009) for maize lines divergently transformed in the NCED (9-cis-epoxycarotenoid dioxygenase) gene. Plants of ‘sense lines’ (i.e. those with enhanced ABA production with respect to the ‘antisense’ ones) had improved expression of mRNA for aquaporin PIP genes and related protein contents. Variations in gc may also relate to fluctuations in atmospheric vapour pressure deficit (VPD). Increased VPD can promote stomatal regulation through upregulation of foliar ABA biosynthesis triggered by reduced leaf turgor (McAdam and Brodribb, 2016). Maize hybrids vary in transpiration response to both soil water content (Fig. 1.6a) and VPD (Fig. 1.6b) (Gholipoor et al., 2013).

3.2.4  Water use efficiency As a summer crop with broad adaptation (Sections 1 and 3.2.1), maize is usually exposed to periods of water scarcity, hence the agronomic importance of water use efficiency (WUE) for biomass and GY. Owing to difficulties for separation between T and Es in the field (Villalobos and Fereres, 1990), accurate assessment of transpiration-based WUE for biomass production (WUEB,T) is usually limited to controlled conditions, with ranges between 4.1 and 8.4 mg g− 1 (equivalent to 41–84 kg ha− 1 mm− 1). Part of this variation is explained by differences in VPD (Fig. 1.6c). Most field surveys define WUE as biomass (WUEB,ET) or GY in relation to ETc (WUEGY,ET), traits that are more variable (Table 1.1) than WUEB,T. Reasons for the large variation in WUE based on ETc are primarily linked to the inclusion of unproductive water loss from the soil (Fig. 1.6d), evidenced by the marked increase in both WUEB,ET (Otegui et al., 1995a) and WUEGY,ET (Yu et al., 2018) obtained when soil mulching minimises Es (Table 1.1). When GY is considered, variations in WUE may be also linked to differences in HI. The frontier line (FL) fitted to data with maximum biomass per unit ETc in Fig. 1.6d has a slope close to maximum WUEB,T in Fig. 1.6c and the maximum in Table 1.1 and represents potential maize water productivity for biomass in field conditions for the evaluated data set (Grassini et al., 2009).

3.2.5  Management practices under water deficits When the probability of water deficit at flowering is high, a decrease in maize stand density reduces plant-to plant competition and avoids plant growth rate at flowering close to threshold values for ear growth suppression (Section 4.1). This practice would also result in a conservative use of soil water during the vegetative period and thus in higher water availability for reproductive growth (Curin et al., 2020; Hao et al., 2019). Wide rows would also result in a conservative use of water during the vegetative period, but as in low plant density, the combination of hybrid, stand density, and row spacing can modify crop response markedly, depending upon their relative effect on water loss from plants and soil along the cycle. For instance, Barbieri et al. (2012) reported enhanced use of soil water reserves early in the cycle under narrow rows, which may produce an unfavourable distribution between vegetative and reproductive stages of the type described by Passioura (2006) for wheat crops exposed to terminal drought in Mediterranean environments. The low stability of maize HI in response to available resources per plant (Echarte and Andrade, 2003) indicates that there is a need for careful adjustment of stand density to environmental conditions and input level (Al-Naggar et al., 2015;

Maize Chapter | 1  17

FIG. 1.6  Maize soil–plant–atmosphere coupling and water use efficiency. Transpiration response of two maize hybrids to the (a) fraction of transpirable soil water (FTSW) and (b) vapour pressure deficit (VPD). EOT, end of treatment. Adapted from (a) Ray and Sinclair (1997) and (b) Gholipoor et al. (2013). (c) Response to VPD for biomass production based on plant transpiration (WUEB,T); data correspond to hybrids included in (a). (d) Biomass and GY response to crop evapotranspiration (ETc); the FL was fitted to data with increased biomass per unit increase in ETc. Adapted from (c) Ray et al. (2002) and (d) several references of Table 1.1.

Hernández et al., 2014), particularly in drought-prone environments (Rotili et al., 2019). Some new hybrids, however, have a more stable response of GY to variations in stand density than others (Di Matteo et al., 2016; Mansfield and Mumm, 2014). Varying sowing date is a recommended strategy to move a crop stage away from constraints such as seasonal drought, heat stress, frost, and biological adversities. In many maize production areas, delaying sowing is recommended to avoid or alleviate water deficits and heat stress at the critical flowering period (Maddonni, 2012; Mercau and Otegui, 2015; Rotili et al., 2019), provided the decline in the photothermal environment and early frosts (Bonelli et al., 2016; Maddonni, 2012) do not penalise GY excessively because of the anticipated arrest of grain filling (Section 2.2).

3.3  Capture and efficiency in the use of nutrients N, phosphorus (P), potassium (K), sulphur (S), and zinc (Zn) are the most widespread limiting nutrients for maize in the main producing regions (Table 1.2). Additionally, some of these regions are characterised by acid soils (e.g. Brazil) owing to naturally high (toxic) aluminium (Al) levels together with very low availability of macronutrients. Such conditions demand liming to produce (Table 1.2). Here we address the most important features of mineral nutrition, with emphasis in N.

18  Crop Physiology: Case Histories for Major Crops

TABLE 1.1  Estimated maize water use efficiency (kg ha− 1 mm− 1) for biomass or grain yield based on crop evapotranspiration. WUE Biomass

Source of variation

Reference

Year and Sowing date (Irrigated crops)

Otegui et al. (1995a)

Year and Sowing date (Rainfed crops + soil mulch)

Otegui et al. (1995a)

18–34

Soil amendments

Faloye et al. (2019)

43–50

Water regimes

Zhang et al. (2017)

27–34

Year (variable degree of stress)

Tolk et al. (2016)

53–64

Year (Rainfed crops)

Kresović et al. (2016)

55–68

Year (Full irrigation)

Kresović et al. (2016)

42–53 65–83

GY

a

b

35–44

Hybrid × Stand density

Curin et al. (2020)

26–57

Environment × Stand density

Curin et al. (2020)

19.5–21.5

Farming systems (Continuous maize vs rotation)

Hussain et al. (2019)

9.5–14.5

Soil amendments

Faloye et al. (2019)

21–29

Water regimes

Zhang et al. (2017)

12–19

Year (variable degree of stress)

Tolk et al. (2016)

24–34

Year (Rainfed crops)

Kresović et al. (2016)

24–36

Year (Full irrigation)

Kresović et al. (2016)

b

11–27

Hybrids, water regimes, and years

Nagore et al. (2017)

17–23

Hybrids × stand density

Curin et al. (2020)

12–31

Environment × stand density

Curin et al. (2020)

14–16

Hybrids (under water deficit)

Hao et al. (2019)

15–20

Water regime

Hao et al. (2019)

17–18

Stand density

Hao et al. (2019)

20–22

N level

Hernández et al. (2015)

20–25

Climate zones and soil textures (current climate)

Rodrigues Pinheiro et al. (2019)

16–23

Climate zones and soil textures (future climate)

Rodrigues Pinheiro et al. (2019)

16–29

Mulching and tillage systems

Yu et al. (2018)

10–13

Water regime and salinity level

Yuan et al. (2019)

In each group, bold indicates maximum and underlined data indicate minimum. a

For the period under complete ground cover.

b

Hybrids of different breeding eras.

3.3.1  Nutrient absorption, assimilation, accumulation, and remobilisation Under potential growing conditions (Section  3.1), the maximum N accumulation in shoots of maize hybrids grown at optimal stand density varies between 240 and 320 kg ha− 1, depending upon the hybrid, with maximum accumulation rates between 2.7 and 3.7 kg ha− 1 d− 1. These high rates remain relatively constant between V5 and R4-R5 (Fig. 1.7b), although peaking slightly before flowering in concurrence with the increase in ei (Eq. 1.2) that drives CGR and total biomass (Fig. 1.7a). The amount of N accumulated at flowering varies between 60% and 74% of the total N at harvest (Ciampitti and Vyn, 2013a), although this proportion has tended to decrease among modern hybrids (55%), highlighting the increasing importance of post-flowering N absorption (Ciampitti and Vyn, 2012; Mueller and Vyn, 2016). This trend is in agreement with the improved post-silking biomass production of modern hybrids (Luque et al., 2006). N is accumulated in vegetative parts until 10–15 days after flowering and then partially mobilised to the grains. The source–sink ratio (capacity to provide assimilates to the grains/capacity of the grains to accumulate the assimilates) is the main influence on N mobilisation

Maize Chapter | 1  19

TABLE 1.2  Soil fertility characteristics of the main maize production zones. Country

pH

Liming

Soil fertility

Organic matter

N

P

K

S

Ca

Mg

Zn

B

USA

5.5–8.0

4–5

1–4

2–4

4–5

4–5

4–5

3–4

3–5

2–4

2–4

2–3

China

5.0–8.0

4–5

3–4

3–5

4–5

4–5

3–5

3–5

2–4

2–4

2–5

2–3

Brazil

4.0–8.0

5

4–5

3–5

4–6

4–6

4–6

3–5

4–6

2–4

2–5

3–4

Argentina

5.5–8.0

2–3

1–4

3–5

3–5

3–5

2–5

3–5

2–4

2–3

2–5

2–3

EU–27

5.5–8.0

3–4

2–4

2–5

3–5

3–5

3–5

3–5

3–5

2–3

2–4

2–3

Ukraine, Russia

5.5–8.0

1–2

1–3

2–3

2–4

3–4

2–4

2–4

2–4

2–3

2–3

1–2

India

3.0–4.0

2–3

1–4

3–5

4–5

4–5

3–5

3–4

3–5

3–4

2–5

2–3

Mexico

5.5–8.0

2–3

3–4

3–5

4–5

3–5

4–5

3–5

3–5

3–4

2–4

2–3

Indonesia, Philippines, Vietnam

5.5–8.0

2–3

3–4

3–5

4–5

4–5

4–5

3–5

3–5

3–4

2–5

2–3

South Africa, Egypt, Nigeria Ethiopia

5.0–8.0

3–4

3–4

3–5

4–6

4–5

4–5

3–5

3–5

3–4

2–5

2–3

Deficiencies

Score

None

1

Few

2

Slight

3

Moderate

4

Severe

5

Very severe

6

Adapted from Bardy Prado et al., 2012; Choudhary et al., 2014; FAO-UNESCO, 1974; Gonzalez Castorena, 2010; Hengl et al., 2015; INEGI, 2007; Jones et al., 2013; Leenaars et al., 2014; Li et al., 2016; Plaza et al., 2018; Schmidt et al., 2011; SEMARNAT y CP, 2003; SEMARNAT y UACh, 2003; Teixeira Guerra et al., 2014; Tian et al., 2010.

(Borrás et al., 2002; Uhart and Andrade, 1995b). Source limitations (e.g. drought, defoliation, etc.) lead to low assimilate availability for N absorption and reduction during the grain-filling period, increasing N mobilisation and reducing kernel N concentration. Contrarily, adequate assimilate availability per kernel during grain filling decreases N mobilisation and increases N absorption and its concentration in kernels (Borrás et al., 2002; Uhart and Andrade, 1995b). During grain filling, the crop can mobilise between 28 and 100 kg N ha− 1, according to the source/sink ratio (Uhart and Andrade, 1995b). These values represent between 18% and 42% of the N in vegetative biomass at 15 days after flowering. Hybrids vary in N mobilisation capacity (Ciampitti and Vyn, 2014; Mueller and Vyn, 2016; Uhart and Andrade, 1991). The contribution of N mobilisation to N in kernels during grain filling can range between 15% and 70% (Ta and Weiland, 1992; Uhart and Andrade, 1995b). The dynamics of N accumulation and mobilisation in the plant are reflected in the N HI (N in grain/N in shoot), which varied between 0.59 and 0.73 (Table 1.3), according to the source–sink ratio and the hybrid (Ciampitti et al., 2013b; Liu et al., 2017; Uhart and Andrade, 1995b). Therefore in maize for grain production, 41%–27% of the N in shoot remains in the stubble if it is not used as forage or source of bioenergy (Table 1.3). P starts accumulating in the plants at maximum rate after V5–V6. At flowering, the crop has accumulated between 45% (Fig. 1.7c) and 55% (Bender et al., 2013) of the total P at harvest. The P HI ranges between 75% and 80%. The dynamics of K accumulation in the crop is anticipated compared to N and P. Almost all K uptake is usually completed by flowering, and there may be a partial decline respect to the maximum at physiological maturity (Fig. 1.7d) because of some loss of senesced leaves and panicles. The K HI varies between 23% and 33% (Bender et al., 2013; Ciampitti et al., 2013a), so most of the absorbed nutrient returns to the soil with crop residues. Other macronutrients (Table 1.3) have low HI (e.g. Ca 7%, Mg 28%) as several micronutrients (e.g. B 25%, Fe 36%, and Cu 29%), except Mo (63%–65%) and Zn (50%–55%).

20  Crop Physiology: Case Histories for Major Crops

30

2500 N0

2000 52%

1500 1000

60% 500

N content (g m–2)

Biomass (g m–2)

N224

N224

N0

5 4 3

44%

2 45%

0

500

1000

K content (g m–2)

P content (g m–2)

6

0

N0

20 74%

15 10

66%

0

(b)

1

(c)

N224

5

0

(a)

25

30

N224

25

N0

119%

20 15

112%

10 5

0 1500 2000 (d) 0 500 Thermal time from VE (°Cd)

1000

1500

2000

FIG. 1.7  Evolution from crop emergence to maturity of (a) BT and its content of (b) N, (c) P, and (d) K of maize crops grown at a stand density of 79,000 plants ha− 1 and two N rates (N0: no N added; N224: 224 kg N ha− 1). The arrow indicates the mean date of silking, and the percent next to each line is the value at silking with respect to the total amount at maturity. Redrawn from Ciampitti, I.A., Camberato, J.J., Murrell, S.T., Vyn, T.J., 2013a. Maize Nutrient Accumulation and Partitioning in Response to Plant Density and Nitrogen Rate: I. Macronutrients. Agron. J. 105, 783–795. https://doi. org/10.2134/agronj2012.0467.

3.3.2  Effects of nutrients on crop development, growth, and grain yield Owing to large requirements for crop production, even mild N deficiencies may reduce maize biomass (Ciampitti et al., 2013b), HI (D’Andrea et  al., 2009) and grain quality (Section  4.4). N deficiencies do not reduce leaf number (TLN, Section 2) and consequently have little (if any) effect on crop development (Uhart and Andrade, 1995a) but may cause large reductions in leaf expansion and leaf persistence (Uhart and Andrade, 1995a), affecting the LAI and consequently, ei (Eq. 1.1). Both IPARi and RUE (Eq. 1.1) decrease under N deficiency, reducing CGR and both shoot and root biomass (Uhart and Andrade, 1995a). Reductions in N availability can also increase the ASI with variable effects across genotypes (D’Andrea et al., 2013; Debruin et al., 2018; Rossini et al., 2020), supporting the value of this secondary trait for adaptation to poor soil N (Lafitte and Edmeades, 1994). Reduction in seed set (Section 4.1) does not lead to proportional decreases in forage quantity and quality because the excess of assimilates can be stored as reserves in the stems, which increase between 6% and 38% under N deficiency (Uhart and Andrade, 1995b). Failures up to 90% of seed set reduced total dry matter 23% and increased stem biomass 55%–60%, without a significant effect on dry matter digestibility or total protein percent of the forage at harvest (Dalla Valle et al., 1998, 2008).

3.3.3  Nutrients diagnosis and fertilisation requirements 3.3.3.1 Nitrogen Supply–demand balance This method, summarised in Eq. (1.3), has been widely used in maize N diagnosis. b  GY  N initial  N mineralised  N stubble  N fertiliser  N loss

(1.3)

with b = N absorbed t− 1 of grain, GY = grain yield goal, N initial = mineral soil N at sowing, Nmineralised = N mineralised during the crop cycle, Nstubble = N supplied by the stubble, Nfertiliser = N from fertilisers, and Nloss = N losses from the system. The maize crop absorbs 15–22 kg of N per t of grain, and a total amount of absorbed N larger than 266 kg N ha− 1

TABLE 1.3  Traits describing macro and micronutrient use by a maize crop with grain yield of 12 t ha− 1. Macronutrients Traits measured at physiological maturity

N

P2O5

K2O

Micronutrients Mg

S

Zn

Mn

kg ha− 1

B

Fe

Cu

g ha− 1

Total requirement

286 (266–307)

114 (100–133)

202 (181–225)

59 (52–66)

26 (24–28)

498 (448–563)

542 (496–793)

83 (67–101)

1376 (1224–1569)

141 (132–155)

Harvested with grain

166 (145–188)

90 (73–108)

66 (57–78)

17 (15–20)

15 (13–16)

308 (269–353)

72 (62–87)

19 (13–32)

248 (218–285)

41 (30–49)

Nutrient harvest index (%)

58 (51–62)

79 (70–82)

33 (27–37)

29 (25–33)

57 (52–60)

62 (60–65)

13 (11–16)

23 (17–31)

18 (17–22)

29 (21–33)

Data correspond to the average of six hybrids grown at two locations (DeKalb and Urbana, IL, USA) during 2010 and are expressed as the mean and the range between maximum and minimum values (in parenthesis). Nutrient harvest index represents the quotient between nutrient harvested with grain and total requirement (in %). Adapted from Bender, R.R., Haegele, J.W., Ruffo, M.L., Below, F.E., 2013. Nutrient uptake, partitioning, and remobilization in modern, transgenic insect-protected maize hybrids. Agron. J. 105, 161–170. https://doi. org/10.2134/agronj2012.0352.

22  Crop Physiology: Case Histories for Major Crops

has been documented for producing a GY of 12 t ha− 1 in the US (Table 1.3; Ciampitti and Vyn, 2012; Djaman et al., 2013). Large N requirements are indicative of a low N use efficiency (NUE), which recognizes different components. Agronomic NUE (NUEA, kg of grain kg− 1 applied N) is the product between the physiological or internal NUE (NIE, kg of grain kg− 1 absorbed N) and the fraction recovered from fertiliser (NRE, kg N in biomass kg− 1 applied) (Ciampitti and Vyn, 2014; Novoa and Loomis, 1981). As N availability decreases, NIE increases from approximately 43 to 56 kg of grain kg− 1 of N absorbed, whereas NRE usually varies between 0.70 and 0.40 (Ciampitti and Vyn, 2012). Variation of NIE among hybrids may range between 37 and 70 kg of grain kg− 1 of N absorbed (Ciampitti and Vyn, 2014). The synchrony between crop N demand and indigenous N supply is the main variable that explains changes in NRE (Ciampitti and Vyn, 2014). Ninitial and Nmineralised during the season depend on the amount and composition of soil organic matter, soil temperature, and water availability. Water effects on N use are particularly critical in a summer crop as maize (Djaman et al., 2013). Initial soil N can be measured, whereas organic matter mineralisation can be measured or estimated with models based on potential conditions (N0) affected by soil moisture and temperature. From evaluations in wheat and maize fields performed in the Pampa of Argentina, Nmineralised can vary from 22 to 232 kg N ha− 1, depending on soil organic matter, soil moisture, and temperature (Reussi Calvo et al., 2018). Measurement of N-ammonium released during soil anaerobic incubation improved the estimation of maize N needs by 29% and 46% for the cool southeast Pampas region and North Humid Pampa of Argentina, respectively (Orcellet et al., 2017). The preceding crop modifies N availability (30–100 kg N ha− 1) depending on the stubble C/N ratio, temperature, and soil moisture (Ranells and Wagger, 1996). N losses include leaching, denitrification, and volatilisation. Low soil buffer capacity, high soil pH, urease activity, temperature, wind speed, and soil moisture promote N volatilisation generating losses of applied N from 2 to 50%. Denitrification occurs in anaerobiosis, high C availability, and presence of denitrifying bacteria, with losses of applied N estimated in 2.5%–70% (Nieder et al., 1989). Their correct estimation is critical for robust Eq. (1.3) outputs. The efficiency factor (N absorbed/N available-applied) for Ninitial and Nfertiliser is similar (0.4–0.6) while for Nmineralised is ≈ 0.7–0.8 (Meisinger, 1984). Soil determinations Probably the most widespread method for the diagnosis of maize N needs is based on soil N-nitrate content at sowing (0–60 cm) plus N added with fertilisation. For the main maize producing region of Argentina, Alvarez (2008) defined a critical value of 180 kg N ha− 1 for GYs close to 8 t ha− 1 using data from the 1980s (when traditional tillage systems predominated), whereas Correndo et al. (2018) recently reported critical values of 293–304 kg N ha− 1 for GYs of 11–14 t ha− 1 in the same region, mostly under no-till. A common weakness of this approach is the large dispersion of data (r2 ≤ 0.50), with N thresholds strongly modified by GY potential, soil texture, Nmineralisation, and previous crop (Orcellet et al., 2017). Plant determinations Among plant determinations, N dilution curves capture the allometric relationship between critical N, i.e. the concentration of N in shoot to maximise growth, and biomass (Gastal et al., 2015). A nitrogen nutrient index (NNI) is defined as the ratio between actual and critical N concentration. Analysing the N dilution curves for different hybrids and regions of the world, a single equation was obtained. Values of NNI = 0.85 at V5 and NNI = 0.80 at V15 (15 d before flowering) associated with maximum GY (Ciampitti and Vyn, 2013b; Greenwood et al., 1990). Djaman and Irmak (2017) estimated the critical NNI of 0.9 near physiological maturity. The relationship between relative GY and nutrient concentration in leaves and stems responds usually to linear + plateau function. On the basis of field studies between 2010 and 2016 in the US Corn Belt, Kovács and Vyn (2017) established a N threshold of 30 g kg− 1 in the ear leaf at silking for achieving 95%–100% of maximum biomass (range of 8–30 t ha− 1) and GY (range of 4–18 t ha− 1). For maximising GY in the Humid Pampa of Argentina, Uhart and Echeverría (2000) reported thresholds of 28 g kg− 1 for leaves at V6, 25 and 13 g kg− 1 for leaves and stems at V15, and 3.5 g kg− 1 for stems and 12 g kg− 1 for grains at harvest. Iversen et al. (1985) estimated 30 days after emergence as the optimum sampling time for nitrate concentration in the stem as indicative of plant N status and established a critical concentration between 11 and 16 g N-NO3 kg− 1 in the base of the stalk to achieve 95% of maximum GY. Sainz Rozas et al. (2001) determined critical values that varied between 4.3 and 10.4 g N-NO3 kg− 1 at V6, whereas a threshold of 0.4 g N-NO3 kg− 1 at R6 was reported in Argentina and the USA (Blackmer et al., 1997; Echeverria et al., 2001). These thresholds need local calibration for fertilisation recommendations elsewhere. Chlorophyll concentration in leaves is common among plant determinations and is usually linked to soil plant analysis development (SPAD) measurements. In a study in 93 sites in Iowa (rainfed) and Nebraska (irrigated), USA, Schepers et al. (1992) established 43.3 as the critical SPAD 502 threshold as representative of possible N deficiency at anthesis. Similar

Maize Chapter | 1  23

values were determined by Rashid et  al. (2005), whereas Ziadi et  al. (2008) acknowledge strong year and site effects. Hybrids may differ substantially in SPAD readings even when exposed to similar soil N supply, but genotypic variability in the chlorophyll/protein relationships should be considered when using chlorophyll-based measurements for N-status assessment (Antonietta et al., 2019). The prediction accuracy can be improved using the N sufficiency index (NSI) based on a well-fertilised tester. The sensitivity of this method is high enough after V6. The NSI ranged between 0.92 and 0.99 at V8, V10, V15, and R1 (Sainz Rozas et al., 2019). Simulation models Several models (CERES-Maize, APSIM, WHCNS, CropSyst) are presently used to explore N restrictions to crop GY, which have been locally used for management decisions, including fertilisation (He et al., 2012; Liu et al., 2011; Mercau and Otegui, 2015; Monzon et al., 2014; Morris et al., 2018). Their basis is the combination of climates, soils, water recharge, crop management, N availability, etc. to simulate GY. The results are synthesised in probabilistic (i.e. based on historic or simulated weather records) GY response curves for different soil N supplies (i.e. Ninitial + Nfertiliser). Tovihoudji et  al. (2019) found that the DSSAT model adequately estimated the positive effects of N fertilisation on the long-term average maize GY when compared with checks. Puntel et al. (2016) tested the APSIM model to simulate maize GY and the economic optimum N rate (EONR) using a large dataset (16-year field experiments) from central Iowa with good results for the long-term response of GY to N but great uncertainty for the EONR (relative root mean square errors of 12.3% and 36.6%, respectively). Remote sensing Remote sensing contributes to characterise intra- and inter-plot variability and, together with field determinations, improve the understanding of environmental limitations to crop production. After reviewing 108 studies, Griffin et al. (2005) reported benefits for the use of sensor-based variable fertiliser rate for sugar beet, maize, and wheat (in 80%, 72%, and 20% of cases, respectively). Similar trends were obtained for maize and wheat in Argentina (Bongiovanni and LowenbergDeBoer, 2006). Holland and Schepers (2010) generated a N fertiliser recommendation based on spatially variable inseason remote sensing data and established the EONR. Their model accommodates management zones, pre-sowing N applications, manure mineralisation, legume credits, nitrate in irrigation water, and crop growth stage. Ransom et  al. (2020) have conducted simultaneous comparisons of multiple N fertiliser rate recommendation tools, including canopy reflectance sensing using RapidSCAN CS-45 (Holland Scientific, Lincoln, NE), across 49 sites in eight US Midwest states and three growing seasons. Tool performances were compared to the EONR. Only 10 of 31 tools (mainly soil nitrate tests and canopy reflectance) produced N rate recommendations that correlated at least weakly with the EONR (r2 ≤ 0.20). In general, remote sensors are complementary to the other N assessment methods, simplifying the field monitoring (Morris et al., 2018). 3.3.3.2  Other nutrients Phosphorus P-Bray soil nutrient deficiency thresholds for maize varied between 13 and 20 mg kg− 1 (Bray y Kurtz I, Mehlich 3, Olsen) in Argentina, USA, China, and Switzerland (Cadot et al., 2018; García et al., 2015; Leikam and Mengel, 2007; Wang et al., 2016). The thresholds could be affected by P mineralisation (Pmin), soil texture, and previous crop. There are no methods to estimate Pmin. Fine-textured soils have a lower P threshold than coarse-textured soils, and the previous crop could release 2.8–16.5 kg of P ha− 1 (Correndo, 2018; Maltais-Landry et al., 2014; Varela et al., 2014). Sulphur S-SO4 soil nutrient deficiency threshold (0–20 cm) for maize ranges between 7 and 10 mg kg− 1 (Carciochi et al., 2016). Variability of S-SO4 thresholds could be related to SO4 content in deep layers of the soil and water table presence and to S mineralisation during the growing cycle. Carciochi et al. (2016) reported that mineralisable N determined by short-term anaerobic incubation (Nan) was associated with S mineralisation and explained 62% of the variation in the response to S fertilisation in maize. The threshold of Nan was established at 54 mg N kg− 1. Potassium The threshold for interchangeable K soil deficiency varies between (1) 100 and 120 mg kg− 1 for soils with a cation exchange capacity (CIC) of 10–15 meq 100 g− 1 and (2) 150–170 mg kg− 1 for CIC > 10–15 meq 100 g− 1 (Eckert, 1994; Leikam and Mengel, 2007). K has a negative interaction with Ca and Mg (Ca + Mg/K  1 and the vertical arrow the threshold plant growth rate for plant barrenness, whereas values in the upper box represent the evaluated stand densities (SD, in plants m− 2). In (c), the vertical arrow indicates the mean optimum SD across the 13 evaluated hybrids. In (d), all data are relative values respect to controls signalled by arrows at the (0,0) ordered pair; fitted model in dark grey ± 10% interval in light grey. Adapted from (a): Andrade, F.H., Vega, C., Uhart, S., Cirilo, A., Cantarero, M., Valentinuz, O., 1999. Kernel Number Determination in Maize. Crop Sci. 39, 453–459. https://doi.org/10.2135/cropsci1999.0011183X0 039000200026x; (b): Andrade, F.H., Echarte, L., Rizzalli, R.H., Della Maggiora, A., Casanovas, M., 2002b. Kernel number prediction in maize under nitrogen or water stress. Crop Sci. 42, 1173–1179. https://doi.org/10.2135/cropsci2002.1173; Andrade, F.H., Cirilo, A.G., Uhart, S.A., Otegui, M.E., 1996. Ecofisología del cultivo de maíz. Dekalb Press, p. 292; (c): Hernández, F., Amelong, A., Borrás, L., 2014. Genotypic differences among argentinean maize hybrids in yield response to stand density. Agron. J. 106, 2316–2324. https://doi.org/10.2134/agronj14.0183; (d): Borrás, L., Slafer, G.A., Otegui, M.E., 2004. Seed dry weight response to source–sink manipulations in wheat, maize and soybean: a quantitative reappraisal. F. Crop. Res. 86, 131–146. https://doi.org/10.1016/j.fcr.2003.08.002.

of the method (Pagano et al., 2007; Rattalino Edreira and Otegui, 2013), particularly in association with flowering dynamics models (Borrás et al., 2007, 2009), which are crucial for improving the nick between male and female inbreds in seed production (Hallauer et al., 1988). Increased broad stress tolerance of new hybrids appears to be related to an increase in partitioning of assimilate to the developing ear from a very early stage, which probably reduces PGRcp and EGRcp thresholds to prevent plant barrenness and a steeper initial slope (Echarte et al., 2000) and a shorter ASI (Campos et al., 2004).

4.2  Kernel weight GY also depends on KW and partial trade-offs between kernel number and weight occur but do not impair increases in KN to translate into improved GY. Maize source–sink ratio during grain filling (i.e. the assimilates availability to fill the kernels) is well represented by the quotient between plant growth during the effective grain filling period and the number of kernels established during the critical period (Borrás and Otegui, 2001; Cirilo and Andrade, 1996; Maddonni et  al., 1998). Grain-filling duration could be reduced when the source is strongly limited during kernel growth (Egharevba et al.,

Maize Chapter | 1  27

2010; Jones and Simmons, 1983). This shortened grain filling reflects the high dependency on current assimilates of maize kernels (Borrás et al., 2004), although some genotypic differences exist in the capacity to hold high KW when assimilates available per kernel are reduced (Borrás and Otegui, 2001). Decreases in the source–sink ratio during the effective grain filling period reduced KW, as observed for the intensifying negative effects of soil N deficiency (Hisse et al., 2019). In contrast, increasing the ratio had minimum positive effects (Fig. 1.8d). This lack of KW response to increased assimilate availability during the effective grain-filling period indicates that maize plants set an individual kernel sink potential early in grain filling, which cannot be increased by improved growing conditions in subsequent stages but can be severely reduced by an unfavourable environment (Borrás et al., 2004; Gambín et al., 2006). Differences in maize potential KW because of genotypes or environments are related to the source–sink ratio established early in grain filling and are associated with changes in kernel growth rate (KGR) during the effective grain-filling period. This rate is usually the main determinant of potential KW in maize (Borrás et al., 2003b; Saini and Westgate, 1999) and depends on the sink capacity established early during development (Jones and Schreiber, 1996; Reddy and Daynard, 1983). Then, PGRcp per kernel estimates this source–sink ratio, underscoring that the importance of the critical period is not limited to KN determination, because it also modulates the potential KW and consequently GY. Alvarez Prado et al. (2013) analysed the genetic bases of physiological processes determining KW in field-grown maize using 245 RILs derived from the IBM Syn4 population (B73 × Mo17). They established positive (and consistent across environments) genetic correlations between KW, KGR, and kernel maximum water content on maize chromosomes 2, 6, 9, and 10 respective, whereas only one consistent quantitative trait locus (QTL) was found for KW, kernel filling duration, and kernel desiccation rate. No common consistent QTL was detected for KGR and kernel filling duration. They highlighted that the co-localisation of consistent QTL for KW, KGR, and maximum water content suggests a common genetic basis for these critical secondary traits. In contrast, Mandolino et al. (2016) found a common QTL for potential KW, KGR, and kernel filling duration on chromosome 5 after mapping 181 RILs derived from a dent × flint Caribbean cross, alerting on the need to explore different physiological strategies for KW determination in different genetic backgrounds.

4.3  Biomass partitioning Genetic GY gains for maize in the past decades in Argentina associated with improved KN m− 2 at optimum stand density, enhanced post-silking biomass, and enhanced biomass allocation to reproductive sinks (Echarte et al., 2004; Luque et al., 2006). Physiological traits representative of pre-silking growth (i.e. canopy development and intercepted radiation up to silking) had almost no effect on these gains; differences among hybrids arose at the start of the critical period and were evident as improved RUE, PGRcp, and biomass partitioning to reproductive organs during that period (EGRcp/PGRcp). Kernel number responded to these trends in biomass production and partitioning. Improved biomass after silking allowed for an almost constant source–sink ratio during active grain filling (Lee and Tollenaar, 2007), which avoided a trade-off between GY components (Sadras, 2007), with the concomitant improvement in GY (Luque et al., 2006). Biomass allocated to kernels may come from current photosynthesis or from reserves stored as stem water soluble carbohydrates (SWSC). The relative contribution of each source has traditionally been estimated by comparing individual KW and the plant growth per kernel during the grain-filling period (PGKgf) (Borrás and Otegui, 2001; Cirilo and Andrade, 1996). When KW ≈ PGKgf, it is assumed that stored reserves were not used during the grain-filling period nor accumulated in other organs. Reserve use per set kernel increases when KW > PGKgf, whereas reserves are accumulated when PGKgf > KW (D’Andrea et al., 2016). Reserve use during active grain filling can vary widely in response to growing conditions that modify the initial reserves at R2 and their subsequent demand (Rattalino Edreira et al., 2014). Hybrids with large kernels combined with high KNP represent an enhanced demand of assimilates and consequently, an enhanced dependence on reserves that might explain the reduced KW stability among modern maize hybrids in certain environments (Echarte et al., 2006). This response is substantially affected by growth conditions during active grain filling, which modify current plant growth and the actual dependence of kernels on reserves (Borrás et al., 2004; Cirilo and Andrade, 1996; Hisse et al., 2019; Rattalino Edreira et al., 2014). Adverse photothermal environments during this stage are particularly critical for crops sown late in spring (Bonelli et al., 2016) and/or those grown at high latitudes (Ruget, 1993), where declining irradiance is accompanied by low temperatures that may affect RUE (Andrade et al., 1993) and therefore crop growth (Tsimba et al., 2013), increasing the dependence on reserves (Kiniry and Otegui, 2000). Maize breeding has improved stay green and canopy health (Tollenaar and Aguilera, 1992) and RUE (Curin et al., 2020; Luque et al., 2006). It has also produced a delay in the age-related decline in photosynthetic rate, especially under N stress (Tollenaar and Lee, 2011). This improved photosynthetic performance could be responding to the larger sink size in modern hybrids (i.e. feedforward effect) and highlights the importance of breeding for an increased photosynthetic activity during grain filling. This effort, however, should not be limited to the effective grain-filling period. The enhanced demand of reserves in high-yielding modern hybrids suggests

28  Crop Physiology: Case Histories for Major Crops

that high photosynthetic activity during late stem elongation, pollination, and early grain growth is essential for assimilate provision to the ear (Schussler and Westgate, 1994) and for reserves accumulation in the stem to cope with grain filling and reduced lodging risk. High accumulation of SWSC may be particularly important in environments prone to terminal stress (Ouattar et al., 1987; Rattalino Edreira et al., 2014).

4.4  Grain quality Phenotypic plasticity of KW is usually small, reflecting a strong genetic control (Hallauer et al., 1988; Prado et al., 2014). Nevertheless, KW responds to reductions in assimilate availability during grain filling as mentioned earlier, which may impair kernel composition (Borrás et al., 2002) and quality properties for industrial purposes (Cirilo et al., 2011; Mayer et al., 2014; Tanaka and Maddonni, 2008).

4.4.1  Kernel hardness Kernel hardness is related to bulk density, storability, insect damage of stored grain, breakage susceptibility, milling characteristics, dry and wet milling yields, and production of special foods (Pomeranz et al., 1986). Maize dry milling industry demands high kernel hardness to maximise yield of coarse fractions (flaking grits) during grinding (Chandrashekar and Mazhar, 1999). The wet-milling market demands intermedium kernel hardness to obtain higher starch yield (Eckhoff, 2004). For animal feed, soft endosperm with higher digestibility is preferred (Rooney et al., 2005). Maize endosperm is composed of vitreous and floury portions, and kernel hardness and density result from the balance between these portions. Flint maize kernels with hard endosperm are still highly demanded for breakfast cereals, snacks, polenta, and brewing (Chandrashekar and Mazhar, 1999; Lee et al., 2007). Argentina is presently the single exporter of nonGMO flint maize to the EU, where special import permits allow its use provided grain quality attains specific standards. The non-GMO flint maize has physicochemical characteristics that make it the preferred raw material for dry milling (Litchfield and Shove, 1990; Rooney et al., 2005). It is highly demanded because of its high milling yield of large endosperm grits and the particular quality that provides to a wide variety of end-use products (Macke et al., 2016). Starch and protein are the main components of maize endosperm, and both have been mechanistically related to kernel hardness (Gayral et al., 2016). Starch granules are embedded within a protein matrix that stretches while starch granules grow. Upon kernel desiccation, the protein matrix is torn in sections where it is thin and labile, and air-filled spaces appear resulting in the floury endosperm. Instead, polyhedral starch granules grow embedded within thicker and stronger sections of the endosperm protein matrix to yield the vitreous endosperm fraction. Within this part, no void spaces are formed during kernel desiccation, thus leading to a compact and vitreous appearance and a hard texture (Watson, 2003). Cerrudo (2018) reported that higher kernel protein concentrations are commonly observed among flint type hybrids with greater proportion of vitreous endosperm (mean of 10.3%; range 9.3%–12.0%) when compared to dent types with predominant floury endosperm (mean of 9.4%; range 8.4%–10.5%). In particular, zeins concentration (a prolamin-type protein found in the corn endosperm) is correlated with kernel hardness (Gerde et al., 2016; Kljak et al., 2018; Robutti et al., 1997). Starch concentration and composition are also central to endosperm hardness. Endosperms with high amylose proportion are harder and denser than endosperms with high amylopectin proportion, suggesting that increased amylose concentration provides more amorphous regions, thus resulting in more compressible polyhedral starch granules that characterise the vitreous endosperm (Dombrik-Kurtzman and Knutson, 1997; Robutti et  al., 2000). Amylose/starch ratio would be modified by changes in starch branching enzyme activity. Lenihan et al. (2005) hypothesised that low temperature would increase this enzyme activity, while high temperature would have the opposite effect, affecting amylose proportion in the starch. Analysing several environments in Argentina, Martínez et al. (2017) reported that air temperature during the effective grain-filling period of hybrids with different endosperm texture was the environmental factor that better accounted for the variation in kernel starch composition, modifying the proportion of amylose in the starch (amylose/starch ratio). Moreover, they confirmed that increases in ear temperature explained the increases in amylose/starch ratio in maize kernels, particularly for treatments applied early during grain filling (Martínez et al., 2019). Dry milling performance is directly associated with kernel hardness, which can be expressed as its mechanical resistance to milling (Wu et al., 2010). Kernel coarse-to-fine ratio of particles derived from the mill is an excellent indicator of hardness (Blandino et al., 2013). A high coarse-to-fine ratio is typical of hard kernels and is associated with elevated dry milling yields. Genetic determinants are the main contributors to common field variations in endosperm hardness. Heritability values of 0.49, 0.65, and 0.80 were established for flaking-grit yield, dry-milling efficiency, and test weight, respectively, as estimators of kernel hardness (Macke et  al., 2016). However, the growing environment can also affect kernel hardness (Cerrudo et al., 2017; Mayer et al., 2019; Tamagno et al., 2016). Enhanced grain dry-milling quality was

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e­ stablished for flint maize exposed to environments or management that improved the source–sink ratio during the postsilking period (Cirilo et al., 2011). For instance, an improved ratio is strongly associated with enhanced incident SR and increased biomass as related to early respect to late sowings at mid-latitudes (Bonelli et al., 2016; M.E. Otegui et al., 1995b). According to this, while no sowing date effects on kernel hardness were found at low latitudes (Abdala et al., 2018; Gerde et al., 2016), Cerrudo et al. (2017) reported decreases in the coarse-to-fine ratio under late sowings in environments with short and cool summers that promote low photosynthetic source capacity during grain filling.

4.4.2  High-oil maize and acidic specialties Maize kernels with high oil concentration are preferred for livestock and poultry feed rations because of their energy value and as a substitute for animal fats (Thomison et al., 2003). Traditional maize hybrids produce kernels with an oil concentration of ≈ 6% (Maddonni and Otegui, 2006), but kernel oil concentration exhibits genetic variability that enables breeding for this trait (Laurie et al., 2004). Hence maize hybrids with a high kernel oil concentration could be obtained by crossing parental lines selected for this trait. Unfortunately, GY and other agronomic characteristics of these high-oil populations are poor, so they are not used in commercial production (Laurie et al., 2004). An alternative way to achieve high oil maize production is based on the exploitation of the xenia effect on kernel composition (Letchworth and Lambert, 1998), which depends upon the use of high-oil parents as pollen donors. This strategy, described as top-cross (Thomison et al., 2002), does not modify GY and KW of the maternal genotype but changes embryo and endosperm growth rates and embryo oil deposition, improving kernel oil concentration (Tanaka and Maddonni, 2008). This attribute in maize kernels shows a strong stability for a wide range of post-flowering source–sink ratios because of a stable embryo–kernel ratio and embryo oil concentration (Tanaka and Maddonni, 2008). Modifying source–sink ratio with thinning and intensity of shading during the effective grain-filling period, Tanaka and Maddonni (2008) found that only severe shading at early stages of kernel growth reduced the final embryo–kernel ratio and the embryo oil concentration. Then, maize kernel oil concentration seemed to be commonly sink-limited. Vegetable oil quality is linked to fatty acid composition; see Chapter 16: Sunflower, Section 4 for comparison. Oleic acid is nowadays the preferred fatty acid for edible purposes because it combines a hypo-cholesterolemic effect and a high oxidative stability (Mensink and Katan, 1989). Temperature during kernel formation accounts for most of the variation in fatty acid composition across years, locations, and sowing dates, and a linear response was detected for oleic acid percent to daily mean temperature in traditional (Izquierdo et al., 2009) and in high oleic-acid hybrids (Zuil et al., 2012). In both types, the oleic acid percent also responded to the amount of intercepted radiation per plant during grain filling (Izquierdo et al., 2009; Zuil et al., 2012). Then, increasing daily mean temperature and/or intercepted SR per plant (up to a saturation level) increased the proportion of oleic acid at the expense of linoleic and/or linolenic acid. Consequently, management practices that increase temperature and intercepted SR per plant during grain filling (e.g. sowing date, plant density, location, and fertilisation) could increase oleic acid percent in maize oil, irrespective of the genotype.

5  Concluding remarks: Challenges and opportunities In this chapter, we outlined the main maize production areas and the role of this crop in farming systems and presented the factors and mechanisms that control development, growth, capture, and efficiency in the use of resources, GY, and kernel quality. A distinctive aspect of maize production was the marked increase during the past two decades when compared with the second half of the 20th century. The outstanding feature was that its production increase has been primarily sustained by area expansion rather than by improved GY, defying the predominant paradigm of supporting production in the latter rather than in exploiting fragile lands. Maize is presently the crop that covers the largest cultivated area, has the largest production, and is an important component in many relevant productions systems. In this scenario, the biggest challenge for the next decades will be to satisfy two contrasting social demands: to increase food production in amount and quality and to decrease the environmental impact linked to input-based agriculture. The pressure will be particularly important for maize, a species characterised by high potential GYs as much as by its sensitivity to resource availability to make those GYs possible. Improved resource use efficiency as much as GY stability across environments would be the main target of future breeding and crop management efforts. Thus the knowledge and quantification of the ecophysiological factors and mechanisms underlying maize development, growth, and GY determination are valuable to (1) design knowledge-intensive crop management strategies for specific genotype and environment combinations oriented to a high and sustainable production in quantity and quality and (2) to understand the differential responses of maize to management practices among cultivars, environmental conditions, and

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production systems. This approach constitutes a low-cost technology that helps matching crop demands and ­environmental offer and is expected to improve the efficiency in the use of environmental resources and inputs. It would also help to solve some input shortage problems that are presently threatening maize production in less developed countries as much as environmental constraints. The identification and analysis of factors and processes that regulate maize growth and GY in interaction with the environment can also provide conceptual and practical tools to improve maize breeding efficiency by identifying relevant traits for increasing GY potential and GY stability across environments, disentangling complex genotype by environment interactions and interactions among relevant traits. The success of this enterprise is highly dependent upon the development of rapid, accurate, and affordable phenotyping methods to assist breeders and to extend modelling predictions from gene expression to agroecosystems. Improved crop modelling is also expected to help redesign cropping and breeding strategies and to guide public policies for sustainable land use.

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J. 95, 147–154. https://doi.org/10.2134/agronj2003.0147. Tian, H., Chen, G., Zhang, C., Melillo, J.M., Hall, C.A.S., 2010. Pattern and variation of C:N:P ratios in China’s soils: a synthesis of observational data. Biogeochemistry 98, 139–151. https://doi.org/10.1007/s10533-009-9382-0. Tojo Soler, C.M., Sentelhas, P.C., Hoogenboom, G., 2005. Thermal time for phenological development of four maize hybrids grown off-season in a subtropical environment. J. Agric. Sci. 143, 169–182. https://doi.org/10.1017/S0021859605005198. Tolk, J.A., Evett, S.R., Xu, W., Schwartz, R.C., 2016. Constraints on water use efficiency of drought tolerant maize grown in a semi-arid environment. F. Crop. Res. 186, 66–77. https://doi.org/10.1016/j.fcr.2015.11.012. Tollenaar, M., 1977. Sink-source relationships during reproductive development in maize. A review. Maydica 22, 49–75. Tollenaar, M., Aguilera, A., 1992. Radiation use efficiency of an old and a new maize hybrid M. Agron. J. 84, 536–541. Tollenaar, M., Hunter, R.B., 1983. A photoperiod and temperature sensitive period for leaf number of maize. Crop Sci. 23, 457. https://doi.org/10.2135/ cropsci1983.0011183X002300030004x. Tollenaar, M., Lee, E.A., 2011. Strategies for enhancing grain yield in maize. Plant Breed. Rev. 34, 37–82. https://doi.org/10.1002/9780470880579.ch2. Tollenaar, M., Dwyer, L.M., Stewart, D.W., 1992. Ear and kernel formation in maize hybrids representing three decades of grain yield improvement in Ontario. Crop Sci. 32, 432. https://doi.org/10.2135/cropsci1992.0011183X003200020030x. Tovihoudji, P.G., Akponikpè, P.B.I., Agbossou, E.K., Bielders, C.L., 2019. Using the DSSAT model to support decision making regarding fertilizer microdosing for maize production in the sub-humid region of Benin. Front. Environ. Sci. 7. https://doi.org/10.3389/fenvs.2019.00013. Troyer, F., 2001. Temperate corn—background, behavior, and breeding. In: Corn: Chemistry and Technology. CRC Press, pp. 393–466, https://doi. org/10.1201/9781420038569.ch14. Tsimba, R., Edmeades, G.O., Millner, J.P., Kemp, P.D., 2013. The effect of planting date on maize: phenology, thermal time durations and growth rates in a cool temperate climate. F. Crop. Res. 150, 145–155. https://doi.org/10.1016/j.fcr.2013.05.021. Uhart, S.A., Andrade, F.H., 1991. Source-sink relationships in maize grown in a cool-temperate area. Agronomie 11, 863–875. Uhart, S.A., Andrade, F.H., 1995a. Nitrogen deficiency in maize: I. effects on crop growth, development, dry matter partitioning, and kernel set. Crop Sci. 35, 1376–1383. https://doi.org/10.2135/cropsci1995.0011183X003500050020x. Uhart, S.A., Andrade, F.H., 1995b. Nitrogen and carbon accumulation and remobilization during grain filling in maize under different source/sink ratios. Crop Sci. 35, 183–190. https://doi.org/10.2135/cropsci1995.0011183X003500010034x. Uhart, S.A., Echeverría, H.E., 2000. Diagnóstico de la fertilización. In: Andrade, F.H., Sadras, V.O. (Eds.), Bases Para El Manejo Del Maíz. El Girasol y La Soja. INTA y Facultad de Ciencias Agrarias-UNMP, Argentina, Balcarce, pp. 235–268. Uribelarrea, M., Cárcova, J., Otegui, M.E., Westgate, M.E., 2002. Pollen production, pollination dynamics, and kernel set in maize. Crop Sci. 42, 1910– 1918. https://doi.org/10.2135/cropsci2002.1910. Van Oosten, M.J., Pepe, O., De Pascale, S., Silletti, S., Maggio, A., 2017. The role of biostimulants and bioeffectors as alleviators of abiotic stress in crop plants. Chem. Biol. Technol. Agric. 4, 5. https://doi.org/10.1186/s40538-017-0089-5.

Maize Chapter | 1  43

Van Opstal, N.V., Caviglia, O.P., Melchiori, R.J.M., 2011. Water and solar radiation productivity of double-crops in a humid temperate area. Aust. J. Crop. Sci. 5, 1760–1766. van Roekel, R.J., Coulter, J.A., 2011. Agronomic responses of corn to planting date and plant density. Agron. J. 103, 1414–1422. https://doi.org/10.2134/ agronj2011.0071. van Roekel, R.J., Coulter, J.A., 2012. Agronomic responses of corn hybrids to row width and plant density. Agron. J. 104, 612–620. https://doi.org/10.2134/ agronj2011.0380. Varela, M.F., Scianca, C.M., Taboada, M.A., Rubio, G., 2014. Cover crop effects on soybean residue decomposition and P release in no-tillage systems of Argentina. Soil Tillage Res. 143, 59–66. https://doi.org/10.1016/j.still.2014.05.005. Varlet-Grancher, C., Bonhomme, R., Chartier, M., Artis, P., 1982. Efficience de la conversion de l’energie solaire par un couvert vegetal. Acta Oecologica Oecologia Plant. 3, 3–26. Varvel, G.E., Wilhelm, W.W., 2003. Soybean nitrogen contribution to corn and Sorghum in Western Corn Belt rotations. Agron. J. 95, 1220–1225. https:// doi.org/10.2134/agronj2003.1220. Vega, C.R.C., Sadras, V.O., Andrade, F.H., Uhart, S.A., 2000. Reproductive allometry in soybean, maize and sunflower. Ann. Bot. 85, 461–468. https:// doi.org/10.1006/anbo.1999.1084. Villalobos, F.J., Fereres, E., 1990. Evaporation measurements beneath corn, cotton, and sunflower canopies. Agron. J. 82, 1153. https://doi.org/10.2134/ agronj1990.00021962008200060026x. Vogel, E., Donat, M.G., Alexander, L.V., Meinshausen, M., Ray, D.K., Karoly, D., Meinshausen, N., Frieler, K., 2019. The effects of climate extremes on global agricultural yields. Environ. Res. Lett. 14. https://doi.org/10.1088/1748-9326/ab154b. Wang, J., Wang, E., Yang, X., Zhang, F., Yin, H., 2012. Increased yield potential of wheat-maize cropping system in the North China plain by climate change adaptation. Clim. 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Image source: Shiv Mirthyu from Pixabay

Chapter 2

Rice Shu Fukai and Len J. Wade University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD, Australia

1 Introduction In the LT Evan’s book ‘Crop physiology – some case studies’, Murata and Matsushima (1975) contributed the rice (Oryza sativa L.) chapter and described crop physiological understanding of rice at the time on dry matter production, generative growth, and yield limitation by grain storage capacity and assimilate supply during grain filling, with emphasis on the influence of timing of nitrogen (N) fertilisation. Their work was almost solely limited to fully irrigated conditions and mostly in temperate environments. For the intervening 45 years since the publication of the book, crop physiological understanding of rice increased greatly, including growth under water-limiting environments in the tropics. Global rice yield roughly doubled in the period, and high yielding varieties have been grown with different management. This chapter summarises recent advances in crop physiology of rice through sections on crop development, growth and resources, grain sink and source availability determining yield, response to abiotic factors, and effects of crop management on grain yield and quality. The concluding section suggests priority areas for further research. This first section introduces the global significance of rice, classification of rice cropping systems based on water availability, and key management options for rice establishment, water saving technologies, and mechanisation that are taking place in many rice-growing areas.

1.1  Global significance of rice More than half the population in the world consumes rice as a staple food, and 90% of the rice production and consumption take places in Asia. The most common system of rice culture is in irrigated lowlands where the crops are grown with standing water during most of the season in paddy fields with bunds to store and secure water supply. This unique rice-growing environment is not conducive to most other crops, and hence rice is commonly grown as a sole crop, and intercropping or mixed cropping is very limited. In Asia, rice farmers had traditionally grown the crop to provide sufficient food for the family, and this subsistence nature continues to the present time in many countries, and hence the rice traded at market is smaller when compared to other major cereals such as wheat. Nevertheless, rice produced for international markets has increased. According to FAO statistics for 2017, world-wide rice area is 167 Mha, production 770 Mt, with average yield of 4.60 t ha− 1. In 1961, rice area was 115 Mha, production 216 Mt, and yield 1.87 t ha− 1. Thus in the 56 years, yield increased 2.5 times, area 1.45 times, and production 3.56 times (Fig. 2.1). The rice productivity increase in the early period coincided with the green revolution where semi-dwarf high-yielding varieties were grown with increased fertiliser, particularly N in the irrigated fields. The increase continued with further development and adoption of new technologies, although there is now a sign of reduced rate of increase. As in other crops, rice has experienced climate change in recent years, and this change is likely to continue. Atmospheric CO2 concentration has increased, and this is expected to have contributed to increased dry matter production of rice as in other crops (Wang et al., 2015). Global mean surface temperature increased by 0.85°C from 1880 to 2012 and is expected to increase by 1.0–3.7°C by 2100 according to IPCC (2013). The magnitude of temperature increase depends on both the location and time of the year (Shimono, 2011). Increased temperature has hastened phenological development and generally had adverse effect on rice growth and yield. The trend in rainfall patterns is less clear, although there are changes in seasonal rainfall in some areas (Prabnakorn et al., 2018). Section 4.2 addresses rice responses to abiotic factors, which can form a basis for estimation of rice production under possible climate change scenarios. Crop Physiology: Case Histories for Major Crops. https://doi.org/10.1016/B978-0-12-819194-1.00002-5 Copyright © 2021 Elsevier Inc. All rights reserved.

45

46  Crop Physiology: Case Histories for Major Crops

Global rice area, producon and yield since 1961 9 8 7 6 5 4 3 2 1 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017

0

area (x100 mha)

producon (x100 mt)

yield (t/ha)

FIG. 2.1  Change in global rice area, production, and yield since 1961. Source: FAOSTAT.

1.2  Rice ecosystem classification with emphasis on water availability Rice ecosystems are commonly classified based on water availability. The most common ecosystem is irrigated lowland rice where rice paddy field is flooded with shallow water depth of less than 0.3 m after the crop is established until just before harvesting. In some areas, rice fields may be flooded with relatively shallow stagnant water (0.3–0.5 m for most of the season) or deeper water, and deep-water and floating rice ecosystems may be adopted. Another major ecosystem is rainfed lowland rice, which is practised in areas of no or limited irrigation water, where crops may experience stress because of soil water deficit at some stages. In the irrigated and rainfed lowland ecosystems, there are bunds to maintain standing water in rice paddies, and crops are commonly grown anaerobically with saturated soil. Rainfed lowland rice may experience anaerobic conditions during the growing period, but in the period of low rainfall, the crop is grown aerobically. Lowland fields are mostly located in plains or towards lower parts of sloped land, and with heavy rainfall, lowland crops may be submerged, i.e. water level exceeds the plant height, and they can be severely damaged. In these lowland areas and also deep water and floating rice areas, often only rice crops can be adapted to the excess water conditions, particularly during wet season; without rice, the area would be wetland. In areas where water is in short supply, upland rice ecosystem may be practised. Upland rice is grown mostly in hilly areas and commonly without irrigation. Upland fields do not have bunds to store water above the soil surface, excess water may be lost as run-off, and crops are grown mostly aerobically. Aerobic rice is a relatively new term for rice mainly grown in irrigated lowlands but in aerobic soil without standing water. The aerobic rice has been researched recently in an attempt to maintain high irrigated yield with minimal water deficit while reducing the high irrigation water requirement of lowland rice ecosystem. Rao et al. (2017) and Chauhan et al. (2017) further describes rice ecosystems. Rice is a C3 plant adapted to warm temperate–tropical environments. It is grown widely from the tropics to the temperate areas. Often indica varieties are grown in hot–warm areas, and japonica varieties are common in warm temperate areas. Rice is grown as a single crop in summer in temperate areas, while in the tropics, it is grown commonly in the wet season but can also be double cropped in longer wet seasons, or wet season plus dry season, or even triple cropped, where irrigation water is available. It can be grown in rotation with non-rice crops: for example, rice-wheat rotation is widely practised in the Indian Subcontinent and in the Central East of China. Fischer et al. (2014) identified several Mega Environments (ME) for rice. Irrigated lowland rice consists of three major MEs, and their cropping systems are determined mostly by temperature; one ME for temperate area where only one rice crop is grown in a year, another with rice double cropped with other crops in the warm tropics and subtropics, and the third for warm to hot tropics where double cropping of rice is practised. In this chapter, we emphasise water availability as a key factor determining growth of the rice crop, and management options are considered in relation to the growth environment, particularly water availability. With secured water supply available in irrigated areas, rice is commonly grown with high input, and high yields can be achieved with high yielding varieties, and harvested crops are sold commercially, except some that are kept for home consumption. On the other hand, growth and yield of rainfed rice depends strongly on seasonal rainfall, and with high risk of not achieving high yield, input is generally low, and yield may not be high even in a season of high rainfall. Genotypes differ in their adaptation to water availability, including excess water, and suitable genotypes have been developed and are grown under particular land and crop management (Wade et al., 1999b). Water shortage is a serious threat to the rice industry in many growing regions, and development of management methods to use limited water efficiently is a major challenge (Prasad et al., 2017b).

Rice Chapter | 2  47

1.3  Crop management There are several management options available for rice growers. This section concentrates on crop establishment, water saving, and mechanisation. The effect of crop management on crop development, growth and resource requirement, and grain yield and quality is described in Sections 2, 3, and 4.3, respectively. Other management options such as selection of varieties, time of planting, fertiliser rates, and timing are integrated into different sections throughout the chapter.

1.3.1  Crop establishment The most common method for crop establishment, particularly in Asia, is transplanting where seedlings are raised in a nursery under more controlled conditions and transplanted to the main fields using commonly 15–40-day-old seedlings. Transplanting is not common in some countries, for example the USA and Malaysia, as described by Kumar and Ladha (2011) in their review of direct seeding. Transplanting is labour-intensive requiring often about 30 people to transplant 1 ha in 1 day, including the time of pulling seedlings, carrying them from the nursery to the main field, and transplanting (Fukai et al., 2019; Xangsayasane et al., 2019b). Different types of crop establishment and some of their main characteristics are listed in Table 2.1. Broadcasting, the most common method of direct seeding, particularly in Asia, requires only one to two people to sow 1 ha (Fukai et al., 2019) and is adopted when there is a lack of labour for manual transplanting. There are different methods in direct seeded rice (Kumar and Ladha, 2011). Wet direct seeding is practised when the soil is saturated with water and commonly, the soil puddled before sowing, and thus land preparation is similar to transplanting. On the other hand, in dry direct seeding, cultivation takes place while soil is still dry. These methods are often related to water availability; in irrigated fields, wet direct seeding is commonly practised, while in rainfed lowland where water environment is not favourable, dry direct seeding is more common. In some areas, particularly where red rice is grown, weedy rice is a problem, and water seeding is practised where seeds are broadcast in standing water (Kumar and Ladha, 2011). Transplanting is common when soil in the main field is saturated with water, and this may often take 2 months after the onset of the wet season. On the other hand, direct seeding particularly dry direct seeding is often practised earlier in the season. This method may also be adopted when there is not sufficient rain during the early part of the wet season and seedlings become too old for transplanting, so as a last resort, farmers may broadcast seed with the hope that rain will come soon after sowing. For flood-prone areas where taller seedlings are needed for transplanting into standing water, late transplanting or double transplanting may be needed (Sharma, 1995; Satapathy et al., 2015). In addition to transplanting and direct seeding, rice can be established from ratooning, as is common in sugarcane (see Chapter 21: Sugarcane). Ratooned rice is established as a regrowth of the crop when it is harvested, and the crop establishment cost is greatly reduced. Commonly rice is ratooned only once, but there is a possibility that ratooning can be extended for additional cycles, especially with perennial rice.

1.3.2  Water-saving methods Owing to increasing water scarcity and low water use efficiency (WUE), water-saving technologies have been developed in some irrigated rice systems. For example, water table depth has decreased in many parts of the world, particularly in China and India, promoting the need for water-saving technologies in the affected areas (Prasad, 2011). In rainfed lowland areas TABLE 2.1  Common rice establishment methods. Transplanting

Direct seeding

Manual transplanting

Transplanter

Broadcasting

Cultivation condition

Wet

Wet

Wet

Dry

Dry water seeding

Dry

Row pattern

Rows not common

Rows (around 25 cm)

No rows

No rows

No rows

Rows (20–25 cm common)

Labour requirement

20–30

3–4

1–2

1–2

NA

2

Method

Seed drill

Labour requirement (the number of people required for planting 1 ha in 1 day) is for Lao PDR. From Xangsayasane, P., Phongchanmisai, S., Vuthea, C., Ouk, M., Bounphanousay, C., Mitchell, J., Fukai, S., 2019b. A diagnostic on-farm survey of the potential of seed drill and transplanter for mechanised rice establishment in Central Laos and Southern Cambodia. Plant Prod. Sci. 22, 12–22.

48  Crop Physiology: Case Histories for Major Crops

with no irrigation water, water-saving technologies are generally not available. One possibility for both irrigated and rainfed lowlands is the use of dry direct seeding, which decreases the total water requirement when compared with puddled fields with transplanted crop because as high water loss through the puddling process is removed. Alternate wetting and drying irrigation (AWD) has recently gained popularity. Rice is grown in irrigated lowland fields, and water level is allowed to drop to commonly 15 cm below the soil surface before irrigation to bring the water level to above the surface, and this is repeated throughout the season. This is now practised where the full amount of irrigation water cannot be secured or is too costly (Bouman et al., 2007). Aerobic rice is grown with no standing water in the field in Brazil (Pinheiro et al., 2006), parts of China (Prasad, 2011), and incipiently in northern Australia. Aerobic rice is grown with ample irrigation to minimise potential water stress effects. Rice fields may not be puddled, and the crop may be planted with a seed drill and grown aerobically. The aerobic system saves water when compared to fully flooded or AWD and allows for rotation with non-rice crops. Land preparation, planting, irrigation, and harvesting of rice may be repeated in a similar manner to the non-rice crop, which follows after the rice in the wet season is harvested. Advantages/limitations of these water-saving methods are described further in Sections 3.2 and 4.3.2.

1.3.3 Mechanisation Traditionally, rice was managed manually, particularly in south and southeast (SE) Asia, but with increased labour cost and labour shortage, mechanisation is increasing where suitable machinery is available and affordable. Mechanisation is commonly adopted for land preparation, planting, and harvesting. This may be based on a two-wheel tractor (hand tractor) that the farmer owns or on a contracting service. It is common for farmers in Asia to own a two-wheel tractor for land preparation, for planting using a seed drill, and harvesting using a reaper. Because of labour shortage and its cost, the transplanter has been used in Eastern Asia, while the seed drill has started to be used in south and SE Asia (see Table 2.1). Mechanical planters can improve crop establishment. A common feature of a mechanical seeder, such as the seed drill, transplanter, and drum seeder is seeding in rows, which ease subsequent crop management. In some areas such as in California and Australia, rice seed is aerially sown in flooded paddies. A combine harvesting service has become available in many rice growing areas and is well adopted by farmers. The adoption of mechanised rice production affects some aspects of crop growth and grain yield and quality (Section 4.4). Mechanisation is often adopted as rice cropping moves from subsistence to commercial agriculture (Clarke et al., 2018). Grain quality, particularly milling quality, becomes important for marketing purposes, so the broken rice fraction should be avoided. A key factor for assessing milling quality is head rice yield (head rice recovery), the proportion by weight of unbroken milled rice (80% of maximum length retained after milling) to weight of rough paddy rice, is used in this chapter. Other grain quality parameters for chemical composition, such as amylose content and protein content, are also important, and they are reviewed in Fitzgerald et al. (2009).

2  Crop structure, morphology, and development This section discusses crop establishment first, followed by phenological development with emphasis on flowering, and concludes with an outline of shoot and root development.

2.1  Germination and seedling emergence Transplanting has an advantage of good establishment with seedlings raised in areas where water may be more controlled, and often fertiliser is applied for healthy seedlings suitable for transplanting. When the main fields are saturated with water, seedlings are transplanted, and water level is raised as seedlings become taller. Older seedlings are used commonly in rainfed lowland areas because farmers may need to wait for main paddy fields to be saturated with water for transplanting. Also in rainfed lowlands where water control is not readily available, young seedlings risk submergence with heavy rainfall after transplanting. For transplanted rice, seed beds provide favourable conditions for germination and early growth. This protection is not available for direct seeded rice, and germination, seedling emergence, and early vigour are important, particularly for direct seeded rice in both lowland and upland ecosystems, including aerobic rice. Water control may be available for irrigated lowland or aerobic rice but may not be available for rainfed lowland rice. Even where irrigation water is available, delicate water management is required not to inundate germinating seed and young seedlings yet to provide sufficient water for their growth. Thus Kato and Katsura (2014) consider early vigour to be one of most important genotypic characteristics for successful aerobic culture. Rapid leaf development to secure a quick increase in leaf area index (LAI) is required for aerobic culture. This could be partly achieved by appropriate genotypes such as tropical japonica types.

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2.1.1  Importance of seedbed in direct seeded rice Direct seeding requires precise water management for emergence, crop establishment, and weed management. Often broadcasted crops are not as even as transplanted ones (Hayashi et al., 2009), particularly when land preparation is not thorough and land level is uneven (Rickman et al., 2001). There could be large areas in which seedlings fail to emerge, and sometimes farmers transplant seedlings into such areas. In addition, broadcasted crops may have seed buried to different soil depths, when broadcasting is followed by harrowing, as is commonly practised. Weeds may grow where rice plants are missing or slow to emerge. Thus it is important to have good rice seed with high germination rate and vigour for quick establishment, and in some cases, seed priming may be used to promote quick establishment (Farooq et al., 2011). Similarly, coating of seeds with calcium peroxide is used to assist good establishment in wet seeding or water seeding (Yamauchi and Chuong, 1995). Iron coating appears useful in anchoring seed in water seeding (Yamauchi, 2017). Germination is important particularly for direct seeded crop in rainfed lowlands where water control is limited, and often the fields are affected by flooding or water logging after seeding. Low oxygen availability in the soils reduces germination and could result in failure of establishment. While rice can grow well under flooded conditions with the development of aerenchyma tissues that transport oxygen through the plant under standing water, it is susceptible to excess water during germination and early growth (Yamauchi et al., 1993). Some genotypes are more tolerant of flooding (anaerobic germination (AG)) because they are able to break starch into simple sugars (Ismail et al., 2009). Genotypes IR64-AG131 and IR64-AG132 introgressed with quantitative trait loci (QTL) for AG (AG1) were able to germinate, and seedlings emerged from the field at more than 200% higher rate than IR64 even after 21 days flooding following seeding (Lal et al., 2018). The improved establishment in genotypes with AG1 increased effective tillers m− 2 and grain yield in three establishment ­methods: dry direct seeding, wet direct seeding using the drum seeder, and broadcasting. Lal et al. (2018) showed that higher seed rate and fertilisation, particularly phosphorus (P), improved crop establishment.

2.1.2  Lodging in broadcasted rice The position of the crown in broadcasted crops is commonly shallow. When compared with transplanted crops, broadcasted crops often require higher number of seeds, resulting in a higher established plant density. These conditions are inductive for plant lodging, and the percent of lodged crops is commonly higher in broadcasted crops than in transplanted crops (Xangsayasane et al., 2019a). Thus lodging resistant varieties are required for broadcasted crops. Lack of labour to transplant seedlings or lack of early season rain with unsaturated soil for manual transplanting may direct farmers to direct seeding with suitable varieties that can emerge quickly from the soil and that do not lodge.

2.1.3  Deep planting Deep planting can compromise rice seedling emergence. However, Hanviriyapant et al. (1987) showed the benefit of deep seeding using a seed drill or by dibbling when surface soil dry out after a rainfall event in rainfed lowland or with delayed planting after an irrigation event. They found that top soil (0–5 cm) dried out quickly, while the 5–10 cm layer was wetter at 10 days after irrigation. This soil moisture condition resulted in the optimal planting depth of 4–6 cm for cv Pelde in northern Australia when irrigation was applied on the same day and shifted to 6–8 cm and 8 cm on 10 and 15 days after irrigation, respectively. The maximum emergence decreased from about 80% to 50%–40% with delay in sowing after irrigation. Seedling vigour as measured by shoot dry weight at 43 days after planting followed the pattern of seedling emergence. An advantage of delayed sowing after irrigation was better control of weeds with cultivation before planting. Using a seed drill, seed placement can be deeper than broadcasted crops. In rainfed lowlands in the Philippines, mean seed depth of broadcasted rice was 16–35 mm in a 2-year study under the experimental conditions of Ohno et al. (2018) and 16–23 mm for Bautista et al. (2019), while seed depth with a seed drill mounted on a two-wheeled tractor was 16–32 mm. This depth can be lowered further with the seed drill if required. Thus with a seed drill, planting depth can be below that of the broadcasted crop, with higher water availability in the deeper soil assisting establishment of drill-planted crops, even when establishment of broadcasted crops may be poor or even fail (Xangsayasane et al., 2019b). The drill-planted crops are not only better in establishment but also form rows that are easier for weed control when compared with broadcasting (Kumar and Ladha, 2011). However, with deep planting, varieties that can emerge from deeper soils are required (Fukai et al., 2019; Xangsayasane et al., 2019b). Early vigour would also be important for drilled crops. There is a large genotypic variation for seedling emergence from deep soil. Lee et al. (2017) found this variation to be associated with mesocotyl length for seed sown at 5 cm depth. Using 57 rice genotypes, they found indica genotypes had the longest mesocotyl, while japonica genotypes had the longest coleoptile. Using chromosome segment substitution lines (CSSL) of Nipponbare and Kasalath, they identified two QTL that are associated with mesocotyl length. Similarly, Ohno

50  Crop Physiology: Case Histories for Major Crops

et al. (2018) found variation in mesocotyl length and seedling emergence among varieties. When sown at 85 mm depth, emergence ranged from greater than 80% to less than 1% in popular varieties.

2.2  Phenological development There are three main growing phases in rice: vegetative, reproductive, and grain filling. The vegetative phase covers from planting to panicle initiation (PI), and its duration varies greatly depending on growing environment and genotypes, particularly their photoperiod sensitivity. Reproductive stage commences with PI and ends with anthesis, while grain filling covers the period from anthesis to maturity. In irrigated lowlands, time of seeding and transplanting may be selected to suit crop growth and high yield. However, as time of seeding and transplanting may be dictated by the availability of water, this could affect crop phenological development and subsequently yield formation. In these cases, photoperiod-sensitive varieties may be selected to give more flexibility, although they may not be high yielding (Ouk et al., 2007).

2.2.1  Drivers of phenological development Time to flowering is strongly controlled by photoperiod and/or temperature during the vegetative stage before PI. Time to flowering is mostly determined by photoperiod with some interaction with temperature in photoperiod-sensitive genotypes, while temperature drives phenological development of photoperiod-insensitive varieties. Other factors that contribute to time to flowering are described at the end of this section. Rice is a short-day plant, and some varieties respond strongly to photoperiod; in this case, flowering date may be similar despite large differences in planting time. Others respond weakly to photoperiod, and in this case, delay in planting results in some delay in flowering. Photoperiod-insensitive varieties flower after a certain number of days if temperature is constant during early growth. Thus often photoperiod-sensitive varieties such as KDML105 in Thailand are considered to flower on the calendar date of 25 October, while the insensitive varieties are often expressed as the number of days to flower or maturity. Photoperiod sensitivity may be expressed as a photoperiod sensitivity index (PSI), calculated as 1- (delay in flowering)/(delay in planting). Thus if a variety is strongly photoperiod-sensitive and always flowers on the same date, delay in flowering with late planting is 0 day, and hence PSI = 1.0. On the other hand, if flowering is delayed by 15 days with 60 days delay in planting, PSI = 0.75. In the insensitive genotypes, 60 days delay in planting results in 60 days delay in flowering, assuming temperature is constant during the growth period, PSI = 0.0. The range of PSI in rainfed lowland rice varieties in Thailand and Laos was 0.23–0.82 (Fukai, 1999). The period from planting to flowering for photoperiod-sensitive varieties is commonly divided into three phases; the basic vegetative phase (BVP) or juvenile phase when the plants do not respond to photoperiod, the photoperiod-sensitive phase (PSP), and post-photoperiod-sensitive phase (PPP) (Yin et al., 1997). The BVP is longer under lower temperatures (Collinson et al., 1992). A base temperature of 8°C is often assumed in rice phenology models (Fukai, 1999). However, more recent work has shown that photoperiod has some effect even after PI, and this can affect the flowering date up to 10 days in some varieties (Yin and Kropff, 1998). In earlier experiments in temperature-controlled rooms, the effect of temperature on time to flower was considered as of heat-sum/thermal time type defined by three cardinal temperatures (the base temperature, the optimum temperature at which the rate of development to flower is the highest, and the high-temperature limit), and the rate of development between cardinal temperatures was interpolated from linear regressions (Summerfield et al., 1992). More recent work that examined photothermal effects simultaneously indicated that the responses were not linear but better described by a skewed bellshaped curve (beta function); hence the use of a bilinear heat sum model overestimated the rate of development to flower when temperature was below 30°C (Awan et al., 2014). Modelling by Yin et al. (1997) based on their earlier experiments indicated that minimum time to flowering of 17 varieties varied between 35 and 74 d, with japonica varieties tending to flower earlier (35–44 days). For these varieties, BVP ranged from 12 to 40 days under these optimum conditions. Optimum day temperature ranged from 28 to 35°C among varieties, and this was 2–3°C higher than optimum night temperature for most varieties. These optimum temperatures were commonly a few degrees lower in japonica varieties. Their model contained five parameters and was able to predict flowering date of 12 varieties in 3 countries. Because temperatures before flowering are more or less constant and often around the optimum in the tropics in the wet season, e.g. lowland rice in the Mekong region, the effect of temperature before flowering on time to flower may be small. In this case, photoperiod has profound effect on flowering time as indicated earlier. However, shoot apex is underwater in small plants, hence water rather than the air temperature affects rice phenological development. Water temperature is more stable and often a few degrees higher during night than air temperature, and this

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should promote development. Experimental data support the use of water temperature until the panicle is exposed to the air during the booting stage (Shimono et al., 2005). Flowering time of rice is sensitive to drought (Puckridge and O'Toole, 1980), and severe stress causes a long delay (Lilley and Fukai, 1994). Longer delay in flowering of a variety is an indication of its susceptibility to drought and is related to panicle water potential (Jongdee et al., 2002). Under an infertile and acidic sandy soil (Aeric Paleaqualt) common for rainfed lowland rice in Thailand, flowering was delayed, and application of farmyard manure promoted flowering by 4–7 days (Wonprasaid et al., 1996). Rice flowering is accelerated by mild N stress but impeded by severe N stress.

2.2.2  Global warming effect Recent warming has hastened rice phenological development. For example, in Punjab and Pakistan, mean air temperature during the transplanting-maturity period of rice increased by 0.5–1.2°C decade− 1 between 1980 and 2014, with an associated decrease in mean time between transplanting and maturity by 6.4 days decade− 1, which was significant in 8 of 10 locations (Ahmad et al., 2019). Effect of temperature increase in 1981–2009 on rice phenological development was examined for 202 stations across China (Zhang et al., 2016). Mean temperature increase during emergence to maturity was about 0.45°C decade− 1. Mean growth duration from emergence to maturity decreased in 92% of cases, and a significant negative correlation between growth duration and mean air temperature was obtained in 54% of the stations. In some stations, the change in varieties towards later maturing during the study period resulted in no significant effect of temperature increase on growth duration. However, the effect of climate change varied between single crop per year in the northeast and north region and double crops per year in the humid south region in China (Zhang et al., 2014a, 2016). Zhang and Tao (2013) mentioned that temperature variability also increased in northeastern China, and this may have prolonged the growing season. Shimono (2011) examined the change in temperature for 1961–2010 for northern Japan where cold often affects grain yield. During these years, temperature increased up to June, and this resulted in heading occurring 0.7–1.9 days decade− 1 earlier. However, there was no significant temperature increase during reproductive growth in July–August, and this resulted in lower temperature at booting when cold has severe effect on spikelet sterility. Current heading date is slightly earlier than the optimal heading date, and with increased temperature, heading would occur even earlier. Crop management could be altered, but the development of cold-tolerant varieties is also required for the region.

2.2.3  Crop establishment methods Crop establishment methods affect flowering date, particularly in rainfed lowland rice. Transplanting of seedlings delays flowering because plants take several days to recover from uprooting and transplanting. This delay was particularly prolonged when old seedlings were used in photoperiod-insensitive varieties in rainfed lowland, and Immark et al. (1997) considered this seedling age effect to be more important than the sowing date effect in varieties considered almost photoperiod insensitive (PSI = 0.23). In their work, delay in transplanting of seedlings from 25 to 45 days old delayed flowering by 5–9 days. Thus while the use of old seedlings will reduce the time the crop spends in the main paddy fields, the saving is partially offset by the delay in flowering. The growing period of transplanted crops in the main field is reduced by a few weeks, although total duration including nursery time may be somewhat larger, and this reduced main field period can be particularly advantageous in marginal environments for rice; for example, in temperate areas where the seedling nursery can be protected from cold weather in spring before transplanting, and seedlings are transplanted to the main fields when the weather is warm so the crop matures before the onset of cold weather (Hoshikawa et al., 1995). It should be noted that crops can be direct-seeded well before the field is ready with saturated soil water for transplanting, particularly with dry direct seeding. Thus with early sowing, the time of harvesting of direct-seeded rice crop may be earlier than the transplanted crops. Seed drill for dry direct seeding requires early planting, before the soil is saturated with water (Xangsayasane et al., 2019b). With the earlier time of sowing and emergence when dry direct seeding is used, the crop may be harvested earlier if photoperiod insensitive varieties are used. With such conditions of varying time of planting, photoperiod-sensitive varieties are often advantageous to avoid flowering in a heavy rain period (Fukai et al., 2019; Xangsayasane et al., 2019b). This can provide an opportunity for a short-duration post-rice crop such as mungbean (Samson et al., 2020). Recently, Vote et al. (2019) explored pond water availability to subsistence farmers in Champassak Province of Lao PDR, and limited supplementary water from small farm ponds could be used to secure rainfed rice or to improve family nutrition with a small area of short-duration post-rice vegetables or grain legumes.

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2.2.4  Crop ripening and maturity Grain-filling duration is affected by temperature and could range from 25 days in the tropics to 50 days in temperate areas. Physiological maturity is the time of maximum grain yield, but grain moisture content keeps decreasing after physiological maturity. Time of harvesting is an important decision for farmers, particularly when rice is produced for marketing. Early harvesting during grain ripening, particularly before physiological maturity, reduces both grain yield and milling quality with a high proportion of immature grain. Early harvesting with high moisture content may also result in extra cost of drying. On the other hand, as the grain ripens and grain moisture declines, fissured grain percent at harvest increases, resulting in increased broken rice percent after milling, and hence head rice yield (unbroken milled rice/rough paddy rice) decreases (Bunna et al., 2019a,b). A heat sum of 450–500oCd (base temperature = 10°C) appears to be the optimum for harvesting with maximum head rice yield for both wet and dry season crops in Laos (Xangsayasane et al., 2019c). The grain moisture content at maximum head rice yield appears to be high at 25%–26% for wet season rice in Cambodia (Bunna et al., 2019b) when compared to southern USA, for example, 21% at Arkansas (Siebenmorgen et al., 2013). High-yielding, quick-maturing, photoperiod-insensitive varieties have contributed to cropping system intensification in Asia. Thus varieties grown in the dry season are mostly photoperiod-insensitive in the Mekong region (Fukai and Ouk, 2012). Quick maturity is often required because the season before the main wet season is often limited because of cold weather or insufficient water. This limitation is further constrained owing to shift from transplanting to direct seeding, for example, in Central China (Xu et al., 2018).

2.3  Shoot development and growth Shoot development is affected by crop establishment, and often increased seed rate promotes shoot development of the canopy. In the work of Lal et al. (2018), direct-seeded rice established from a seed rate of 60 kg ha− 1, compared to 40 kg ha− 1, had higher seedling population, taller plants, more tillers m− 2, higher LAI (5.4 vs. 4.2) at flowering, and more panicles (269 vs. 168 panicles m− 2) but small difference in grain yield. The number of tillers increases during the tillering stage for about 1 month from transplanting, and the maximum tiller number depends on growing conditions such as plant spacing, N availability, and genotype. However, not all tillers produce panicles, and commonly, late tillers senesce particularly under poorer conditions. The number of panicle-bearing tillers, but not maximum tiller number, was correlated with mean daily solar radiation for 6 weeks after transplanting, for crops grown under favourable conditions across locations in Japan (Murata and Matsushima, 1975). Genotypes with fewer tillers, such as the New Plant Type (NPT), would reduce the wasteful non-productive tillers, but because of the limitation in tillering capacity, a closer spacing may be required (Peng et al., 2008). Rice tillers profusely and high-yielding varieties tend to have a high proportion of productive tillers. Kato and Katsura (2014) showed tiller production was reduced when seedlings were grown under flooded than under aerobic conditions, resulting in higher panicle numbers in aerobic, although this may be related to the higher number of plants established when compared to transplanted crops. A medium level of standing water covering the tillering nodes would suppress tiller emergence. Broadcasted crops with higher plant density than transplanted crops maintain higher tiller density and higher shoot biomass from tillering to maturity, but plant height may be similar or even slightly lower (Naklang et al., 1996). Leaf area development depends on environmental and management factors, such as seed rate mentioned earlier, and N availability (Murata and Matsushima, 1975). New leaves are produced and often appear at a constant rate (phyllochron) on each tiller. The flag leaf is the last leaf to appear before heading and is generally smaller than leaves produced earlier. Leaves commonly senesce and LAI decreases during grain filling (San-oh et al., 2004), which is accelerated by high temperature (Kim et al., 2011). Some genotypes have an ability to maintain leaf area often called ‘stay-green’ characteristics; for example, one genotype lost leaf area between heading and maturity at the rate of 0.02 LAI d− 1, while two others lost at the rate of around 0.06 LAI d− 1 (Huang et al., 2019). Varieties with maintenance of high green leaf area are important for assimilate production during grain filling (Peng et al., 2008). Premature leaf senescence may occur in varieties with high N concentration and high N content in grain (Wei et al., 2017a). Once PI is achieved, the young panicle starts to develop. Bracts, primary branches, and secondary branches are differentiated, and at about 10–12 days after PI, spikelets are differentiated in secondary branches (Murata and Matsushima, 1975). The potential number of spikelets per panicle is determined during this early reproductive stage, while some spikelets are aborted during the late reproductive stage (Kato and Katsura, 2010). In the later reproductive stage, florets are differentiated, and maturation of pollen ends the reproductive stage. The period of pollen development to flowering is susceptible to

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environmental stresses such as extreme temperatures and drought, causing male sterility, and subsequently, reduced spikelet fertility, and often, reduced grain yield. Flowering takes place over several days within a panicle. A spikelet bears only one floret in rice, and this reduces flexibility to adjust to the environments that are available relative to most other cereal crops. There are six stamens in the rice floret. At anthesis, the lodicules become turgid, and their increased volume causes the anther to dehisce and pollen to shed, some of which are intercepted by the stigma. Spikelets developed early and mostly located on primary branches, often called superior spikelets, flower early than inferior spikelets on secondary branches, which often fail to become fully filled grain (Mohapatra et al., 2011). Detailed morphological development of above-ground organs is described in Yoshida (1981).

2.4  Rood development and growth Rice root system differs greatly under anaerobic and aerobic conditions. There are two distinct types of roots in flooded lowland rice; one is a superficial dense hairy root system in the top soil layer (< 1 cm), where oxygen is still available. The other type is a nodal root-lateral root complex that develops below the aerobic soil-flood water interface (Ladha et al., 1998). The nodal roots are thick (> 0.5 mm) and aerenchymatous, and oxygen is transported through the aerenchyma to lateral roots that are mostly fine and less than 0.1 mm in diameter. These lateral roots take up most water and nutrients. Aerenchyma formation in the root cortex is promoted under anaerobic conditions. When water is ponded, development of root aerenchyma is essential for passage of oxygen, with the sclerenchyma barrier to radial oxygen loss essential to keeping oxygen available to growing root tips in flooded soils (Ismail, 2018). Root growth is reduced after PI in both upland and lowland conditions (Naklang et al., 1996), resulting in a decrease in root–shoot ratio with root mass at maturity below 20% of total mass, particularly in lowlands. Kato and Okami (2010) found in Japan that root weight increased to around flowering in flooded and aerobic conditions, but the root mass was less than 10% of total biomass at maturity.

2.4.1  Shallow root system One common feature of lowland rice is a shallow root system under anaerobic conditions. For example, Pantuwan et al. (2002) described the top 15 cm soil containing 84%–87% of total root weight. Similarly, Naklang et al. (1996) showed most roots were in the top 10–15 cm layer under lowland conditions. In the experiments of Naklang et al. (1996), not only shoot growth but also root growth was higher in broadcasted crops than transplanted crops, particularly in the shallow soil layer of 0–10 cm, while differences in shoot and root growth were generally small among the four varieties they examined. Root growth is particularly sensitive to soil water availability. In saturated soil, root growth may be limited to the surface layer where a large number of fine roots develop (Kato et al., 2013). The general lack of deep roots in lowland conditions may be because of lack of oxygen in lower soil layers, but other physiological factors also affect deep root growth (Gowda et al., 2011). Lack of deep water may not cause much problem in irrigated lowland rice because sufficient water is taken up from the surface roots except around midday. However, this could be an issue in rainfed lowland rice where standing water often disappears, but roots are not able to grow deeper perhaps because of the hard pan that may have developed after continuous puddling (Pantuwan et al., 2002). Samson et al. (2002) reported genotypic differences in ability for roots to grow into deeper layers as water deficit proceeded in Rajshahi Bangladesh. Root plasticity was considered important under the fluctuating soil water conditions in the rainfed lowlands (Kano-Nakata et al., 2011). Dry direct seeding or aerobic system could minimise this problem. The large number of surface roots common in lowland rice may decrease sharply under aerobic conditions in which the number of nodal roots is reduced, resulting in a higher proportion of total roots in deeper soil in the study of Kato and Okami (2010) in a temperate area. In their study, aerobic root growth was limited during early stages, and the crop was susceptible to water stress showing stomatal closure. When the soil surface dried, the aerobic crop was able to extract water from subsurface layers, but stomatal aperture decreased sharply with decrease in soil water potential below − 50 kPa at 20 cm soil depth. Siopongco et al. (2008, 2009) demonstrated chemical and hydraulic root signals for stomatal and transpirational adjustments in rice as soils dried. When soil surface water is reduced to around field capacity, roots may start to descend (Kato et al., 2013). However, once soil water content decreases further, root growth may be reduced sharply. Soil strength as measured by penetrometer increases sharply with decrease in soil water potential after an irrigation event in aerobic rice, and this would impair root elongation (Kato et al., 2013).

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In upland-grown rice without irrigation, water stress may affect both shoot and root growth (Naklang et al., 1996). In a comparison of four varieties in various upland and lowland conditions over 3 years, upland crops had slightly lower total root mass and a higher proportion of deep roots (> 30 cm) in upland conditions (Naklang et al., 1996). Variety difference in root dry matter was small, except that lowland-adapted IR20 had more surface roots than others under transplanted lowland conditions.

2.4.2  Deep roots Unlike lowland conditions, upland rice, including aerobic, can develop functionally important deep roots. Root angle is important in determining the vertical distribution of roots, as demonstrated by Kato et al. (2006) and further by Ramalingam et  al. (2017). Thus the proportion of roots with a steep angle (i.e. 45–90 degrees from the horizontal) was associated with deep roots (> 30 cm soil layer). The expression of deep root characteristics was less pronounced in compacted soils (Ramalingam et al., 2017), although water may be available below the hardpan (Yano et al., 2006). Recent studies have shown genotypic variation in root angle or deep-root ratio under aerobic conditions (Kato et al., 2013) or upland conditions (Kitomi et al., 2015; Ramalingam et al., 2017); thus it may be feasible to develop rice varieties with deeper root systems for aerobic conditions with reduced requirements for frequent irrigation. Wade et al. (2015) reported two genomic hotspots for root growth under drought and grain yield in rainfed lowland conditions. Ramalingam et al. (2017) mentioned four QTL that were associated with deep root angle. Ishimaru et al. (2017) found that when compared with IR64, its introgression line with IR64 background (YTH183) with higher yield potential had steep-angle deeper roots, accessing more water from deeper soil layers under AWD. Kato et al. (2011) also found YTH183 had higher root length density at 15–30 cm and 30–70 cm soil layers but not in the 0–15 cm layer. The importance of steep-angle roots with larger deep root ratio has been found by others in water limiting conditions, and deep root gene dro 1 was identified (Uga et al., 2013). Deep root as drought progressed was the implication in Samson et al. (2002), Clark et al. (2002), and Wade et al. (2015). See Shashidhar et al. (2012) for root methods. However, it should be noted in most studies (Naklang et al., 1996; Kato et al., 2013) that roots are considered deeprooted when they are in soil depth of only 30–45 cm (Naklang et al., 1996), 15–30 cm (Kato et al., 2013), and 30–60 cm (Ramalingam et al., 2017). Fukai and Inthapan (1988) showed that water uptake in aerobic rice was limited to 60 cm depth, and this was shallower than in maize (Chapter 1: Maize, Section 3.2.2) or sorghum.

3  Growth and resources 3.1  Capture and efficiency in the use of radiation Radiation interception and radiation use efficiency (RUE) are used to dissect the growth of irrigated rice in response to crop management, crop physiological status (e.g. leaf N concentration), and genotype. RUE is commonly calculated as the ratio of shoot biomass and total amount of radiation intercepted during a growth period. In this chapter, RUE is expressed on the basis of intercepted photosynthetically active radiation (PAR); here, we use a factor of two to transform RUE from shortwave radiation to PAR.

3.1.1  Crop growth analysis with radiation interception Radiation interception increases with LAI almost linearly for small canopies, but the rate of increase in radiation interception decreases with larger LAI and eventually reaches a maximum. A similar pattern applies when variation in LAI is created by agronomic treatments such as plant density and fertiliser N rate. For example, Weerakoon et al. (2000) examined the effect of N rate under two CO2 concentrations and showed that radiation interception increased sharply to 50% with LAI ≈ 2, but the rate of increase in interception decreased with the maximum interception obtained at LAI ≈ 6. Increased N rate from 0 to 200 kg ha− 1 increased LAI and radiation interception greatly, while there was also an increase in RUE. Radiation interception during early growth may be influenced by seed rate and planting pattern such as row spacing. Wide rows may reduce radiation interception during early growth, and this is a possible reason for reported disadvantages from the transplanter machine; increased plant density per row may then increase dry matter production and grain yield (Xangsayasane et al., 2019b). Differences in yield between a tropical environment at International Rice Research Insstitute (IRRI) in the Philippines and a cooler environment in Hubei, China associated with lower RUE under higher temperature (Wang et al., 2016a). For a common set of varieties, total dry matter (TDM) was higher in Yunnan (southern China) with higher incident radiation and higher total radiation intercepted than in Kyoto (Japan) with no changes in RUE (Katsura et al., 2008).

Rice Chapter | 2  55

In this analysis, radiation interception was estimated from the extinction coefficient k and LAI, and variation in soil and management may have also differed. Firm conclusions can be obtained when comparison is made at the same location, with experimental manipulations such as plant density and varieties. Alternatively, by considering comparable management, the hierarchy of limitations over locations or ecosystems can be evaluated in cross-site analyses (Wade et al., 1998; Stuart et al., 2016). Deshmukh et al. (2017) showed that lower biomass production in upland conditions in the wetter year was mostly because of lower RUE (2.3 vs. 3.0 g MJ− 1) with no difference in total radiation intercepted. On the other hand, Katsura et al. (2010) showed aerobic rice in temperate Japan produced higher biomass than flooded rice in three of four comparisons, and this was associated with similar or slightly higher RUE in aerobic rice (2.5–3.0 g MJ− 1) than in flooded lowland rice (2.4–2.7 g MJ− 1). This high RUE in aerobic rice was associated with higher N uptake exceeding 200 kg ha− 1 when compared with that under flooded condition (150–170 kg ha− 1). Thus well-irrigated and well-fertilised, such as 180 kg N ha− 1 in the work of Katsura et al. (2010), aerobic rice appears capable of similar radiation interception and similar or higher RUE when compared to the flooded lowland rice. However, lower RUE in aerobic condition than in flooded condition was reported in experiments at IRRI (Clerget et al., 2014). Radiation interception may be reduced under drought because of reduced leaf area growth, leaf rolling, and leaf death (Boonjung and Fukai, 1996a). In upland experiments in which crops of different ages were subjected to drought periods of 23–35 days, the drought effect on RUE depended on the crop age (Boonjung and Fukai, 1996a). When the crop was 33 days old at the commencement of drought, water requirement was small, severe water stress did not develop, and the crop adapted through reduced LAI and radiation interception, with almost no effect on RUE. In the older crop with radiation interception exceeding 60% and hence higher water demand, leaf water potential decreased rapidly, and the reduction in RUE from 2.3 to 1.2 g MJ− 1 mostly contributed to the reduction in biomass production.

3.1.2  Radiation use efficiency as reflection of leaf photosynthesis rate Rice RUE is commonly about 2.8 g MJ− 1 under favourable conditions (Sinclair and Horie, 1989; Sinclair and Muchow, 1999). However, RUE is affected by weather and soil. For example, water stress often reduces the rate of photosynthesis and hence RUE (Boonjung and Fukai, 1996a), as mentioned earlier. Thus in an aerobic condition that has induced slight water stress, RUE may be reduced. Similarly, soil N availability and leaf N status affect photosynthesis and RUE (Sinclair and Horie, 1989). Experiments in Korea showed that increased N increased both RUE as related to leaf photosynthesis and intercepted radiation as related to LAI, with higher biomass production during early growth (Xue et al., 2016). In experiments in China, integration of higher plant density, late split N application, and post-anthesis shallow alternate wetting and drying (AWD) increased grain yield of early and late sown rice by 33%–37% over present practice (Qin et al., 2013). These yield gain related to both increased radiation interception and increased RUE. Qin et al. (2013) suggested that the late N application at PI-flowering and post-anthesis shallow AWD improved late N availability, maintaining photosynthesis. Total N uptake at flowering and N recovery at maturity were higher in these superior management practices. Usefulness of late N application was also noted in Peng et al. (2006). As leaves age and start to senesce, the rate of photosynthesis decreases, resulting in reduced RUE towards maturity (Horai et al., 2013). Thus maintaining a high photosynthetic capacity and hence high RUE during grain filling appears critical for high yield. Stay-green varieties that maintain green leaf area during grain filling would be expected to maintain higher rate of photosynthesis and RUE to meet the assimilate requirement to fill grain. On the other hand, leaf senescence may be associated with the transport of N and C to meet the grain filling demand; in this case, RUE would decrease. Yield advantage of ‘super’ hybrid rice is partially associated with its high RUE in late growth stages (Huang et al., 2016). Experiments in Guangxi Province, China showed that higher RUE was associated with higher photosynthesis at heading and milk grain in the field. Glasshouse experiments show that the ‘super’ hybrid had higher chlorophyll a and Rubisco contents than the ordinary hybrid. While the yield advantage of the ‘super’ hybrid was also demonstrated in subtropical areas of Hunan Province in China, this was more related to a slightly longer growth duration and higher intercepted radiation, while there was no significant variety difference in RUE (Zhang et al., 2009c). They also found that these hybrids did not necessarily require more N fertiliser.

3.1.3  Radiation use efficiency as related to canopy structure While RUE often reflects the leaf photosynthetic rate (Sinclair and Horie, 1989), it is also affected by canopy structure in rice. Higher RUE can be related to high leaf inclination angle from the horizontal, i.e. erect leaves. For a canopy of mostly horizontal leaves, radiation is mostly intercepted by the top leaves, and light flux density at the leaf surface may be above or close to light saturation for photosynthesis with lower efficiency of conversion of solar radiation. With more erect, ­radiation

56  Crop Physiology: Case Histories for Major Crops

is more evenly distributed on large leaf surface areas with higher efficiency of conversion. It is well known in rice that high leaf inclination angle with smaller extinction coefficient k allows better penetration of radiation into canopy depth and with reduced mutual shading, so canopy photosynthesis is higher (Monsi and Saeki, 2005). Leaf inclination angle may vary because of crop management or varieties. Rice planted with one plant per hill had better canopy structure when compared to three plants per hill at the same plant density (San-oh et al., 2004). During reproductive and grain filling stages, these crops intercepted more than 90% of incident radiation, but crop growth rate was higher in the more even plant distribution with higher leaf inclination angle. San-oh et al. (2004) showed that more even distribution of plants with one plant hill− 1 resulted in larger leaf inclination angle (e.g. flag leaf 79–81 degrees) with smaller extinction coefficient (k = 0.46–0.50) when compared with the three plants per hill (74–75 degrees, k = 0.74–0.82). Transplanted crops and dibbled direct-seeded crops were also compared, and under good crop establishment with no weed problems, direct-seeded crops with even plant distribution and one plant per hill produced the highest LAI, light interception, dry matter, and grain yield (San-oh et al., 2004). In the experiments of Gu et al. (2017), AWD outyielded flooded because of increased RUE and reduced k in AWD. Root oxidation activity was higher in AWD than in flooded conditions, and this was associated with increased cytokinin production. The authors considered this may be a reason for increased N allocation to the higher canopy position contributing to higher RUE. The importance of maintaining N in the top canopy layer for high canopy photosynthesis was also suggested by Ladha et al. (1998), but they considered that with most N already in the top layer, there is limited scope for further improvement of this trait. Comparison of a historic set of 14 varieties in Middle Reaches of Yangtze river showed improvement in pre-heading RUE from 2.2–2.7 g MJ− 1 to 3.1–3.5 g MJ− 1 over the period of 1936–2005 (Zhu et al., 2016). This is a mean annual increase of 0.43%, which was smaller than the mean annual increase in grain yield of 1.17%, implying that factors other than RUE contributed to yield improvement. The importance of canopy structure has been highlighted in hybrid rice; ‘super’ hybrid rice varieties have high inclination angles in three top leaves (Peng et al., 2008). These leaves are long and narrow with their LAI exceeding 6 and are also more erect compared to the standard hybrid, with a smaller k allowing more solar radiation to penetrate the deeper canopy, contributing to increased canopy photosynthesis. This, together with panicle position within a canopy, is considered to be advantageous, with high conversion efficiency of intercepted radiation. Comparison of 3D canopy structure can be made more readily with the availability of digital technology (Hua et al., 2016). Hua et al. (2016) demonstrated the effect of Prostrate Growth 1 gene which was more prevalent in wild rice; YIL18 with the gene had about 10 degrees lower leaf inclination angle than the variety Teqing, which is known for erect growth habit. Prostrate type growth with large tiller angle from the main stem favours rapid canopy spread and competition with weeds. However, gradually, this trait was lost during rice domestication. Thus selection of rices with improved seedling vigour and weed competitiveness may be useful for direct-seeded conditions where weeds are a concern (Zhao et al., 2006; Kumar et al., 2009). Conversely, improved rices have more erect tillers, and tiller angle-controlling genes have been identified recently (Dong et al., 2016). It may be advantageous to combine prostrate early growth for weed suppression and erect growth later for yield potential, perhaps using tissue-specific promoters.

3.2  Capture and efficiency in the use of water Crop growth may be analysed from the amount of water taken up by the crop and efficiency of water use for biomass production (Passioura, 1977); this analysis provides information that could help reduce water use in irrigated rice and reduce the effect of water stress in rainfed rice. WUE or water productivity in this chapter is defined as the ratio of grain yield to total water used in the rice field, unless stated otherwise.

3.2.1  Water balance in lowlands Fig. 2.2 shows the water balance in a rice paddy field on a sloped land. One common issue with lowland crop is its high water requirement because deep percolation and seepage are large components of the water balance. Seepage can be high, although this water lost from a field may be utilised in a lower field, and hence on a whole catchment basis, water loss may be smaller than expected from single-field estimates. Owing to evaporation from the water surface, well-irrigated rice may have slightly larger evapotranspiration than crops grown without standing water. However, rice fields may lose water through deep percolation, particularly where the soil is not clay or porous; subsoil compaction can be used to reduce percolation loss on sandy soils (Sharma et al., 1995), and puddling will also help to reduce this loss, which is commonly practised in transplanted and wet direct-seeded rice. However, a hard pan to reduce deep percolation loss favours waterlogging and compromises other crops in the rotation (Mitchell et al.,

Rice Chapter | 2  57

FIG. 2.2  Water balance in lowland rice paddy field on a sloped land. Source: Courtesy of Dr. Mitsuru Tsubo.

2013). This may be a particular problem when heavy machinery is used in cultivating rice. Deep ripping can be practised to reduce the hard pan strength, and this can increase the growth and yield of non-rice crops (Vial et al., 2013). Inthavong et al. (2011b) developed a water balance model for lowland rice fields in which deep percolation rate was estimated from clay content. Shallow root depth in lowland rice crops predisposes them to drought when rainfall is limited and irrigation water is not available. These rainfed lowland fields are often located on sloping land (Fig. 2.2), and upper fields lose water more quickly than lower fields (Inthavong et al., 2012). Thus it is common to plant early maturing varieties late in upper fields and harvest early and to plant late maturing varieties in lower fields early and harvest late (Fukai and Ouk, 2012), with lower fields having greater soil water holding capacity, longer ponded water duration, and higher yield than upper fields (Samson et al., 2004).

3.2.2  Water requirement and water use efficiency 3.2.2.1  Effect of crop establishment methods Transplanting of seedlings commonly takes place when soil is saturated with water and shallow standing water may just cover the soil surface. From the onset of the wet season, paddy fields are often wet cultivated (puddled), and significant amount of water may be lost by the time of transplanting. In a study of porous soil, 10% of total irrigation may be saved by dry seeding (Sudhir et al., 2014). Thus direct seeding, particularly dry direct seeding that could be planted early at the beginning of wet season, could save water that is otherwise required before transplanting. However, in some fields with high rate of deep percolation, more water may be lost without puddling in dry direct seeding (Sudhir et al., 2011a,b). Dry seeding required less water in the first month when compared to water seeding but did not save water for the whole growth period when the same flooded condition was maintained after the first month (Linquist et al., 2015). On the other hand, transplanted crops spend fewer weeks in the main paddy fields, and this would save some water. In the study of Sudhir et al. (2014) at IRRI in the Philippines, water use was 2817 mm in the wet direct-seeded rice, 2315 mm in the transplanted crops, and only 2141 mm in dry direct-seeded rice crops. Kumar and Ladha (2011) examined 44 studies from different countries where irrigation water use was estimated for direct seeded and transplanted crops. Transplanted fields used an average of 1372 mm of irrigation water, while this was reduced by 12% in wet direct seeding and 21% in dry direct seeding. Water saving and water productivity of irrigated directseeded rice is reviewed by Farooq et al. (2011). They showed examples of increased water productivity in direct-seeded rice.

58  Crop Physiology: Case Histories for Major Crops

3.2.2.2  Effect of water-saving methods The main characteristics of dry direct seeding, together with two other methods of water saving, AWD and aerobic rice, are summarised in Table 2.2. Water saving in AWD is owing to at least partly to reduced percolation and seepage when water level is reduced from fully flooded conditions. Just before irrigation when soil water level was lowest in AWD-mild (or shallow AWD), stomatal conductance was reduced and leaf transpiration rate was about 20% lower than the flooded conditions in experiments in Jiangsu Province, China (Zhang et al., 2009b), indicating water saving was partly because of reduced transpiration. Meta-analysis of Carrijo et al. (2017) concluded that in AWD-mild where watering was applied to maintain soil water potential at 15 cm above − 20 kPa, WUE increased by about 25% with a corresponding decrease in water use. Saving of irrigation water may be even larger in the wet season when overall irrigation requirement is lower. However, water saving can also be large in dry seasons. In dry season, AWD experiments in the Philippines, soil water tension was about 10–15 kPa when water level declined to 15 cm, while at 30 cm, the water tension often exceeded 20 kPa (Lampayan et al., 2015b). In these experiments, where water saving was about 50% in any treatments, growth was slightly affected by an AWD treatment for irrigation at a water level of 30 cm below soil surface. Water productivity (yield per unit irrigation + rainfall) almost doubled over the flooded control in AWD for irrigation at 25 cm below soil surface. Deshmukh et al. (2017) reported total biomass under AWD was similar to that in flooded lowland in a wetter year, but it was about 25% lower in AWD in drier year. On the other hand, biomass under upland condition was 25%–40% lower than that under flooded condition. Water stress would have reduced biomass production and water productivity in these conditions. In very leaky lowland soils in Tokyo, water productivity was very low (0.16–0.17 kg m− 3), but AWD increased it to 0.48–0.68 kg m− 3 and upland conditions to 0.62–0.71 kg m− 3 (Deshmukh et al., 2017). In other experiments at the same location, flooded lowland rice used more than 3000 mm of water, while aerobic rice used 800–1300 mm with resultant water productivity of 0.23–0.26 and 0.75–0.84 kg m− 3, respectively (Kato et al., 2009). They conducted another set of experiments in the lowland fields where water use was about 1500 mm and water productivity was 0.54 kg m− 3. Aerobic rice used 790–910 mm of water, with water productivity of 0.78–0.96 kg m− 3. Thus aerobic rice saved a large amount of water and water productivity increased substantially over the flooded lowland culture. In Brazil, Reis et al. (2018) found water productivity in aerobic rice was about double that of flooded rice. 3.2.2.3  Other factors Under non-flooded irrigated, dry direct seeding in NW India (Pal et al., 2017), where four lines were planted three times in each of 2 years, grain yield and water use differed among genotypes and sowing time combinations. High-yielding genotype and shorter growing duration had higher water productivity (1.40 kg m− 3) than other combinations (range 0.85– 1.32 kg m− 3). For late-sown crops, higher yielding varieties (HYVs) had higher TDM and N uptake at anthesis and higher translocation of assimilates during grain filling with high harvest index. Lampayan et al. (2015a) compared water use of three seedling ages, 14, 21, and 30 days old at transplanting. Water requirement was reduced with older seedlings as the time the crop spent in the main field was reduced. The 21 day-old seedlings produced the highest yield and the highest water productivity. TABLE 2.2  Main characteristics of water-saving methods. Method

Cropping system

Dry direct seeding

Lowland-irrigated or rainfed, directseeded

AWD

Aerobic rice

Puddling (wet cultivation)

Water level

Water saving

Potential issues

Not puddled

Maintenance of standing water desired

Water saving in land preparation but possibly increased percolation

Percolation water loss, water stress development, weeds, and poor establishment

Irrigated lowland, transplanted or direct seeded

Puddled or not puddled

Water level fluctuate as designed

Water saving when water level is reduced

Increased cost and labour for water management

Irrigated in lowland area, direct-seeded

Not puddled

No standing water desired

Water saving with reduced percolation and seepage

Weeds and poor establishment

Rice Chapter | 2  59

3.3  Capture and efficiency in the use of nutrients 3.3.1 Nitrogen Availability of affordable N fertiliser and of semi-dwarf varieties was critical to the green revolution in the 1960s and 1970s, greatly increasing rice production, particularly in irrigated lowlands. However, the recent increases in N fertiliser costs, and the environmental concerns with excess N fertilisation, have resulted in research to improve the efficiency of N use so that smaller amount of N fertiliser could be applied to produce the same or even higher yield. Sections 3.3.1.1 and 3.3.1.2 are mostly for irrigated rice, while Section 3.3.1.3 is for water stress conditions. Definitions used in this section include relationships between crops with no N fertiliser (0 N) and N-fertilised crops (N applied); supply of other nutrients is the same in both 0 N and N applied treatments. We define the following: N recovery efficiency  RE   100   shoot N uptake in N applied  N uptake in 0 N  / amount of N applied

(2.1)

Agronomic NUE   grain yield in N applied  grain yield at 0 N  / amount of N applied

(2.2)

Internal NUE = grain yield / total N uptake

(2.3)

Physiological NUE   grain yield in N applied  grain yield at 0 N  /  N uptake in N applied  N uptake in 0 N 

(2.4)

Partial factor productivity of applied N  PFPN   grain yield / amount of N applied

(2.5)

Similar definitions apply to other nutrients, as discussed for P and K further. 3.3.1.1  Plant N uptake and the fate of N in the field Nitrogen uptake and plant N concentration In main paddy fields after transplanting or direct seeding, the rates of seedling growth and N uptake are low. This is followed by rapid dry matter production and high N uptake that tend to maintain high plant N concentration. Over 60% of total plant N is located in the leaves for around 60 days after transplanting (Ladha et al., 1998). N is critical for leaf expansion and light interception and photosynthesis and RUE (Section 3.1.2). The rate of total plant N uptake starts to decline often before heading, while crop growth rate for TDM production may still be at its maximum. Thus N concentration in the plant decreases with time, but N concentration at a given growth stage varies depending on N availability to the plant. Thus shoot N concentration declined from about 3.5% after transplanting to just below 2% towards maturity under favourable N conditions, but this decline was from 2.5% to about 1% under severely N-limiting conditions for japonica rice in east China (Ata-Ul-Karim et al., 2013). Critical N concentration (Nc) is the shoot N concentration below which growth is affected. Decline in N concentration may be quantified against shoot dry matter, and such Nc dilution curve is available for rice (Sheehy et al., 1998; Ata-Ul-Karim et al., 2013). The Nc dilution curve can be used as a diagnostic indicator of N fertiliser requirement, and subsequent grain yield can be estimated from N concentration determined at PI or even earlier (Tahir Ata-Ul-Karim et al., 2016; Ata-Ul-Karim et al., 2017). The proportion of N in the leaves declines as leaves senesce and translocate N to grain, with leaf N proportion below 10% by maturity (Ladha et al., 1998). Leaves senesce from those in lower canopy, but as those in the upper position start to senesce, canopy photosynthesis decreases and the rate of assimilate translocation to grain declines, and this reduced source supply could become a limiting factor for high grain yield, particularly when grain sink demand is high (see Section 4). The proportion of N stored in leaves earlier and translocated to grain is much greater than from other organs. While translocation of carbon also takes place during grain filling, particularly under unfavourable conditions for photosynthesis, carbon in grain is mainly derived from current photosynthesis that prevails during grain filling (Kumar et al., 2006). Nitrogen losses from the field For crops grown with standing water, ammonium (NH4+) is the main N form in the soil available for rice plants. Lateral roots that are finer with high root length density, rather than primary nodal roots, take up most N from the soil (Ladha et al., 1998). Fertiliser N is commonly broadcast just before transplanting or wet direct seeding and incorporated into the puddled soil and also during growth as top dressing, often at tillering and PI or heading. Fertiliser N is typically NH4+-based or urea that hydrolyses to NH4+ within a few days of application and moves in the water. N concentration in the water spikes for several days before returning to a very low level. NH4+ in the water may be lost by volatilisation of NH3, particularly when

60  Crop Physiology: Case Histories for Major Crops

solution pH is high, while lateral roots may take up NH4+ from the soil (Ladha et al., 1998). NH3 volatilisation is the main form of gaseous loss from rice fields as losses from denitrification are small (Cassman et al., 1996). Ammonia volatilisation occurs in both flooded and non-flooded periods, and the loss can be commonly as high as 24%–32% when fertiliser is applied under non-flooded conditions (Linquist et al., 2013). Ammonia volatilisation can reduce germination and seedling growth when urea fertiliser is applied at sowing in dry direct-seeded rice (Qi et al., 2012). When standing water is lost and the bulk of soil becomes aerobic, NH4+ is quickly converted to NO3−, and with subsequent flooding, N can be denitrified and lost as N2; AWD, where aerobic condition is followed by anaerobic condition, regularly favours these processes. In lowland, N leaching is small because most N is in the form of NH4+, which is attracted to negatively charged soil particles, and puddled rice soils have low water permeability (Linquist et al., 2013). However, estimates with soil WHCNS (water heat carbon nitrogen simulator) showed greater loss from leaching than volatilisation (Liang et al., 2019). Some N fertiliser applied may be immobilised and may not be available to the present rice crop. In rice, global N RE (Eq. 2.1) is about 46% and agronomic NUE (Eq. 2.2) about 22 kg kg− 1 (Ladha et al., 2005). Wang et al. (2018) using 15N showed that low RE did not necessary mean that all N was lost because nearly 15% of applied N remained in the 0–20 cm soil layer. In their experiments in China, RE varied from 21% to 36%. At maturity, 51% of 15N was in the plant and soil, and hence 49% was considered to be lost as volatilisation and denitrification. They showed that RE depends on the inherent soil N level; if it is high and grain yield of control without N application is high, fertiliser N applied may not be taken up at the maximum rate, and RE may be low. On the other hand, if soil N is low, then the plants rely more on applied N, and RE may be high. The low recovery and NUE can be improved with better fertiliser management and other methods. Cassman et al. (1996) estimated that commonly 10%–65% of applied N fertiliser is lost in rice. One method to improve RE is to reduce N losses through ammonia volatilisation and other processes. Ammonia volatilisation loss is higher when urea is broadcast to flood water than when it is incorporated into the soil, and deep placement of urea reduced the N loss by 33% and increased the yield by 10% (Yao et al., 2018). Different types of controlled-release fertiliser are now available, which can reduce the N loss and increase the grain yield (see further). 3.3.1.2  Nitrogen use efficiency under favourable conditions This section describes NUE under irrigation, and the next section considers NUE under water deficit. Under favourable conditions where no other factors are limiting yield to any large extent, often grain yield increases non-linearly with increase in applied N, until the point where yield levels off at the optimum N rate for grain yield. For example, under dry direct seeding in northern India, grain yield increased sharply with 60 kg N ha− 1, and the maximum grain yield was achieved at 120 kg N ha− 1, with no further yield increase at 180 kg N ha− 1 (Mahajan et al., 2012). Agronomic NUE decreased from 19.3 to 13.0 and further 8.3 kg kg− 1 with the increase in N rate from 60 to 120 and then to 180 kg ha− 1. The optimum N rate for the maximum yield is generally higher than that for the maximum return to N application, and this difference in N rate depends on the cost of N fertiliser and farm gate rice price. As a result of increased N rate, grain yield increased in many countries in recent time, but agronomic NUE generally decreased. In China, which accounts for 37% of global inorganic N consumption for rice, rapid increase in N rate resulted in decline in agronomic NUE; 15–20 kg kg− 1 in 1958–63, 9.1 kg kg− 1 in 1981–83, and in Zhejiang province to 6.4 kg kg− 1 in more recent times (Peng et al., 2006). According to Peng et al. (2006), about 7% of total N produced in the world was applied to rice in China, and on average, rice farmers applied 145 kg N ha− 1 in 1997, and a high proportion was applied within 10 days of transplanting. Timing of fertiliser application affecting NUE A number of papers demonstrate over-fertilisation in China. Under irrigation, there has been major progress in N management that reduces N input and risk of environmental pollution without reducing yield. Peng et al. (2006) found that farmers in China could reduce N input in early stages without sacrificing grain yield in experiments at four major rice-producing provinces comparing farmer’s practice and modified practice. The farmer’s practice was set at 180–240 kg N ha− 1 of which 56%–85% was applied as basal fertiliser within 10 days of transplanting. In their modified practice, the total was reduced to 130–170 kg N ha− 1 by reducing the proportion of basal to 38%–79% but keeping the amount of N top dressing the same at each site. The mean grain yield in modified practice was higher than the farmer’s practice (7.6 vs. 7.2 t ha− 1). Mean total N uptake was slightly lower in the modified practice (178 vs. 194 kg N ha− 1), resulting in higher internal NUE. In these experiments, mean control yield (the same fertiliser input except N which was 0 kg ha− 1) was high (6.4 t ha− 1), and mean N uptake of the control was also high (95 kg ha− 1), resulting in PFPN about 50% higher in the modified practice (52 vs. 35 kg kg− 1). The highest PFPN of 124 kg kg− 1 was achieved when total N rates of 60 kg ha− 1 were split with 35% applied at basal, 20% at mid-tillering, 30% at PI, and 15% at heading; the mean yield of this treatment was 7.4 t ha− 1, which was not

Rice Chapter | 2  61

significantly different from the 7.6 t ha− 1 of the modified practice. Thus shifting N application towards reproductive stage can save a large amount of N. Generally, early N application, i.e. basal and top dressing at tillering, has lower N fertiliser recovery than that applied later in reproductive stage (Wang et al., 2018). However, they also showed that N applied basally tend to remain in the soil at maturity, and it could be available to subsequent crops. Since Peng et al. (2006) was published, similar work on modified farmer’s practice was examined at different locations/ years with overall similar results; for example, the work of Sui et al. (2013) at seven different sites in Jiangsu Province. In Chen et al. (2015b), multi-split application of 15 kg ha− 1 every week increased grain yield with the lowest total N input but with the highest N recovery of 64%–79% in four locations among all N application treatments. Sometimes modified farmer’s practice treatment reported from China included, in addition to modified rate and timing, increased hill density or plants per hill, increased P and K rate, and AWD. While the yield, NUE, and economic benefit may increase in the modified practice and is of great practical use for local farmers, it is difficult to pinpoint exactly the cause of such improvement, e.g. Hubei Province ((Zeng et al., 2012; Chen et al., 2015a; Chen et al., 2015b), Jiangsu Province (Zhang et al., 2018), and Hunan Province (Xie et al., 2019)). These modified practices, with an increase in the amount of N applied in the reproductive stage but a decrease in the vegetative stage together with other changes in agronomy, often resulted in increased recovery of applied N and increased sink size (spikelet number m− 2), which appeared to be responsible for increased yield. The work at IRRI, Philippines showed that late N application was also productive in drill-planted irrigated lowland rice (Liu et al., 2019). When compared to the current practice of equal amounts of split N application at seedling, mid-tillering, and PI (standard), shifting the application from seedling to heading increased the yield by 6%–7%, with the same total N rate. Total N uptake was higher with equal splits at mid-tillering, PI, and heading stages than the standard N treatment. Thus replacing the seedling stage application with the heading stage application increased N uptake in both wet and dry seasons, with apparent N recovery exceeding 70% in the former. The N that was taken up after heading increased yield in association with higher LAI and crop growth rate during grain filling, higher grain set (filled grain percentage), and higher harvest index. In these examples mentioned earlier, predetermined fixed amounts of N were applied at particular times, regardless of plant N status, thus there was no variation in the N rate in different areas with varied soil N status. This could be modified to tailor fertilisation to plant N status. Commonly, colour chart, SPAD meter, and N dilution curves (Ata-Ul-Karim et al., 2013, 2017) are used to improve NUE and grain yield; the former can be readily used by farmers. Direct determination of leaf N concentration can be used, such as practised in Australia at PI. Site-specific N management Site-specific N management (SSNM) considers the site-specific indigenous N supply using the information obtained from unfertilised controls. SSNM may be considered as an empirical model to optimise N, P, and K application (Dobermann et al., 2002). The NPK supply at a site or region is determined from indigenous N, P, and K supply plus nutrients from fertiliser assuming fertiliser recovery rate for N, P, and K, for example, 50% for N. Nutrient demand is estimated to achieve maximum yield at the site, often 80% of estimated potential yield. Thus SSNM intends to match the application with the plant N demand (Cassman et al., 1996). In-season adjustment is then made from N concentration determination to improve NUE. The concept was tested in irrigated fields in seven locations in six countries in Asia, and the overall result was 7% increase in grain yield over farmers practice on fertiliser application. Peng et  al. (2006) tested SSNM with grain yield without N application set at 5 t ha− 1. SSNM (fixed time application) produced the highest yield of 7.7 t ha− 1, with total N application of 110–130 kg ha− 1, resulting in agronomic NUE of 11.8 kg kg− 1. The yield and agronomic NUE were significantly higher than the farmer’s practice (7.2 t ha− 1 and 3.6 kg kg− 1) but not significantly higher than the modified practice mentioned earlier. SSNM has shown to be advantageous over farmer’s practice in N management in subsequent experiments in China. For example, about 5%–8% increase in grain yield was observed over the farmer’s practice treatment, with a higher increase in AWD than in continuously flooded conditions, in the experiments conducted in Jiangsu Province (Liu et al., 2013). With decreased total N rate in SSNM, total N uptake was less, but internal N use efficiency and PFPN were higher. However, N application time was different between these treatments, and hence both the rate and timing were confounded making it difficult to specify the benefit of determination of plant N status before determining N application rate. Controlled-release N fertiliser One way of applied N fertiliser matching with the timing of plant N demand is the use of controlled-release fertiliser. Fertiliser is coated with water-insoluble protective materials so that N is released slowly to better match with crop growth and its N requirement. Alternatively, urease inhibitors can reduce NH3 volatilisation and nitrification ­inhibitors

62  Crop Physiology: Case Histories for Major Crops

can reduce nitrification–denitrification. Meta-analysis by Linquist et al. (2013) showed that urease inhibitors, nitrification inhibitors, and controlled-release N fertiliser modestly increased yield by about 10% in alkaline (pH > 8.0) but not in acidic soils (pH  japonica conventional (JC) inbred > IH (Wei et al., 2017a). The lower yield of IH (mean 9.9 t ha− 1 vs. 11.6 t ha− 1 in JIH) was associated with lower spikelets m− 2 despite single grain weight being the highest among the three groups. JIH had more spikelets panicle− 1, which was almost double that in JC and more than 50% greater than in IH. Thus increased sink size in JIH with increased panicle size resulted in overall higher yield. This was accompanied by JIH producing the highest biomass during grain filling; thus the high sink size was matched with its capacity to supply assimilate source. They also had the longest grain filling duration ensuring sufficient time to meet the demand to fill in the larger sink. Most panicle dry weight increase was met by current assimilate during grain filling; stem weight decreased slightly during grain filling (less than 100 g m− 2), and contribution of NSC was small with a maximum of about 10% in IH. Shorter grain filling in IH with lower grain yield was associated with high grain N concentration; Wei et al. (2017a) suggested this high N concentration and higher total N content in grain caused premature leaf senescence because of greater N translocation from leaves to grain, with shorter grain filling resulting in lower grain yield in IH. Huang et al. (2019) compared three varieties, JIH, IH, and inbred indica, with different sink sizes under low N input across 3 years in the Middle Reaches of Yangtze River in China. They showed that JIH had (1) high yield associated with high sink size, and the latter was associated with its ability to produce more spikelets for given biomass or N uptake. JIH had more primary and secondary branches in a panicle with a large number of spikelets differentiated and becoming fertile; (2) JIH also produced more assimilate and absorbed more N during grain filling. The higher biomass production was associated with higher RUE and NUE and better canopy structure of JIH; and (3) JIH also headed earlier than others, and biomass at heading may be less than others, but grain filling duration was longer. 4.1.3.3  Other factors affecting genotypic variation in grain yield One key factor determining genotypic variation in grain yield is RUE, according to the study of 12 varieties in two planting densities in wet and dry seasons in the Philippines (Dingkuhn et al., 2015). RUE was higher in varieties with lower specific leaf area but was also affected by the variety’s ability to maintain green leaves during grain filling. Thus RUE was higher in varieties with stay-green traits. Modelling confirmed the benefit of partial stay-green to potential yield but also showed that some degree of leaf senescence was required for N translocation from leaves to grain. There was genotypic variation in maintaining chlorophyll content during grain filling in a population developed from a bi-parental cross, and several QTL were identified (Yamamoto et al., 2017). There are cases of genotypic variation in use of available assimilates to fill grain that has caused variation in yield. For example, Okamura et al. (2018) showed a high-yielding variety depleted all the carbohydrate reserves at maturity, whereas a lower-yielding counterpart had unused residual carbohydrates in the stem at maturity and low grain set of 53% across 4-year experiments. Use of a larger amount of the reserve to fill the grain in a HYV was also noted in the work of Ishimaru et al. (2017), where IR64 and introgression line YTH183, which was derived from the cross between a large panicle sized NPT and IR64 were compared in 14 experiments under fully flooded and water-saving conditions across three Asian countries in the tropics. Varieties were similar in total biomass. The -yielding ability of YTH183 was noted for two countries earlier (Kato et al., 2011). Under flooded conditions, they found no difference in spikelets panicle− 1 nor grain set, but YTH183 produced heavier grain and had a higher harvest index. Similarly, Ishimaru et al. (2017) found YTH183 out-yielded IR64 by 1.2–1.3 t ha− 1 or 22.8%–32.3% in fully flooded and water-saving conditions, respectively, because of larger single grain weight and harvest index in most of the 14 environments. The heavier grain was related to greater grain width and thickness. Grain growth curves showed that the advantage in grain growth in YTH183 occurred during the latter part of grain filling, particularly in the bottom part of the panicle. NSC in the stem at heading was similar between the two varieties but decreased during grain filling more greatly in YTH183, indicating its ability to mobilise stored reserves resulting in heavier grain. They also found that YTH183 had more vascular bundles with

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larger panicle neck diameter contributing to the translocation of assimilates during grain filling. The NSC available in the stem at heading was not fully utilised to fill grain in IR64, and thus availability of NSC at heading may not be critical for its subsequent use. Zhang et  al. (2013b) demonstrated that three high-yielding japonica varieties that differed in panicle size, that is, Yangfujing-8 (YFJ8), Lianjing-7 (LJ7), and Huaidao-9 (HD9) with small, medium and large panicles, differed in their response to timing of N application (PI, spikelet differentiation, and heading) in China. HD9 had fewer panicles than YFJ8 and LJ7. HD9 produced higher yield with N application at spikelet differentiation and heading than at PI (10.4–10.7 vs. 9.5 t ha− 1). In this variety, total spikelets produced with early N application at PI appeared excessive, so source was insufficient and grain set and single grain weight were lower, resulting in lower yield. NSC availability spikelet− 1 at heading was also the lowest in this variety, and NSC availability spikelet− 1 was associated with grain set and activity of SUS enzyme. Total biomass production at maturity also tended to be greater with early N application at PI, although the rate of leaf photosynthesis remained higher when N was applied later at heading. Thus the variety with large panicle (HD9) required more assimilate during grain filling, which became available with N application at spikelet differentiation—heading. The small panicle variety (YFJ8) produced the highest yield with earlier N application (10.2, 9.2, and 7.9 t ha− 1 with N application at PI, spikelet differentiation, and heading, respectively), and the medium panicle variety (LJ7) produced higher yield with application at PI and spikelet differentiation than at heading (9.8–9.8 vs. 8.2 t ha− 1). With YFJ8 and LJ7, N application at PI and spikelet differentiation stages increased spikelet number m− 2 without greatly affecting grain set and single grain weight. Thus there was a clear genotype by environment by management interaction (G × E × M) in the high-yielding environment (about 10 t ha− 1). Time of N application may need to be adjusted with genotype, in that a variety with a large sink size and a large panicle size may require N application later in the reproductive stage, to ensure sink size does not become too large, and source supply is maintained to fill grain.

4.2  Response to abiotic factors Section 4.2.1 describes rice response to water deficit that may develop in rainfed lowland or upland conditions. Submergence, a problem unique to rice-growing ecosystems, is the focus of Section 4.2.3, while effect of increased CO2 is described in Section 4.2.2. Sections 4.2.4 and 4.2.5 deal with rice responses to extreme temperatures, and genotypic and management adaptations, and Section 4.2.6 describes response to salinity.

4.2.1  Water deficit Rice is quite sensitive to even mild soil water deficit, and hence maintaining favourable soil water condition is essential for high yield. Unless soil is saturated with water, maintaining high water potential at shallow soil depth is required. For example, in upland conditions, soil water potential at 20 cm and 40 cm depth may be maintained at around − 10 kPa and − 30 to − 40 kPa, respectively, for maximum growth (Kato et al., 2013). Kato and Okami (2011) have shown thresholds from − 15 kPa to − 25 kPa at 20 cm soil depth for maintaining transpiration. Yield of upland rice is sensitive to mild soil water deficit when compared with other crops such as sorghum and maize (Inthapan and Fukai, 1988). This is partially related to rice’s shallower root system, which becomes even shallower when confined to a small soil volume by ponding of water. The effect of drought on grain yield depends on the timing, intensity, and duration of the stress in relation to crop development. Water deficit during establishment may compromise transplanting; stress during tillering may reduce number of tillers and panicles; stress during reproductive stage reduces spikelet number; and stress during flowering may increase spikelet sterility and reduce grain set (Boonjung and Fukai, 1996b). We thus outline different types of drought as a framework to identify management and varieties to improve adaptation. 4.2.1.1  Types of drought and genotype × management options For rainfed lowland rice, three types of drought are considered: (1) early season drought that compromises transplanting; (2) intermittent drought between rainfall events, and (3) terminal drought that develops during growth and continues until the crop matures or is killed. Of 32 observations in 3 years across northeast Thailand, four fields had continuous drought throughout the growing period, terminal drought developed very frequently, most fields lost standing water by 1 week after flowering, and intermittent drought developed in 20 fields (Monkham et al., 2018). These patterns would be similar in the Mekong region, although the frequency of drought may be somewhat less in neighbouring countries in the region (Tsubo et al., 2009; Inthavong et al., 2012). In the Mekong, drought is more severe in higher topo-sequence positions with reduced water availability (Fukai and Ouk, 2012).

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Management options (Table 2.3) should be tailored to type of drought. Knowledge of the phenological development of candidate varieties and of the local environment would help in identifying the appropriate time of planting. Yield is reduced if standing water disappears before flowering (Jearakongman et al., 1995; Tsubo et al., 2009), and hence planting time, particularly of photoperiod-insensitive or mildly sensitive varieties, needs to be sufficiently early to escape from late season drought, which is quite common in rainfed lowland rice in Thailand and elsewhere (Fukai and Ouk, 2012; Monkham et al., 2018). On the other hand, early planting, particularly of photoperiod-sensitive varieties, results in a long time between planting and flowering, and crops may lodge under high soil fertility (Fukai, 1999). Strongly photoperiod-sensitive varieties commonly flower late, and this predisposes to late season drought. In some countries such as Cambodia, sowing of late-flowering strongly photoperiod-sensitive varieties has been discouraged, and earlier flowering varieties have been developed. Early season drought is also common, but farmers may be able to transplant using older seedlings or younger seedlings produced during the drought period. Other farmers may broadcast after the dry period if there is still sufficient time for crop growth to maturity. However, they may seed drill and plant deeper in the soil where water is still available. Varieties with early vigour (Sandhu et al., 2015) may be used for quick emergence and overcoming the common problem of weeds in direct-seeded fields, and those with early vigour together with an ability to emerge from deep sowing need to be developed. Intermittent drought is also common, and breeding efforts are being made to develop adapted varieties (Xangsayasane et al., 2014; Monkham et al., 2015). Sahbhagi Dhan is a drought-tolerant variety with stable yield released in South Asia; it has often shown high emergence rate under dry soil and a high proportion of total root length as lateral roots (Anantha et al., 2016). Similarly, line IR57514-PMI-5-B-1-2 is a drought-tolerant donor (Fukai and Ouk, 2012) and was used as a parent of varieties subsequently released in SE Asia. Because of the complexity of drought development at different toposequence positions in different seasons, often there is strong genotype and environment interaction for grain yield in rainfed lowland rice; for example, in Thailand and Laos (Cooper et al., 1999) or across rainfed lowlands worldwide (Wade et al., 1999c). Acuna et al. (2008) used G × E analysis to show adaptation to one type of drought did not necessarily provide adaptive advantage in other kinds of drought. This necessitates repeating multilocation trials before adapted varieties could be selected and slows down rice improvement. 4.2.1.2  Adaptive traits Rice is considered to be a drought avoider (Kamoshita et al., 2008), with key avoidance traits, including ability to maintain a higher leaf water potential and better root systems for water uptake. In rainfed lowlands, high-yielding varieties under drought maintain high leaf water potential (Pantuwan et al., 2002) and turgor that minimises delay in heading (Homma et al., 2004). Turgor maintenance was associated with higher osmotic adjustment in leaves (Kamoshita et al., 2004). Similarly, in upland conditions, varieties that maintained leaf water potential had lower spikelet sterility and higher grain yield under drought (Jongdee et al., 2002). Maintenance of leaf water potential was associated with larger xylem diameter in the stem and hence its ability to transport water within the plant (Sibounheuang et al., 2006).

TABLE 2.3  Types of drought in rainfed lowland rice and management and variety options available. Types of drought

Option 1

Option 2

Option 3

Management

Drill seeding

Dry broadcasting

Transplanting

Variety

Early vigour

Early vigour

Short duration or photoperiod-sensitive

Management

Optimum nutrition

Variety

Drought avoidance

Management

Early planting

Variety

Short duration, insensitive

Photoperiod sensitive

Drought avoidance

Early season

Mild intermittent

Terminal drought

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Roots play a major role in drought adaptation. Henry et al. (2011) showed in rainfed lowland experiments at IRRI that varieties with high root length density at 30–45 cm extracted more water and maintained cooler canopy, which was consistent with earlier results from Bangladesh, in which genotypes differed in the ability to increase root length density below 15 cm soil depth as water deficit progressed (Samson et al., 2002). Gowda et al. (2011) reviewed root characteristics associated with drought resistance in rice. Deep and coarse roots were key traits for drought adaptation in upland conditions, but adaptive traits were not so straightforward in rainfed lowlands. This was partly related to the hard pan resulting from puddling that helped reduce deep percolation but impeded root penetration to deep soil layers, especially as the soil dried. Hence coarse nodal roots could help deeper root growth and access to stored water. Another issue was the functionality of deeper roots dependent on the transport of oxygen through aerenchyma. Large vascular bundle diameter could help maintain high hydraulic conductivity and hence high plant water status under drought. Aquaporins may be involved in hydraulic conductance. Genotypic variation in root traits such as root number, root diameter, and root growth plasticity have been listed and their roles tested under controlled conditions (Azhiri-Sigari et al., 2000; Kamoshita et al., 2000), but the functional implications of these traits for yield under drought are not clear (Gowda et al., 2011).

4.2.2  Effect of increased CO2 concentration 4.2.2.1  Crop growth As rice is a C3 plant, the rate of photosynthesis and dry matter production increase with increase in CO2 concentration over current and expected ranges. Findings in controlled environments were confirmed by free air CO2 enrichment experiments (FACE) (Sakai et al., 2001; Yang et al., 2006b). Meta-analysis by Wang et al. (2015) showed about a 25% increase in shoot dry matter of rice in response to increase in mean CO2 to 630 μL L− 1, although the increase was smaller in FACE than in the controlled environments. The analysis also showed a 17% mean increase in light-saturated photosynthesis and a proportionally similar decrease in stomatal conductance. The effects of CO2 enhancement are greater at vegetative stage and decrease with time. For example, in FACE at Jiangsu, 200 μL L− 1 increase in CO2 concentration increased TDM by 40%, 30%, 22%, 26%, and 16% at tillering, PI, heading, mid ripening, and grain maturity, respectively (Yang et al., 2006a). Measurement of canopy CO2 exchange also showed a 33% increase in growth with increased CO2 concentration in the first 3 weeks of growth but a diminishing effect that disappeared by heading (Sakai et al., 2001). With CO2 enhancement, LAI increased, particularly when high N was applied, and this faster leaf area development also helped to increase TDM production when the leaf canopy was still small. At later stages, leaf area is often fully developed, and solar radiation should be fully intercepted, so that the effect of enhanced CO2 is based only on increased rate of photosynthesis. If sufficient N is not applied, N supply from the soil may not meet the increased N requirement for greater TDM in the later growth stage, resulting in diminished effect of CO2 enhancement. Wang et al. (2015) showed N was critical for crop response to elevated CO2. Total N uptake increased with CO2 enhancement, but the TDM increase was also large, so N concentration in the plants, and particularly in leaves decreased, indicating N becomes a more limiting factor for growth with CO2 enhancement. Fertiliser N application required to maximise the effect of CO2 enhancement is influenced by variety (Hasegawa et al., 2019). If on the other hand, the leaf N concentration during grain filling was maintained above 15 mg g− 1, which was considered the threshold affecting RUE (Ata-Ul-Karim et al., 2013), crop growth may not be limited by N as shown by De Costa et al. (2006). 4.2.2.2  Grain yield and quality In their meta-analysis of 125 studies, Wang et al. (2015) showed 20% increase in mean grain yield under increased CO2 concentration and smaller effects in FACE than in growth chambers. Yield gains with increased CO2 are usually larger under non-limiting N, with some exceptions (Yang et al., 2007; Shimono et al., 2008). The mean response of hybrids was greater than indica and japonica inbred varieties in terms of biomass production, but the increase in tiller number and panicle number was smaller and HI decline was greater than others. Japonica inbred varieties responded most in terms of tiller production and tended to have more response in grain yield. It may be that these hybrids had limited capacity to respond to increased assimilate availability. In cool areas, warm temperature may increase the effect of CO2 enhancement. For example, Shimono et  al. (2008) reported that the positive effect of CO2 on growth was 6% in 1 year when mean air temperature was 18.4°C, while it was 17% in another year when temperature was 19.9°C. In a larger study covering 11 years, Hasegawa et al. (2016) showed the effect of CO2 enhancement on grain yield varied by 0%–21% with nil or reduced response under low and high temperatures. The grain yield increase because of CO2 enhancement was associated mostly to increased panicle number (12%) and grain number (15%), a slight (5%) increase in spikelet fertility, and no significant increase in single grain weight

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(Wang et al., 2015). The FACE by Zhang et al. (2015) showed that increased yield associated with enhanced CO2 was mediated by increased grain set for spikelets at the bottom of the panicle. Increased assimilate availability in vegetative stage to panicle development stage increased panicle number and spikelet number m− 2 while that after heading increased grain set (Sakai et al., 2001; De Costa et al., 2006; Yang et al., 2006b). Enhanced CO2 also effected grain quality. Elevated CO2 decreased protein content and increased grain chalkiness. Yang et al. (2007) showed decreased head rice yield (the ratio of whole milled grain/paddy rice) by 13% with CO2 enhancement, and hence the positive effect on grain yield reported in the literature may be overestimated when milled whole grain for marketing is considered. They reported a reduction in amylose content, and an increase in peak viscosity as a result of reduced grain hardness, and hence improved eating quality under elevated CO2.

4.2.3 Submergence Submergence is a major problem in many rice-growing countries in SE and South Asia, particularly along the coast of Bangladesh, India, and Vietnam. Flooding has two distinct classes and mechanisms: flash flooding of short duration, in which cessation of elongation and energy-saving allows growth to restart when floodwaters retreat (e.g. QTL SUB1); and deep-water flooding to several metres for several months, in which plant elongation is required for foliage to remain above the water to allow respiration and photosynthesis (e.g. QTL Snorkel) (Sripongpangkul et al., 2000; Das et al., 2005; Hattori et al., 2011). Transient complete submergence of less than 2 weeks caused by flash flood with heavy rainfall is common and affects over 20 Mha of rice (Mackill et al., 2012). Submergence is common during germination—establishment in direct-seeded rice fields, but also causes severe problems after seedling emergence in both transplanted and direct-seeded crops when the plants are still short. Submergence effects on germination and seedling emergence are discussed in Section 2.1. Submergence reduces both the radiation available for photosynthesis and the oxygen level. The damage to the submerged plants is exacerbated with increased submergence depth, higher temperature, and increased turbidity because these conditions reduce photosynthesis and increase respiration requirement. When susceptible rice plants are submerged, the level of ethylene increases, plants elongate, and chlorophyll is degraded. This consumes energy and the carbon and hastens plant mortality under transient submergence, particularly when the photosynthetic rate is reduced severely (Mackill et al., 2012). Some landraces can withstand submergence for up to 2 weeks, while susceptible varieties commonly die in complete submergence after 1 week. Owing to the frequency of the problem in these areas, improved varieties with high-yield potential are not grown because of their susceptibility to submergence. From one of the tolerant land races in India, FR13A was selected, and the QTL for submergence tolerance (SUB1) was identified on chromosome 9 (Mackill et  al., 2012). Expression of SUB1 is induced by ethylene, which inhibits plant elongation and hence saves energy within the plants resulting in survival for about 2 weeks. SUB1 is effective from 4 days after germination to 2 weeks before flowering. Markerassisted backcrossing was used to introgress the gene into several mega varieties in South and SE Asia. These SUB1 varieties behave like the mega varieties when there is no flood in terms of grain yield and grain quality but survive submergence much longer. Comparisons of the mega varieties with or without SUB1 show the yield advantages of SUB1 varieties from 1 to more than 3 t ha− 1 (Ismail et al., 2013). These SUB1 introgression varieties were adopted very rapidly, covering areas exceeding 1 Mha by 2012. In addition to flash flooding, long-term partial stagnant flooding is common in low-lying areas. There are some varieties tolerant to long-term partial stagnant flooding, but this is not related to SUB1 (Singh et al., 2011). Deep-water and floating rices require an entirely different strategy because prolonged flood depth and duration precludes a tolerance strategy. Here, stems must elongate with the rising floodwater so that oxygen can continue to be transported to roots through aerenchyma (Sripongpangkul et al., 2000; Das et al., 2005; Hattori et al., 2011). Similarly, tolerance for submergence during germination (AG) is independent of, but compatible with, SUB1 (Mackill et al., 2012). Development of multi-stress tolerant varieties is continuing, for example, pyramiding SUB1 and brown planthopper resistance into Thai jasmine rice KDML105 (Korinsak et al., 2016).

4.2.4  High temperature 4.2.4.1  Reproductive growth Increased temperature has large effect on phenological development as described in Section  2, but it also has adverse effect on growth through reduction in RUE (see Section  3.1), reproduction, and yield. Similar to the effect of drought (Section 4.2.1), the effect of high temperature depends on timing, intensity, and duration of hot spells. Rice is susceptible to heat about 9 days before anthesis when the male gametophytes develop (Satake and Yoshida, 1978). High temperature damage in rice is particularly severe to the male reproductive organs. The damage may be reduced with deep standing water, which may buffer the effect on young developing panicles from high daytime air temperature

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(Jagadish et al., 2015). Thus one of the challenges of water-saving technologies such as aerobic rice is to ensure the plants cope with the exposure of young panicles to extreme temperatures in the absence of standing water. Rice is considered most susceptible to heat on the day of flowering because it causes sterility and hence affects yield directly. Several reviews are available on this topic (Jagadish et al., 2015; Arshad et al., 2017; Prasad et al., 2017a). The heat-damaging temperature in rice at flowering is considered 35°C (Satake and Yoshida, 1978) and is lower than that of other tropical and sub-tropical cereals (Prasad et al., 2017a). Heat-induced spikelet sterility is common with high daily temperature at flowering. For example, spikelet sterility was about 11% when average maximum temperature at around heading was below 33°C but increased to over 23% when temperature exceeded 37°C, with the variety TDK1 in the dry season in Lao PDR (Ishimaru et al., 2016). When the crop was shaded during flowering, the air temperature decreased by more than 2°C, and the sterility was decreased. In Tamil Nadu, spikelet sterility of cv. Coimbatore 51 was below 10% with average maximum temperature at heading below 36.5°C, but it increased to over 33% when air temperature exceeded 38°C. However, in southern Australia where rice is grown under irrigation in the dry season in inland areas with high potential evapotranspiration and large day and night temperature variation, air temperature exceeding 40°C at flowering did not induce spikelet sterility in cv. Langi (Matsui et al., 2007). Owing to the low relative humidity of around 20%, high evaporative cooling lowered panicle temperature by almost 7°C below air temperature. The number of anthers that dehisce and the distance of dehiscence at anthesis are important in determining the number of shed pollen, and long dehiscence at the base of the thecae of this variety contributed to heat tolerance, probably through the release of large number of pollen grains onto the stigma (Matsui and Omasa, 2002). High temperature at flowering reduces anther dehiscence (Matsui and Kagata, 2003), the number of pollen grains intercepted by the stigma and spikelet fertility (Matsui et al., 1997). Growing with standing water in lowlands secures high water uptake by the roots and hence continuous transpiration, unless stomata close at midday, and a high transpiration rate reduces leaf temperature by evaporative cooling, and hence this acts as heat avoidance mechanism. This ability would be reduced in humid areas where transpiration would be lower. Because of the evaporative cooling effect, adverse effects of heat may be less severe in irrigated than in rainfed conditions, and also in dry than in humid conditions, for a given air temperature. In the work of Matsui et al. (1997) at IRRI using open-top chambers in the field in the dry season, a 4°C increase over ambient temperature reduced spikelet fertility from 85% to 78%. Spikelet fertility varied daily according to the maximum temperature. Enhanced CO2 concentration by 300 μL L− 1 had no effect on spikelet fertility, but when it was combined with increased temperature, spikelet fertility decreased from 87% to 66%. This interaction appears associated with enhanced CO2 reducing stomatal conductance, and the resultant reduced transpiration increased canopy temperature. Thus CO2 enhancement reduced the critical temperature, above which spikelet fertility is significantly reduced, by 1–2°C. Pollen germination and pollen tube elongation may be affected by high temperature causing spikelet sterility (Jagadish et al., 2015). However, opening of spikelets within a plant takes a few days, and the spread of spikelet opening further increases in a plant community. Thus an extended heat period causes a stronger effect on spikelet sterility percent. An example of the effect of heat duration is seen in 3-year outdoor potted experiments that examined the heat stress duration effect at anthesis and during grain filling on spikelet fertility (Shi et al., 2016). When the plants were at anthesis and also at 12 day after anthesis, they were brought into temperature-controlled rooms with varied day/night temperature at 32/22°C, 35/25°C (close to the field conditions), 38/28°C, and 41/31°C for 2, 4, and 6 days, respectively (Shi et al., 2016). While spikelet fertility and grain yield were not affected for 4 days at 35/25°C when compared with 4 days at 32/22°C at anthesis, spikelet fertility decreased at the higher temperatures. Increased heat duration from 2 to 6 days at anthesis reduced spikelet fertility. Spikelet fertility and yield were also reduced when temperature treatments were applied at 12 days after anthesis, although the effect was less pronounced compared to the treatments at anthesis. Sun et al. (2018) extended the work of Shi et al. (2016) mentioned above to measure spikelet sterility in response to timing of heat during grain filling. Spikelet sterility at extremely high temperature (44/34°C) for 6 days was the highest when it was imposed at anthesis (65% vs. 8% in control), was reduced at 6 days after anthesis (25%), and was small at 12 days after anthesis (12%). This timing effect was associated with the time of flowering of spikelets at different positions within a panicle. The temperature threshold for spikelet sterility was 35–36°C depending on the variety. The temporal spread of flowering in the controlled room (about 8 days) was important in determining the sensitive time to heat stress. However, the flowering pattern in the field appears even more spread; for example, 6.4 days from the first flower to 50% flowering when dry and wet season data of 292 varieties were pooled at IRRI (Bheemanahalli et al., 2017). 4.2.4.2  Grain yield In comparing responses of different crops to a wide range of temperatures in sun-lit temperature-controlled rooms, Boote et al. (2005) found rice to have an optimum temperature of 25°C for grain yield. The response of total biomass was less

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pronounced than grain yield, and the response of harvest index followed the pattern of grain yield with a threshold temperature of 36°C, above which HI was 0. This threshold was lower than most crops they examined. While some other crops adjusted to temperature extremes by producing a smaller grain size, rice maintained similar grain size, but grain set responded greatly to temperature. Higher temperature commonly reduced grain filling duration, and this often reduces grain yield with lower assimilate partitioning to grain. For example, in the temperature-controlled experiment by Kim et al. (2011), moderately high temperature after heading (31/23°C) compared to 25/17°C and 19/11°C accelerated grain filling, maturity, and leaf senescence. The variety used, japonica variety Ilpumbyeo, matured before full leaf senescence, and hence assimilate was still available when grain filling was completed, and all grains were filled. They obtained similar results in a field experiment in Korea by comparing two crops planted 44 days apart, where mean daily temperature during grain filling was 24.4°C and 21.9°C. Thus the advantage of maturing in cooler condition was longer grain filling and increased assimilate partitioning to panicles. In the meta-analysis of enhanced CO2 effect, Wang et al. (2015) showed the interaction of enhanced CO2 and high temperature among 19 studies. For increased temperature alone, there was a large negative effect on yield (33% reduction), associated with reduced spikelet fertility and HI. When both CO2 and temperature were increased, yield decreased by 7%. However, this interaction also depended on the ambient temperature; thus 1°C increase resulted in decrease of 9%–10% in warm areas (mean ambient temperature of around 25°C or 30°C) but not in the cooler area with a mean temperature around 20°C. Importance of night-time temperature Field experiments that directly compared the effect of daytime temperature on rice yield are limited. Shah et al. (2014) used heaters and blowers in open fields with plastic walls to increase daytime temperature alone, night-time alone, and both day- and night-time temperature by about 2°C over the ambient temperature from booting to maturity in a set of nine varieties, four japonica and five indica. The ambient temperature varied greatly during the experiment period in 2 years, but the maximum ambient temperature reached 32–34°C, and the minimum ambient temperature was about 16°C towards maturity in both years. Heating day and night reduced yield by 21%–30% on average of 9 varieties. Decrease in grain yield was less than 10% when daytime temperature alone was increased, while warmer night-time temperature alone reduced yield by 5% in the first year and 31% in the second year. Warming reduced yield more in japonica than in indica varieties. The high day- and night-time temperatures increased spikelet sterility (16% in both indica and japonica in years 1 and 12 and 40% in indica and japonica, respectively, in year 2) and lower harvest index (15% and 19% in indica and japonica, respectively, in year 1 and 17% and 42%, respectively, in year 2). While high daytime temperature at anthesis may induce spikelet sterility (Section 4.2.4.1), high night-time temperature is often more detrimental to grain yield than daytime temperature (Peng et al., 2004). While night-time temperature of 25°C compared to 21°C during vegetative stage had almost no effect on plant growth, the higher night-time temperature commencing at PI increased dark respiration and promoted degeneration of spikelets that had been differentiated, resulting in fewer spikelets and hence reduced sink size (Laza et al., 2015). High night-time temperature during grain filling may be considered unfavourable with increased respiration losses and reduced assimilate partitioning to grains. An example of high temperature reducing assimilate partitioning can be seen in a temperature-controlled experiment during grain filling where assimilate production at high temperature (32°C) compared to 22°C was not reduced despite increased respiration loss, but assimilate distribution to panicle was reduced, and this caused reduction in grain yield (Cheng et al., 2010). For a critical view on the effect of temperature on respiration and growth, see Chapter 19: Cassava, Section 3.1.5. At IRRI, six hybrid and two inbred varieties were compared in field experiments with two night-time temperatures: 23°C in controls and 29°C in crops heated with closed chambers; chambers were open during the day (Shi et al., 2016). Mean grain yield reduction was 13.4% in dry season and 18.6% in wet season, and head rice yield was also generally decreased, while chalkiness increased with increased night-time temperature. Yield decrease was more pronounced in hybrids than inbred varieties. Using the same field chamber system, Shi et al. (2017) tested three varieties in two N levels in both wet and dry seasons. Increasing N application rate did not alleviate the adverse effect of high night-time temperature. Higher night-time temperature did not affect spikelet sterility in the field experiment (Shi et al., 2013), and this contrasts with temperature-controlled studies where often higher night-time temperature was used, and the effect was severe causing increased spikelet sterility (Jagadish et al., 2015). Genotypic variation There is large genotypic variation in heat tolerance in rice (Pasuquin et al., 2013). Prasad et al. (2006) grew 14 varieties of different species (O. sativa, Oryza glaberrima, and interspecific varieties), ecotype-subspecies (tropical indica, temperate

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japonica, and tropical japonica) in temperature-controlled glasshouses where mean temperature was 5°C higher than ambient temperature. They found no negative effect of high temperature on vegetative growth, but grain yield decreased sharply at high temperatures in most varieties. The grain yield variation was associated with spikelet sterility and harvest index, but there was no clear trend of high temperature tolerance among different species or subspecies. The variation in spikelet sterility was related to pollen production and pollen interception by stigma. Variation in anther dehiscence also contributed to the variation in pollen interception by stigma (Matsui and Omasa, 2002). Using 292 diverse indica varieties at IRRI, 33°C was the threshold beyond which spikelet sterility increased (Bheemanahalli et al., 2016). Spikelet sterility increased at a rate of 0.26% per oC h with base temperature of 33°C. As temperature increases in the morning when rice plants flower, indica varieties with early morning flowering show lower spikelet sterility (Bheemanahalli et al., 2017). Ishimaru et al. (2010) identified a QTL for early morning flowering from wild rice Oryza officinalis, and it was further fine mapped (Hirabayashi et al., 2015). Incorporating such a trait into breeding populations is promising in reducing spikelet sterility under high temperature. Future global warming effect In the global meta-analysis of many simulated studies of the effect of temperature change in the future, Challinor et al. (2014) indicate that rice yield, and wheat and maize yields, would decrease with a global warming of 2°C or greater without adaptation, but rice yield can be maintained or enhanced with crop adaptation. It should be, however, pointed out that different simulations by different groups produced quite variable results, and hence the close monitoring of actual temperature change and crop response is required. Effective adaptation would include the change in time of planting and varieties (Osborne et al., 2013). With increased temperature, growth duration may become too short for effective interception of solar radiation and crop growth, resulting in reduced grain yield, and thus varieties with larger thermal requirement are required for adaptation to global warming.

4.2.5  Low temperature Cold is a problem in some temperate areas and also in high altitude areas in the tropics. Rice is most susceptible to cold at the young microspore stage resulting in male sterility (Satake and Hayase, 1970). Thus in these areas, rice is planted at a time that will result in the young microspore stage, which is in about 14 days before flowering, taking place in the warmest time of the year, and often protected by increasing standing water to cover young panicles (Farrell et al., 2006b). When mean minimum temperature for a month is below 17°C, male sterility could develop. When cold weather develops during vegetative and reproductive stages, floral development is delayed, and this could also affect fertilisation. In addition to the cold problem during reproductive stage, low temperatures at planting and towards harvesting often become limiting, and quick maturing varieties are required that fit well in the period of warm temperature. Transplanting can be advantageous because a protected seedling nursery can be heated up with the sun, and seedlings can be transplanted when the temperature is high enough before transplanting to the main fields (Hoshikawa et al., 1995). Optimum time of planting is considered to be the time that would minimise the chance of cool weather occurrence for a given variety. Thus optimum time of planting is determined from the knowledge of genotypic flowering time response and historical local weather pattern, as demonstrated by Farrell et al. (2006b) for the Riverina region in southern Australia. While indica varieties are generally more susceptible to cold at any growth stage, there is genotypic variation against cold at young microspore stage (Ye et al., 2009). Genotypes may be screened using cold water or cold air and by determining spikelet sterility. Cold tolerance mechanisms at young microspore stage and flowering are illustrated in Fig. 2.4. Cold temperature at young microspore stage reduces the number of pollen grains. Cold temperature at flowering may reduce (1) anther dehiscence, mediated by reduced pollen water uptake and pollen swelling, (2) interception efficiency by stigma, and (3) pollen germination (Mitchell et al., 2016). Often genotypes with a large number of pollen in the anther are coldtolerant at the young microspore stage (Farrell et al., 2006a). Those with a higher pollen number tend to have more pollen intercepted by the stigma, increasing the chance of fertilisation. Anther dehiscence appears important for securing a larger number of pollen intercepted by the stigma in the young microspore under cold conditions, and genotypic variation has been found in responses to cold temperature at this stage (Susanti et al., 2019). While Fig. 2.4 is based on the findings of the effect of cold, the effect of other stresses in rice follows a similar pattern of processes that lead to male sterility, resulting in reduced grain number panicle− 1. Increased N application often makes the plants more susceptible to cold at the young microspore stage. Increased N causes more tillering and increased panicle number, which could result in a smaller number of pollen grains in each anther (Gunawardena et al., 2003b). Gunawardena et al. (2003a) showed that removing tillers increased pollen number per anther and reduced spikelet sterility.

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FIG. 2.4  Diagram of cold stress at booting or flowering on development of male sterility in rice.

4.2.6 Salinity Saline environments for rice may range from flash flood and stagnant flood in flood-prone regions, through tidal saline with fluctuating water depths in coastal and delta regions, to dryland salinity in drought-prone regions. Salinity is a complex problem because plants may be exposed to osmotic stress, and to Na+ and Cl− ion toxicity, as salt concentrations rise, sometimes in conjunction with flooding and heavy metal toxicities (Ismail and Horie, 2017). Various strategies have been identified for salinity tolerance, including salt uptake and exclusion, compartmentation, compatible solutes, reduced distribution to shoots, dilution through continued transpiration and growth, and combinations of these (Ismail and Horie, 2017). Physiological studies suggest salinity tolerance is most associated with low shoot sodium concentration, compartmentation to older leaves, tolerance within leaves, and greater plant vigour (Yeo et al., 1990). Crops rarely tolerate salt concentrations greater than one-third seawater, so for differences in performance (not only survival), ion transport mechanisms in conjunction with continued growth were essential (Flowers, 2004). In contrast, halophytes were able to tolerate high tissue concentrations of Na+ and Cl−, through regulation of membrane transport, synthesis of compatible solutes, and ability to deal with reactive oxygen species (Flowers and Colmer, 2015). Chapter 20: Sugar Beet (Section 3.3.2) is an illustrative contrast whereby a halophyte plant absorbs and utilises sodium physiologically. The first release of a salinity-tolerant rice cultivar, PSB-Rc50 ‘Bicol’, was derived from anther culture by combining the high yield of IR5657-33-2 with the salinity tolerance of IR4630-22-2-5-1-3 (Senadhira et  al., 2002). Bangladeshi cultivars BRRIdhan-47, BRRIdhan-61, and BINAdhan-8 performed better under dry-season salinity, so were preferred by farmers (Islam et al., 2016). ABA priming was considered effective against saline/alkaline stress (Wei et al., 2017b), while multi-environment screening separated tolerance to salinity from tolerance to sodicity (Krishnamurthy et al., 2017). Although genetic control of salinity tolerance was complex, the evidence nevertheless suggested a few QTL of major effect could be pyramided using molecular approaches (Ismail et al., 2007). For example, Thomson et al. (2010) demonstrated the Saltol gene from the salt-tolerant cultivar Pokkali was able to control shoot Na+/K+ homeostasis. These authors advocated a backcrossing programme because multiple tolerant alleles were closely associated with the Saltol locus. Aala and Gregorio (2019) screened tolerant lines from Bangladesh and Philippines and reported a diversity of alleles from the tolerant-check FLA78, offering promise of further recombination. Likewise, in examining the correlation of salinity-induced senescence with whole-plant and leaf-blade sodium concentration, Platten et al. (2013) found seven major and three minor alleles closely associated with the gene OsHKT1;5. Rahman et al. (2016) explored novel genetic sources and found successful landraces effectively limited sodium transport to the shoot, including for seedling tolerance. These mechanisms were associated with reduced accumulation of Na+, increased accumulation of K+, and lower Na+/K+ ratios in leaves. These reports seemed consistent with the many sources, many genes, but

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single mechanism philosophy for rice performance under salinity (Ismail et al., 2007; Platten et al., 2013). Leaf relative water content also showed promise for screening rice for salinity tolerance, because it was highly correlated with low Na+/K+ ratio and was fast, simple, cheap, and quantitative (Suriya-Arunroj et al., 2004). Comprehensive reviews of physiological and molecular mechanisms to salinity tolerance, how to combine multigenic salinity tolerance in a coordinated manner, and rice responses to salinity stress have recently been provided by Islam et al. (2019), Gupta et al. (2019), and Riaz et al. (2019), respectively. Radanielson et al. (2018) used Oryza V3 model to examine trait combinations for enhanced rice performance under salinity tolerance and reported three promising strategies: (1) shortduration rice to escape late exposure to increasing salinity as water availability declined (or late irrigation of long-duration rices for the same purpose), (2) include salt tolerance traits bTr and bPN above 12 dS m− 1, and resilience trait aSalt of 0.11 for 60%–70% yield in up to 16 dS m− 1, and (3) increase the value of the tolerance parameter b by 1% would increase yield by 0.3%–0.4%. Despite the extensive knowledge of critical mechanisms for salinity tolerance, there has been limited progress in breeding and especially the successful release of tolerant rice cultivars, suggesting a need to better utilise molecular and genomic approaches to combine these complex strategies into a single salinity-tolerant rice cultivar (Ismail and Horie, 2017).

4.3  Crop management for yield and quality 4.3.1  Crop establishment Crop establishment methods include manual transplanting, mechanical transplanter, broadcasting, seed drill, and ratooning, and their suitability depends on environmental conditions, availability of labour, and infrastructure. 4.3.1.1  Comparison of direct seeding and transplanting Yield In Thailand where broadcasting has increased gradually to about 50% of total rice areas over the last 50 years, the yield of broadcasted crops was lower than that of transplanted crops in earlier years, but it has become similar more recently because farmers have been adopting suitable technology for direct seeding (Suwanmontri, 2018). If crop establishment is good and weeds are not an issue, the broadcasted crop may out-yield the transplanted crop (Naklang et al., 1996), but often different establishment methods produce similar yields (Xangsayasane et al., 2019b). Broadcasting often results in low or uneven establishment and lower yield than transplanted crops (Naklang et al., 1996) or drill-planted crops (Kumar and Ladha, 2011). Weedy fields may not be suitable for direct seeding, particularly for broadcasting (Kumar and Ladha, 2011). A higher seed rate in broadcasting could help under such conditions because the rice competes more strongly against weeds (Basnayake et al., 2006). Seed drilling produced better yield than broadcasting in extremely dry conditions, where broadcasted crops almost failed completely (Xangsayasane et al., 2019b). Comparison of yield between direct-seeded and transplanted rice is reviewed by Farooq et al. (2011). Kumar and Ladha (2011) compared the yield of transplanted and direct-seeded crops from six Asian countries; yield was similar in most cases, although in India and Pakistan, the mean yield of dry direct-seeded rice was lower than transplanted crop, while in Bangladesh and the Philippines, wet direct seeding produced higher yield than transplanted crops. However, farmers may choose a particular method of establishment to maximise their financial return rather than crop yield, resulting in their opting for broadcasting, particularly if they can readily control weeds (Fukai and Ouk, 2012). Sometimes, they use older seedlings for transplanting, particularly if the soil is not saturated with water, and the potential yield may be reduced as transplanting is delayed. Weeds Commonly transplanted fields are wet-cultivated and during this process, some weeds that may have emerged are cultivated out. Transplanting with seedlings of often 10–20 cm height has an advantage in competing against weeds that have emerged after last harrowing conducted often just before transplanting. This size difference gives the rice a competitive advantage over the weeds. Another reason is that after transplanting, rice fields have standing water, with its depth being increased as rice seedlings grow taller, and anaerobic conditions suppress germination and emergence of weeds. Thus transplanted rice fields commonly have less weeds when compared with direct-seeded fields. For details, see a few reviews (Kumar and Ladha, 2011; Matloob et al., 2015; Rao et al., 2017). Direct-seeded fields are prone to weed problems, and yield of direct seeded rice can be reduced greatly by weeds. There was a negative correlation between rice yield and weed seed number in a research station experiment at CARDI, Cambodia (Kamoshita et al., 2016). Ikeda et al. (2008) showed that yield reduction started when weed dry weight exceeded 48 g m− 2, and yield was reduced by 22%, with each 100 g m− 2 increase in weed weight with maximum yield reduction of 54%.

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With the change to direct seeding, particularly in dry direct seeding, often herbicide use increases sharply, as found in Thailand where the use of herbicides in dry-seeded rice increased from 36% to 92% between 1998 and 2009 (Pandey et al., 2012). This often results in changes in weed composition in rice fields, resulting in increases in grassy weeds such as weedy rice (red rice) that are difficult to control with any selective herbicide (Kumar and Ladha, 2011). With drill sowing, however, banding of fertiliser with the seed increases early seedling vigour, and young ducklings can be used to reduce early weed competition, with benefits to grain yield (Sengxua et al., 2019). 4.3.1.2 Ratooning Ratooning, whereby the rice crop is allowed to regrow after harvest from tiller buds, has been practised in China for over 1700 years (Wang et al., 2020). Historically, grain yields have been low (Santos et al., 2003), but with careful management, yields of 28.6%–64.3% of the plant crop can be attained (Wang et al., 2019). With increasing water scarcity and climate change, interest in ratooning as a green, resource-efficient technology is increasing, for sustainable systems with reduced environmental impact (Yuan et al., 2019). Ratooning reduces costs by savings in labour for transplanting and crop establishment and reduced fertiliser requirements. Less greenhouse gas is produced because less fossil fuel is consumed, soil is submerged for less time, and organic matter is reduced. Although yields are lower, grain quality remains high, and returns to the farmer are improved (Wang et al., 2020). Attaining consistently higher ratoon yields requires careful management of the crop to minimise lodging and carryover of pests and diseases. Dry- or wet-direct seeding saved additional labour over transplanting in establishing the plant crop (Dong et al., 2017). Improved ratoon performance was associated with application of 100 kg N ha− 1 at 15 days after heading in the plant crop to encourage tiller buds and of 100 kg N ha− 1 at 1–2 days after stubble cutting in the ratoon crop to promote their growth (Wang et al., 2019). At tiller bud regeneration, a shallow water depth is essential for regrowth and tiller survival (or 69% yield loss), which is also aided by shorter stubble and timely N application (Bahar and Dedatta, 1977; Nakano and Morita, 2008). Shorter stubble encourages regeneration from basal nodes for stronger tillers and panicles (Harrell et al., 2009). Regeneration rates in direct-seeded rice were higher in hybrid rice than in inbred cultivars because of greater dry matter per stem at the first harvest (Chen et al., 2018). Ling et al. (2019) added a reserve pool submodule to the ORYZA-v3 model to investigate critical factors for rice ratooning. Consistent with the experimental results above, the most sensitive phases were initial ratoon tiller development and early production and allocation of dry matter in the ratoon crop. Ziska et al. (2018) used models to demonstrate ratoon rice could be used as an adaptive management tool under climate change by allowing rice systems to migrate along a south– north transect in the southern Mississippi Valley. Wang et al. (2020) suggested a specialised harvester may be needed to reduce rolling of stubble during ratoon harvest, although timely soil drying in mid-late grain filling before harvest can reduce this. Greater attention to breeding rice varieties for enhanced ratoon performance was advocated, along with improved mechanical harvesters (Wang et al., 2020). A change to government policy in China was also encouraged to strengthen research on mechanisms for high ratoon yields to secure sustainable increases in rice production for food security and environment benefit (Lin, 2019). 4.3.1.3  Perennial rice With global population increasing, pressure on the resource base and impact of climate change, even marginal lands, which currently support 50% of the world’s population, are at risk of degradation under annual cropping, and they must be farmed sustainably in future to meet the ever-increasing demands for food and livelihood (Eswanan et al., 1999; Tilman et al., 2011). Perennial grains show promise in meeting these conflicting needs for protection of fragile lands while also allowing farmers to support themselves and their families (Glover et al., 2010). To do so, perennial grains must stabilise land and soil resources while at the same time contributing to grain and/or forage in mixed crop-livestock systems (Batello et al., 2014). In rice-based systems, with populations rising rapidly, favourable land with access to irrigation is largely utilised, and marginal lands of low fertility that are dependent on rainfall and vulnerable to climate change are being increasingly targeted to meet the food gap, the need to develop perennial rice as a component of sustainable farming systems is urgent (Wade, 2014). Following successful hybridisation between O. sativa (L.) and Oryza longistaminata (Tao and Sripichitt, 2000), efforts to develop perennial rice commenced (Hu et al., 2003; Sacks et al., 2006; Zhang et al., 2014b), with the long-term goal of breeding perennial rice to stabilise fragile soils in rainfed lowland and rainfed upland rice-based systems. Four papers have specifically reported on performance of perennial rice in the field (Zhang et al., 2017, 2019; Huang et al., 2018; Samson et al., 2018), indicating perennial rice may have promise in a number of rice-based systems. Perennial rice derivatives were reported to survive, regrow, and yield successfully across a diverse range of environments in southern China and Lao PDR, with perennial rice PR23 identified as a prime candidate for release to farmers,

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based on its broad adaptation and high yield over environments (Zhang et al., 2017). Other genotype groups showed preferential adaptation to dry season, wet season, or more tropical conditions. The paper concluded that regrowth success and maintenance of spikelet fertility over regrowth cycles were important for adaptation of perennial rice, especially to low minimum temperatures at higher altitude and rainfall deficit in lower altitude sub-humid conditions. Huang et al. (2018) then examined the suitability of PR23 for release to farmers under irrigated paddy conditions by comparing perennial rice PR23 with two seasonally replanted annual rice genotypes, RD23 and HXR7, across nine ecological regions in southern Yunnan Province of China, and across scales, from experimental plots to smallholder fields to commercial areas. Overall, the grain yield of PR23 was similar to those of the preferred annual rice cultivars in these conditions, but the economic analysis indicated substantial labour savings for farmers by growing the perennial instead of the annual rice varieties. PR23 was considered acceptable in grain size and grain quality, so farmers were keen to grow PR23 because of reduced costs and especially labour savings. Samson et al. (2018) extended these comparisons to rainfed lowland environments in the subhumid tropics of Lao PDR. While yields were lower in the ratoon crop, all perennial rice derivatives were able to survive the dry season with access to life-saving irrigation. This was promising because the annual rice RD23 was unable to ratoon under these conditions and had to be resown. Ratoon grain yields of several perennial rice lines were comparable to replanted annual RD23, which was also promising under those wet-season rainfall-deficit conditions. Recently, Zhang et al. (2019) reported a combination of high-yield potential, strong regrowth, and earlier maturity resulted in higher performance over environments and regrowth cycles, with PR23 outstanding and able to perform similarly to the seasonally replanted annual check, BN21, over up to six growth cycles. Further understanding of longevity in perennial rice is required. How many ratoon cycles can be grown before replanting is needed? Is there systematic yield decline over regrowth cycles? If so, can any such decline be arrested through improved management or improved disease resistance? What trade-offs may occur as a result of the perennial growth habit, and can they be compensated by any improved resource capture in the perennial varieties? Are there benefits from including perennials, such as improved sustainability, biodiversity, soil health, or livestock integration? Some of these challenges may be best addressed using long-term experiments, to ensure valid comparisons, as is presently being implemented in China.

4.3.2  Water-saving technologies 4.3.2.1  Alternate wetting and drying irrigation In AWD irrigation, adding water when soil water level reaches to 15 cm below the soil surface is considered to be neutral for yield (Bouman et al., 2007). In their meta-analysis of AWD irrigation, Carrijo et al. (2017) concluded that AWD-mild where field was irrigated to maintain soil water potential above − 20 kPa at 15 cm, yield was not affected in most cases. However, when the soil dried below − 20 kPa (AWD-severe), grain yield was reduced on average by 23%. AWD, particularly AWD-severe, was found to exhibit less adverse effect on yield under higher soil organic carbon. Mahajan et al. (2012) also showed an interaction effect in AWD in experiments in northwest India; without N application, the effect of mild water stress (at − 20 kPa over − 10 kPa) reduced yield from 5.90 to 3.77 t ha− 1, but when N was applied at 60 kg ha− 1 or greater amount, there was no effect of water stress. Whether grain yield under AWD can be maintained the same as the yield under flooded conditions also depends on the varieties (Sandhu et al., 2017). For example, Chu et al. (2018) found that AWD adapted varieties can produce the same yield but not those that are adapted only to flooded paddy conditions. Under flooded paddy conditions, the two varieties produced similar yield and expressed physiological attributes such as root biomass, but under AWD, the adapted variety produced higher root dry matter, root density, root oxidation activity, and higher activities of enzymes that convert sucrose to starch in grains. Recent work from China shows that the timing of AWD can be important and mild AWD during the grain-filling period can increase grain yield over the flooded crop. Gu et al. (2017) in Jiangsu Province compared flooded and AWD with constant N management. Nitrogen uptake and hence N recovery was about the same at 37%–38%, but AWD increased panicles m− 2, grain set, and yield (12%–14%), with agronomic NUE increasing from about 13 kg kg− 1 to 18 kg kg− 1. Grain yield increase in AWD-mild over the flooded control appeared to be associated with improved root aeration and increased concentration of cytokinins in roots and shoot (Zhang et al., 2009b) (see Chapter 16: Sunflower, Section 3.1.2 for the role of root cytokinins on canopy senescence). Zhang et al. (2009b) demonstrated in experiments in Jiangsu, China, that after wetting, stomata conductance was increased to the same as, but leaf photosynthesis exceeded, that in the flooded conditions, contributing to increased shoot and root dry matter, particularly during grain filling. Enzyme activities that were associated with sucrose to starch conversion in grain were also higher in AWD-mild, and this also contributed to its 11% higher grain yield. A similar yield advantage in AWD-mild was further demonstrated in 3 N rates (100, 200 and 300 kg ha− 1) also in Jiangsu (Wang et al., 2016b).

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Despite the advantage of saving water and no yield penalty of AWD-mild, Carrijo et al. (2017) indicated AWD was not adopted well except in China where a similar system of draining in the mid-season has been practised. A secure source of irrigation water is required where irrigation water is costly for wide adoption of AWD. Lampayan et al. (2015b) demonstrated an economic benefit of AWD and mentioned technology adoption in Vietnam, Bangladesh, and the Philippines. Nitrous oxide emissions are significant in rice in flooded soil conditions but are reduced when ponded water disappears, as in rainfed lowland, AWD, and especially, aerobic and upland conditions (Kirk et al., 2019; Kirk and Kronzucker, 2005). 4.3.2.2  Aerobic rice Aerobic rice can produce similar or even higher yield as demonstrated in temperate areas (Kato et al., 2009). Harvest index and yield components were mostly similar under aerobic and flooded conditions. In the work of Kato et al. (2009) in two locations in 2 years, the first aerobic rice was grown in upland areas after growing soybean and wheat for the previous 5 years, while in lowland fields, rice was grown once a year and fallowed between the rice crops. Thus it is possible that rotation with non-rice crops might have provided more favourable conditions to aerobic rice when compared to continuous cropping of flooded rice. Kato and Katsura (2014) review aerobic rice production and list high yields achieved at different locations. Aerobic rice in temperate areas often produced yields exceeding 9 t ha− 1, while yield in the tropics appears limited to about 8 t ha− 1. Prasad (2011) suggested partially aerobic rice system, including AWD and saturated soil culture, may be more suitable in the tropics. In soils of high percolation across 3 years in Brazil, Reis et al. (2018) compared four water management treatments at planting (flooded, AWD, saturated soil, and aerobic). Rainfall was sufficient for rice growth, and the aerobic rice with no standing water did not require any irrigation other than that at the time of fertiliser application. They showed the highest yield in the aerobic conditions where N uptake was the highest during early growth. The yield advantage of aerobic rice was expressed mostly through increased panicle density and to lesser extent through increased grain number panicle− 1, and assimilate availability during grain filling was sufficient to meet the large demand created by the large sink size. The advantage of aerobic over flooded rice was also demonstrated by Katsura and Nakaide (2011). In their work, the NSC reserve available in the shoot and the dry weight increase during grain filling was higher in aerobic than flooded conditions. They found that root oxidation activity was greater in the aerobic conditions because of higher oxygen availability, while it decreased during grain filling in the flooded conditions, and this, together with higher soil N availability, appeared to have helped production of more assimilate to meet the higher demand by the larger sink size. Aerobic rice may have higher N uptake than flooded rice, resulting in leaves maintaining high photosynthetic capacity, in turn providing assimilates to support more spikelets and a larger sink for high yield potential in temperate areas (Kato and Katsura, 2010) but not in the tropics (Clerget et al., 2014). Kato and Katsura (2014) mentioned the genotypic variation in spikelet production efficiency, the spikelets produced per unit N uptake, a key character associated with high yield. However, they suggested high sink capacity of aerobic rice does not always result in high yield because there may not be sufficient source supply to fully fill all the spikelets produced, and hence yield may become source limited. As mentioned in Section 4.2, rice is sensitive to mild soil water deficit, and without standing water, aerobic rice can be water stressed more readily and growth can be reduced any time during crop growth. With mild stress and with reduced evaporative cooling as a result of stomatal closure, high temperature can damage rice badly. If water stress develops during mid-panicle development to flowering, it can affect not only panicle exertion and grain set (see Section 4.2) but also potential grain size through the reduction in husk size (Katsura and Nakaide, 2011), reducing grain yield when compared to flooded rice. Genotypes with resistance to mild soil water deficit are required for sustainable aerobic rice, and this is incorporated in most aerobic rice breeding programmes in the world. Pinheiro et al. (2006) described the aerobic rice breeding in Brazil; blast resistance, drought resistance, high-yield potential, and lodging resistance were gradually incorporated into new varieties, and these varieties out-yielded other semi-dwarf or upland type varieties under favourable aerobic conditions. High grain quality was then added as a breeding objective. Kato and Katsura (2014) listed characteristics of major aerobic rice breeding systems presently practised. They noted that tropical japonica type was well adapted to aerobic culture, and indica-japonica crosses were commonly used for aerobic rice breeding. Prasad (2011) also described major breeding objectives for aerobic rice in different countries; a common objective was for drought tolerance and the other, responsiveness of grain yield to high input such as fertiliser. Thus drought-tolerant upland varieties and high-yielding lowland varieties are also commonly used to develop varieties suitable for aerobic conditions. These breeding programmes produced a number of varieties well adapted to aerobic conditions such as Apo. Table 2.4 lists some key issues, management options, and desirable traits associated with aerobic rice. Incorporation of these traits is expected to increase adaptation to aerobic conditions. Deep rooting may be important in securing water from depth under water-saving methods (Section 2.4). Other related issues are discussed in Section 2.1 (germination and emergence) and Section 4.3.2 (water saving).

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TABLE 2.4  Key issues, management options, and variety traits associated with aerobic rice. Issues

Management

Traits

Establishment

Soil water content may be low

Planting time window, deep sowing

Long mesocotyl, quick germination

Weed

With no standing water, weeds may compete with rice

Weed control measures, land preparation, herbicide

Early vigour

Mild intermittent stress

Stress may develop between irrigation events

Water at prescribed soil water potential

Deep and extensive roots, stomata open in mild stress conditions

Nutrient availability

P may be less available in aerobic soils, particularly low pH soils

Soil amendments

High P use efficiency

Soil water availability

Conversion of lowland fields

Hard pan disruption, avoid heavy clay soils

Deep penetration ability with thick roots

Extreme temperatures

Without standing water, plants are exposed to ambient temperature

Possibly change the time of planting to escape from extreme temperatures

Heat tolerance and cold tolerance

Without transplanting and no standing water to control weeds, aerobic rice could be severely affected by weeds. In the aerobic rice study of weed control in the Philippines in wet and dry seasons, Chauhan and Johnson (2010) showed 94%– 96% yield loss if weeds were not controlled. Weed control was required for a longer time period in 30 cm rows compared with 15 cm rows, with uncontrolled weed biomass being about 10% greater in wide rows. The critical period of weeding to achieve 95% weed-free field was from 15 to 64 days after sowing for 30 cm rows and from 17 to 56 days after sowing in 15 cm rows in the dry season. The critical period was slightly shorter in both row spacings in the wet season. Higher rice plant density also helped to reduce weed problem in aerobic rice (Chauhan et al., 2011). Aerobic and anaerobic flooded conditions provide different soil physical and chemical conditions. Thus there are cases where aerobic rice experience unfavourable conditions compared to flooded rice. For example, in low pH soils, aerobic rice may encounter difficulty in P uptake because P is more immobilised in aerobic soil conditions, and fine roots are reduced with the loss of standing water (Kato and Katsura, 2014). Similarly, excess Al, which may not be an issue in lowland rice where ample water reduces the potential impact of these ions, could cause toxicity under aerobic conditions. Continuous rice cropping in aerobic conditions can cause severe disease and insect pest issues, such as soil-borne root nematode; the topic is well reviewed in Prasad (2011). Prasad (2011) showed cases where the yield of aerobic culture relative to flooded culture decreased as the number of continuous crops of aerobic rice increased. Pinheiro et al. (2006) showed 1 year of soybean with 4 years of rice doubled the yield in a 5-year continuous rice (1.16 vs. 2.58 t ha− 1), while 2 years of rice and 3 years of soybean produced a rice yield of 4.32 t ha− 1. While there may be yield decline with continuous lowland rice production, the detrimental effect of continuous rice cropping is much more severe in upland aerobic conditions, with root-knot nematodes implicated as an important factor in upland rice (Prot and Matias, 1995). Root damage was reduced by soil flooding and was eliminated by soil fumigation.

4.4 Mechanisation Mechanisation such as the use of mechanical planters and combine harvesters may not greatly affect crop growth, development, and yield, but it can often affect the gross margin of the farmers (Fukai et al., 2019), while reducing labour requirement, and reducing drudgery, especially for women and children. The advantages of machinery are better captured with varieties that are suitable for mechanised rice production (Table 2.5, Fukai et al., 2019). Field capacity of the combine harvester was reduced by 38% in lodged crops, and hence varieties with lodging resistance are required, particularly when rice was established from broadcasting (Xangsayasane et al., 2019b). The seed drill has an advantage over broadcasting when the soil surface is dry because of its ability to plant deeper in the soil (Xangsayasane et al., 2019b). Rice varieties suitable for deep planting need to be identified. As drill is used before soil is saturated with water, often planting takes place much earlier than the time of transplanting. This could cause a problem if photoperiod-sensitive varieties are planted too early because flowering may take place during the peak rainy season. Thus there is a need to identify genotypes suitable for use with seed drills.

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TABLE 2.5  Variety characteristics required for mechanised rice production (Fukai et al., 2019). Characteristics

Type of operation

Note

Lodging resistance, reduced canopy bulkiness

Combine harvesting

Particularly broadcasted crops

Shattering resistance

Combine harvesting

Particularly old indica varieties

Photoperiod sensitivity

Seed drill

Avoiding flowering at peak rainy period from early planting

Seedling’s ability to emerge

Seed drill

Seed may be planted at depth in moist soil

Canopy spread

Seed drill, transplanter

Filling initial gap quickly, weed control

Combine harvesting coupled with an artificial dryer can improve grain quality and marketability (Fukai et al., 2019). When rice is grown for subsistence, i.e. home consumption, farmers may not have concern on grain quality as long as the variety meets their preferred type such as glutinous/non-glutinous, aroma, colour, and hardness. When farmers are harvesting rice manually, sun drying of the plants in the field is common and is often acceptable for home consumption. On the other hand, marketing often requires consistent physical and milling quality such as low broken rice percent, and this can be achieved more readily with combine harvesting and artificial drying. As a combine harvests the crop and also threshes it to produce paddy rice with high moisture content, sun drying in the field requires more resources when compared with manually harvested rice crops, and the milling after sun drying commonly produces a lower head rice yield than artificial dryers (Xangsayasane et al., 2019c). Different types of artificial dryers are now available, and the best drying practice depends on the dryer’s energy efficiency, greenhouse gas emissions, and cost–benefit (Nguyen Van et al., 2019).

5  Concluding remarks: Challenges and opportunities With the changes in external factors, rice cropping is changing, and this provides challenges and opportunities. Here, we identify six aspects that need the attention of rice crop physiologists, agronomists, soil scientists, and others.

5.1  Adaptation mechanisms to reduced water input in irrigated system Shortage and affordability of water supply have become a major limiting factor in many rice-growing areas, and water-saving methods are required. In these areas, irrigation water may be available, but the traditional flooding system may not be sustainable, and different methods have been examined, including dry direct seeding, AWD, and aerobic rice.

5.1.1  Dry direct seeding Alternative planting systems such as dry direct seeding are changing soil characteristics, with the likelihood of greater root access to deeper soil layers, to provide additional plant demands in rainfed lowland and other ecosystems. Under drill seeding, root access to deeper soil layers should be improved, offering prospects for rice in the rainfed lowlands to access additional resources from below the surface layer during dry periods.

5.1.2 AWD AWD was developed to save water in irrigated lowlands and seems to work well in different countries. There are some reports that indicate AWD produce yield that can exceed the traditional flooded rice, particularly AWD applied during grainfilling period. This advantage could be because of increased N availability or increased aeration that vitalise root activities. Understanding the mechanisms could lead to appropriate N and water management, with possible improvement in grain yield, NUE, and WUE.

5.1.3  Aerobic rice The sensitivity of rice to mild soil water deficits limits the use of aerobic system. This is partially because of varieties developed for flooded system, which may not be adapted to the aerobic system. For example, shallow root system suits flooded conditions, but deep roots are often required for the aerobic system. In transplanted rainfed lowlands, the hard pan limits root growth, and adapted varieties may not always possess deeper root

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systems. In aerobic s­ ystems, the hard pan does not commonly develop, and water in deeper soil should be more accessible to varieties with deeper roots. Adapted varieties in rainfed lowland system possess ‘avoidance’ mechanism, and they can maintain high plant water potential (Kamoshita et al., 2008). This is an adaptation strategy suitable in systems where water availability during growth is uncertain. However, in aerobic system, irrigation water is available, and hence ideally available soil water is fully exploited with stomata open so that crop growth continues at maximum rate. Thus introduction of some ‘tolerance’ strategy would achieve this, possibly, including stomata that are not strongly sensitive to mild soil water deficit, so manipulation of root signals may be promising. The water management practice would consider the sensitivity of stomata and effective root depth. Similar to AWD, exposure to aerobic condition may be advantageous for N uptake and root health, and management practices may require changes; for example, fertiliser N rate, timing, and form.

5.2  Adaptation mechanisms for drought avoidance in rainfed lowland rice Genotypic variation in leaf water potential and the advantage of varieties that maintain high water potential is well documented, but the underlying mechanisms are not well understood. Usefulness of a deep root system in extracting more water has been suggested, but little experimental evidence is available on the extent of the advantage of deep root system, e.g. to what access to deep-soil water explains the variation in leaf water potential? High-throughput, reliable, and affordable methods to quantify root system and water extraction in lowland field are required, especially for direct-seeded systems. Improved understanding in conductance of water in the plant is also required, as well as osmotic adjustment, dehydration tolerance, and root signals in assisting maintenance of leaf water potential.

5.3  Adaptation mechanism for mechanised rice farming 5.3.1  Direct seeding, particularly drill planting While farmers are increasingly adopting direct seeding, crop establishment is inferior relative to transplanted crops. Drill planting provides generally better establishment as long as seed is placed in moist soil and not too deep in the soil that allows seedling emergence. Genotypes appear to differ in their ability to emerge from deep soil, but clear understanding of the underlying mechanisms will allow us to find the interrelationship among soil depth, moisture content, and genotype. Screening genotypes for deep placement and lower soil moisture content than field capacity may be required for identification of genotypes suitable for direct seeding in water-limiting conditions. Good and quick establishment helps to minimise the damage by weeds, which is a major issue in direct-seeded rice, including drill-planted crops. Well-adapted varieties would allow earlier planting at the onset of wet season, and new cropping system could evolve.

5.3.2  Combine harvesting Lodging is a major problem for combine harvesting. Lodging tends to be more of a problem in direct-seeded crops, particularly broadcasted crops. Understanding mechanisms of lodging and identifying screening methods for lodging resistance would be useful.

5.4  Factors determining grain set Grain set is sensitive to internal and external factors and plays a key role in grain yield (Section 4.1). The fate of spikelets during grain filling requires a detailed study. While this may be determined mostly by availability of assimilate, source supply alone may not be sufficient in determining grain set. The extent of other limiting factors such as assimilate transport through phloem and starch synthesis requires further work.

5.5  Enhancing yield potential Grain yield in rice has increased greatly in the past 50 years with application of appropriate inputs and with improved genotypes. The latter has achieved varieties adapted to biotic and abiotic stresses and those with higher yield potential. Improving yield potential has been achieved mostly through improved grain sink capacity rather than improvement in CO2 assimilation. Research on improving leaf photosynthetic rate would be worthwhile. This may be through improved carboxylation capacity such as C4 rice or through improved CO2 transport system. Enhancing yield potential is likely to have a direct impact on rice productivity, particularly in irrigated lowlands and favourable rainfed lowlands.

86  Crop Physiology: Case Histories for Major Crops

5.6  Head rice yield Head rice yield influences the commercial value of the crop. It varies greatly depending not only on harvesting time and postharvest management but also rice fissuring at the time of harvest (Bunna et al., 2019b; Xangsayasane et al., 2019c). Genotypes differ in head rice yield (Vongxayya et al., 2019), but plant factors determining head rice yield are not well understood. Grain response to milling time or degree of milling appears different among varieties, but this could be further investigated.

Acknowledgement We thank Amelia Henry, Yoichiro Kato, Toshihiro Hasegawa, Abdel Ismail, Shaobing Peng, and Guy Kirk for their helpful comments on the manuscript during its development and Mitsuru Tsubo for the figure on water balance (Fig. 2.2).

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Wade, L.J., Amarante, S.T., Olea, A., Harnpichitvitaya, D., Naklang, K., Wihardjaka, A., Sengar, S.S., Mazid, M.A., Singh, G., McLaren, C.G., 1999a. Nutrient requirements in rainfed lowland rice. Field Crop Res. 64, 91–107. Wade, L.J., Fukai, S., Samson, B.K., Ali, A., Mazid, M.A., 1999b. Rainfed lowland rice: physical environment and cultivar requirements. Field Crop Res. 64, 3–12. Wade, L.J., McLaren, C.G., Quintana, L., Harnpichitvitaya, D., Rajatasereekul, S., Sarawgi, A.K., Kumar, A., Ahmed, H.U., Sarwoto, Singh, A.K., Rodriguez, R., Siopongco, J., Sarkarung, S., 1999c. Genotype by environment interactions across diverse rainfed lowland rice environments. Field Crop Res. 64, 35–50. 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Priming of rice (Oryza sativa L.) seedlings with abscisic acid enhances seedling survival, plant growth, and grain yield in saline-alkaline paddy fields. Field Crop Res. 203, 86–93. Wei, H., Hu, L., Zhu, Y., Xu, D., Zheng, L., Chen, Z., Hu, Y., Cui, P., Guo, B., Dai, Q., Zhang, H., 2018a. Different characteristics of nutrient absorption and utilization between inbred japonica super rice and inter-sub-specific hybrid super rice. Field Crop Res. 218, 88–96. Wei, H., Meng, T., Li, X., Dai, Q., Zhang, H., Yin, X., 2018b. Sink-source relationship during rice grain filling is associated with grain nitrogen concentration. Field Crop Res. 215, 23–38. Wissuwa, M., Ae, N., 2001. Genotypic variation for tolerance to phosphorus deficiency in rice and the potential for its exploitation in rice improvement. Plant Breed. 120, 43–48. Wissuwa, M., Yano, M., Ae, N., 1998. Mapping of QTLs for phosphorus-deficiency tolerance in rice (Oryza sativa L.). Theor. Appl. Genet. 97, 777–783. 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Xangsayasane, P., Phongchanmisai, S., Bounphanousai, C., Fukai, S., 2019a. Combine harvesting efficiency as affected by rice field size and other factors and its implication for adoption of combine contracting service. Plant Prod. Sci. 22, 68–76. Xangsayasane, P., Phongchanmisai, S., Vuthea, C., Ouk, M., Bounphanousay, C., Mitchell, J., Fukai, S., 2019b. A diagnostic on-farm survey of the potential of seed drill and transplanter for mechanised rice establishment in Central Laos and southern Cambodia. Plant Prod. Sci. 22, 12–22. Xangsayasane, P., Vongxayya, K., Phongchanmisai, S., Mitchell, J., Fukai, S., 2019c. Rice milling quality as affected by drying method and harvesting time during ripening in wet and dry seasons. Plant Prod. Sci. 22, 98–106. Xie, X., Shan, S., Wang, Y., Cao, F., Chen, J., Huang, M., Zou, Y., 2019. Dense planting with reducing nitrogen rate increased grain yield and nitrogen use efficiency in two hybrid rice varieties across two light conditions. 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Rice seedling establishment as affected by cultivar, seed coating with calcium peroxide, sowing depth, and water level. Field Crop Res. 41, 123–134. Yamauchi, M., Aguilar, A.M., Vaughan, D.A., Seshu, D.V., 1993. Rice (Oryza-sativa L) germplasm suitable for direct sowing under flooded soil surface. Euphytica 67, 177–184. Yang, J.C., Zhang, J.H., 2010. Grain-filling problem in 'super' rice. J. Exp. Bot. 61, 1–4. Yang, L., Huang, J., Yang, H., Dong, G., Liu, G., Zhu, J., Wang, Y., 2006a. Seasonal changes in the effects of free-air CO2 enrichment (FACE) on dry matter production and distribution of rice (Oryza sativa L.). Field Crop Res. 98, 12–19. Yang, L., Huang, J., Yang, H., Zhu, J., Liu, H., Dong, G., Liu, G., Han, Y., Wang, Y., 2006b. The impact of free-air CO2 enrichment (FACE) and N supply on yield formation of rice crops with large panicle. Field Crop Res. 98, 141–150. Yang, L., Wang, Y., Dong, G., Gu, H., Huang, J., Zhu, J., Yang, H., Liu, G., Han, Y., 2007. The impact of free-air CO2 enrichment (FACE) and nitrogen supply on grain quality of rice. Field Crop Res. 102, 128–140. Yano, K., Sekiya, N., Samson, B.K., Mazid, M.A., Yamauchi, A., Kono, Y., Wade, L.J., 2006. Hydrogen isotope composition of soil water above and below the hardpan in a rainfed lowland rice field. Field Crop Res. 96, 477–480. Yao, Y., Zhang, M., Tian, Y., Zhao, M., Zhang, B., Zhao, M., Zeng, K., Yin, B., 2018. Urea deep placement for minimizing NH3 loss in an intensive rice cropping system. Field Crop Res. 218, 254–266. Ye, C., Fukai, S., Godwin, I., Reinke, R., Snell, P., Schiller, J., Basnayake, J., 2009. Cold tolerance in rice varieties at different growth stages. Crop Pasture Sci. 60, 328–338. Yeo, A.R., Yeo, M.E., Flowers, S.A., Flowers, T.J., 1990. Screening of rice (Oryza-sativa-L) genotypes for physiological characters contributing to salinity resistance, and their relationship to overall performance. Theor. Appl. Genet. 79, 377–384. Yin, X.Y., Kropff, M.J., 1998. The effect of photoperiod on interval between panicle initiation and flowering in rice. Field Crop Res. 57, 301–307. Yin, X., Kropff, M.J., Nakagawa, H., Horie, T., Goudriaan, J., 1997. A model for photothermal responses of flowering in rice II. Model evaluation. Field Crop Res. 51, 201–211. Yoshida, S., 1981. Fundamentals of Rice Crop Science. International Rice Research Institute, Los Banos, the Philippines. Yoshinaga, S., Takai, T., Arai-Sanoh, Y., Ishimaru, T., Kondo, M., 2013. Varietal differences in sink production and grain-filling ability in recently developed high-yielding rice (Oryza sativa L.) varieties in Japan. Field Crop Res. 150, 74–82. Yuan, S., Cassman, K.G., Huang, J.L., Peng, S.B., Grassini, P., 2019. Can ratoon cropping improve resource use efficiencies and profitability of rice in Central China? Field Crop Res. 234, 66–72. Zeng, X.M., Han, B.J., Xu, F.S., Huang, J.L., Cai, H.M., Shi, L., 2012. Effects of modified fertilization technology on the grain yield and nitrogen use efficiency of midseason rice. Field Crop Res. 137, 203–212. Zhang, S., Tao, F.L., 2013. Modeling the response of rice phenology to climate change and variability in different climatic zones: comparisons of five models. Eur. J. Agron. 45, 165–176. Zhang, H., Xue, Y., Wang, Z., Yang, J., Zhang, J., 2009a. Morphological and physiological traits of roots and their relationships with shoot growth in “super” rice. Field Crop Res. 113, 31–40. Zhang, H., Xue, Y.G., Wang, Z.Q., Yang, J.C., Zhang, J.H., 2009b. An alternate wetting and moderate soil drying regime improves root and shoot growth in rice. Crop Sci. 49, 2246–2260. Zhang, Y., Tang, Q., Zou, Y., Li, D., Qin, J., Yang, S., Chen, L., Xia, B., Peng, S., 2009c. Yield potential and radiation use efficiency of “super” hybrid rice grown under subtropical conditions. Field Crop Res. 114, 91–98. Zhang, H.M., Xu, M.G., Shi, X.J., Li, Z.Z., Huang, Q.H., Wang, X.J., 2010. Rice yield, potassium uptake and apparent balance under long-term fertilization in rice-based cropping systems in southern China. Nutr. Cycl. Agroecosyst. 88, 341–349. Zhang, Q.C., Wang, G.H., Feng, Y.K., Qian, P.Y., Schoenau, J.J., 2011. Effect of potassium fertilization on soil potassium pools and rice response in an intensive cropping system in China. J. Plant Nutr. Soil Sci. 174, 73–80. Zhang, Y., Zhang, C.C., Yan, P., Chen, X.P., Yang, J.C., Zhang, F.S., Cui, Z.L., 2013a. Potassium requirement in relation to grain yield and genotypic improvement of irrigated lowland rice in China. J. Plant Nutr. Soil Sci. 176, 400–406.

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Zhang, Z., Chu, G., Liu, L., Wang, Z., Wang, X., Zhang, H., Yang, J., Zhang, J., 2013b. Mid-season nitrogen application strategies for rice varieties differing in panicle size. Field Crop Res. 150, 9–18. Zhang, S., Tao, F.L., Zhang, Z., 2014a. Rice reproductive growth duration increased despite of negative impacts of climate warming across China during 1981-2009. Eur. J. Agron. 54, 70–83. Zhang, S., Wang, W.S., Zhang, J., Ting, Z., Huang, W.Q., Xu, P., Tao, D., Fu, B.Y., Hu, F.Y., 2014b. The progression of perennial rice breeding and genetics research in China. In: Batello, C., Wade, L.J., Cox, T.S., Pogna, N., Bozzini, A., Chopianty, J. (Eds.), Perennial Crops for Food Security. FAO, Rome, pp. 27–38. Zhang, G., Sakai, H., Usui, Y., Tokida, T., Nakamura, H., Zhu, C., Fukuoka, M., Kobayashi, K., Hasegawa, T., 2015. Grain growth of different rice cultivars under elevated CO2 concentrations affects yield and quality. Field Crop Res. 179, 72–80. Zhang, S., Tao, F., Zhang, Z., 2016. Changes in extreme temperatures and their impacts on rice yields in southern China from 1981 to 2009. Field Crop Res. 189, 43–50. Zhang, S.L., Hu, J., Yang, C.D., Liu, H.T., Yang, F., Zhou, J.H., Samson, B.K., Boualaphanh, C., Huang, L.Y., Huang, G.F., Zhang, J., Huang, W.Q., Tao, D.Y., Harnpichitvitaya, D., Wade, L.J., Hu, F.Y., 2017. Genotype by environment interactions for grain yield of perennial rice derivatives (Oryza sativa L./Oryza longistaminata) in southern China and Laos. Field Crop Res. 207, 62–70. Zhang, H., Yu, C., Kong, X., Hou, D., Gu, J., Liu, L., Wang, Z., Yang, J., 2018. Progressive integrative crop managements increase grain yield, nitrogen use efficiency and irrigation water productivity in rice. Field Crop Res. 215, 1–11. Zhang, S.L., Huang, G.F., Zhang, J., Huang, L.Y., Cheng, M., Wang, Z.L., Zhang, Y.N., Wang, C.L., Zhu, P.F., Yu, X.L., Tao, K., Hu, J., Yang, F., Qi, H.W., Li, X.P., Liu, S.L., Yang, R.J., Long, Y.C., Harnpichitvitaya, D., Wade, L.J., Hu, F.Y., 2019. Genotype by environment interactions for performance of perennial rice genotypes (Oryza sativa L./Oryza longistaminata) relative to annual rice genotypes over regrowth cycles and locations in southern China. Field Crop Res. 241. Zhao, D.L., Atlin, G.N., Bastiaans, L., Spiertz, J.H.J., 2006. Developing selection protocols for weed competitiveness in aerobic rice. Field Crop Res. 97, 272–285. Zhu, G.L., Peng, S.B., Huang, J.L., Cui, K.H., Nie, L.X., Wang, F., 2016. Genetic improvements in rice yield and concomitant increases in radiation- and nitrogen-use efficiency in middle reaches of Yangtze River. Sci. Rep. 6. Ziska, L.H., Fleisher, D.H., Linscombe, S., 2018. Ratooning as an adaptive management tool for climatic change in rice systems along a north-south transect in the southern Mississippi valley. Agric. For. Meteorol. 263, 409–416.

Image source: Manfred Richter from Pixabay

Chapter 3

Wheat Gustavo A. Slafera, Roxana Savinb, Dante Pinochetc, and Daniel F. Calderinid a

ICREA, Catalonian Institution for Research and Advanced Studies, and Department of Crop and Forest Sciences, University of Lleida—AGROTECNIO Center, Lleida, Spain, bDepartment of Crop and Forest Sciences, University of Lleida—AGROTECNIO Center, Lleida, Spain, cInstitute of Agricultural Engineering and Soil Science, Universidad Austral de Chile, Valdivia, Chile, dInstitute of Plant Production and Protection, Universidad Austral de Chile, Valdivia, Chile

1 Introduction 1.1  Wheat origin, production, and yield Wheat is one of the most widely cultivated crops in the world (Leff et al., 2004; Fischer et al., 2014; Fig. 3.1a and b); grown from Japan in the east to the US plains in the west; from Scandinavia and Canada in the north to the Patagonia and New Zealand in the south; from the sea level in many countries to more than 1700 m.a.s.l. in Nepal. It contributes about 20% of energy and protein in human diets worldwide (Braun et al., 2010) and is therefore critical to food security (Reynolds et al., 2012). Global wheat area averaged 220 Mha over the past 5 years (Section 1.2). About one-third of wheat is irrigated (Frenken and Gillet, 2012). Wheat is the second most irrigated cereal after rice, and 37% of the irrigated wheat area is in Asia. In the context of climate change and water scarcity, sustainability of wheat cropping systems is a challenge for farmers, scientists, and policymakers (Section 3.2). Wheat originated in the Levant (Vavilov, 1940—English version 1992; Abbo and Gopher, 2020). This area of the Middle East features a large diversity of Triticum L. species; for example, T. aethiopicum, T. araraticum, T. boeoticum, T. dicoccoides, T. dicoccum, T. carthlicum, T. ispahanicum, T. karamyschevii, T. macha, T. monococcum, T. sinskajae, T. spelta, T. timopheevii, T. turanicum, T. urartu, T. vavilovii, and T. zhukovskyi and related species such as Aegilops spp. The evolution of domesticated wheat was characterised by interspecific hybridisation events, showing positive correlation between increased ploidy and agricultural achievement (Dubcovsky and Dvorak, 2007). In the first hybridisation event, the diploid Triticum urartu (2n = 2x = 14, AA genome) and presumably Aegilops speltoides (2n = 2x = 14, BB genome) generated the tetraploid Triticum turgidum spp. durum (2n = 4x = 28, AABB genome). In the second hybridisation event, the tetraploid wheat and the diploid Aegilops tauschii (2n = 2x = 14, DD genome) formed the hexaploid Triticum aestivum (2n = 6x = 42, AABBDD genome) (Dubcovsky and Dvorak, 2007; Matsuoka, 2011). Aegilops spp. has provided additional genetic variability captured in synthetic wheats developed by the International Maize and Wheat Improvement Center (CIMMYT). A retrospective analysis on the development and utilisation of synthetic hexaploids in the CIMMYT Global Wheat Programme found that 20% of the lines sampled in two international yield trials were synthetic-derived with an average contribution of 15.6% (Rosyara et al., 2019). Owing to its global importance, this chapter focuses on T. aestivum with occasional comments on durum wheat Triticum turgidum spp. durum, mainly in Section 5 with a focus on grain quality.

1.2  Trends in production, area, and yield The global wheat production increased almost linearly at a rate of 8.7 Mt y−  1 during the past 60 years (Fischer et al., 2014; Fig. 3.1c). Although a linear trend is apparent (R2 = 0.96; P < .001), changes in slope indicate three periods (Fig. 3.1b): (i) a first linear period 1961–82, with a rate of 11.3  ±  0.54a Mt y−  1, (ii) followed by a lower rate increase between 1983 and 2002 with a rate of 5.7  ±  0.92 Mt y−  1 with a peak of 591 Mt in 1990, and (iii) a final period between 2003 and 2018, showing a recovery with a rate of 11.6  ±  1.21 Mt y−  1. Wheat production over the last 5 years averaged ~ 750 Mt. The major wheat producers are China (124.9 Mt y−  1), India (91 Mt y−  1), Russia (60.2 Mt y−  1), the US (56.7 Mt y−  1), and France (37.5 Mt y−  1). These five countries produce 52% of global wheat, and China and India together account for almost one-third of it (Fig. 3.1c). a. Standard error, unless otherwise specified. Crop Physiology: Case Histories for Major Crops. https://doi.org/10.1016/B978-0-12-819194-1.00003-7 Copyright © 2021 Elsevier Inc. All rights reserved.

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100  Crop Physiology: Case Histories for Major Crops

FIG. 3.1  (a) Wheat production, (d) harvested area, and (g) grain yield across the world averaged between 2009 and 2018; (b, e, h) trends from 1961 to 2018; and (c, f, i) averages from 2009 to 2018 for the top five countries in each of the three variables. Data from: FAOSTAT, 2020. Food and Agriculture Organization of the United Nations. http://www.fao.org/faostat/en/#home.

In the first half of the 20th century, the acreage cultivated with wheat increased in the Americas, Australia, and parts of Africa and contributed to increased global production (Calderini and Slafer, 1998). Since the 1960s, global wheat acreage stabilised between 200 and 240 Mha (Fig. 3.1e). Presently, the largest areas of wheat are in India, Russia, China, the US, and Australia, accounting collectively for half of the global wheat growing area (Fig. 3.1f). Grain yield has been the major driver of wheat production increase since the 1960s, in turn driven by improved varieties and management. Despite the strong linear trend for yield between 1961 and 2018, changes in slope indicated three periods, similar to those for production: 1961–82 with a rate of 407  ±  22.3 kg ha−  1 y−  1, 1983–2003 with a rate of 332 ± 23.4 kg ha−  1 y−  1, and 2004–18 with a rate of 507 ± 43.8 kg ha−  1 y−  1 (Fig. 3.1h). The apparent levelling-off between 1990 and 2003 at global level (Fig. 3.1h) was also documented at national scales (e.g. Calderini and Slafer, 1998). In addition to yield, trends in yield variability are also an important issue and will be even critical in the near future because of the challenge of the increasing food demand in a changing climate (Section 4.1.3). In a detailed study evaluating yield trends and variability of 168 crops in 224 countries from 1961 to 2014, Arata et al. (2020) concluded that wheat yield increased in 75% of the areas considered in the study (e.g. North America, Western Europe, etc.) and showed no trends in 17% and decrease in 8% of the area. Regarding yield stability across the crops, only 27.6% of the series showed increased variability, with the highest and lowest yield variability found in the West Asia and North Africa (WANA) region and in Western Europe and North America, respectively. Differences in climate variability and management development seem to be the most likely causes explaining the differential yield variability (Arata et al., 2020). In the most productive areas, accounting for 75% of wheat global production, climate variability explained 36% of the year-to-year yield variation (Ray et al., 2015). For example, in China and India, the top wheat producers (Fig. 3.1c), 32% of their yield variation was associated to precipitation and both temperature and precipitation variability, respectively (Ray et al., 2015). Present average global yield is ~ 3400 kg ha−  1 (FAOSTAT, 2020). The countries with the higher yields—Ireland, the Netherlands, Belgium, New Zealand, and UK—average 7900–9200 kg ha−  1 (Fig. 3.1i) but do not have large growing areas and therefore do not coincide with those leading production (cf. Fig 3.1i, f, and c).

Wheat Chapter | 3  101

Grain weight (mg grain -1)

Average grain weight

YIELD

Number of plants (m-2)

Number of spikes per plant

Number of spikes per m2

Number of grains per spikes

Number of grains (spikelet-1)

Number of plants per m2

Number of grains per m2

Number of grains (spike-1)

Number of spikes (plant-1)

Number of grains (m-2)

Number of grains per spikelet Number of spikeletsper spike

Number of spikelets (spike-1)

Number of spikes (m-2)

FIG. 3.2  Yield determination as a product of yield components with relationships illustrating frequent negative relationships between yield components for wheat grown under field conditions and in dense stands. Modified from Slafer, G.A., Savin, R., 2006. Physiology of crop yield. In: Goodman, R. (Ed.), Encyclopedia of Plant and Crop Science. Taylor & Francis Group, NY, USA; Slafer, G.A., Savin, R., Sadras, V.O., 2014. Coarse and fine regulation of wheat yield components in response to genotype and environment. Field Crop Res. 157, 71–83.

2  Crop structure, morphology, and development 2.1  Yield determination Yield is the most important trait designing breeding strategies and management practices and is also the most complex trait because it is the final outcome of multiple interactions between developmental and growth processes, the focus of this chapter.

2.1.1  Yield components Owing to its complexity, crop physiologists, breeders, and agronomists have tried to decompose yield into simpler components. The most usual approach considers yield as the product of the number of grains per m2 and average grain weight (GW). The former is the product of spikes per m2 and grains per spike. Spikes per m2 is in turn the product of plants per m2 and spikes per plant, and grains per spike is the product of spikelets per spike and grains per spikelet (Fig. 3.2). Despite its popularity, this approach has a major drawback: components are not independent of each other, particularly under field conditions of dense stands, and they commonly relate negatively to each other (Fig. 3.2). This approach is useful retrospectively but is unsuitable to predict yield response to particular management or breeding interventions because the mechanisms for the trade-offs between components are only partially understood. For the component number of grains per m2, feedbacks determine a true compensation, likely related to their simultaneous generation (Section 2.2); therefore resources allocated to one component could be detracted from its complementary. Then, improving one component may not result in a net yield increase (Slafer, 2003). The negative relationship between grains per m2 and average GW is less likely to represent feedback processes because the crop first sets the grains and then fills them (Section 2.2). Two elements that contribute to a true feedback are the overlap between the determination of grain number and potential GW (i.e. the size of the ovaries; Section 2.1.3) and the overlap between grain set and storage of water-soluble carbohydrates, which in turn can contribute to fill the grains. Indeed, virtual lack of feedback does not mean that the negative relationship between average GW and grain number may not represent competition. Competition could arise under short supply of assimilates to satisfy the demand of growing grains; that is, final GW would reflect the availability of resources per grain set, and grain growth would continue or would have a higher rate should not assimilate availability during grain filling be restrictive. But that lack of simultaneity between the setting and the effective growth of the grains indicates other likely causes for the negative relationship, not involving competition for resources amongst growing grains during effective grain filling (Miralles and Slafer, 1995; Acreche and Slafer, 2006). The simplest noncompetitive cause would be that any additional grain, increasing grain number per m2, is bound to be constitutively smaller because it belongs to a lower hierarchy; that is, more distal position within spikelets or in lower-rank spike.

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Owing to the relevance of the negative relationship between average GW and grains per m2, many studies investigated whether grains are source- or sink-limited during the effective period of grain filling. The most common relationship between yield and the number of grains per m2 over large yield ranges (Slafer et al., 2014) would support that grains hardly compete amongst them; otherwise, compensation would result in a scattered relationship between yield and grain number. However, this interpretation overlooks the possibility that management and breeding interventions may increase simultaneously and similarly, the strength of both sinks and sources during grain filling and then the positive relationship between yield and grain number would hold. The noncompetitive explanation for the negative relationship between grain number and GW is further supported by the limited response of average grain size to decrease (e.g. shading or defoliation) or increase (e.g. degraining and thinning plants in the plot) the source–sink ratio during grain filling in wheat (e.g. Borrás et al., 2004; Calderini et al., 2006; Pedro et al., 2011; Serrago et al., 2013; González et al., 2014).b This is also consistent with (i) the downregulation of photosynthesis because of weak sink during grain filling (e.g. Acreche and Slafer, 2009; Serrago et al., 2013) and (ii) substantial amounts of reserves of water-soluble carbohydrates that often remain in vegetative tissues at physiological maturity (e.g. Serrago et al., 2013). All in all, it seems that wheat (and other grain crops) evolved a conservative strategy conducive to high source:sink ratio (Reynolds et al., 2005; Serrago et al., 2013; Borrill et al., 2015) that would ensure grain fill and viable seed size in most circumstances (Sadras, 2007; Sadras and Slafer, 2012). Therefore yield could be increased if the number of grains were improved without penalties in the potential size of the grains and vice versa, if potential grain size were improved without penalties in grain number. In this scenario, it is imperative to understand the determination of grain number per m2 and potential grain size. Both traits are largely determined in a relatively short phenological window that is, therefore, known as the critical period for yield determination.

2.1.2  Grain number determination The number of grains per m2 is as complex as, and more laborious to determine than, yield itself. Hence we need to identify simpler traits putatively related to this major yield component. As mentioned earlier, yield numerical components are unsuitable for prospective analyses because of feedback processes (Section 2.1.1). An alternative approach to identify critical determinants of grains per m2 has been outlined in the pioneering work of Tony Fischer. Fischer (1985) compiled experiments in which crop growth was reduced by sequential, brief shading periods (Section 3.1) and high-temperature accelerating development (Section 2.2.1) at different stages of development. He reported that the number of grains per m2 and yield were particularly sensitive to stress imposed close to anthesis but not for stress in earlier or later periods (Fig. 3.3a), although components of grain number are produced throughout the whole growing season to slightly after anthesis (see also Section 2.2). This pattern has been verified for different cultivars and growing conditions (e.g. Fischer and Stockman, 1980; Savin and Slafer, 1991; Slafer et al., 1994; Abbate et al., 1995, 1997; 1 Yield sensitivity to source-strength

50% anthesis

Grains per m2 (% of unshaded control)

100

0

0

(a)

-80

20 -60 -40 -200 Days to Anthesis

40

Sowing

(b)

Anthesis Time from sowing to harvest

Maturity

FIG. 3.3  (a) Number of grains per m2 was sensitive to shading during a narrow developmental window from ~ 30 days before to ~ 10 days after anthesis; this is roughly from the onset of stem elongation to the lag phase of postanthesis development when grain set is determined. (b) Dynamics of sensitivity of yield to source-strength from sowing to maturity. Modified from (left panel) Fischer, R.A., 1985. Number of kernels in wheat crops and the influence of solar-radiation and temperature. J. Agric. Sci. (Camb.) 105, 447–461; (right panel) Slafer, G.A., Savin, R., 2006. Physiology of crop yield. In: Goodman, R. (Ed.), Encyclopedia of Plant and Crop Science. Taylor & Francis Group, NY, USA. b. Exceptionally, GW is reduced with reductions in source strength during grain filling under conditions such as severe leaf diseases (Serrago et al., 2011), extreme lodging (Acreche and Slafer, 2011), or extreme shading (Beed et al., 2007; Sandaña et al., 2009). However, there is a lack of symmetry, whereby this reduction in final GW in response to extreme source reduction does not imply that grain growth in the unstressed control was source-limited (Serrago et al., 2013).

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FIG. 3.4  (a) Timing of spike growth before anthesis, in a narrow developmental window before anthesis, indicating on the ordinate the SDWa. Dashed lines are two hypothetical aims of breeding and management to increase sink strength and yield: improving growth or partitioning over the same period or extending the duration of the spike growth period maintaining growth rate and partitioning. (b) Relationship between grains per m2 and SDWa. Dashed line represents the improvement from either of the two mechanisms mentioned in a, and dotted line represents an improved sink strength because of an increased fruiting efficiency (FE), represented by the slope of the relationship. Based on: (a) Kirby, E.J.M., 1988. Analysis of leaf, stem and ear growth in wheat from terminal spikelet stage to anthesis. Field Crop Res. 18, 127–140; González, F.G., Slafer, G.A., Miralles, D.J., 2003. Floret development and spike growth as affected by photoperiod during stem elongation in wheat. Field Crop Res. 81, 29–38. (b) Fischer, R.A., 1985. Number of kernels in wheat crops and the influence of solar-radiation and temperature. J. Agric. Sci. (Camb.) 105, 447–461; Abbate, P.E., Andrade, F.H., Culot, J.P., 1995. The effects of radiation and nitrogen on number of grains in wheat. J. Agric. Sci. 124, 351–360; Demontes-Meynard, S., Jeuffroy, M-H., Robin, S., 1999. Spike dry matter and nitrogen accumulation before anthesis in wheat as affected by nitrogen fertilizer relationship to kernels per spike. Field Crop Res. 64, 249–259; Dreccer, M.F., Schapendonk, A.H.C.M., Slafer, G.A., Rabbinge, R., 2000. Comparative response of wheat and oilseed rape to nitrogen supply: absorption and utilitarian efficiency of radiation and nitrogen during the reproductive stages determining yield. Plant Soil 220, 189–205.

Demontes-Meynard et al., 1999; Dreccer et al., 2000; Demontes-Meynard and Jeuffroy, 2004; Ugarte et al., 2007; Prasad and Djanaguiraman, 2014). Furthermore, the relationship between crop growth and partitioning in this developmental window often accounts for yield responses to breeding and management (Section 3.1). Thus although crop growth is virtually always source-limited (increasing source strength increases growth and vice versa), wheat yield is source-limited only during this developmental period from ~ 3 weeks before to ~ 7 days after anthesis (Fig. 3.3b). The period of determination of grain number coincides with the period of active spike growth before anthesis (Fig. 3.4). Floret primordia develop within the juvenile spikes before anthesis, determining sequentially the number of fertile florets and the number of grains (Section 2.2). This explains the strong relationship between the number of fertile florets or grains and the spike dry weight at anthesis (SDWa; Fig. 3.4). Fruiting efficiency explains the scatter in the relationship between grain number and spike dry weight (Box 3.1). Although not always completely independent (e.g. Dreccer et al., 2009; Lázaro and Abbate, 2012), the potential tradeoff between fruiting efficiency and SDWa can be avoided (Bustos et al., 2013; García et al., 2014; Elía et al., 2016; Ferrante

Box 3.1  Fruiting efficiency as a determinant of grain number Fischer (1984) developed a physiological model of grain number based on the strong relationship between grain number and SDWa and recognised that there would be some variation in the slope of that relationship. This slope was first termed ‘spike fertility index’ (Fischer, 2011 and references therein) and was overlooked because of the large impact of Rht genes and nitrogen (N) fertilisation on spike dry weight. More recently, this trait gained in relevance because of the difficulties in increasing spike dry weight, with no further reducing plant height and the need to limit N fertilisation. It was then renamed ‘fruiting efficiency’ (e.g. González et al., 2011, 2014; Pedro et al., 2011; Ferrante et al., 2012; Reynolds et al., 2012; Bustos et al., 2013; García et al., 2014; Foulkes and Reynolds, 2015) because other indices are defined as unitless ratios like harvest index (HI) or leaf area index (LAI), whilst efficiency is reserved to define output-to-input ratios (e.g. water or nitrogen use efficiency (NUE)), as discussed by Slafer et al. (2015). Attention to fruiting efficiency increased during this century (Fischer, 2011; Slafer et al., 2015). The physiology of fruiting efficiency is related to that of the developmental dynamics of floret primordia (as described in Section 3) and the fate of pollinated ovaries (to set a grain or to abort), as recently described elegantly by Pretini et al. (2020). As a cleistogamous plant, wheat, most fertile florets set grains, and commercial cultivars show low rates of grain abortion. Therefore differences in fruiting efficiency in modern cultivars are normally related to the rates of survival of floret primordia determining the number of fertile florets at anthesis. (Continued)

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BOX 3.1  Fruiting efficiency as a determinant of grain number—cont’d Accelerating development of labile florets improves fruiting efficiency. As the survival of floret primordia initiated responds to availability of assimilates, there are two alternative ways to improve fruiting efficiency. The two mechanisms are (i) diminishing the carbohydrate demand of individual florets and (ii) improving the partitioning between structural parts of the inflorescence and florets. The former would be useless to improve yield because the resulting florets would have smaller ovaries determining a reduction in potential grain size (see issue 2.3). On the other hand, improved partitioning of the resources in favour of growing florets within the juvenile spike at the expense of structural parts would not lead to a trade-off between fruiting efficiency and GW. In this case, a noncompetitive reduction in average grain size may arise by a higher proportion of constitutively smaller grains (Fig. 3.B1). Several recent studies have screened elite materials for fruiting efficiency that could be exploited in breeding. A relatively large degree of variation has been reported whenever modern cultivars or elite lines were compared (see Table 2 in Slafer et al., 2015), and it has been shown that selecting for fruiting efficiency (or a proxy to it) would effectively increase grain number and yield in wheat (Pedro et al., 2011; Alonso et al., 2018). The genetic bases for this trait are emerging (Basile et al., 2019; Gerard et al., 2019). A validation of QTLs for fruiting efficiency in independent F2 populations has been reported (Pretini et al., 2020).

FIG. 3.B1  Schematic representation of two alternative explanations for a negative relationship between average grain weight and fruiting efficiency. Left: a constitutive reduction in floret size bringing about a trade-off between weight of the grains (not any average weight) and fruiting efficiency resulting in no yield gain from increased fruiting efficiency. Right: a nonconstitutive alternative hypothesis in which the size of individual grains is not affected but the proportion of grains of smaller size potential is increased, and then increasing fruiting efficiency does produce yield gain. Reproduced with permission from Slafer, G.A., Elia, M., Savin, R., García, G.A., Terrile, I.I., Ferrante, A., Miralles, D.J., González, F.G., 2015. Fruiting efficiency: an alternative trait to further rise wheat yield. Food Energy Secur. 4, 92–109; Slafer, G.A., Kantolic, A., Appendino, M., Tranquilli, G., Miralles, D.J., Savin, R., 2015. Genetic and environmental effects on crop development determining adaptation and yield. In: Sadras, V.O., Calderini, D.F. (Eds.), Crop Physiology Applications for Genetic Improvement and Agronomy, second ed. Elsevier, Amsterdam, The Netherlands. pp. 285–319.

et al., 2017). Consequently, fruiting efficiency and SDWa are useful for the prospective analysis of management and breeding aiming to increase grains per m2. For this reason, this developmental window of time from ~ 3 weeks before to ~ 7 days after anthesis is known as the ‘critical period’ for yield determination because it is the period when major changes in yield are expected, associated to the number of grains set and their potential weight (see further and Section 2.2.1). Breeding and management shall, therefore, aim to improve crop growth and/or partitioning in that particular window.

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2.1.3  Determination of potential grain weight Average GW is a major component of yield and is an important quality trait (Section 5.1). Agronomic characteristics such as seedling vigour and crop establishment may be affected by grain/seed and embryo size (Richards and Lukacs, 2002). As discussed earlier, average GW is related to grain weight potential (GWP). Therefore this section is focused on the determination of GWP, whilst the interaction with environmental and management factors influencing the realisation of GWP will be considered in Sections 3 and 4. Bremner and Rawson (1978) defined GWP as ‘the intrinsic capacity of grains to accumulate dry matter’. For an operational definition, we paraphrase the definition of potential yield (Evans and Fischer, 1999): GWP is the GW of a cultivar when grown in environments to which it is adapted, with nutrients and water nonlimiting and pests, diseases, weeds, lodging, and other stresses effectively controlled. In contrast to indeterminate and semideterminate crops such as pulses and canola where grain growth overlaps with vegetative growth (e.g. Chapter 8: Soybean; Chapter 11: Peanut; Chapter 15: Faba bean, and Chapter 17: Canola), the growing grain is the dominant sink of wheat after anthesis. Wheat leaves, stems, and roots end their growth at anthesis, except for the accumulation of soluble carbohydrates in stems, which can extend to 1–2 weeks after anthesis (Ehdaie et al., 2006; Dreccer et al., 2009). In wheat and other determinate crops, the grain growing period is defined between anthesis and physiological maturity. Considering the time-course of grain growth, and that a grain exists as such after ovule fertilisation, most efforts to understand GWP determination focused on grain filling. The simplest approach to study GWP has been through the characterisation of the rate and duration of dry matter accumulation in grain in absence of growth restrictions. In general, genetic variations in GWP seemed more related to the rate of grain filling than to the duration from anthesis to maturity (e.g. Asana and Williams, 1965; Millet and Pinthus, 1984; Loss et al., 1989; Wardlaw and Moncur, 1995; Miralles et al., 1996; Calderini and Reynolds, 2000). However, the underlying causes of differences in grain growth rate are poorly understood. Indeed, if grain growth is mainly sink-limited (Section 2.1.1), GWP should be established even before the start of grain growth, and differences in rate of grain growth may well be the consequence, rather than the cause, of differences in the capacity of the grains to grow. A number of studies have challenged the assumption that GWP is determined between the onset of grain filling and physiological maturity (Ugarte et al., 2007; Hasan et al., 2011; Simmonds et al., 2016). Indeed, treatments imposed before anthesis such as high temperature (Wardlaw, 1994; Calderini et al., 1999) or removal of florets from central spikelets (Calderini and Reynolds, 2000) modified potential and actual GW. These studies showed that the determination of both grain number and GWP overlap during a wider period than previously assumed, that is, at least between booting and a week after anthesis (Calderini et al., 2001; Parent et al., 2017). The effect of preanthesis treatments affecting GWP was also found in barley and triticale (Ugarte et al., 2007), sorghum (Yang et al., 2009) and sunflower (Cantagallo et al., 2004; Lindström et al., 2006; Castillo et al., 2017). As a consequence, the understanding of GWP determination in wheat should consider the processes immediately before and after anthesis. In addition, GWP has related to either inner or outer tissues of the grain in wheat (Brinton and Uauy, 2019). The association between final GW and endosperm cell number (Brocklehurst, 1977; Gleadow et al., 1982; Nadaud et al., 2010) supports the assumption that the capacity of the grain to accumulate carbohydrates into the inner tissues regulates GWP. On the other hand, the relationship between final GW and the volume of the floret cavity raised the hypothesis that maternal tissues could delimit a volume available for the growth of the endosperm (Millet, 1986). More recently, three grain traits have been identified as key drivers of GWP in wheat: carpel weight, grain length, and stabilised water content, that is, water content at the water plateau (Hasan et al., 2011). The effect of preanthesis conditions on GW of wheat has been ascribed to carpel/ovary weight at anthesis (Calderini and Reynolds, 2000; Calderini et al., 2001; Simmonds et al., 2016; Xie et al., 2015; Reale et al., 2017). The association between final GW and carpel weight at anthesis had been previously reported for barley (Scott et al., 1983); linear relationships between these traits are evident in wheat (Fig. 3.5) and have been reported through the assessment of wheat genotypes (Hasan et al., 2011; Yu et al., 2015; Simmonds et al., 2016). This association was also found in sunflower (Cantagallo et al., 2004; Castillo et al., 2017) and sorghum (Yang et al., 2009). The association between final GW and carpel/ovary weight supports the hypothesis that GWP is regulated by the maternal grain tissues, taking into account that the ovary becomes the pericarp. This hypothesis is supported by (i) the sensitivity of GWP to treatments during fast carpel growth between booting and anthesis (Calderini and Reynolds, 2000; Ugarte et al., 2007) and (ii) the association between final GW and the maximum dry matter of pericarp reached early after anthesis (Herrera and Calderini, 2020). Moreover, the relationships between final GW and both ovary size and endosperm cell number as determinants of GWP are not mutually exclusive because division of endosperm cells is a centripetal process, starting from the pericarp. Therefore the number of endosperm cells could be linked to the size of the pericarp (the ovary wall) and

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FIG. 3.5  Relationships between grain weight and (a and b) carpel weight at pollination and (c and d) stabilised water content of 105 doubled haploid lines plus the parental cultivars GW carried out in two experimental years (year 1: a and c; year 2: b and d) in Valdivia, Chile. Data from: Hasan, 2011. Ph.D. thesis: Physiological bases of grain weight determination and associated QTL markers in wheat (Triticum aestivum L.). Universidad Austral de Chile. p. 102.

the internal volume delimited by pericarp, as has been hypothesised (Hasan et al., 2011; Brinton and Uauy, 2019; Kino et al., 2020). An important implication of the correlation between size of ovaries and grain is that improvement in fruiting efficiency should not be at the expense of a reduction in resource demand by florets (Box 3.1). Grain length was positively associated with length of the pericarp cells across ploidies, including bread, durum, and diploid wheat genotypes (Muñoz and Calderini, 2015); this has been validated by Brinton et al. (2017) with wheat NILs for a QTL associated with GW in chromosome 5A. The line carrying this QTL had both heavier grains and larger pericarp cells than the line lacking this QTL. However, grain width was also related to final GW when mapped populations, elite cultivars, and ancestral wheat species were compared (Gegas et al., 2010). The association between GW and maximum water content has been widely recognised in wheat (Borrás et al., 2004 and references therein). Therefore although our understanding of GWP determination is incomplete, few traits seem key in building up sequentially GWP and final GW (Fig. 3.6). These traits will be considered when discussing the effects of environmental, breeding, and management practices on GW (Sections 3 and 4). Our knowledge of genes associated with GWP has advanced noticeably over the past decade in wheat (Brinton and Uauy, 2019) and other crops, mainly rice (e.g. Ma et al., 2019; Yuan et al., 2019). The gene GW2 has been validated as a negative regulator of grain size and weight (Yang et al., 2012; Simmonds et al., 2016; Wang et al., 2018; Zhang et al., 2018). However, most attempts to increase GWP with this gene in wheat have not improved yield because of a strong trade-off with grain number per m2 (Brinton et al., 2017; Wang et al., 2018; Song et al., 2007). For instance, triple mutant lines of TaGW2 gene increased GW by 20% over the wild types, with no impact on yield (Wang et al., 2018). This may reflect increases in GWP at the expense of reductions in fruiting efficiency owing to having fertile florets with larger carpels (Ferrante et al., 2015), a trade-off that should be avoided when identifying useful genes to increase yield through increasing GWP [alike useful traits to improve yield through increasing fruiting efficiency should avoid constitutive reductions in GWP (Box 3.1)]. Expansins and XTH genes influence grain size through their effect on cell wall loosening (McQueen-Mason et  al., 1992; Cosgrove and Jarvis, 2012; Calderini et al., 2020a). For example, expansins and others pericarp cell-wall genes were down-regulated in response to high postanthesis temperature with reduction in actual GW (Kino et al., 2020). The expression of these genes has been associated with cell and grain length in wheat (Lizana et al., 2010; Muñoz and Calderini, 2015), barley (Radchuk et  al., 2011), and sunflower (Castillo et  al., 2018). Recently, it was found a putative gene on chromosome 5A associated with a QTL region for grain length, which would encode an expansin protein in durum wheat

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FIG.  3.6  Grain weight determination in wheat. From booting to anthesis, florets develop and the ovaries grow at their highest rate. Expansin gene expression increases from booting until a peak during the lag phase and declines afterwards. From anthesis to physiological maturity, the pericarp cells elongate, as affected by expansins, endosperm cell division, and water inflow. Grain dry mater accumulation rate firstly increases and then decreases along the grain-filling phase (responsible for the typical sigmoid curve describing grain dry weight along time after anthesis). At physiological maturity, grain dry matter accumulation ends and grains dehydrate further. Modified from: Calderini, D.F., Castillo, F., Arenas, A., Molero, G., Reynolds, M.P., Craze, M., Bowden, S., Milner, M.J., Wallington, E.J., Dowle, A., Gomez, L.D., McQueen-Mason, S.J., 2020a. Overcoming the trade-off between grain weight and number in wheat by the ectopic expression of expansin in developing seeds leads to increased yield potential. New Phytol. (accepted).

(Mangini et al., 2020). Additionally, Choi et al. (2018) showed association between GW2 and an expansin gene regulating GW in rice. On the other hand, the natural variation associated with long glume and lemma has been mapped to a single semidominant P1 locus on chromosome 7A in T. polonicum, and studies confirmed the linkage of P1 locus with grain size (Watanabe et al., 1996; Okamoto and Takumi, 2013). This information about genes linked to GW is important to connect physiological processes and traits with their molecular bases to improve wheat GWP (Brinton and Uauy, 2019). However, as we discussed throughout the three subsections of Section 2.1, understanding potential trade-offs is critical. Overlooking trade-offs can lead to misleading conclusions; for example, the association between TaTPP-6AL1 and GW lead the authors to conclude that ‘…TaTPP-6AL1 and its functional marker are valuable to improve yield in wheat breeding’, whereas yield and grain number were not measured (Zhang et al., 2017). On the other hand, the ectopic expression of expansins seems to overcome the trade-off between GW and grains per m2 (Calderini et al., 2020a), but more studies are needed.

2.2  Crop phenology In addition to changes in grain retention and threshability, changes in phenology have been critical in domestication of crop species (Doebley et al., 2006; Gao et al., 2017; Haas et al., 2019; Lu et al., 2020) and remains critical to match crop and

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environment for two reasons (Sadras and Dreccer, 2015). Firstly, extreme events (e.g. frost, heat) during the critical period disrupt reproduction and impair floret development, grain set, and yield. Secondly, yield is proportional to the duration of the critical period, and the crop growth rate and partitioning to reproductive structures during the critical period. Hence manipulating sowing date and cultivar phenological type to match critical stages with favourable environmental conditions is central to risk management for yield (e.g. Richards, 1991; Araus et al., 2002; Flohr et al., 2017, 2018; Dreccer et al., 2018; Hunt et al., 2019). The large developmental plasticity of wheat accounts for its broad geographical spread (Section 1.2). Agronomic and genetic studies generally focus on the period from sowing to anthesis (or heading) as a single trait, but time to anthesis includes phenotypically distinct phases in terms of generation of particular organs, responsiveness to environmental factors, and genetic modulation. In this section, we describe (i) the phases of wheat development and the organogenesis that takes place in these phases with emphasis on yield components and (ii) environmental and genotypic factors determining the length of the developmental phases. Fig. 3.7 illustrates the time course of wheat development, highlighting the initiation, appearance, and growth of shoot organs and the timing of production of yield components. Spikes per m2 and grains per spike feature phases of generation (tillering and floret initiation) and degeneration (tiller mortality, floret death) whose balance determines their final number. Although crop development is a continuous process, we frequently divide the crop cycle into phases to analyse the dynamics of organogenesis and the effects of genetic and environmental factors altering the rates of development. Time to anthesis is conveniently divided into a vegetative phase from sowing to floral initiation and a reproductive phase from floral initiation to anthesis (e.g. Slafer and Rawson, 1994; Kirby et  al., 1999), the latter frequently divided into earlyreproductive and late-reproductive phases, with the stage of terminal spikelet initiation as a cut-off (e.g. Ochagavía et al., 2017). A complementary approach considers the number of leaves initiated in the main shoot during the vegetative phase and the phyllochron, that is, the time between the appearance of successive leaves, plus the time from appearance of flag leaf to anthesis (e.g. Jamieson et al., 1998). Time to anthesis plus the duration of grain filling complete the growing cycle. In the rest of this section, we first describe organogenesis generically (Section 2.2.1) during particular phases of development (Section 2.2.2) and then discuss environmental (Section 2.2.3) and genetic (Section 2.2.4) sources of variation in duration of those phases and on rate of development of particular organs.

2.2.1  Generation, appearance, and growth of organs 2.2.1.1  Initiation of leaves, spikelets, and florets Seeds of wheat have the embryo with a plumule (the embryonic shoot) and a radicle (primary root). The plumule includes the coleoptile, approximately four leaf primordia and the dome-shaped shoot apex that will be responsible for the development of more leaves first and reproductive organs later. Indeed, development of an individual includes leaf initiation started in the mother plant (Fig. 3.7) (Kirby and Appleyard, 1987; Hay and Kirby, 1991). Immediately after sowing, seed imbibition takes place and leaf primordia initiation is resumed. The initiation of new leaf primordia, as single ridges on opposite and alternating sides of the apex, follows an almost linear dynamic (Fig. 3.9a; Kirby et al., 1987; Delécolle et al., 1989; Kirby, 1990). The time between the initiations of two consecutive leaf primordia is called the leaf plastochron and can be estimated as the reciprocal of the rate of leaf initiation. Leaf initiation continues, still as single ridges, until the apex switches from initiating leaf primordia to initiate spikelet primordia at the time of floral initiation; this switch is paralleled by a morphological change of the apex from dome-shaped to a cylindrical, more elongated structure (but still differentiating single ridges). The number of initiated leaves is the final leaf number in the main shoot and depends on the duration from sowing to floral initiation and the leaf plastochron of each genotype in the specific conditions of growth. Indeed, final leaf number reflects variation in phenology with genotype (G), environment (E) and their interaction G × E. At floral initiation, the meristematic apex starts differentiating spikelet primordia. Again, the dynamic of spikelet initiation is linear from floral initiation to the formation of the last spikelet (the terminal spikelet of the spike). The maximum number of spikelets per spike is a function of both the duration of the period from floral initiation to terminal spikelet and the spikelet plastochron, that is, the time between the initiations of two consecutive spikelet primordia, which is estimated as the reciprocal of the rate of spikelet initiation. The linear relationship between spikelet primordia number returns a single rate of spikelet initiation that is faster than the rate of leaf initiation (Fig. 3.9b). Traditionally, it has been assumed that the stage of double ridge evidences the transition from vegetative to reproductive apex, which is the reason for considering this stage as critical for understanding wheat development. But the first spikelets are initiated as single ridges before double ridge and are morphologically undistinguishable from leaf primordia (Delécolle et al., 1989; Kirby, 1990; Fig. 3.9a). The exact time of floral initiation can only be dated a posteriori, when the accumulated number of primordia is determined from

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FIG. 3.7  Key developmental stages of wheat from sowing to harvest in an arbitrary time scale. Boxes underneath illustrate (i) the appearance of the apex/ spike, (ii) the four major component phases, (iii) the timing of differentiation or growth of organs, and (iv) the timing of formation and definition of yield components. Modified from: Slafer, G.A., Rawson, H.M., 1994. Sensitivity of wheat phasic development to major environmental factors: a re-examination of some assumptions made by physiologists and modellers. Aust. J. Plant Physiol. 21, 393–426; Miralles, D.J., Slafer, G.A., 1999. Wheat development. In: Satorre, E.H., Slafer, G.A. (Eds.), Wheat: Ecology and Physiology of Yield Determination. Food Product Press, New York, USA, pp. 13–43; Slafer, G.A., Kantolic, A., Appendino, M., Tranquilli, G., Miralles, D.J., Savin, R., 2015. Genetic and environmental effects on crop development determining adaptation and yield. In: Sadras, V.O., Calderini, D.F. (Eds.), Crop Physiology Applications for Genetic Improvement and Agronomy, second ed. Elsevier, Amsterdam, The Netherlands. pp. 285–319.

seedling emergence and the final leaf number is determined from the appeared leaves over the growing season: we can determine the time when the first primordia in excess of final leaf number was initiated. Central spikelets develop first, and further differentiation progresses acropetally and basipetally. By the time of terminal spikelet, the individual florets would have been already started to develop in the firstly initiated spikelets (in the middle third of the spike; Fig. 3.8). Floret initiation within each of the spikelets progresses acropetally from the floret most proximal to the rachis (e.g. Sibony and Pinthus, 1988; Kirby, 1988; Miralles et al., 1998; González et al., 2003, 2005b; Ferrante et al., 2010, 2013a). Associated with this hierarchy, the carpels forming the ovary at anthesis and the mature grain are larger for proximal florets than for more distal positions. Floret initiation within each spikelet is indeterminate and continues approximately until flag leaf emergence or early booting (Kirby, 1988; González et al., 2003, 2005b; Ferrante et al., 2010, 2013a), reaching a maximum of 6–12 floret primordia per spikelet, depending on the spikelet position within the spike (Sibony and Pinthus, 1988; Youssefian et al., 1992; Miralles et al., 1998). However, unlike leaf and spikelet primordia that always survive, only a small proportion (15%–30%) of all the initiated florets reaches the stage of fertile florets at anthesis (Fig. 3.8). Massive flower and fruit abortion is a universal feature of angiosperms (Stephenson, 1981).

110  Crop Physiology: Case Histories for Major Crops

Spikelet primordia

Terminal spikelet

Carpel of proximal floret

W 3.5 W 5 W9 W 10 Floret developmental stages

(A)

(B)

Spike

Spikelets within the spike

Floret within the spikelet

0

W1

.5

.5

W

(C)

W5 3.5

W7

.5 W7

W8

Time

Spike at Grain set. anthesis soon after anthesis

ly

mon

(com ent ) W 9 velopm l florets e a d mal proxim r o N in

Late degeneration (commonly in intermediate florets) Early degeneration (commonly in distal florets)

FIG. 3.8  Wheat floral development as shown (a) schematically and (b) photographically from early spikelet primordia differentiation to anthesis, with selected floret developmental stages. (c) Floral development in the scale of Waddington (W#) with time (upward arrow) for florets that develop normally to achieve the fertile floret stage at anthesis and setting grains afterwards. The downward arrows show floral degeneration either early or late during development. The drawings and photographs are not to scale; as a reference, the width of a floret is 0.10 mm at W3.5, 0.15 mm at W5 0.15 mm, 0.30 mm at W7.5, and 1.60 mm at W10. Reproduced with permission from Ferrante, A., Savin, R., Slafer, G.A., 2010. Floret development of durum wheat in response to nitrogen availabilities. J. Exp. Bot. 61, 4351–4359.

This period from terminal spikelet to anthesis coincides with that in which the juvenile spikes, in which florets are developing, do actually grow (Fig. 3.9b). This supports the idea that floret death reflects a possible competition for assimilates because increasing the availability of resources for spike growth increases the rate of development of labile florets, reduces floret death, and increases the number of fertile florets (González et al., 2005a; Ghiglione et al., 2008; Ferrante et al., 2010, 2013a, 2020). In addition, the onset of floret mortality that had been proposed to be a developmental process (e.g. Bancal, 2009) has also been proven to be related to the growth of the spikes (e.g. González et al., 2011; Ferrante et al., 2013b). Thus the more the spike can grow at these critical stages, the more florets can reach the stage of fertile florets and grains afterwards, irrespective of whether this growth is dependent on crop growth or partitioning or whether it is because of agronomy or genetic drivers (Section 4.1); see Slafer (2003) and Slafer et al. (2005) for a more comprehensive discussion on this issue. 2.2.1.2  Appearance of leaves and tillering and growth of stems, spikes, and grains The wheat shoot is a succession of vegetative phytomers. Each phytomer is composed of a node, an internode, a leaf comprising sheath and blade or lamina, and an axillary tillering bud. A wheat plant at anthesis is composed of a variable number of phytomers that depends on the final leaf number, although not all organs grow in all phytomers. The leaves grow in all phytomers, but internodes do not expand in the first phytomers, hence the lack of a true stem until well advanced the growing cycle. From seedling emergence to the onset of stem elongation, a false stem is formed by successive leaf sheaths, whilst only the first phytomers have tillers that will actually grow in agronomic conditions. Seedling emergence is the appearance of the tip of the first leaf through the coleoptile, which is adapted to push through soil whilst protecting the first leaf that will emerge through its extreme. From then on, leaves appear at more or less constant rate (Fig. 3.9b) if final leaf number is relatively small (e.g. < 8 leaves), until the appearance of the flag leaf. The phyllochron

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FIG. 3.9  Dynamics of (a) leaf, spikelet, and floret primordia initiation, (b) leaf appearance, tillering and tiller mortality, and growth of stems and spikes, and (c) grain growth, including the dynamics of grain volume and grain moisture, both in absolute terms and as a percentage of grain fresh weight. Arrows show timing of key developmental/morphological stages.

is longer than the plastochron, implying that the higher the number of leaf primordia initiated (frequently related to longer periods of leaf initiation), more leaves will have to appear after floral initiation (Hay and Kirby, 1991) and consequently, the longer the reproductive phase from floral initiation to anthesis. When the number of leaves initiated exceeds a certain threshold (e.g. > 8), the phyllochron of early leaves is shorter and the rate of leaf appearance faster than that of late appearing leaves (e.g. Jamieson et al., 1995; Calderini et al., 1996; Slafer and Rawson, 1997; González et al., 2005a; Ochagavía et al., 2017). The dynamics of leaf appearance is important because it largely determines the duration of the time to anthesis and also because it is related to other developmental processes (e.g. Kirby, 1990). Tillering, a particular type of lateral branching in grasses (see also Chapter 4: Barley; Chapter 6: Oat; Chapter 2: Rice; Chapter 5: Sorghum), is partially related to the dynamics of leaf appearance, at least whilst the availability of resources, or environmental signals related to that availability, does not limit their growth. The coleoptile is a modified leaf with a tiller bud. The first tiller is the coleoptile’s, but it may not emerge through the soil (Baker and Gallagher, 1983). All other tillers emerge through the sheath of the leaf of that phytomer. These ‘leaf tillers’ start appearing in coordination with leaf appearance (e.g. Klepper et al., 1983; Rickman et al., 1983; Masle, 1985), which makes it predictable and relevant for crop simulation (e.g. Porter, 1985; McMaster et al., 1991). There is a lag of ~ three phyllochrons for the starting of tillering from seedling emergence. From then on, one additional primary tiller (those emerging through main shoot leaf sheaths) will emerge from the following leaves at ~ one phyllochron interval. The rate of tiller emergence per leaf appeared (or per phyllochron) is constant for primary tillers. Tiller buds are also initiated in phytomers of the tillers; therefore primary tillers may produce secondary tillers, emerging from the leaf sheaths of the tiller leaves; tertiary tillers are also potentially possible (although unexpected in plants grown at normal crop densities), emerging from leaves of the secondary ones. The increase in tiller number is linear only for a short period and exponential (following a Fibonacci series; Masle, 1985), when higher order tillers start to appear. Under agronomic conditions, this free tillering proceeds as expected only until a restriction in availability of resources inhibits the growth of new tillers (Fig. 3.9b); that is, only in the earliest part of the cycle, when plants are rather isolated. When resources become scarce mainly because of interplant competition, the rate of tillering diminishes, and frequently, some tillers die. The mortality of tillers starts with the younger tillers, contributing to the convergence and synchrony in flowering and maturity of the crop (Hay and Kirby, 1991). There is not a developmental stage marking the end of tillering and the onset of tiller mortality, but in agronomic conditions, this roughly coincides with the onset of stem elongation (Fig. 3.9b; Rawson, 1971). The reason for such coincidence is that elongating internodes became a dominant sink at this stage, reducing the availability of assimilates to alternative sinks (including tillers to appear and very young tillers that have not produced the structures necessary not to require assimilates from others shoots). Tiller death continues until reaching self-supporting tillers and then stabilises, resulting in a certain number of tillers that corresponds to the number of tiller spikes of the crop (Fig. 3.9b). The top ~ four to six internodes elongate from the onset of stem elongation to anthesis (Fig. 3.9b). The elongating process is related to the dynamics of leaf growth and appearance (Kirby et al., 1994), and the elongating internode at any time is two phyllochrons delayed respect to the leaf of the same phytomer (McMaster et al., 1991). The length of each elongated internode is inversely proportional to its order: the later the longer, being the last elongating internode, the spike peduncle.

112  Crop Physiology: Case Histories for Major Crops

Spike growth takes place in very few (~ 3) weeks immediately before anthesis (Kirby et al., 1987; González et al., 2003). Although highly developed, the spike has not accumulated much dry matter until well entered the period of stem growth (Fig. 3.9b). In this very short period of spike growth, florets complete their development (as described earlier) returning a strong correlation between the number of fertile florets or grains and spike dry matter at anthesis (Section 2.1.2). After anthesis, grains became the dominant sink. In many cases, grains grow more than the crop, highlighting the contribution of reserves accumulated beforehand to fill the grains. The grain-filling period can be divided into four phases, characterised by the dynamics of grain growth after pollination (Fig. 3.9c; e.g. Bewley and Black, 1985; Loss et al., 1989; Stone and Savin, 1999). The first phase is known as either ‘lag phase’ (referring to a delay in starting growth) because it features active grain development but negligible growth, ‘grain set phase’ because pollinated fertile florets may abort in this period immediately after anthesis (Fig. 3.7), or ‘watery ripe’ because grain water uptake drives a rapid increase in volume, reaching > 70% moisture (and then, if pressed with the fingers, it breaks easily and release a watery fluid). In this phase, cell division and development are incipient for endosperm first and embryo later. The endosperm creates the sink strength for each individual grain, through producing the endosperm cells where dry matter will be accumulated during effective grain filling. It is then the period when GWP, which was initially established by the size of the ovary, is finally determined associated with the number of endosperm cells and the volume of the grain (Section 2.1.3). The second phase shows a strongly linear GW increase at maximum rates, during which most of GW is realised (Fig. 3.9c). Grain water content stabilises reaching a ‘grain water plateau’, that is, a period in which the amount of water in the grain remains more or less unchanged, and the water percentage is linearly reduced. Most, 70% or more, of the dry matter is starch (Section 5.1) and therefore the driving force for grain growth. Protein is a small proportion of dry matter but critical for grain quality (see Section 5.1). Complex carbohydrates, relevant as a source of dietary fibres (mainly arabinoxylans; Shewry et al., 2020 and references quoted therein), are accumulated in this phase. This stage is known as ‘milky ripe’ (Fig. 3.9c) because if pressed with the fingers, it breaks easily and release a white fluid resulting from the mixture of starch and still high water content in the grain. The third phase of ‘dough grain’ is actually the last of grain growth, when accumulation of dry matter occurs at decreasing rates and water content diminishes, reducing the relative water content of the grains (then, when pressing the grains with the fingers, it breaks with increasing difficulty because grain growth progresses releasing a content similar to a dough, until towards the end of the phase the grain cannot be broken by hand, although the pericarp is still soft and pressing with nails leave an indentation). This phase ends at ‘physiological maturity’ when dry matter peaks and the grain enters a quiescent state (Bewley and Black, 1985). Water content at physiological maturity is still high, ~ 37% (Calderini et al., 2000), but the grain is hard (cannot be indented by pressing with the nails). In the last phase from physiological maturity to commercial harvest of the crop, grain water content decreases, first quickly and then more slowly because the driving force for this water loss is the difference in water potential between the grain and the surrounding air (Fig. 3.9c).

2.2.2  Phenological phases and scales There are two main approaches to divide the cycle into phases, which are similar for postanthesis but differ strongly before anthesis. The most common approach recognises the external morphology of the plants and splits vegetative and reproductive periods shifting at flowering. In wheat, there are distinct morphological processes before flowering like appearance of leaves, tillering, and the elongation of internodes with a certain degree of overlapping. Therefore the scale of development frequently used for agronomic research and farming decisions (e.g. spraying of agrochemicals, fertilisation) considers two digits in the ‘decimal code’ of Zadoks et al. (1974). The first digit, from 0 to 9, refers to the main stage or organ, and the second digit quantifies the advancement of that stage or number of organs. Thus the scale of Zadoks describes development into stages from 00 and 99. Main stages from 0 to 3 refer to vegetative organs (0: germination, 1: leaves, 2: tillers, and 3: internodes), from 4 to 6 refer to the condition of the spike (4: booting, 5: heading, and 6: anthesis), and from 7 to 9 to stages of grain growth and development. The earlier scale of Feeks (1941), popularised by Large (1954), is less detailed with only 1 digit for each of the stages (from pretillering and tillering, stages 1–5; through stem extension and heading, stages 6–10 to ripening, stage 11). The scale of Haun (1973) focuses on appearance of leaves; that is the stage of Haun is a number describing the number of leaves (and the fraction thereof) that have appeared in the main shoot. All in all, considering the external morphology, wheat can be divided into four main phases, the first three comprising development to anthesis. These phases are (i) crop establishment, from sowing to the onset of tillering, when the seedlings would approximately have three expanded leaves (Section 2.2.1), (ii) tillering, from the appearance of the first tiller to the

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onset of stem elongation, commonly coinciding with the cessation of tillering (Section 2.2.1), (iii) stem elongation, from first node detectable above the soil to anthesis, embracing relevant stages, such as flag leaf appearance, booting, and heading, and (iv) grain filling, describing the progress in relation to the water content of the grain from watery through milky and dough to hard (Section 2.2.1). This approach is practical but has two significant limitations. First, phases are not clearly defined, for example, tillering may cease before or after the onset of stem elongation, depending on availability of resources and plant density (Section 2.2.1). Secondly, and more relevant, it does not reflect the actual physiological stage of the apex, where development does take place. For that reason, in this chapter, we described the development in wheat divided in four phases considering the developmental features of the apex: the vegetative, early reproductive, late reproductive, and grain-filling phases (Figs 3.7 and 3.10). Vegetative phase is, when the apex develops vegetative organs, characterised by leaf initiation. It starts with seed imbibition (in practical terms, with sowing) and lasts until floral initiation. The morphology of the apex changes from a dome to an elongated cylinder, in all cases producing single ridges corresponding to leaf primordia. In this phase, all leaves are initiated, and final leaf number is determined. Early reproductive phase is from floral initiation, through double ridges, glume primordium, and lemma primordium, to terminal spikelet stages. The apex shape changes considerably from cylindrical with single ridges corresponding to the first spikelets initiated to the formation of double ridges and then widening with development of organs at each particular spikelet. In this phase, all spikelets are determined. Quantitative scale based on the morphogenesis of the spike, ovary and pistil of florets

Spikelet detail

Developmental score

Description Transition apex.

1.5

Early double ridge stage. Double ridge stage.

2

Glume primordium present.

3

Lemma primordium present.

3.25

Floret primordium present. Stamen primordium present.

3.5

Pistil primordium present. Carpel primordium present.

4.25

Carpel extending round three sides of ovule.

5

Stylar canal closing; ovarian cavity enclosed on all sides but still open above.

5.5

Stylar canal remaining as a narrow opening; two short round style primordia present.

6

Stylar begin elongating.

6.5

Stigmatic branches just differentiating as swollen cell on styles.

7

Unicellular hairs just differentiating on ovary wall; stigmatic branches elongating.

7.5

Stigmatic branches and hairs on ovary wall elongating.

8

Stigmatic branches and hairs on ovary wall continue to elongate; stigmatic branches from a tangled mass. Styles and stigmatic branches erect; stigmatic hairs differentiating.

8.5

Styles and stigmatic branches spreading outwards. Stigmatic hairs well developed.

9.5

W3.5

2.5

W2

W1.5

W2.5

W3

W∼3

4

W3.25

W3.5

Spike

4.5

W4

W3.5 W4.5

W5

W7

W6.5

W6

W5.5

9

Styles curved outwards and stigmatic 10 branches spread wide; pollen grains on welldeveloped stigmatic hairs.

W7.5

W8

W8.5

W9

W9.5

W10

waddington et al., 1983

FIG.  3.10  Floral developmental stages according to the Waddington scale (Waddington et  al., 1983), with pictures taken from the experiments of Ferrante et al. (2013a) from early spikelet initiation at the transition of the apex to reproductive development to the stage of the fertile floret (W10), with details of selected developmental stages focused on the development of the spike until the initiation of the terminal spikelet (W3.5) in each individual floret primordium from then onwards. Reproduced with permission from Oxford University Press: Ferrante, A., Savin, R., Slafer, G.A., 2013a. Floret development and grain setting differences between modern durum wheats under contrasting nitrogen availability. J. Exp. Bot. 64, 169–184.

114  Crop Physiology: Case Histories for Major Crops

Late reproductive phase is of floret development and stem elongation. Florets are developed inside each spikelet initiated in the previous phase. The development of florets includes the initiation and growth of each of the floret organs of both androecium (pistil) and gynoecium (ovary). Through their development, florets reach the stage of fertile floret at anthesis or stop developing and die (Section 2.2.1 and Fig. 3.8). Grain-filling phase is when GWP is first established and grain growth follows. It is further subdivided into stages considering the consistency of the grains revealing the relative water and starch contents, and then reflecting the advancement in grain growth from anthesis to physiological maturity: watery, milky, dough and hard stages (Section 2.2.1). The decrease in grain water content through grain filling could be used as a reliable and simple method to more quantitatively estimate the degree of grain development (Box 3.2). Owing to their relevance, different scales for apex and floret development have been produced (e.g. Gardner et al., 1985; Waddington et al., 1983), and guides have been popularised (mainly Kirby and Appleyard, 1987). The scale of Gardner divides apex development into eight stages, from the vegetative apex to terminal spikelet. The scale of Waddington focuses more on the further development of the florets (Fig. 3.10).

Box 3.2  Quantifying grain development through its moisture content

(a)

Days after anthesis

(b)

Physiological maturity

Weight or water % ofaverage grains

Grain water percentage (%)

The most common characterisation of postflowering development progress towards maturity has been qualitative, dividing the development into loosely defined grain stages, such as ‘watery’, ‘milky’, ‘dough’, and ‘hard’ grain. As this characterisation is based in the proportion of water in grains, it is possible to put forward a quantitative developmental estimate based on the actual grain water content, reflecting the proportion of the time to maturity already elapsed at any time the moisture content of growing grains is measured. For this to be realistic, there must be a steady change in this variable during the whole postflowering period, and for it to be of universal application (a developmental scale applicable to all genotypes of a particular species and to different crop managements), there should be uniform performance across cultivars and environmental conditions. The scheme (Fig. 3.B2) indicates that grain growth and grain moisture content dynamics are strongly variable depending on the genotype and the environment, determining large differences in final GW. However, the relationship between grain growth and its water content seem much more stable because there is a positive relationship between the rate of grain growth and the rate of water percentage reduction (the higher the slope of grain dry matter gain, the smaller—more negative—the rate of water percentage in grains). If the final GW is normalised (by referring in each case the GW at any time between anthesis and maturity as a percentage of the final GW), there seems to be a universal sharp negative relationship between the grain moisture percentage and GW normalised; so that disregarding profound differences in final GW and in the dynamics of grain growth, all crops within a particular species reached physiological maturity at a rather similar water content in the grains. Evidences in maize (Saini and Westgate, 2000; Borrás et al., 2003; Borrás and Westgate, 2006), wheat (Schnyder and Baum, 1992; Calderini et al., 2000), sorghum (Gambín and Borrás, 2005), soybean (Swank et al., 1987), and sunflower (Rondanini et al., 2007) have shown that final GW is achieved at, or near to, a particular moisture content, irrespective of the actual size of the grains (affected by genetic or environmental factors)*, revealing that dry matter accumulation in developing grains and the concurrent loss of water are closely related phenomena.

Moisture content at physiological maturity

Grain weight relative to final

FIG. 3.B2  Schematic representation of the dynamics of grain weight and water content considering three contrasting grain-filling environmental conditions (a), and relationship between grain growth (normalised as grain weight at any time between anthesis and maturity as a percentage of the final grain weight) and its relative water content (b).

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BOX 3.2  Quantifying grain development through its moisture content—cont’d Thus it seems that duration of grain filling is determined by the interaction between reserve depositions and declining cellular water content, where deposition of reserves such as starch replaces water until critical minimum moisture content is reached. As, for each crop, (i) water percentage at flowering and at maturity are rather constant (for a wide range of grain-growing conditions and of final GWs), and (ii) it decreases linearly across the range from flowering to physiological maturity, and it can be proposed that the progress of grain development towards maturity may be trustworthily based on the water content of the grains. For instance, if for wheat, the limits are ~ 80% water content just after anthesis and ~ 40% at physiological maturity (Calderini et al., 2000), it can be directly established what proportion of the grain-filling period has elapsed at any time we measure grain moisture content in the field. This quantitative assessment allowing determining how much of the grain filling has been already completed may be instrumental in management decisions such as when to apply a desiccant to the crop to advance harvest without losing yield (e.g. Calviño et al., 2002). *In some extreme conditions, moisture content at maturity may also be affected within a crop, although assuming a constant value for a particular crop seems justified for realistic agronomic conditions. Reproduced with permission from Slafer, G.A., Kantolic, A., Appendino, M., Tranquilli, G., Miralles, D.J., Savin, R., 2015. Genetic and environmental effects on crop development determining adaptation and yield. In: Sadras, V.O., Calderini, D.F. (Eds.), Crop Physiology Applications for Genetic Improvement and Agronomy, second ed. Elsevier, Amsterdam, The Netherlands. pp. 285–319.

2.2.3  Environmental factors affecting wheat development Comparing the phenology of a particular genotype across sowing dates or locations highlights the environmental effect. Whereas wheat phenology can respond to water, nutrients, and radiation (e.g. Rawson, 1993; Rodriguez et al., 1994; Arisnabarreta and Miralles, 2004; Angus and Moncur, 1977), the responses are small and inconsistent (Slafer, 1995; Hall et  al., 2014). Phenology of indeterminate crops such as quinoa (Chapter  7: Quinoa, Section  2.2.2.) and pulses (Chapter 10: Chickpea, Section 2) is responsive to soil stress, including drought and salinity. The main environmental factors affecting wheat phenology are temperature, including temperature per se and vernalisation, and photoperiod (Slafer and Rawson, 1994). 2.2.3.1  Temperature per se The positive effect of temperature per se on the rate of development is ‘universal’ in that all phases for all genotypes are similarly sensitive to temperature (Aitken, 1974; Miralles and Slafer, 1999). Furthermore, this effect is the same in other crops (e.g. Parent and Tardieu, 2012) and other ectotherm organisms (Gillooly et al., 2002). The effect is ‘positive’ because, at least for a large range of temperatures, the rate of development is accelerated, and the duration of the phenological phases is reduced, when plants are exposed to higher temperatures (Slafer and Savin, 1991; Miralles and Slafer, 1999; Porter and Gawith, 1999, and references quoted therein). The most common model of developmental response to temperature features a linear increase in developmental rate between the base Tb and the optimum temperature To and a linear decline between To and the maximum temperature Tm (Fig. 3.11a).c The TT model (Monteith, 1984) is the calendar time weighted by the thermal conditions in which plants are developing with units of degree days (°Cd). When the temperature increases, there is a proportional acceleration of developmental processes resulting in a reduction of the calendar time required for the completion of the phase (Fig. 3.11a). Considering this effect, the duration of the phase becomes invariable in terms of TT, resulting from multiplying the calendar time by the temperature affecting the development, instead of days. Indeed, the physiological robustness of the concept relies on this linear relationship whose slope represents the reciprocal of the TT required for completion of this phase at any temperature ranging from the base and the optimum thresholds. Effective temperatures calculated as the daily mean minus the base temperature, with daily mean calculated from hourly temperatures or as the mean of the maximum and minimum daily

c. The literature is inconsistent in naming these thresholds. We use ‘optimum’ for the independent variable when the dependent variable is negatively affected if exposed to values of the independent variable either below or above that threshold (like in the case of temperature in Fig. 3.10). On the other hand, we use ‘critical’ for the threshold at which the dependent variable reaches its maximum, and any values below the critical negatively affects the dependent variable, but values above the critical are all equally ‘optimum’ (like in the case of photoperiod or vernalisation in Fig. 3.10; but also for the relationship between radiation interception and LAI, see Section 3.1).

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FIG. 3.11  Relationship between the rate of development and (a) temperature and (b) vernalisation or photoperiod. (c) Timing during the crop cycle when wheat is sensitive to these three factors: temperature per se, solid line; vernalisation, dashed line; photoperiod, dotted line. In A, rate of development increases with temperature between the base (Tb) and optimum temperatures (To) and declines between To and Tm. In B, the rate of development increases linearly with vernalisation (duration of a period with vernalising temperatures) or photoperiod and plateaus after a critical photoperiod or critical vernalisation threshold. In this example, the rate of development refers to either the whole phase of sowing to anthesis or to any particular phase that is sensitive to these factors. In the relationship with temperature, the reciprocal of the slope of the linear regression between Tb and To is the thermal time (TT−  1) required to complete the phase considered at any temperature between these thresholds and using the estimated Tb. The slope of the relationship with photoperiod or vernalisation is the sensitivity of the cultivar to these factors. From Slafer, G.A., 2012. Wheat development: its role in phenotyping and improving crop adaptation. In: Reynolds, M.P., Pask, A.J.D., Mullan, D.M. (Eds.), Physiological Breeding I: Interdisciplinary Approaches to Improve Crop Adaptation. CIMMYT, Mexico DF, pp. 107–121.

t­ emperature. It has been suggested that for early developmental phases, when the internodes are underground, soil temperature should be used instead of air temperature (Jamieson et al., 1995; Vinocur and Ritchie, 2001), but the advantage of using soil temperature is unclear (McMaster et al., 2003; McMaster and Wilhelm, 2003). Empirically, the process consists in ‘accumulating’d daily mean temperatures above the base, and through estimating duration of phases through TT, the duration becomes independent of temperature, and therefore the observed TT differences could be used to analyse sensitivity to the other factors (photoperiod and vernalisation). The base temperature cannot be accurately determined experimentally (by definition, the duration of the phase—and therefore of the experiment—would be infinite), and therefore it is always indirectly estimated. Although the model is applicable to all phenological phases and genotypes, the actual base and optimum temperatures vary with genotype and phase (e.g. Angus et al., 1981; Slafer and Savin, 1991; Rawson and Richards, 1993; Slafer and Rawson, 1995a; Porter and Gawith, 1999). As temperature accelerates not only the rate of development but also the rate of primordia initiation, there are not clear effects of temperature per se on the final number of leaves initiated; and advanced anthesis under higher temperatures is related to the increased rate of leaf appearance. 2.2.3.2 Vernalisation Vernalisation is the requirement for an exposure to a period of low temperatures to allow (qualitative response) or accelerate (quantitative response, i.e. to shorten the phase) development. The effect is mainly effective in early stages of development and is a mechanism evolved in temperate species to avoid flowering and grain filling during periods of high risk of frost. Fulfilment of vernalisation requirements dominates the response to photoperiod (see further) ensuring that even if wheat is sown early in autumn (with still relatively long days and warm temperature), anthesis will not occur until the following spring (Dubcovsky et al., 2006; Hemming et al., 2008). The stimulus is perceived directly by the active shoot apex (Chouard, 1960; Amasino, 2004), that is, from seed imbibition onwards; indeed, vernalisation may take place during grain filling in the mother plant. ‘Winter’ wheat genotypes have a strong sensitivity to vernalisation, which is lacking or reduced in ‘spring’ genotypes (Slafer and Rawson, 1994; Valle et al., 2009). Normally, winter wheats are sown in fall, their vernalisation requirements avoid them to advance in development until after the winter, and flower in spring soon after the risk of late frosts has been minimised. In locations where winter wheats would need to vegetate through very harsh winters, spring wheats are the alternative, and as these wheats do not d. Plants do not accumulate temperature anywhere! Researchers, agronomist, breeders, modellers, and others interested in assessing and/or predicting the progress of development accumulate temperatures over days as a practical tool for quantitatively taking into account the large effect it has on the rates of development (Fig. 3.10).

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require vernalisation when sown in spring and, as they do not require the exposure to a period of low temperatures, they can flower in early summer. Vernalisation occurs in a wide range of temperatures, from −  1°C to 15°C (Porter and Gawith, 1999), but most effective temperatures are between ~ 1°C and 8°C (Flood and Halloran, 1984; Brooking and Jamieson, 2002). The rate of development of a sensitive genotype increases (and the duration of the phase decreases) with longer exposure to vernalising temperature and saturates after a threshold (Fig.  3.11b). The slope of the response represents the sensitivity to vernalisation, and the rate of development at critical or longer vernalisation periods represents the earliness per se (Section 2.2.4) of the genotype; spring genotypes are mostly insensitive to vernalisation, and the development vs vernalisation response is normally a horizontal line with intercept representing its earliness per se. Unlike temperature per se, wheat is sensitive to vernalisation only in early developmental stages (Fig. 3.11), which is expected from the evolutionary interpretation of the trait, that is, to avoid reproduction before being exposed to winter. Thus vernalisation affects mainly the rate of vegetative development and that of the early reproductive phase (Slafer and Rawson, 1994; and references quoted therein). As vernalisation affects the rate of development of the vegetative phase much more than the rate of primordia initiation, exposure to vernalisation reduces the final number of leaves initiated (and then wheats sown in fall normally initiate many more leaves, normally > 12, than those sown in spring, normally seven to nine). 2.2.3.3 Photoperiod In addition to vernalisation, plants evolved the capacity to use photoperiod as a cue to speed up or slow down development towards flowering; strictly, plants respond to the duration of the dark period (e.g. Pearce et al., 2017). Leaves detect photoperiod by changes in the isomer form of phytochrome (Legris et al., 2017 and references quoted therein). Under inductive photoperiods (long days in wheat), leaves produce a signal (florigen; a sort of hormone) that moves to the apex where it accelerates the rate of development, inducing flowering (Wigge et al., 2005; Zeevaart, 2006). The response to photoperiod cannot start until at least seedling emergence (when leaves start to perceive the length of the day) and normally starts immediately after emergence because wheat lacks a ‘juvenile phase’e (Hay and Kirby, 1991; Slafer and Rawson, 1995b) and continues during the late reproductive phase (Miralles et al., 2000; Whitechurch and Slafer, 2002; González et al., 2003, 2005a; Fig. 3.11c). In genotypes with no vernalisation requirement, long photoperiod at seedling emergence can induce reproductive development immediately, and final leaf number would be the number of leaves primordia in the embryo plus those initiated from sowing to seedling emergence (both together ~ six to seven leaves) (Hay and Kirby, 1991). Wheat is a long-day plant, which implies that development slows down when photoperiod is shorter than the critical (Fig. 3.11b), and therefore the phase becomes longer if the response is quantitative, as in commercial cultivars, or prevented if response is qualitative, virtually inexistent in agronomically adapted materials (Major, 1980; Slafer and Rawson, 1994). As photoperiod affects more the rate of development of the phase than the rate of primordia initiation, photoperiods shorter than the critical generally increase the number of primordia initiated (e.g. Rawson, 1993; Major, 1980; Slafer and Rawson, 1996; González et al., 2002, 2003; Miralles et al., 2003). The response to photoperiod is characterised by at least three parameters: a critical photoperiod, the sensitivity to photoperiod (represented by the slope), and earliness per se (Fig. 3.11b). Within the available variation, even within commercial cultivars or elite material, it is possible to find genotypes that are insensitive (the rate of development is that determining the earliness per se at any photoperiod) and a large degree of sensitivity levels (magnitude of the slope in Fig. 3.11b) (Slafer and Rawson, 1994; Ochagavía et al., 2017; Pérez-Gianmarco et al., 2018).

2.2.4  Genotypic differences and main genetic factors Genetic variation is associated with three groups of genes. Two of them, the photoperiod (Ppd) and vernalisation sensitivity genes (Vrn) account for the majority of genotypic variation in phenology and are primarily responsible for coarsetuning adaptation (Griffiths et al., 2009). However, when genotypes are screened for phenology under long days and after being vernalised, there is ‘residual’ variation that by definition is independent of photoperiod and vernalisation sensitivities (Appendino and Slafer, 2003). These relatively minor differences are ascribed to genes of earliness per se (Eps; Slafer, 1996; Snape et al., 2001), responsible for fine-tuning adaptation (Griffiths et al., 2009; Zikhali et al., 2014; Ochagavía et al., 2018).

e. An initial phase in which a photoperiod-sensitive genotype is insensitive to photoperiod, determining a longer minimum duration of vegetative development and therefore a relatively higher minimum number of leaves that must be initiated before the apex becomes reproductive (such as in the case of maize, e.g. Kiniry et al., 1983; Chapter 1: Maize).

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Ppd, Vrn, and Eps may alter different phases of development. However, most frequently, these genes were identified, and their effects quantified, considering time to heading or anthesis as a single phase. Recent efforts have been made to determine the effect of individual developmental genes (or their interaction) on particular phenological phases and associated dynamics of primordia initiation (e.g. González et al., 2005c; Ejaz and von Korff, 2017; Ochagavía et al., 2017, 2018, 2019; Pérez-Gianmarco et al., 2018, 2019). Results are incipient and more work would be required to reach general ­conclusions. For that reason, in this section, we focused on the genetic factors controlling developmental rates considering time to heading or anthesis. We will comment only superficially on the most critical genes involved in genotypic variation in development; for a more comprehensive view and discussions on gene action pathways, please see the recent review on the issue by Hyles et al. (2020). Genotypic variation in vernalisation sensitivity in bread wheat is largely controlled by the Vrn-1 family of major genes. However, other Vrn genes have been identified (e.g. Cockram et al., 2007). These include Vrn-2 that is more relevant in diploid wheat and barley but not in commercial bread wheat (Dubcovsky et al., 2006), Vrn-B3 in chromosome 7B, formerly Vrn5, but not very relevant in determining genetic variation within commercial wheats (Yan et al., 2006), and Vrn-D4 (Yoshida et al., 2010). Thus most studies have focused on this family of genes, integrated by Vrn-A1 (formerly, Vrn1), Vrn-B1 (Vrn2), and Vrn-D1 (Vrn3) located in chromosomes 5A, 5B, and 5D, respectively (Flood and Halloran, 1986; Snape et al., 2001). Each of these genes has a dominant (Vrn-1a) and recessive allele (Vrn-1b) that confer insensitivity and sensitivity, respectively. Winter wheats would have the recessive alleles in all three Vrn-1 genes. Vrn-A1 has stronger effects than Vrn-B1 and Vrn-D1, and therefore genotypes with the Vrn-A1a are spring wheats (e.g. Appendino and Slafer, 2003; Yan et al., 2004). Vrn-1 genes are expressed in the apex regulating the transition from vegetative to reproductive stages, and in leaves, expression that is relevant to allow the photoperiod response to occur in photoperiod-sensitive winter wheats (see Fig. 2 in Hyles et al., 2020). Major photoperiod-sensitivity genes are Ppd-1 located in chromosome 2. These are Ppd-A1 (formerly, Ppd3), Ppd-B1 (Ppd2), and Ppd-D1 (Ppd1) located in chromosomes 2A, 2B, and 2D, respectively (Scarth and Law, 1984; Snape et al., 2001; Beales et al., 2007). Each of these genes has a dominant (Ppd-1a) and recessive allele (Ppd-1b) that confer insensitivity and sensitivity, respectively. In general, Ppd-D1a has the strongest effect (Snape et al., 2001; Yang et al., 2009; Bentley et al., 2013; Kiss et al., 2014; Jones et al., 2017; Ochagavía et al., 2017), although not always this superior strength is evident (e.g. Stelmakh, 1998; Tanio and Kato, 2007; Bentley et al., 2011), and Ppd-A1 and Ppd-B1 are also recognised as important factors controlling photoperiod sensitivity in wheat (e.g. Bentley et al., 2013). This variability reflects that the interaction with the background or the source of the dominant allele (Ochagavía et al., 2017) may affect the strength of the effect. Also the insensitivity is normally clearer with the joint action of two or more genes, that is, the effects of these genes would mainly be additive (Shaw et al., 2012; Ochagavía et al., 2017). Despite the Ppd-1 are the main genes recognised for photoperiod sensitivity, there must be other genes also contributing to this sensitivity (Bloomfield et al., 2018; Hyles et al., 2020). Earliness per se are a set of heterogeneous genes (Worland et al., 1994), each one independent of the others and are considered in conjunction only because of their final effect on time to anthesis: all of them affect the rate of development independently of the photoperiod and vernalisation and are responsible for relatively minor differences in phenology when plants are grown under saturating photoperiod and vernalisation (Slafer, 1996). They cannot be easily identified in other conditions because their effects can be masked by Vrn and Ppd genes (Sukumaran et al., 2016; Zikhali et al., 2014). Owing to their relatively small effect, Eps genes can be critical for fine-tuning adaptation (Griffiths et al., 2009; Gomez et al., 2014). They are a large number of genes reported to exist in virtually all chromosomes (Kamran et al., 2014; Lopes et al., 2015). In principle, the term used to designate these genes (per se) was based on the idea that they affected the rate of development independently of the environment (see references in Slafer, 1996). But it has been hypothesised that at least some Eps genes would actually be temperature-sensitivity genes (Slafer, 1996). The lack of insensitivity to temperature (Section 2.2.3) does not preclude variation in the degree of sensitivity. In the absence of photoperiod and vernalisation effects, Slafer and Rawson (1995c) showed genotypic variation in responsiveness to temperature and genotypic differences in maximum rate of development responsive to temperature (Slafer and Rawson, 1995d). More recently, the Eps × temperature interaction was explicitly reported, firstly in T. monococcum (Bullrich et al., 2002; Appendino and Slafer, 2003) and later in T. aestivum (Ochagavía et al., 2019).

3  Capture and efficiency in the use of resources 3.1  Capture and use efficiency of radiation Photosynthesis returns high-energy organic compounds (C6H12O6) from CO2 and H2O with radiation as the source of energy. Yield is the product of shoot biomass and HI (Eq. 3.1), and biomass is a function of incident photosynthetic a­ ctive

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radiation (PARi), the fraction of that radiation intercepted by the canopy (fRI) and the radiation use efficiency (RUE) (Monteith, 1977; Grifford et al., 1984): Yield  Biomass  HI

(3.1)

Biomass  IPARi  fRI  RUE

(3.2)

3.1.1  Dynamics of radiation interception Grain yield is closely associated with shoot biomass, mainly when environment and management are the driving forces of yield variation (Fischer, 1993; Cossani et al., 2009), although genotypic variation in yield is also related to that in biomass, particularly comparing lines of wheat that are all semidwarf and elite germplasm (e.g. Bustos et al., 2013; García et al., 2013). Grain yield is closely associated with shoot biomass across genotypes, environmental and management conditions (e.g. Sadras and Slafer, 2012; Bustos et al., 2013; García et al., 2013), as illustrated in Fig. 3.12. As yield is mostly source-limited during the critical period for grain number determination (Section 2.1.2 and Fig. 3.3), reaching full radiation interception at the onset of the critical period (Fig. 3.13) is crucial to maximise the crop growth rate, grain number, and yield. For example, a large amount of intercepted radiation in the critical period is one of the key environmental conditions to explain the high yield (≥  12 t ha−  1) achieved in southern Chile (e.g. Sandaña et al., 2009; Bustos et al., 2013; Box 3.3).

FIG. 3.12  Relationship between yield and shoot biomass at harvest for wheat, barley, and triticale crops grown in Valdivia, Chile. The cumulative PAR intercepted by the crop up to anthesis is shown for wheat. Data from: Quiroz, J., 2010. Rendimiento y producción de biomasa de trigo, cebada y triticale bajo riego y secano durante el llenado de grano en sur de Chile (MSc. thesis). Universidad Austral de Chile, p. 71.

FIG. 3.13  Fraction of intercepted PAR of spring wheat during the crop cycle. The arrow shows anthesis and the horizontal line shows 95% of radiation interception. Phenological phases are: S, sowing; Em, seedling emergence; DR, double ridge; An, anthesis; PM, physiological maturity. Reproduced with permission from Mera, M., Lizana, X.C., Calderini, D.F., 2015. Cropping systems in environments with high yield potential of southern Chile. In: Sadras, V.O., Calderini, D.F. (Eds.), Crop Physiology: Applications for Genetic Improvement and Agronomy, second ed. Academic Press, Elsevier, pp. 111–140.

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Box 3.3  Potential yield of winter vs spring wheat The assumption that potential yield is higher in winter than in spring wheat is common and influences farmer decisions. However, potential yield is similar in spring and winter wheats in high-yielding environments. Despite a short season (~  4.5 months, from late August to mid-January), spring wheat cultivars and lines yielded from 12 to 16 t ha−  1 in southern Chile (Sandaña et al., 2009; Bustos et al., 2013), which is similar to high-yield potentials achieved in North Europe with winter wheats. In this environment, spring canola produced 8 t ha−  1, with 50% of grain oil concentration (Calderini et al., 2020b). How could spring wheat (and spring types of other temperate crops) have potential yields similar to those of the winter types? Why these similar potential yields are not seen in other regions, particularly in the northern hemisphere? A major difference is the severe winter in the North and rather mild winter in the South. Therefore in most wheat-growing regions of the northern hemisphere, it is not possible to sow wheat in winter, whilst wheat is commonly sown in winter in the southern hemisphere (Fig. 3.B3). Thus in the northern hemisphere, delaying sowing of spring wheat until the soil temperature allows a normal seedling emergence (generally, towards early spring) and returns a low photothermal quotient during the critical period for grain number determination with respect to that of winter wheats. On the other hand, in the southern hemisphere, winter wheats are sown in late autumn-early winter, whilst spring wheats are sown over the winter, so that in all cases, the critical period of winter and spring wheats overlaps. Therefore the different sowing windows between hemispheres strongly conditions the potential and actual yield of spring vs winter wheat; but when they have similar photothermal conditions during the grain set and grain filling, potential yield seems to be similar between both wheat types. This reinforces the relevance of the critical periods for yield determination (Section 2.1.2).

FIG. 3.B3  Winter and spring wheat crop cycles in the northern and southern hemispheres. Note that sowing and maturity dates are a broad average example. Dotted vertical lines show the solstices and equinoxes.

The fraction of radiation intercepted by the canopy fRI depends on the radiation attenuation coefficient k and LAI: fRI  1  exp

 k  LAI 

(3.3)

In wheat, k ranges between 0.33 and 0.46 (Calderini et al., 1995) and varies with cultivar (Bustos et al., 2013) and crop geometry (Abichou et al., 2019). This trait is affected by the canopy optical properties, chiefly the angle of insertion of the leaves and tillering, and consequently, it changes with phenology. Wheat NILs of contrasting height and canopy architecture varied in k, with k = 0.8 for a double dwarf line (Miralles and Slafer, 1995), which is similar to planophile crops such as sunflower (Chapter 16: Sunflower, Section 3.1.3). The time-course of LAI in an expanding canopy typically conforms to a logistic pattern with a lag phase after seedling emergence depending on the phyllocron (Fig. 3.9) and leaf expansion. At the beginning of tillering (Section 2.2 and Figs 3.7 and 3.9), LAI grows fast and could reach 6 or more, well above the critical LAI required to achieve maximum radiation interception ~ 95% (LAIc; that normally ranges between 3 and 4), in crops well supplied with water and nutrients. In unstressed crops, maximum LAI is often reached around booting (Zadoks 4.5), when flag leaves had been already fully expanded. Leaf senescence starts before anthesis by tiller mortality (Section 2.2 and Fig. 3.7) but is more evident during grain filling. Postanthesis senescence is rarely a constraint because wheat yield is sink-limited during grain filling (Section 4.1). Hence the relevance of stay green for yield may be associated with other traits such as cooler canopy rather than with supply of assimilates. Interception of radiation varies with cultivar and management, as illustrated in Fig. 3.14. For instance, cultivar Otto intercepted more radiation early in the season than Quijote and Pumafen (Fig. 3.14a) despite that all had similar k. This differential capacity to intercept radiation at earlier stages would be related to early vigour, in turn related to differences

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FIG. 3.14  Fraction of intercepted PAR of (a) three spring wheat cultivars, (b) a spring wheat at optimum (S1) and delayed (S2) sowings in Valdivia, southern Chile, and (c) a spring wheat sown at conventional (300 pl m−  2) and low (45 pl m−  2) plant densities. The arrows show anthesis and the horizontal line 95% of radiation interception. Data from Mera, M., Lizana, X.C., Calderini, D.F., 2015. Cropping systems in environments with high yield potential of southern Chile. In: Sadras, V.O., Calderini, D.F. (Eds.), Crop Physiology: Applications for Genetic Improvement and Agronomy, second ed. Academic Press, Elsevier, pp. 111–140; Bustos, D.V., Hasan, A.K., Reynolds, M.P., Calderini, D.F., 2013. Combining high grain number and weight through a DHpopulation to improve grain yield potential of wheat in high-yielding environments. Field Crop Res. 145, 106–115; and unpublished data.

in phyllochron, tillering, and leaf expansion rates (Section 2.2.1). Crops sown at the optimum date reached LAI close to LAIc at least 20 days before anthesis even in spring wheat cultivars grown in the southern hemisphere (Box 3.3), whilst a late-sown crop developed faster and did not reach full radiation interception at anthesis (Fig. 3.14b). Seasonal radiation interception was 1619 MJ m−  2 in S1 and 1232 MJ m−  2 in S2 (Fig. 3.14b), with a corresponding difference in shoot biomass at harvest (S1 = 21.6 and S2 = 18.2 t ha−  1). Well-managed crops under two contrasting plant population densities are compared in Fig. 3.14c. Accumulated intercepted radiation up to anthesis was 879 MJ m−  2 in the conventional plant density compared to 677 MJ m−  2 at low plant density. The effect of nutrient availability on radiation interception has been widely reported in wheat and is largely mediated by LAI (e.g. Fischer, 1993; Salvagiotti and Miralles, 2008; Sandaña et al., 2012). Fischer et al. (1993) reported a range of maximum LAI between 0.5 and 9 in response to nitrogen supply. LAI and radiation interception fully accounted for the impact of the combined N and sulphur (S) supply on crop growth rate and shoot biomass of wheat (Salvagiotti and Miralles, 2008). Similarly, phosphorus deficiency reduced LAI with no effect on k (Sandaña et al., 2012).

3.1.2  Radiation use efficiency RUE is a measure of crop-level photosynthesis often calculated as the slope of the zero-intercept regression between biomass and intercepted radiation (Monteith, 1977; Verón et  al., 2005). Leaf photosynthesis increases nonlinearly with irradiance (Fig. 3.15a) and saturates at ~ 1000 μmol PAR m−  2 s−  1, although this depends on the position of the leaf on the canopy and the distribution of both radiation and nitrogen in the canopy (Dreccer et al., 2000). Crop photosynthesis is also asymptotic with irradiance, although the levels of irradiance saturating canopy photosynthesis are much higher than those saturating single leaf photosynthesis (Fig. 3.15b). This is because the extinction of light in the canopy means leaves in lower layers of the canopy rarely saturate; hence crop photosynthesis normally increases linearly with intercepted solar radiation

FIG.  3.15  Relationships between (a) leaf photosynthesis and irradiance, (b) crop photosynthesis and irradiance, (c) crop photosynthesis and radiation interception, and (d) shoot biomass and accumulated intercepted PAR of wheat crop in southern Chile under optimum management. Data from: (d) Quiroz, J., 2010. Rendimiento y producción de biomasa de trigo, cebada y triticale bajo riego y secano durante el llenado de grano en sur de Chile (MSc. thesis). Universidad Austral de Chile, p. 71.

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(Fig. 3.15c); most leaves (those below the upper layer) would never experience saturation, and upper-layer leaves may only be saturated in the central hours of the day. RUE of an unstressed, well-managed wheat crop was 3 g MJ−  1 PAR in the high-yielding environment of southern Chile (Fig. 3.15d). This RUE corresponded with a crop growth rate of 300–320 kg ha−  1 d−  1 during the linear phase of shoot biomass accumulation. A range of 2.4–3.0 g MJ−  1 PAR was reported for wheat as achievable efficiencies (Sinclair and Muchow, 1999; Stockle and Kemanian, 2009). Wheat RUE varies with ontogeny and commonly decreases from anthesis to maturity (Sinclair and Muchow, 1999; Stockle and Kemanian, 2009). The fall in postanthesis RUE has been ascribed to a higher crop respiration, the ageing of the photosynthetic tissues, and leaf senescence processes. Even before viewing the starting of senescence by naked eye, leaves start remobilising N to the grains, and photosynthesis is intimately related to leaf N concentration (del Pozo et al., 2007; Moreau et al., 2012). In addition, because grain growth is commonly sink-limited (Section 4.1.1), a weak sink may downregulate photosynthesis during grain filling (e.g. Acreche and Slafer, 2009; Serrago et al., 2013). Indeed, breeding and introgressing semidwarf genes (both increasing postanthesis sink strength) have reduced the gap between pre and postanthesis RUE (Calderini et al., 1997; Miralles and Slafer, 1997), in line with the suggestion that postanthesis sink strength in wheat is the main driving force for postanthesis growth (Reynolds et al., 2005). Therefore very high-yielding wheats with a strong sink during grain filling showed similar RUE after and before anthesis (Bustos et al., 2013), leading to the hypothesis that increasing postanthesis sink strength would concomitantly result in indirect improvements in source-strength owing to this feedback, maintaining postanthesis RUE at similar levels of preanthesis (Bustos et al., 2013). Comparisons of historic collections of genotypes revealed improved RUE with selection for yield over decades in UK and Australia (Shearman et al., 2005; Sadras et al., 2012). Improved RUE of wheat in Australia was independent of leaflevel photosynthesis and associated with higher nitrogen uptake and a relaxation in the extinction of nitrogen relative to the extinction of radiation—newer varieties with higher RUE have greener leaves and more radiation at the bottom of the canopy (Sadras et al., 2012). Consistently, Richards et al. (2019) showed that erectophile lines yielded 13% more than planophile lines, and most of this yield advantage was associated with a higher shoot biomass (11%), although they did not measure RUE. Furthermore, G × E for visual scores of canopy architecture was low, and significant QTL associated with canopy architecture were identified on most chromosomes (Richards et al., 2019). NILs of different height had RUE from 2.14 g MJ−  1 PAR in double dwarf lines with poor radiation distribution within the canopy to 2.88 g MJ−  1 PAR in semidwarf lines with optimal height (Miralles and Slafer, 1997). A genome-wide association study (GWAS) showed that traits associated with RUE and final biomass at various growth stages that explained 7%–17% of phenotypic variation in yield (Molero et al., 2019). Similarly, lines with higher RUE before and after anthesis produced 20% more biomass than the best parental and current cultivars, reaching RUE of 3.8 g MJ−  1 PAR during the preanthesis period, that is, 380 g m−  2 d−  1 (Bustos et al., 2013; García et al., 2013); the reasons for the high RUE are unknown in this case. High vapour pressure deficit (VPD) reduces RUE in wheat (Kemanian et al., 2004; Dreccer et al., 2018; Rodriguez and Sadras, 2007). Kemanian et al. (2004) attributed changes of RUE between 1.6 and 3.2 g MJ−  1 of PAR to differences in VPD. Correcting RUE by VPD may be useful for comparisons amongst experiments. High proportion of diffuse radiation increases RUE (Sinclair and Muchow, 1999; Stockle and Kemanian, 2009), and crop simulation models account for it (Asseng et al., 2015). In a north–south transect in eastern Australia, with a range of diffuse radiation from 0.4 to 0.6, RUE increased 1.6-fold per unit increase in diffuse radiation (Rodriguez and Sadras, 2007). Changes in RUE from 1.7 to 2.14 g MJ−  1 of PAR have been reported between nitrogen fertilised and control treatments in wheat (Caviglia and Sadras, 2001). This result is in agreement with the link between N availability and RUE (García et al., 1988; Fischer, 1993). Salvagiotti and Miralles (2008) found no effect of N and S fertilisation on wheat RUE. Differences in N effect on RUE are likely because of soil N content of the control treatments, highlighting the differential sensitivity of RUE from radiation interception in wheat and in other crop species (e.g. Chapter 4: Barley, Section 3). Similarly, Sandaña and Pinochet (2011) did not find changes in RUE under different soil phosphorous (P) concentrations even though the crop growth rate ranged between 257 and 425 kg ha−  1 d−  1. A negative effect was found only for severe P deficiency reducing shoot biomass > 60% relative to the control, when RUE decreased 30%–40% relative to the control. Consistently, RUE of wheat was insensitive to Al toxicity in a range of Al saturation between 0% and 35%, with the reduction in shoot and root biomass ascribed to LAI and radiation interception (Valle et al., 2009).

3.2  Capture and efficiency in the use of water Water deficit is the main abiotic stress affecting wheat yield worldwide (Ding et al., 2018; De Oliveira Silva et al., 2020b). The balance between reference evapotranspiration and rainfall varies amongst environments, as illustrated in Fig. 3.16. In Valdivia, rainfall often meets or exceeds evaporative demand (Fig. 3.16a), hence the water-limited yield potential Yw is close to yield potential Yp (Fischer, 2015). In this environment, yield potential is high because of the favourable ­photothermal

Wheat Chapter | 3  123

FIG.  3.16  Cumulative rainfall and evapotranspiration (ET0) during the growing season in two contrasting environments: Valdivia (Chile) (a) and Roseworthy (Australia) (b). Bars show the regular spring wheat season from sowing (S), depicting the time of anthesis (At) and physiological maturity (PM) in each environment. Data of rainfall and ET0 corresponding to 2018–19 season. Data from: Valdivia (https://agrometeorologia.cl/) and Roseworthy (https://www.longpaddock.qld.gov.au/silo/point-data/).

regime during grain set and grain filling (Mera et  al., 2015), and yield of farmers in this region averaged ~  8 t ha−  1 in 2018 (ODEPA, 2019). Furthermore, volcanic soils with high water-holding capacity, 180 mm of available water (Dörner et al., 2015), buffer occasional dry spells between rainfall events, mainly in late grain filling. On the other hand, evaporative demand typically exceeds rainfall at Rosewothy (Fig.  3.16b), especially during key phases for yield determination (Section  2.1). Owing to the combination of rainfall seasonality and soils with low water-holding capacity, stored soil water at sowing is a small source of water for crops compared to in-season rainfall in environments like this (Sadras and Rodriguez, 2007). Hence the gap between Yw and Yp is large (van Ittersum et al., 2013), with yield averaged ~ 4 t ha−  1 in 2018 (Crop and Pasture Report South Australia, 2019). Therefore there is a wide range of conditions where management of water has high impact on wheat yield, as shown for regions of Australia (Dreccer et al., 2018). In this regard, environmental characterization of water stress has been developed during the last years, which is relevant for both breeding and crop management. Drought patterns have been identified by using crop simulation models over long-term climatic records. For example, Chenu et al., (2013, 2018) identified four major drought patterns over space and time for the wheat-belt of Australia (see Chapter 4: Barley). Passioura (1977) advanced a water-based model of crop yield: Y  ET  WUE  HI

(3.4)

where Y is yield, ET is seasonal evapotranspiration, Water use efficiency (WUE) is biomass per unit ET, and HI is harvest index. The three terms of the identity are not unrelated, but the model remains useful to assess breeding and management strategies (Passioura, 2006; Araus et al., 2008; Reynolds and Tuberosa, 2008).

3.2.1  Crop evapotranspiration Crop evapotranspiration depends on water availability in the soil, evaporative demand, and the capacity of the crop to use soil water. Crops rely on water stored in the soil before sowing and rainfall during the growing season (Fig. 3.17). Runoff, deep drainage, and soil evaporation are unproductive losses. In addition, soil physical and chemical constraints may restrict root growth and function. Fallow is a common practice to store water in soil in different wheat cropping systems (e.g. Fischer, 2009; Savin et al., 2015; Zhang et al., 2015) and has been reviewed by Passioura and Angus (2010), Hunt and Kirkegaard (2011), and Pittelkow et al. (2015). A metaanalysis showed the impact of no-till on wheat yield depended mainly on three variables: aridity index (the degree of dryness of the climate), irrigation, and N rate (Pittelkow et al., 2015). Consequently, contrasting effect of no-till has been reported on average wheat yield relative to conventional tillage. In Mediterranean climates, wheat yield is higher under no-till, especially in water-stressed years. In the US, the positive effect of no-till is apparent for wheat often grown in semiarid areas. In the Mediterranean environment of central Chile, no-till improved water infiltration and storage in the soil, but the effect on yield depended on the precipitation because no-till favoured yield in dry years but penalised it in wet years (Brunel et al., 2013; Brunel-Saldias et al., 2018). The metaanalysis showed that the average wheat yield across all the studied cases decreased 2.6% in no-till wheat relative to when the crop follows conventional tillage (Pittelkow et al., 2015).

124  Crop Physiology: Case Histories for Major Crops

FIG. 3.17  Variables and processes driving crop water uptake. Based on Passioura, J.B., Angus, J.F., 2010. Improving productivity of crops in waterlimited environments. Adv. Agron. 106, 37–75. https://doi.org/10.1016/S0065-2113(10)06002-5.

Direct seeding in doubled-cropped wheat–soybean in Argentina, Brazil, and the US (Calviño and Monzón, 2009) and in rice–wheat systems in the Indo-Gangetic Plains (Bhushan et al., 2007) effectively improves capture of water. In these no-till doubled-cropped systems, the capture and efficiency in the use of resources, especially water, have been improved (Caviglia et al., 2004; Bhushan et al., 2007). For example, Caviglia et al. (2004) calculated that the rainfall capture efficiency increased from 0.51 (dimensionless) in sole crops to 0.71 in double crops. Crop water uptake depends on the depth and distribution of the root system. The usual pattern of wheat rooting depth and density has been extensively reviewed (Thorup-Kristensen and Kirkegaard, 2016). The wheat root system grows fast during the vegetative period, reaches almost half of its final biomass by double ridge stage, and peaks around anthesis. The growth of the root system is in turn related to the early vigour in wheat crops (Turner and Nicolas, 1987). Early vigour not only correlates with deeper root systems but also improves water use (WU) by reducing the proportion of water lost by direct evaporation from the soil, and thus increases the proportion of top-soil water used in transpiration (Richards, 1991; Rebetzke and Richards, 1999). The main attributes conferring early vigour appear to be low-specific leaf weight (i.e. thin leaves) and large embryo (LópezCastañeda et al., 1996). Other factors contributing to variation in the early vigour include the rate of leaf appearance (Whan et al., 1991) and the associated pattern of tillering, which also includes the possibility of coleoptile tillers that can improve early leaf area and ground cover (Liang and Richards, 1994; Bort et al., 2014; Rebetzke and Richards, 1999; Zhao et al., 2019). Nutrient deficiencies may restrict water availability for crops (Angus and van Herwaarden, 2001; Sadras and Roget, 2004; Cossani et al., 2012; De Oliveira Silva et al., 2020a). Early fertilisation improves early vigour that often increases water capture by the crop. For instance, Wang et al. (2018) showed that N fertilisation increased dryland wheat root length density and water uptake in deeper layers. Therefore the healthier and well-nourished the crop, the greater is its water extraction capacity (Angus and van Herwaarden, 2001). Other root traits that should be considered for increasing crop water uptake are: (i) root architecture (Lynch, 2007, 2019), such as lateral branching, thinner roots, length, and density of root hairs (useful also for capturing low mobile nutrients such as phosphorus) and (ii) the reduction of root metabolic cost, which has been pointed out for favouring soil exploration and water uptake (Lynch, 2007). Several workers around the world are presently studying these traits, and various attempts to develop high-throughput screening facilities are being developed and genetic markers for traits (ThorupKristensen and Kirkegaard, 2016), although allocation of resources to root growth may depend more on management than breeding (e.g. Allard et al., 2013). Improvements in water capture associated with larger root systems in semiarid regions need to be considered cautiously. This proposition assumes that the root system of current varieties is insufficient to capture available water. A number of studies challenge this assumption. Passioura (1983) advanced the notion of redundant root systems for Australian wheat

Wheat Chapter | 3  125

varieties. In a comparison of wheat released over five decades, Aziz et al. (2017) found selection for yield associated with smaller root system in winter-rainfall environments of Australia. Fourteen bread wheat genotypes covering 100 years of Swiss wheat breeding were grown in 1.6 m tall columns in the greenhouse under well-watered and drought conditions. Rooting depth diminished with year of release under well-watered conditions but not under early water stress (Friedli et al., 2019). In semiarid region of Shaanxi province in China, breeding favoured larger root systems with no increase in water capture (Sun et al., 2020). In a study with pot-grown plants, modern wheat had less root redundancy and higher yield in water-limited environments than an older counterpart (Zhu and Zhang, 2013). Indeed, root pruning may increase yields of winter wheat in semiarid conditions (e.g. Hu et al., 2015, 2019).

3.2.2  Water use efficiency WUE could be defined from short-term gas exchange (mol of CO2 mol of H2O) to biomass or yield per unit seasonal ET. At crop level, wheat WUE for biomass ranges from 29 to 105 kg ha−  1 mm−  1 and for yield, from 5.4 to 24 kg ha−  1 mm−  1 (French and Schultz, 1984; Barraclough et al., 1989; Passioura, 1996; Abbate et al., 2004; Steduti and Albrizio, 2005; Sadras and Angus, 2006; Sadras and Lawson, 2013; Fan et  al., 2018). WUE is commonly estimated from successive shoot biomass samples at different phenological stages and ET estimates using soil water balance or lysimeter. Carbon isotope discrimination (Δ13C) has been used as a surrogate for WUE in wheat breeding (Condon et al., 2002, 2004). The principle behind this trait is that open stomata associated with a high availability of CO2 and high discrimination against the heavier C isotope (13CO2) and the contrary is true when stomata are partially or totally closed (Farquhar and Richards, 1984; Araus et al., 1993). However, the classical model of C discrimination has shown inconsistent results mainly under low photosynthesis conditions, but new approaches seem to reinforce this indirect method to quantify WUE (Busch et al., 2020). Oxygen discrimination (18O/16O) has been associated with yield and stomatal conductance in irrigated wheat (Barbour et al., 2000). WUE declines with increasing VPD (Abbate et al., 2004; Sadras and Angus, 2006). Although the atmospheric water demand could be neutralised when WUE is corrected by VPD, the WUE is still dependent on water availability because the lower the water availability, the higher the WUE (Fig. 3.18). The CO2 concentration also affects WUE, and the increasing CO2 can positively impact on WUE of C3 species such as wheat (Asseng et al., 2015). This was confirmed for wheat in plot experiments under terminal stress at high CO2 concentration, that is, 700 ppm (Dias de Oliveira et al., 2013). But even though increased CO2 concentration can improve WUE of wheat under water stress, it cannot offset the thermal increase and increased leaf temperature (Lopes et al., 2012). Management of crop residue, row spacing, and irrigation can reduce soil evaporation and increase both transpiration and WUE (Hatfield and Dold, 2019). The change from furrow to micro-irrigation has been proposed and reviewed recently (Fan et al., 2018), adjusting WU to optimising WUE and balancing crop water traits and yield. However, maximum yield and maximum WUE are not always compatible goals, and the compromise between crop and water production has been

FIG.  3.18  Relationship between wheat shoot biomass (DW) and WU weighted by the VPD under high (open symbols) and low (closed symbols) water availabilities in different locations of Argentina. Reproduced with permission from: Abbate, P.E., Dardanelli, J.L., Cantarero, M.G., Maturano, M., Melchiori, R.J.M., Suero, E.E., 2004. Climatic and water availability effects on water-use efficiency in wheat. Crop Sci. 44, 474–483. https://doi. org/10.2135/cropsci2004.4740.

126  Crop Physiology: Case Histories for Major Crops

proposed (Fereres et  al., 2014). The trade-off between crop production and WUE has been reviewed by Fereres et  al. (2014), who emphasised the importance of environmental conditions such as temperature, VPD, and solar radiation on this trade-off and a lower impact by the genotype.

3.2.3  Harvest index Stresses at early stages have little impact on HI (Section 2.1), but HI is sensitive to stress, especially thermal and water stress, at later stages (Unkovich et al., 2010). Therefore there is room to increase yield by reducing the negative impact of water stress on HI, which is a common feature in Mediterranean conditions. Studies with plants in containers to manipulate the dosage of water showed a positive relationship between HI and the ratio between WU after anthesis and seasonal WU (e.g. Passioura, 1977; Richards and Townley-Smith, 1987; Sadras and Connor, 1991). The association between HI and partitioning of WU seems weaker in the field (Unkovich et al., 2010). In a recent study assessing the variation of HI in Australia, water shortage and high temperature were the main variables affecting HI; HI was negatively associated with floret and stem sterility, spike density and GW (Porker et al., 2020), reinforcing the importance of the key periods for yield discussed in Section 2.1. For crops relying on stored soil water, limiting water uptake during the vegetative growth could favour HI and yield (Passioura, 2006). Terminal drought affects GW, and maintenance of grain filling would mitigate the negative impact of water stress on both GW and HI. Carbohydrate reserves can buffer shortage of current photosynthesis, but trade-offs between reserves and grain number and between reserves and root growth (Lopes and Reynolds, 2010; del Pozo et al., 2016; Ovenden et al., 2017) have been reported, and the association between yield and water-soluble carbohydrates has not been confirmed (Sadras et al., 2020); therefore further research is needed to understand the physiological role of stored carbohydrate reserves.

3.3  Capture and efficiency in the use of nutrients Macronutrients such as nitrogen (N), phosphorus (P), and potassium (K) are usually applied as fertilisers for high yielding wheat; however, yield response to N supply has been found even at lower yields (Fig. 3.19). Indeed, as discussed in the previous section (Section 3.2), there is an interaction between nutrients and water so that under water limited yields there could be a yield response to fertilisation as the availability of nutrients would alleviate the level of water stress by increasing water capture and WUE in water stressed low-yielding environments (Angus and van Herwaarden, 2001; Sadras and Roget, 2004; Cossani et al., 2012; Wang et al., 2018; De Oliveira Silva et al., 2020b). Underfertilisation of N mines organic matter from soils (Angus and Grace, 2017) and overfertilisation contributes reactive nitrogen to the environment (Fageria and Baligar, 2005; Yang et al., 2017). P overfertilisation can also cause en-

FIG. 3.19  Relationship between grain yield with and without fertiliser under rainfed conditions for wheat (triangles) and barley (circles) in Morocco, Jordan, Italy, and Spain. Dotted line is the relationship 1:1. Reproduced with permission from Elsevier Savin, R., Slafer, G.A., Cossani, C.M., Abeledo, L.G., Sadras, V.O., 2015. Cereal yield in Mediterranean-type environments: challenging the paradigms on terminal drought, the adaptability of barley vs wheat and the role of nitrogen fertilization. In: Crop Physiology, second ed. Applications for Genetic Improvement and Agronomy, pp. 141–158.

Wheat Chapter | 3  127

vironmental issues such as eutrophication (Schindler et al., 2016). Therefore a balance between the crop requirements and availability of nutrients should be considered when assessing the required levels of fertilisation in order to increase the sustainability of the agroecosystem.

3.3.1  Nutrient absorption, assimilation, accumulation, and mobilisation Crop uptake can be calculated as: Crop uptake  Available nutrient  BD  H  NuUpE

(3.5)

where available nutrient is the amount of nutrient available in the soil (usually 20 cm of depth for immobile nutrients, and root depth for mobile nutrients ~ 100 cm in wheat) as indicated by chemical indexes; BD, is the bulk density of the soil (g cm−  3), H is the soil rooting depth from where the nutrient is captured by the crop (dm), and NuUpE (nutrient uptake efficiency) quantifies the ability of the crop to capture a particular nutrient from the soil (kg kg−  1). 3.3.1.1  Nutrient uptake efficiency NuUpE depends on the crop root traits and nutrient mobility in the soil (Thorup Kristensen, 2001). Regarding the root characteristics, when nutrients being analysed are mobile in the soil, there are two complementary traits characterising this efficiency: the capacity of the root to explore the soil profile and the efficiency of roots in capturing N (i.e. N uptake per unit root length). A surrogate often used to quantify the former is the rate of root soil penetration (Rasmussen et al., 2015). Wheat rooting depth penetration rate ranges from 1.0 to 1.5 mm d−  1, compared with 1.5 and 2.3 mm d−  1 in nonlegume dicots. Winter and spring wheat had a similar rate (1.3 mm d−  1) but winter wheat reaches a depth of 2.2 m, twice that of spring wheat, due to longer growth period (Thorup-Kristensen et al., 2009; Rasmussen et al., 2015). Breeding would have increased rooting depth under limiting availability of soil resources (e.g. Friedli et al., 2019). Regarding N uptake per unit of root length, it would be negatively related to root thickness (Melino et al., 2015; Corneo et al., 2016). Then it is possible to improve NuUpE through selecting for thinner roots which would have more capacity to extract nutrients (Aziz et al., 2017). For nutrients with low mobility such as P, root length density, root hairs and associated traits in the upper 20 cm of the soil are a key (Goos et al., 1993; Lynch, 2007). Topsoil foraging can be improved through greater production of axial roots, shallower axial root growth angles, greater lateral root density, reduced root metabolic cost, and greater root hair length and density (Lynch, 2019). However, these traits can be overridden by other crop singularities such as organic acid secretions that solubilise P in soil (Sandaña and Pinochet, 2014). The relative importance of the acquisition of nutrient by wheat roots is evidenced in three main mechanisms: (i) the mass flow, which is largely dependent on water flow and soil solution concentration, (ii) diffusion, which is dependent on soil characteristics particularly of the soil buffer capacity, porosity tortuosity, water content and the concentration gradient from soil particles to root surface, and (iii) the rhizosphere effect, which is particularly important for the interaction between crop roots, soil and microorganisms (Table 3.1). Nutrients are taken up by plants roots in a regulated manner and are distributed along the plant according to the crop demand during the crop cycle, where requirements and sinks change with phenology (Barracough et al., 2014 and Section 2.2). Three phases can be distinguished in the process of nutrients uptake and distribution during the ontogeny (Fig. 3.20): (a) a first phase ruled by the space colonisation where root and canopy are growing and the crop behaves more as an individual plant than a population, colonising the space in both ways above and below ground, (b) a second phase of nutrient accumulation, mainly in leaves and stems, and (c) a final phase where nutrients are mobilised from vegetative sources to reproductive sinks as spikes and mainly grains after anthesis. During the first phase, nutrients came from the seeds reserves (mainly form the endosperm) and is expressed as vigour and initial capacity to produce carbohydrates after emergence and the initial colonisation of space, which relies on fast growth and root contact with the soil solution. At this stage, the explored soil volume is small and the seminal roots start the soil colonisation (Fig. 3.20); meanwhile the aboveground space is limited by crop cover to capture radiation (Section 3.1). As plants cover the soil and roots grow into deeper soil layers, wheat reaches its maximum nutrient uptake, and the efficiency of this process is one of the major determinants of crop growth together with water capture (Section 3.2). At heading, nutrient translocation from vegetative organs becomes more important and increases as seeds grow. This last phase is important for grain and seed quality (Section 5.2). To achieve optimal nutrient use efficiency, crops need to maximise the uptake and then the internal process of nutrient cycling or recycling.

128  Crop Physiology: Case Histories for Major Crops

TABLE 3.1  Relative influence of the mechanisms of nutrient acquisition by wheat.

N

Mass flow

Diffusion

Rhizosphere effect (wheat roots-soil)

**

**

*

Bacterial association

***

**

Mycorrhizas, organic acids

**

*

Organic acids

*

pH changes

*

pH and Eh changes

*

Bacterial association

**

pH changes, mycorrhizas

*

pH changes

P K

*

Ca

***

Mg

***

S

**

*

Fe

*

*

Mn

**

Zn

*

Cu

**

Ni

**

Cl

***

B

**

*

Mo

**

*

*

Modified from Gregory, P.J., Crawford, D.R., McGowan, M., 1979. Nutrient relations of winter wheat: 2. Movement of nutrients to the root and their uptake. J. Agric. Sci. 93, 495–504. https://doi.org/10.1017/S0021859600038193; Hinsinger, P., Bengough, A.G., Vetterlein, D., Young, I.M., 2009. Rhizosphere: biophysics, biogeochemistry and ecological relevance. Plant Soil 321, 117–152. https://doi.org/10.1007/s11104-008-9885-9; Giehl, R.F.H., von Wirén, N., 2014. Root nutrient foraging. Plant Physiol. 166, 509–517. https://doi.org/10.1104/pp.114.245225; Lynch, J.P., 2019. Root phenotypes for improved nutrient capture: an underexploited opportunity for global agriculture. New Phytol. 223, 548–564. https://doi.org/10.1111/nph.15738.

FIG. 3.20  The three phases of wheat crop nutrition. The scale is Zadoks et al. (1974).

3.3.2  Effects of nutrients on wheat growth 3.3.2.1  Nutrient uptake and partitioning Fig. 3.21 shows the time-course of N, P, and K accumulation and partitioning in wheat during the crop cycle. The maximum rate of nutrient uptake occurred between tillering and stem elongation, and the maximum amount of nutrient remobilisation from vegetative to reproductive organs (when grain nutrient accumulation exceeds that of the crop) is from the end of the grain lag phase to physiological maturity depending on the environmental conditions, especially temperature and water availability (Malhi et al., 2006; Maillard et al., 2015). Nutrient accumulation in wheat tissues could be divided into two groups considering their time-course relative to the biomass accumulation. N, P, K, S, Ca, and Fe accumulate in advance to biomass and, on the contrary, Mg, Zn, Cu, Mn, and B are delayed, especially at the early developmental stages, taking into account that the nutrient time-course is affected by the availability of the element in the soil. At anthesis, around 70%, 80%, and 90% of the total uptake of N, P, and K, respectively, occurs (Malhi et al., 2006; Clarke et al., 1990; Fig. 3.21 and Table 3.2). Nutrient accumulation in high-yielding wheat (>  8 t ha−  1) ranges from 240 to 300 kg N ha−  1, 35 to 40 kg P ha−  1, 160 to 200 kg K ha−  1, 50 to 60 kg Ca ha−  1, 15 to 20 kg Mg ha−  1, and 15 to 20 kg S ha−  1. This productivity requires maximum

Wheat Chapter | 3  129

FIG. 3.21  Relative dry matter, N, P, and K accumulation and partitioning amongst leaves, stems spikes, and grains during the wheat crop cycle (from emergence to maturity) in Valdivia, Chile. The scale is Zadoks et al. (1974). Data from Sandaña, P., Pinochet, D., 2014. Grain yield and phosphorus use efficiency of wheat and pea in a high yielding environment. J. Soil Sci. Plant Nutr. 14. https://doi.org/10.4067/S0718-95162014005000076; Clunes, J., Pinochet, D., 2020. Effect of slow‐release nitrogen on the nitrogen availability in an andisol and the critical nitrogen concentration in wheat. Agron. J. 112, 1250–1262. https://doi.org/10.1002/agj2.20131.

a­ccumulation rates up to 6.5 kg N ha−  1  d−  1, 1.2 kg P ha−  1  d−  1, and 3.2 kg K ha−  1  d−  1 from the end of tillering to anthesis. However, at yield ~  6 t ha−  1, the maximum nutrient uptake rate declines to 3.2–5.7, 0.3–0.6, 3.9–7.0, and 0.45–0.6 kg ha−  1 d−  1 for N, P, K, and S, respectively (Malhi et al., 2006). Most of total nutrient uptake at maturity is in grains because they accumulate over 70% of the N and P and 31%–64% of the S, Mg, Mn, and Zn. Less than 20% corresponds to K, Ca, Na, Cl, and Fe (Hocking, 1994). Vegetative tissues provide a substantial amount of the nutrients to grain: almost 100% of K is remobilised from stems and leaves, over 70% of the N and P, and between 15% and 51% of S, Mg, Cu, and Zn. Mobilisation of Ca, Fe, Mn, Na, and Cl from vegetative tissues is negligible. 3.3.2.2  Crop nutrient demand Crop nutrient demand can be estimated as:









Crop nutrient demand kgha 1  Yield 100 kgha 1  Nc  1  Wc  / HI

(3.6)

where yield is the yield target (i.e. the estimated expected yield should the crop not experience deficiencies of the nutrient under consideration), Nc is the nutrient critical concentration measured at harvest or when the maximum of the nutrient is reached (expressed as kg 100 kg−  1), Wc is the water content of grain at harvest (dimensionless), and HI is the harvest index. In general, all the factors from the right part of this equation are unified in a ‘factor of Demand’ (fDem), which is fDem = Nc × (1  −  Wc)/(HI) rearranging as:











Crop nutrient demand kgha 1  Yield t ha 1  fDem kg t 1 agronomic product



(3.7)

fDem is the relationship between actual yield and crop nutrient uptake at its minimal optimal nutrition. This value can be obtained from experiments where different quantities of N available in soil are supplied and maximum yield (Ymax or 90%–95% of Ymax) is obtained with the minimal amount of the nutrient available for the crop.

130  Crop Physiology: Case Histories for Major Crops

TABLE 3.2  Most typical values for fDem and extraction factors by wheat. Extraction factors Typical Nutrient

fDem

Grain Typical

Range

Straw Typical

Range

17.0–29.0

7.5

3.5–9.3 0.4–1.0

−  1

Macronutrients (kg t ) N

21.18

22.50

P

3.55

3.50

3.0–5.6

0.8

K

13.49

4.50

4.5–6.7

12.7

Ca

5.07

0.37

0.3–1.2

5.0

2.5–7.2

Mg

1.58

1.20

0.7–2.1

1.6

0.7–2.0

1.26

1.40

1.0–2.8

0.7

0.5–2.5

S

11.0–15.5

−  1

Micronutrients (g t ) Fe

46.64

38.0

9–96

40.0

40–300

Mn

68.59

25.0

20–45

60.0

20–60

Zn

24.35

25.0

7–64

15.0

9–25

Cu

4.01

5.1

2–44

2.5

1–7

B

6.53

1.5

0.5–3

6.0

1–10

Mo

0.35

0.4

0.2–0.5

0.3

0.2–0.5

From: FAO, 1971. A Study on the Response of Wheat to Fertilizers. FAO Soils Bulletin 12. Series number 0253-2050. Food and Agricultural Organization of the United Nations, Rome, Italy 131 p; GRDC (Grain Research & Development Corporation), 2016. Section 5. Wheat—Nutrition and Fertilisers. https://grdc.com. au/__data/assets/pdf_file/0029/373907/GrowNote-Wheat-South-05-Nutrition.pdf; Fan, M.S., Zhao, F.J., Fairweather-Tait, S.J., Poulton, P.R., Dunham, S.J., McGrath, S.P., 2008. Evidence of decreasing mineral density in wheat grain over the last 160 years. J. Trace Elem. Med. Biol. 22, 315–324. https://doi.org/10.1016/j. jtemb.2008.07.002; Murphy, K., Reeves, P.G., Jones, S.S., 2008. Relationship between yield and mineral nutrient concentration in historical and modern spring wheat cultivars. Euphytica 163, 381–390. https://doi.org/10.1007/s10681-008-9681-x; Marles, R.J., 2017. Mineral nutrient composition of vegetables, fruits and grains: the context of reports of apparent historical declines. J. Food Compos. Anal. 96, 93–103. https://doi.org/10.1016/j.jfca.2016.11.012.

fDem is the inverse of the nutrient utilisation efficiency (NuUtE); that is, amount of grain produced per unit of N uptake expressed in kg of yield to total kg of nutrient in the crop (Moll et al., 1982). This trait requires periodic updates because it changes with genetic improvement of both yield and nutrient use efficiency (Clarke et al., 1990; de Oliveira Silva et al., 2020b). Wheat breeding in the long term has consistently improved NUtE (e.g. Calderini et al., 1995). More recently, approaches from the genetic perspective have shown high variation in the component of the NuUE in wheat (Gaju et al., 2011; Guo et al., 2012), which gives an opportunity to improve the crop nutrient use. The critical nutrient concentration is the minimum concentration for maximum growth (Greenwood et al., 1986; Lemaire and Gastal, 1997; Sadras and Lemaire, 2014; Justes et al., 1994). This concept is useful to characterise the nitrogen status of the crop through the estimation of the nitrogen nutrition index. Curves relating shoot critical N concentration (Nc, g kg−  1) and shoot dry matter (DM, t ha−  1) have the form: Nc  a  DM  b

(3.8)

where Nc is the critical concentration of nitrogen in the shoot biomass, and DM is the shoot biomass of the crop. The a and b parameters that characterise the dilution curve are species-dependent (Lemaire et al., 2008). With a 38.5 to 53.5 and b 0.44 to 0.59 for wheat (Justes et al., 1994). Cadot et al. (2018) proposed a similar approach for P, showing similar behaviour and relationships than N, demonstrating also that N and P are both associated by the N:P stoichiometry in wheat and other crops (Sadras, 2006; Lemaire et al., 2019). However, Hoogmoed and Sadras (2016, 2018) showed that both water-soluble carbohydrates and crop water status can affect the dilution curve in wheat. This is in agreement with Zörb et al. (2018), who found a decrease of grain N grain concentration with the increase of the crop yield (dilution effect, see Calderini et al., 1995), in agreement with

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de Oliveira et al. (2020a), who showed the risk of extrapolating the dilution curve across different agroecosystem and environments. Nevertheless, the crop demand factor or NuUtE can be useful to estimate wheat nutrient demand for an expected yield considering modern wheat cultivars with high NUE.

4  Yield responsiveness to management and breeding 4.1  Yield responsiveness to management and breeding 4.1.1  How management practices affect yield Wheat yield is the product of biomass and HI (Eq. 3.1) and is the emergent of the interaction amongst G × E × M (genotype, environment, and management). Wheat yield is closely associated with shoot biomass (Fig. 3.12), with the exception of wheat breeding (Section 4.1.2) and terminal drought (Section 3.2). Both biomass and yield are modified by the growing environment (weather and soil conditions) and management. The same is true for the key window of yield determination (Fig. 3.3), which affects both the set of grain number and potential GW (Sections 2.1.2 and 2.1.3). Management practices impact on yield through affecting yield determinant traits have been discussed earlier. To illustrate this, we selected a few management practices reviewing briefly their impact on yield determination. 4.1.1.1  Sowing date, density, and arrangement Wheat yield shows an optimum response to sowing date where suboptimal dates have a penalty on yield by exposing the crop to unfavourable conditions such as frost and supra-optimal sowing dates negatively affect yield by shorter crop cycle, lower photothermal quotient (PTQ) during the critical period and/or unfavourable conditions during grain filling, decreasing shoot biomass, and especially, HI. One of the main objectives when sowing date is scheduled is to match key developmental phases for yield determination (Section 2.1) and favourable environmental conditions avoiding stresses. Winter, facultative, and spring wheats cultivars should be sown at the time to assure the highest PTQ during the critical period for grain number determination (Fischer, 1985; Savin and Slafer, 1991; Ortiz-Monasterio et al., 1994; Menéndez and Satorre, 2007). The need to consider PTQ when radiation interception is maximised makes it necessary to displace the critical period in spring wheat grown in the N hemisphere to times of the year with decreasing PTQ; something that is not an issue in the southern hemisphere (or other wheat regions with mild winters) in which it is possible setting the critical period of winter and spring wheats at the most favourable PTQ, that is, to concur anthesis date in winter and spring wheats because spring wheat in these conditions can be sown in winter (Box 3.3). Therefore sowing date sets the crop Yp and the gap with attainable (Yat) and actual (Yac) yields (van Ittersum et al., 2013). These gaps are because of the occurrence of water and nutrient stresses, plagues, diseases, weeds competition, frost, lodging, sprouting, and hail, amongst others. To narrow the gap between Yp and both Yat and Yac, the crop should reach the LAIc at the beginning of the critical period, that is, ~ 20 days before anthesis or earlier (Figs 3.13 and 3.14b). The cultivar and sowing date choices are two key decisions to be made by farmers before the crop is sown. Additionally, but associated with these choices, plant density is an important complementary decision because they will affect LAI and radiation interception dynamics (Fig. 3.14c). In wheat, it is generally accepted that GY stabilizes ~ 100 plants m−  2 following an asymptotic shape equation for well-adapted cultivars and at optimum sowing date (Frederick and Marshall, 1985; Spink et al., 2000; Whaley et al., 2000; Lloveras et al., 2004; Valério et al., 2009; Dai et al., 2013). However, recent studies demonstrated that similar yield could be reached at lower plant rates. Fischer et al. (2019) showed that yield of 7 t ha−  1 was recorded under both conventional (200–300 plants m−  2) and very low (20 plants m−  2) plant rates. This corroborates previous studies such as that of Darwinkel (1978), who showed that seed rates lower than 100 plant m−  2 achieved similar GY than higher densities and Bustos et al. (2013), showing similar yield between conventional (350 plants m−  2: 11 t ha−  1) and low plant rate (44 plants m−  2: 11.2 t ha−  1). Hasan et al. (under review) confirmed these results, supporting that low seed rate (20–44 plants m−  2) do not penalise wheat yield in a range from 7 to 12 t ha−  1 across different cultivars and environments (Bustos et al., 2013; Fischer et al., 2019; Hasan et al., under review). Under low plant density, lower radiation interception is expected, as demonstrated by Bustos et al. (2013 and Fig. 3.14c), which is apparently compensated by a higher RUE through the crop cycle, that is, 2.5 and 3.8 g MJ−  1 in conventional and low plant rate, respectively, averaged across cultivars (Bustos et al., 2013). Also, higher spikes per plant, grains per spikelet, and occasionally, higher thousand GW compensated the lower number of plants challenging the consensus assuming an uneven trade-off amongst yield components at plant rates lower than 100 plants m−  2. Undoubtedly, changes in plant density go hand in hand with plant distribution on the ground. Lower plant rate decreases rectangularity (the ratio between plant distance across and within rows), increasing the Red:Far red ratio and neighbours perception (Evers et al., 2006; Abichou et al., 2019). This enhances tillering, reduces plant height, increases HI, and allows higher grain number per spike and sometimes, improved thousand GW.

132  Crop Physiology: Case Histories for Major Crops

4.1.1.2  Fertilisation and irrigation Nitrogen fertilisation is required not only for reaching high yields, undoubtedly in highly productive environments, but also to reduce the gap between actual and attainable yield in low-yielding conditions (Fig. 3.19). Therefore N fertilisation is a common farmer’s practice that modifies the offer of resources impacting on crop growth rate. N fertilisation improves yield through increasing the crop growth rate during the critical period of grain number (and potential GW) determination. This is mainly because higher N availability accelerates the expansion of LAI advancing then the levels of radiation interception by the crop (not affecting the extinction coefficient of the canopy; Fischer, 1993). As N fertilisation increases LAI, at moderate fertilisation doses, the extra N uptake is normally diluted in more LAI, and therefore leaf N concentration does not change much, and consequently, RUE is also maintained. At higher doses where N availability increases more than what is required for LAI responses, there is also an increase in RUE, although the magnitude is normally smaller than that on radiation interception (Fischer, 1993). Across experiments, N affected the crop growth rate during the critical period affecting the biomass accumulated in the reproductive organs, and higher SDWa and more fertile florets were measured under increased N availability (Fischer, 1993; Demontes-Meynard and Jeuffroy, 2004; Prystupa et al., 2004; Ferrante et al., 2013a, 2017; Fig. 3.10). And any effects of N at earlier stages of development, although affecting early growth, do not affect yield if the crop growth rate during the critical period is not compromised; that is why there are no penalties in postponing N fertilisation until late tillering even when growth until then had been penalised by N stress (Fischer, 1993). It is based on this physiological determination of yield that N-fertilisation practices start delaying the application to late tillering or even to the onset of stem elongation in wheat (which is convenient as it is easier to estimate the expected yield when part of the season has elapsed, and it may also reduce N losses if applied too early and winter is rainy). Noteworthy, the impact of P fertilisation showed similar effects on wheat yield and its physiological determinants than N with only little differences. In two independent studies assessing P availability on wheat carried out in Argentina (Lázaro et al., 2009) and Chile (Sandaña and Pinochet, 2011), P fertilisation increased LAI, radiation interception, and crop growth rate. Across the experiments and P treatments, a unique and positive association was found between grain number per unit area and SDWa and between the last and the CGR during the critical period (Fig. 3.22). Other crop traits such as k and RUE were not modified (Lázaro et al., 2009; Sandaña and Pinochet, 2011). Irrigation is also a key management strategy for reducing the gap between Yp and Yat  −  Yac. In India, the study of historical data of irrigated wheat showed that yield increased 13% powered by irrigation between 1970 and 2000 (Zaveri and Lobell, 2019). These authors also showed that irrigated wheat was less sensitive to heat than the rainfed crops. Nevertheless, yield increments had slowed during past years. In addition, as pointed out in Section 1.1, in the context of water scarcity, to maintain irrigated wheat cropping systems is a big challenge regarding that irrigated wheat accounts for ~ 70 Mha the second most irrigated cereal area after rice (FAOSTAT, 2020). The direct effect of irrigation is on water uptake increasing the crop evapotranspiration and transpired water increasing biomass production, however negatively affecting WUE (Fig.  3.18). In a Mediterranean environment, differences between irrigated and rainfed conditions consistently showed linear associations between yield and grain number for barley, bread, and durum wheat (Cossani et al., 2012). Positive associations were also found between yield and shoot biomass and between grain number and PTQ during the critical period calculated as the intercepted PAR (Cossani et al., 2012).

FIG. 3.22  Relationship between (A) grain number and spike dry weight at anthesis and (B) spike dry weight at anthesis and crop growth rate during the critical period for grain number determination in treatments with (closed symbols) or without (open symbols) P fertilisation from experiments carried out in Argentina (squares; Lázaro et al., 2009) and Chile (circles; Sandaña and Pinochet, 2011). Reproduced with permission from Elsevier Sandaña, P., Pinochet, D., 2011. Ecophysiological determinants of biomass and grain yield of wheat under P deficiency. Field Crop Res. 120, 311–319. https://doi.org/10.1016/j.fcr.2010.11.005.

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4.1.1.3  Management of other constrains In addition to nutrient and water scarcity, management of other constrains are highly important in wheat cropping systems. For example, acidic soils (pH < 5.5) account for about 30% of the world’s land (~ 3950 Mha), excluding ice areas, and it has been estimated that over 50% of the world’s potential arable lands are acidic (von Uexküll and Mutert, 1995). An asymptotic shape response has been reported for wheat yield to pH in Oklahoma soils where grain yield stabilised at soil pH 5.8 (Lollato et al., 2019). Wheat sown in acidic soils is often affected by Al toxicity (Kariuki et al., 2007; Lollato et al., 2013), decreasing yield 30% or more depending on Al concentration (Costa et al., 2003; Kariuki et al., 2007). Two main management strategies are used by farmers to neutralise the negative impact of Al toxicity: (i) liming and (ii) cultivar choice. Regarding the latter, there is a wide range of variability in sensitivity to Al. Whilst yield of Al sensitive cultivars could be affected by relatively low soil Al concentrations, Al-tolerant cultivars could grow without important yield penalties when grown in soils with up to ~ 0.5 cmol (+) kg−  1 of exchangeable Al in acidic soils and andisoils, which is equivalent to ~ 16% of Al saturation (Al concentration expressed as a percentage of total exchangeable base cations), depending on the soil type (Coutinho, 1990; Valle et al., 2009). On the other hand, the application of liming depends on soil pH, soil buffer capacity, and Al concentration. For example, in soils with pH 5, the quantity of liming to apply is about 4 t ha−  1, but this depends on soil characteristics (because the type and quantity of clays and other colloids determine the buffer capacity). Lime application improves the root growth, which is the wheat organ most impacted by Al toxicity, allowing the capture of water and nutrients facilitating the shoot growth by increasing LAI and radiation interception (Valle et al., 2009, 2011). As in previous sections, grain yield across a wide range of soil Al concentrations associated with shoot biomass and grain number (Tang et al., 2003; Kariuki et al., 2007; Valle et al., 2009), and the capture of radiation has been found as the main cause of the penalty of Al toxicity on wheat shoot biomass and yield as under other soil constraints such as nitrogen deficiency and compactness (Abbate et al., 1995; Sadras et al., 2005). Lately, silicon has been proposed as a soil Al toxicity amendment (Vega et al., 2020 and references therein). Other soil constrains are saline and sodic soils, estimated from 830 to 932 Mha, more than 6% of the world’s land, which is rising (Acosta-Motos et al., 2017; Genc et al., 2019). Indeed, it is estimated that over 50% of global arable land will be salinised by 2050 (Jamil et al., 2011). Soil salinisation emerges as an important constraint, particularly in arid and semiarid regions of the world with hotspots in Pakistan, China, US, India, Argentina, Sudan, central and western Asia, and in the Mediterranean coastline (Cuevas et al., 2019). Yield reductions of 50% in durum wheat under dryland salinity (James et al., 2012), 88% in bread wheat under irrigation with high salinity water (Jafari-Shabestari et al., 1995), and 70% under sodicity have been reported (Rengasamy, 2002). In controlled conditions experiments, transpiration accounted for 90% of the variation of shoot growth in barley and wheat (Harris et al., 2010). Bread wheat is moderately salt-tolerant (Munns et al., 2008), being the threshold by 100 mM NaCl (about 10 dS m−  1). Durum wheat is less salt-tolerant than bread wheat, that is, 6.8–8.6 dS m−  1 (Francois et al., 1986). Other soil constrains such as Bo toxicity has been recently reviewed (Landi et al., 2019).

4.1.2  Impact of wheat breeding on grain yield and next steps Improving yield is a permanent aim of wheat breeding, and this objective has been reinforced in the present century by the challenge of an increasing world population and food demand (Ray et al., 2013), which has been recently estimated to peak in 2064 when global population reaches 9.73 billion (8.84–10.9) people (Vollset et al., 2020). The aim of improving potential yield was achieved by wheat breeding in most of the countries along the 20th century (Slafer et al., 1994; Calderini et al., 1999; Foulkes and Reynolds, 2015). In Fig. 3.23, the relative genetic gains of yield of several countries, and also different studies for the same country, are shown. Across the studies, average gain of yield was 0.74% y−  1. Only five countries (Brazil, Chile, China, England, and Mexico) and nine studies showed genetic gains ≥ 1% y−  1. However, it is important to take into account that the shorter the evaluated period, the higher the genetic gain estimated (Fig. 3.23). For example, the average genetic gain from studies evaluating ≤ 20 years was 1.74% y−  1, whilst the average of studies analysing ≥ 50 years was 0.59% y−  1 (Fig. 3.23). Additionally, the later the period, the higher the gain because genetic gain increased slowly in the first half of the 20th century and higher during the second half (see Fig. 2 in Calderini and Slafer, 1999). The Green Revolution, led by Nobel laureate Dr. Norman Borlaug, was a quantum leap increasing both potential and actual yield, but wheat yield was increased step by step during the 20th century, even before the 60s (e.g. Austin et al., 1980; Slafer and Andrade, 1989; Siddique et al., 1989; Fig. 3.23). Plant height was slowly decreased by wheat breeding since 1900 (see Fig. 16.2 in Calderini et al., 1999), and Nazareno Strampelli in Italy was a forerunner of wheat plant height reduction and yield improvement. Even though, plant height reduction was jumped up by the introgression of the Rht alleles from Norin 10 in the 60s, which consolidated the wheat yield improvement worldwide, with few exceptions as in low-yielding

134  Crop Physiology: Case Histories for Major Crops

FIG. 3.23  Relative genetic gain in yield reported for different countries and periods of bread (closed circles) and durum (open triangles) wheat. This figure only included studies in which genetic gains were evaluated through growing side-by-side cultivars released at different times in the same experiment. Genetic gains were calculated as the ratio between the absolute genetic gain and the average yield as a percentage and they are shown by the colour and intensity of the bars representing the period analysed in each study, and exact values are explicit at the right of the bars. Drawing by Gabriela Carrasco-Puga.

Wheat Chapter | 3  135

environments, such as in some areas of Australia (Hyles et al., 2020). Main changes because of the introgression of the Rht alleles associated to the improved yield were the increase of HI with similar shoot biomass and augmented grain number per land unit without change or a small negative effect on GW (Gale and Youssefian, 1985; Slafer et al., 1994; Calderini et al., 1999; Foulkes and Reynolds, 2015). Consistently, linear associations between yield and HI were reported across countries when wheat cultivars released at different times since the first half of the 20th century were evaluated together in the same experiment (e.g. Austin et al., 1980; Calderini et al., 1995; Fischer et al., 1998; Brancourt-Hulmel et al., 2003; Shearman et al., 2005; Royo et al., 2008; Acreche et al., 2008; del Pozo et al., 2014; Mondal et al., 2020). A positive association between HI and the yield-plant height ratio was also reported (Calderini et al., 1999). The plant height reduction allowed a rearrangement of shoot biomass towards the reproductive organs as shown in Table 3.3. Different authors found an optimal response of wheat yield to plant height (Richards, 1992; Miralles and Slafer, 1995; Flintham et al., 1997), ranging between 0.70 and 0.90 m (Fig. 3.24). This parabolic response was explained by the tradeoff between biomass and HI in NILs for plant height, where biomass production was affected by lower RUE in the double dwarf line and HI was lower in the standard height in both cases compared against the semidwarf wheat line (Miralles and Slafer, 1995). The same is true for HI, which was estimated to have an upper threshold of c. 62% (Austin et al., 1980). Having modern cultivars already an optimum plant height and a HI, which is close to the threshold, it implies that tools and criteria successfully exploited in the past would have little value to further improve yield, highlighting that future reductions of stem biomass seem to be an uphill battle for wheat breeding (as shown in Table 3.4), where the calculated value of 15% of stem plus sheaths biomass by Austin et al. (1980) has not been reached. Therefore the strategy of continuing increasing wheat yield by stressing plant height is not feasible, and the realistic way is to improve biomass or other alternative was to further improve spike growth before anthesis (Rivera-Amado et al., 2019). Likely, the opportunity of still improving wheat TABLE 3.3  Grain yield and stem dry weight in two wheat cultivars released at different times in Argentina. Year of release

Grain yield (g m−  2)

Stem weight (g m−  2)

1920

319.9

839.1

1990

649.1

496.4

Difference

329.3

−  342.7

Data from: Calderini, D.F., Dreccer, M.F., Slafer, G.A., 1995. Genetic improvement in wheat yield and associated traits. A re‐examination of previous results and the latest trends. Plant Breed. 114, 108–112. https://doi.org/10.1111/j.1439-0523.1995.tb00772.x.

FIG. 3.24  Schematic representation of the optimum wheat yield in response to plant height. Based on Richards, R.A., 1992. The effect of dwarfing genes in spring wheat in dry environments. I. Agronomic characteristics. Aust. J. Agric. Res. 43, 517–527. https://doi.org/10.1071/ar9920517; Miralles, D.J., Slafer, G.A., 1995. Individual grain weight responses to genetic reduction in culm length in wheat as affected by source–sink manipulations. Field Crop Res. 43, 55–66; Flintham, J.E., Börner, A., Worland, A.J., Gale, M.D., 1997. Optimizing wheat grain yield: effects of Rht (gibberellin-insensitive) dwarfing genes. J. Agric. Sci. 128, 11–25. https://doi.org/10.1017/s0021859696003942.

136  Crop Physiology: Case Histories for Major Crops

TABLE 3.4  Biomass partitioning into grain, chaff, leaf lamina, stem, plus sheaths. Austin et al. (1980) (four most modern cultivars)

Austin et al. (1980) (theoretical maximum HI)

Consort Herefordshire, UK (mean 1996/1997 and 1997/1998)

Crop component

g m−  2

%

g m−  2

%

g m−  2

%

Grain

707

49

895

62

1 103

56

Chaff

143

10

181

13

195

10

Leaf lamina

139

10

139

10

183

 9

Stem + sheaths

453

31

226

15

490

25

Measured and calculated data. Data from: Foulkes, M.J., Hawkesford, M.J., Barraclough, P.B., Holdsworth, M.J., Kerr, S., Kightley, S., Shewry, P.R., 2009. Identifying traits to improve the nitrogen economy of wheat: recent advances and future prospects. Field Crop Res. 114, 329–342.

yield by plant height is only feasible for some low-yielding environments where taller semidwarfs have been proposed (Hyles et al., 2020). In historic sets of wheat cultivars, few studies found a positive association between grain yield and shoot biomass, RUE, or photosynthesis (Calderini et al., 1997, Fischer et al., 1998; Shearman et al., 2005; Reynolds et al., 2007; Sadras and Lawson, 2011; Xiao et al., 2012). In Argentina and Mexico, RUE increased after anthesis, apparently driven by the increase of sink size (Calderini et al., 1997; Reynolds et al., 2007). In UK and Australia, selection for yield increased preflowering RUE (Shearman et al., 2005; Sadras et al., 2012). Shearman et al. (2005) speculated the improvement of the intrinsic photosynthetic rate as the likely cause of higher RUE cultivars released in UK after 1983. In Australia, the increase of the preanthesis RUE was apparently driven by changes in nitrogen uptake and distribution of nitrogen and radiation into the canopy profile (Sadras et al., 2012). Shifts in canopy architecture and improvements in leaf photosynthesis or associated traits have been found as a consequence of wheat breeding in China and Chile (Xiao et al., 2012; Sun et al., 2014; del Pozo et al., 2016). However, the phenotypic response of biomass to wheat breeding has been either negligible or modest across the studies, likely because genotypes with higher biomass were obtained by indirect selection for grain yield. To accelerate the development of higher biomass cultivars in the future, key traits have been proposed, that is, from the enhancement of leaf photosynthesis (Parry et al., 2011) to the increase of RUE (Reynolds et al., 2012) as aims per se. The biomass increase of wheat seems physiologically feasible, and genetic variability for this trait has been reported from long ago (Sharma, 1993) to recently (Molero et al., 2019). However, trade-off between HI and shoot biomass has been reported during past years (Duan et al., 2018; Molero et al., 2019; Rivera-Amado et al., 2019) preventing from a simple strategy and view of increasing wheat biomass. Grain number is a key yield component (Section 2.1) and was linearly associated with wheat yield improvement across the world as mentioned earlier. This was possible by the higher dry matter of the spikes at anthesis and more florets reaching the floret fertile category as a consequence of plan height reduction and higher shoot biomass partitioning to the spike until anthesis (e.g. Gale and Youssefian, 1985; Slafer et al., 1994; Miralles et al., 1998). Therefore changes in the spike growth rate and/or the spike-growing period would improve this key trait (Figs 3.4 and 3.7–3.10). Photoperiod sensitivity during the late reproductive phase (Fig. 3.7) has been explored as a way to enlarge the spike fast-growing period and in turn increase fertile floret and grain number (Slafer et al., 2001; Miralles and Slafer, 2007). In addition to spike dry matter, fruiting efficiency (Section 2.1.2 and Box 3.1) could also contribute to the increase of grain number. Recently, Pretini et al. (2020) identified 37 QTL for this trait in a double haploid population of hexaploid wheat. GW has been much less affected by wheat breeding showing no change or even a slight decrease (Siddique et al., 1989; Sayre et al., 1997) across wheat cultivars released in different eras. However, GW increase has been found mainly in cultivars released after the 80s. For example, the positive impact of wheat breeding on GW was found in Argentina (Calderini et al., 1995), Australia (Sadras and Lawson, 2011), and in areas of China (Zheng et al., 2011). Nevertheless, contrasting results could be pointed out in Argentina because the GW increase after 1980 (Calderini et al., 1995) did not continue as it was shown in a recent study updating Argentinian wheat breeding until 2011 (Lo Valvo et al., 2018; Fig. 3.23). On the other hand, in Australia, GW showed two phases: a first period when this trait decreased from 1957 to 1982 and a following phase of GW increase (Sadras and Lawson, 2011). In China, negligible changes in GW have also been reported (Xiao et al., 2012), and the impact of breeding on GW seems to be dependent on the China’s province and breeding programme. Although the results are contrasting, GW is a trait to be considered for improving wheat yield. As described in Section 2.1.3, genes

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a­ ffecting GW have been reported, especially during the past 10 years and QTL associated with this component (Lizana et al., 2010; Yang et al., 2012; Wang et al., 2018; Zhang et al., 2018; Brinton and Uauy, 2019; Mangini et al., 2020), but a concern to bear in mind is the trade-off between GW and grain number reported recently for different wheat populations (Quintero et al., 2018; Molero et al., 2019). However, a recent report seems to have overcome the trade-off between GW and grains per m2 (Calderini et al., 2020a). Wheat breeding also impacted on the use of resources, especially nutrients and water. Higher NUE was a common effect found across countries when NUtE is calculated as the ratio between yield and absorbed N (Fischer and Wall, 1976; Feil and Geisler, 1988; Slafer et al., 1990; Calderini et al., 1995; Ortiz-Monasterio et al., 1997), although few exceptions were also reported (Acreche and Slafer, 2009; Sadras and Lawson, 2013). Similar results were found for PUE (Calderini et al., 1995). On the other hand, N uptake was not modified by wheat breeding in most of the countries (Calderini et al., 1995; Foulkes and Reynolds, 2015). However, improvement of N uptake or N uptake efficiency was also found in CIMMYT, Spain, and Australia (Ortiz-Monasterio et al., 1997; Muurinen et al., 2006; Acreche and Slafer, 2009; Sadras and Lawson, 2013), and genetic variability for both N uptake and N utilisation has been demonstrated (Le Gouis et al., 2000). The rate of N uptake increase ranged between 0.12 and 1.20 kg N ha−  1 y−  1 (Austin et al., 1980; Ortiz-Monasterio et al., 1997; Giunta et al., 2007; Sadras and Lawson, 2013). As a result of the balance between N grain yield and N uptake, grain N concentration was decreased across most of the wheat breeding studies owing to the dilution of N because the rate of yield dry matter was higher than the rate of grain N increase. In addition to the challenge of increasing yield, wheat breeding is also defied by improving the efficiency of nutrients uptake and use. Evidences of the feasibility of them have been shown during the past wheat breeding (Ortiz-Monasterio et al., 1997; Sadras and Lawson, 2013), and deep roots, higher root length density, and specific root density have been recommended for improving the uptake of mobile nutrients in the soil such as N (Cormier et al., 2016). Leaf and canopy photosynthesis per unit of N, the distribution of nitrogen and radiation into the canopy profile, and N remobilisation have been considered for increasing NUE (Cormier et al., 2016). Importantly, the co-limitation between N and water (Sadras, 2004; Cossani et al., 2010; Cossani and Sadras, 2019) supports that selection for yield positively affects both water and nitrogen economies. For immobile nutrients in the soil as P and K, the focus is on the topsoil, where these nutrients are available. Positive associations between P uptake and the length of surface basal roots were found and the higher the basal root angle, the lower the P uptake, especially in soils of low P content (Lynch, 2007). This author suggested that a promising breeding strategy for improving the uptake of these nutrients would be the same than used for tolerance of abiotic stress, that is, to assess a wide range of genotypes for the expression of specific tolerance traits. However, a trade-off between the root architecture proposed for immobile nutrients uptake and water uptake was pointed out by Lynch (2007). Fewer studies evaluated the impact of wheat breeding on water uptake and WUE. In Australia, no trends were found for wheat evapotranspiration in cultivars released between 1958 and 2007 (Sadras and Lawson, 2013). However, when the authors plotted data from different Australian experiments, a linear association was found between yield per transpiration unit (WUE) and year of release from 1918 to 2007. Water-associated traits were evaluated in CIMMYt’s cultivars released between 1962 and 1988, where modern cultivars showed higher stomatal conductance and lower canopy temperature than older ones (Fischer et al., 1998); however, water uptake and WUE were not measured. As in nutrients, roots were the aim for increasing water uptake, and deep roots is a need for this objective not only for water-limited environments but also for high rainfed environments, where N and other nutrients are leaching (Thorup-Kristensen and Kirkegaard, 2016). The recent release of a transgenic wheat apparently tolerant to water deficit, carrying a mutated version of the gene HaHB4 from sunflower (González et al., 2019), opens the opportunity for improving both WUE and Yw.

4.1.3  Perspectives of wheat under climate change Climate change is one of the main challenges for agriculture in this century (Sadras and Calderini, 2009, 2015; Ray et al., 2013; Fischer and Connor, 2018), accounting for global temperature increase (both in daytime and night-time temperatures and variations between winters and summers), rainfall variation, CO2 increment, and higher frequency of extreme events; for example, heat spells producing heat shocks and heavy rains producing waterlogging (IPCC, 2018). Climate change has already affected wheat yields as was reported by Iizumi et al. (2018) through a counterfactual analysis, where the authors found that climate change decreased global mean yield of wheat by 1.8% between 1981 and 2010. Amongst the projected environmental changes, the fact that crops will be exposed to higher temperatures is rather accurately predicted (e.g. Challinor et al., 2014). Temperature increase ranges from 1.3°C to 1.7°C by averaging this century (2046–65) and between 1.8°C and 3.1°C by the end of the century (2080–99) (IPCC, 2018). This global warming will be heterogeneous along the world, predicting higher increases in the northern hemisphere than in the southern hemisphere and also high in tropical regions (Fig. 3.25). Large areas of major wheat producing countries will be affected by global warming such as North America, the Mediterranean Basin, the Indian subcontinent, eastern Asia, and Australia (Figs 3.1 and 3.25).

138  Crop Physiology: Case Histories for Major Crops

FIG. 3.25  Scenarios of global warming for years 2046 (left panel) and 2081–2100 (right panel) and present wheat cropping countries. Temperature change is indicated by colours and wheat country production, the size of grey circles. Modified from: IPCC-AR5 2013, Collins, M., Knutti, R., Arblaster, J., Dufresne, J.-L, Fichefet, T., Friedlingstein, P., Gao, X., Gutowski, W.J., Johns, T., Krinner, G., Shongwe, M., Tebaldi, C., Weaver A.J., Wehner, M., 2013. Long-term climate change: projections, commitments and irreversibility. In: Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J. , Nauels, A., Xia, Y., Bex, V., Midgley, P.M. (Eds.), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA; FAOSTAT, 2020. Food and Agriculture Organization of the United Nations. http://www.fao.org/faostat/en/#home.

Predictions based on assembles of a large number of different simulation models showed that wheat production is estimated to be penalised significantly by raising temperatures (in average by c. 6% °C−  1) (Asseng et al., 2015). Recognising the detrimental effect of raising temperatures, that the problem is even more complex, because not only the average temperatures will increase but also the frequency of heat waves (Meehl and Tebaldi, 2004; Seneviratne et al., 2014); and the increase in frequency will not only occur in regions already exposed to heat waves but also in cooler regions (Semenov, 2007). This is a rather relevant distinction because heat waves produce yield penalties much stronger than expected from the raise in average temperature not only accelerating development and reducing growth but also more directly impairing reproductive processes (Slafer and Savin, 2018). Additional information accounting for the effect of climate change on wheat and other cereal crops in different areas of the world has recently been reported by O’Leary et al. (2018). The forecast of rainfall and cloudiness, affecting two key environmental factors for crops: water availability and radiation, has been less precise than temperature, especially rainfall. However, climate models simulate that global mean precipitation will increase with global warming but with high variation along the globe. Increases in precipitation are predicted for high latitudes and decreases in mid-latitude, mainly in summertime, except in eastern Asia. In addition, decreases in rainfall

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in subtropical areas are estimated by climate multimodel ensemble. In agreement, cloud cover is expected to decrease in low and middle latitudes and substantial increases at high latitudes (IPCC, 2018). In this context, the response of wheat to climate change has been estimated by crop simulation models (e.g. Meza and Silva, 2009; Makowski et al., 2020, and references therein), but especially by ensemble models, which has been demonstrated as more accurate than single models when crop temperature response is simulated (Asseng et al., 2015). Early simulations of ensemble models, considering only temperature increase, showed a negative impact of global warming on wheat yield as expected in a range of 15–32°C. Across different wheat cropping areas, simulated grain yield decreased 5% °C−  1 of temperature increase. This negative impact was mainly because of the increase in the developmental rate (Section 2.2.3), reducing biomass and grain number per unit area because of lower accumulated intercepted radiation (Sections 2.1.1 and 3.1). However, few positive impacts were also found in water- and nitrogen-limited conditions by avoiding the stresses ending the crop cycle (Asseng et al., 2015). More recently, Asseng et al. (2019) simulated wheat yields in 60 world areas considering two temperature increases (2°C and 4°C) and two CO2 concentrations (360 and 550 ppm) for the period 2040–69. Generally, negative impacts on yield were simulated for low‐ and mid‐latitude locations and positive effects in high‐latitude locations. Most of the yield responses ranged between −  10% and 10%. Variability in wheat yield by the impact of climate change was also shown in other studies (e.g. Rosenzweig et al., 2014). However, we must be careful when extrapolating the conclusions from studies using crop simulation models that still need to consider the magnitude of effects from heat waves compared to that from constantly higher temperatures (Slafer and Savin, 2018). Field experiments were also carried out to evaluate the impact of climate change on wheat, mainly global warming. As it was shown by modelling, field experiments reported negative impact of increasing temperature on wheat yield. Most of the field experiments increased temperature by portable chambers equipped with heaters (e.g. Savin et al., 1996). These studies generally focused on key phases for yield and components, taking into account that heat waves are forecasted for the future, and the impact of temperature on crop yield depends on the developmental stage of the crop at which the thermal increase occurs. Also, higher night temperatures were studied in field conditions by this methodology. Increased temperatures during key periods for yield determination (Section 2.1) showed yield decrease by 4.5%–5% C−  1 (Ugarte et al., 2007; Lizana and Calderini, 2013; García et al., 2016). When night temperature was increased in wheat and barley, similar results were found, that is, 7% °C−  1 (García et al., 2015). These studies also shown that when yield components were considered, grain number reduction ranged between 4% and 6% °C−  1 and GW by 1%–4% °C−  1 (Lizana and Calderini, 2013; García et al., 2015, 2016). The effect of the temperature increase on grain number was analysed in more detail by García et al. (2015) that the impact of increased temperature was because of the higher developmental rate, shortening the duration of the critical period, reducing radiation capture with negative consequences for biomass production. Similar explanation was found in other experiments where the higher developmental rate was the cause of yield penalty under increased temperature (Lizana and Calderini, 2013). Few studies combined temperature and CO2 increases in field plot experiments. Dias de Oliveira et al. (2013) evaluated the response of wheat to increased temperature (2°C, 4°C, and 6°C) and CO2 concentration (700 mL L−  1) showing that in irrigated plots, grain yield was increased 9% (averaged across the two assessed cultivars) under increased CO2 and temperature (2°C), but this reversed when temperature was increased by 4°C (GY: ~7%) and 6°C (GY: ~10%). In this experiment, terminal drought was also tried. The effect of both increased temperature and terminal drought was counterbalanced by higher CO2 by increasing yield by 30% and 18.5% (averaged across cultivars) under 2°C and 4°C, respectively. On the contrary, CO2 increase was unable of compensating drought under the higher thermal increase (6°C) decreasing yield by 7.5% (similar in both cultivars). Although these results are useful, the positive impact of increased CO2 concentration on wheat would be lower until the middle of this century because CO2 concentration for this time is expected to reach 550 ppm (RCP8.5), and values of 700 ppm or higher are projected for the end of the century (https://www.ipcc-data.org/observ/ ddc_co2.html).

5 Quality 5.1  Grain quality traits Wheat is used for various types of breads, noodles, biscuits, cakes, pasta, etc., and in nonfood applications such as starch, vital dry gluten, biodegradable plastics, and ethanol, amongst others (Day et al., 2006; Uthayakumaran and Wrigley, 2010). Wheat quality has, therefore, different meanings depending on the end-use, and the step in the value chain from breeding, production in the field, commercialisation, manufacture of end product, and consumer (Rondanini et al., 2019). The diversity of markets and of uses means that breeders must provide for a similarly wide variety of products when they select for quality type (Wrigley, 1994).

140  Crop Physiology: Case Histories for Major Crops

Carbohydrate, mostly starch, accounts for 70% of the weight in the mature wheat grain (Stone and Savin, 1999; Shewry, 2009), with protein accounting for 8%–22% (Peña et al., 2002; Shewry, 2007). Carbohydrates and protein are synthesised during the grain-filling phase described in Section 2.2.2. The timing when these components are synthesised and deposited during grain growth are described elsewhere (e.g. Stone and Savin, 1999; Shewry et al., 2012). The most common grain quality traits for commercialisation are test weight, moisture content, protein content, and particular grain defect limits (sprouted, fungal, or insect damage) or weed seed limits and other contaminant limits (Wrigley, 1994). These attributes are used for segregation or grading wheat by grain buyers and traders to separate sound wheat, suitable for human consumption, from weather-damaged and disease- or drought-affected grain of lesser value. Some countries have more complex system of grading than others, but there are many common attributes and values amongst the different standards (Delwiche, 2010). Some quality traits can be used to predict end-product quality. The ultimate test of the suitability of wheat for any end product is to manufacture the end product, using scale test methods. However, as manufacture of the end product is timeconsuming and usually requires a large sample, research has been performed to link quality traits and end-product traits. To estimate the end-quality product dough, rheological measurements and small-scale laboratory tests are often used in bread wheat mills and bakery industry (Fu et al., 2020). Wheat quality can be simplified to three key traits: grain hardness, grain protein concentration, and dough or protein quality. The relationships between these three traits have been summarised graphically by Moss (1973) and are widely used (Fig. 3.26). For the production of a particular quality end product, there is a relatively narrow range of grain protein concentration of each endosperm hardness type (Fig. 3.26). Grain endosperm hardness is a measure of the resistance to deformation (Turnbull and Rahman, 2002). It is determined by the way starch granules and proteins are packed in the endosperm cells. Common hexaploid wheat (T. aestivum L.) endosperm texture ranges from very soft to hard, whereas the tetraploid durum wheat (T. turgidum L. ssp. durum) presents the hardest grains across all ploidies (Pauly et al., 2013). Hardness is largely controlled by genetic factors (e.g. values of 89% narrow-sense heritability are usually reported, Jernigan et al., 2018), but it can be affected by the environment and factors such as moisture, lipid, and pentosan content (Turnbull and Rahman, 2002). Endosperm hardness affects the particle size after milling, water absorption by the flour, and milling yield. On the other hand, for the bakery industry, the endosperm hardness is a predictor of the suitability for a particular end product (Fig. 3.26).

FIG.  3.26  Relationship between grain protein content, endosperm type, and end uses of wheat flour. Reproduced with permission from Australian Institute of Agricultural Science & Technology Moss, H.J., 1973. Quality standards for wheat varieties. J. Aust. Inst. Agric. Sci. 39, 109–115.

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Grain protein content varies depending on environment and crop management practices (Peña et al., 2002; Shewry, 2007), and these variations are much larger than variations due to genotype (Aguirrezábal et  al., 2015). Grain protein concentration is mainly due to variations in the quantity of carbon compounds (i.e. starch, Jenner et  al., 1991), whilst the quantity of nitrogen compounds (i.e. proteins) per grain is relatively stable. The relationship between carbon and nitrogen compounds leading to a final concentration of protein in the grain can most simply be explained by the effects of environmental factors during the grain-filling period on the rate and duration of accumulation of starch, oil, and protein (Aguirrezábal et al., 2015). And, as shown by Jenner et al. (1991), their depositions in the grain are relatively independent from each other and are controlled differently. Although protein content is usually used for grade and market quality (Wrigley and Bekes, 2004), there is not a linear association with dough quality because protein composition is different from protein content (Stone and Savin, 1999; Wrigley and Bekes, 2004).

5.2  Grain proteins, nutrients, fibre, and healthy traits Around 85% of the endosperm protein is gluten, a very large complex primarily composed of polymeric (multiple polypeptide chains linked by disulphide bonds) and monomeric (single chain polypeptides) proteins, known as glutenins and gliadins, respectively (MacRitchie, 1994; Peña et  al., 2002). These storage proteins are controlled by over 100 genes located at different loci (Shewry and Halford, 2002), coding for high molecular-weight-glutenin subunits (HMW-GS), low-­molecular-weight-glutenin subunits (LMWGS), α/β-gliadins, γ-gliadins, and ω-gliadins. The chemical description and classification of the different proteins involved in gluten can be found in comprehensive reviews by Shewry and Halford (2002) and Peña et al. (2002). When gluten is hydrated and mixed and or kneaded, it forms a continuous network of proteins, which provide the cell structure to a loaf of bread (Stone and Savin, 1999). Glutenins confer elasticity, whilst gliadins confer viscous flow and extensibility to the gluten complex. Thus gluten is responsible for most of the viscoelastic properties of wheat flour doughs and is the main component conditioning the use of a wheat variety in bread and pasta-making. Gluten viscoelasticity, for end-use purposes, is commonly known as flour or dough strength (Peña et al., 2002). 5.2.1.1  Grain nutrients, fibre, and healthy traits Wheat products contribute to dietary fibre, which in turn promotes human health. For instance, the pentosan content (mainly arabinoxylan and to a lower degree β-glucans) reduces the risk of chronic diseases, such as diabetes, cardiovascular diseases, and colorectal cancer (De Munter et al., 2007; Vitaglione et al., 2008; Aune et al., 2011), which are particularly relevant in countries with ageing populations. Consumption of these dietary fibres by the European population is well below the recommended levels (Shewry et al., 2014). Minor components, including lipids, terpenoids, phenolics, minerals, and vitamins, are dietary important (Shewry et al., 2013). These components differ in their distribution within the grain. For instance, the starchy endosperm (recovered as white flour on milling) contains low contents of cell wall components, minerals, and phytochemicals, whereas the pure bran, that is, aleurone layer, outer layers of the grain, and the embryo, lack in starch and are enriched in minor components with nutritional and health benefits (Shewry et al., 2013).

5.3  Sensitivity of grain quality traits to environmental stresses GW and composition depend on the genotype, but most of the quality traits are highly conditioned by the environment and by the genotype–environment interaction (Gooding and Davies, 1997; Savin and Molina-Cano, 2002; Wrigley and Bekes, 2004; Aguirrezábal et al., 2015). The two major environmental stresses that alter grain composition and quality are high temperature and drought. The effect of heat stress on wheat grain quality is well documented (Stone, 2001 and references therein), and the occurrence of heat stress will likely increase with climate change. Global climate models predict an increase in mean ambient temperature between 1.8°C and 5.8°C by the end of this century (IPCC, 2018), and also, more intense and extremes temperatures are predicted (Meehl and Tebaldi, 2004; Seneviratne et al., 2014). Grain quality is likely to be affected by higher mean temperatures, higher maximum (Meehl and Tebaldi, 2004; Seneviratne et al., 2014), moderately high temperatures (in the c. 20–32°C range, Wardlaw and Wrigley, 1994) and higher night temperatures (Shi et al., 2010), as illustrated in Table 3.5. However, most reports are from experiments in controlled conditions and are thus inconclusive (Slafer and Savin, 2018). In general brief periods of heat stress with temperatures higher than 32–35°C may alter flour, dough, and baking quality (Blumenthal et al., 1993); these effects have been related to an increased gliadins/glutenins ratio (Blumenthal et al., 1991; Triboi et al., 2000) and a decrease in the proportion of the larger molecular size glutenins (Wardlaw et al., 2002). On the other hand, moderately high temperatures of 20–32°C have a positive effect on dough properties (Randall and Moss, 1990; Wrigley et al., 1994) and have been reported to lead to changes in the gliadin fraction composition (Daniel and Triboi, 2000, 2001).

142  Crop Physiology: Case Histories for Major Crops

TABLE 3.5  Examples of the effect of different type of heat stress on the major grain quality attributes in wheat. Grain quality attributes Type of heat stress

Grain weight

Starch content/ quality

Protein content

Protein quality

Dough quality

Daily maximum or heat waves

−/=

−

+/=

−/+/=

−/+

✓

✓

✓

✓

Condition

References

✓

Chamber

Randall and Moss (1990)

✓

✓

Glasshouse

Blumenthal et al. (1995)

✓

✓

✓

Field

Graybosh et al. (1995)

✓

✓

✓

✓

Glasshouse

Stone et al. (1997)

✓

✓

✓

✓

Chamber

Wardlaw et al. (2002)

✓

✓

✓

✓

Glasshouse

Spiertz et al. (2006)

+/=

−/+

−/+

✓

✓

✓

✓

Glasshouse

Stone et al. (1997)

✓

✓

✓

✓

Field

Daniel and Triboi (2000)

✓

✓

✓

✓

Glasshouse

Wardlaw et al. (2002)

?

?

?

✓

Chamber

Prasad et al. (2008)

✓

Field

García et al. (2016)

✓ ✓

Moderately high

Night

−

−

✓

−

?

Negative, positive, and equal symbols indicate a reduction, an increase, and no significant modifications compared to the unheated control, respectively. The question mark symbols indicate that there is no report so far on the effects of that particular type of stress on grain quality attributes.

Regarding dietary fibre, minerals, and the other minor components, differences in the amount have been found amongst genotypes (Ortiz-Monasterio et al., 2007; Rakszegi et al., 2008; Shewry et al., 2013; De Santis et al., 2018), and also the concentration may vary under heat stress and drought (Zhang et al., 2010; Rakszegi et al., 2014) or N availability (Shi et al., 2010).

5.4  Grain quality and crop management Grain yield and quality are determined throughout the growing season, but important decisions that will strongly affect them should be taken before planting. Some grain attributes with high heritability will be decided by the genotype chosen, such as the colour of the wheat grain (white, yellow, purple), which is strongly determined by the ability of genotype to accumulate lutein or anthocyanin, the endosperm hardness (Turnbull and Rahman, 2002), or the composition of the starch; although commercial wheat genotypes usually contain ∼  25% amylose and ∼  75% amylopectin, at present, a large number of mutants have been discovered and used as specialty quality genotypes. For example, a high-amylose type contains 55%–70% amylose and 45%–30% amylopectin, and a waxy mutant contains almost 100% amylopectin and no amylose (Hung et al., 2006; Bird and Regina, 2018). The starches of these wheats have unique characteristics that promote specific application for food processing and enhance nutritional quality of staple foods (Hung et al., 2006; Bird and Regina, 2018). Although final protein content and the amount of certain proteins are modulated by the environment and the G × E × M interaction, differences amongst wheat varieties in gluten viscoelastic properties (i.e. strength and extensibility) are mainly associated to different combinations of high and low molecular glutenins (Bonafede et al., 2015). The development and utilisation of gene-specific markers for these glutenins and gliadin alleles have dramatically improved the selection efficiency of breeding materials with desirable genes. However, most low-molecular glutenins and gliadin genes comprise complex populations of gene with high allelic variation; therefore their contributions in bread quality still need to be solved (Rasheed et al., 2014).

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5.4.1.1  Nitrogen and other nutrient fertilisers Nitrogen fertilisation is one of the most widely applied management practices in grain crops worldwide. In many regions, crops are frequently well fertilised to maximise productivity. Addition of nitrogen fertiliser also affects grain quality (Stone and Savin, 1999), modifying not only the protein percentage but also the gliadin:glutenin ratio (Saint Pierre et al., 2008). However, it is important to understand the key aspects for obtaining a particular outcome: the amount of initial nitrogen content in the soil, the water availability throughout the growing season, the timing of nitrogen application, and the potential yield and quality of the genotype. As elegantly shown by Fischer et al. (1993), starting from a low level of nitrogen availability, the first increment of nitrogen fertiliser increases the yield and protein content in the grain, but the response of starch is usually the greater (Jenner et al., 1991; Calderini et al., 1999; Aguirrezábal et al., 2015). Therefore nitrogen application increases the yield but decreases the protein concentration when grain yield is in the linear response to nitrogen. This is generally reported as the negative relationship between protein concentration and grain yield (see Stone and Savin, 1999; Aguirrezábal et al., 2015, Fig. 3.27, left panel). Before the critical level of nitrogen is attained, the response of starch and protein accumulation enters a second region of response, in which additional nitrogen fertiliser will often have a reduced (but still positive) effect on starch accumulation and a proportionally greater impact on protein accumulation. The net effect of nitrogen in the second region of response is therefore an increase in yield, and a comparatively large increase in protein percentage (Fig. 3.27, left panel), which is not always a profitable management option. If additional fertiliser is applied, the amount of starch in the grain may not be affected because the maximum possible yield is attained by that particular environment and genotype, and also a maximum genetic amount of protein percentage will be attained (Stone and Savin, 1999). An interesting summary of the effects of environmental variables at a given nitrogen supply and source–sink ratio can be found in Aguirrezábal et al. (2015, see Fig. 17.8 therein) Thus the final decision on the amount of nitrogen fertiliser to add should come from the expected yield responses at each site (source–sink relationship) and also depend on the temperature, water availability, and the end use product required by the local industry and profitability. Often, when nitrogen fertiliser increases and/or timing of application in near heading or anthesis, protein percentage increases between 0.5% and 6% units (Fischer et al., 1993), resulting in an increase in both gliadins and glutenins (Fig. 3.27, right panel), but gliadins increase preferentially over glutenins, and consequently the gliadin:glutenin ratio increases (Stone and Savin, 1999; Triboi et al., 2000; Dupont and Altenbach, 2003; Saint Pierre et al., 2008; Fig. 3.27). These variations may result in decrease in gluten strength and mixing properties. However, other researchers have reported that increasing nitrogen fertilisation did not affect the relative amount of gliadins and glutenins (Dupont and Altenbach, 2003; Johansson et al., 2013) or a reduction in the size of glutenin polymers (Naeem et al., 2012; Johansson et al., 2013). These differences could be attributed to genotype and allocation of nitrogen to the different protein subunits but also to different environmental backgrounds. Martre et al. (2003, 2006) successfully simulated crop traits on both grain yield and protein concentration. In fact, important advances have been made in modelling protein components using a modified Sirius Quality model, which partitioned N into structural/metabolic and major storage proteins within the routine, which provided prediction of the gliadin and glutenin fractions. Further discussions in this issue can be found in Aguirrezábal et al. (2015) and Nuttall et al. (2017). An additional aspect regarding nitrogen fertilisation is the increase in some micronutrients concentration (Shi et al., 2010). Proper nitrogen fertilisation may enhance Zn, Cu, and Fe concentrations and could be regarded as a way to reduce

FIG. 3.27  Relationship between grain protein content and grain yield (a), and total grain protein and the percentage of monomeric and polymeric proteins (b). Modified from (a) Fischer, R.A., Howe, G.N., Ibrahim, Z., 1993. Irrigated spring wheat and timing and amount of nitrogen fertilizer. I. Grain yield and protein content. Field Crop Res. 33, 37–56. (b) Saint Pierre, C., Peterson, C.J., Ross, A.S., Ohm, J.B., Verhoeyen, M.c., Larson, M., Hoefer, B., 2008. White wheat grain quality changes with genotype, nitrogen fertilization, and water stress. Agron. J. 100, 414–420. https://doi.org/10.2134/ agronj2007.0166.

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human malnutrition (Shi et al., 2010). Grain sifting could also be a strategy for selecting high-micronutrient concentration grains because proximal grains from the spike (which also are larger in size) have higher micronutrient concentration than distal and smaller grains (Calderini and Ortiz-Monasterio, 2003). Another nutrient that has been found to reduce gluten quality is S (Wrigley et al., 1980; Moss et al., 1981; Flæte et al., 2005) because the proportion of S-poor to S-rich prolamins is dependent on S availability (Moss et al., 1981) and then, a change in the nutritional quality owing to a major reduction in the proportion of all the essential amino acids present (Wrigley et al., 1980). In addition, changes owing to S deficiency were associated with a decrease in dough extensibility, an increase in resistance to extension, and a consequent deterioration of baking quality (Moss et al., 1981); whilst loaf volume was increased significantly by S in some experiments (Zhao et al., 1999). N:S ratio have been found to be a better indicator of loaf volume than protein concentration alone (Moss et al., 1981; Zhao et al., 1999).

6  Concluding remarks: Challenges and opportunities Wheat has been part of the human history and a key crop in the Neolithic Revolution supporting the dawn of civilisation. From the Middle East, wheat has spread all over the world, and nowadays, it is the crop with the larest acreage across the continents. Everything suggests that wheat will continue as one of the most important staple food crops in the future in spite of the big challenge of balancing intensification and sustainability in a context of climate change. In this chapter, we described wheat development and growth characteristics considered in general and focused on the critical periods for yield determination (corresponding to the periods when grain number and potential size of the grains is determined), which is common across the different types of wheat and across ploidies and species. Regarding developmental patterns, we considered the organogenesis of the major organs determining sources and sinks for the crop and the environmental and genetic factors affecting crop development, which is essential for crop adaptation and productivity, considering the three types of wheats regarding the developmental patterns: winter, alternative, and spring. Regarding growth, the importance of radiation interception as a determinant process and a key for biomass production has been shown. When constrains were reviewed in Sections 3 and 4.1, a common feature emerged, namely, the uptake of resources and their use efficiency. About radiation, although the capture of this fundamental source of energy for photosynthesis has explained differences in biomass and yield production across plant densities, water and nutrients availabilities and even under constrains such as Al toxicity, the future improvement of radiation capture seems to be little promising because k is very conservative in wheat, and the possible gain in soil cover by LAI is narrow. As a result, most efforts are being focused on RUE. Genetic variability exists in biomass and RUE, and some examples were shown in this chapter; however, the trait has been elusive for genetic improvement in biomass production of cultivars. Another strategy consisted in increasing growth specifically during the critical period for yield determinantion, discussed in Section 4.1.2, through modifying the length of the period when the spike grows to increase grain number. Photoperiod sensitivity has been proposed for this aim, but more work is still needed to make this hypothesis real. Looking for higher GW, a huge work has been carried out in wheat and other crops searching for both QTL and genes controlling this yield component. The gain in knowledge of physiological and molecular bases of GW determination has showed a quantum leap during the past 20 years and lines with higher GW than wild types have become real; however, this increase was not conveyed in higher yield because of the trade-off between grain number and weight. Therefore several bottlenecks should be solved during the next years to successfully increase wheat production to match the increment of food demand. Climate change is a central challenge for agriculture and natural systems. The knowledge and certainty on magnitude of climate variables change is variable. CO2 and temperature increases are more accurately predicted than rainfall. As shown in Section 4.1.3, the increase in temperature has a negative impact on wheat yield; however, less clear is the impact of both temperature and CO2 increases, although yield increases cannot be denied, especially for northern areas, where the crop cycle could be extended. We also call the attention to the fact that most predictions are based on the expected impact of increasing average temperature, but the likely effects of heat waves may be more damaging and are mostly ignored so far. Finally, grain quality is an important trait. Quality traits are highly affected by G × E ×  M interaction. These aspects were extensively discussed in Section 5.3 and the main management practices conditioning wheat grain quality. The industrial requirements depending on the wide type of products derived from wheat grains were also presented, highlighting the properties of glutenins and gliadins, whose balance is key for the bread industry. In addition to the traditional quality aspects, we also covered superficially nutraceutical properties. Protein and elements (Fe and Zn) content have become a central issue in breeding programmes and research (Biofortification Challenge Program, CGIAR). Opportunities for further increasing grain quality aspects will increase with the understanding of their determination.

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Image source: Authors

Chapter 4

Barley Daniel J. Mirallesa, L. Gabriela Abeledob,c, Santiago Alvarez Pradob,c,d, Karine Chenue, Román A. Serragob,c, and Roxana Savinf a

Department of Plant Production, School of Agriculture, University of Buenos Aires and IFEVA-CONICET, Buenos Aires, Argentina, bDepartment of Plant Production, School of Agriculture, University of Buenos Aires, Buenos Aires, Argentina, cCONICET, Buenos Aires, Argentina, dIFEVA, Buenos Aires, Argentina, eUniversity of Queensland, Brisbane, QLD, Australia, fDepartment of Crop and Forest Sciences, University of Lleida—AGROTECNIO Center, Lleida, Spain

1 Introduction 1.1  Global trends in harvested area and yield Barley is the fourth most cultivated cereal in the world. Of the 680 Mha cultivated with cereals, about 47 Mha or 7% correspond to barley (FAOSTAT, 2019). About 70%–85% of barley is used for animal feed, 15%–20% for malting, and around 5% is retained for seed (Fischbeck, 2001). In diverse regions of the world, barley is predominantly grown as feed grain where other crops such as maize cannot be cultivated because of short growing season, cool spring, rainfall deficiency, and high atmospheric evaporative demand. In these areas, especially in developed countries, barley straw is used for feeding and animal bedding. The world production of barley has increased from the beginning of the 1960s to the beginning of the 1990s at a rate of 3.4 Mt. y− 1. However, world production was reduced at the rate of 3.3 Mt. y− 1 from 1990 to 2000, then stabilised at ca. 140 Mt. after 2000 (Fig. 4.1a). A similar trend was observed in harvested area because it increased from the 1960s to the beginning of the 1980s at a rate of 1.46 M ha y− 1. However, from the 1980s until now, harvested area was reduced at rate of 1.07 M ha y− 1 (Fig. 4.1b). In contrast to harvested area, yield has consistently increased from the 1960s at a rate of 25 kg ha− 1 y− 1, reaching a current average world yield of about 3 t ha− 1 (Fig. 4.1c). Considering only malting barley, total production in 2018/19 was 141 Mt. (E-malt.com/USDA), with 40% of the total production in the European Union, followed by Russia (12%), Canada (6.2%), Australia (5.5%), Ukraine (5.4%), Turkey (5.2%), Kazakhstan (3%), Argentina (3%), USA (2.4%), and Iran (2.2%) (FAOSTAT, 2019). Most projections estimate that cereal production must increase by 50% or even more by 2050 to ensure food security (Fischer et al., 2014). Therefore the understanding of key processes determining crop development, growth, and yield in cereals, including barley, is relevant to facilitate crop breeding and improve management practices. Moreover, it is crucial to understand how physiological processes interact with the environment, especially given challenges related to climate change. In this context, characterising barley responses to climate change scenarios is required to develop adaptation strategies and guarantee sustainable food production for a growing global population (Howden et al., 2007).

2  Crop structure, morphology, and development Two botanical types of barley can be distinguished, two- and six-row genotypes, depending on the number of fertile and well-developed spikelets at each node of the rachis. Test weight, kernel weight, and kernel plumpness are normally higher in two-row types than in six-row barley (Ullrich, 2002). Often, six-row barley has higher grain protein concentration than two-row, although this highly depends on crop management. Cultivars of both types suit the requirements of the malting and brewing industries. Traditionally, two-row malting barley has been used in Europe, Australia, and South America, whilst six-row malting barley has been more commonly used in North America (Savin et al., 2012). Double cropping using the wheat–soybean rotation is a common practice in many countries around the world to promote the intensification of crops within rotations. However, late harvest times in wheat result in delayed sowing and yield penalty in soybean (Calviño et al., 2003). In this context, barley represents a better option than wheat preceding soybean in crop rotation because it is

Crop Physiology: Case Histories for Major Crops. https://doi.org/10.1016/B978-0-12-819194-1.00004-9 Copyright © 2021 Elsevier Inc. All rights reserved.

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Production (M t)

166  Crop Physiology: Case Histories for Major Crops

200 180 160 140 120 100

80 60 40 20 0

(a)

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1960

1970

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Harvest area (M ha)

90 80

70 60 50

40 30 20 10 0

1950

(b)

1960

1970

1980 1990 Year

2000

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3.5

Yield (t ha-1)

3.0 2.5

2.0 1.5

y = 0.025x - 47.5 R² = 0.92

1.0 0.5

(c)

0.0 1950

1960

1970

1980 1990 Year

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FIG. 4.1  Time trends of (a) production, (b) harvested area, and (c) yield of barley globally (from 1960 to 2018). In (c), the dotted line represents the linear regression. Data From FAOSTAT, 2019. http://www.fao.org/faostat/es/. Verified February 2020.

harvested earlier than wheat. Alvarez Prado et al. (2013) demonstrated that earlier field release by barley is mainly owing to earlier flowering time because the grain-filling and drying periods of both wheat and barley were similar. Crop development is crucial to crop adaptation, and in particular to yield and grain quality, in a specific environment. Thus it is important to understand the physiology of crop development and identify the genetic and environmental factors affecting the duration of vegetative and reproductive phases during which yield components are formed. In this section, we describe: (1) the stages, development phases, the interaction between the different phases, and how yield components are determined, (2) the dynamics of the initiation and appearance of both vegetative and reproductive organs, and (3) the role of temperature, vernalisation, and photoperiod as the main environmental drivers of crop development.

2.1  Differentiation of vegetative and reproductive organs The development of barley can be partitioned into three major phases, i.e. vegetative, reproductive, and grain filling (Fig. 4.2). The timing of developmental phases can be associated with a particular yield component (Fig. 4.2). However, some developmental phases are more important than others for grain yield (GY) determination. In cereals, including barley (Arisnabarreta and Miralles, 2008a; Cossani et al., 2009), GY is better explained by the number of grains per unit of land area than other traits (Miralles and Slafer, 1999; García et al., 2013; Ferrante et al., 2017), and weaker relationships have been reported between GY and average grain weight. Key reproductive phases occur from the beginning of spikelet initiation to heading as critical (Arisnabarreta and Miralles, 2008a). Final grain number depends more on grain and/or spike survival than on the initiation of structures that may potentially produce a grain. In two-row barley, the survival of spikes per unit area is more important than grains per spike to determine

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Time

Stages Sw E

Phases

FI

DR

TM

MP

Hv

Grain filling Phase

Reproductive Phase

Vegetative

PM

Hd BGF Spike and stem grow th

Leaf intiation

Development processes

Spikelet initiation Tillers initiation

Spikelet mortality Tillers mortality

Grain set

Leaf intiation

Leaf appearance -2

Plants m

Survival

Grains m

spike

Survival

-2

plant-1

Grains spikelet-1

Spikelets spike -1

Yield components

Environmental control

Tillers

plant-1

Grain filling

Grain weight

Photoperiod Vernalization Temperature

FIG. 4.2  Barley development throughout the crop cycle: Sowing (Sw), seedling emergence (E), floral initiation or ‘collar’ stage (FI), double ridge (DR), triple mound (TM), maximum number of total primordia initiated in the apex (MP), heading (Hd), beginning of grain-filling period (BGF), physiological maturity (PM), and harvest (Hv). Boxes indicate different phases, developmental processes, and yield components formation. Environmental factors that control the length of different phases are also indicated. Adapted from Slafer, G.A., Rawson, H.M., 1994a. Sensitivity of wheat phasic development to major environmental factors: a re-examination of some assumptions made by physiologists and modellers. Aust. J. Plant Physiol. 21, 393–426.

the final number of grains, while in six-row barley, both spikes per unit area and grains per spike contribute highly to the number of grain per unit area (Garcia del Moral et al., 1991b; Arisnabarreta and Miralles, 2008a, 2015).

2.2  Dynamics of initiation and appearance of vegetative and reproductive organs 2.2.1  Leaf and spikelet initiation into the apex The mature barley embryo contains three to four leaf primordia (Kirby and Appleyard, 1986). Fig. 4.3 shows the transformation of the apex in the main shoot across plant development immediately after germination. From germination to seedling emergence, the apex initiates one to three new leaf primordia, depending on sowing depth because this practice modifies the thermal time between sowing and emergence and thereby the number of leaves initiated in the apex. At seedling emergence, the shoot apex has five to seven leaf primordia initiated. The initiation of leaf primordia in the apex occurs at a single rate for a particular genotype under constant environmental conditions. The interval between the initiation of two consecutive leaf primordia (viz. plastochron) is commonly around 40–50°Cd (degree days), above a base temperature of 0°C (Kirby et al., 1987; Delécolle et al., 1989; Miralles and Richards, 2000). During the vegetative phase, the apical meristem produces single ridges, which will become actual leaves. At the beginning, the apex has a conical shape (dome) of about 0.2 mm in length that later elongates (Fig. 4.3). The maximum number of leaves in the main shoot is reached at the time of cessation of leaf initiation, when the apex changes from vegetative to reproductive, thus starting the initiation of spikelet primordia. The first visual evidence of the change from vegetative to reproductive apex is the appearance of a double ridge (Fig. 4.3). This double ridge corresponds to both a smaller primordium of the last leaf primordium, which does not develop further, and the upper and bigger ridge that will become the first developed spikelet of the spike (Bonnett, 1966; Kirby and Appleyard, 1986). The apex at this time is about 0.5 mm long, and this stage is usually referred to as the beginning of floral initiation (FI in Fig. 4.2). However, it should be noted that

168  Crop Physiology: Case Histories for Major Crops

Reproductive stages

Vegetative stages

Grain filling

Lemma Primordium Spikelet Primordium

Dome

Leaf Primordium

DR

Leaves initiation

Collar

TM

Spikelet initiation

Spikelet abortion

Grain setting

Grain Filling

Floret development Anther primordium

Ovary

Fertile Floret

Floret primordium

FIG. 4.3  Morphological changes in the apex of main stem barley throughout developmental stages. See Fig. 4.2 for abbreviations.

about half of the total number of spikelet primordia have already started to develop by this time (Kirby and Appleyard, 1986). Even though it is generally accepted that the visual evidence of the change from vegetative to reproductive stage is the appearance of the ‘double ridge’, the true change is named ‘collar’ stage that occurs well before the appearance of the first visible double ridge in most agronomic conditions (Fig. 4.3). Spikelet primordia are initiated faster than the leaf primordia. When the total number of primordia (leaves + spikelets) is plotted against thermal time, there is an inflection point that generally coincides with the time of collar stage (i.e. floral initiation in Fig. 4.3). Owing to the lack of unequivocal morphological change in the apex to identify the collar stage, the occurrence of this stage can be determined only by subtracting final leaf number from the accumulated number of primordia. The next important apical stage in barley is ‘triple mound’, in which the spikelet primordium differentiates three protuberances corresponding to the central and lateral spikelets. At this stage, two- and six-rowed varieties look similar, but the lateral spikelets do not develop further in two-rowed varieties (Bonnett, 1966). Spikelet initiation ceases when awn primordia on the most advanced spikelets are evident (Fig. 4.4). The number of spikelet primordia initiated in barley varies between 10 and 45, depending on genotype and environment (Kirby, 1977; Kitchen and Rasmusson, 1983). Maximum number of spikelet primordia coincides with the beginning of stem elongation, and the young spike is about 3 mm long and still at the ground level or slightly below ground (Fig.  4.4). After the maximum number of spikelets is reached, a proportion of later initiated primordia at the tip of the shoot apex does not progress to mature spikelets. Thus, about 30%–40% of the maximum number of spikelet primordia initiated abort before ear emergence (Kirby and Faris, 1972; Kernich et al., 1996). The number of fertile florets per spike is closely related to the number of spikelet survival in both two- and six-row barley, and thereby the higher the rate of spikelet primordia mortality, the lower the spikelet survival (Arisnabarreta and Miralles, 2004).

2.2.2  Leaf emergence All the leaf primordia initiated (those initiated in the mother plant and new primordia initiated after seed imbibition) must appear between germination and the emergence of the spike from the sheath of the last appeared leaf (flag leaf). Thus the time to heading strongly depends on the number of leaves initiated in the main shoot and the rate at which these leaves emerge. The phyllochron, defined as the period between the appearance of two successive leaves, is estimated as the reciprocal of the rate of leaf appearance. Therefore the duration from seedling emergence to heading can be calculated as (1) the product of the total number of leaves initiated (i.e. leaves differentiated in the embryo plus leaf differentiating during the vegetative period up the collar stage) and the phyllochron, plus (2) the time from the flag leaf appearance to heading, frequently assessed to last ca. two phyllochrons. The phyllochron can be affected by genetic and environmental factors.

Number of primordia (leaves+spikelets)

Barley Chapter | 4  169

Maximum Number of spikelets

MP

Hd Rate of spikelet initiation

Collar Final Leaf Number

DR Rate of leaf initiation

Leaf primordia diferentiated in the embryo

Time after emergence (days or °Cd) FIG. 4.4  Schematic relationship between cumulative primordia in the apex (leaves and spikelets) and time after emergence. See Fig. 4.2 for abbreviations.

Genetic variability in phyllochron ranges from 50 to 97°Cd in barley (Frank and Bauer, 1995; Kernich et al., 1995a). The environmental conditions also affect the rate of leaf emergence, especially when measured in days per appeared leaf (Kirby et al., 1985a; Kirby and Perry, 1987; Masle et al., 1989). Using thermal time to account for temperature (Klepper et al., 1982; Cao and Moss, 1989; Slafer and Rawson, 1994b; Frank and Bauer, 1995), the phyllochron from seedling emergence to flag leaf emergence is more or less constant (Baker et al., 1980; Kirby et al., 1985b; Cao and Moss, 1989). Some studies reported changes in phyllochron with crop ontogeny, with higher rates of appearance for early leaves than for later leaves (Stapper and Fischer, 1990; Jamieson et al., 1995a; Hotsonyame and Hunt, 1997; Slafer and Rawson, 1997; Miralles and Richards, 2000). This generally occurs when the final number of leaves is increased in response to short photoperiods or the lack of vernalisation requirements in sensitive genotypes such as winter type barley (Slafer and Rawson, 1997; Miralles and Richards, 2000). Thus a single genotype could show a single or two phyllochron values depending upon the final number of leaves initiated. Other factors such as photoperiod, carbohydrate reserves, and moderate water and nutrients stresses have little effect on the phyllochron in wheat and barley (Cutforth et al., 1992; Frank and Bauer, 1995; Hall et al., 2014).

2.2.3 Tillering The number of spikes established per unit area is the most important numerical component of final number of grains per unit area and yield, especially in two-row genotypes (Garcia del Moral et al., 1991b; Dofing and Knight, 1992; Garcia del Moral and Garcia del Moral, 1995; Alvarez Prado et al., 2013). The tillering process can be divided into three phases: (1) tillering appearance, (2) maximum number of tillers per plant, and (3) net tiller mortality, and at the end of the tiller mortality, the final tiller number is established. Under non-restrictive conditions, the first tiller appears when the third or fourth leaf emerges on the main stem. Once the first tiller has emerged, the next tillers appear following a synchrony of a tiller (or even more) per phyllochron depending on the available resources (Salvagiotti and Miralles, 2007). However, it has been reported that differences prevail between wheat and barley in tillering dynamic because barley initiates more tillers per leaf than wheat (Alzueta et al., 2012). Tiller numbers increase rapidly during the first few weeks after the emergence of the first tiller, reach a maximum shortly after floral initiation, and it is assumed that the end of tiller appearance is related to the beginning of stem elongation (Garcia del Moral and Garcia del Moral, 1995; Miralles and Richards, 2000), which signals the beginning of tiller mortality (Borràs-Gelonch et al., 2012). The rate of tiller initiation is closely associated with the supply of resources (nutrients, water and radiation) (Evers et al., 2006) and determines, together with the duration of the tillering cycle, the maximum number of tillers (Alzueta et al., 2012). Cessation of tillering appearance could be related to (1) stem elongation because most of the carbohydrates are allocated to stem growth and/or (2) changes in intensity and quality of radiation inside the canopy. Evers et al. (2006) reported that tillering cessation in wheat occurred when the fraction of photosynthetically active solar radiation intercepted by the canopy exceeds a threshold (0.40–0.45) and red:far-red ratio drops below 0.35–0.40. During the period of tiller appearance, the rate of tiller appearance is the main driver of maximum tiller number; however, there is also a strong negative correlation between final number of tillers and tiller mortality rate (Alzueta et al., 2012). Tiller mortality often begins after floral initiation in main shoots because developing tillers compete with

170  Crop Physiology: Case Histories for Major Crops

l­imited success for available assimilates against developing spikelets and florets on the main stem (Lauer and Simmons, 1988; Garcia del Moral and Garcia del Moral, 1995). The proportion of tillers that senesces without contributing to GY varies with the cultivar and environment (Simmons et al., 1982; Garcia del Moral and Garcia del Moral, 1995; Salvagiotti et al., 2009; Alzueta et al., 2012). Barley cultivars vary in both maximum tiller production (Kirby, 1967; Cannell, 1969a; Garcia del Moral and Garcia del Moral, 1995) and tiller mortality (Garcia del Moral and Garcia del Moral, 1995). In general, two-rowed barleys are higher tillering than six-rowed cultivars, and the winter types generally produce more tillers than the spring ones (Kirby and Riggs, 1978; Garcia del Moral and Garcia del Moral, 1995). Long photoperiod, high temperature, and high plant density reduce tillering (Cannell, 1969b; Simmons et al., 1982; Garcia del Moral and Garcia del Moral, 1995), while high radiation intensity and high water and nitrogen availabilities promote formation and growth of secondary tillers (Cannell, 1969b; McDonald, 1990; Salvagiotti et al., 2009; Alzueta et al., 2012) or at least reduce their mortality. Competition among shoots for nutrients, radiation, and water seems to be one of the principal causes for tiller mortality in barley. Tillers that had at least three fully emerged leaves at jointing (Kirby and Jones, 1977) or were over a third the height of the main stem (Garcia del Moral and Garcia del Moral, 1995) are more likely to survive.

2.3  Genotypic and environmental drivers of barley development The major environmental drivers of barley development are temperature (both temperature per se and low temperature associated with the vernalisation) and photoperiod (Ellis et al., 1988; Slafer and Rawson, 1994a). Temperature per se affects all developmental phases, while photoperiod and vernalising temperatures affect the rate of development in particular phases. Other factors related to level of nutrients in the soil, water availability, plant density, and radiation have small or null effect on the time to heading (Miralles and Slafer, 1999; Hall et al., 2014) and will not be discussed here.

2.3.1 Temperature Temperature affects all genotypes and every developmental phase, from seed imbibition to maturity. The duration of crop phases responds non-linearly to temperature (Fig. 4.5a). The rate of development, calculated as the reciprocal of the duration of the phase, increases linearly with temperature up to a maximum and decreases linearly for higher temperatures; parameters of the rate-temperature model include three cardinal temperatures: base, optimum, and maximum (Fig. 4.5b). The reciprocal of the slope of the linear relationship between rate of development and temperature is the thermal time measured in °Cd that determine the duration of a phase at any temperature within the range between the base and optimum (Fig. 4.5).

2.3.2 Vernalisation

Duration of the Phase (d)

Rate of development (d-1 )

Although spring barley generally lacks vernalisation requirements, the transition from vegetative to reproductive stage in winter type barley cultivars requires vernalising temperatures, typically in the range between 3°C and 12°C (Trione and Metzger, 1970). Vernalising temperature, contrary to photoperiod, is experienced directly by the apex meristem and the embryo of the imbibed seed. Vernalisation mainly affects the length of the vegetative phase and hence the final leaf number (Figs 4.2 and 4.6a). In fact, the number of leaves is commonly used as an indicator of vernalisation sensitivity once the other requirements were satisfied (Kirby et al., 1985b; Rawson and Zajac, 1993). Although it is widely recognised that vernalisation acts during the vegetative phase, the vernalisation effects during the spikelet initiation phase have been reported for wheat (Rahman, 1980).

(a)

Tempertaure (ºC)

(b)

Base

Optimum

Critical

Tempertaure (ºC)

FIG. 4.5  Relationship between (a) duration of a particular phase and (b) the reciprocal of the duration (i.e. the rate of development) with temperature. Base, optimum, and critical temperatures are indicated.

Barley Chapter | 4  171

2.3.3 Photoperiod

Rate of development (d -1)

Barley is a quantitative long-day species. Increasing photoperiods accelerate development phases and reduce the length of each phase up to the optimum threshold. Increases in photoperiod over the threshold do not modify the length of the developmental phase (Fig. 4.6b). Photoperiod stimulus is perceived by phytochrome of the leaves, and the signal is transmitted to the apex (Evans, 1987). Barley plants respond to photoperiod once the tip of the first leaf emerges. As many other crops (soybean—Collison et al., 1993; maize—Kiniry et al., 1983; Chapter 1: Maize, in this book; and sunflower—Villalobos et al., 1996), barley may exhibit a juvenile phase of insensitivity to photoperiod, during which cultivars do not respond to inductive photoperiods (Takahashi and Yasuda, 1970; Yasuda, 1982; Roberts et al., 1988). However, this point is not completely clear as some cultivars growing under long photoperiods exhibit only ca. six leaves and do not seem to feature a significant juvenile phase (Hay and Ellis, 1998; Miralles and Richards, 2000). The phase from sowing to floral initiation is sensitive to photoperiod. Changes in the duration of this phase, together with the duration of juvenile phase, modify the number of final leaves initiated in the apex (Fig. 4.2). Thus, long days reduce the number of leaves formed on the main shoot, while short days extend the period of leaf initiation increasing the final leaf number. Although daylength modifies the duration of the vegetative phase by altering the final leaf number, photoperiod does not affect the rate of leaf initiation (Miralles and Richards, 2000). Conversely, once the apex changes from vegetative to reproductive, photoperiod influences the rate of spikelet initiation. Although some positive effects of long photoperiod on the rate of spikelet initiation have been reported, they are smaller than those on the rate of phasic development during spikelet initiation (i.e. reciprocal of the phase duration). Consequently, these opposing effects when photoperiod is extended (i.e. increases in the rate of spikelet initiation and reduction in the duration of the initiation phase) are not fully compensated, resulting in fewer spikelets initiated under long days (Miralles and Richards, 2000; Perez-Gianmarco et al., 2018). In addition, daylength affects not only the duration of vegetative phase but also the duration of the late reproductive phase of stem growth, during which a variable number of previously initiated spikelets dies. Experiments based on reciprocal photoperiod transfers (i.e. expose reciprocally the plant to contrasting photoperiods) at the time of stem growth initiation demonstrated that the duration of the late reproductive phase was largely determined by the daylength to which they were exposed at that time (Kernich et al., 1996; Miralles and Richards, 2000). As photoperiod affects the duration of reproductive

Duration of the Phase (ºCd)

(a)

(b)

No-requirement

Low

Intermediate High

TV

Vernalisation (weeks)

Photoperiod Sensitvity(°Cd h-1 )

Earliness “per se”

TP

Photoperiod (h)

FIG. 4.6  Relationship between (a) rate of development and vernalisation for different vernalisation requirements (no requirement, low, intermediate, high) and (b) duration of the phase for different photoperiods. TV (vernalization threshold) and TP (photoperiod threshold) indicate the respective thresholds at which vernalisation and photoperiod requirements are saturated.

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period, and as the number of grains per unit area is defined during the late reproductive (when spikelet and tiller mortality occur), increasing the duration of reproductive phases could be used to promote a higher number of grains through increasing the assimilates for the spikes (Slafer et al., 1996; Miralles et al., 2000).

3  Growth and resources 3.1  Capture and efficiency in the use of radiation 3.1.1  Canopy size and radiation interception GY can be described as the product of shoot biomass produced during the crop cycle and the proportion of biomass allocated to grain (i.e. harvest index (HI)). In barley, biomass is usually a larger source of variation in yield than HI. Biomass accumulation depends on: (1) the intercepted solar radiation and (2) the radiation use efficiency (RUE) (Monteith, 1977; Gardner et al., 1985). According to the equation derived from Beer’s Law (Monteith and Unsworth, 1990), the fraction of incident solar radiation intercepted by the crop (RI) is a function of leaf area index (LAI) and the extinction coefficient k, which is determined by canopy architecture. The value of LAI at which 95% of intercepted radiation is reached can be considered as the ‘critical LAI (LAIc)’ (Fig. 4.7). RI  t   1  exp

  k LAI   t

(4.1)

Intercepted radiation (%)

LAI, and hence the fraction of intercepted radiation, increases from crop emergence to the end of flag leaf expansion. In unstressed crops, leaf area dynamics is mainly driven by the expansion of leaves with little or no contribution of senescence until flag leaf emergence. Hence in well-watered barley crops, the maximum proportion of intercepted radiation is normally achieved just before or during the flag leaf expansion. As was stated earlier (see Fig. 4.7), the LAI to achieve 95% radiation interception is defined as the critical LAI (LAIC) and depends on canopy architecture characterised by the extinction coefficient (Fig. 4.7). Barley canopies with higher extinction coefficient (planophile canopies) require lower LAIC than crops with more erectophile canopies (see Fig. 4.7). Accordingly, adjusting crop management to ensure high LAI and photosynthesis by the crop for as long as possible allow crops to intercept more radiation and produce more biomass during their crop cycle. The dynamics of LAI is driven by leaf senescence at more advanced crop stages. Senescence is a complex process modulated by environmental and genetic factors. It involves two important crop-level events: (1) the turnover of the photosynthetic apparatus and the concomitant (2) mobilisation of nitrogen (N) from the leaves to the grains. The rate of senescence and the mobilisation of leaf N are related to the source–sink ratio and to the N status of the plant (Masclaux et al., 2000). Depending on the genotype, barley could mobilise up to 90% of the N from the leaves to the grains. Soil N content has a strong influence on the senescence rate (Schildhauer et al., 2008). High N levels in the soil generally delay crop senescence, while low N enhances senescence (Martre et al., 2006). Stay-green traits—their physiology, genetics, and environmental modulation—are well understood in sorghum (Chapter 5: Sorghum, Sections 3.2.1, 3.3.3, 4.3.1) and to a lesser extend in wheat (Christopher et al., 2016, 2018). LAI accounts for the total leaf area of the crop and could be separated in two components: leaf number and leaf size. Leaf size is highly sensitive to growing conditions. Usually the first leaves (represented in Fig. 4.8 as the bottom layer L5) are smaller than the leaves that appeared during jointing. However, flag leaf in barley (indicated as L1 layer in Fig. 4.8) is smaller than the rest of the leaves. Fig. 4.8 illustrated wheat and barley leaves are index for different layers throughout the

95%

Critical LAI

Leaf Area Index FIG. 4.7  Relationship between intercepted radiation and leaf area index. Lines show barley crops contrasting in the light extinction coefficient (k), i.e. higher (solid line) and lower (dotted line) coefficients. The arrows show the critical LAI.

Barley Chapter | 4  173

Leaf layers

Top L1 L2 L3 L4 L5 Bottom

0.0

0.5 1.0 1.5 Leaf area index of each layer

2.0

FIG. 4.8  Leaf area index of different layers in barley (dashed) and wheat (solid) canopies with similar total leaf area index (LAI = 6). L1 and L5 represent the top and bottom layers, respectively. The sum of the different layers represents the total LAI. Source: R. Carretero and L. Iriarte (unpublished).

profile of the canopy around anthesis. The barley leaf size profile is different to wheat, where the flag leaves is often larger than the others (Fig. 4.8). The number of leaves per m2 is the product of number of plants per m2, number of leaves per tiller, and number of tillers per plant. The number of plants is represented by the plant density, usually chosen by the farmers previous to sowing. The number of leaves per tiller is a function of plastochron (usually 40–50°Cd per leaf in barley) and the duration of leaf differentiation (i.e. from seedling imbibition to double ridge). The number of tillers, however, is a consequence of a complex process involving initiation and degeneration of structures occurring during the tillering phase (see the previous section). The tillering phase can be divided into four stages (Fig. 4.9): (1) tillering appearance, (2) maximum number of tillers per plant, (3) tiller death, and (4) at the end of tiller death phase is defined the final number of tillers. Six-rowed barley genotypes generally establish fewer tillers per plant than two-rowed types with similar time to flowering (Kirby and Riggs, 1978; Garcia del Moral and Garcia del Moral, 1995; Arisnabarreta and Miralles, 2004). The capacity to produce more tillers per plant is important for radiation capture during the pre-flowering period, especially in low input productions systems (i.e. low N applications, late sowings, and short maturity genotypes). Increases in N promote tiller appearance rate determining a higher maximum number of tillers per plant (Prystupa et al., 2003; Abeledo et al., 2004). However, evidences have demonstrated that the higher the tillers initiated, the lower the tillers survival as was described in the previous section of this chapter, causing a partial counterbalance between the rates of tiller initiation and tiller mortality (Berry et al., 2003; Alzueta et al., 2012).

3.1.2  Radiation-use efficiency (RUE)

Number of tillers per plant

RUE for non-stressed barley crops varies from ca. 1.8 to ca. 3.0 g MJ− 1, similar to wheat (Sinclair and Muchow, 1999). Water stress, N deficit, and low temperature can reduce RUE (Jamieson et al., 1995b; Gallagher and Biscoe, 1978; Andrade et al., 1993; Kemanian et al., 2004). However, leaf expansion is usually more sensitive to water and nutrient stress than leaf photosynthesis (Sadras and Milroy, 1996; Salah and Tardieu, 1997). Thus the initial negative effect of water and nutrient stress on crop biomass is generally related to reduction in the radiation intercepted more than in RUE.

(i)

(ii)

(iii)

(iv) Thermal time from emergence (°Cd)

FIG. 4.9  Time-course of tillers per plant: (1) tiller initiation phase, (2) plateau once maximum tiller number is reached, (3) tiller mortality phase, and (4) final fertile tiller number, which agree with the number of spikes per plant (and per unit area).

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3.2  Capture and efficiency in the use of water 3.2.1  Environmental characterisation of water stress Crop yield improvement relies on the identification of genotypes better adapted to their production environment. However, complex genotype–environment interactions (GEI) typically contribute to yield variability, hindering the identification of superior genotypes, especially where complex abiotic stresses, such as drought, are frequent (Chenu et al., 2013; Chenu, 2015). In such situations, characterisation of the crop environment is important to understand GEI (e.g. Löffler et al., 2005; Chenu et al., 2011). In a global analysis from ICARDA-CIMMYT, 750 barley GY trials in 75 countries were analysed, grouping sites across years that represent similar selection environments (Hernandez-Segundo et al., 2009). The authors clustered environments into three main groups: (1) cool with intermediate precipitation; (2) warmer and drier; and (3) cool with the highest average precipitation (Hernandez-Segundo et al., 2009). While this approach identifies mega-environments across the world, it does not help in understanding which cultivars can be grown within the geographical area targeted by a breeding programme, i.e. the target population environments (TPE) (Comstock, 1977). This is partly because of the lack of information on the timing and intensity of environmental variables such as rainfall and the vapour pressure deficit (VPD). Using crop modelling, Chenu et  al. (2009) looked at water stress experienced by barley crops over the crop cycle for representative management practices. Over the long-term, they identified four major drought patterns for barley in North-East Australia (Fig. 4.10a): ET1 comprised situations where the crop was not water-limited by or only experienced short-term stresses; ET2 was characterised by late stresses, starting around flowering and relieved around mid-grain filling; ET3 had stresses beginning during the vegetative period and relieved during grain filling; and ET4 had stresses beginning a bit later than ET3 but continuing through to crop maturity. For the studied area, the frequency of occurrence of the ­environment types varied greatly over time and spatially. Management practices also had a strong impact on the environment type. Delayed sowing tended to have the opposite impact to an increase in initial soil water, with a decrease

FIG.  4.10  (a) Relationship between water-stress index and thermal time around flowering for four drought environment types in the North-eastern barley-growing region of Australia. Overall, the frequency of environment types ET1, ET2, ET3, and ET4 was 16%, 53%, 10%, and 21%, respectively. (b) Frequency of occurrence of the different environment types for each combination of sowing date and initial soil water used in the simulations. Sowing date increased from the earliest (1) to the latest (5) in 2-week intervals. Initial soil water increased from the lowest (1: most severe conditions) to the highest (5: less severe conditions), each representing 20% of the initial soil water encountered in the first set of simulations performed for each site over 119 years. Frequency data correspond to all the simulations performed (all sites over 119 years). Adapted from Chenu, K., Mcintyre, K., Hammer, G., 2009. Environment characterisation as an aid to improve barley adaptation in water limited environments, in: Barley Symposium, pp. 1–9.

Barley Chapter | 4  175

in the frequency of ET1 and ET2 and an increase in the frequency of ET3 and ET4 (Fig. 4.10b). The impact of initial water tended to increase with later sowing for ET2, ET3, and ET4, while the proportion of ET1 became more marginal. The frequency of occurrence of the four environment types varied across regions and years (Fig. 4.11), being generally correlated with seasonal rainfall. For example, while the Lockyer Burnett region received marginal rainfall and had a high proportion of low-mild ET1–2 stresses (ca. 80%) and a relatively low proportion of severe ET3–4 stresses (ca. 20%), other

FIG. 4.11  Frequencies of drought environment type (pies) in the different regions of the north-eastern barley-growing area of Australia, and simulated yield distribution for each environment type (box plots). Data are based on simulations over 119 years. The environment types are presented in Fig. 4.10a. The size of the pie is proportional to the barley planted area in the associated region. Adapted from Chenu, K., Mcintyre, K., Hammer, G., 2009. Environment characterisation as an aid to improve barley adaptation in water limited environments, in: Barley Symposium, pp. 1–9.

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regions with significantly higher cumulative rainfall (northern Darling Downs and the Dubbo regions) had a lower proportion of low-mild ET1–2 stresses (71% and 68%, respectively) and a higher proportion of severe ET3–4 stresses (29% and 32%, respectively; Fig. 4.11). This highlights the value of this type of approach for environment characterisation for barley production.

3.2.2  Root architecture and functionality Insufficient soil water availability or a high environmental demand, even in well-watered plants, can change plant water status resulting in a water deficit situation (Tardieu et al., 2018). This depends on both the capacity of the root system to supply water to shoots (Lobet et al., 2014) and the potential of shoots to transpire, which combines evaporative demand and shoot characteristics (Monteith, 1977). Roots play a vital role in resource uptake, provide anchorage, and interact with organisms in the soil. Defined as the spatial distribution of roots throughout the soil space, the root system architecture is complex and depends on many underlying processes, such as root elongation, curving and branching (Lynch, 1995; Rich and Watt, 2013). Furthermore, the root system architecture of a crop is influenced by the environment and do influence the efficiency and timing of water capture and extraction in cereals (Kondo et al., 2000; Manschadi et al., 2006; Pennisi, 2008). Root system of barley comprises seminal roots and nodal or secondary roots (Forster et al., 2007). The number of seminal roots was increased through domestication (Grando and Ceccarelli, 1995), with current varieties showing from 3.6 to 6.5 seminal roots (Robinson et al., 2016, 2018). Root angle for seminal roots has an ample variation ranging from 12 to 89 degrees and has been proposed as a proxy to study the genetic variability of the root system architecture (Robinson et al., 2018). Both traits showed a highly variable genetic correlation with GY, mainly explained by the environment. Roots interact with environmental factors such as soil type and strength (Bingham and Bengough, 2003; Rich and Watt, 2013), nutrient heterogeneity and availability (Drew, 1975), and management practices that influence crop water use (Richards et al., 2002). Part of the interaction with the soil type is related to secondary traits such as root hairs and mucilage exudation, which play an important role in the uptake of water from drying soils by increasing the contact surface (Carminati et al., 2017) and the soil water retention, maintaining the rhizosphere wet (Ahmed et al., 2014). Other important root traits, directly related with water extraction, are root depth and density (Fig. 4.12). Both traits influence the amount of soil water that can be supplied to the shoot (Manschadi et al., 2006). Crops frequently fail to extract all available water in the lower half of the root zone because of low root density at deeper layers (Barraclough and Weir, 1988). These differences in root length density at different depths may be associated with the speed at which roots elongate to depth or may be related to proliferation rate at each soil layer (Fig. 4.12). In barley, as in other cereals, maximum root length occurs at around anthesis (Lugg et al., 1988), reaching between 1.5 and 2 m under no water restrictions.

FIG. 4.12  (a) Extractable soil water of barley growing in two experiments in Queensland, Australia. (b) Profiles of root length density of barley at the end of the crop cycle. Based on Thomas, S., Fukai, S., Hammer, G.L., 1995. Growth and yield response of barley and chickpea to water stress under three environments in southeast Queensland. II.* Root growth and soil water extraction pattern. Aust. J. Agric. Res. 46, 35–48. https://doi.org/10.1071/ AR9950035.

Barley Chapter | 4  177

3.2.3  Scaling from leaf to canopy: From stomatal conductance to water use efficiency Many physiological mechanisms triggered in plants by water deficit act in a short term, such as the stomatal conductance, impacting over more complex traits such as transpiration rate. For instance, an increase in transpiration rate because of the increase in the environmental demand tends to cause partial stomatal closure (Mott and Parkhurst, 1991), thereby stabilising transpiration rate. Because hourly plant-level transpiration rates correlate with leaf stomatal conductance (Alvarez Prado et al., 2018; Chenu et al., 2018), it is possible that genotypes and species differ in plant-level transpiration rates related to differences in leaf stomatal conductance. Barley is an anisohydric species, meaning that it cannot prevent leaf water potential to drop when soil dries (Tardieu and Simonneau, 1998). It has been proposed that differences between isohydric and anisohydric behaviours mainly result from how stomatal pores at the leaf surface close under water deficit and control plant transpiration (Buckley, 2005). In this regard, reductions in stomatal conductance are observed in barley under water stress (Fig. 4.13a), which can lead to reduced GY (González et al., 1999). Reductions in stomatal conductance are directly related to soil water depletion (González et al., 1999; Fig. 4.13b) and vary depending on the genotype, with those with slight reductions in stomatal conductance between well- and limited-water conditions showing a high level of osmotic adjustment (Fig. 4.13c).

FIG. 4.13  (a) Relationship between stomatal conductance and time during the water-stress period for a control (white points) and a water stress (black triangles) condition. (b) Relationship between stomatal conductance and soil water content. (c) Relationship between the difference in stomatal conductance between well-watered and water stress conditions in eight barley genotypes. (a and c) From González, A., Martı́n, I., Ayerbe, L., 1999. Barley yield in water-stress conditions. Field Crop. Res. 62, 23–34; (b) Adapted from Borel, C., Simonneau, T., This, D., Tardieu, F., 1997. Stomatal conductance and ABA concentration in the xylem sap of barley lines of contrasting genetic origins. Aust. J. Plant Physiol. 24, 607–615.

178  Crop Physiology: Case Histories for Major Crops

Mechanisms involved in the stomatal response to environmental conditions are multiple and have different quantitative effects on stomatal conductance. Scaling up to the whole plant or canopy cannot be considered as a sum of individual mechanisms whose weight would be independent of environmental conditions. All leaves in a canopy are not identical and do not experience identical microclimate (McNaughton and Jarvis, 1991). Small changes in stomatal or canopy conductance have a variable impact on transpiration at the canopy scale, depending on how well the saturation deficit at the leaf surface is coupled to that of the ambient air. In general, the degree of sensitivity of transpiration from a single leaf to changes in conductance of that leaf varies according to the exposure to wind and so can vary according to whether the leaf is located in a glasshouse, in a leaf chamber, or out in the field (Jarvis and Mcnaughton, 1986). However, transpiration does decline as a result of stomatal closure when soil water supply becomes limiting (Jarvis and Mcnaughton, 1986) or when evaporative demand is high (Chenu et al., 2018). In the context of crop improvement, it is advisable to identify component traits that underpin the phenotypic expression of more complex traits (such as yield) and are more suitable for selection by virtue of a reduced environmental dependency, reduced GEI, and closer alignment to underpinning genetics (Hammer et al., 2006). Despite the dependence of transpiration efficiency on environmental conditions and its complex nature, transpiration efficiency itself was suggested as a trait for largescale phenotyping because it is a proxy of the stomatal conductance (Chenu et al., 2018). Transpiration efficiency, defined at the plant level, corresponds to the plant dry biomass (with or without the root system) produced per unit of water transpired (Fig. 4.14a). It is generally measured in sealed containers that exclude soil evaporation and deep drainage and differs from the crop water use efficiency (WUE), which typically includes soil evaporation and deep drainage and excludes root biomass. Biomass accumulation in barley is linearly related to cumulative transpiration (Kemanian et  al., 2005; Fig.  4.14a), with important variations in the slope (Albrizio et al., 2010; Kemanian et al., 2005), principally associated with VPD. As observed in other crops (Abbate et al., 2004), the normalisation of transpiration by daytime VPD in barley decreased the scatter of the data standardising them in a single relationship (Fig. 4.14b). VPD encapsulates the combined effects of air temperature and relative humidity and is the main driving force of the whole-plant transpiration rate (Monteith, 1995). In natural environments, both temperature and relative humidity contribute to the variation in VPD. On a sunny day, VPD typically increases as temperature increases and relative humidity decreases progressively throughout the day. In dry environments, this increase takes place during most of the day, with VPD increasing three- to four-fold over a few hours. Because both transpiration and CO2 intake occur through the stomata, transpiration rate responses to increasing VPD have been linked both theoretically and experimentally to WUE (Fig. 4.14b) and yield under different water regimes. For a given VPD, the variation in short-term WUE of the leaf tissue (WUEph, i.e. the ratio of CO2 assimilation to transpiration), and in the long-term whole-plant transpiration efficiency (on GY basis or on total biomass basis), arises from difference in photosynthetic rate and/or stomatal conductance. Under water deficit, stomata would close, limiting transpiration rate more than photosynthetic CO2 uptake, which in turn increases WUEph (Larcher, 1995). Under moderately high temperature, a high VPD has a positive effect on barley GY per plant, leading to a larger transpiration efficiency on GY basis (Sanchez-Diaz et al., 2002). A selection criterion based on transpiration efficiency of plants is the carbon isotope discrimination (Δ13C) of plant dry matter. It provides time-integrated information of plant performance during the crop cycle (Sanchez-Diaz et al., 2002). Despite its accuracy, this technique is expensive and not widely adopted.

FIG. 4.14  (a) Relationship between biomass and cumulative transpiration and (b) cumulative transpiration normalised by the air VPD of spring barley for an early (first) and late (second) sowing date (SD). (c) Relationship between transpiration-use efficiency (g biomass kg− 1 H2O) and VPD of the air. Kemanian, A.R., Stöckle, C.O., Huggins, D.R., 2005. Transpiration-use efficiency of barley. Agric. For. Meteorol. 130, 1–11, Reproduced with the permission of Elsevier.

Barley Chapter | 4  179

3.3  Capture and efficiency in the use of nutrients In this section, we focus on N, with a brief mention for other nutrients. N is generally the most important nutrient in terms of deficiencies in production systems around the world. Excess N is also a problem in malting barley because high grain protein content is an undesirable trait for the industry.

3.3.1  Soil nitrogen acquisition Soil is the source of N for plants. Only a small fraction (nearly 5%) of the soil N is in inorganic forms, primarily as ammonium (NH4+), nitrite (NO2−), and nitrate (NO3−). The NH4+ ion is the preferred form in which plants uptake N because it requires less energy for reduction. However, owing to the high nitrification rate, N is more commonly available as nitrate in well-aerated soils. Nitrate in the soil solution moves rapidly to the roots by mass flow (Oyewole et al., 2013) and is absorbed by root cells mediated by a low-affinity transport system (LATS) that is constitutive and a high-affinity transport system (HATS) that is regulated by intracellular nitrate consumption (Orsel et al., 2002). The barley HATS for nitrate uptake is similar to those reported in other species such as Zea mays L., although maize and barley feature quantitatively different response to the external nitrate level (concentration dependence is rare in barley; Glass et al., 1992). Once nitrate is absorbed by roots, it can be assimilated by the roots or translocated to aerial organs through the xylem. Studies with 15NH4 estimated that roots contributed ca. 20%–30% of whole-plant nitrate reduction (Gojon et al., 1986). Before its incorporation as amino acid, the nitrate is reduced to nitrite by the nitrate reductase enzyme and then to NH4+ by the nitrite reductase enzyme.

3.3.2  Efficiency in the use of nitrogen and its partitioning to the grains Barley yield increases asymptotically in response to soil N availability, as also observed in other crops (Fig. 4.15). The maximum yield achieved and the N level that saturates the response depends on the cultivar and the environmental conditions throughout the growing season. The higher the soil water availability, the higher the soil N level that saturates the yield response, especially in cultivars with high yield potential (Abeledo et al., 2011). The efficiency in the use of N for yield (NUEY, kg kg− 1 N; Table 4.1) can be interpreted as the GY achieved per unit of N available in the soil, and in barley, it varies between 15 and 55 kg kg− 1 N. NUEY depends on: (1) the amount of N absorbed by the crop per unit of N available in the soil (N uptake efficiency, NUpE, kg N kg− 1 Nsoil), and (2) the N utilisation efficiency for yield (NUtEY, kg kg− 1 Nabsorbed), which represents the ratio between yield and N absorbed (Eq. 4.2; Table 4.1). Thus barley yield (GY) can be expressed as the product between soil N availability (Nsoil + fertiliser) and the efficiency at which that N is absorbed by the crop and converted into yield:















GY kg ha 1  Nsoil kg N ha 1  NUpE kg kg 1 N  NUtEY kg kg 1 N



(4.2)

Grain yield (Mg ha-1)

8

6

4 NUEY 2

0 0

40 80 120 160 Soil nitrogen availability (kg N ha-1)

200

FIG. 4.15  Relationship between grain yield and soil nitrogen availability at sowing for barley. The slope of the linear phase represents the efficiency in the use of nitrogen for grain yield (NUEY). Based on Abeledo, L.G., Calderini, D.F., Slafer, G.A., 2011. Modelling yield response of a traditional and a modern barley cultivar to different water and nitrogen levels in two contrasting soil types. Crop Pasture Sci. 62, 289–298.

180  Crop Physiology: Case Histories for Major Crops

Similarly, the shoot dry biomass at maturity (SHB) depends on N uptake efficiency and the N utilisation efficiency for shoot biomass (NUtEB):















SHB kg ha 1  Nsoil kg N ha 1  NUpE kg kg 1 N  NUtEB kg kg 1 N



(4.3)

In barley, NUpE ranges between 0.35 and 0.51 kg kg− 1 N and NUtEY from 31 to 67 kg kg− 1 N, while NUtEB is stable around 100 kg kg− 1 N (Abeledo et al., 2008; Bingham et al., 2012). NUpE is strongly dependent on the availability of water in the soil (Liu et al., 2018). Modern barley cultivars have a significantly higher NUtEY than older cultivars (Abeledo et al., 2008; Bingham et al., 2012). The agronomic efficiency in the use of nitrogen for grain yield (ANUEY, kg kg− 1 N; Table  4.1) is the increase in yield per each unit of fertiliser applied. ANUEY for barley varies between ca. 3 and 60 kg kg− 1 N (Muurinen et al., 2006; Anbessa and Juskiw, 2012; Cossani et al., 2012; González et al., 2019). The variation in the ANUEY values results from the combined variation in NUpE, NUtEY, and the proportion of the N available for the crop from the fertiliser in relation to the native soil N, with low amount of native soil N improving ANUEY; Gaju et al., 2011). Type, timing, and mode of fertiliser application also modified ANUEY (Sieling et al., 1998; Lázzari et al., 2005). In addition, the capture and efficiency in the use of a nutrient is conditioned by the presence of other nutrients (e.g. Prystupa et al., 2004). For example, ANUEY can be increased with an increased availability of zinc in the soil when that nutrient was deficient in the soil (González et al., 2019). The total N absorbed by a crop at maturity (kg N ha− 1) is determined by both the N available in the soil and the N uptake efficiency. The ability to capture resources changes in barley throughout the ontogeny. In barley, the N uptake up to heading is approximately 80% of the total N seasonal absorption (Boonchoo et al., 1998; Lázzari et al., 2005). Therefore N content in grains depend on the efficiency of N mobilisation towards the grains from N stored in the vegetative biomass at heading (Przulj and Momcilovic, 2001; Muurinen et al., 2007; Abeledo et al., 2008). Environmental variations throughout the crop cycle modify the proportion of N absorbed by the crop at heading in relation to that assimilated throughout the whole growing season. Under potential growing conditions, the amount of N uptake during the pre-heading period is the main source of variation between cultivars (Feingold et al., 1990; Przulj and Momcilovic, 2001). Grain nitrogen yield is defined at maturity and can be explained as:









GNY kg N ha 1  BNm kg N ha 1  NHI

(4.4)

where GNY is the grain nitrogen yield, BNm is the amount of N absorbed by the crop in shoot biomass at maturity, and NHI is the nitrogen harvest index (i.e. the ratio of N in grains to total N in shoot biomass). NHI varies between 55% and 85% (Lázzari et al., 2005; Abeledo et al., 2008; Bingham et al., 2012).

TABLE 4.1  Traits related to the efficiency in the use of nitrogen. Trait

Definition

Abbreviation

Unit

N use efficiency for yield

GY (kg ha− 1) per unit of N available in the soil (kg N ha− 1)

NUEY

kg kg− 1 N

N uptake efficiency

Amount of N uptake by the crop (kg N ha− 1) per unit of N available in the soil (kg N ha− 1)

NUpE

kg N kg− 1 N

N utilisation efficiency for yield

GY (kg ha− 1) per unit of N absorbed by the crop at maturity (kg kg− 1 N)

NUtEY

kg kg− 1 N

N utilisation efficiency for shoot biomass

Shoot biomass (kg ha− 1) per unit of N absorbed by the crop at maturity (kg kg− 1 N)

NUtEB

kg kg− 1 N

Agronomic N use efficiency for yield

GY (kg ha− 1) per unit of N added by fertilisation (kg N ha− 1)

ANUEY

kg kg− 1 N

Adapted from Muurinen, S., Slafer, G.A., Peltonen-Sainio, P., 2006. Breeding effects on nitrogen use efficiency of spring cereals under northern conditions. Crop Sci. 46, 561–568.

Barley Chapter | 4  181

Differences in the concentration of N in the grains and, therefore, in N harvested per unit area, depend on the amount of N absorbed by the crop throughout the crop cycle and the proportion of N that is partitioned to the grains (Eqs 4.2–4.4). Variations in NUtEY modify grain N concentration because increases in NUtEY determine decreases in the concentration of N in grains (Sadras, 2006).

3.3.3  Critical nitrogen dilution curve The concentration of N in shoot biomass decreases with increasing crop biomass. Dilution curves relate critical N concentration Nc (the minimum concentration of shoot N for maximum growth rate) and shoot biomass W (Lemaire and Gastal, 2009):



Nc  %   a  W  b Mg ha 1



(4.5)

where parameters a and b are species-dependent (Lemaire et al., 2008). In barley, a = 4.76 and b = 0.39 were determined for shoot biomass above 1.79 Mg ha− 1, while a constant Nc = 3.77% was determined for biomass below this threshold (Zhao, 2014). The critical N dilution curve is useful to characterise the N status of the crop through the estimation of the nitrogen nutrition index (NNI), defined as the ratio between the actual N concentration Na and Nc. NNI below 1 indicates N limits growth, and NNI above 1 indicates luxury consumption of N (Fig. 4.16). The inverse of the critical N concentration indicates the amount of shoot biomass produced per unit of N uptake by the crop and allows to characterise NUtEB (Eq. 4.3).

3.3.4  Relationship between grain yield and grain protein concentration The concentration of protein in grains (GPC) is calculated by the concentration of nitrogen in the grain (GNC) and affected by a conversion factor: GPC  %   GNC  %   CF

(4.6)

where CF is a conversion factor of N into protein, typically 6.25. However, Mariotti et al. (2008) suggested CF = 5.60, while FAO uses 5.83 for barley (FAO, 2003). In grain crops, there is often a trade-off between GY and grain N concentration. This is important for both malting barley, which requires grain protein concentration in a narrow range of 10%–12%, and feed barley, which requires higher protein content. To analyse the relationship of barley between GY and the concentration of N, we compiled a data set from the literature (Fig. 4.17). Just in few cases, it was possible to combine high GYs with the range of GNC demanded by the brewing industry (Fig. 4.17). The trade-off between GNC and GY in grain crops also applies for barley as well (Fig. 4.14a and b). However, exceptions can be found in which there was no trade-off (Fig. 4.14c and d). Most likely, the level of soil N availability mediates on whether or not this trade-off is expected. Those conditions in which the trade-off was not observed may correspond to luxury consumption of N for the environment under study.

FIG. 4.16  Relationship between nitrogen concentration in shoot biomass and crop shoot biomass in barley. Based on Zhao, B., 2014. Determining of a critical dilution curve for plant nitrogen concentration in winter barley. Field Crops Res. 160, 64–72.

182  Crop Physiology: Case Histories for Major Crops

FIG. 4.17  Relationship between grain nitrogen concentration and grain yield reported in four studies. The dotted lines represent the range of GNC demanded by the malting industry. The figure was built considering the references that are indicated in each figure. Holm, L., Malik, A.H., Johansson, E., 2018. Optimizing yield and quality in malting barley by the governance of field cultivation conditions. J. Cereal Sci. 82, 230-242. https://doi.org/10.1016/j. jcs.2018.07.003; Marinaccio, F., Reyneri, A., Blandino, M., 2015. Enhancing grain yield and quality of winter barley through agronomic strategies to prolong canopy greenness. Field Crops Res. 170, 109-118. http://dx.doi.org/10.1016/j.fcr.2014.10.002; Prystupa, P., Ferraris, G., Ventimiglia, L., Loewy, T., Couretot, L., Bergh, R., Gómez, F., Gutierrez Boem, F.H., 2018. Environmental control of malting barley response to nitrogen in the Pampas, Argentina. Int. J. Plant Prod. 12, 127–137. https://doi.org/10.1007/s42106-018-0013-3.

GNY can be calculated with Eq. (4.4), and it can also be defined as the product between GY and GNC:









GNY kg N ha 1  GY kg ha 1  GNC  % 

(4.7)

GNY is the amount of nitrogen that is extracted from the paddock with harvest, and varied between 10 and 210 kg N ha− 1, with a common value around 100 kg N ha− 1.

3.4  Requirement of other nutrients Table 4.2 shows barley nutrient requirement per unit GY, and Fig. 4.18 represents total requirement as a function of yield. Barley has a high demand for N and potassium (K) per unit of yield. N is the nutrient with the highest extraction rate in grain (i.e. the highest nutrient through the harvest process). In contrast, barley has low phosphorus (P) extraction rate in grains and low P requirements despite its high PHI (Table 4.2). Sulphur (S) is another nutrient with a high HI (associated with the constitution of S amino acids in the barley grain). TABLE 4.2  Barley nutritional requirement per unit of grain yield, the rate of extraction in grain, and the nutrient harvest index (NuHI) for nitrogen (N), phosphorus (P), potassium (K), sulphur (S), calcium (Ca), and magnesium (Mg). Trait

N

P

K

S

Mg

26

4

20

4

3

Extraction in grain (kg t )

15

3

5

2

1

NuHI

0.58

0.75

0.25

0.50

0.33

Total uptake per unit yield (kg t − 1

− 1

)

From Ciampitti, I.A., García, F.O., 2007. Requerimientos Nutricionales. Absorción Y Extracción De Macronutrientes Y Nutrientes Secundarios I. Cereales, Oleaginosos E Industriales (in Spanish). Archivo Agronómico # 11. IPNI.

Barley Chapter | 4  183

Nutrient requeriment (kg ha-1)

350

N P

280

K S

210

Mg 140 70 0 0

3

6

-1

9

12

G rain yield (Mg ha ) FIG. 4.18  Relationship between total nutrient uptake per unit of harvested area (i.e. nutrient requirement) and grain yield for barley crops, for nitrogen (N), phosphorous, potassium (K), sulphur (S), calcium (Ca) and magnesium (Mg). Based on Ciampitti, I.A., García, F.O., 2007. Requerimientos Nutricionales. Absorción Y Extracción De Macronutrientes Y Nutrientes Secundarios I. Cereales, Oleaginosos E Industriales (in Spanish). Archivo Agronómico # 11. IPNI.

4  Grain yield and quality As in other crops, GY can be expressed as a function of accumulated biomass during the crop cycle (BT) and the partitioning of shoot biomass to grain (i.e. HI): GY  BT  HI

(4.8)

HI is considered a conservative trait varying between 0.38 and 0.48 (Arisnabarreta and Miralles, 2015). GY often has a closer association with aboveground biomass than with HI (Arisnabarreta and Miralles, 2015). GY can also be described throughout its numerical components: grain number (GN) and grain weight (GW). Moreover, GN can be described through other physiological components, including the crop growth rate and partitioning to reproductive structures as the spike as will be described further.

4.1  Grain number and the critical period As stated earlier, GY is closely correlated to the number of grains per unit area. This yield component is defined from midstem elongation to the beginning of the awn appearance (or first spikelets appearance in the case of awnless cultivars) over the flag leaf ligule. The period comprised between those stages (i.e. from mid-stem elongation to awn appearance) is named ‘critical period’ (Arisnabarreta and Miralles, 2008a) because both the number of fertile tillers and the number of fertile spikelets per spike are defined, and thereby the number of grains per unit area is finally established. Developmentally, the critical period of barley is similar to that of wheat and oat (Fig. 1 in Preface). Grain number can be divided in different yield components as was proposed by Fischer (1984) in wheat: GN  SDWHD  FE HD

(4.9)

SDWHD  Ds  CGR  Ps

(4.10)

CGR   PARia  RUE  / Ds

(4.11)

where SDWHD is the spike dry weight at heading; FEHD is the fruiting efficiency, i.e. number of grains per unit spike dry weight at heading; Ds is the duration of the spike growth period, which in barley is defined between maximum number of spikelet primordia (MNP) and heading (HD) stages (Arisnabarreta and Miralles, 2008b); CGR is the crop growth rate around the spike growth period; Ps is the proportion of dry weight partitioned to the spike; and PARia is the accumulated photosynthetically active radiation intercepted between MNP and HD. Grain number shows different associations with the components described earlier. For instance, grain number per unit area was significantly associated with SDWHD and PARia. In the same way, CGR is significantly correlated with PARia

184  Crop Physiology: Case Histories for Major Crops

(Arisnabarreta and Miralles, 2015). Studies in barley showed that the proportion of biomass partitioned to the spike was positively correlated with the size of the spike at the beginning of stem elongation, and the partitioning to reproductive organs is increased when nutrients (e.g. N) are increased, promoting a higher spike:stem ratio for the same spike dry weight at the beginning of stem elongation. Under potential growing conditions, greater amounts of assimilates allocated to the spike during the pre-heading phase have a strong impact on the number of fertile florets and grains per unit area (Slafer and Rawson, 1994a; Miralles et al., 1998, 2000; González et al., 2003; Prystupa et al., 2004; Arisnabarreta and Miralles, 2008b, Arisnabarreta and Miralles, 2015). Therefore any stress altering the crop physiological status (i.e. growing conditions below the potential) during that phase has an important negative impact on grain number per unit area, and thereby on yield, more so than during other phases of the crop cycle (Fischer, 1985). This is why a strong positive association between grain number and photothermal quotient, Q (i.e. the ratio between PAR intercepted by the crop and temperature during the pre-flowering period - critical period) was found in barley by Arisnabarreta and Miralles (2008a).

4.2  Grain filling As in many other grain crops, the rate of dry matter accumulation in barley grains is initially slow, increasing to a nearly constant rate up to physiological maturity (Gallagher et al., 1976; Alvarez Prado et al., 2013). Usually, grain growth rate ranges from 0.9 to 2.2 mg d− 1 depending on the supply of assimilates and position of the grain within the spike, with grains in the middle of the spike having the highest growth rates (Gallagher et al., 1976; Scott et al., 1983). Final grain size relates to both the rate of growth and duration of grain filling. It is important to highlight that the environmental conditions immediately before anthesis affect the potential size of the grain (Scott et al., 1983; Ugarte et al., 2007). In two-rowed barley, grains are similar in size between both rows. In six-rowed varieties, all three spikelets at each node of the rachis are fertile, and while the central grains are symmetrical, the lateral grains are asymmetric to a greater or lesser extent, each with a right-handed or left-handed bias (Briggs, 1978). Thus the stability of two-row barley grain weight has great significance for malting because penalties apply if grains size does not meet the industry requirements. Dynamic of grain filling is correlated with the dynamic of grain water content (Borras and Westgate, 2006; Alvarez Prado et al., 2013); see for comparison Chapter 1: Maize, Section 2.1 and Chapter 16: Sunflower, Section 2.1. Developing grains accumulate more water in absolute term (mg water) than reserves immediately after flowering until water content is maximised relatively early during the grain-filling period and remains stable during a ‘hydric plateau’ period. The hydric plateau is a short period during middle of grain filling during which the entry and exit of water into the grain are equalised. Once the hydric plateau is finished, grains start to lose water until harvest moisture is reached. The water loss rate during grain filling is negatively associated with the duration of grain filling (Gambín et al., 2007) and can be assumed that grain moisture (in relative terms) at physiological maturity in barley is close to 50% (Bingham et al., 2007; Alvarez Prado et al., 2013). The causes that determine the end of growth in the grain of cereals are still unclear but could be related to a diminishing ability for starch synthesis, caused by enzyme dehydration, rather than to a lack of carbohydrates (Biscoe et al., 1973). The rapid loss of water in the barley grain during ripening seems to be associated with a raise in the concentration of abscisic acid in the endosperm that could increase pericarp permeability, causing grain dehydration (King, 1976; Mounla, 1979).

4.3  Barley uses and grain quality As it was already pointed out, barley has three distinct uses: malting, feed, and food. Nowadays, the most important uses of barley worldwide are feed and malting (Edney, 2010). However, barley was initially used as human food in many parts of the world (Baik and Ullrich, 2008), but it was transformed into animal feed or beer-making material because wheat and rice gained importance (Newman and Newman, 2006). In fact, barley is still the major food in some countries from Asia, Africa, and the Andean region of Ecuador, Perú, and Bolivia. Recent studies have shown that barley has great nutritious quality for human and animal health (e.g. Wood, 2007; Arcidiacono et al., 2019). As a consequence, there is a renewed interest in the USA, Canada, and Europe for food uses of grain barley (Baik and Ullrich, 2008). Most research and breeding effort on barley quality has focused on malting because it has a premium price over feed barley (Ullrich, 2002). In general, physical characteristics (test weight or plump uniform grains) influence feed barley price, and in fact, feed types are designated because they do not reach malting standards (Ullrich, 2002). In this section, we focus on malt as an ingredient for alcoholic beverages and, in particular, for beer production. In barley (as in other field crops), the end-product quality has traditionally been related to the composition and structure of the seed at harvest maturity, as determined by the genotype, the environment, and the management practices (Rondanini et al.,

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2019). There is no simple, clear group of variables that are unanimously regarded as defining the barley quality of grain or malt. Quality requirements in malting barley represent a consensus of the specifications required by commercial brewers to efficiently produce their products consistently with desired properties or traditional methodologies (Savin and MolinaCano, 2002). For brewing, quality traits align fundamentally on the target type of beer (Edney, 2010). Two processes can be recognised: (1) the transformation of barley grains into malt (malting) and (2) the brewing of the malted grains. Briefly, malting is a controlled germination process in which, under adequate humidity and temperature, the enzymes responsible for the degradation of cell walls and the protein matrix are synthesised to facilitate the access of amylolytic enzymes of yeast to starch. In this stage, the grain components are transformed into soluble sugars. The manufacture of beer consists in producing a sugary wort that in the presence of yeast produces an alcoholic fermentation of soluble sugars (Edney, 2010). Therefore the assessment of malt barley quality begins after harvest and continues after malting. The analyses cover physical, biochemical, and metabolic characteristics (Table 4.3). Standardised protocols are followed with strict criteria depending on the malting and brewing products. Assessments are often divided in barley quality prior to malting and the quality of the malt (Table 4.3). A high-quality malt barley cultivar must have a series of physical and biochemical characteristics that favour high malt extract for a given malting process. Varietal purity and germination are the most important quality requirements (Edney, 2010). Each malt variety may differ in its potential and processing characteristics and must germinate uniformly to avoid malting problems. Also, the weight and screening percent of the grains are important for malting uniformity (mainly during germination). The requirement is that the proportion of grains with a screening > 2.5 mm exceeds 85%–90% of the sample (Table 4.3). Protein content is one of the main quality attributes; Section 3.3.4 analysed the trade-off between grain protein and yield. In general, there is a negative relationship between protein content and malting quality that defines a target of 10%–12% protein for malting. Although high percents of proteins reduce malt quality, some protein is necessary to obtain adequate levels of enzymes during the industrial process. β-glucans should not exceed 3.5% as they may cause reductions in starch degradation and other problems in brewing, such as reduced rates of wort separation and beer filtration and the formation of hazes and precipitates. From a biological point of view, malt extract represents a measure of the solubility of the different components of the grain. It is a complex character that results from the interaction of various biochemical processes that are controlled by several genes (Edney, 2010) and usually must be at least 80% according to the industry requirements (Table 4.3). In addition, the presence of high levels of proteins in the malt causes problems when precipitating during brewing (Smith, 1990), but as in grain, minimum levels of protein are necessary to ensure enzymatic production. High levels of βglucans in malt means an incomplete degradation of cell walls, which decrease the quantity of the extract produced (Fincher and Stone, 1993) and, on the other hand, form high viscosity aqueous solutions that cause problems in the filtering process during brewing (Fincher and Stone, 1993). Free amino nitrogen (FAN) is an indication of available nitrogenous compounds for yeast nutrition (Edney, 2010), and diastatic power is a quantification of starch-degrading enzymes required for distilling (Edney, 2010). All in all, the final quality of the malt is influenced by environmental and managements factors that affect the growth and development of the grain and the malting process (Savin et al., 2004). The malting and brewing processes act on the raw material, i.e. barley grains, the quality of which is strongly dependent on their composition. The environment during

TABLE 4.3  Physical and chemical traits and standards for barley grain and malt. Traits

Requirement for industry

Grain

Colour Varietal purity Germination Screening > 2.5 mm Optimum total proteins β-glucans

Yellowish, disease-free 100% At least 95% 85%–90% 10%–12%  80% 4%–5.5% >150 ppm   35°C) during reproductive phase (Prasad et al., 2006, 2009; Djanaguiraman et al., 2014; Singh et al., 2016). Short episodes of high temperatures between 36°C and 38°C around flowering have been reported to be damaging to a sorghum crop in Australia. However, genetic diversity can be utilised to breed varieties with improved tolerance to heat stress. Genotypic differences in heat tolerance do exist in sorghum (Nguyen et al., 2013). The effect of high temperature on seed set operates for about 12–15 days between flag leaf and the start of grain filling. This effect on seed set is cumulative and is a function of both intensity and duration. There are two aspects: (a) threshold temperature and (b) tolerance above the threshold. Singh et al. (2015) reported genotypic differences in seed set percentage for both the threshold temperature (36–38°C) and the tolerance to increased maximum temperature above that threshold. Similar threshold temperatures of 36°C for maize (Dupuis and Dumas, 1990) and 38°C for rice (Tenorio et al., 2013) have been reported. APSIM simulations have indicated that increasing the threshold temperature is more important than the temperature above that threshold for sorghum, where an increase in threshold temperature minimised the adverse yield effects significantly. But selection of genotypes for increased heat tolerance above the threshold temperature is also important because 1–5°C temperature increase is predicted in future (CSIRO, 2007). The US sorghum breeding programme reports lower thresholds (i.e. 33°C) and lack of genetic diversity for heat tolerance (Tack et al., 2017). However, in this study, heat stress was assessed by the yield reduction in a large metaanalysis of trials across a wide range of temperature conditions. However, this analysis did not allow the effects of heat stress on the reproductive biology to be separately assessed. In any case, the main effect of high temperature appears to be on the reproductive biology, especially pollen germination and seed set (Prasad et al., 2006), whereas the effects on plant growth and photosynthesis are considered to be minor (Jain et al., 2007; Prasad et al., 2008; Nguyen et al., 2013). Heat stress resulting in poor seed set can be compensated by increased mass of the grain (Yang et al., 2012), but in another study, reduced seed set in heat-susceptible genotypes was not compensated by increased seed mass (Singh et al., 2015). Similarly, Prasad et al. (2006) reported no effect on seed mass, although seed set percentage significantly declined as temperatures increased from 32°C to 36°C. In fact, decreased seed mass did not eventuate until temperatures reached 40°C. Findings of these studies, and increasing incidents of short episodes of high temperatures > 35°C around flowering, reinforce that the adverse effects of climate change on grain yield in sorghum crops are more likely to be owing to increased incidence of heat stress rather than drought. Therefore more emphasis on tolerance to heat stress is warranted in breeding programmes (Lobell et al., 2015). Sorghum originated in the semiarid tropics and is generally sensitive to low-temperature stress (Yu et  al., 2004). However, in growing regions of Australia, this is not an issue unless late-sown sorghum is maturing in decreasing autumn temperatures in northeastern Australia. Low-temperature stress ( 300 000 plant per ha). As a result, yield response to increasing plant density tends to flatten at relatively low densities (Board, 2000; De Bruin and Pedersen, 2009; Cox et al., 2010). In stressful conditions (e.g. soil water deficit), branching and leaf expansion temporarily cease; in contrast, primordia formation and leaf appearance are largely unaffected. Similarly, LAI development is modest when the crop cycle length is too short (Edwards and Purcell, 2005). Hence increasing plant density may be needed in these situations to ensure a reasonable leaf area at the time of the critical period. Row spacing can also be used to increase absorption of solar radiation. Narrowing row width in soybean fields leads to earlier canopy closure, which may increase capture of incoming solar radiation during the critical period for yield determination (Andrade et al., 2002). In favourable environments in the Corn Belt, narrowing row width (from 76 to 38 cm) is a management option to increase capture of solar radiation when LAI development is insufficient for near-full absorption of the incident radiation during the critical period. For example, Andrade et al. (2019) found a positive yield response to narrowing row width in environments where LAI development was limited by a short duration of the VE-R3 phase owing to late sowing and/or use of early MG cultivars. In contrast, yield response to narrow row width was nil in optimally managed soybean sown early using a full-season MG. In Argentina, narrowing row width from 52 to 35 cm is also common when growing an early-maturing soybean cultivar in early sowings or when soybean is sown after

FIG. 8.11  (a) Fraction of absorbed photosynthetically active radiation (fA) as a function of green leaf area index. The fA was estimated as the ratio between absorbed and incident photosynthetically active radiation (PAR). (b) Seasonal dynamics in the fraction of PAR that was absorbed by the green canopy (fA), transmitted to the soil (fT), and reflected back to the atmosphere (fR). (c) Aboveground dry matter (including abscised leaves) as a function of accumulated absorbed PAR during the crop season. The linear regression was fitted within the range in which increasing radiation resulted in increased dry matter accumulation; slope of the relationship represents the radiation-use efficiency. Data from seven high-yield experiments (range: 5.3–6.7 Mg ha− 1) conducted in Nebraska (USA) at 76-cm row spacing during two-crop seasons (2016 and 2017). See text for explanation on calculation of developmental stages. Adapted from Cafaro La Menza et al. (2020).

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harvest of a winter cereal crop; in both cases, the goal is to compensate for the shortening of the vegetative phase and the low water availability early in the season (late sowing). Narrowing rows also helps weed control. As documented in the 1960s by Shibles and Weber (1965), dry matter accumulation in soybean is proportional to the amount of IPAR, with the slope of the relationship (with zero-intercept) representing the radiation-use efficiency (RUE). Despite its general stability, RUE can vary with temperature, CO2 concentration, total radiation, proportion of diffuse solar radiation, leaf N content, soil water deficit, and O3 level (Sinclair and Muchow, 1999 and references cited therein). Measured RUE in optimally managed irrigated soybean experiments in Nebraska was 2.19 g MJ− 1 APAR (or 2.14 g MJ− 1 IPAR) for most part of the crop season (Fig. 8.11c), which falls within the range reported in the literature (Andrade, 1995; Sinclair and Muchow, 1999; Van Roekel and Purcell, 2014 and references cited therein) and is well below that reported for maize (3.8 g MJ− 1 APAR; Lindquist et al., 2005). The RUE differential between soybean and maize reflects differences in photosynthetic pathway (C3–C4), canopy architecture, and energy content of both vegetative and reproductive organs. Slightly lower RUE, observable in just the early and late parts of the season, may be attributable to early low temperature and lower leaf photosynthetic capacity and the late decline in leaf N content in senescing canopies (Rochette et al., 1995; Sinclair and Muchow, 1999). Rattalino Edreira et al. (2020) estimated average conversion of the total (VE–R7) incident PAR into seed yield to be ca. 0.8% in producer soybean fields in the Corn Belt, with an upper limit between 1.0% and 1.2%. In the 1980s, soybean breeders measured canopy apparent photosynthesis (CAP) using closed field chambers to examine the correlation between CAP and yield. The correlation was relatively strong (r = 0.6), and newer cultivars released in the 1980s had higher CAP than older cultivars released in the prior decade, but not many cultivars were examined (Boerma and Ashley, 1988; Ashley and Boerma, 1989). In theory, selection for higher CAP would be expected to enhance yield, but CAP is an unsuitable trait for breeding applications. About 25 years later, comparison of a historical set of 24 MG III cultivars released from 1923 to 2007 showed that all three traits in the Monteith (1977) equation, efficiency in light interception, RUE, and harvest index (HI), were higher in modern cultivars (Koester et al., 2014, 2016). Total daily leaf CO2 uptake (A′), maximum rate of Rubisco carboxylation (Vc,max), electron transport (Jmax), and night respiration rate were measured for the same set of cultivars on 14 different days spanning V5–R6 in each growing season. Maximum photosynthetic capacity (based on Jmax and Vc,max) and night respiration rates did not change consistently with year of cultivar release. On 8 of the 14 measurement days, linear regression of A′ on cultivar release year was statistically significant, totalling a + 12% total increase in A′ over 85 years (calculated based on those measurement days with a significant trend). Higher A′ in newer– older cultivars was associated with greater rates of leaf photosynthesis in the afternoon during the R3–R6 phase as a result of greater stomatal conductance when soil water content was high. Seed yield has nearly double during the same 80-year time span, suggesting a very modest contribution of increased leaf photosynthesis to the overall yield gain. Transgenic approaches have been effective at deploying cultivars with tolerance to herbicides; these cultivars have been massively adopted by producers in USA, Argentina, and Brazil during the past 25 years, facilitating weed control and reducing labour. An unintended consequence has been build-up of herbicide-resistant weeds. More recently, insectresistant cultivars have been released in South America to control Lepidoptera. Increasing yield through radical changes in leaf photosynthesis efficiency through the use of transgenic approaches has received considerable attention (and funding) in recent years (Zhu et al., 2007, 2010). Despite expectations to deliver commercially available cultivars with 50% higher yield potential within 10–15 years (Long et al., 2006), these efforts have not yet resulted in any cultivar release with one or more transgenic changes leading to a proven superior yield. Changes in leaf photosynthesis do not necessarily translate into changes in CAP as it has been documented by Pettigrew et al. (1989) when comparing chlorophyll-deficient soybean isolines–their normal pigmented wild type. Stimulated by this apparent ‘overinvestment’ of N in chlorophyll, there have also been recent efforts to develop reduced-chlorophyll cultivars. Proponents claim that these cultivars would exhibit better light distribution within the canopy, which, together with reallocation of more plant N to rubisco, might lead to higher RUE and yield (Ort et al., 2011, 2015). This approach has been unsuccessful so far at developing cultivars with higher (or even similar) yield performance than normal pigmented cultivars as a result of lower IPAR early in the season, without any detectable change in RUE (Slattery et al., 2017).

3.2  Capture and efficiency in the use of water 3.2.1  Capture of water The soybean root system is characterised as diffuse but has three distinct morphologically defined components: the primary tap root that originates as the radicle from a germinating seed, the lateral roots, often referred to as secondary roots that emerge from the taproot, and the tertiary roots that originate from lateral roots (Lersten and Carlson, 2004; Torrion et al., 2012). The primary root is strongly geotropic and typically has a large diameter (Mitchell and Russell, 1971). Similar to

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other crops with taproot systems, soybean has nearly two-thirds of the roots in the upper 30 cm of the soil profile (Fan et al., 2016). Despite qualitative differences in root system type between soybean and maize (taproot–fibrous), distribution of root dry matter and root length with depth is remarkably similar between the two crops (Nichols et al., 2019). In soil without physical or chemical constraints, the rate of root growth is modulated by soil temperature and ceases during pod setting and early seed filling (Fig. 8.10b). Reported daily rates of root growth ranged from 1.2 to 3.9 cm d− 1 (Ordóñez et al., 2018 and references cited therein). The upper range of daily root growth rates is consistent with the maximum rate of 4 cm d− 1 proposed by Calmon et al. (1999). Once the roots reach a soil layer and attain a critical root length density in the layer, soil water content decreases quickly with time, especially when the transpiration demand is greater than the available soil water (Dardanelli et al., 2003, 2004). The rate at which water content declines in a given soil layer also depends upon physical factors that impede a uniform root distribution (e.g. high clay content and soils with vertic properties). In deep soil layers, water extraction rate is lower as a result of insufficient time to develop the root system. Soybean root systems can reach a maximum depth from 150 to 220 cm (Bland, 1993; Borg and Grimes, 1986; Dardanelli et al., 1997, 2004; Kaspar et al., 1978; Fan et al., 2016; Ordóñez et al., 2018). Cultivar MG, physical or chemical soil constraints, and presence of water table influence root distribution, maximum root depth, and the time when the latter is attained. Ordóñez et al. (2018) found that maximum rooting depth in soybean grown across 10 site-sowing date combinations in Iowa, USA varied from 88 to 154 cm and was closely related with the depth of water table near the time when root growth ceased. Similarly, low pH and high Al concentration reduces root grow in tropical weathered soils; these soils require periodic lime or gypsum application to alleviate acidity to ensure deeper root systems, greater water and nutrient extraction, and sustain high crop yields (Marsh and Grove, 1992; Caires et al., 2008; Pivetta et al., 2011; Battisti and Sentelhas, 2017). Another example of physical constraints to root depth is a petrocalcic horizon (‘caliche’) in the southeastern Pampas that limits crop water extraction, increasing the chances of water stress in the middle of the summer crop season (Calviño and Sadras, 1999; Sadras and Calviño, 2001). As a result, yield of soybean and other summer crops decreases with reduced soil depth. Despite its relatively late critical period for yield determination (Fig. 8.8) and greater plasticity to tolerate episodic water stresses, soybean is not the best option for this environment because it consumes most of the available soil water before the critical period. More suitable management alternatives to deal with shallow soils include (1) switching to winter crops that grow during a time of the year with substantially lower evaporative demand (winter and spring) and (2) sowing determinate summer crops (e.g. maize) at low plant density to reduce transpiration during the vegetative phase and deferred soil water to the critical period for yield determination (Calviño and Cerrudo, personal communication). Transitory water stress can be detrimental to soybean yield and the differential sensitivity of yield to water stress in relation to crop development has been widely investigated (e.g. Korte et al., 1983; Kadhem et al., 1985; Andriani et al., 1991). In these studies, the yield penalty owing to water stress was minimal or nil when water stress occurred at R1–R2 but high and consistent when it coincided with R3–R6. These findings were consistent with the critical period shown in Fig. 8.8a. A practical application of these results is that, in environments with high available soil water at sowing, as it is the case of most agricultural soils in Nebraska, irrigation can be deferred until the onset of R3 with little risk of water stress, generating yields similar to yields of fully irrigated crops during the whole season but using less irrigation (Torrion et al., 2014). Whilst these studies focused on water stress owing to insufficient water supply, transitory waterlogging events owing to excessive precipitation, presence of shallow water tables, and/or poor soil drainage can also lead to yield reduction, especially when timing of water excess coincides with reproductive stages (Scott et al., 1989; Linkemer et al., 1998; Nosetto et al., 2009). Underlying physiological mechanisms include impaired root capacity to absorb water and nutrients, reduced N fixation, lower leaf expansion and photosynthetic rates, and earlier leaf senescence (Oosterhuis et al., 1990; Bacanamwo and Purcell, 1999; Boru et al., 2003). Not surprisingly, it has been challenging to incorporate soybean to lowland rice-based systems (Bajgain et al., 2015; Theisen et al., 2017).

3.2.2  Water use efficiency Leaf stomata must be open to allow the entry of CO2 into the leaf for photosynthesis, thus allowing the simultaneous exit of H2O (i.e. transpiration) from a humid leaf interior. Dry matter accumulation and consequently, seed yield are inextricably linked to transpiration (Passioura, 1977; Sinclair et al., 1984). Indeed, a linear response of total plant biomass to seasonal transpiration has been documented for most annual crops and, in general, there is minimal variation in water-use efficiency (WUEDM; dry matter per unit of transpiration) within C3 or C4 species (Sinclair et al., 1984). Daytime VPD influences WUE with lower VPD increasing WUE (Tanner and Sinclair, 1983; Sinclair et al., 1984). When WUEDM is normalised to a seasonal daytime VPD of 1 kPa, and roots are accounted for its calculation, the resulting WUEDM (typically referred to ‘kd’ in the literature) for soybean is relatively stable across environments, with values ranging from 4.0 to 4.4 kPa (Tanner and

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Sinclair, 1983; Suyker and Verma, 2010; Connor et al., 2011), which are roughly half of those reported for maize. Hence early sowing in the Corn Belt not only allows greater capture of the seasonal available solar radiation but also shifts the crop cycle towards a time of the year with lower VPD and hence higher WUEDM (Purcell et al., 2003). Ultimately, seed yield will depend, in addition to the amount of crop transpiration and daytime VPD, on the partitioning of dry matter to seed (see Section 3.4). Using producer-reported data from rainfed and irrigated soybean fields in Nebraska, Grassini et  al. (2015) derived a boundary function with x-intercept = 73 mm and slope = 9.9 kg ha− 1 mm− 1 for the relationship between yield and water supply (Fig.  8.12a). These two parameters are biophysically meaningful: the x-intercept gives a coarse estimate of the seasonal soil evaporation, whilst the slope estimates WUEY. The boundary function defined for Nebraska appears also to provide a reasonable upper limit for rainfed and irrigated soybean crops in other environments (Fig. 8.12b). The estimated soybean WUEY of 9.9 kg ha− 1 mm− 1 was similar to reported values for other legume and oilseed crops, ranging from 7 to 12 kg ha− 1 mm− 1 (Sadras et al., 2011; Connor et al., 2011 and references cited therein). In contrast, soybean WUEY was lower than those reported for cereal C3 crops such as wheat and barley (range: 20–30 kg ha− 1 mm− 1), primarily because of higher synthesis cost of seed biomass and differences in seasonal VPD (Specht et al., 1999, 2014). Likewise, soybean WUEY was well below maize WUEY of 30–40 kg ha− 1 mm− 1, which is also explained, in addition to seed biomass composition, by their respective C3–C4 photosynthetic pathways. The distribution of data points in Fig. 8.12a indicates no further yield increase for water supply ≥ 650 mm, suggesting that such amount of water supply, when adequately sustained by well-distributed precipitation or irrigation events during the growing season, should be sufficient to satisfy crop water requirements for highest yields in the western Corn Belt. Many irrigated fields exceeded this threshold, suggesting ample room to reduce irrigation without hurting yield by adjusting irrigation scheduling based on crop water requirements (Gibson et al., 2019). Interestingly, for any given water supply, producer yield was typically higher in irrigated than in rainfed fields, and this was especially notable for fields with abundant water supply (> 650 mm) (Fig. 8.12a). An ill-distributed precipitation pattern during the growing season might expose rainfed crops to transitory water stress during the critical period, even in fields with seasonal water supply exceeding 650 mm. Similarly, intense precipitation events early in the season would favour unproductive water losses. Other nonwater related factors also limited rainfed productivity. For example, fields located in best soils and flat terrain are typically allocated to irrigated production. Likewise, rainfed fields received less intensive management (less frequent P fertilisation, foliar fungicide) and were sown later when compared with irrigated fields, which reduces crop cycle length and exposes the critical period to less favourable environmental conditions (Fig. 8.9). Although much research effort has been expended in the past to improve legume crop WUEY through breeding (Vadez et al., 2014), those efforts have had, so far, only minimal impact. For example, use of carbon isotope discrimination (CID) as a proxy for WUEY has generated mixed results that, when coupled with a high per-sample cost of CID, has not persuaded many soybean breeders to routinely use it. Another reason is that cultivars exhibiting slower soil water extraction (commonly refer to as ‘slow wilting’ phenotypes) can, in principle, ‘save’ water that can be used at a later crop stage (assuming the saved water is not lost via soil evaporation). However, slow wilters are notorious slow growers and typically have

FIG.  8.12  (a) Relationship between producer-reported soybean yield and seasonal water supply in rainfed and irrigated fields. Water supply was estimated as the sum of available soil water at sowing, in-season precipitation, and total irrigation. Parameters of the boundary function are shown. (b) Relationship between soybean yield and seasonal crop evapotranspiration based on data reported in the literature for 116 rainfed and irrigated crops grown in experimental field plots in the western Corn Belt (Elmore et al., 1988; Specht et al., 1989; Payero et al., 2005; Suyker and Verma, 2009; Aiken et al., 2011), Argentina (Dardanelli et al., 1991; Della Maggiora et al., 2000), and Italy (Casa and Lo Cascio, 2008). Dashed black line in (b) indicates the linear regression for the pooled data (r2 = 0.70). Adapted from Grassini et al. (2015).

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s­ ignificantly lower RUE than fast wilters (Ries et al., 2012). Slow wilters are thus best used in production systems with a high frequency of terminal drought, where yield is already severely limited by insufficient water. Low RUE genotypes do not fare well in the high-yield soybean production regions of the Corn Belt, where protracted droughts are infrequent, although short intermittent periods of precipitation scarcity do occur. In the past decade, the focus on WUE improvement has shifted towards genotypes in which the transpiration response to VPD is not linear (which is common in most high-yielding commercial cultivars) but instead exhibits a linear-plateau (L-P) response in which leaf transpiration ceases to increase after the L-P breakpoint is reached (Fletcher et al., 2007; Gilbert et al., 2011; Sinclair et al., 2017). The physiological foundation for this L-P pattern has not been fully characterised but in essence is a mid-day type of stomatal closure triggered when the VPD reaches a genotype-dependent specific value. This is essentially a water-saving trait, but it is now termed an ‘effective water use’ strategy (i.e. shifting crop water use from earlier to later in the season). Proponents claim that the conservation of soil water through this shift would improve crop growth because the water shift should enhance HI (Sinclair, 2018). However, it remains to be seen if genotypes exhibiting limited transpiration also suffer from a lower RUE that slow wilters exhibit owing to stomatal closure lessening photosynthetic carbon gain. A thorough review on conservative water use strategies in legume crops can be found elsewhere (Blessing et al., 2018). The foregoing approaches are effectively designed to lessen stomatal conductance, thereby improving WUE by reducing the WUE denominator. Despite this effort, commercial successes are rare. In a key paper documenting the change in transpiration parameters over the course of 80 years of breeder selection focused solely on yield, Koester et al. (2016) documented that modern soybean cultivars could upregulate their stomatal conductance in the days after soil water content was increased as a result of a precipitation event (or an irrigation event), compared to the older cultivars that could not do that. This suggested that intense breeder selection for yield generated another correlated response that amounted to a more opportunistic stomatal behaviour in modern varieties (i.e. respond with open stomata when soil water was available). In reality, this is an under-appreciated inductive stomatal response that leads to greater carbon gain in modern cultivars grown with intermittent rather than protracted or terminal drought and soils with high plant-available water holding capacity. So-called conservative water saving strategies typically exhibit a constitutive nonresponse to greater soil water availability and thus continue to save water that could have been used for more plant growth. This discovery of stomatal upregulation follows on the heels of intensive review of greater stomatal conductance and its impact on crop productivity (Roche, 2015), and the fact that modern soybean cultivars have cooler canopies because of their greater stomatal conductance (Keep et al., 2016). Similar findings have been reported for cotton (Radin et al., 1994) and wheat (Amani et al., 1996; Fischer et al., 1998).

3.3  Capture and efficiency in the use of nitrogen Soybean has ca. four times higher N requirements per unit of seed mass than does maize. Total N assimilation is proportional to dry matter accumulation: a soybean crop accumulates ca. 33 and 80 kg N ha− 1 Mg− 1 of aboveground dry matter and seed yield, respectively (Salvagiotti et al., 2008; Bender et al., 2015; Tamagno et al., 2017) (Fig. 8.13a). Hence a total N uptake of 240 kg N ha− 1 would be required to sustain current yield in the USA, Argentina, and Brazil of ca. 3 Mg ha− 1. About 75% of accumulated N in soybean is absorbed after R3. Soybean exhibits an attenuated dilution curve when compared with other C3 crops (Divito et al., 2016) (Fig. 8.13b), which is independent from variation in management and environmental factors (Divito et al., 2016). Analyses of dilution curves in soybean only considered samples collected before R5 because the intense N remobilisation from nonseed organs to seeds during the seed filling modifies the dilution pattern (see Section 3.4). Soybean rarely receives N fertiliser in producer fields, except for (sometimes) a small application as ‘starter’ at sowing. Hence soybean relies on: (1) absorption of N that is available in the soil from organic matter mineralisation, residual soil inorganic N left unused by previous crop, and dry and wet atmospheric deposition and (2) reduction of atmospheric N2 by rhizobia (Bradyrhizobium japonicum) in the root nodules. Soybean nodules are determinate because cell division ceases early during nodule development and the final spherical shape results from cell enlargement rather than cell division (Hirsch, 1992). Reduced N compounds are exported out of the nodule as ureides and transported through xylem to the shoots where they are catabolised (McClure and Israel, 1979; Atkins et al., 1982). Symbiotic N fixation in legumes involves substantial changes in morphology and physiology, with the root nodule representing an added sink for carbon that competes with other plant organs. The practice of inoculation involves coating the seed with inoculum produced from cultured bacteria. In fields without history of soybean cultivation, it is important to have seeds inoculated over a number of years (Dunigan et al., 1984; Brutti et al., 1998). For example, soybean was a relatively new crop in the Cerrados, requiring seed inoculation with proper strains for adequate nodulation for N fixation (Alves et al., 2003). In contrast, inoculation is no longer practised in areas with long history of soybean cultivation where Bradyrhizobium strains are well established, as it is the case of the Corn Belt (De Bruin et al., 2010; Leggett et al., 2017).

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FIG. 8.13  (a) Seasonal dynamics for total (circles) and fixed N (triangles) in aboveground dry matter (ADM) in soybean. Inset shows the physiological nitrogen-use efficiency derived from the slope of the relationships between ADM or seed yield (at 13% moisture content) and accumulated N at physiological maturity (R7 stage) based on Cafaro La Menza et al. (2019). (b) Relationship between shoot N concentration and ADM. The Generic N dilution curve proposed by Greenwood et al. (1990) for C3 crop species is shown for comparison purposes (dashed curve; y = 5.67 × − 0.5). Average shoot nitrogen concentration and ADM at R1, R3, and R5 stages are shown. Soybean data were collected in five high-yield experiments (range: 5.3–5.8 Mg ha− 1) conducted in Nebraska (USA) during two-crop seasons (2016 and 2017). Crops did not receive N fertiliser during the crop season. See text at the beginning of Section 3 for explanation on calculation of developmental stages. Adapted from Cafaro La Menza et al. (2020).

The symbiosis between the host plant and the bacteria occurs early in the crop cycle: it can be recognised from nodules on the fine roots at V3. After a lag phase with small N fixation, the nodule starts to supply N to the host plant, but it is not until pod setting (R3–R5) that N supply from fixation becomes significant, supplying most of the N accumulated during seed filling (Fig. 8.13a). In high-yield fields in fertile soils in Nebraska, N fixation has been estimated to contribute with ca. 65% of total N accumulated in aboveground biomass at R7 (Cafaro La Menza et al., 2020), which is consistent with other reports from the Corn Belt and elsewhere (Salvagiotti et al., 2008; Ciampitti and Salvagiotti, 2018; Córdova et al., 2019). Although any factor that affects crop growth would invariably impact N fixation, soil variables with greatest influence on N fixation include N and water content, P supply, pH, and temperature (Cassman et al., 1980; Zhang et al., 1995; Purcell et al., 1998; Collino et al., 2015; Santachiara et al., 2019). The contribution of N fixation to total N uptake correlates negatively with the indigenous N supply (Santachiara et al., 2017; Tamagno et al., 2018; Cafaro La Menza et al., 2019), which is consistent with the observation that high soil N prevents formation of new nodules and, especially, reduces N fixation of existing nodules (Norman and Krampitz, 1946; Thornton, 1947; Allos and Bartholomew, 1955; Pate and Dart, 1961; Streeter, 1988; Denison and Harter, 1995; Arrese-Igor et al., 1997). N fixation is more sensitive to soil water deficit or excess compared with other plant physiological processes such as leaf expansion, photosynthesis, and uptake and assimilation of inorganic soil N (Purcell et al., 1998, 2004; Bacanamwo and Purcell, 1999). One of the implications of these differentials in sensitivity to water stress is that soybean will rely more on soil mineral N when water becomes limiting or excessive. The role of P supply on N fixation is discussed in Section 3.5. As soybean yield continues to increase, it is uncertain the degree to which indigenous soil N and N fixation can meet the proportionally higher N requirements. For example, yields ranging from 4.5 to 6.0 Mg ha− 1 had an associated N uptake requirement from 360 to 480 kg N ha− 1. Given that indigenous soil N typically ranges between 100 and 150 kg N ha− 1 across producer fields in the Corn Belt, a question of concern is whether N fixation can fully meet crop N requirements. Cafaro La Menza et al. (2017, 2019) investigated this issue in irrigated experiments in USA and Argentina, following a meticulous protocol involving a ‘control’ treatment (hereafter called ‘zero-N’) that forced the crop to rely on site-specific biological N fixation and indigenous soil N supply and a ‘full N’ treatment specially designed to provide the crop with an ample N supply to optimally match seasonal crop N demand. These authors found a clear N limitation in high-yield environments exceeding 4.5 Mg ha− 1, with the yield difference between treatments averaging 0.6 Mg ha− 1 as a result of differences in N uptake. Recent research has aimed to close this N-driven yield gap in a cost-effective way by evaluating the yield response to different combinations of N-fertiliser type, amount, placement depth, and timing, and its associated economic profitability (e.g. Salvagiotti et al., 2009; McCoy et al., 2018). In general, yield response to N fertiliser application has been inconsistent and not cost-effective as a result of the trade-off between N fixation and soil N absorption (Salvagiotti et al., 2008; Mourtzinis et al., 2018a; Tamagno et al., 2018). Opportunities to alleviate this trade-off have been proposed, although a proof-of-concept is pending and the timeline for impact is uncertain (Denison, 2015). Without

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changes in N fixation efficiency, N supply will become (if not already) a yield-limiting factor in high-yield soybean production environments (> 4.5 Mg ha− 1) as producers in those systems continue to fine-tune their agronomic practices and adopt high-yielding cultivars. Experimental evidence indicates that new cultivars accumulate more N than older ones. Specht et  al. (1999) and Kumudini et al. (2002) noted that total N accumulation was greater in higher yielding modern Canadian cultivars as a result of greater N accumulation during seed filling but not earlier in the crop cycle. The same authors could not detect statistical differences in N harvest index (NHI). Interestingly, their data also indicated that this arose from proportionally more N provided from fixation than from soil N uptake, which is consistent with the notion that, as N requirement increases with higher yields, more N needs to come from fixation (Santachiara et al., 2017). In a separate study, Rotundo et al. (2014) assessed 17 biomass and N traits in a pool of elite cultivars—25 from Argentina and 65 from USA. Consistent with previous studies, the authors found larger N uptake as the key driver explaining high yields. Interestingly, some highyield cultivars had higher N uptake during the vegetative phase (VE–R1) and seed filling (R5–R7), whilst others relied mostly on N uptake during the R1–R5 phase. Highest-yielding cultivars differed in the physiological strategies to attain maximum yield. For example, some of them combined high NUE and HI traits, whilst others exhibited high NHI and similar seed N concentration.

3.4  Dry matter and nitrogen partitioning Dry matter and N accumulation in soybean follow a sigmoidal pattern, with maximum rates during pod setting and early seed filling (Fig. 8.14). In near-optimal conditions, ca. 75% of total dry matter and N accumulation occurs after R3. Early in the season, roots, leaves, and stems have priority for the new plant dry matter. Subsequently, partitioning to roots declines compared with the amount of dry matter that is allocated to growing leaves, petioles, and stems. During seed filling, most of the new dry matter is partitioned to the growing pods whilst dry matter of stems and petioles declines, indicating some remobilisation of nonstructural carbohydrates to the growing seeds as documented in earlier studies (Hume and Criswell, 1973; Stephenson and Wilson, 1977; Yamagata et al., 1987). Back-of-the-napkin calculations on the efficiency to remobilise nonstructural carbohydrates for seed production have yielded estimates of ca. 0.5–0.6 g seed g− 1 (Borras et al., 2004). In near-optimal conditions, the ratio between seed and shoot biomass at R7, that is HI, is ca. 40%, which is lower compared with cereal crops such as wheat and maize (HI range: 0.45–0.55).b This differential reflects the higher construction costs associated with synthesis of seed oil and protein (Amthor et al., 1994). When evaluated on a glucose equivalent basis, soybean HI averaged 0.49, which is comparable to the HI reported for cereal crops (Cafaro La Menza et al., 2019). Although HI is relatively stable across a range of G, E, and M practices (Spaeth et al., 1984; Egli et al., 1985; Egli, 1988), it can change when vegetative growth is favoured over reproductive growth or vice versa. For example, HI tends to decline with increasing duration of the crop cycle owing to more vegetative growth (Schapaugh Jr. and Wilcox, 1980; Egli, 2011). Similarly, HI can change as a result of water deficit, with direction and magnitude of the change in HI depending upon timing of water stress in relation with vegetative and reproductive growth (e.g. Pandey et al., 1984a; Specht et al., 1986; Andriani et al., 1991). Before seed filling, most N resides in leaves, stems, and petioles, mostly as a constituent of the rubisco enzyme (Fig. 8.14). Nitrogen remobilisation from nonseed organs to the growing seed is larger compared with dry matter remobilisation. During the seed filling, ca. 60% of the N accumulated in leaves, stems, and petioles at R5 is remobilised to seed (Ortez et al., 2019; Cafaro La Menza et al. 2020), leading to quick leaf senescence and decline in leaf N and RUE, which has been referred to as ‘self-destruction’ (Sinclair and de Wit, 1975, 1976). So, whilst the number of seeds set during R3–R6 imposes an upper limit to yield, the capacity of the crop to meet the carbon and N demand of the growing seeds ultimately determines the final yield, which is consistent with the source–sink co-limitation proposed for soybean by Borras et al. (2004). At R7, ca. 70% of total aboveground N is located in the seed biomass (i.e. NHI). The large N requirement per unit of seed yield, together with a high NHI, typically leads to a negative N balance in soybean fields, with the latter estimated as N fixation minus grain N removal (Giller and Cadisch, 1995; Santachiara et al., 2017). This apparent negative N balance (which does not account for N in root and nodule biomass and from rhizodeposition) may be partially alleviated when soybean is rotated with cereal crops receiving N fertiliser inputs as is the case in the dominant maize–soybean rotation in the Corn Belt (Tenorio et al., 2020).

b

Estimated HI based on aboveground biomass and seed yield on a dry-matter basis; aboveground biomass included abscised leaves and petioles (Cafaro La Menza et  al., 2017, 2019). Many studies do not account for abscised leaves and petioles, leading to an overestimation of HI as discussed by Schapaugh Jr. and Wilcox (1980).

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FIG. 8.14  (a and b) Dry matter and nitrogen (N) in roots, leaves, stems plus petioles, pod walls, and seeds, expressed as a fraction of the total dry matter or accumulated N at R7 stage. (c and d) Derivatives from the lines fitted in (a) and (b) indicating the relative rate in dry matter and N accumulation. Positive rates indicate accumulation, and negative rates indicate remobilisation. See text at the beginning of Section 3 for explanation on the calculation of developmental stages. Adapted from Cafaro La Menza et al. (2020). Root biomass was estimated following Setiyono et al. (2010).

3.5  Other nutrients Seasonal patterns of accumulation of other mineral nutrients (P, K, S, Ca, Mg, Fe, Mn, Zn, B, and Cu) follow sigmoidal patterns similar to the ones reported for dry matter and N in Section 3.4 (Harper, 1971; Hanway and Weber, 1971; Bender et al., 2015; Gaspar et al., 2017a, 2018). Maximum rates of nutrient uptake occur around R3, except for K that peaks earlier (R1–R3). Nutrients with high HI (besides N) include P, S, and Cu, ranging from 60% to 80% of total nutrient accumulation at R7 (Bender et al., 2015; Gaspar et al., 2017a, 2018). Leaves are the major source of remobilised N, P, and Cu, whilst stems and petioles are major sources of remobilised K (Bender et al., 2015; Gaspar et al., 2017a, 2018). In contrast, Ca, Mg, Mn, B, and Fe exhibited remarkable low HI (10%–40%), whilst K and Zn showed intermediate HI (40%–50%).c Nutrient deficiencies affect both plant growth and N fixation as it has been reviewed elsewhere (e.g. Divito and Sadras, 2014; Gonzales-Guerrero et al., 2014). Soybean has relatively large P and K requirements and large removal of these two nutrients occurs via the harvested seed (Bender et al., 2015; Gaspar et al., 2017a). Based on nutrient accumulation in aboveground dry matter, Tamagno et al. (2017) established mean N:P ≈ 11 and N:K ≈ 2. Given these ratios, a crop that produces 3 Mg ha− 1, which is similar to average yields in USA, Argentina, and Brazil, would require uptakes of 22 kg P ha− 1 and 120 kg K ha− 1. Unlike P and N, which are mostly (ca. 75%) absorbed after R3, ca. half of accumulated K at R7 is absorbed before R3 (Bender et al., 2015; Gaspar et al., 2017a). Phosphorus and K are mobile in the plant, and deficiency symptoms are observed in older leaves. Plants with P deficiency exhibit reduced growth and smaller leaflets (Chiera et al., 2002; Gutierrez-Boem and Thomas, 1999). Soybean is sensitive to soil P deficiencies because nodules are formed at the expense of root length density, which, in turn, is needed for efficient P uptake (Cassman et al., 1980). Likewise, there is a direct effect of P on the growth and survival of rhizobia and their capacity for nodulation and fixation (Cassman et al., 1981b; Singleton et al., 1985). Symptoms of K deficiency are well defined (chlorosis followed by necrosis), beginning at the leaflet margins and moving inwards over the leaflet (Sinclair, 1993). Insufficient K reduces photosynthesis, growth, and partitioning to seed (Huber, 1984; Parvej et al., 2015; Singh and Reddy, 2017); K-deficient plants tend also to be more susceptible to pathogens and insect pests (Amtamann et al., 2008).

c

Estimated nutrient HI was calculated based on nutrient amount in aboveground and seed biomass.

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Raising P availability to meet crop requirements and support N fixation through application of P fertiliser is needed in highly weathered tropical soils to overcome their large P fixation capacity (Cassman et al., 1993). Identification and alleviation of soil constrains was critical for soybean expansion into the Cerrados (Sanchez, 2019). Under natural vegetation, these soils have low pH (range: 4–5), low cation exchange capacity, and high concentration of Al and Fe oxides with a large capacity to fix P. Hence, periodic lime application is needed to increase pH, reduce Al activity, and alleviate Mg and Ca deficiencies. Likewise, after conversion into agriculture, a large amount of P fertiliser is needed to overcome the high fixation capacity of Al and Fe oxides before P can be made available for crops; smaller amounts are needed in subsequent years. Periodic fertiliser K application is also needed. Application of fertiliser P (but not K) is also common in the Pampas (but with smaller rates compared with the Cerrados). In addition to fertilizer, second-crop soybean also relies on residual soil P from previous wheat crop (ReTAA, 2019). Fertiliser P application is less common in the Corn Belt than in the Cerrados and Pampas, with ca. 40% of the fields receiving P fertiliser; K fertilizer is applied in ca. 40% of the fields, which are mostly concentrated in the central and eastern parts of the Corn Belt (USDA-ERS, 2019). Application of P and K fertiliser is guided by soil nutrient tests before sowing, although there have been efforts to use foliar analyses to diagnose nutrient deficiencies and estimating fertiliser requirements (Cassman et al., 1981a; Yin and Vyn, 2004). Reports on S deficiency in soybean have increased in recent decades probably associated with the reduction in atmospheric sulphate deposition from industrial pollution and increasing use of high-analysis fertilisers with less incidental S (e.g. Gutierrez Boem et al., 2007; Hitsuda et al., 2008; Salvagiotti et al., 2012; Kaiser and Kim, 2013). Main symptom of S deficiency is small, yellow–green leaves at the top of the plants, resembling those produced by other immobile nutrients (Sinclair, 1993). Besides yield penalty owing to reduced leaf area expansion and photosynthesis (Sexton et al., 1997), S limitation can also lead to changes in protein composition (Hitsuda et al., 2008). Main S source is mineralisation from soil organic matter; not surprisingly, responses to S fertiliser addition is more likely in soils with low organic matter and/or fields subjected to environmental and management factors that reduce mineralisation rates. Because soil S tests are unreliable, nutrient ratios have been proposed as an alternative to identify S-deficient fields (Hitsuda et al., 2004; Salvagiotti et al., 2012; Divito et al., 2015). A thorough review about S nutrition in soybean is available elsewhere (Hitsuda et al., 2008). Highly productive soils usually contain sufficient quantities of micronutrients for optimum crop growth (Mallarino et al., 2017). Besides their role on the physiological processes that are common to all plants, B, Mo, Co, Fe, Zn, and Ni are important for N fixation (Munns, 1977; Evans and Russell, 1971; Klucas et al., 1983; Gonzales-Guerrero et al., 2014). Seed and foliar fertiliser applications are common to overcome micronutrient deficiencies in the Cerrados, mostly from B, Cu, Zn, and Mn (Fageria and Baligar, 2001; Campo et al., 2009; de Jesus Lacerda et al., 2017). In contrast, micronutrient deficiencies are rare in agricultural soils in the Corn Belt and Pampas, except for specific environments (Mallarino et al., 2017). For example, Mn and Fe are two common micronutrient deficiencies in alkaline soils of the Corn Belt. Because both Mn and Fe are immobile in the plant, the deficiency occurs in the youngest upper leaves, with the leaves turning yellow and veins remaining green (Sinclair, 1993). Symptoms of Fe deficiency are typically referred to as iron deficiency chlorosis (IDC). Soil or foliar fertiliser application can help correct Mn deficiency (Randall et al., 1975), whilst selection of tolerant cultivars and/or use of iron chelates are options to mitigate IDC (Kaiser et al., 2014; Liesch et al., 2011). A thorough review on micronutrients deficiencies in soybean production in the Corn Belt is available elsewhere (Mallarino et al., 2017).

4  Yield and quality 4.1  Yield potential and yield gaps The yield gap is the difference between average on-farm yield and the yield potential defined by solar radiation, temperature, and genotype, and also by water availability in rainfed crop systems (Lobell et al., 2009; van Ittersum et al., 2013; Global Yield Gap Atlas, 2019). This definition of yield potential reflects an upper biophysical limit to what might be attainable for any recent crop cultivar grown on a given field; hence, the magnitude of the yield gap estimates the degree of yield improvement that could still be captured with adjustments in crop management. Specht et al. (1999) and Sinclair and Rufty (2012) reported that a yield potential of ca. 6 Mg ha− 1 can be used as a ‘functional’ upper limit for on-farm soybean yields in favourable environments in the Corn Belt. Experiments in high-yield environments in the Corn Belt and elsewhere have confirmed this upper limit (Spaeth et al., 1987; Van Roekel and Purcell, 2014; Cafaro La Menza et al., 2017, 2019; Zanon et al., 2016). On-farm soybean yields of ca. 6 Mg ha− 1 are currently achieved in the Corn Belt but only under the best possible G × E × M interaction across a large geographic area (see Rattalino Edreira et al., 2020a, b). Hence, this fixed value of yield potential is not meaningful for fields located in other climates and soil types or where water supply is not sufficient to meet crop water requirements. Based on producer data from six regions in Nebraska during 8 years (2004–11), Grassini et al. (2014b) estimated the average yield gap for irrigated soybean using the 95th percentile of the yield d­ istribution in

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each region-year as a proxy to yield potential. These authors found that average yield gap ranged from 12% to 20% of the estimated yield potential, with the latter ranging from 4.5 to 5.0 Mg ha− 1 across the six regions. In subsequent studies, Grassini et al. (2015) and Rattalino Edreira et al. (2017) combined crop modelling and boundary-function analysis, based on site-specific weather and current management practices (sowing date, cultivar MG, and plant density) from farmer soybean fields over many seasons, to determine a yield gap that represents 22% and 13% of the yield potential estimated for rainfed (4.8 Mg ha− 1) and irrigated soybean (5.7 Mg ha− 1) in the Corn Belt. Aramburu Merlos et al. (2015) reported an overall yield gap of 32% of the simulated national yield potential of 3.9 Mg ha− 1 for rainfed soybean in Argentina. Early attempts to estimate yield potential for rainfed soybean in Brazil relied on record yields from contest winning fields (Battisti et al., 2018) or agro-ecological crop models (Sentelhas et al., 2015). Recent efforts to estimate these parameters using wellvalidated process-based models and best available weather and soil databases have yielded an estimated national average yield potential of 5.4 Mg ha− 1 for rainfed soybean in Brazil, with the yield gap representing 45% of the simulated yield potential (Global Yield Gap Atlas, 2019). Underpinning causes for current yield gaps in soybean have received comparably less attention than in cereals. In the Corn Belt, Grassini et al. (2015), Rattalino Edreira et al. (2017, 2020b), and Mourtzinis et al. (2018b), identified late sowing as the key management practice explaining current yield gap in soybean. Their approach, based on analysis of producer survey data, revealed other factors explaining yield gaps, including foliar fungicide and/or insecticide application, tillage method, and P fertiliser application. Following a similar approach, Calviño and Sadras (1999) and later Di Mauro et al. (2018) identified sowing date, previous crop, row spacing, and foliar fungicide and P fertiliser application as potential causes for yield gaps for rainfed soybean in Argentina. Corroborating experimental evidence from the Corn Belt, Di Mauro et al. (2018) also found reduced yield in soybean after soybean compared with soybean rotated with maize (see Section 1). Identification of the causes for yield gaps is needed but not sufficient to close them. In some cases, the solution to close the gap may simply not exist or be too costly. For example, Cafaro La Menza et al. (2017) estimated that N limitation can explain about half of the yield gap measured for irrigated soybean in Nebraska, where current yield potential is 5.7 Mg ha− 1 and average producer yield is 4.5 Mg ha− 1. As explained in Section 3.3, cost-effective measures to close the N-driven gap do not currently exist. In other cases, there may be behaviour factors slowing adoption of a given agronomic technology (Kuehne et al., 2017). For example, whilst optimisation of sowing date seems relatively easy to implement in the Corn Belt, there are many reasons why producers may still be reluctant to sow soybean earlier. The first constraint is a combination of farm logistics and cultural preference as many producers only have one planter, and they prefer to use it for sowing maize first. The second limitation is associated with biophysical factors (i.e. water excess, cold weather) that could often delay sowing. Finally, farmers tend to overestimate the risk associated with seed chilling injury, killing frost, and seed and/or plant stand loss associated with early sowing despite the well-documented benefits of early sowing and associated measures to reduce risk, for example, by using seed treatments and monitoring of soil temperature (e.g. Esker and Conley, 2012; Vossenkemper et al., 2015; Tenorio et al., 2016; Gaspar et al., 2017b). Minimising the yield gap of an individual crop should not compromise the productivity of the entire cropping system and/or increase risk (Guilpart et al., 2017). For example, there is a yield penalty when soybean is sown earlier, or shorter MGs are used in relation to those recommended to maximise soybean yield (as a single crop per year) in the Cerrados (Fig. 8.3). Still, producers prefer early sowings, or shorter MGs when soybean is followed by second-crop maize to minimise the risk of terminal drought in maize and maximise the crop-system productivity (da S. Andrea et al., 2018; Noia Junior and Sentelhas, 2019). Considering trade-offs between closing yield gaps and benefits from other non-yield related factors is also important when evaluating the overall impact of adopting (or not) an agronomic practice. For example, despite the yield penalty associated with no-till adoption in irrigated soybean (Grassini et al., 2015; Rattalino Edreira et al., 2017), other factors can counterbalance this penalty leading to adoption of no-till in irrigated fields to control soil erosion, better capture of pre- and in-season precipitation leading to lower irrigation water requirements, and lowered fossilfuel use for field operations. Finally, when the yield gaps are small and/or agronomic interventions may be too costly or labour-­intensive, it may be wise to look for opportunities to reduce input use without reducing crop yields, as it has been documented for seeding rates and irrigation amount in the Corn Belt (Gaspar et al., 2017b; Gibson et al., 2019), leading to increases in input-use efficiency and farmer profit.

4.2  Seed quality Breeders have found it difficult to simultaneously improve soybean seed yield and seed protein. Despite many claims of achieving that goal, a successful high-yield, high-protein cultivar release has been elusive. Indeed, it has been long known that yield and oil concentration are typically positively correlated, with both traits negatively correlated with protein

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concentration (Burton, 1987; Wilson, 2004). These negative correlations exist whether the source of variation is genetic (Rincker et al., 2014), or agronomic, in both the USA (Assefa et al., 2018, 2019) and Argentina (Bosaz et al., 2019). A multisite evaluation of soybean yield, protein, and oil of a historic set of 168 cultivars of MG II, III, and IV released from 1923 to 2008 documented increases in yield and oil concentration, but concomitant decreases in protein concentration (Rincker et al., 2014). Similar trends have been found for MG III, IV, and V cultivars released between 1980 and 2015 in Argentina (de Felipe et al., 2016). Agronomic practices to simultaneously improve yield and protein concentration have not achieved a similar goal (Assefa et al., 2018, 2019), though increasing seed protein (with minimal impact on seed oil) via seasonally sequential partial applications of high amounts of N fertiliser during the entire crop season may hold promise (Cafaro La Menza et al., 2017). Despite the efforts of both breeders and agronomists towards alleviating the trade-off between yield and protein concentration, a significant downward trend in protein concentration from 1986 to 2019 is observable in USA (Fig. 8.15). These trends are attributable to a decreased seed protein concentration in new high-yielding cultivars that replace old low-yielding ones, as well as a shift of the USA soybean production towards the northwestern areas (Fig. 8.5), where protein concentration is typically lower (Rotundo et al., 2016). We note that soybean seed protein and seed oil yields, when expressed as mass per ha, did in fact, simultaneously increase at respective rates of 0.35 and 0.20 Mg ha− 1 per Mg ha− 1 increase in seed yield (Assefa et al., 2018). However, per ha constituent yields are not of relevance to soybean processors, who crush seed mass to derive meal and oil, and thus must rely on seed concentrations of protein and oil. Major effect protein–oil quantitative trait loci (QTL) have two alleles, one of which enhances protein but depresses oil, and which also depresses yield in those QTL studies that include well-replicated yield tests to reduce Type II error (Patil et al., 2017; Brzostowski et al., 2017). It is often assumed that a high protein–low oil pattern is because of tight linkage

FIG. 8.15  Changes in (a) seed protein concentration, (b) seed oil concentration, and (c) yield for soybean in USA during a 34-year time frame (1986– 2019) based on farmer-submitted harvested seed samples. Values for the last year of the time series (2019) are shown. Data from Miller-Garvin and Naeve (2019) and USDA-NASS.

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of two genes that separately control each trait. If so, the discovery of a cross-over event that would re-phase a two-gene repulsion phase linkage to a coupled phase linkage (i.e. a high protein–high oil phenotype) should be possible. However, a confirmed high protein–high oil recombinant has not been detected in the past 90 years of breeding efforts on evaluating perhaps tens of thousands of lines derived from thousands of mated high–low protein parental lines. This implicitly indicates that pleiotropy, not linkage, should be the null hypothesis in QTL studies based on high–low protein parents (Chung et al., 2003). Indeed, the frequency of the high protein–low oil allele in nearly all QTLs is near-zero in modern high yielding northern USA cultivars (Lee et al., 2019). A better genetic solution would be to identify QTL whose two alleles alter protein but not oil. Such a QTL on soybean chromosome 10 was recently discovered (Phansak et al., 2016; Lee et al., 2019), and initial research indicates that it has an allelic effect for protein of + 0.41% and a low to nil effect for oil (Lee et al., 2019). Remarkably, the counterpart low protein allele is present in 65%–95% in all accessions of MGs 0–X examined by Lee et al. (2019), indicating that the high-protein allele at this QTL offers potential for enhancing relative protein content without depressing oil concentration when introduced into new cultivars. However, this chromosome 10 QTL allele needs rigorous evaluation in different cultivars of differing MG to confirm the initial favourable allelic effects. There is also a question about conservation of mass and energy as improving energy- and N-rich seed components would require higher total biomass and N uptake and/or greater HI and NHI. More realistic opportunities exist to use genetic approaches to modify the fatty acid profile without penalties on yield and protein concentration, for example, to develop high-oleic cultivars for biodiesel production (Clemente and Cahoon, 2009; Graef et al., 2009). In the past, the quality of soybean seed and meal produced in USA was considered inferior to those with higher protein concentration produced in Brazil and Argentina. However, soybean seed quality is now measured more comprehensively by inclusion of the contents of amino acids and sucrose. For example, a recent study found USA soybean meal to be superior for animal feed when compared to meal from Brazil, Argentina, or India because of its higher digestibility, lower fibre, higher average metabolisable energy, and better content of five essential amino acids (Ravindran et al., 2014). The five amino acids (often the most limiting in animal feed rations) are lysine, threonine, and tryptophan for swine and methionine, cysteine, lysine, and threonine for poultry. In that study, USA protein concentration in seed and meal was lower than that of Brazil but not with respect to Argentina. A negative correlation was detected between the concentrations of these five amino acids and protein, but arginine and glutamine/glutamate concentrations correlated positively (Pfarr et  al., 2018). This indicates that, in high-protein phenotypes, increased glutamine/glutamate dilutes the concentration of these key amino acids (Pfarr et al., 2018). The authors concluded that lower seed protein in USA compared to that of other countries may be partially offset by a higher content of the limiting amino acids when used in animal rations. However, the market still uses protein content (rather than amino acid content) as an indicator of the feeding value of soybean seed and meal. The problem is that estimates of amino acid content are too variable for use in market seed component pricing because of unpredictable but significant G × E × M interactions (McClure et al., 2017; Mourtzinis et al., 2017b; Lee et al., 2019). Variation in major seed constituents (i.e. protein, oil) depends on G, E, G × E and, in a lesser degree, M and their interactions with G and E. For example, in central Argentina, G, E, and M accounted for a respective 70%, 27%, and 3% of the modelled variation in seed protein concentration in producer fields (Bosaz et al., 2019). Similar results have been reported for USA (Assefa et al., 2019). Temperature and water availability during seed filling (and their interactions) have been identified as key environmental factors explaining variation in seed constituents (Wilson, 2004; Rotundo and Westgate, 2009; Carrera et al., 2009; Mourtzinis et al., 2017b; Bosaz et al., 2019). As noted earlier, the intrinsic value of harvested soybean seed (at 13% moisture) to processors resides in two valuable constituents: oil (19%) and protein (36%). Carbohydrate (28%) and mineral elements (4%) remain with the protein to produce soybean meal following oil extraction. The relative contribution of protein and oil to overall seed composition is ultimately dependent on the deposition pattern (i.e. timing, duration, and rate) of these components during seed filling. These patterns have been examined by collecting developing seed samples from field-grown cultivars (Rubel et al., 1972; Yazdi-Samadi et al., 1977; Dornbos and McDonald, 1986; Bolon et al., 2010; Poeta et al., 2014). Protein deposition starts earlier than oil deposition; as a result, young seeds exhibit comparably higher protein-to-oil ratio than mature seed. Whilst oil concentration increases linearly during the seed filling, protein concentration increases early during the seed filling but then follows a slightly concave (down then up) pattern for the remainder of seed development. Sources of carbon and N for oil and protein synthesis have been investigated in a number of studies. Yamagata et al. (1987) exposed plants to a 10-h pulse of 13C-labelled CO2 during the first day of seven successive reproductive phases (6–7 days apart) spanning from R1–R7. These authors documented that the seed protein fraction was mostly dependent on photosynthate generated in the first half of seed filling, whereas the lipid fraction was dependent on photosynthate generated during the last half. Remobilisation of N from leaves, petioles, and stems accounts for ca. 60% of the N used for protein synthesis; the rest comes from soil N uptake and N fixation during seed filling (Ortez et al., 2019; Cafaro La Menza et al., 2020). These differential deposition patterns and sources of C and N for protein and oil synthesis need to be considered relative to the timing of temperature and water stress with respect to final protein and oil

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concentrations. Location of seeds on the plant also impact seed constituents: pods at the top of the plant tend to have seeds with higher protein concentration and lower oil concentration than seeds in pods at the bottom of the plant (Escalante and Wilcox, 1993; Bennett et al., 2003). The impact of temperature on protein and oil concentrations of field-grown soybean was evaluated by Piper and Boote (1999) in USA based on data from 20 cultivars ranging from MG 000 to VIII and by Carrera et al. (2009) in Argentina using data from eight cultivars ranging from MG III to VII. The air temperature range during seed filling was nearly identical in both studies (i.e. 15–29°C in USA and 14–27°C in Argentina). In both countries, a near similar quadratic model provided the best fit for the nonlinear increase of oil concentration (from 14% to 22%) in response to temperature increase. This positive association of oil concentration with temperature is consistent with growth chamber studies (Wolf et al., 1982; Dornbos Jr. and Mullen, 1992; Gibson and Mullen, 1996; Wilson, 2004; Rotundo and Westgate, 2009). In contrast, variation in protein concentration over the same temperature range in USA and Argentina field trial studies was much more modest (from 41% to 43%). A U-shaped quadratic model provided the best fit to the observed response of protein concentration to temperature: protein concentration decreased from 43% to 41% when the temperature increased from 14°C to 20°C, with no change from 41% from 20°C to 24°C but then increased from 41% to 43% from 24°C to 28°C. Although significant, temperature accounted for a small fraction ( 28°C) were evaluated (Wolf et al., 1982; Dornbos Jr. and Mullen, 1992; Gibson and Mullen, 1996). Cooler regions tend to produce soybeans with higher sucrose content; that is the case of the north-western fringe of the Corn Belt (Wolf et al., 1982; Kumar et al., 2010). Conflicting results have been reported about the response of oil and protein concentrations to water availability during seed filling. Several authors have reported lower protein and higher oil concentrations with increasing soil water deficit in both field and chamber studies (e.g. Foroud et al., 1993; Specht et al., 2001; Carrera et al., 2009; Wijewardana et al., 2019), whilst others have reported opposite results, that is, higher protein and lower oil concentrations with increasing water limitation (e.g. Dornbos Jr. and Mullen, 1992; Rotundo and Westgate, 2009, 2010; Mertz-Henning et al., 2018). Interactive effects of water stress and temperature on protein concentration have also been reported. For example, Carrera et  al. (2009) found that temperature impact on protein concentration was not detectable in absence of water stress but became discernible with increasing water limitation. The foregoing conflicting findings have hampered our understanding of the influence of G, E, M, and their interactions on seed constituents. Despite efforts to use physiological frameworks to model these responses (e.g. Piper, 1993; Rotundo et al., 2011; Poeta et al., 2014), robust prediction of seed constituents remains elusive.

5  Concluding remarks: Challenges and opportunities Major drivers of soybean development, growth, and seed yield and constituents were reviewed in this chapter. Three major soybean production regions (USA Corn Belt, Brazilian Cerrados, and Argentinean Pampas) served as case studies to illustrate G × E × M interactions. The review of the existing soybean literature allowed the identification of a number of topics deserving further research: (1) identification of causes for yield gaps in present producing areas, especially those where soybean production has recently expanded, (2) understanding the influence of environmental factors on seed constituents, (3) assessing opportunities to alleviate the trade-off involving N fixation and soil N absorption, (4) investigating the apparent negative soil N balance within the context of the cropping sequence, (5) discovering the genetic basis explaining the apparent sensitivity to photoperiod during reproductive stages, and (6) advancing the knowledge of G, M, E, and their interactions to exploit opportunities to increase yield potential and its stability in favourable and water-limited environments.

Acknowledgements We thank Drs Sotirios Archontoulis (Iowa State University, USA), Jessica Torrion (Montana State University, USA), and Claudia Vega (INTA Manfredi, Argentina) for sharing data from previous publications for Figs 8.8 and 8.10. We are also grateful to Drs Sotirios Archontoulis (Iowa State University, USA), Fernando Andrade (INTA Balcarce & University of Mar del Plata, Argentina), Rafael Battisti (University Federal of Goias, Brazil), Kenneth Cassman (University of Nebraska-Lincoln, USA), Shawn Conley (University of Wisconsin, USA), Adriana Kantolic (University of Buenos Aires, Argentina), Seth Naeve (University of Minnesota, USA), Jose Rotundo (Corteva Agriscience, USA), and Fernando Salvagiotti (INTA Oliveros, Argentina) for their useful comments on an earlier version of this chapter. The senior author would like to acknowledge the financial support from the North Central Soybean Research Program (NCSRP) and Nebraska Soybean Board (NSB) for his research programme during the past years.

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Available from: https://www150.statcan.gc.ca/n1/en/type/data (Accessed 1 December 2019). Stephenson, R.A., Wilson, G.L., 1977. Patterns of assimilate distribution in soybeans at maturity. II. The time course of changes in 14C distribution in pods and stem sections. Aust. J. Agric. Res. 28, 395–400. Stewart, D.W., Cober, E.R., Bernard, R.L., 2003. Modeling genetic effects on the photothermal response of soybean. Agron. J. 95, 65–70. Streeter, J., 1988. Inhibition of legume nodule formation and N2 fixation by nitrate. CRC Crit. Rev. Plant Sci. 7, 1–23. Summerfield, R.J., Asumadu, H., Ellis, R.H., Qi, A., 1998. Characterization of the photoperiodic response of post-flowering development in maturity isolines of soyabean [Glycine max (L) Merrill] ‘Clark’. Ann. Bot. 82, 765–771. Sun, C.N., 1957. Histogenesis of the leaf and structure of the shoot apex in Glycine max (L.) Merrill. Bull. Torrey Bot. Club 84, 163–174. Suyker, A.E., Verma, S.B., 2009. Evapotranspiration of irrigated and rainfed maize–soybean cropping systems. Agric. For. Meteorol. 149, 443–452. Suyker, A.E., Verma, S.B., 2010. Coupling of carbon dioxide and water vapor exchanges of irrigated and rainfed maize–soybean cropping systems and water productivity. Agric. For. Meteorol. 150, 553–556. Tamagno, S., Balboa, G.R., Assefa, Y., Kovács, P., Casteel, S.N., Salvagiotti, F., García, F.O., Stewart, W.M., Ciampitti, I.A., 2017. Nutrient partitioning and stoichiometry in soybean: a synthesis-analysis. Field Crop Res. 200, 18–27.

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Tamagno, S., Sadras, V.O., Haegele, J.W., Amstrong, P.R., Ciampitti, I.A., 2018. Interplay between nitrogen fertilizer and biological nitrogen fixation in soybean: implications on seed yield and biomass allocation. Sci. Rep. 8, 17502. Tanner, C.B., Sinclair, T.R., 1983. Efficient water use in crop production: research or re-search? In: Taylor, H.M., et al. (Eds.), Limitations to Efficient Water Use in Crop Production. ASA, Madison, WI, pp. 1–27. Tardieu, F., Reymond, M., Muller, B., Simonneau, T., Sadok, W., Welcker, C., 2005. Linking physiological and genetic analyses of the control of leaf growth under changing environmental conditions. Aust. J. Agric. Res. 56, 937–946. Tenorio, F.A., Grassini, P., Rees, J., Glewen, K., Mueller, N., Thompson, L., Specht, J., 2016. Early Bird Gets the Worm: Benefits of Early Soybean Planting. University of Nebraska-Lincoln Crop Watch. Available from https://cropwatch.unl.edu/2016/early-bird-gets-worm-benefits-early-soybeanplanting. 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Greenhouse studies of nitrogen fertilization of soybeans and lespedeza using isotopic nitrogen. Soil Sci. Soc. Am. Proc. 11, 249–251. Torrion, J.A., Setiyono, T.D., Cassman, K.G., Ferguson, R.B., Irmak, S., Specht, J.E., 2012. Soybean root development relative to vegetative and reproductive phenology. Agron. J. 104, 1702–1709. Torrion, J.A., Setiyono, T.D., Graef, G.L., Cassman, K.G., Irmak, S., Specht, J.E., 2014. Soybean irrigation management: agronomic impacts of deferred, deficit, and full-season strategies. Crop Sci. 54, 2782–2795. USDA-ERS (United States Department of Agriculture – Economic Research Service), 2019. ARMS (Agricultural Resource Management Survey) farm financial and crop production practices. Available from: https://www.ers.usda.gov/data-products/arms-farm-financial-and-crop-production-practices/. (Accessed 1 December 2019). USDA-NASS, 2014. Farm and Ranch Irrigation Survey (2013). 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Vega, C.R.C., Andrade, F.H., Sadras, V.O., Uhart, S.A., Valentinuz, O.R., 2001. Seed number as a function of growth. A comparative study in soybean, sunflower, and maize. Crop Sci. 41, 748–754. Vertucci, C.W., Leopold, A.C., 1983. Dynamics of imbibition of soybean embryos. Plant Physiol. 72, 190–193. Vossenkemper, J.P., Nafziger, E.D., Wessel, J.R., Maughan, M.W., Rupert, M.E., Schmidt, J.P., 2015. Early planting, full-season cultivars, and seed treatments maximize soybean yield potential. Crop Forage Turfgrass Manag. 1, 1–9. Wang, E., Engel, T., 1998. Simulation of phenological development of wheat crops. Agric. Syst. 58, 1–24. Wahua, T.A.T., Miller, D.A., 1978. Effects of shading on the N2 fixation, yield and plant composition of field-grown soybeans. Agron. J. 70, 387–392. Watanabe, S., Harada, K., Abe, J., 2012. Genetic and molecular basis of photoperiod responses of flowering in soybean. Breed. Sci. 61, 531–543. Weber, C.R., Fehr, W.R., 1966. Seed yield losses from lodging combine harvesting in soybeans. Agron. J. 58, 287–289. Wijewardana, C., Reddy, K.R., Bellaloui, N., 2019. Soybean seed physiology, quality, chemical composition under soil moisture stress. Food Chem. 278, 95–100. Wilkerson, G.G., Jones, J.W., Boote, K.J., Buol, G.S., 1989. Photoperiodically sensitive interval in time to flower of soybean. Crop Sci. 29, 721–726. Wilson, R.F., 2004. Seed composition. In: Boerma, H., Specht, J.E. (Eds.), Soybeans: Improvement, Production, and Uses. CSSA, Madison, WI, pp. 621–668. Wolf, R.B., Cavins, J.F., Kleiman, R., Black, L.T., 1982. Effect of temperature on soybean seed constituents: oil, protein, moisture, fatty acids, amino acids, and sugars. J. Am. Oil Chem. Soc. 59, 230–232. Woods, S.J., Swearingin, M.L., 1977. Influence of simulated early lodging upon soybean seed yield and its components. Agron. J. 69, 239–242. 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Zanon, A.J., Steck, N.A., Grassini, P., 2016. Climate and management factors influence soybean yield potential in a subtropical environment. Agron. J. 108, 1–8. Zhang, F., Lynch, D.H., Smith, D.L., 1995. Low root temperature and nodulation, nitrogen fixation, photosynthesis and growth by soybean [Glycine max (L.) Merr.]. Environ. Exp. Bot. 35, 279–285. Zhang, L.X., Kyei-Boahen, S., Zhang, J., Zhang, M.H., Freeland, T.B., Watson Jr., C.E., Liu, X.M., 2007. Modifications of optimum adaptation zones for soybean maturity groups in the USA. Crop Manage. https://doi.org/10.1094/CM-2007-0927-01-RS. Zhu, X.-G., de Sturler, E., Long, S.P., 2007. Optimizing the distribution of resources between enzymes of carbon metabolism can dramatically increase photosynthetic rate: a numerical simulation using an evolutionary algorithm. Plant Physiol. 145, 513–526. Zhu, X.-G., Long, S.P., Ort, D.R., 2010. Improving photosynthetic efficiency for greater yield. Annu. Rev. Plant Biol. 61, 235–261.

Image source: Lachlan Lake

Chapter 9

Field pea Lachlan Lakea, Lydie Guilionib, Bob Frenchc, and Victor O. Sadrasa a

South Australian R&D Institute, and The University of Adelaide, Adelaide, SA, Australia, bL’Institut Agro, Montpellier SupAgro, Department of Biology and Ecology, Montpellier, France, cDepartment of Primary Industries and Regional Development, Merredin, WA, Australia

1 Introduction 1.1  Origin and agronomy Field pea (Pisum sativum L.) is a self-pollinated, diploid (2n = 14), C3 species from the diverse Pisum genus that is part of the Fabaceae family (Smýkal et al., 2015). It is native to the Eastern Mediterranean between Turkey and Iraq and is believed to have been domesticated between 9000 and 10 000 years ago as part of the Neolithic crop assemblage (Zohary and Hopf, 1973; Weeden, 2007; Abbo and Gopher, 2017). Field pea has two wild relatives: Pisum abyssinicum and Pisum fulvum. Although there is some uncertainty, all three species were likely domesticated from Pisum elatius (Weeden, 2007, 2018; Abbo et al., 2009, 2011). Field pea is grown in more than 100 countries with over 90 000 accessions in gene banks (Yadav et al., 2010; Singh et al., 2013). It is commonly grown in rotation with cereals and/or oilseeds as single crop or intercropped. In temperate systems, it is grown either as spring-sown crop in environments with very cold winters such as northern Europe, Canada, and parts of the USA, or as an autumn-sown crop in milder Mediterranean-type environments such as southern Australia. In India and Bangladesh, field pea is sometimes grown as a relay crop in the dry season between successive rice crops (Ali and Sarker, 2013). Field pea may be intercropped with other crops such as canola (rapeseed), faba bean, wheat, or maize, often with improved yield and yield stability when compared to equivalent areas of component crops (Stelling, 1997; Soetedjo et al., 1998; Tan et al., 2020). Field pea, either on its own or in combination with other species, can be used as catch or cover crop, terminated just before sowing another grain or vegetable crop, or as a green or brown manure, terminated at flowering with herbicide or mechanically. Field pea is an important component of cropping systems, particularly in rotation with cereals where it provides ­nitrogen-rich crop residues, broader weed control options, breaks for pests and diseases, and residual soil water for the following crop associated with a relatively short cycle and shallow roots. The associated diversification of crop rotations and reduced nitrogen input improves profitability and provides opportunities in food and feed markets. Field pea is grown for dry seed, fresh peas, and fodder, which are all rich in protein and carbohydrate. The main outlet for protein peas grown in Europe, North America, and Australia is animal feed, but human food outlets expanded considerably in the 2000s. Yellow peas are used by the agri-food and nonfood ingredient industry or traded for human consumption. Green peas are used for breaking and marbled peas for poultry production. Field pea straw is used as forage or bedding for suckler cattle and other slow-growing animals. The top producers and exporters of field pea are Canada, the Russian Federation, and the top importers are India, China, Pakistan, and Bangladesh (FAO, 2018). Field pea straw is sparse; hence the crop facilitates the no-till establishment of the following crop. There are reported yield gains of wheat after field pea in comparison to wheat after wheat that average 0.75 t ha− 1 (~ 10%) in the UK (Vaidyanathan et al., 1987) and 0.45 t ha− 1 (~ 20%) in Western Australia (Seymour et al., 2012). In Australia, the benefits of break crops in terms of mean yield response of the following wheat crop was: oats < canola ≈ mustard ≈ flax < field pea ≈ faba bean ≈ chickpea ≈ lentil ≈ lupin, with an average increase of 1.2 t ha− 1 for grain legumes (Angus et al., 2015). The wheat yield response to legume break crops was relatively greater at high yields (Angus et al., 2015). The inclusion of field pea within a cropping system (especially winter types) favours the spreading of work and diversification of management practices. Sowing and harvesting periods are delayed when compared to the main autumn crops in Europe, and field pea exhibits significant phenological variability, which can be exploited to match environment. In Australia, field pea’s potentially short duration means that it can be sown later than other crops, and it is often sown late for disease and weed management, but yield potential is higher with early sowing. Field pea is usually harvested earlier Crop Physiology: Case Histories for Major Crops. https://doi.org/10.1016/B978-0-12-819194-1.00009-8 Copyright © 2021 Elsevier Inc. All rights reserved.

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322  Crop Physiology: Case Histories for Major Crops

than cereals in Australia (starting from September in drier years) because it is more exposed to terminal moisture stress and can suffer yield loss from pod shattering. By including field pea in rotations with a high proportion of cereals or those of the oilseed–wheat–barley type, field peas help to regulate biological processes within the system (cutting off the cycles of pests, improving soil structure and microorganisms, etc.). In 2017, the operational costs (seed, fertilisation, and treatment products) for field pea in France were between 350 and 500 € ha− 1 depending on the production context, that is, 30 to nearly 100 € ha− 1 less than for rapeseed or wheat. The market price of standard field pea was 20%–45% higher than that of a standard milling wheat over 2012–16 (Inovia, 2017); protein crops also benefit from a financial incentive (coupled aid) to promote protein independence in Europe. Costs for low-rainfall farms in Western Australian in 2013 were A$209 ha− 1 for field pea, A$204 ha− 1 for canola, and A$212 ha− 1 for wheat, depending on the preceding crop. In this case, field pea was less profitable than wheat because its yield was only half of wheat’s. In the USA, field pea has been included in the Farm Act, which means the marketing loan programme provides a minimum return for the crop, recognising its importance in the sustainability of farming systems (Yadav et al., 2010). Field pea yield averaged 1.7 t ha− 1 globally this century despite potential yields above 6 t ha− 1 (Smýkal et al., 2015). Yield improvement averaged 16 kg ha− 1 y− 1 since 1961 (Fig. 9.1), lagging behind wheat (~ 40 kg ha− 1) (FAO, 2018). The rate of yield improvement between 1961 and 1990 was 21 kg ha− 1 y− 1, which has declined afterwards, partially because of the shift of the crop into more marginal environments and lagging research investments.

1.2  Pests and diseases 1.2.1  Insect pests Field pea is susceptible to a range of invertebrate pests, most of which are generalists. During crop establishment, wireworms, armyworms, and cutworms and larvae of various moth and beetle genera, can feed on germinating seeds and roots and shoots of young seedlings. The emerging shoot apex is sensitive to redlegged earthmite (Halotydeus destructor) and lucerne flea (Sminthurus viridis). These are important pests in Australia, which can almost completely destroy an emerging crop. During vegetative growth, various slugs, snails, aphids, and other insects can feed on leaves and other plant parts; snails can also contaminate grain. The pea leaf weevil (Sitona lineatus) is an important pest in most pea-producing areas but not in Australia. Adult weevils feed on leaves, leaving characteristic notches in leaf margins, but larvae feeding on roots and root nodules cause more damage. In Australian crops, larvae of budworms and pod borers such as Helicoverpa spp. and Etiella spp. can cause significant damage to growing pods and seeds and large yield losses. The pea weevil (Bruchus pisorum), actually a beetle, is specific to Pisum spp. in contrast to the more generalist pests above. It feeds on pollen, lays its eggs on the surface of young pods, and the larvae burrow through the pod wall where they enter the developing seed. They remain inside the seed after harvest from where they emerge as adults. Resistance 14

2.2 Area harvested Yield

2.0 1.8 1.6 1.4

8

–1

10

Yield (t ha )

Area harvested (M ha)

12

1.2 6 1.0 4 1960

0.8 1970

1980

1990

2000

2010

Year FIG. 9.1  Global acreage and yield of field pea from 1961 to 2017. Data from FAOSTAT. 2019. Food and Agriculture Organization of the United Nations. http://www.fao.org/faostat/en/#data/QC.FAO.

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was found in P. fulvum, but there is only limited genetic resistance amongst accessions of field pea (Singh et al., 2013). A transgenic field pea has been developed that completely controls the pest but at the expense of unacceptable antinutritional factors for monogastric animals (Morton et al., 2000; Aznar-Fernández and Rubiales, 2019). Specific management packages exist for many of these pests, usually involving insecticides. Some insects can cause severe damage in a very short time if unchecked. For pea leaf weevil and pea weevil, the adult insect is the target rather than the larvae, which do the damage but are inaccessible to insecticide sprays. Likewise, pod borer larvae cannot be controlled chemically once they enter pods. Integrated management not relying entirely on insecticides is encouraged for all these pests.

1.2.2  Fungal and bacterial disease Stem and root rots caused by fungi such as Pythium spp., Rhizoctonia spp., and Botrytis spp. can affect young crops, but the more serious diseases usually occur later on. Blackspot or Ascochyta or Mycosphaerella blight caused by Mycosphaerella pinodes, Ascochyta pisi, and Phoma pinodella is possibly the most damaging pea disease worldwide. Bacterial blight, caused by Pseudomonas syringae pv. pisi and pv. syringae, is widespread and important (Singh et al., 2013). Some fungal diseases, such as Septoria leaf spot, cause little economic damage, but others such as powdery mildew (Erysiphe pisi), Sclerotinia stem rot (Sclerotinia sclerotiorum), and Fusarium wilt (Fusarium oxysporum) vary in their economic importance between regions. Disease management depends largely on crop rotation, fungicides, resistant cultivars, and sowing uninfected seed. Fungicide seed dressings are effective against seedling root and stem rots, and foliar fungicides against later-occurring fungal diseases, but are not always economic. Foliar fungicides have not given economic control of blackspot in Australia (although they can in Canada and Europe), so sowing is often delayed to avoid early-season spore showers, but the delay is at the expense of yield potential. There is cultivar resistance for powdery mildew, Fusarium wilt, and bacterial blight. Seed infection is an important means of transmitting bacterial blight but is secondary for diseases such as blackspot. The effectiveness of crop rotation depends on the host range of the disease and its longevity in the soil and crop residues. Sclerotinia spp. affect a range of oilseeds and legumes, hence limiting rotation options. Fusarium spp. and blackspot fungi can survive in the soil for more than 6 years, hence only long breaks in rotations give effective control (McDonald and Peck, 2009; Tran et al., 2016). Wind-borne blackspot spores released from old crop residues in late summer travel long distances, so crops cannot be grown close to infected residues, which further restricts rotation choices.

2  Crop structure, morphology, and development 2.1  Seed and plant characteristics Individual seed weight varies from 175 to 300 mg. Seed coats can be pigmented or not, with colour ranging from green to yellow. The colouring may be contained within the seed coat and/or the cotyledons. In common with all annual grain crops, seed size of field pea is subject to stabilising natural selection (Sadras, 2007), thus reinforcing agronomic selection for marketing reasons and selection against large seeds, which can cause pod splitting. There are three main phenological types (1) spring, (2) classical winter, and (3) winter ‘Hr’ types, which are highly responsive to photoperiod (Lejeune-Hénaut et al., 2008; Bénézit et al., 2017). Field pea displays three main leaf morphologies: conventional, semi (afila) leafless, or completely leafless, where leaves are replaced with tendrils (Uzun et al., 2005; Mikić et al., 2011; Tafesse et al., 2019). Field pea has an indeterminate growth habit, but some varieties that produce few leaves and flowers over a short period are classed as determinate and tend to dominate the Mediterranean growing regions in southern Australia. Field pea varies for indehiscence or pod shattering (Hradilova et al., 2017). The traditional shattering pod type spreads its seed after maturity causing significant yield loss, whilst the mutation with reduced pod parchment, known as the ‘sugar pod’, almost entirely eliminates shattering, with implications for flexibility of harvest timing (Siddique et al., 2013; Sadras et al., 2019). Plant height at harvest ranges from 0.35 to 1.0 m. The plant is composed of one or several stems with a common organisation (Fig. 9.2). A stem is a succession of phytomeres produced by the apical meristem (Nougarede and Rondet, 1973). Each phytomere is composed of a fragment of stem (internode), one leaf, and one axillary meristem. The stem culminates in a cauline bud containing young phytomere primordia and the apical meristem. The leaf is composed of two stipules, one or several pairs of leaflets, and tendrils at the top of the rachis. In semileafless varieties, leaflets are replaced by tendrils, which improve standability. Before floral initiation, axillary meristems at the lower nodes can either produce branches or abort. After floral initiation, axillary meristems generate reproductive organs. There are one to three reproductive organs on each phytomere. The sequential flowering along the stem leads to a wide heterogeneity of organ stages. Acropetally on the main stem, one can

324  Crop Physiology: Case Histories for Major Crops

FIG. 9.2  Representation of a pea plant. In this example, phytomeres 1–10 are strictly vegetative. The first flowering node is the 11th. Phytomeres 11–14 show successive steps in reproductive organ development from floral bud (phytomere 14) to young pod (phytomere 11). The cauline tip contains the youngest phytomeres and the apical meristem.

successively find the oldest and strictly vegetative phytomeres with leaves and eventually branches, phytomeres with mature leaves and pods, and phytomeres with young leaves, flowers, or floral buds. The stem ends with the apical tip.

2.2 Phenology 2.2.1  Phenological progression Vegetative development is characterised from two stages of phytomere development: the initiation by the apical meristem and the end of foliar organ expansion (i.e. fully expanded stipules). As the phytomere primordia are located in the cauline tip, the determination of the number of initiated phytomeres requires observation of the apex with a magnifying glass or microscope after removing the enclosing leaves. At plant level, the reproductive developmental stages (flowering, beginning of seed filling, and maturity) progress linearly along the stem as a function of cumulative degree days above a base temperature (Ney and Turc, 1993) as illustrated in Fig. 9.3. The potential rates of phenological progression along the stem depend on cultivar. As temperature is the main driver of development, the rates remain quite stable across environments when expressed in number of phytomeres reaching a given stage per degree day. Conversely, the final node number is highly variable (Roche et al., 1999). Development of seeds involves three successive events: fertilisation, beginning of seed filling, and end of seed filling. Fertilisation occurs at flowering and is followed by cell divisions in the embryo with low reserve accumulation. Beginning of seed filling corresponds to the final stage of seed abortion (Duthion and Pigeaire, 1991; Dumoulin et al., 1994). Seed dry matter increases sigmoidally with thermal time from end of cell division until maturity (Ney et al., 1993; Yin et al., 2003). At maturity, the final seed dry weight is reached, and passive dehydration occurs depending on weather. In common with other species, progression of seed filling can be estimated from seed water content (see, e.g. Chapter 3: Wheat, Box 3.2; Chapter 16: Sunflower, Section 2.1). For field pea, Dumoulin et al. (1994) showed that seed filling starts when seed water content falls below 85% and ends when it reaches 55%, which is higher than in other crops.

2.2.2  Photoperiod and temperature The drivers of development from sowing to emergence are soil water content and soil temperature. Assuming an adequate supply of water for germination and growth, temperature is the main phenological driver from emergence to the end of juvenile phase, and from the inductive phase onwards, the drivers are temperature, photoperiod, and water (Roche et al., 1999; Robertson et al., 2002). Developmental models describe the base temperature for development (Tb) ranging from 0°C to 2.4°C, with an optimal temperature (To) of 25–28°C and a maximum temperature (Tm) between 35°C and 38°C. The

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FIG. 9.3  Field pea reproductive development. The progression of key stages, flowering (F), seed filling (SF), and physiological maturity (M) along the stem is represented as a function of thermal time. Note that photoperiod and vernalisation are not considered. At plant level, beginning (B) and end (E) of these key stages determine the period of seed number and seed weight formation (horizontal arrows). NFlo and NFru are the highest flowering and fruiting node, respectively. Modified from Ney, B., Turc, O., 1993. Heat-unit-based description of the reproductive development of pea. Crop Sci. 33, 510–514.

optimum photoperiod (Pb) range is 12.0–12.8 h and thermal time to flowering ranges from 625°Cd and 973°Cd (Ney and Turc, 1993; Jeuffroy and Ney, 1997; Olivier and Annandale, 1998; Weikai and Donald, 1998; Roche et al., 1999; Iannucci et al., 2008; Sadras et al., 2013). Field pea is usually categorised as a facultative long-day plant with a quantitative response to photoperiod and temperature, accelerating development as photoperiod and temperature increase. There are examples of both photoperiodinsensitive (day neutral) and obligate types that do not flower until a threshold photoperiod is met (Berry and Aitken, 1979; Summerfield and Roberts, 1988; Weller et al., 1997; Lecoeur, 2010). Examples of vernalisation have been reported where the position of the first flowering node decreased with temperatures between 4°C and 8°C (Murfet, 1973; Lecoeur, 2010; Weller and Ortega, 2015).

2.2.3  Effect of extreme temperature and water stress The rates of phenological progression, expressed in number of phytomeres reaching a given stage per degree day, are constant over a wide range of conditions. For vegetative development, the rates of phytomere production and leaf appearance are reduced if the fraction of transpirable soil water (FTSW) falls below 0.2 (Lecoeur and Sinclair, 1996; Lecoeur and Guilioni, 1998). Progression rates of flowering, beginning of seed filling, and physiological maturity are not affected by a mild water deficit (Turc et al., 1992; Ney et al., 1994). Severe water deficit can slow down the rate of reproductive development, mainly the rate of flowering along the stem and may also cause the abortion of flowers and embryos (Lecoeur and Guilioni, 2010). In Canadian field experiments, the length of the reproductive phase was longer with increased precipitation (Bueckert et al., 2015). In French field studies, the phenological progression of reproductive stages remained stable for daily average temperature below 25°C, including periods when maximum daily air temperature was around 31°C, whilst in Canadian conditions, temperature above 25.5°C reduced the reproductive period, indicating possible interactions with water (Jeuffroy et al., 1990; Ney and Turc, 1993; Bueckert et al., 2015). Vegetative or reproductive developmental rates did not respond to more severe heat stress, with maximum ambient temperature up to 35°C (Guilioni et al., 1998). High temperature and severe water deficit can change the duration of development, final number of phytomeres, and the position of the highest flowering or fruiting nodes (NFlo and NFru, Fig. 9.3). Water deficit reduces the final number of reproductive nodes and shortens the life cycle (Guilioni et al., 2003; Lecoeur and Guilioni, 2010). The reduction is greater with earlier, more severe, and more persistent stress. Similar effects are observed for moderate temperature stress with maximum daily air temperature around 30°C (Stanfield et al., 1966; Guilioni et al., 1997; Guilioni and Jeuffroy, 2010). This could be viewed as an acceleration of apex senescence and termination of flower production. A severe heat stress may

326  Crop Physiology: Case Histories for Major Crops

have the opposite effect (Guilioni et al., 1997). If the high temperature stress is severe and early enough to cause abortion and abscission of young floral buds, then the final number of phytomeres is increased in direct proportion to the number of flowering nodes with floral abscission (Guilioni et al., 1997).

2.3  Critical period The critical period for field pea yield spans from around the beginning of flowering to the beginning of seed filling, irrespective of cultivar and growing conditions (high temperature, water stress, N deficiency, and low plant density) (Guilioni et al., 2003). In quantitative terms, the critical period has been defined as beginning around flowering to 400°Cd postflowering in French conditions (Jeuffroy et al., 2010) or from 10 days before flowering to 40 days after flowering for cv Nitouche in the high-yielding conditions of southern Chile (Sandaña and Calderini, 2012). For Australian water deficit and heat stress conditions, the critical period was 400°Cd before to 200°Cd after flowering (Sadras et al., 2013). Although there is some variation in the start and finish of the critical period, there is agreement that the most critical point is between flowering and pod set.

3  Growth and resources 3.1  Capture and efficiency in the use of radiation 3.1.1  Radiation interception Interception of photosynthetically active radiation (PAR) is proportional to leaf area index (LAI) and canopy architecture, characterised with the radiation extinction coefficient k (Ishag and Dennett, 1998; Bréda, 2003). The leaves of conventional field pea tend towards horizontal, favouring capture of radiation at early stages, and high LAI compared to semileafless and leafless types (Haboudane et al., 2004). Consequently, radiation distribution into lower layers of the canopy may be limited in vigorous crops (Uzun et al., 2005). Conventional, semileafless, and leafless field peas have different radiation capturing strategies, with tendrils and petioles intercepting 60% of total radiation on leafless peas compared to less than 30% on semileafless or conventional field peas (Heath and Hebblethwaite, 1985). Leafless and semileafless types appear to have a greater photosynthetic efficiency (Heath and Hebblethwaite, 1985). Semileafless varieties can be sown at slightly higher rates than conventionally leaved varieties to compensate for the reduced leaf area. The range of k for field pea is 0.34–1.06 (Heath and Hebblethwaite, 1985; Martin et  al., 1994; Ridao et  al., 1996; Thomson and Siddique, 1997; Ishag and Dennett, 1998; O’Connell et al., 2004). Variation in k arises from variety, row spacing, density, and stage of development (Ishag and Dennett, 1998; O’Connell et al., 2004). Heath and Hebblethwaite (1985) found k ranked leafless > semileafless ≈ leafy. Although water deficit can cause wilting, the dense canopy structure of field pea is rigidly maintained by tendrils with little effect on k (Ridao et al., 1996), but lodging is likely to reduce k (Heath and Hebblethwaite, 1985; O’Connell et al., 2004; Tafesse et al., 2019). LAI is associated with both crop architecture and crop growth rate (Martin et al., 1994). Basal branching of field pea varies with genotype and responds to plant density with a reduction of 0.097 branches per plant for each additional plant m− 2, with higher branching genotypes intercepting maximum radiation at lower densities (Spies et al., 2010). LAI of unstressed conventional and semileafless field pea may reach more than 9, which is comparable to faba bean (Chapter 15: Faba bean, Section 3.1.2) and more than chickpea and some cereals (Ishag and Dennett, 1998; Haboudane et al., 2004; Tesfaye et al., 2006; Sandaña et al., 2012). Such high LAI reduces radiation distribution lower in the canopy with implications for yield of lower fruiting nodes. The proportion of PAR intercepted by the crop or radiation interception efficiency (RIE) (Lecoeur and Ney, 2003) changes during the crop cycle with crop growth rate, LAI, and k. RIE increases sigmoidally with thermal time, RIE remaining low during a latency phase, then increasing exponentially with leaf development, and reaching a plateau (Heath and Hebblethwaite, 1985; Ishag and Dennett, 1998; Guilioni and Jeuffroy, 2010). Low and high temperature as well as water stress limit crop growth rate and delay the maximum LAI and RIE. The latency phase is consequently longer for winter crops. Crop growth rate ranges from 5 to more than 45 g m− 2 d− 1 (Thomson and Siddique, 1997; Ishag and Dennett, 1998; Sandaña et al., 2012; Neugschwandtner et al., 2013; Sadras et al., 2013). Canopy expansion and crop growth are similar to that of oat and faba bean, faster than lentil and chickpea but slower than wheat or barley (Ishag and Dennett, 1998; Siddique et al., 2001; O’Connell et al., 2004; Neugschwandtner et al., 2013, 2014; Lake et al., 2016). A higher proportion of protein in the tissue of field pea compared to cereal and the energy associated with N fixation are possible reasons for this difference.

Field pea Chapter | 9  327

3.1.2  Radiation use efficiency The radiation use efficiency (RUE) of crops is a measure of the conversion of intercepted radiation to biomass. It can be calculated as a function of PAR or solar radiation, using shoots or whole crops, and calculated as the ratio or slope of the regression between biomass and intercepted radiation. Ratios and slopes return similar values if the intercept of the regression is zero, otherwise slopes are biased estimates of RUE (Verón et al., 2005). Here we report RUE defined for shoots and PAR unless otherwise indicated; where relevant, we indicate the method of calculation. Reported RUE ranges from 0.91 to 2.76 g MJ− 1 of intercepted PAR depending on period of measurement, genotype, sowing date, plant population density, and environment (Table 9.1). In Mediterranean conditions where moisture stress is typical, vegetative RUE was similar between Spain (1.4 g MJ− 1) and Australia (1.5 g MJ− 1), with a linear relationship between biomass and intercepted PAR (Martin et al., 1994; O’Connell et al., 2004). The relatively large variation in reported RUE of field pea may reflect actual differences, variations owing to methods of measurement or a combination; however, the indeterminate growth habit, self-shading, and high tissue protein would indicate the potential for greater variation than cereals. Owing to the high energetic cost of protein-rich seed, the RUE of field pea and other grain legumes is generally lower than for cereals, which commonly ranges from ~ 1.4 to 2.9 g MJ− 1 for wheat and more than 3.5 g MJ− 1 for C4 maize (Sinclair and Horie, 1989; Lindquist et al., 2005; Muurinen and Peltonen-Sainio, 2006; Sandaña et al., 2012). Field pea RUE varies with ontogeny (Martin et al., 1994; Ridao et al., 1996; Lecoeur and Ney, 2003) and crop architecture, with semileafless and leafless types intercepting less radiation across the growing season but having a similar final biomass as conventional types (Martin et al., 1994). Heath and Hebblethwaite (1985) reported that lodging decreases RUE. RUE also changes with sowing date, but this is correlated with the change in environmental conditions and the effects on crop photosynthesis (see later). For example, late sowing in Mediterranean environments may expose crops to high temperature in combination with high vapour pressure deficit and water stress, which decreases RUE. Higher proportion of diffuse radiation increases RUE as shown for soybean, sunflower, wheat, and intercropped field pea (Sinclair et al., 1992a,b; Bange et al., 1997; Kanton and Dennett, 2008; Rodriguez and Sadras, 2007). Field pea RIE and RUE are sensitive to water deficit. Lecoeur and Sinclair (1996) showed a threshold FTSW of 0.2 for leaf production and 0.4 for expansion rate for several cultivars. Below the threshold for expansion rate, stomatal conductance and net photosynthesis decreased linearly reducing RUE (Lecoeur and Sinclair, 1996; Guilioni et  al., 2003). Depending on the timing and intensity of stress, water deficit may change the leaf number, leaf area distribution, internode length, and consequently, RIE, RUE, and biomass (Heath and Hebblethwaite, 1985). Field pea RUE declined with average air temperature below 12°C or above 22°C (Guilioni and Jeuffroy, 2010). Consequently, autumn sowings are exposed to suboptimal temperatures for RUE for a significant part of the vegetative phase, and a high temperature often impairs RUE during the reproductive phase, potentially reducing biomass and yield (Guilioni et al., 1998; Guilioni and Jeuffroy, 2010). Despite symbiotic fixation, field pea crops can be N-deficient. N deficiency may reduce branching and change crop architecture (Doré et al., 1998) and reduce the final number of flowering nodes (Sagan et al., 1993; Jeuffroy and Sebillotte, 1997; Roche et al., 1999). Early N deficiency decreases the rate of leaf emergence (Roche et al., 1998) and plant leaf area. Lower leaf N content decreases photosynthesis and RUE (Sinclair and Horie, 1989). In the high-yielding conditions of southern Chile, field pea showed a positive relationship between P supply and intercepted PAR but not RUE (Sandaña et al., 2012). TABLE 9.1  Radiation use efficiency of field pea crops. Country

Measurement period

RUE (g MJ− 1)

Source

NZ

Seasonal

1–2.5

Zain et al. (1983)

Spain

Vegetative

1.43

Martin et al. (1994)

England

Seasonal

0.96–1.46

Heath and Hebblethwaite (1985)

USA

Seasonal

1.67–2.76

Jannink et al. (1996)

Australia

Seasonal

0.91–1.04

Thomson and Siddique (1997)

Australia

Vegetative

1.52

O’Connell et al. (2004)

UK

Seasonal

0.9–2.4

Kanton and Dennett (2008)

Chile

Seasonal

1.13

Sandaña et al. (2012)

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3.2  Capture and efficiency of use of water 3.2.1  Environmental and temporal patterns of water supply and demand In temperate and Mediterranean environments, transient or terminal water deficit is the primary environmental factor limiting yield of field pea (Gan et al., 2009b; Lecoeur and Guilioni, 2010; Blessing et al., 2018). In Mediterranean environments, water supply recharges in autumn, followed by winter-dominant rainfall and gradual drying and rising temperature in spring (Armstrong et al., 1994; Loss and Siddique, 1994; Thomson and Siddique, 1997). However, modelled patterns of water supply and demand in winter rainfall environments of Australia showed that, in common with wheat, the most severe and regular drought has an onset at about 500°Cd before flowering (Sadras et al., 2012). This compromises grain set, and despite severe stress during grain fill, seed number accounts for most of the reduction in yield with drought. In these environments, phenological adaptation is critical with early or late flowering types, shifting the patterns of water use before and after the critical period outlined in Section 2.3 (Mwanamwenge et al., 1998; Siddique et al., 2001).

3.2.2  Capture of water LAI and canopy architecture, as discussed in Section 3.1, influence gas exchange. The rapid canopy expansion of field pea compared to other cool-season grain legumes reduces soil evaporation (Thomson and Siddique, 1997; Thomson et al., 1997; Neugschwandtner et al., 2013). Rapid canopy development may also be associated with a more rapid development of the root system, giving earlier access to water and nutrients when present lower in the profile (Siddique et al., 2012). Semileafless and leafless field peas have relatively conservative water usage with less evapotranspiration early in the season with potential to leave a greater proportion of water available for the reproductive phase, depending on rainfall and soil (Alvino and Leone, 1993; Sandaña and Calderini, 2012). This presents a trade-off between leaf area and evapotranspiration with conventionally leaved types associated with a greater transpiration rate and the potential for earlier water deficit (Alvino and Leone, 1993). Higher evapotranspiration early in the season is less likely to cause stress in winter rainfall environments (see Environment type 1 in Sadras et al., 2012). Issues with early profligate soil water uptake may arise when field pea is grown on a drying profile during a dry winter and spring (Environment type 3 in Sadras et al., 2012). However, the phenological plasticity of indeterminate field pea allows crops to respond to small or intermittent rainfall events common in the low to medium rainfall Mediterranean environment, thus buffering yield losses when compared to determinate crops (Miller et al., 2001; Siddique et al., 2001; Wang et al., 2012; Blessing et al., 2018; Hoffmann, 2019). In bulky, conventionally leaved canopies, self-shading may reduce photosynthesis and transpiration from lower canopy layers. The small fraction of transpiration from either epidermal conductance or imperfectly closed stomata can vary between leaf types, but this accounts for a minor proportion of total transpiration (Sanchez et al., 2001). Osmotic adjustment may help to maintain transpiration through both maintenance of leaf turgor at reduced leaf water potential (Blum, 2017) and capture of water from dry soil (Chapter 16: Sunflower, Section 3.2.3). Turgor maintenance was associated with improved yield under water stress in the field, and turgor maintenance was associated with osmotic adjustment measured in growth chambers (Sánchez et al., 1998). In stressed plants, the concentration of soluble carbohydrates increased up to seven times relative to unstressed controls and was proportional to the osmotic adjustment (Sánchez et al., 1998). However, this relationship is yet to be tested under field conditions. Field pea roots are relatively shallow, fibrous, fine, and have a comparatively large surface area and high radial hydraulic conductance. About 75%–90% of the total root biomass in field pea is typically in the top 0.2–0.5 m of the soil. Roots commonly reach maximum depth of less than 1 m, regardless of soil conditions with isolated reports of deeper roots (Hamblin and Hamblin, 1985; Hamblin and Tennant, 1987; Gregory, 1988; Andersen and Aremu, 1991; Armstrong et al., 1994; Tricot et al., 1997; Merrill et al., 2002). This has implications for summer weed management and the water budget for following oilseed or cereal crops. Root distribution of field pea is thought to respond to soil moisture; however, both increased and reduced rooting depth in response to soil moisture were reported (Merrill et al., 2002; Benjamin and Nielsen, 2006). Field pea root growth is initially slow. After a lag period, the primary or tap root elongates linearly at rates up to 22 mm d− 1 or 0.086 cm d− 1°C− 1, followed by the appearance of first-order laterals acropetally (Gregory, 1988; Tricot et al., 1997; Thorup-Kristensen, 1998; Zhao et al., 2017). The tap root is strongly geotropic, whilst the laterals grow at 45 degrees before turning downward (Gregory, 1988). Expansion continues down into the soil with depth increasing at a maximum rate during the vegetative phase, followed by a reduction in length and density of the first-order laterals that precedes a declining growth rate at flowering; growth persists into the reproductive phase under conducive conditions but generally finishes by late podding (Salter and Drew, 1965; Andersen and Aremu, 1991; Tricot et al., 1997; Merrill et al., 2002; Gan et al., 2009a). The rate of senescence of root tissues vary but tends to rank root hairs > laterals > tap root; rates may also decrease because of interactions with mycorrhizas or increase with soil N content and environmental stress (Thomas and Ougham, 2015).

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Root length density of field pea is lower than for cereals but with higher specific root water uptake (Hamblin and Hamblin, 1985; Hamblin and Tennant, 1987; Armstrong et al., 1994; Martin et al., 1994; Merrill et al., 2002; Benjamin and Nielsen, 2006; Schatz and Endres, 2009; Chaturvedi et al., 2011). Gan et al. (2009a) found that the root biomass of field pea at maturity was approximately one-third of that in canola and wheat with a similar (wheat) or higher (canola) shoot biomass. Under the experimental conditions of Hamblin and Tennant (1987), root length m− 2 of cereals was 5–10 times greater than in field pea despite similar shoot biomass. The small diameter, fine root system of field pea is associated with high radial hydraulic conductance with less distance (shallow cortex) between the soil and the xylem (Hamblin and Tennant, 1987). Hydraulic conductance also depends on the resistance within the cortex associated with aquaporins that facilitate the radial movement of water through the symplastic pathway with a putative role in response to stress (Bramley, 2006; Beaudette et al., 2007; Bramley et al., 2009; Brodribb et al., 2015). In the lateral roots of field pea, a diurnal rhythm of hydraulic conductivity was associated with the expression of aquaporin genes, but there was no correlation between changes in transpiration rate and aquaporin gene expression, indicating a relatively minor role in water movement when compared to the symplastic pathway (Beaudette et al., 2007). Field pea roots produce metaxylem with very low resistance for axial water flow, allowing higher specific-root water uptake and rapid transport of water from the soil into the plant tissue when compared with monocotyledonous species representing an adaptation to small and intermittent rainfall events (Hamblin and Tennant, 1987; Niki and Gladish, 2001; Brodribb et al., 2015; Blessing et al., 2018). Anatomical changes in metaxylem have been demonstrated in chickpea in response to soil moisture; however, increased resistance may limit yield under well-watered conditions (Passioura, 1983; Purushothaman et al., 2013, 2017; Brodribb et al., 2015; Saeed et al., 2016). In experimental conditions of Saskatchewan Canada, total field pea water use was speculated to be limiting yield with significant amounts of water left in the profile after harvest (Wang et al., 2012).

3.2.3  Water use efficiency Crop water use efficiency (WUE) is defined as either biomass or yield per mm of evapotranspiration, and reported ranges for field pea are from 2.5 to 19.1 kg grain ha− 1 mm− 1 (Borstlap and Entz, 1994; Miller et al., 2001; Siddique et al., 2001; McKenzie et al., 2004; Wang et al., 2012; Cernay et al., 2016). Water use efficiency for yield, and particularly for biomass, is usually higher in field pea than in lentil and chickpea (Biederbeck and Bouman, 1994; Miller et al., 2001; Siddique et al., 2001; Chaturvedi et al., 2011; Wang et al., 2012). Possible reasons for superior WUE are higher growth rate and radiation interception at lower temperatures when compared to chickpea, and a lower relative investment in root growth (Knight et al., 1994; Siddique et al., 2001; Benjamin and Nielsen, 2006; Blessing et al., 2018). In some cases, high WUE has been associated with low transpiration, growth, and yield (Zain et al., 1983; Knight et al., 1994; Blessing et al., 2018). Some genotypes of P. fulvum had higher productivity and yield stability in conditions of mild water stress when compared to P. sativum without reduced performance in wetter conditions (Naim-Feil et al., 2017), but results are inconclusive, because comparisons were done in single rows.

3.3  Capture and efficiency in the use of nutrients 3.3.1 Nitrogen Field pea relies upon two sources of nitrogen: fixation of atmospheric N through symbiosis with Rhizobium leguminosarum bv. viciae (Rlv) and soil mineral N (Jensen, 1986; Voisin et al., 2002a). Field pea fixes relatively larger amounts of N compared with lentil or chickpea, with double the seed-N yield reported by Miller et al. (2001). Symbiotic fixation can account for the majority of N acquisition during vegetative growth and may be as high as 92%–97% of total N at flowering and 80% at maturity, representing up to 244 kg N ha− 1 (Rennie and Dubetz, 1986; Evans et al., 1989; Peoples et al., 1995; Armstrong et al., 1997; Beckie et al., 1997; Salon et al., 2001; Reiter et al., 2002; Walley et al., 2007). Molecular, physiological, and agronomic aspects of N fixation have been reviewed comprehensively by Peoples et al. (1995) and Unkovich et al. (2008). Field pea produces indeterminate nodules with an apical meristem that remains functional for weeks to months, elongating and creating distinct nodule characteristics in contrast to the determinate nodules in species such as soybean or common bean (Dupont et al., 2012; Divito and Sadras, 2014; Thomas and Ougham, 2015). The cells in the fixation zone of field pea nodules are functional until around pod fill; however, senescence occurs continuously (Guerra et al., 2010; Thomas and Ougham, 2015). Field pea is in the class of amide transporters in contrast to ureide-transporters such as soybean (Chapter 8: Soybean, Section 3.3) and common bean (Sprent, 1980; Christensen and Jochimsen, 1983; Vance et al., 1985).

330  Crop Physiology: Case Histories for Major Crops

The shoot of field pea seems to control most aspects of the symbiosis through a feedback whereby excess N is translocated through phloem back to the nodulated roots, reducing fixation (Mcinchin and Witty, 2005). Carbon allocated to the nodules and the production of nodules are also under feedback control, where existing nodules prevent the formation of new ones on younger roots. The Rhizobium is primarily responsible for regulating the expression of the nitrogenase gene (Caetano-Anollés and Gresshoff, 1991; Mcinchin and Witty, 2005; Bourion et al., 2007; Laguerre et al., 2007). Field pea root growth and nodule establishment are correlated; however, nodules are a major carbon sink during vegetative growth and reduce root growth compared to unnodulated plants (Schilling et al., 2006). Hypernodulating mutants show restricted root growth and no yield advantage owing to the carbon invested in the nodules (Duc and Messager, 1989; Voisin et al., 2002a; Bourion et al., 2007, 2010; Laguerre et al., 2007). Mcinchin and Witty (2005) reviewed the carbon costs associated with N2 fixation and reduction in legumes. Between 8% and 23% of the net photosynthates during vegetative growth are transferred to the nodules. The estimated associated carbon costs of N2 fixation (2.8–4.8 g C g− 1 N) are higher than the cost of reducing NO3 to NH4 (0.8 to 2.4 g C g− 1-N). In field pea, the estimated cost is from 1.5 to 6.7 g C g− 1 N2 (Schulze et al., 1994; Salon et al., 2001; Voisin et al., 2003; Mcinchin and Witty, 2005). Estimated carbon cost for nitrogenase activity in the nodules for N2 fixation in field pea was 2.03 g C g− 1 N (Schulze et al., 1999). N2 fixation in field pea varies with ontogeny. Fixation rate increased linearly at around 0.3 mg N plant− 1°Cd− 1 from early vegetative growth to peak around flowering and early reproductive growth (Salon et al., 2001). During seed filling, carbohydrates are preferentially allocated to seed, N fixation declines, and N is mobilised from vegetative tissue to seed (Škrdleta et al., 1993; Schulze, 2003; Burstin et al., 2007). Nitrogen mobilisation from vegetative tissue to the seed triggers the release of N compounds from stems, leaves, and roots into the phloem activating, an N-feedback regulation of nitrogenase activity (Salon et al., 2001; Schulze, 2003). Environmental factors affecting biological N fixation include soil mineral nitrogen content, pH, salinity, soil compaction, soil moisture, air CO2 concentration, and radiation (Delgado et al., 1994; Jayasundara et al., 1997; Tricot et al., 1997; Matamoros et al., 1999; Frechilla et al., 2000; González et al., 2001; Salon et al., 2001; Hauggaard-Nielsen et al., 2010; Siczek et al., 2013; Thomas and Ougham, 2015). There is an antagonistic relationship between mineral nitrogen and nitrogen fixation. Field pea nodules are sensitive to nitrate (Škedleta et al., 1984; Jensen, 1987; Sprent et al., 1988; Matamoros et al., 1999; Voisin et al., 2002a,b; Hauggaard-Nielsen et al., 2010). Field peas supplied with 200–250 kg ha− 1 mineral N had between 35% and 50% of total N supplied from fixation with the majority being fixed after flowering. Fixation ceased when mineral N was more than 400 kg N ha− 1 or when it reached more than ~ 2 mol N m− 3 (Salon et al., 2001; Voisin et al., 2002b). Field pea relying on soil mineral N reduces most of the mineral nitrogen (84%–89%) in shoots (Schilling et al., 2006). Soils with pH below 5.5 inhibit nodulation. Salinity and low pH directly affect rhizobial colonisation, nodule development, and the ability of nodules to fix N through reduced acetylene reduction activity, nodule leghaemoglobin content, and respiratory capacity (Delgado et al., 1994; Bolanos et al., 2006). Salinity and low pH reduce shoot and root growth and photosynthates provided to the nodules, indirectly affecting N2 fixation (Brugnoli and Lauteri, 1991). A dry soil also reduces photosynthesis and allocation of carbohydrates to the nodules of field peas, which causes an increased oxygen resistance and decreased bacteriod respiration and N fixation (Armstrong et al., 1997; González et al., 2001; Salon et al., 2001). Soil compaction can reduce root length, nodulation, nitrogenase activity, and total N content (Stirzaker et al., 1996). In potted plants, these compaction effects have been partially offset with the addition of Nod factors (bacterial signalling molecules) that initiate nodule growth (Stirzaker et al., 1996; Siczek et al., 2013). Compaction can increase the thickness of the root cortex, which aids in soil penetration; however, it can decrease the size of the infected nodulated tissue (Siczek et al., 2013). Darkness reduces sucrose and nitrogenase activity in the nodules of field pea with an eventual collapse of metabolism and structural damage (Matamoros et al., 1999; Schulze et al., 1999). Deficiency of phosphorus, potassium and sulphur (Scherer et al., 2008) also compromises N fixation in legumes, including field pea, as reviewed by Divito and Sadras (2014). 3.3.1.1  Critical nitrogen concentration and residual soil nitrogen The critical N dilution curve for field pea described by Ney et al. (1997) relates shoot N concentration (N%) and shoot dry matter (W, t ha− 1): N%  5.08 W 0.32

(9.1)

Critical nitrogen curves define the minimum shoot N concentration for maximum crop growth rate. They are used to define the nitrogen nutrition index (NNI) as the ratio between actual and critical nitrogen concentration. Under conditions not limiting effective nodulation, field pea maintained NNI ≥ 1 for the vegetative period (Ney et al., 1997; Salon et al., 2001). An NNI ≥ 1 at flowering is required for maximum seed set (Ney et al., 1997).

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In common with other pulses (Chapter 8: Soybean, Section 3.3; Chapter 15: Faba bean, Section 5.4), high-yielding field pea draws large amounts of N from the soil with modest contribution to the soil N balance (Senaratne and Hardarson, 1988). The N harvested in shoots of field pea is 2%–3% of total weight, and around 3%–4% in grain, with an N harvest index (HI) of 30%–55% (Armstrong et al., 1997; Beckie and Brandt, 1997; Beckie et al., 1997). About 40 kg of N is removed per tonne of grain (GRDC, 2018). The residual N for the following crop is between 5 and 15 kg ha− 1 t grain− 1 (Jensen, 1994; Beckie and Brandt, 1997). A positive N contribution to the following crop has been estimated to occur when more than 42%–48% of total N is derived from fixation (Kirkegaard et al., 2008), with reported ranges from − 32 to 96 kg N ha− 1 (Senaratne and Hardarson, 1988; Jensen, 1994; Peoples et al., 1995; Stevenson and Kessel, 1996). In Canadian field experiments, the N mineralisation from a field pea stubble in the following crop was between 4 and 45 kg N ha− 1 (Beckie et al., 1997). In the Canadian prairies, starter soil mineral N enhanced field pea vegetative growth with varying effects on yield (up to 11% increase) and seed N content (McKenzie et al., 2001). However, excessive growth and lodging can reduce yield (Voisin et al., 2002a). The most efficient strategy to increase the yield of field pea is speculated to be an addition around the flat pod stage, where N recovery can be as high as 88%. Here plant retrieval and transfer to seed takes place without reducing fixation, provided there is adequate soil moisture. This period coincides with the highest sink demand and declining N2 fixation; however, the result of this strategy depends on environment and soil moisture, with most evidence drawn from controlled conditions (Jensen, 1986; Salon et al., 2001; Voisin et al., 2002b; Bourion et al., 2007). Lower concentration of soil nitrate (0.63 mmol L− 1) prolong nitrogen fixation in older plants (Škrdleta et al., 1993).

3.3.2  Other nutrients Under current Australian conditions, the typical removal of macronutrients (kg nutrient ha− 1 per tonne of grain) is 3.9 (P), 8.0 (K), 1.8 (S), 0.7 (Ca) and 0.9 (Mg); for micronutrients, the amounts (g nutrient ha− 1 per tonne of grain) are 7 (Cu), 28 (Zn), and 14 (Mn) (GRDC, 2018). Deficit of P, K, and S constrain crop growth and can affect nodulation and nitrogen fixation (Jakobsen, 1985; Divito and Sadras, 2014). Shoot and root biomass and nodulation in field pea increased linearly with P rates up to 90 kg P2O5 ha− 1 in field trials in Turkey; the optimal yields were at lower rates, indicating potential penalties associated with excessive vegetative growth (Erman et al., 2009). Trials in high-yielding environments of southern Chile showed uptake efficiencies of 0.49 P g− 1 P available for field pea and 0.44 g P g− 1 P available for wheat (Sandaña and Pinochet, 2014). Field pea returned 125 g yield g− 1 P available in comparison to 195 g yield g− 1 P available for wheat (Sandaña and Pinochet, 2014). Some degree of plasticity has been observed with rooting depth increasing in response to the addition of P (Jin et al., 2014). This is consistent with results from a pot trial showing an increase in root-to-shoot ratio associated with increased availability of P partially mediated by increased carboxylate exudation (primarily citrate) under low P availability (Pearse et al., 2006). Arbuscular mycorrhiza fungi (AMF) form a symbiosis with roots of many plant species, including field pea (Smith and Read, 2010). The mycorrhiza’s hyphae extend the size and reach of the root system and may improve capture of immobile nutrients such as P; other hypothesised benefits include enhanced tolerance to salinity, low pH, water stress, heavy metal toxicity, and pathogens but at extended carbon cost to the plant (Smith and Read, 2010). However, field pea research in southern Australia has shown that beneficial effects on plant nutrient acquisition are likely overstated, except for Zn (Ryan and Angus, 2003; Baird et al., 2010).

4  Yield and quality 4.1  Grain number and weight Grain yield of field pea can be analysed as the product of biomass and HI; but see Tamagno et al. (2020) and Qin et al. (2013) for bias with HI. Grain yield is also described as the product between the components of grain number (the number of plants per unit area, stems per plant, pods per stem, and seeds per pod) and mean seed weight.

4.1.1  Plant population density In high-yielding environments (3 to > 7 t ha− 1) such as those of New Zealand and France, the relationship between yield and plant population density follows an asymptotic response according to genotype with maximum yield for densities greater than 88–100 plants m− 2 (Moot and McNeil, 1995; Doré et al., 1998). At lower plant density, greater branching contributes to higher yields and may compensate for low plant number. Doré et al. (1998) found for cv. Solara a threshold of 115 stems m− 2 below which more grains per stem did not compensate for low stem numbers. Branching might not be sufficient to compensate for low plant numbers when N nutrition is inadequate (Doré et al., 1998).

332  Crop Physiology: Case Histories for Major Crops

4.1.2  Grain number and grain weight In field pea, as in numerous crops, yield variation amongst genotypes and environments is more closely associated with grain number than grain weight (Doré et al., 1998; Ayaz et al., 2004a,b; Poggio et al., 2005; Sadras, 2007; Sadras et al., 2013), as illustrated in Fig. 9.4. At phytomer level, the number of ovules is determined between the initiation of reproductive organ primordia and flowering. At flowering, ovules are fertilised, and seeds develop until the beginning of seed filling, when seed number is fixed (Section 2.2). Owing to the indeterminate growth habit, the period of grain number determination at plant level begins when the first reproductive organs are produced (floral initiation) and ends when the last fruiting node reaches the final stage in seed abortion (beginning of seed filling). In common with most flowering plants, the number of ovules in field pea is rarely limiting (Linck, 1961), and the number of developing seeds always exceeds the final grain number, even if the growth rate is high (Stephenson, 1981). The number of developing seeds seems to be adjusted according to assimilate availability in the plant, but this may be an oversimplification that ignores the sink activity or tissue growth as a driver for photosynthesis (Körner, 2013, 2015). Grain number correlates with plant (or crop) growth rate during a critical period bracketing flowering (Section 2.3). The relationship between seed number per plant and growth rate was linear with the data of Guilioni et al. (2003) and curvilinear for Sadras et al. (2013). The nonlinearity of the relationship may suggest a decoupling between growth and reproduction under conditions that favoured vigorous growth and early canopy closure (Sadras et al., 2013). It could be linked to the direct effect of light quality on reproductive organ abortion in a dense canopy and the indeterminate growth habit resulting in the simultaneous growth of vegetative and reproductive organs. The relationship between seed number and growth rate reflects the plasticity of field pea, which adjusts the number of reproductive sinks in an apparent balance with the availability of assimilates in the plant. Any stress decreasing crop growth rate during the critical period such as water deficit, heat stress, cold, and N deficiency (Section 3) diminishes final seed number (Guilioni et al., 2003). Different architectural phenotypes can achieve similar grain number. Munier-Jolain et al. (2010) reported yield components in two crops with contrasting weather. Both crops presented 120 stems m− 2, 2000 seeds m− 2, and seed weight of 0.25 g but different pod and seed set along the stem. For crops with favourable conditions before flowering and excessive water during flowering, yield was realised on the first eight to nine floral nodes with an average of 2.5 seeds per node. Under severe water stress from the beginning of flowering to mid-flowering, there were only three nodes bearing pods, each one bearing 5.5 seeds on average. Individual seed weight can be analysed in terms of number and size of seed cells or rate and duration of seed growth. In grain legumes, cotyledons are the major storage organ because the endosperm is restricted to a nutrient-rich apoplastic liquid, which is almost totally resorbed at the beginning of the maturation (Marinos, 1970). Maximum cotyledon cell size, which is modulated genetically (Lemontey et al., 2000), and cotyledon cell number (Davies, 1975) determine the potential seed weight. As seed cell divisions span from flowering to the beginning of seed filling, any stress limiting cell division in this period can impair seed weight. Moreover, there is a close relationship between cotyledon cell number and seed growth rate during seed filling (Munier-Jolain et al., 2010). Photoassimilate availability during the time of cell division determines seed growth rate, whereas photoassimilate availability during seed filling drives the seed filling duration (Munier-Jolain 4000

0.30 y = –9E-05x + 0.29 R2 = 0.31

0.20 2000

0.15 y = 5.16x + 483.9 R2 = 0.92

0.10

Grain weight (g)

Grain number (m –2 )

0.25 3000

1000 0.05 0 400

0.00 500

600

700

Grain yield (g m –2 )

FIG. 9.4  Relationship between grain yield and grain number (closed circles) or grain weight (open circle) for semileafless pea Nitouche in southern Chile. Main sources of variation are season and radiation. Fitted lines are linear regressions. Data from Sandaña, P., Calderini, D.F., 2012. Comparative assessment of the critical period for grain yield determination of narrow-leafed lupin and pea. Eur. J. Agron. 40, 94–101.

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et al., 2010). In comparison with seed number, seed size is less responsive to the environment and, for a given growth rate, plants with smaller seed produce more seeds as illustrated in Fig. 9.4 (Sadras, 2007; Gambín and Borrás, 2010).

4.2  Biomass and harvest index In field pea as in many grain legumes, biomass increases sigmoidally from emergence (Martin et al., 1994; Thomson and Siddique, 1997). Correspondingly, crop growth rate increases exponentially with time reaching a maximum soon after flowering and decreasing to negative at maturity (Thomson and Siddique, 1997). Biomass at maturity varies amongst cultivars and growing conditions with up to 14.5 t ha− 1 reported in southern Chile (Fig. 9.5). Higher biomass leads to higher yields (Fig. 9.5), but the relationship varies with environment and genotype consistent with the different relationships between yield and crop growth rate (Guilioni et al., 2003; Sadras et al., 2013). In stressful Australian environments, a nonlinear relationship between crop growth and yield reflects decoupling of vegetative and reproductive growth (Sadras et al., 2013). In high-yield potential environments such as Chile and France, yield may correlate more closely with biomass (Guilioni et al., 2003; Sandaña and Calderini, 2012). Biomass at beginning of flowering is a classical indicator of yield potential at this stage. In European environments, it is generally considered that high yield is achieved when threshold of 300 g dry matter m− 2 is produced at flowering (Munier et al., 2010). A lower biomass at the beginning of flowering reveals preflowering stress such as water deficit or excessive water, compacted soil, and nitrogen deficiency (see Section 3). HI can be considered in two ways. The most common is HI at maturity as an indicator of the overall allocation of plant resources to seed. The second option is to consider the change in HI with time during seed filling. In both cases, HI needs to take into account energy content associated in particular with seed protein and the artefacts of using ratios when allocation is dependent on plant size (Qin et al., 2013; Tamagno et al., 2020). Over a range of environments, a roughly linear relationship exists between yield and biomass. However, it is also true that for the same biomass, HI can vary broadly, indicating a combination of source and sink drivers (Fig. 9.5). The observed linear trend in field pea indicates that the source is a strong driver, but the scatter indicates other limits to HI such as indeterminate growth (Cohen, 1971; Amir and Cohen, 1990). Stresses may reduce seed number and HI (e.g. Guilioni et al., 2003). Shoot N content also influences the final HI. Lecoeur and Sinclair (2001c) showed that the maturity date and the final HI seem to be reached when 80% of the plant N was allocated to seed, regardless of abiotic conditions (Lecoeur and Sinclair, 2001a). This indicates that irrespective of the trophic status of the plant and stresses, the biomass mobilisation requires a minimum amount of N as shown for maize (Uhart and Andrade, 1995). In field pea and lupin, pod wall ratio (pod wall weight/whole pod + seed weight ratio) is a component of HI that incorporates seed per pod, seed abortion, and pod wall thickness; it relates to yield for a wide range of sources of variation (Mera et al., 2004, 2006; Sadras et al., 2013, 2019). Sadras et al. (2019) identified QTL associated with pod wall ratio in field pea. Analysing the dynamics of HI in terms of rate and duration of increase in HI can help to identify the limiting factors during seed set and filling. These two traits can be affected by source:sink dynamics for both carbon and nitrogen. Abiotic 8000 7000

Yield (kg ha–1)

6000 5000 4000 3000 2000 1000 0 0

2000

4000

6000

8000

10000

12000

14000

16000

Shoot biomass at maturity (kg ha –1)

FIG. 9.5  Seed yield as a function of shoot biomass at maturity for various cultivars and growing conditions. Data from Cernay, C., Pelzer, E., Makowski, D., 2016. A global experimental dataset for assessing grain legume production. Sci. Data 3, 160084 (closed circles) and Sandaña, P., Calderini, D.F., 2012. Comparative assessment of the critical period for grain yield determination of narrow-leafed lupin and pea. Eur. J. Agron. 40, 94–101 (open circles).

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or biotic stresses modifying the source or the pattern of sink distribution could change the HI dynamics and its final value. The potential sink size, that is, the number of seeds and the maximum seed size, should also be considered in defining the maximum HI. From beginning of seed filling, HI increases linearly with time (Bindi et al., 1999) or with thermal time (Lecoeur and Sinclair, 2001b). However, the rate of HI increase is variable. For example, Lecoeur and Sinclair (2001b) found a twofold variation in experiments combining genotypes, water and N availabilities, and sowing dates.

4.3  Yield and quality trade-offs Field pea seed has a protein content between 22 and 32 mg g− 1 (Ali-Khan and Youngs, 1973; Blessing et al., 2018). The starch, protein, fibre, vitamins, minerals, and phytochemicals of field pea seed have implications for human and animal nutrition (Singh et al., 2013; Smýkal et al., 2015). A low glycaemic index and reduced starch digestibility results from the intermediate amylose content (Smýkal et al., 2015). In comparison to cereals, the proteins in field pea are low in sulphurcontaining amino acids and tryptophan but high in lysine (Duranti and Gius, 1997). In comparison to other grain legumes, field pea seed is particularly high in isoleucine, valine, and threonine (Duranti and Gius, 1997). Field pea seed also contains antinutritional trypsin inhibitors that can be denatured with heat. The relationship between yield and seed protein content is variable. Quantitative trait loci have been identified for yield and seed protein content in recombinant inbred lines in field trials in Canada (Tar'an et al., 2004). This study showed less than 1% difference between the protein content of the higher (4.2 t ha− 1) and lower yielding (3.8 t ha− 1) parents (Tar'an et al., 2004). Research in Finland found a positive correlation between protein content and yield attributable to climatic variables; protein was positively correlated to temperature sum and negatively correlated to July precipitation (Karjalainen and Kortet, 1987). Earlier work in Canada showed a negative correlation between yield (range from 1.8 to 4.2 t ha− 1) and protein concentration (range: 26.3%–29.4%). The correlation was strongest in the drier environments; P and N fertilisation increased protein (Sosulski et al., 1974).

5  Concluding remarks: Challenges and opportunities A growing world population, changing climate, the increasing need for affordable, high-quality protein, and the increased awareness of the role of legumes in rotations for sustainable agriculture is driving interest in pulses such as field pea. Present trends in global trade and prices are mostly driven by increased demand, particularly from India and China. Against the backdrop of increasing demand, the rate of yield improvement is lagging for several causes, including the cultivation of field pea into marginal cropping land and lagging research effort. To capture opportunities of rapidly growing markets and to compete with other crops, research opportunities and priorities should focus on yield improvement for both current and anticipated conditions, with a major focus on adaptation to drought and thermal stress. Improvement of seed quality may be important in emerging markets. Field pea diversity in physiological, morphological, and architectural traits provides opportunities for breeding and agronomy, which can be further expanded with wild genotypes such as P. fulvum. Potential avenues for yield improvement are increasing resource capture and use efficiency with different root and leaf/stem arrangements, improved root and leaf physiology, and improving N fixation. Our understanding of critical physiological processes is a major bottleneck. The assumption of carbon assimilate as the main driver of growth and yield needs critical revision (Körner, 2015). Theory is needed to integrate the economies of carbon, water, and N in pulses (Cossani and Sadras, 2018; Kunrath et al., 2018). Quantifying the overall system implications of field pea in rotations and intercrops, such as the residual water and nitrogen contributions to the following crops or companion crops, is important.

Acknowledgements We thank Neil Turner and Thomas Sinclair for their thoughtful comments and the Grains Research and Development Corporation for supporting our research in grain legumes.

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Image source: Authors

Chapter 10

Chickpea Vincent Vadeza,b, Amir Hajjarpoorb, Lijalem Balcha Korbuc, Majid Alimaghama, Raju Pushpavallid, Maria Laura Ramireze, Junichi Kashiwagif, Jana Kholovab, Neil C. Turnerg, and Victor O. Sadrash a

Institute for Research and Development (IRD), UMR DIADE, University of Montpellier, Montpellier, France, bInternational Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Crop Physiology Laboratory, Patancheru, Telangana, India, cEthiopian Institute of Agricultural Research (EIAR), Debre Zeit Research Center, Debre Zeit, Ethiopia, dCSIRO Agriculture and Food, Floreat, WA, Australia, eMycology and Mycotoxicology Research Institute (UNRC-CONICET), Río Cuarto, Córdoba, Argentina, fCrop Science Lab., Research Faculty of Agriculture, Hokkaido University, Sapporo, Japan, gSchool of Agriculture and Environment, Faculty of Science, The University of Western Australia, Perth, WA, Australia, hSouth Australian R&D Institute, and The University of Adelaide, Adelaide, SA, Australia

1  Introduction and agronomic context 1.1  Origin and ecology Chickpea (Cicer arietinum L.) is the primary source of food protein for about 20% of the world population and is the second largest pulse crop after common bean, with a global production of about 17.2 Mt from 17.8 Mha (FAOSTAT, 2018). Chickpea and its most closely related wild relatives are mostly self-pollinated diploids with eight chromosomes and genome size of about 750 MB. Cultivated chickpea and its wild progenitor, Cicer reticulatum, originate from Southeastern Anatolia, in modern Turkey (von Wettberg et al., 2018). Chickpea is believed to have been domesticated about 10 000 years ago, with the oldest archaeological remains traced to northern Syria around 5000 BC. However, archaeological remains are scant and chickpea could well be confused with pea in a number of cases, and dates of its introduction to sub-Saharan Africa and the Indian subcontinent are unclear (Redden and Berger, 2007). Part of the reason for a limited genetic diversity in this crop could relate to the shift during early cultivation from winter to summer cropping to escape Ascochyta blight and the relatively narrow expansion of the wild progenitors (Abbo et al., 2009). However, a recent recollection of wild progenitors has shown a larger diversity than what was available in the gene banks (von Wettberg et al., 2018). Cultivated chickpea is erect, contrary to its wild progenitor, which is believed to have been a domestication criterion. Chickpea is divided into two main groups: (1) Desi, featuring a small seed with dark and thick seed coat that usually needs dehulling for consumption; this type predominates in South Asia and sub-Saharan Africa and (2) Kabuli, featuring large seed with lighter and thinner seed coat that is consumed as whole seeds; this type dominates in West Asia and Mediterranean regions. Kabuli plants are less vigorous than their Desi counterparts (Lamichaney et al., 2016). While these two types have often been seen as two distinct subgroups, molecular analysis is ambiguous (e.g. Roorkiwal et al., 2014).

1.2  The role of chickpea in farming systems In India, chickpea is cultivated as a sole crop during the dry winter season on approximately 9 Mha between 14°N and 29°N in rotation with rice, maize, or fodder sorghum in the North, rice or pearl millet in the Eastern Plain Zone, sorghum or pearl millet in the Central zone, soybean in Madhya Pradesh, and cotton in Tamil Nadu (Majumdar, 2011). Chickpea used to be prevalently cultivated in the northern states, although the spread of Aschochyta blight has pushed the crop further south, where it presently dominates. The cropping season ranges from 150 to 160 days in the North to 100 days in the South (Ali and Kumar, 2003). Chickpea relies on stored soil moisture, with terminal water stress in about 60% of the situations (Hajjarpoor et al., 2018). However, the water limitation varies across regions, depending on seasonal rainfall or access to irrigation. A recent environmental characterisation has identified six subregions (Fig. 10.1). In three of the six subregions, water deficit reduced yield by about 70%, falling from irrigated yields of about 2.9 t ha− 1 to rainfed yields of about 1.0 t ha− 1. Modelling and experiments have shown that a single irrigation of 40 mm during grain filling could increase grain yield by 30%–40% (Vadez et al., 2012a). Crop Physiology: Case Histories for Major Crops. https://doi.org/10.1016/B978-0-12-819194-1.00010-4 Copyright © 2021 Elsevier Inc. All rights reserved.

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344  Crop Physiology: Case Histories for Major Crops

FIG. 10.1  Clustering of chickpea production zones into six homogenous system units (HSUs). From Hajjarpoor, A., Vadez, V., Soltani, A., Gaur, P., Whitbread, A., Suresh, D., Murali, B., Gumma, K., Diancoumba, M., Kholová, J., 2018. Characterization of the main chickpea cropping systems in India using a yield gap analysis approach. Field Crop Res. 223, 93–104. https://doi.org/10.1016/j.fcr.2018.03.023.

Ethiopia is among the top 10 chickpea-producing countries in the world and accounts for over 90% of African grain production (Asfaw et al., 2010; Pachico, 2014; Fikre, 2016), mostly (95%) from the highlands. Farmers use chickpea for soil fertility repair, as a food and feed in smallholder crop-livestock systems, and more recently, to meet demand by the local industries and export markets (Fikre, 2016). Present yield averages about 2.0 t ha− 1 (CSA, 2017/18) compared the attainable yield by leading farmers around 3.5 t ha− 1 (Fikre et al., 2018). Main constraints include excess or deficit of water, nutrient deficiency, degraded soils (Tamene et al., 2017; Vanlauwe et al., 2010), and the use of landraces with low yield potential (Abdulkadir et al., 2017; Amede et al., 2002; Robson et al., 1981; Korbu et al., 2016). Chickpea is traditionally sown at the end of the rainy season from early September to October (Bejiga and Tullu, 1982). Rains of the main cropping season often start in

Chickpea Chapter | 10  345

June, and precipitation peaks in July and August in most growing areas. Early sowing is usually associated with severe waterlogging (Jutzi and Abebe, 1987), where surface drainage methods such as broad bed and furrows increase yield (Mamo et al., 1994; Ayana, 2014; Agegnehu and Sinebo, 2012). Waterlogging also exposes the crop to higher risks of wilt/root rot diseases causing plant mortality (Agegnehu and Sinebo, 2012; Bejiga et al., 1997). The yield of late-sown chickpea, with September rainfall below 50 mm, is constrained by moisture stress during grain filling (Bejiga and Anbessa, 2002). However, the decline in temperature from October to December following the cessation of rain implies less effects from heat stress under Ethiopian condition than in India for late-season crops. Therefore, mid-August to early September seems the ideal sowing period in the majority of chickpea-growing areas of Ethiopia (Bejiga and Tullu, 1982; Bejiga et al., 1994; Agegnehu and Sinebo, 2012). In Australia, chickpea is grown in summer rainfall environments, relying mostly on stored soil water, and in winter rainfall environments, where it relies on in-season rainfall. Historically, chickpea was grown as a break-crop in wheat-based rotations, was replaced by canola when Ascochyta blight devastated the crop across southern Australia, but favourable prices and Ascochyta-resistant genotypes have driven a renewed interest and expansion of the crop in recent years, particularly in areas where chickpea is grown on stored soil moisture and the risk of Ascochyta is lower. Chickpea provides benefits to the following cereal crop, and its effect extends up to the second crop after chickpea (Angus et al., 2015).

2  Crop structure, morphology, and development Chickpea morphology varies from prostrate to erect. Traditionally, chickpea in India used to be weeded and harvested manually, but high labour costs are driving mechanisation requiring morphologically adapted cultivars. NBeG47 is the first such cultivar released in 2016. It is anticipated that mechanisation of the chickpea harvest, coming with a more erected and taller plant type of the crop in India, may lead to changes in the density of sowing because more erect plants should enable a higher sowing rate. Chickpea is mostly cultivated as a spring crop in Mediterranean countries such as Turkey, to a certain extent as a winter/ spring crop in Israel, Spain, and Turkey, where Ascochyta blight is an endemic threat, as a winter crop in Mediterranean environments of Australia, and as a winter crop in tropical and subtropical climates of Southeast Asia and Australia. Chickpea phenological development is modulated by genetics, temperature, and photoperiod, and it also responds to soil water content and salinity (Summerfield et al., 1981; Roberts et al., 1985; Ellis et al., 1994; Soltani et al., 1999, 2006a,b; Robertson et al., 2002; Chauhan et al., 2019). Chickpea is a long-day plant with photoperiod below 11 h delaying flowering (Soltani et al., 2006b; Roberts et al., 1980; Summerfield et al., 1981). Soltani et al. (2006b) summarises cardinal temperatures for chickpea development and reports small genetic variation in cardinal temperatures and photoperiod threshold among four genotypes. In common with other pulses, the most sensitive developmental window for yield response to stress in chickpea is about 200°Cd after flowering (Lake and Sadras, 2014). For a given location, the combination of variety and sowing date is usually tailored to manage the risk of cold, heat, and drought in relation to this critical developmental window. For instance, chickpea is usually sown from late November to December in northern India (Berger et al., 2006), allowing first flowers to avoid both low temperature and Aschochyta blight, a fungal disease that spreads under cool, wet, and humid conditions. In southern India, chickpea is sown in October leading to flowering and grain filling in January–February, to avoid the hot summer with maximum temperatures over 35°C in mid-February. Breeding programmes in India have thus developed cultivars with phenological adaptation to either northern or southern latitudes (Berger, 2007). However, broadly adapted cultivars have also been identified (Berger et al., 2006). A recent study has shown that cultivars putatively adapted to the North and to the South had longer and shorter phenophases (Vadez et al., 2013). However, modelling output showed that 43 biological days from emergence to flowering favours yield across latitudes of India, which would ease breeding and multilocation testing. Flowering seems to respond nonlinearly to water stress (Robertson et al., 2002; Soltani et al., 1999), and prediction of flowering has improved when top soil water content is considered in addition to temperature and photoperiod (Soltani et al., 1999; Chauhan et al., 2019). Salinity delays flowering (Vadez et al., 2007; Pushpavalli et al., 2015), and delayed flowering is partially responsible for about 50% lower yields, measured on individual plants grown in large pots (Pushpavalli et al., 2015).

3  Growth and resources 3.1  Capture and efficiency in the use of radiation Dry matter production is the product of incident photosynthetic active radiation (PAR), the fraction of that radiation intercepted by the crop, and the radiation use efficiency (RUE). Intercepted radiation is determined from an exponential function of crop leaf area index (LAI) and the extinction coefficient of the canopy, k. Hughes et al. (1987) reported k for total

346  Crop Physiology: Case Histories for Major Crops

FIG. 10.2  (a) Chickpea plant leaf area as a function of main stem node number described by a power function as y = xb for different plant densities (15, 30, 45, 60 plants m− 2) and (b) dependency of the coefficient of the power function on chickpea plant density (Soltani et al., 2006b).

solar radiation of prostrate chickpea line ILC482 of 0.61 ± 0.019 and of 0.47 ± 0.031 for the erect line ILC72. Soltani et al. (2006a) reported k = 0.51 for Hashem, a locally adapted Kabuli cultivar in Iran. Chickpea leaf area growth can be described by a power function: y = xb where y is the plant leaf area, x is the main stem node number and b is a coefficient that depends on plant population density (Fig. 10.2). Node production on the main stem starts after emergence and terminates at first-pod stage. Photoperiod (for chickpea varieties sensitive to photoperiod), temperature, water availability and phyllochron (thermal time period between emergence of successive leaves) determine the time available for node production on the main stem (Soltani et  al., 2006c). A shorter phyllochron would favour earlier canopy closure. At later stages of development, the amount of photosynthate partitioned to leaf and specific leaf area limit the daily increase in plant leaf area (Soltani and Sinclair, 2011). During seed growth, leaf senescence depends on the daily amount of nitrogen (N) mobilised from the leaf to grain (Soltani and Sinclair, 2011; Soltani et al., 2006c). Water-deficit decreases chickpea leaf area expansion in proportion to stress severity (Singh, 1991; Soltani et al., 2000). Leaf expansion declines linearly when fraction of transpirable soil water (FTSW) reaches 0.5 (Soltani et al., 2000). Seasonal RUE, on a PAR and shoot biomass basis, ranges from 0.7 to 2.1 g MJ− 1 (Table 10.1). Owing to leaf senescence and the synthesis cost of protein in seed, RUE decreases during grain filling (Lake and Sadras, 2014). RUE was also shown to be related to grain yield, although this relation depended on interactions with the phenological stage and the environment (Lake and Sadras, 2016). Fig. 10.3a presents the relationship between RUE and temperature showing a linear increase between 3°C and 14°C, a peak between 14°C and 22°C, and a decline to zero between 22°C and 36°C (Soltani et al., 2006a). In agreement with this pattern, Singh et al. (1982) reported that the photosynthetic rate of chickpea increased with increasing air temperature from 10°C to 17°C and peaked at 22°C. Whether a linear plateau model is used (Soltani and Sinclair, 2012) or a nonlinear model (Singh and Rama, 1989), RUE declines when approximately two-thirds of the available water in the soil has been used (Fig. 10.3b). RUE is lower in crops with higher extinction coefficient (value range for chickpea: 0.50–0.80) because canopies with erect leaves have better radiation distribution and a lower proportion of radiation-saturated leaves than a canopy of flat leaves (Ayaz et al., 2004). RUE increases with increasing CO2 concentration according to (Goudriaan et al., 1984):  C RUE x  RUE 0   1  b  ln  x   C0 

   

(10.1)

where RUEx is RUE at a CO2 concentration lower or higher than the reference, RUE0 is RUE at the reference CO2 concentration C0, and Cx is the target CO2 concentration. The coefficient b is 0.4 in C4 plants and 0.8 in C3 plants. There are few reports about CO2 concentration effect on chickpea dry matter production (Pal et al., 2008; Jin et al., 2012; Rai et al., 2016; Chakrabarti et al., 2019). One of these reports found a 24% increase in RUE following an elevation in CO2 concentration to 580 ppm (Saha et al., 2015). In Fig. 10.4, the results of these papers were used to evaluate accuracy of Eq. (10.1), where the coefficient variation of actual–calculated RUE was 15.4%, suggesting that the method is accurately adjusting RUE for CO2 concentration in chickpea.

Chickpea Chapter | 10  347

TABLE 10.1  Summary of RUE reported for chickpea. Location

Water regime

PAR or solar

RUE (g MJ− 1)

References

Dolby, Australia

Irrigated

PAR

1.53

Leach and Beech (1988)

Patancheru, India

Irrigated

Solar

0.67

Singh and Rama (1989)

Valenzano, Italy

Irrigated

Solar

1.20

Albrizio and Steduto (2005)

Dire Dawa, Ethiopia

Irrigated

PAR

2.07

Tesfaye et al. (2006)

Gorgan, Iran

Irrigated

Solar

0.79–1.18

Soltani et al. (2006a)

Adelaide, Australia

Irrigated

PAR

1.30

Lake and Sadras (2017)

Aleppo, Syria

Rainfed

Solar

0.30–0.93

Hughes et al. (1987)

Canterbury, New Zealand

Rainfed

PAR

1.25

Ayaz et al. (2004)

Dire Dawa, Ethiopia

Rainfed

PAR

1.68

Tesfaye et al. (2006)

Saskatoon, Canada

Rainfed

PAR

1.18–1.93

Li et al. (2008)

Adelaide, Australia

Rainfed

PAR

0.87

Jahansooz et al. (2007)

Adelaide, Australia

Rainfed

PAR

0.98

Lake and Sadras (2017)

Data based on shoot biomass and intercepted radiation.

FIG. 10.3  Relative change of RUE for chickpea as a function of temperature (a) (Soltani et al., 2006a) and (b) fraction of transpirable soil water change (Singh and Rama, 1989; Soltani and Sinclair, 2012); data points represent the average values for 10–15 intervals during the postrainy seasons of 1984, 1985, and 1986 at Patancheru, India.

FIG. 10.4  Relative change of RUE in chickpea as a function of carbon dioxide concentration changes based on Goudriaan et al. (1984) method. Points are real data from literature where average reference carbon dioxide concentration (C0) was around 380 ppm (Pal et al., 2008; Jin et al., 2012; Saha et al., 2015; Rai et al., 2016; Chakrabarti et al., 2019), and the curve represents the model.

348  Crop Physiology: Case Histories for Major Crops

3.2  Capture and efficiency in the use of water This section shows varying water stress patterns and the adaptive value of individual canopy and root traits. Box  10.1 ­illustrates integration of physiological processes in a modelling framework.

Box 10.1  Modelling to integrate physiology and agronomy Experimental work is usually limited by the number of seasons, sites, cultivars, and management combinations that are affordable, and information on crop development, growth, and yield is usually fragmented. Crop simulation models complement experiments and integrate knowledge quantitatively, allowing to test G × E × M interactions in diverse scenarios. For chickpea, much of the modelling work has used the Simple Simulation Modelling framework (Soltani and Sinclair, 2012). An outline of the simple simulation modelling framework Plant phenology is critical for crop adaptation, especially for chickpea that grows within constrained time boundaries, facing terminal drought in many cases, increasing temperature at the end of its growing season in most growing regions, or cold stress at flowering in others (Vadez et al., 2013; Soltani et al., 2016; Kaloki et al., 2019). Soltani and Sinclair (2011) used the concept of biological day for predicting phenological stages in chickpea. In this approach, on each calendar day, a fraction is calculated to reflect the inhibition of potential development because of restricting functions, which in chickpea are temperature, photoperiod, and soil water content (Soltani and Sinclair, 2012). The duration of a phenological stage can be defined as the minimum number of days required for progress through a stage under optimum conditions. Leaf area is modelled in two stages; firstly, the main stem node number is predicted as a function of temperature and water availability. Then, plant leaf area is computed on the basis of an exponential relationship between leaf area and the main stem node number (Fig. 10.2a), taking into account plant population density (Fig. 10.2b) and the influence of environmental and management factors such as nitrogen availability (Soltani and Sinclair, 2011). Radiation use efficiency RUE (Section 3) is modelled to allow the calculation of daily biomass production (Soltani and Sinclair, 2012). The model varies RUE between vegetative and reproductive stages and accounts for the effect of atmospheric CO2 (Soltani et al., 2007); see also Fig. 10.4. Biomass accumulates as a function of PAR intercepted by the canopy and RUE. LAI and extinction coefficient k are determinants of radiation interception (Sinclair, 1986; Hammer et al., 2010; Robertson et al., 2002). The biomass is then allocated between root and shoot according to allometric coefficients. A biphasic pattern was found in chickpea for biomass partitioning between leaves and stems before first-seed stage (Soltani et al., 2006d). With low total dry matter, 54% of biomass produced was allocated to leaves, but at higher levels of total dry matter, that is, under favourable and prolonged conditions for vegetative growth, this portion decreased to 28%. During the period from first-pod to first-seed, 60% of biomass produced was partitioned to stems, 27% to pods, and 13% to leaves. During the period from first-seed to maturity, 83% of biomass was partitioned to pods. A general water balance is used similar to other crops. Water availability is set by a root front that penetrates the soil from emergence and down to a set soil depth, with chickpea-specific coefficient driving the deepening of the root front. Crop transpiration is derived from daily cumulated biomass, from the relationship between dry matter and transpiration, taking VPD into consideration (Tanner and Sinclair, 1983). A nitrogen balance is calculated in the model, because N is both critical for the photosynthetic apparatus and for filling grains. Soltani and Sinclair (2011) assume that at 14 biological days after sowing, N2 fixation activity becomes fully active to meet the nitrogen needs of the crop. Model applications Kaloki et al. (2019) identified 21 parameters through multiple linear regression on chickpea yield, which accounted for 91% of the total variation in yield in a 2-year experimental dataset. Considering these traits/parameters, simulations were conducted in multiple years (100) and locations (50) across the Australian grain belt in comparison with commercial genotypes. Ideotypes incorporating key traits and targeting specific environments were designed (Kaloki et al., 2019). To optimise chickpea phenology in different latitudes of India, Vadez et al. (2013) found that the optimum flowering time for each location related more to the incoming rainfall of the location rather than to its latitude. The results of this simulation suggested that an optimal phenology fitted many of the latitudes in India, and that breeding of future chickpea cultivars should strongly consider matching plant phenology with the amount of rainfall expected in the target region. Vadez et al. (2013) also simulated the effect of extending root traits, altering soil depth, and applying one supplementary irrigation at grain filling in multiple years and locations of India. Extending maximum depth of soil water extraction by roots led to an 8%–12% yield benefit. Against expectations, increasing the rate of root growth to reflect the characteristics of an existing root trait QTL on LG5 caused a 4%–6% yield penalty in all situations. This yield loss was a consequence of more rapid consumption of soil water at depth, resulting in a greater soil–water deficiency late in the season during the seed filling period. Changing the rate of leaf area development rate associated with the root QTL locus on LG5 had no impact on yield, except for a yield increase at the two highest yielding locations. The greatest yield benefit (about 30%) was obtained by irrigating the crop with 30 mm at the beginning of seed growth (Vadez et al., 2012a), a prediction that was confirmed experimentally.

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BOX 10.1  Modelling to integrate physiology and agronomy—cont’d Soltani et al. (2001) used a crop model and long-term weather data to analyse the response of chickpea to limited irrigation. They showed that in northwest Iran, chickpea experiences terminal drought that starts between flowering and the beginning of seed growth, severely reducing grain yield by 67% when compared to irrigated conditions. Simulated grain yield showed a large response to limited irrigation when the first irrigation was applied at the beginning of seed growth, resulting in a yield increase of 81% (739 kg ha− 1) compared to rainfed conditions. For this irrigation, 124 mm water was required, assuming an irrigation efficiency of 100%. Information on bio-geo-physical properties (weather, soil, crop, and management) of the major chickpea growing regions of India were collated and used to identify major cropping scenarios (Hajjarpoor et al., 2018). Analysis of the gap between actual and potential yields was clustered into six distinct homogeneous system units (HSU), within which similar system responses to genetic or management intervention would be expected. This study revealed, for example, that drought was not a problem in all units, contrary to the common belief, and limitations specific to each HSU were highlighted, with recommendations for breeding and/or management strategies.

3.2.1  Environmental patterns of water supply and demand Four types of drought are prevalent for chickpea in Australia, featuring a supply/demand ratio close to one up until flowering, and increasing stress with progressively later onset of water deficit associated with actual grain yield (Fig. 10.5). The supply/demand ratio was calculated daily with a crop simulation model. Supply represents the daily water available to the crop, either from rainfall, irrigation, or available in the soil profile accessible to the roots, and the water demand of the canopy is a function of radiation, ambient temperature, and vapour pressure deficit. The lack of preflowering stress, in environments where both field pea and wheat feature early-onset drought, has been attributed to the slow growth of chickpea early in the season (Lake et al., 2016). Increase in the supply/demand ratio in scenario #4 reflects late rainfalls. Scenario #1 was the least severe, as indicated by the highest yield predicted in this scenario, followed by scenario #2 that experienced a late postflowering stress (decrease in the supply/demand ratio) (Fig. 10.5b). In South Asia, chickpea predominantly relies on stored soil water with characteristic terminal drought, but irrigation and expansion to southern India with significant in-season rain cause departures from this pattern (Hajjarpoor et al., 2018).

3.2.2  Canopy traits In field studies, the rate of leaf photosynthesis decreased from about 30 μmol m− 2 s− 1 to near zero as the midday leaf water potential decreased from − 0.5 to − 3 MPa (Leport et al., 1998, 1999). When the soil water stress began to affect leaf gas exchange during seed filling, the yield in the stressed crop was 77% of that in the irrigated crops, whereas for water stress beginning at flowering, the yield under water stress was 30% of that under irrigation (Leport et al., 1998, 1999). Glasshouse experiments with two chickpea cultivars showed that in drying soil, FTSW thresholds were ~ 58% for stomatal conductance and transpiration, 40%–45% for leaf photosynthesis, and ~ 38% for predawn leaf water potential (Pang et al., 2017). Flower (b)

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FIG. 10.5  Patterns of water stress environments for chickpea in Australia (a) and predicted frequency distribution of grain yield in each of the four main stress scenarios (b). From Lake, L., Chenu, K., Sadras, V.O., 2016. Patterns of water stress and temperature for Australian chickpea production. Crop Pasture Sci. 67(2), 204–215. https://doi.org/10.1071/CP15253.

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and pod production continued until the FTSW below 20%, although seed set failed for FTSW below 57% (Pang et al., 2017). Failure of seed set could be related to increasing concentration of abscisic acid in the pod wall and developing seed, lack of carbohydrates, or both (Pang et al., 2017). For crops relying on stored soil water, genotypes have been identified with lower canopy vigour and transpiration restrictions under high VPD contributing to slower water use at vegetative stage and more water available for grain filling (Zaman-Allah et al., 2011a,b; Vadez et al., 2013). Low early vigour, however, would favour soil evaporation and poor competition with weeds. Genotypic differences in canopy conductance and canopy transpiration can be captured with thermal imagery (Kashiwagi et al., 2008; Zaman-Allah et al., 2011a). Several QTL for canopy temperature depression, used as a proxy for continued transpiration, were identified (Purushothamana et al., 2015).

3.2.3  Root traits Chickpea has a typical taproot system from which lateral roots branch. Significant G × E interaction has been observed in root growth, with genetic diversity reported in rooting depth at maturity from 0.6 to 1.5 m, depending on soil physical and chemical composition (Gregory, 1988). Most chickpea germplasm reached maximum root biomass between 35 and 50 days after sowing under rainfed conditions in a 1.2-m deep Vertisol in southern India (Kashiwagi et al., 2005). Root depth and root length density have been associated with a QTL on chromosome 4 (Varshney et al., 2014) and associated with higher yield under terminal drought (Varshney et al., 2014). This QTL was later shown to co-locate with a QTL for early vigour (Sivasakthi et al., 2018). Genotypic differences in the hydraulic make up of chickpea genotypes contrasting for transpiration restriction under high VPD have been observed, with aquaporins (membrane proteins in charge of water transport) playing a potential role in the root hydraulic conductance (Bramley et al., 2013; Sivasakthi et al., 2017).

3.2.4  Water use efficiency Under severe terminal drought, higher water use efficiency (WUE) could contribute to drought tolerance (Bramley et al., 2013; Zaman-Allah et al., 2011a). The discrimination of the 13C carbon isotope (Δ13C) can be used as a surrogate for WUE, showing a negative correlation with cumulative WUE (Farquhar et al., 1982, 1989). A chickpea reference collection of 280 accessions (Upadhyaya et al., 2008) showed large genetic diversity in Δ13C, which correlated with both the grain yield and harvest index (HI) under terminal drought (Krishnamurthy et al., 2013). Yield under terminal drought was also positively associated with Δ13C at flowering (Kashiwagi et al., 2013), indicating a negative correlation between WUE in the vegetative phase and grain yield. However, there was a positive correlation between water use in the vegetative phase and grain yield under mild drought conditions, indicating that vigorous plants and higher transpiration in the vegetative phase, supported by a vigorous root system, could be adaptive for mild drought (Palta and Turner, 2019).

3.3  Capture and efficiency in the use of nutrients 3.3.1 Nitrogen The nitrogen requirements for chickpea are not well defined because of the generalised assumption that the crop can fulfil its requirements through the biological fixation of N (BNF). Consequently, there is a common assumption among farmers that chickpea fertilisation is not justified economically (Wolde-Meskel et al., 2018), but in low-N soils, a starter dose of 10–15 kg N ha− 1 aids early growth before BNF is established (Loss et al., 1998). There is experimental evidence for N, phosphorus (P), and sulphur (S) fertilisation enhancing plant growth, biomass, and grain yield and BNF in chickpea (Woldesenbet et al., 2013; Amede et al., 2002). Symbiotic N2 fixation decreases to 60%–80% in soil with mineral N above 100 kg ha− 1 (Guinet et al., 2018). This percentage also depends on the productivity potential; for instance, under the high-yielding Ethiopian conditions, chickpea can fix up to 140 kg N ha− 1 or 80% of the total N requirement (Keneni et al., 2012; Fikre, 2016). Furthermore, chickpea residues could contribute around 70 kg N ha− 1 to following crops (Ghosh et al., 2007). Several reports indicated that chickpea can add more than 30% N to the soil and reduce 60%–100% N requirements (~ 40 kg N ha− 1) of the next cereal component such as wheat and teff (Abdulkadir et al., 2017; Kanwar and Virmani, 1987). Chickpea transports N from the nodules in the form of amides. This has a particular advantage in the case of water stress. Indeed, the BNF of amide exporters has similar sensitivity to water stress as photosynthesis (Serraj and Sinclair, 1995), so that under water stress, N accumulation from BNF and carbon fixation from photosynthesis decline similarly, and there is no N deficiency. This contrasts with legumes that are ureide exporters, such as soybean, in which BNF decreases at higher soil moisture contents than photosynthesis.

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3.3.2  Other nutrients P deficiency constrains yield in some soils (Srinivasarao et al., 2006). Genotypes vary in traits related to P acquisition from low P soils, including total root length, root surface area, root hair cylinder volume, and root carboxylate exudation, particularly malonate, which solubilises soil P (Pang et al., 2018a,b). Additionally, BNF can acidify the rhizosphere and contribute to mobilising insoluble P (Gunes et al., 2007; Tamene et al., 2017). Chickpea favoured the uptake of P, K, Fe, Zn, and Mn in wheat (Gunes et al., 2007) and P in maize plants (Li et al., 2004); Tamene et al. (2017) outlined the role of chickpea in modifying soil properties in relation to major nutrients, particularly in acidic soils where P fixation is a problem (Schlecht et al., 2006).

3.3.3 Salinity Salt stress can be a problem for chickpea and has been the object of extensive studies over the last few decades (see review by Flowers et al., 2010). In India, salinity arose under irrigation with brackish water. Some Indian soils also have sodicity that undermines soil structure and makes them prone to waterlogging. Some Australian soils also feature sodicity and dryland salinity (Rengasamy, 2002). A large-scale screening of 263 Desi and Kabuli accessions showed a fivefold variation in salinity tolerance (Vadez et al., 2007). Crosses of parents with contrasting yield under salinity (tolerant × sensitive: ICC1431 × ICC6263; JG62 × ICCV2; JG11 × ICCV2) revealed QTL for seed yield or traits related to seed yield such as seed number in all three populations (Vadez et al., 2012b), with two regions common to all three populations, one on CaLG05 related to plant vigour and one on CaLG07 related to higher reproductive success (Vadez et al., 2012c; Pushpavalli et al., 2016). In all the three studies, however, the parents used for the development of the RILs were adapted to Indian environments and extrapolation to other environments is not warranted. In South Australia, Atieno et al. (2017) screened 245 diverse accessions from ICRISAT reference set, and a bi-parental RIL population developed from Genesis836 (salt tolerant) and Rupali (salt sensitive), two cultivars well adapted to Australian nonsaline environments. As found earlier (Pushpavalli et al., 2015), a locus on CaLG05 associated with the number of filled pods, while major genomic regions were identified on CaLG04 and CaLG07 that associated with yield-related traits under salinity (Atieno et al., 2017; Atieno, 2017). Salinity usually reduces growth rate, plant height, and shoot biomass in chickpea (Atieno et al., 2017), but these traits appeared unrelated to seed yield under salinity (Vadez et al., 2012c). In contrast, a higher number of tertiary branches and flowers under nonsaline conditions were linked to higher seed yield under saline conditions in saline-tolerant genotypes. This suggests that a higher number of reproductive sites under nonsaline conditions, and maintaining a large number of these under salinity, maybe adaptive under salt stress (Vadez et al., 2012c). This was also the conclusion from the initial large screening (Vadez et al., 2007). Unlike heat and drought stress (Pang et al., 2017), salinity has no effect on pollen or pistil function (Turner et al., 2013), but pod abortion was found to be higher in salt-sensitive lines compared to salt-tolerant lines (Atieno et al., 2017; Turner et al., 2013). In many studies, filled pod number and seed number are key determinants of seed yield under salinity (Pushpavalli et al., 2015; Pushpavalli et al., 2016; Turner et al., 2013). In chickpea, salinity stress delayed flowering in some studies but not others (Pushpavalli et al., 2016; Turner et al., 2013). The delay in first flower correlates positively with severity of salinity (Dhingra et al., 1996; Dhingra and Varghese, 1993). In recent studies, when a group of salt-tolerant and salt-sensitive lines were exposed to salinity, the salinity-tolerant group flowered earlier, and the delay in the appearance of the first flower was smaller (maximum 10 days) than in the saltsensitive group (up to 25 days) compared to the plants exposed to nonsaline conditions (Pushpavalli et al., 2019, 2016). This delay in flowering with salinity was positively correlated to yield loss. Thus the flowering-time delay under salinity may be useful as a proxy in selecting tolerant material in large populations.

4  Yield and quality 4.1  Yield and its components Grain yield is the product of plant number per surface area, grain number per plant, and average grain weight. These components are, however, linked by tight compensatory mechanisms that preclude yield improvement from their individual manipulation (Slafer et al., 2014). For example, crop yield is unresponsive to plant number per unit area within agronomic ranges (Siddique et al., 1984). Likewise, double podding could increase seed number per plant but is neutral for yield, although it can contribute to yield stability (Rubio et al., 1998; Cho et al., 2002). In common with all annual crops (Sadras, 2007; Slafer et al., 2014), chickpea accommodates environmental variation through grain number, whereas average grain weight is conserved (e.g. Lake and Sadras, 2014). In common with soybean (Chapter 8: Soybean, Section 2, Fig. 8.8),

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chickpea grain number is linearly associated with crop growth rate in the critical period (Lake and Sadras, 2016). In chickpea, the onset of most critical period is about 400°Cd before flowering with maximum sensitivity to stress at 200–400°Cd after flowering (Lake and Sadras, 2016). In several annual crops, including chickpea (Soltani et al., 1999), HI increased linearly during most of grain filling. This attribute was used to model chickpea yield assuming a constant rate of increase of HI (dHI/dt) over time until maturity (Soltani et al., 1999). Departures from linearity can arise from differences in phenological development until the beginning of seed growth (Soltani et al., 2005). Seed growth then depends on both the biomass at the beginning of seed growth, which depends on previous growth conditions, and on biomass accumulation during seed growth. For example, increased temperature and water stress during seed filling would hasten phenological development, reduce potential seed growth, and reduce HI, as illustrated in sowing date trials affecting the photothermal environment (Sadras et al., 2015) and crops grown under severe water stress (Siddique and Sedgley, 1986).

4.2  Seed quality Chickpea is an important part of subsistence diets and food security (Young et al., 1990; Agriculture and Agri-Food Canada, 2006a,b; Sathe, 1996) and is increasingly recognised as a part of a healthy diet throughout the world (Jukanti et al., 2012). Chickpea and soybean are the only two legume crops that provide all essential amino acids (Young et al., 1990; Sathe, 1996) and are important sources of vitamins (B1, B2, B5, and B6), minerals (Zn, Ca, Mg, and Mn), carotenoids, and carbohydrates. Seed quality, however, has not been a major focus in breeding, besides the typical seed attributes of the two main types, the search for large-seeded Kabuli, the colour of the testa, and easiness to split in Desi. There are a variety of traditional chickpea preparation and processing methods that include soaking, decortication, grinding, sprouting, fermentation, boiling, mashing, roasting, parching, frying, and steaming (Roy et  al., 2010). Fresh grains in India and Ethiopia are consumed either boiled or roasted, which require (1) earliness of podding, so that farmers can benefit from a price premium when the fresh crop is brought on the market; (2) green seed that is preferred by consumers and is putatively richer in pro-vitamin A (Box 10.2). Chickpea quality is hampered by seed contaminations and fungi. Seeds and infected harvest debris are the main sources of primary infections, and the level of seed damage depends on environmental conditions, such as relative humidity, dew, and temperature. Many fungi commonly isolated from chickpea seeds and chickpea by-products are potential mycotoxinproducers of aflatoxins, ochratoxin A, and trichothecenes, among others. The main mycotoxin producers are in the genera

Box 10.2  Stay green and green seed phenotypes Green seediness of legume variants occurs at a low frequency in germplasm (Pundir et al., 1985) and associates with extended greenness of leaves resulting from disruption of senescence (Penmetsa et al., pers. comm.). Such extended chlorophyll retention in shoot forms a separate category of stay-green phenotypes. Multiple studies across a variety of plants indicate that the ‘stay-green’ protein (Mendel’s recessive I locus in garden pea; Sato et al., 2007) is involved in senescence processes, including the one in green-seeded chickpea (Sivasakthi et al., 2019). Its loss-of-function results in the failure to degrade chlorophyll in leaf and seed and results in ‘stay-green’ phenotype in a wide range of plants (Thomas and Ougham, 2014). Stay-green proteins and closely related members work in concert to recruit chlorophyll catabolic enzymes (Sakuraba et al., 2012) to degrade chlorophyll whose unquenched presence during senescence might be toxic to plants [e.g. by production of reactive oxygen species (ROS)]. This extended chlorophyll retention also results in extended retention of ROS scavenging machinery such as the carotenoids cycle (including, e.g. pro-vitamin A). Recently, 40 stay-green accessions and a set of 29 BC2F4-5 stay-green introgression lines using a stay-green donor parent ICC16340 and two Indian elite cultivars (KAK2, JGK1) as recurrent parents (Sivasakthi et al., 2019) were tested. The green cotyledon trait in chickpea was controlled by a single recessive gene that was invariantly associated with the extended chlorophyll retention. The chickpea ortholog of Mendel’s I locus of garden pea, encoding an STG protein, was likely to underlie the persistently green cotyledon phenotype of chickpea (Kholova et al., pers. comm.). Further sequence characterisation of this chickpea ortholog CaStGR1 revealed the presence of five different molecular variants (alleles), each of which is likely a loss-of-function of the chickpea protein (CaStGR1) involved in chlorophyll catabolism. The wild type and green cotyledon lines were also tested for adaptation to dry environments and traits linked to agronomic performance in contrasting conditions. The plant processes linked to disrupted CaStGR1 gene did not functionality affect transpiration efficiency or water use. Photosynthetic pigments in grains, including provitaminogenic carotenoids important for human nutrition, were twofold to threefold higher in the stay-green type. Agronomic performance did not correlate with the presence/absence of this particular stay-green allele. Therefore allelic variation in chickpea CaStGR1 did not compromise traits linked to environmental adaptation and agronomic performance and is a candidate for biofortification of provitaminogenic carotenoids in chickpea.

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Aspergillus, Penicillium, Fusarium, and Alternaria. Mycotoxins can be acutely or chronically toxic, or both, depending on the kind of toxin, the dose, the health, the age, and nutritional status of the exposed individual or animal, and the interactions between mycotoxins (Bräse et al., 2009; Wu et al., 2014). Because mycotoxins are unavoidable, it is relevant to trace them through the food and feed chains. International regulations restrict imports of raw materials contaminated with aflatoxins, ochratoxin A, deoxynivalenol, and trichothecenes type A (van Egmond et al., 2007). Natural contamination with aflatoxins and ochratoxin A has been reported in chickpeas and chickpea-based products in India, Turkey, Pakistan, and Iran (Ramirez et al., 2018). Although there are few studies, aflatoxin levels seem to be fairly high in stored chickpea seeds in comparison with those in processed chickpeas (flour, roasted). Aflatoxin B1 in chickpeas is a major public health concern, and studies are needed for minimising or eliminating them. The prevention of contamination with toxicogenic fungi during harvest, processing, and storage is the best way to control aflatoxin accumulation.

5  Concluding remarks: Challenges and opportunities We have outlined some opportunities to meet the main challenges of chickpea adaptation to stresses, including heat, drought, and salinity. There is genetic variation for adaptation to many of these stresses but trait-based selection, as well as genetic and genomic technologies, has largely failed to capitalise on this variation. The way chickpea has been cultivated and managed for decades in the developing world is bound to change; for example, widespread mechanisation would require more erected phenotypes. Chickpea yield gaps highlight opportunities for improved agronomic management. For instance, water applied at grain filling would bring about major yield increases. Farming system work is needed to revisit the allocation of water among cereals and pulses and to exploit fully the genetic variations in the symbiotic nitrogen fixation potential. Crop simulation models are particularly useful to address this and other questions holistically capturing G × E × M interactions. The role of chickpea in the betterment of diets is now recognised and calls for a shift in the profile of targeted varieties and towards crop quality standards. Work is especially needed to investigate the influence of G × E × M on seed quality.

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Efficient water use in crop production: research or re-search? In: Taylor, H.M., Jordan, W.R., Sinclair, T.R. (Eds.), Limitations to Efficient Water Use in Crop Production. American Society of Agronomy, Madison, WI, pp. 1–27. Tesfaye, K., Walker, S., Tsubo, M., 2006. Radiation interception and radiation use efficiency of three grain legumes under water deficit conditions in a semi-arid environment. Eur. J. Agron. 25 (1), 60–70. Thomas, H., Ougham, H., 2014. The stay-green trait. J. Exp. Bot. 65, 3889–3900. Turner, N.C., Colmer, T.D., Quealy, J., Pushpavalli, R., Krishnamurthy, L., Kaur, J., Singh, G., Siddique, K.H.M., Vadez, V., 2013. Salinity tolerance and ion accumulation in chickpea (Cicer arietinum L.) subjected to salt stress. Plant Soil 365, 347–361. Upadhyaya, H.D., Dwivedi, S.L., Baum, M., Varshney, R.K., Udupa, S.M., Gowda, C.L.L., Hoisington, D., Singh, S., 2008. Genetic structure, diversity, and allele richness in composite collection and reference set in chickpea (Cicer arietinum L.). BMC Plant Biol. 8, 106. Vadez, V., Krishnamurthy, L., Serraj, R., Gaur, P.M., Upadhyaya, H.D., Hoisington, D.A., Varshney, R.K., Turner, N.C., Siddique, K.H.M., 2007. Large variation in salinity tolerance in chickpea is explained by differences in sensitivity at the reproductive stage. Field Crop Res. 104, 123–129. Vadez, V., Soltani, A., Sinclair, T.R., 2012a. Modelling possible benefits of root related traits to enhance terminal drought adaptation of chickpea. Field Crop Res. 137, 108–115. https://doi.org/10.1016/j.fcr.2012.07.022. Vadez, V., Krishnamurthy, L., Thudi, M., Anuradha, C., Colmer, T., Turner, N., Siddique, K., Gaur, P., Varshney, R., 2012b. Assessment of ICCV 2 x JG 62 chickpea progenies shows sensitivity of reproduction to salt stress and reveals QTL for seed yield and yield components. Mol. Breed. 30, 9–12. Vadez, V., Rashmi, M., Sindhu, K., Muralidharan, M., Pushpavalli, R., Turner, N.C., Krishnamurthy, L., Gaur, P.M., Colmer, T.D., 2012c. Large number of flowers and tertiary branches, and higher reproductive success increase yields under salt stress in chickpea. Eur. J. Agron. 41, 42–51. Vadez, V., Soltani, A., Sinclair, T.R., 2013. Crop simulation analysis of phenological adaptation of chickpea to different latitudes of India. Field Crop Res. 146, 1–9. https://doi.org/10.1016/j.fcr.2013.03.005. van Egmond, H.P., Schothorst, R.C., Jonker, M.A., 2007. Regulations relating to mycotoxins in food: perspectives in a global and European context. Anal. Bioanal. Chem. 389, 147–157.

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Vanlauwe, B., Bationo, A., Chianu, J., Giller, K.E., Merckx, R., Mokwunye, U., Ohiokpehai, O., Pypers, P., Tabo, R., Shepherd, K.D., Smaling, E.M.A., Woomer, P.L., Sanginga, N., 2010. Integrated soil fertility management: operational definition and consequences for implementation and dissemination. Outlook Agric. 39, 17–24. Varshney, R., Thudi, M., Nayak, S., Gaur, P., Kashiwagi, J., Krishnamurthy, L., Jaganathan, D., Koppolu, J., Bohra, A., Tripathi, S., Rathore, A., Jukanti, A.K., Jayalakshmi, V., Vemula, A., Singh, S.J., Yasin, M., Sheshshayee, M.S., Viswanatha, K.P., 2014. Genetic dissection of drought tolerance in chickpea (Cicer arietinum L.). Theor. Appl. Genet. 127, 445–462. von Wettberg, E.J.B., Chang, P.L., Basdemir, F., Carrasquila-Garcia, N., Korbu, L.B., Moenga, S.M., Bedada, G., Greenlon, A., Moriuchi, K.S., Singh, V., Cordeiro, M.A., Noujdina, N.V., Dinegde, K.N., Sani, S.G.A.S., Getahun, T., Vance, L., Bergmann, E., Lindsay, D., Mamo, B.E., Warschefsky, E.J., Dacosta-Calheiros, E., Marques, E., Yilmaz, M.A., Cakmak, A., Rose, J., Migneault, A., Krieg, C.P., Saylak, S., Temel, H., Friesen, M.L., Siler, E., Akhmetov, Z., Ozcelik, H., Kholova, J., Can, C., Gaur, P.M., Yildirim, M., Sharma, H.C., Vadez, V., Tesfaye, K., Woldemedhin, A.F., Tar'an, B., Aydogan, A., Bukun, B., Penmetsa, R.V., Berger, J., Kahraman, A., Nuzhdin, S.V., Cook, D.R., 2018. Ecology and genomics of an important crop wild relative as a prelude to agricultural innovation. Nat. Commun. 9, 649. https://doi.org/10.1038/s41467-018-02867-z. Wolde-Meskel, E., van Heerwaarden, J., Abdulkadir, B., Kassa, S., Aliyi, I., Degefu, T., Wakweya, K., Kanampiu, F., Giller, K., 2018. Additive yield response of chickpea (Cicer arietinum L.) to rhizobium inoculation and phosphorus fertilizer across smallholder farms in Ethiopia. Agric. Ecosyst. Environ. 261, 144–152. Woldesenbet, L., Haile, W., Beyene, S., 2013. Response of chickpea (Cicer arietinum L.) to nitrogen and phosphorus fertilizers in Halaba and Taba, Southern Ethiopia. Ethiop. J. Nat. Resour. 13 (2), 115–128. Wu, F., Groopman, J.D., Pestka, J.J., 2014. Public health impacts of foodborne mycotoxins. Annu. Rev. Food Sci. Technol. 5, 351–372. Young, V.Y., Bier, D., Pellet, P., 1990. Reply to J Millward. Am. J. Clin. Nutr. 51, 494–496. Zaman-Allah, M., Jenkinson, D.M., Vadez, V., 2011a. Chickpea genotypes contrasting for seed yield under terminal drought stress in the field differ for traits related to the control of water use. Funct. Plant Biol. 38, 270–281. Zaman-Allah, M., Jenkinson, D.M., Vadez, V., 2011b. A conservative pattern of water use, rather than deep or profuse rooting, is critical for the terminal drought tolerance of chickpea. J. Exp. Bot. 62, 4239–4252.

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Image source: Authors

Chapter 11

Peanut Rao Rachaputia, Yashvir S. Chauhanb, and Graeme C. Wrightc a

Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Gatton, Australia, Department of Agriculture and Fisheries, Kingaroy, QLD, Australia, Queensland Alliance for Agriculture and Food Innovation (QAAFI), University of Queensland, Australia, cPeanut Company of Australia, Kingaroy, Queensland, Australia b

1 Introduction Peanut (Arachis hypogaea), also known as ‘groundnut’ in some countries, is one of the most popular dual-purpose (oilseed and food) legume crops of the world. It is cultivated either as a sole crop in semiarid tropics or in rotation with other crops (e.g. cereals, cotton, and sugarcane) under high input conditions. Most parts of the peanut plant have some commercial use. For example, in countries where peanut is cultivated as an oilseed crop, vegetative parts of the plant are used as nutrient-rich fodder, and the seeds for oil and by-products, that is, shells and meal in livestock feed. Peanut, being a legume, can produce nodules on the roots in a symbiotic association with Bradyrhizobium and fix atmospheric nitrogen through symbiosis. In situations where shoot dry matter (DM) is left behind on the soil after harvesting of pods, the residual nitrogen in vegetative parts and roots is utilised by subsequent crops. For instance, an irrigated peanut crop grown in rotation with sugarcane can fix a total of about 300 kg N ha− 1, and the crop residue after harvesting of pods would contribute up to 100 kg N ha− 1 for the subsequent cane crop (Toomsan et al., 1995). However, in semiarid regions, the residual peanut stubble is also collected and used as livestock fodder.

1.1  Area, production, and yield The world peanut production has increased from 14 Mt in 1960s to more than 41 Mt in 2012, reflecting an annual growth rate of 3.85% between 1991 and 2012, with the bulk of this increase in production and area occurring in China (Di and Yi-Ru, 2012). However, since 2012, the global annual growth rate dropped and stagnated at 0.65%. Peanut is presently grown in over 26 Mha worldwide with a total production of over 45 Mt (Table 11.1). China and India remain the world’s largest producers of peanut, accounting for over 40% and 15% of the world production, respectively, followed by the USA (7.2%). The rest of the world’s peanut production comes mainly from Nigeria, Senegal, Sudan, Myanmar, and Chad. Rainfed peanut production dominated through the 1950s and 1960s. Irrigated production, which started in the early 1970s, presently constitutes about 20% of the total peanut area. About 80% of the world peanut production comes from rainfed regions of the semiarid tropics, where temperature, rainfall, vapour pressure deficit (VPD), and solar radiation, in addition to soil nutrient disorders, are the major sources of variation in yield (Nageswara Rao et al., 2012). Under changing climate scenarios of increased temperatures and variable rainfall, Singh et al. (2012) predicted a 6%– 44% decrease in peanut yields across regions of India by 2050. Challinor et al. (2007) likewise projected up to 70% reduction for rainfed peanut production areas of India by 2100. It is, however, imperative to explore genetic and management avenues to minimise impacts of climate on yield and quality, especially in the rainfed areas because they still contribute to the bulk of global peanut production (Box 11.1).

2  Crop structure, morphology, and development During the past 3500 years, the cultivated peanut has become the largest gene pool of the Arachis genus, including two subspecies fastigiata and hypogaea, which are further separated into botanical types (Pasupuleti and Nigam, 2013). The cultivated botanical types, ‘Spanish bunch’, ‘valencia’, ‘virginia bunch’, and ‘virginia runner’ typically differ in phenology, branching habit, pod, and seed size, making them adaptable for specific environments (Sections 2.3.3 and 2.4). Crop Physiology: Case Histories for Major Crops. https://doi.org/10.1016/B978-0-12-819194-1.00011-6 Copyright © 2021 Elsevier Inc. All rights reserved.

361

362  Crop Physiology: Case Histories for Major Crops

TABLE 11.1  Area, production, and pod yield in the top 10 peanut producing countries in 2017/18. Production (Mt)

Pod yield (t ha− 1)

4.62

17.1

3.71

India

4.93

6.7

1.35

3

USA

0.72

3.2

4.49

4

Nigeria

2.70

3.2

1.19

5

Senegal

1.25

1.4

1.41

6

Sudan

1.80

1.4

0.78

7

Myanmar

0.89

1.4

1.55

8

Chad

0.95

1.0

1.05

9

Argentina

0.38

0.9

2.27

10

Cameroon

0.43

0.6

1.40

25.92

44.9

1.73

World rank

Country

1

China

2

World

Area (Mha)

Data from: Foreign Agricultural Service/USDA, Office of Global Analysis, 2019. Oil Seeds, World Markets and Trade, p. 11.

Box 11.1  Implications of climate change to peanut Climate change can impact agriculture in several ways. At low latitudes, climate change is projected to result in increased temperature, reduced rainfall, and increased frequency of extreme weather events such as floods and droughts (IPCC, 2018). The projected increases in temperature would reduce yield and crop duration, with faster development (Gordo and Sanz, 2010; Dimes et al., 2008). The timing and frequency of extreme temperature events (> 35°C) could be more critical than the increase in the mean temperature (White et al., 2006). Peanut tolerates high temperature at vegetative stages but only for a short period during the reproductive phase; prolonged stress at critical periods can reduce seed set, seed filling, and yield (Porter and Semenov, 2005). Prolonged exposure to end-of-season drought coupled with elevated temperatures predispose peanut crop to aflatoxin contamination (Dorner et al., 1989; Rachaputi et al., 2002). However, irrigation management, appropriate sowing date, and short maturity genotypes can be effective in minimising the aflatoxin risk of rainfed peanut in southeast Queensland (Rachaputi et al., 2002). Resource capture-based crop models can analyse the impact of these climate factors on peanut crop performance (e.g. Hammer et al., 1987; Carberry et al., 1993). Application of crop models in peanut has been focussed on the impact of in-crop variability of climatic factors on crop productivity and associated product quality (aflatoxin risk). Apart from estimating the likely impact of high temperature and water limitations, crop models can also assist in developing strategies to mitigate against these risks. Decision support systems have been developed for peanut to assist in crop management. See Aflatoxin risk prediction and irrigation scheduling in Box 11.2. The ongoing climate change also affects photoperiod sensitivity of peanut. Photoperiod had no effect on partitioning of dry matter to pods under low temperature, but partitioning to pods increased under short days compared to long days at higher temperatures (Nigam et al., 1994). This suggests that in future climates with higher mean temperature (Houghton et al., 2001; IPCC, 2007), photoperiod × temperature interactions affecting pod yield might become more prominent in the regions/sowings with long days (> 13 h) and elevated temperatures (> 26°C) during pod formation.

Phenological descriptors for peanut are based on visually observable vegetative (V) and reproductive (R) events (Boote, 1982). The V phases are determined by the number of developed nodes with fully expanded trifoliate leaves on the main stem. The R stages are divided into eight events to describe development of pods and seeds (Table 11.2).

2.1  Sowing to emergence Germination of peanut is determined by temperature and moisture in the seeding zone (~ 5 cm deep). Often temperature limits germination and emergence rather than soil moisture because peanut is either sown at optimal water status in rainfed regions or sown into dry soil followed by an irrigation. Suboptimal (> 15°C) soil temperatures affect germination and crop establishment. Limited genotypic variation in base temperature (Tb) of 10°C for germination and significant variation

Peanut Chapter | 11  363

TABLE 11.2  Descriptors of phenological reproductive phases in peanut. Crop phase

Description

R1

Beginning of flowering

R2

Beginning of peg

R3

Beginning of pod

R4

Full pod

R5

Beginning seed

R6

Full seed

R7

Beginning maturity

R8

Harvest maturity

Data from: Boote, K.J., 1982. Growth stages of peanut (Arachis hypogaea L.). Peanut Sci. 9, 35–40.

for optimum temperature (Topt) for maximum germination (28–36°C) in peanut (Leong and Ong, 1983) suggest that rate of germination and crop establishment could be slowed in suboptimal soil temperatures. Leong and Ong (1983) reported that in a cooler soil (19–22°C) peanut emergence was less than 50%, whilst emergence was 70%–80% in a warmer soil (25–31°C). In regions where peanut is sown under suboptimal soil temperatures, mulching with either plastic or organic material showed promise. Plastic mulch contributed to increase in peanut area and production from 9 Mt in 1995 to 17 Mt in 2017 in China (Hu et al., 1995).

2.2  Emergence to flowering During the initial vegetative growth phase, the rate of leaf appearance correlates linearly with soil temperature (Awal and Ikeda, 2002). The plants growing in a comparatively warmer soil produced more leaves on their branches than on the main axis. This phenomenon of increasing leaf number on branches in warmer soil gives plants the initial vigour for establishment. Peanut is amongst the earliest flowering crops because it is insensitive to photoperiod during the vegetative phase and hence has a shorter preflowering phase. However, the crop is sensitive to photoperiod from the start of the reproductive phase (Bagnall and King, 1991; Nigam et al., 1998). Whereas photoperiod has no effect on time to 50% flowering, twice as many flowers are produced under short photoperiod (10 h) when compared with a long (16 h) photoperiod (Bagnall and King, 1991). Peanut generally responds favourably to water deficit applied from emergence to start of flowering (preflowering phase), resulting in increased pod yields of up to 24% (Nageswara Rao et al., 1985, 1988; Puangbut et al., 2010; Jongrungklang et al., 2013) through a range of morphological and physiological mechanisms (Wright et al., 1991; Reddy et al., 2003). Water deficit during the preflowering phase could also increase root growth (Rucker et al., 1995; Puangbut et al., 2009). Recovery following the release of preflowering water deficit had a stronger influence on final yield than tolerance to water deficit during the preflowering drought phase (Puangbut et al., 2010).

2.3  Flowering to maturity 2.3.1 Temperature Pollen development and viability in peanut declined linearly as day temperature increased from 28°C to 48°C, resulting in reduced pod set (Prasad et al., 1999). A negative relationship between pod number and the time to first flower appearance at high temperature (40/28°C) (R2 = 0.93, P 40°C) during pod development could be a yield barrier. Whilst plastic mulching on seed bed improved crop establishment in suboptimal temperatures, it was detrimental to pod setting and pod development in some environments where soil temperatures exceed 40°C during reproductive phase (Nalawade and Patil, 2000). Peanut pods with seeds develop at a depth of 1–10 cm, hence the importance of soil temperature. The optimum soil temperature for pod and seed yield is 23°C (Cox, 1979), which is well below the optimum temperature for vegetative growth and development. However, soil temperature of >38°C reduced DM accumulation, flower production, and seed mass (Golombek and Johansen, 1997; Prasad et al., 2001) (Table 11.3). Lack of relationship between leaf area and pod number or pod weight suggests that pod development is controlled by factors other than carbon assimilation. High soil temperature had no effect on the root–shoot ratio because both root and shoot growth are reduced in similar proportions. However, high soil temperature reduced total DM, number of pods, and seed size, without affecting the number of pegs and harvest index (HI) (Table 11.3) despite a decrease in the number of pods and seed size (Craufurd et al., 2003).

2.3.2 Water Fig. 11.1 summarises effects of timing of water deficit on peanut yield. Moderate water deficits from emergence to flowering can result in higher yields compared to fully irrigated control. Sensitivity to water deficit increases progressively during the reproductive phase. Most significant effects were observed for water deficit during seed filling. Water deficit from the start of flowering to start of seed growth water deficit, depending upon its intensity, can reduce total DM by 20%–50% and pod yield by 30%–60% compared to the fully irrigated control (Nageswara Rao et al., 1985). Kheira (2009) showed that water deficit during the early flowering, late flowering, early pegging, and pod formation stages reduced seed yield by 28%, 39%, 36%, and 41%, respectively, relative to the fully irrigated control. The greatest reduction in total DM (30%–60%) and pod yield (30%–90%) occurred with prolonged water deficit from the start of seed growth to maturity. Soil water deficit during pod filling needs be considered in the light of the indeterminate nature and the subterranean fruiting habit of the peanut. Water deficit during pegging could increase surface soil strength, impairing pegging and limiting pod set and seed number (Haro et al., 2008, 2010; Collino et al., 2001; Underwood et al., 1971). The greater reduction in pod yield relative to total DM could be because of hard topsoil, which could prevent pegs to penetrate the dry soil (Underwood et al., 1971; Boote et al., 1976). Fruit initiation continues after the start of seed growth, so water deficits during pod filling can reduce both the initiation and development of pods (Matlock et al., 1961; Boote et al., 1976; Pallas et al., 1979; Underwood et al., 1971; Ono et al., 1974). Pegs may fail to develop into pods because the growth of pods in the soil may be affected because of inadequate moisture in the root zone (Allen et al., 1976; Boote et al., 1976), or lack of calcium uptake by developing pods (Skelton and Shear, 1971).

2.3.3  Interactions between temperature and water, and between temperature and photoperiod The effects of high temperature and water stress during pegging (R2) through to full seed development (R6) were additive and temporary for both vegetative growth and pod yield and disappeared as soon as high-temperature stress was removed (Kakani et  al., 2015). The study concludes that genotypes tolerant to water stress would generally be tolerant to high

Peanut Chapter | 11  365

FIG. 11.1  Peanut yield responses to timing of drought stress (flowering = 0) in relation to unstressed controls (dotted horizontal line). Modified from Nageswara Rao, R.C., Singh, S., Sivakumar, M.V.K., Srivastava, K.L., Williams, J.H., 1985. Effect of water deficit at different growth phases of peanut. I. Yield responses. Agron. J. 77, 782–786 (circle); Puangbut, D., Jogloy, S., Vorasoot, N., Akkasaeng, C., Kesmala, T., Rachaputi, R.C.N., Wright, G.C., Patanothai, A., 2009. Association of root dry weight and transpiration efficiency of peanut genotypes under early season drought. Agric. Water Manag. 96, 1460–1466, field and glass house (square, triangle); Nautiyal, P.C., Ravindra, V., Zala, P.V., Joshi, Y.C., 1999. Enhancement of yield in groundnut following the imposition of transient soil-moisture stress during the vegetative phase. Exp. Agric. 35, 371–385 (diamond).

t­ emperature under field conditions. Water management, in combination with appropriate genotypes, is expected to mitigate yield losses under water deficit (Villegas and Challinor, 2016). Conversion of flowers to pegs and then to pods is reduced under long photoperiods. Nigam et al. (1998) observed significant temperature × photoperiod interactions in all the three botanical types, including virginia, Spanish, and valencia, with the sensitivity to photoperiod increasing with increasing temperature. Photoperiod has a significant effect on total DM (shoot + root), with the genotypes producing up to 72% greater DM under long-day compared to short-day treatments, with leaf weight accounting for 76% of the variation in DM (Nigam et al., 1998). Environments with daily mean temperatures above 24°C showed significant photoperiod responses, with long days resulting in reduced pod to peg ratio, compared to short days. Short days increased pod numbers compared to long days, suggesting that pod to peg ratio could be used as a potential indicator to assess genotypic sensitivity to photoperiod (Nigam et al., 1998). Despite significant effects of photoperiod on reproductive growth, major seed quality traits such as proportion of sound mature seed, oil content, and 100 seed weight remained unaffected (Dwivedi et al., 1990).

2.4  Combining sowing date and genotype to match growing environment The strategy of matching reproductive stages to favourable environmental periods by manipulating sowing windows to avoid high temperature or water stress seeks to stabilise yield. In the Mediterranean-type environments, early sowing (between mid-May and early June) increased yield owing to suitable temperature, longer crop cycle (~ 160 days or 2400– 2500°Cd), and more solar radiation during the growing period, allowing both the main and double crop production with acceptable yield (Caliskan et al., 2008). In tropical environments of Dorner in Sudan, early sowing (June) increased pod yield, oil content, and iodine number but reduced the shelling percentage compared to later (August) sowing (Nur and Gasim, 1978). A multiyear and multisite field study in Argentina involving two peanut cultivars grown under irrigated conditions and five sowing dates between 17 October and 21 December showed that yield could be maximised by advancing sowing. Delaying sowing reduced pod yield owing to sink limitations (Haro et al., 2007). The ‘short’ duration characteristic of Spanish and valencia genotypes, enables their adaptation to the short length season of the rainfed tropics. The virginia bunch types are semispreading and of medium duration (5 months), whilst the runner (or full season) types are spreading with alternate branching and of equivalent of longer duration (5–6 months). The virginia bunch and runner types produce large seeds suitable for confectionary markets. These genotypes are generally adapted to should this be wetter instead of wet regions where irrigation is available. Modelled plant extractable soil moisture in red ferrosols of Kingaroy, in northern Australia, showed that short-duration types (Spanish and valencia) reached maturity by 1700°Cd, with residual plant extractable soil moisture (PESM) close to 20 mm (Fig. 11.2). In contrast, virgina bunch and virgnia runner types took 2000°Cd and 2500°Cd, respectively, with

366  Crop Physiology: Case Histories for Major Crops

Extractable soil moisture (full = 119 mm)

120 100 80 60 40 20 0 0

1000 2000 Thermal time from sowing (oCd)

3000

FIG. 11.2  Dynamics of extractable soil water for short- (circle), medium- (triangle), and long- (square) duration peanut genotypes simulated using 62 years of climate data for Kingaroy, Queensland, Australia.

PESM below 20 mm (unless there is a rainfall event) presents an associated risk of yield loss and quality owing to water stress (Fig. 11.2). Intercropping short- and long-duration varieties could potentially stabilise peanut pod yield in environments with variable season length. A field study intercropping of short (Spanish types) and long duration (virginia types) resulted in the Land Equivalent Ratio (LER, the ratio of the area under sole cropping to the area under intercropping needed to give equal yield) of 1.25 for pod yield and total biomass despite moderate or severe water deficits at the end of the season (Nageswara Rao et  al., 1990). However, the success of this approach depends on the farmer’s ability to harvest the early maturing genotype without major disturbance to the long-duration crop, which presents a practical problem for mechanised farming systems. As a result of growing uncertainty of rainfall towards the end of season and lack of irrigation facilities, growers in rainfed regions of southeast Australia are beginning to consider short-duration types to escape end-of-season drought. However, short-duration types have lower pod yield potential in good rainfall years (e.g. ~ 75%) compared to full season maturity lines (Graeme Wright, personal communication). As the new short-duration lines with a higher yield potential are developed, growers will most likely favour short-duration types.

3  Growth and resources 3.1  Capture and efficiency in the use of radiation Crop biomass (with seed weight adjusted for energy content) can be defined as the product of cumulative radiation incident on the crop (R), the fraction of incident radiation intercepted by the crop (f), and the radiation-use efficiency (RUE) for biomass production (calculated using photosynthetically active radiation PAR in most cases) (Monteith, 1977; Sinclair and Muchow, 1999). In most cases, the incident radiation incident above the canopy and fractional radiation intercepted by canopy can be measured using line quantum sensors or tube solarimeters. The relationship between leaf area index (LAI) and f is linear until LAI reaches about 3 and becomes curvilinear thereafter. However, leaf movements in response to water deficits and incident solar radiation can cause diurnal variation in f. Many legumes, including peanut, respond to water limiting or supra-optimal solar radiation conditions through para-heliotropism, a phenomenon of folding and orienting their leaves to sun, thereby reducing the intensity of radiation on the leaf surface. This phenomenon may be advantageous in the semiarid tropics where water deficit often limits transpiration, preventing evaporative cooling, heating leaves at temperatures several degrees above air temperature, and causing tissue damage (Gates, 1968; Sullivan et al., 1977). In situations where paraheliotropism plays a role in canopy leaf movement, the realistic values for k (the canopy extinction coefficient) provide more accurate simulation of radiation interception using LAI. A reduced k (more upright leaves) allows better penetration of radiation into leaf canopies, thus illuminating more leaf area with lower intensity of PAR, increasing canopy photosynthesis whilst decreasing heat load caused by radiation. This would be expected to increase the RUE when biomass is source-limited (Bell et al., 1993). The observed changes in leaf orientation indicate that peanut has dynamic mechanisms that favour radiation interception when water is freely available but maintain RUE when water becomes limiting. Combining peanut cultivars, sowing dates, and densities in the field, Kiniry et al. (2005) demonstrated that as the k increased from 0.3 to 1.0, RUE decreased from 2.75 to 1.5 g MJ− 1. An LAI of 5–6 and a mean k of 0.60–0.65 appear to be normal for peanut grown under high input conditions in many regions (Kiniry et al., 2005). However, leaf area development is influenced by genotype and environmental factors. In general, the peanut canopy intercepts close to 100% solar

Peanut Chapter | 11  367

r­ adiation when LAI reaches 3.0–3.5. This is usually achieved by adjusting the row spacing depending on genotype and regional recommendations. Regional agronomic practices aim to achieve > 3.0 LAI by flowering and maintain the LAI > 3.0 throughout the seed-filling period to ensure filling pods are not source-limited. Seasonal conditions affect RUE with reports of 0.88 g MJ− 1 (Matthews et al., 1988b), 0.62–0.86 g MJ− 1 (Wright et al., 1993), 1.59–1.91 g MJ− 1 (Bell et al., 1993), 1.69–2.11 g MJ− 1 (Bell and Wright, 1994), 2.49–3.02 g MJ− 1 (Chapman et al., 1993), and 1.2 g MJ− 1 (Kiniry et al., 2005). RUE was 39% higher in the warmer (33/23°C) compared to the cooler night environment (33/17°C) (Bell et al., 1992). In environments where night temperatures were > 20°C, RUE was more responsive to increased specific leaf nitrogen (SLN). However, peanut SLN declines faster under drought stress (DeVries et al., 1989), which affects RUE. The variable nature of RUE in peanut as evidenced from these reports clearly indicates that this efficiency factor is strongly influenced by environment (e.g. night temperature, water stress, nutrition, etc.). Arkebauer et al. (1994) discuss reasons for this observed variation, which include form of carbon used (shoots + roots), characterisation of solar radiation (total v’s)–PAR), and the timescale over which RUE calculations are made.

3.2  Capture and efficiency in the use of water Genotypic variation in peanut under water-limiting conditions and associated physiological mechanisms have been the focus of research since 1980s (Nageswara Rao et al., 1985; Pimratch et al., 2008; Zurweller et al., 2018). However, improving yield under drought has proved difficult because of the large G × E interaction. To identify traits contributing to yield in water-limited environments, the model proposed by Passioura (1986) has been adopted for peanut: Y  T  TE  HI

(11.1)

where, Y is pod yield, T is the amount of water transpired by the crop, TE is transpiration use efficiency (g of DM produced per kg transpiration), and HI is harvest index (ratio of pod weight to DM).

3.2.1 Transpiration Biomass accumulation is usually proportional to the amount of water transpired by the plant. On loamy and clay soils, roots and soil water extraction have been recorded at depths in excess of 100 cm (Nageswara Rao et al., 1989; Matthews et al., 1988b; Prabowo et al., 1990; Wright et al., 1991). Rooting depth and root length density are associated with water extraction in water-limited environments (Songsri et al., 2008), although Ludlow and Muchow (1990) warned that measurements of rooting depth and root length density do not necessarily give an indication of a genotype’s ability to extract soil water. Measurements of soil water extraction can, however, provide useful but indirect information on root function (Ketring, 1986). Soil, weather, and crop factors such as temperature, atmospheric VPD, plant population, and soil water content affect crop transpiration, and thus extraction of water by plants. Nageswara Rao et al. (1989) found that total length and weight of the root system decreased in wider rows and at low plant density, although significant amounts of root biomass were still found in the interrow space of the sparse plantings (12 plants m− 2). Soil water extraction from depth was more rapid in the denser stands and was associated with greater root production at deeper soil layers (Simmonds and Williams, 1989). Several studies quantified peanut transpiration in field and mini-lysimeters and its relationship with total DM (Matthews et al., 1988a,b; Wright et al., 1994; Puangbut et al., 2009; Ketring, 1984). Some of these studies showed a positive relationship between root and shoot dry weights (Ketring, 1984; Wright et al., 1994).

3.2.2  Transpiration efficiency Matthews et al. (1988a) demonstrated variation in TE between peanut genotypes with similar transpiration (T). Wright and Nageswara Rao (1994) described genotypic, environmental, and management factors affecting TE in peanut and reinforced the finding of variation in TE as an important factor in peanut crop water relations. However, measurement of soil water extraction in field experiments is labour-intensive and expensive. Farquhar et al. (1982) postulated that at constant VPD, carbon isotope discrimination (Δ13C) during CO2 assimilation estimates the ratio of the internal CO2 concentration in the leaf (Ci) to ambient CO2 concentration (Ca) in C3 plants. Lower A (rate of photosynthesis per unit leaf area) was associated with lower Ci/Ca and greater TE (Farquhar et al., 1989). In agreement with this theory, A was negatively correlated with TE in a number of C3 crops, including peanut (Hubick et al., 1986; Wright et al., 1988). Nageswara Rao and Wright (1994) concluded that ∆13C is a useful surrogate for selecting peanut genotypes with improved TE under drought. Measurement of ∆13C in dried leaf tissues could be quick but is costly because it requires access to a mass spectrometer and skills to operate the equipment yet creating the need for cheaper and reliable surrogate. Several studies reported that TE and ∆13C correlate with leaf traits associated with photosynthetic capacity per unit leaf area such as specific leaf area (SLA) or SPAD

368  Crop Physiology: Case Histories for Major Crops

Chlorophyll Meter Reading (SCMR) (Nageswara Rao and Wright, 1994; Nageswara Rao et al., 2001; Sheshshayee et al., 2006; Arunyanark et al., 2008). Thicker leaves (high SCMR and low SLA) have higher TE. However, leathery leaves could also result in high SCMR, which doesn’t represent high chlorophyll density per unit leaf area. Krishnamurthy et al. (2007) reported that the relationships of SLA and SCMR with TE could be limited to specific stages of stress, and the percentage of variation accounted in these relationships was too low to merit their use as indirect indicators of TE variation. Vadez and Ratna Kumar (2016) observed no relationship of SCMR and SLA with TE in peanuts grown in PVC tubes. These discrepancies in the relationships between TE with its surrogates in the above studies could be due to variations in methodology, including crop stages, position of the leaf, and water status of the plant when SCMR and SLA are measured. This suggests further research is needed to standardise the techniques of using TE surrogates. Water-use efficiency (WUE) is expressed on the basis shoot DM or yield and evapotranspiration (ET). Several studies showed a positive relationship between seasonal ET and pod yield (Kheira, 2009; Idinoba et al., 2008). For rainfed peanut, deep tillage to increase rainfall infiltration into soil and stubble retention to minimise soil evaporation can potentially increase WUE (Unger et al., 1991). Water-saving irrigation strategies have also been explored to improve WUE (Kheira, 2009).

3.2.3  Harvest index Harvest Index (HI) is the ratio of pod DM to shoot DM. Water deficits have significant effects on HI mainly by affecting total plant DM (Fig. 11.3). However, the relationship between pod DM and HI in peanut is generally strong, irrespective of water deficit (Nageswara Rao et al., 1985; Ratnakumar and Vadez, 2011). In high rainfall and humid environments, infestation by foliar pests and diseases can decrease the photosynthetically active leaf area, especially during pod-filling phase, and thus can affect HI.

4  Capture and efficiency in the use of nutrients 4.1 Nitrogen Peanut relies on two sources of nitrogen: derived from N fixation by nodules through a mutual symbiotic relationship with rhizobia and direct uptake of mineral N by roots. In general, yield response to inoculation by Bradyrhizobium was observed in the field where peanut had not been sown previously (Reddy et al., 1981). Peanut responded positively to inoculation when inoculant was applied in the seed furrow rather than to seed before sowing.

4.1.1  N fixation Within an active nodule, rhizobia carry out the reduction of N2 to ammonia using the enzyme complex nitrogenase. The nitrogenase reaction is energy-intensive. Further energy is required to assimilate the products of fixation into amino acids

0.70 0.60

Harvest Index

0.50 0.40 0.30 0.20 0.10 0.00 0

2000

4000

6000

8000

10000

12000

-1

Dry matter (kg ha ) FIG. 11.3  Relationship between shoot dry matter and harvest index of peanut grown under different timing and intensities of drought during 1980–81 (y = 4E − 05x + 0.1629, R2 = 0.56, circles) and 1981–82 (y = 7E – 05x − 0.052, R2 = 0.88, squares) at ICRISAT, India.

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(Sprent, 1994). The energy cost for nitrogen assimilation ranks N fixation > nitrate > ammonium (Raven, 1988; Vance and Heichel, 1991). Because nitrogenase is inactivated by lack of availability of ‘O2’, and yet the bacteria need O2 to produce ATP to drive the reaction, nodules must maintain a high flux of O2 at a low concentration from the ambient soil medium to the site of nitrogen fixation. The oxygen transport is facilitated by an extensive intercellular space network and by the presence of the oxygen-carrying pigment, haemoglobin, in the host cells. The presence of haemoglobin gives active nodules a distinct reddish pink colour, and it is closely coupled with the nitrogenase activity. N uptake is assumed to comprise three processes: (1) mass flow, which is a function of transpiration, (2) the nitrate concentration in soil solution, and (3) active uptake and diffusion estimated in terms of the rate at which plants can assimilate nitrate from soil and fixation (Sinclair, 1986). If N demand cannot be satisfied by mass flow, then it is supplied by either diffusion or N2 fixation, depending on the legume species (Robertson et al., 2002). The process of N2 fixation is sensitive to water deficits and crop age. Peanut is able to keep the nodules functional during stress; hence its nitrogenase activity is relatively stable and recovers faster upon re-watering compared to other grain legumes (Venkateswarlu et al., 1990). Bell et al. (1994) found that unlike in other legumes, the rate of N2 fixation in peanut does not decline during the later stages of pod fill. This study also found that there was no difference between virginia and Spanish bunch types in N2 fixation, and rate of accumulation of fixed N could be accounted for by a common linear function of energy-adjusted above-ground DM. In peanut, the nitrogen fixation efficiency (NFE), defined as g N fixed per kg of above-ground DM ranged from 0.5 to 17.9 g N (kg DM)− 1. Despite high rates of N2 fixation throughout pod filling, N2 fixation alone was unable to satisfy the N demand of the developing pods. To meet this deficit, uptake of mineral N continued throughout the pod fill period, and N was also remobilised from vegetative parts to developing pods. Bell et al. (1994) also found that the mobilised N was almost exclusively derived originally from N2 fixation. N fixation is also discussed in other legume chapters, for example, Chapter 8: Soybean; Chapter 13: Lentil; and Chapter 15: Faba bean.

4.1.2  Response to soil mineral N Response of peanut to fertiliser N application has been inconsistent across regions, which led peanut to earn the title of an ‘unpredictable legume’ (Cummins, 1986). These variable responses were attributed to differences in climate, soil nutrients, management, and cultivars. Most consistent responses to fertiliser N have been observed in situations where soil conditions were not suitable for nodulation (Reid and Cox, 1973). Fertiliser N inhibits root hair infection and nodule initiation, development, and function (Gibson, 1977). Cope et al. (1984) found no responses to mineral N application for the runner type in Georgia and Alabama, whilst Patel et al. (1988) found that application of N increased yield in only once in five years in India. Mali et al. (1988) found that application of 20 kg N ha− 1 as a basal dose increased N uptake and pod yield in India. Responses of legumes to applied nitrogen are best understood in soybean (Chapter 8: Soybean, Section 3.3).

4.2 Calcium Availability of calcium (Ca) to peanut pods is a major limiting factor to production in many regions of the world, particularly in noncalcareous soils. Because pods do not transpire, they do not receive xylem-transported Ca from the roots (Skelton and Shear, 1971); the pods must absorb Ca directly from the surrounding soil through mass flow for their development (Chahal and Virmani, 1973; Wolt and Adams, 1979; Zharare et al., 2009). This interesting aspect plays an important role in calcium management for achieving high yield, quality, and seed germination. Ca requirements are greatest at the start of gynophore swelling, and Ca deficiencies in later stages can result in the failure to fill the pod with seed, a phenomenon known as ‘pops’. However, moisture stress in the pod zone can decrease Ca uptake and thus induce Ca deficiency in peanuts (Hallock and Allison, 1980; Rajendrudu and Williams, 1987). Differences between Spanish and virginia bunch types in pod distribution pattern and seed size may have some relevance to the Ca uptake by developing pods. Pods of bunch cultivars from within a 5 cm radius of the tap root, whilst in virginia runner types, most of the pods set within a radius of up to 30 cm from the main stem. Although seed size varied depending on the botanical type, no significant differences in Ca requirement for seed growth were observed between bunch and runner cultivars despite the higher pod zone Ca requirement for virginia types, and there is no evidence for an influence of seed size on internal Ca requirements (Zharare et al., 2009). A comparative analysis across a range of Ca environments showed that the shelling percentage (seed-to-shell ratio) of runners declined less rapidly compared to bunch types, indicating that runner type peanut varieties were more efficient in exploiting Ca at lower soil Ca availability in the pod zone (Hartmond et al., 1994). However, as a part of agronomic package for peanut, Ca is routinely applied to the soil during the pod development phase. Gascho and Davis (1994) adequately covered calcium nutrition in peanut.

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4.3 Phosphorus Phosphorus (P) has low solubility and poor mobility in the soil (Gulati et al., 2008) and P deficiency compromises shoot and root growth, photosynthesis, sugar translocation, nodule formation, and N fixation (Rotaru and Sinclair, 2009; Udvardi and Poole, 2013; Yadav et al., 2017). The BNF in bacteroids, as well as ammonium assimilation into amino acids and ureides in the plant cell fraction of nodules, require a large amount of P in energy transfer. The molecular mechanism, including maintenance of the P-homeostasis in nodules for rhizobia–legume symbiosis, is emerging as the main adaptive strategy to enhance P utilisation in P-deficient soils. Less than 5% soil P is absorbed by the plants in the form of HPO 4 2− and H 2 PO 4 − (Mitran et al., 2018). In acid soils, the inorganic phosphates are complexed with aluminium and iron, and thus become unavailable for plant uptake (Mohsin et al., 1995; Merbach et al., 2010). Some soil-borne bacteria such as Agrobacterium, Acetobacter, and Azotobacter solubilise phosphate enhancing P availability (Ogut et al., 2010; Walpola and Yoon, 2013; Kaur and Sudhakara Reddy, 2014). Native phosphate-solubilising bacteria (PSB) can be used to improve P availability and yield of crops (Richardson et al., 2001; Mohammadi et al., 2011) including peanut (Taurian et al., 2010; Anzuay et al., 2015). In acid lateritic soils (Alfisol) of the peanut-growing areas with pH  7.4, particularly calcareous soils with a high content of calcium carbonate (> 15%), which are typical of semiarid and arid climates. Pasupuleti et  al. (2015) observed significant differences in intrinsic ability amongst peanut genotypes in the uptake of Iron (Fe) and zinc (Zn) from soil. There was a significantly positive relationship between levels of Fe and Zn in seed (Fig. 11.4), suggesting that higher Zn seed content achieved either by genotypic selection or by zinc fertilisation strategies would lead to high Fe content in peanut.

5  Grain yield and quality 5.1  Grain yield We have analysed peanut pod yield in relation to development and capture and efficiency of resources in previous sections. Here, we focus on breeding for yield.

60 Seed Fe Concentration (mg g-1)

58 56 54 52 50 48 46 44 42 40 50

60 70 80 Seed Zn concentration (mg g-1)

90

FIG. 11.4  The relationship between levels of Zn and Fe in peanut seeds for 64 genotypes grown in alfisols at ICRISAT, India. Each value is mean of 4 seasons and 4 replications (y = 0.47x + 18.0, R2 = 0.59). Courtesy: Pasupuleti, J., Nigam, S.N., Abhishek, R., Anil Kumar, V., Manohar, S.S., Venuprasad, R., 2015. Iron and zinc concentrations in peanut (Arachis hypogaea L.) seeds and their relationship with other nutritional and yield parameters. J. Agric. Sci. 153, 975–994.

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Most peanut breeding programmes focus on pod yield, seed size, quality traits, and resistance for foliar diseases (Isleib et al., 2001). Genetic gains in yield correlate with increasing number of reproductive structures, reproductive efficiency (percentage of flowers progressing to pods and seed), and seed weight (Coffelt et al., 1989; Haro et al., 2013). As yield has a relatively low heritability, this approach may not be the most efficient (Ntare and Williams, 1998; Chapter 3: Wheat, Section 2.1). Furthermore, pod yield can only be accurately measured in later progeny populations, so measurement of physiological traits in ­intermediate generations, before lines are fully fixed, offers the possibility of selecting physiologically adapted material before yield testing begins. Between 2000 and 2010, a large ACIAR-funded collaborative project involving breeders, physiologists, and modellers tested whether indirect selection using the trait-based approach could improve the efficiency of selection in large-scale ­peanut-breeding programmes (Cruickshank et al., 2002; Nigam et al., 2005). A total of 276 progenies developed from four selected crosses (192 genotypes in 12 environments in India and 82 genotypes in 7 environments in Australia) were tested for yield. This research has found that both direct (yield-based) and indirect (trait-based) selection methods for yield were able to identify high-yielding genotypes under water-limited conditions, with up to 30% pod yield advantage compared to local check varieties, suggesting that parental selection was more critical than the breeding method. This research concluded that (1) a physiological model enabled identification of ‘key’ functional traits for breeding; (2) parental selection (rather than the progeny selection method) had a major bearing on the breeding for drought resistance; (3) in India, traitbased selection was only marginally superior in most drought patterns, excepting mid-season droughts involving recovery; (4) in Australia, both approaches were equally efficient. However, the selection index effectively retained higher TE genotypes that were more likely to be rejected in direct pod yield selection; (5) co-selection for TE and pod yield is possible.

5.1.1  Ideotype breeding The concept of plant or crop ideotype is important for breeding because it provides a framework of which important traits are needed to be combined into a single cultivar and their relative priority for selection. For peanuts, the crop ideotype varies across regions, production systems, and the end-use in the market. The Australian Peanut Breeding Programme (APBP) is a case study for the application of ideotype breeding (Suriharn et al., 2011; Gauffreteau, 2018). It has focused on highyielding, early maturity varieties that escape end-of-season drought and aflatoxin issues critical in southern Queensland (Chauhan et al., 2010). Crop modelling has demonstrated that end-of-season droughts occur in over 60% of years for traditional long maturity (150 days) virginia types (Cruickshank et al., 2002). This can be ameliorated with earlier maturing types of ~ 120 days (Chauhan and Wright, unpublished data). Fig. 11.5 supports this effect, with the overall frequency of end-of-season drought (groups ‘ec3’ and ‘ec4’) being reduced from over 60% with a long maturity type to under 40% with an early maturing type. As discussed earlier in Section 2, much of the early maturity germplasm in the global gene banks is of Spanish botanical type, which lacks high-yield potential, has poor foliar disease resistance, small seed size, and is generally poorly adapted to the Australian environment and preferred market type (i.e. large seed virginia type). These deficiencies along with other

FIG.  11.5  APSIM-simulated patterns of drought (no stress, mid-season, moderate terminal, and severe terminal drought) for (a) early-season cv. Taabinga and (b) full-season cv. Streeton. Frequencies of drought for (a) cv. Taabinga: no stress 29.9%, mid-season stress 20.2%, moderate terminal stress 25.9%, and severe terminal stress 24.2%; and (b) cv. Streeton: no stress 18.5%, mid-season stress 14.9%, moderate terminal stress 32%, and severe terminal stress 34.6%. Courtesy: Chauhan and Wright (unpublished).

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market required traits of high oleic fatty acid (Norden et al., 1987), high blanchability (Wright et al., 2018), and acceptable taste meant that a major introgression effort to incorporate all these traits in a single adapted genotype was required. In the late 1990s, a major breeding effort was therefore initiated to develop adapted early maturity varieties using an ideotype breeding approach incorporating the traits in Table 11.4. Between 2007 and 2018, significant genetic progress in grain yield potential, seed size (as increase Jumbo %), and foliar disease resistance (as seed yield under high foliar disease pressure) has been achieved, with Fig. 11.6 showing this advancement for four varieties released from the APBP since 2007.

5.2  Seed quality Aflatoxins are a major issue for quality of peanut and are discussed on Box 11.2.

5.2.1 Utilisation The dual-purpose nature of peanut has led to the evolution of two major markets, one for peanut snack foods, candies, and butter and another for oil and by-products such as meal and shell. The large and higher quality seeds are traded as snack food, whereas small seeds are used for edible oil used for cooking. Nearly 82% of the global peanut production is used for oil, 12% as seed, and 6% as feed (Mehrotra, 2011). However, the global production of peanut oil is around 5.5 Mt compared to 73.5 Mt of oil-palm, 56.9 Mt of soybean, 27.9 Mt of rapeseed, and 19.4 Mt of sunflower (Foreign Agricultural Service/ USDA, Office of Global Analysis, 2019). Peanut shell is also used for composting, mulching, and feed or fuel pellets (Anyang General International Co., Ltd., 2019). TABLE 11.4  Traits for early-maturity peanut ideotype for the Australian Peanut Breeding Program. Trait

Target

Harvest index

> 50%

Seed yield

> 90% of long season type (150 days)

Maturity period

Short duration, 125–135 days (selection based on mature seed % > 78%)

Resistance to late leaf spot and rust

High seed yield under unsprayed conditions (Subbarao et al., 1990)

Seed %

 78% seed content

Blanching %

> 85% blanchability (Wright et al., 2018)

Oil content %

> 48% up to 55%

Fatty acid content %

Oleic acid > 75%; linoleic acid  45% (seeds riding over a 9.1 mm screen)

Flavour and taste

Acceptable taste/flavour (> 5.0 on PCA taste panel)

Other

Resistance to soil borne diseases (CBR, Sclerotinia)

6 60

Jumbo seed (%)

yield (t/ha)

5 4 3 2 1 0 Taabinga Redvale Tingoora (2018) (2014) (2010)

Walter (2007)

50 40 30 20 10 0 Taabinga Redvale Tingoora Walter (2018) (2014) (2010) (2007)

FIG. 11.6  (a) Pod (squares) and seed (bricks) yield, and (b) seed size (as Jumbo %) under irrigated conditions for four early maturing lines released by the APBP since 2007. LSDs at 5% are 0.136 for pod yield, 0.10 for seed yield, and 1.1 for Jumbo seed %.

Peanut Chapter | 11  373

Box 11.2  Application of peanut physiology to monitor and manage aflatoxin risk Aflatoxin is a potent carcinogen with implications in liver and other human diseases, including childhood stunting. It is produced by Aspergillus flavus, which contaminates peanuts in the field between 40°N and 40°S latitudes. Aflatoxin contamination of peanuts is mainly driven by weather, with underlying genetic and management components contributing to susceptibility and risk. In particular, hot and dry conditions during reproduction are key risk factors that predispose preharvest A. flavus infection and aflatoxin production (Sanders et al., 1993; Cotty and Jaime-Garcia, 2007; Cotty et al., 2008). Harnessing climate dependencies of A. flavus and other related species to predict preharvest contamination in the peanut was investigated widely (Thai et al., 1990; Parmar et al., 1997; Craufurd et al., 2006; Chauhan et al., 2010). Several researchers were able to manipulate aflatoxin contamination by altering soil water status and temperature or agronomy (Cole et al., 1985; Blankenship et al., 1984; Rachaputi et al., 2002). Chauhan et al. (2010) used APSIM to count the number of days that meet two conditions: temperature between 22°C and 36°C and fractional available soil moisture (FASW)  80%) before processing for the manufacture of confectionary products, including peanut butter and snack foods (Singh et al., 1996; Sanders et al., 1999). Blanchability is strongly dependent on genotype with broad-sense heritability from 0.74 to 0.97 (Shokraii et al., 1985; Cruickshank et al., 2003). The speed breeding techniques developed for peanut could be adopted to fast tracking varieties with high yield and blanchability (O'Connor et al., 2013).

5.2.5  Oleic to linoleic acid ratio (hi-oleic) Norden et  al. (1987) first identified a peanut genotype containing ∼ 80% oleic acid and ∼ 2% linoleic acid with health benefits discussed in Section  5.2.1. Large G × E × M interactions were noted for the hi-oleic trait in peanut (Andersen and Gorbet, 2002). Lower temperatures during seed development were associated with a more unsaturated oil owing to enhanced synthesis of linoleic acid. However, the actual impact of seed maturity is dependent on genotype, climatic conditions, and genotype-by-climate interactions (Andersen and Gorbet, 2002). Whilst some studies reported an increase in oleic acid and reduction in linoleic acid with seed maturity (Hinds, 1995; Mozingo et al., 1985; Worthington et al., 1972; Young et al., 1972), other studies have shown a reduction in oleic acid and an increase in linoleic acid with maturity (Hashim et al., 1993; Lynd and Ansman, 1989), whereas Knauft et al. (1986) observed no influence of maturity on oil chemistry. Chapter 16: Sunflower (Section 5.1.1) and Chapter 17: Canola (Section 4.3) outline genotypic and environmental sources of variation in oil, with emphasis on fatty acid composition.

5.3  Trade-offs between yield and quality traits The association between yield and oil or protein content in peanut varies with genotype, management, and environment interactions. Meta and Monpara (2010) and Pasupuleti et al. (2015) reported a positive correlation between pod yield and oil content, whilst Chiow and Wynne (1983) reported negative association between the two traits. Pasupuleti et al. (2015) also reported a trade-off between oil content and 100-seed weight, in large seeds, generally used for confectionary purposes, which is a desirable character. Dwivedi et al. (1990) reported no relationships between oil content and 100-seed weight in ungraded sample but found a positive relationship when the sample has been graded to avoid immature or shrivelled seeds. Given the changing nature of the fatty acids in immature seeds, it is essential to analysis mature seeds for reproducible results.

6  Concluding remarks: Challenges and opportunities Peanut has been described as ‘the unpredictable legume’ because of its unpredictable responses to inputs (Cummins, 1986). The world peanut production has increased from 14 Mt in 1960s to over 41 Mt in 2012; however, the global annual growth rate of peanut production dropped and stagnated at 0.65% since 2012. The bulk of the world peanut production (~ 80%) is rainfed in the semiarid tropics, where temperature, rainfall, VPD, and solar radiation are the major factors influencing the pod yield. The vegetative phase of peanut is adapted to moderate water deficits and elevated temperatures, whilst the reproductive phase is extremely sensitive. Water deficit during the vegetative phase often increased pod yields through a range of morphological and physiological mechanisms. The greatest reductions in pod yield with prolonged water deficits after flowering in common with other legumes (e.g. Chapter 15: Faba bean, Section 5.2.; Chapter 10: Chickpea, Section 4.1) and canola (Chapter 17: Canola, Section 2.2.2). The possibility of increasing peanut yield using strategic irrigation, where in crops are allowed to be exposed to some degree of stress in the early stages and timely irrigation during pod formation and filling are worth investigating. Peanut is amongst the earliest flowering legume crops because it is insensitive to photoperiod during the vegetative phase and hence has a shorter preflowering phase. However, the crop is sensitive to photoperiod from the start of the reproductive phase, with declining peg to pod ratio under long photoperiods. Significant temperature × photoperiod interactions have been observed in all the three botanical types, including virginia, Spanish and valencia. How such interactions could be exploited in enhancing wide and specific adaptation and in determining agronomic performance should be investigated. These interactions are likely to play substantial role in improving adaptation to climate change. Many studies have shown genotypic variation in transpiration efficiency, and the value of Δ13C as a screening tool for improved transpiration under drought. N fixation declines with both water deficit and plant age in general. However, nitrogenase activity in peanut was found to be more stable compared to other grain legumes.

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Peanut pods directly absorb calcium from soil medium, hence the importance of calcium fertilisation for yield and quality, particularly in noncalcareous soils. P deficiency can directly affect root growth, photosynthesis, sugar translocation, and N fixation. Native phosphate-solubilising bacteria can be used to improve P availability. Yield-based and trait-based selection were equally efficient to identify high-yielding genotypes under water-limited conditions, with up to 30% yield advantage compared to local checks, suggesting that parental selection was more ­critical than the breeding method. Short-duration genotypes with more rapid development through the reproductive phase are important for crop adaptation to high temperatures. Possibilities of more synchronous flowering and podding habit for more assured input environments should be investigated. With increasing drought frequency in semiarid tropics, there is interest in breeding shorter duration cultivars. However, much of the short-duration germplasm available in the global gene banks has low yield in favourable environments, and poor seed quality and foliar disease resistance. Major introgression effort to incorporate all these traits into adapted early-maturing genotypes has been successful over the past decade in Australia. Development of cultivars with phenological plasticity to take advantage of escape mechanism under terminal drought and ability to extend reproductive development in more favorable environments will be desirable. Such cultivars may need to incorporate higher dormancy levels to prevent germination of early formed pods. The physiological principles developed in the past decades have been successfully applied in robust peanut crop models for prediction of yield across diverse environments and environmental and management factors affecting aflatoxin risk.

Acknowledgements Authors would like to express their sincere thanks to Dr. S.N. Nigam, retired principal peanut breeder, ICRISAT, for his discussions and comments on the early draft of this chapter.

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Nigam, S.N., Nageswara Rao, R.C., Wynne, J.C., Williams, J.H., Fitzner, M., Nagabhushanam, G.V.S., 1994. Effect and interaction of temperature and photoperiod on growth and partitioning in three groundnut (Arachis hypogaea L.) genotypes. Ann. Appl. Biol. 125, 541–552. Nigam, S.N., Nageswara Rao, R.C., Wynne, J.C., 1998. Effects of temperature and photoperiod on vegetative and reproductive growth of groundnut (Arachis hypogaea L). Agro. Crop Sci. 181, 117–124. Nigam, S.N., Chandr, S., Rupasridevi, K., Manohar, B., Reddy, A.G.S., Nageswara Rao, R.C., Wright, G.C., Reddy, P.V., Deshmukh, M.P., Mathur, R.K., Basu, M.S., Vasundhara, S., Vindhiya Varman, P., Nagda, A.K., 2005. Efficiency of physiological trait-based and empirical selection approaches for drought tolerance in groundnut. Ann. Appl. Biol. 146, 433–439. Norden, A.J., Gorbet, D.W., Knauft, D.A., Young, C.T., 1987. Variability in oil quality among peanut genotypes in the Florida breeding program. Peanut Sci. 14, 7–11. Ntare, B.R., Williams, J.H., 1998. Heritability and genotype x environment interaction for yield and components of a yield model in segregating populations of groundnut under semi-arid conditions. Afr. Crop. Sci. J. 6 (2), 119–127. Nur, I.H., Gasim, A.A.E., 1978. Effect of sowing date on groundnuts in Sudan Gezira. Exp. Agric. 14, 13–16. Obyrne, D.J., Knauft, D.A., Shireman, R.B., 1997. Low fat-monounsaturated rich diets containing high-oleic peanuts improve serum lipoprotein profiles. Lipids 32, 687–695. O'Connor, D.J., Wright, G.C., Dieters, M.J., George, D.L., Hunter, M.N., Tatnell, J.R., Fleischfresser, D.B., 2013. Development and application of speed breeding technologies in a commercial peanut breeding program. Peanut Sci. 40 (2), 107–114. https://doi.org/10.3146/PS12-12.1. Ogut, M., Er, F., Kandemir, N., 2010. Phosphate solubilization potentials of soil Acinetobacter strains. Biol. Fertil. Soils 46, 707–715. Ono, Y., Nakayama, K., Kubota, M., 1974. 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Toomsan, B., McDonagh, J.F., Limpinuntana, V., Giller, K.E., 1995. Nitrogen fixation by groundnut and soyabean and residual nitrogen benefits to rice in farmers' fields in Northeast Thailand. Plant Soil 175, 45–56. Udvardi, M., Poole, P.S., 2013. Transport and metabolism in legume-rhizobia symbioses. Annu. Rev. Plant Biol. 64, 781–805. Underwood, C.V., Taylor, H.M., Hoveland, C.S., 1971. Soil physical factors affecting peanut pod development. Agron. J. 63, 953–954. Unger, P.W., Stewart, B.A., Parr, J.F., Singh, R.P., 1991. Crop residue management and tillage methods for conserving soil and water in semi-arid regions. Soil Tillage Res. 20, 219–240. Upadhyaya, H.D., 2005. Variability for drought resistance related traits in the mini-core collection of peanut. Crop Sci. 45, 1432–1440.

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Image source: CIAT Flickr

Chapter 12

Common bean Millicent R. Smitha,b and Idupulapati M. Raoc,⁎ a

School of Agriculture and Food Sciences, University of Queensland, Gatton, QLD, Australia, bQueensland Alliance for Agriculture and Food Innovation (QAAFI), Gatton, QLD, Australia, cInternational Center for Tropical Agriculture (CIAT), Cali, Colombia

1 Introduction Common bean (Phaseolus vulgaris L.) is the most important grain legume for human nutrition worldwide (Beebe, 2012). This is attributed to its wide consumption and cultivation, and nutritional and agronomic role, that complement those of cereals and other sources of carbohydrates such as root crops and plantain in the tropics of Latin America and eastern and southern Africa (Broughton et al., 2003). P. vulgaris is the most widely grown and economically important Phaseolus species amongst all edible bean seed types (pinto, kidney, navy beans, and green beans; runner beans, string beans, half runners, snap beans, French, and haricot beans). Common bean is nutrient rich, with both protein and complex carbohydrates, vitamins (A, C, and folate), dietary fibre, and biologically important minerals such as Ca, Mg, K, Cu, Mn, Fe, and Zn (Cavalieri et al., 2011; McClean et al., 2011; Beebe, 2012). Common bean per capita consumption ranges from 50 to 60 kg per year in Rwanda, Kenya, and Uganda (Buruchara et al., 2011).

1.1  Climatic zones Due to variation in growth habit (bush to climbers) and growth cycles (2–10 months), production of common bean can occur across a range of climatic zones (Beebe et al., 2013) from 60° North to 32° South latitude and from sea level to 3000 m above sea level (Thung and Rao, 1999). It grows optimally at temperatures of 18–24°C. During flowering, a maximum temperature exceeding 30°C during the day and 20°C during the night can limit yields. Rainfed crops require moderate amounts of rainfall (300–600 mm), but adequate amounts are essential during and immediately after flowering. Generally, common bean is considered a short-season crop with most varieties maturing between 65 and 110 days from emergence to physiological maturity. Maturity can extend up to 200 days after sowing amongst climbers that are used in cooler upland elevations (Graham and Ranali, 1997). Common bean is adapted to deep well-drained, sandy loam, sandy clay loam, or clay loam soils with a clay content of between 15% and 35% and with no nutrient deficiencies (Thung and Rao, 1999). Heavy clay soils with poor oxygenation and capping clay sands are not suitable. Thus it will not grow well in soils that are compacted, too alkaline, or poorly drained. The crop is sensitive to soil-borne diseases and should be grown in a rotation.

1.2  Major growing regions Common bean is predominantly grown in Latin America, Africa, and Asia (Table 12.1). China is also emerging as an important common bean producer and exporter. Production of common bean is difficult to accurately estimate in some countries due to confusion with other legume species (Beebe et al., 2013); however, estimates suggest annual dry bean gross production to be 26.5 M t (FAOSTAT, 2017). Although India and Myanmar register a massive number of hectares, common bean is confused with those of other grain legumes, especially Vigna mungo and Vigna radiata that are widely grown in these two countries and were classified as Phaseolus species at one time (Beebe, 2012).

*

Present address: Plant Polymer Research Unit, National Center for Agricultural Utilization Research, Agricultural Research Service, United States Department of Agriculture (USDA-ARS), Peoria, IL, United States. Crop Physiology: Case Histories for Major Crops. https://doi.org/10.1016/B978-0-12-819194-1.00012-8 Copyright © 2021 Elsevier Inc. All rights reserved.

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386  Crop Physiology: Case Histories for Major Crops

TABLE 12.1  Volume of dry bean production, area, and yield for top 10 bean-producing countries in the world in 2010 and 2014. Production (million metric tons) for 2010 and 2014 Country

2010

2014

Area (million hectares)

Yield (t ha− 1)

India

4.87

4.110

10.0

0.411

Brazil

3.20

3.294

3.186

1.034

Myanmar

3.03

4.651

3.017

1.541

China

1.53

1.050

0.600

1.750

United States of America

1.44

1.311

0.667

1.965

Mexico

1.16

1.273

1.681

0.757

United Republic of Tanzania

0.95

1.114

1.134

0.982

Uganda

0.46

0.877

0.674

1.300

Kenya

0.39

0.616

1.052

0.585

Argentina

0.34

0.430

0.350

1.228

Global

23.2

26.5

30.6

0.867

Data from, FAOSTAT, 2017. Food and Agriculture Organisation of the United Nations Production and Trade Data. http://faostat3.fao.org/home/E.

The production figures from Latin America and East Africa are more reliable as common bean is the most important food legume within these regions (Beebe, 2012). Brazil is by far the most important country, representing more than 50% of area and production in Latin America, followed by Mexico with another 20%. Central America and parts of the Caribbean likewise present significant production or consumption for the size of their populations. In the Andean region, bean production is more modest, highly concentrated in some locations, and can occur in complex systems of multiple crops. Within eastern and southern Africa, most production occurs in the mid-altitude highlands between 1200 and 2000 m, from Ethiopia to South Africa. Three East African countries (Kenya, Tanzania, and Uganda) are amongst the leading bean producers in the world. This is in part due to a unique partnership model, Pan African Bean Research Alliance, involving Centro Internacional de Agricultura Tropical (CIAT) and its research partners, together with effective breeding and seed-delivery systems that have helped to reach millions of beneficiaries with improved bean varieties in Africa (Buruchara et al., 2011; Mukankusi et al., 2018).

1.3  Role in farming systems Beans are both major components and niche crops in different production systems (Beebe, 2012). The four main bean production systems in the tropics are: (i) monocropped beans in favourable environments, (ii) associated beans as a crop of primary importance, (iii) associated beans as a secondary crop, and (iv) monocropped or associated beans in fragile or uncertain environments (Voyset, 1998). These production systems vary widely in altitude, latitude, and intensity of cropping. The monocropped indeterminate climbers are the most primitive land races, and these were selected in association with maize at mid- to high altitudes. These are not only locally important in the highlands of Mexico, Guatemala, and the Andes of South America but are also gaining importance due to higher seed yield (~ 4 t ha− 1) in eastern Africa. Bush beans (indeterminate or determinate) in the tropics are cultivated in other cropping systems and in environments far beyond those of climbing beans, especially at lower altitudes and higher temperatures. Bush beans are often intercropped in traditional mixed systems, often with maize, or occasionally with cassava (Chapter 19: Cassava, Section 6.5.2), sorghum or pigeon pea in Central America, Brazil, and eastern and southern Africa (Hyman et al., 2008). In Brazil, common bean is cultivated in three cropping seasons, two of them rainfed, the wet (September to December) and dry (January to March) seasons, whereas the winter crop is irrigated. The rainfed bean-growing area represents 93% (2.3 million ha) of the Brazilian production area (Lana et al., 2018). Productivity in these rainfed areas is almost half when

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FIG. 12.1  Estimated percentage of area for cropping systems under which common bean is grown by region in Africa. Reproduced with permission from, Katungi, E., Chiuri, W., Ojara, M., Ongom, B., Farrow, A., 2019. Bean production in Africa. In: Farrow, A., Muthoni-Andriatsitohaina, R. (Eds.), Atlas of Common Bean Production in Africa, second ed. Pan-Africa Bean Research Alice Better Beans for Africa PABRA, pp. 23–33.

compared with that in the irrigated winter season due to dry spells and high temperatures. Most bean production (70%) is by small farmers who do not have access to irrigation systems. The rainfed bean production area has been decreasing with a small increase in irrigated area in the past decade. In sub-Saharan Africa (SSA), common bean is primarily grown for food security and income generation of smallholder farmers, in holdings that rarely exceed 2 ha. It is consumed and traded by more than 100 million households (Buruchara et al., 2011). Common bean is grown on 7.6 Mha in SSA as a sole crop or is intercropped. About 38% is produced in sole crop. Shade tolerance and early maturity make common bean suitable for intercropping with maize, sorghum, banana, coffee, cassava, and other grain legumes such as groundnut (Katungi et al., 2019; Fig. 12.1). Because of a combination of factors, including low inputs, infertile soils, biotic, and abiotic stress, common bean yield is low, typically below 1 t ha− 1 in most tropical countries (Beebe, 2012). Increasingly, market opportunities are driving incentives for increased inputs as observed amongst farmers in Argentina, Brazil, and Mexico (Beebe, 2012). Despite strong efforts for genetic improvement, seed yield has not increased dramatically, except where management and modernisation of agricultural practices have occurred.

1.4  Implications of climate change Climate change will likely limit the land suitable for common bean production due to drought and heat stress in the tropics (Beebe et al., 2011). Global metaanalysis projected that decreases of about 5% in crop productivity are expected for every degree of warming above historic levels, and that adapted crops yield roughly 7% greater than nonadapted crops (Challinor et al., 2014). Simulations of historical and future crop suitability for nine major crops of SSA indicated that transformational changes are likely for all crops. For common bean, as much as 60% of the current suitable growing areas will become unsuitable (1.85 Mha) by the end of the century (Rippke et al., 2016). Thus bean-based cropping systems require major attention to alleviate the impacts of climate variability and change. In some bean-production regions, the effects of climate change are already experienced through increased frequency of drought and/or excess rainfall, which alter incidence of disease (Beebe et al., 2011). Modelling suggests that climate change will have an impact on suitability of production of common bean in Latin America and SSA (Beebe et al., 2011). Rainfall is predicted to reduce in key production areas in Latin America (central Mexico, northeastern Brazil) and Africa (South Africa, northern Tanzania, southern Kenya, and low altitude Ethiopia), whereas to increase in others (Cameroon, south central DR Congo, central Angola, and northern Mozambique) (Beebe et al., 2011). For example, climate models tested for one of the largest bean-producing states (Goias) in Brazil predict by 2030 for the dry target population environments, the probability of occurrence of drought (reproductive and/or terminal) would increase from 29.6% (baseline) to approximately 70% and for the wet target population environments, it would

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FIG.  12.2  Changes in suitability of production in Africa from 2014 to 2050. Reproduced with permission from, Ojara, M., Perez, J., Farrow, A., Carmona, S. Laderach, P., 2019. Current and future climatic suitability of bean in Africa. In: Farrow, A., Muthoni-Andriatsitohaina, A. (Eds.), Atlas of Common Bean Production in Africa, second ed. Pan-Africa Bean Research Alliance Better Beans for Africa PABRA, pp. 109–119.

increase from 16% (baseline) to approximately 43% (Heinemann et al., 2017). Heat stress will be widespread across production regions. Changing climatic conditions will also further degrade soil quality, as warmer temperatures and intense rainfall accelerate mineralisation of organic matter and increase erosion rates (St. Clair and Lynch, 2010). Production areas will be impacted differently by climate change depending on the region. For instance, within Central America, increases in maximum temperatures are expected to reduce yields dramatically in some regions requiring farmers to shift production to other crops, whereas in other regions, farmers’ adaptation is feasible (Eitzinger et al., 2017). In SSA, of the area that is currently deemed ‘very suitable’ for production, none will be very suitable by 2030, whereas by 2050, only 32% of the area currently designated as moderately or better suited for common bean production will remain so (Ojara et al., 2019). By 2050 in SSA, the predicted reduction in suitability to grow beans is 60%–80% for parts of eastern DR Congo and southwestern Uganda and 40%–60% for in Uganda, Ituri in eastern DR Congo, much of western Kenya, the Mara in Tanzania, Guinee Conakry, and Cameroon (Fig. 12.2). However, there are also some areas, where suitability for bean is predicted to improve, particularly highland areas of Ethiopia, Burundi, DR Congo, and Tanzania, which will be 20%–40% more suitable for bean than currently. Changes in climate across production regions, alongside a significant need to increase yields means crop improvement must focus on mitigating the effects of drought, heat, and disease. Strategies

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will need to be developed to assist farmers in partially or completely transitioning from common bean production to other crops (Rippke et al., 2016).

2  Crop structure, morphology and development 2.1  Morphological variation Common bean grows as either an erect herbaceous bush, up to 20–60 cm high or as twining, climbing vine up to 2–5 m long. It has a taproot with many adventitious roots, and the stems of bushy types are rather slender, pubescent, and many branched, whereas in the twinning types, the stems are prostrate for most of their length and rise towards the end. The trifoliate leaves, borne on long green petioles, are green or purple. Leaflets are 6–15 cm long and 3–11 cm broad, and the inflorescences are axillary or terminal, 15–35 cm long racemes. The flowers are arranged in pairs or solitary along the rachis, white to purple and typically papillonaceous. Once pollinated, each flower gives rise to one pod, and the pods are slender, green, yellow, black, or purple, sometimes striped. The pods can be cylindrical or flat, straight, or curved, 1–1.5 cm wide and up to 20 cm in length and may contain 4–12 seeds. The seeds are 0.5–2 cm long, kidney shaped, and highly variable in colour depending on the variety: red, white, green, tan, purple, grey, or black.

2.2  Taxonomy and gene pools The taxonomy of Phaseolus was revised by Debouck (1999). The Genus Phaseolus includes common bean (P. vulgaris) and four other cultivated species: the scarlet runner bean (P. coccineus, known for its red/scarlet flower); year-bean (P. dumosus, aka P. polyanthus, name changed in 1995); tepary bean (P. acutifolius); and lima bean (P. lunatus). These species have similar origins in the Americas, the tepary bean is found in northwest Mexico and southern Arizona, scarlet runner and year-beans are found in southern Mexico and the highlands of Guatemala and the lima bean mirrors the common bean in that it has two centres of origin: one in the Andean region and the other in Middle America. There are over 50 species of wild beans in existence. The wild bean originated across a wide geographic area in the tropics and subtropics of Latin America from north central Mexico to northwest Argentina and is found in forest clearings with well-defined wet and dry seasons (Toro et al., 1990). The wild bean evolved patterns of growth and development that assure survival with important implications for genetic improvement (Beebe et al., 2008). Significant changes have occurred with domestication and further agronomic selection (Smartt, 1990). These include: (i) loss of seed dormancy; (ii) changed growth habit (from climbing or half-runner to bush); (iii) photoperiod insensitivity; (iv) reduction in fibre and selection of the green bean type of pod from the original dehiscent pod; (v) greatly increased seed size (from 20 to 50 mg in wild material to > 200 mg in cultivated material); and (vi) white seeds that are preferred by the canning industry. Domestication of common bean occurred independently in two major centres, resulting in two major gene pools Middle American and Andean, that vary on yield and other physiological traits (Kwak and Gepts, 2009). Four gene pools of the wild bean were detected by amplified fragment length polymorphism analysis (Tohme et al., 1996) that evolved in succession. The cultivated Middle American gene pool displays clearly distinct races (Mesoamerica, Jalisco, Durango, and Guatemala) but with fluid borders or subdivisions depending on methods and criteria used to evaluate diversity and the specific populations under study (Diaz and Blair, 2006). Distinction between the races would be dependent on their respective adaptation regimes and growth habits, indeterminate bush Durango types occurring in the dry highlands of Mexico and climbing race Jalisco types in the wet highlands. In terms of world production of Middle American types, the race Mesoamerica is the most important one, followed by race Durango, whereas races Jalisco and Guatemala are of restricted local importance for production but are important sources of traits for breeding. Races in the Andean pool were defined by Singh (1981) based on growth habit and physiological adaptation: race Nueva Granada with bush growth habit with adaptation to mid- to lower altitudes; highland race Peru with climbing habit; and temperate race Chile with indeterminate bush habit. The Andean gene pool is substantially less variable than the Middle American gene pool (Blair et al., 2007). The complementarity of races and their contribution as sources of traits have emerged as an underlying theme of genetic improvement. More than 30 000 domesticated and 1000 wild accessions of common bean are stored in the germplasm collection at CIAT, Colombia. The impact of domestication on common bean is becoming clearer. A recent study shows that domestication through direct selection for seed weight led to the unconscious selection of genes that have an impact on root growth through a developmental pleiotropic effect (Singh et al., 2019). Overall, the extensive germplasm collection and diversity of environments from which common bean evolved provide an important source of traits for genetic improvement (Cortes and Blair, 2018).

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As common bean is highly polymorphic (Fageria and Santos, 2008), the species is adapted to a wide range of ecological niches (Freytag and Debouck, 2002). Related species are an attractive option for traits to broaden the genetic base of common bean, especially for adaptation to more extreme environments (Souter et al., 2017). Species that can be crossed with P. vulgaris represent an important genetic resource for crop improvement and cover the secondary gene pool (P. dumosus and P. coccineus, wild P. costaricensis and P. albescens) and tertiary gene pool (P. acutifolius and P. parvifolius). P. lunatus is the fifth domesticated species within the genus. It is classified into a quaternary gene pool in relation to P. vulgaris and cannot be crossed with common bean.

2.3  Phenological development 2.3.1  Vegetative development Germination of P. vulgaris is epigeal, whereas P. coccineus is hypogeal. Both species germinate in about 6–8 days under optimum conditions (typically minimum soil temperature of 18°C, more adaptive during short days). Mature seeds do not normally show dormancy. Water is imbibed through the micropyle, the raphe, and the hilum, but uptake through the seed coat is negligible (Korban et al., 1981). Seed germination begins when the seed takes in water rapidly, causing the inner layers to swell and split the seed coat and other coverings. The radicle then emerges and starts its downward growth into the soil. The hypocotyl elongates and straightens, raising the cotyledons above the ground. As the epicotyl begins to lengthen and straighten, the first leaves, called plumules, emerge. Small-seeded cultivars tend to germinate and multiply their seed weight more rapidly than large-seeded ones when grown at a relatively high temperature (28°C; Laing et al., 1984). At a low temperature (12°C), large-seeded cultivars tend to germinate more quickly than small-seeded ones (Austin and Maclean, 1972). This fits with the observation that cultivars adapted to cooler climates tend to have larger seeds. The rate of seed germination of common bean is most rapid at 29–34°C, with the exact optimum temperature depending on the cultivar. Below 8°C, germination does not occur (White and Montes, 1993). Following germination, the vegetative growth stages continue up to approximately 40 days, which involves the development of roots, trifoliate leaves, nodes, and branches. Each vegetative stage may be divided into periods, determined by counting the number of fully expanded trifoliate leaves on the main stem, where V1 is emergence, V3 is the first trifoliate leaf and V4 is the third trifoliate leaf. Under ideal conditions, growth occurs exponentially up until the reproductive stage begins.

2.3.2  Reproductive development The reproductive stages occur from approximately 40 to 94 days, and these are indicated by flowering, pod formation, pod fill, and maturity. Each reproductive stage may be divided into periods, where R6 is flowering, R7 is pod formation, R8 is pod filling, and R9 is physiological maturity (Fig. 12.3a).

2.4  Determinancy and growth habit Common bean is described as either determinate or indeterminate. Tanaka and Fujita (1979) described the determinate habit as having stems that terminate in a flower cluster, typically the bush type, whereas the indeterminate habit stems terminate in a vegetative bud cluster and can be either a bush, semiclimbing, or climbing bean. Generally, determinate cultivars flower and mature early, and the transition of the terminal shoot meristem from vegetative to reproductive state results in a terminal inflorescence in the axil of the older leaf primordia. This is in contrast with indeterminate cultivars in which the terminal shoot meristem continuously produces modular units until senescence, each one consisting of a leaf and an inflorescence. Plants of indeterminate cultivars will have a terminal shoot meristem that remains in a vegetative state throughout the production of vegetative and reproductive structures. Thus stem termination has great effects on plant height, flowering and maturity period, amount of branching, length of internodes on the main stem, and node production, which conditions how many flowers and leaves, and therefore pods and seeds, are produced. Improved understanding of the genetic control of vegetative growth and flowering time in common bean will enable genetic manipulation of major components of yield. Typically, indeterminate genotypes have more stable yield due to the longer growth cycle and ability to cope with flower abortion and pod abscission due to abiotic stress. Indeterminate genotypes continue their vegetative growth during flowering, which can result in long developmental stages, variable maturing times, and a long critical period determining the yield of most grain legumes (Lake and Sadras, 2014). Indeterminate growth habit with extended period of flowering (and pod set) contributes to phenotypic plasticity to cope with stressful environments. However, indeterminacy may cause direct competition between vegetative and reproductive growth, and abiotic stress conditions can increase the relative ­distribution

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FIG. 12.3  (a) The stages of development of a common bean plant and (b) the stages of development relative to time of sowing (days after planting) of four common bean cultivars of different growth habit types grown at CIAT-Palmira (mean annual temperature of 24°C). Modified from, Fernandez, F., Gepts, P., López, M., 1986. Etapas de desarrollo de la planta de frijol comín (Phaseolus vulgaris L.). CIAT, Cali, Colombia.

of carbon to reproductive tissues (Polania et al., 2016c; Rao et al., 2017). Debouck and Hidalgo (1984) further classified beans into four growth habits, where type I is erect determinate bush, type II is erect indeterminate bush, type III is indeterminate straggling, and type IV is indeterminate climbing. Plant growth habit influences plant development and maturity (Fig. 12.3b).

2.5  Critical stages of crop development Over the course of the growth cycle, there are key periods that are known to be important for yield. Transition to the reproductive stage can be weak in common bean. Under some environmental stimuli, for example, rainfall late in the growth cycle, the plant may revert back to the vegetative stage interrupting reproductive development (Beebe, 2012). Typically, the cycle is complete within 70–90 days. The length of the growth cycle is important for yield development, as each day of reduction in growth cycle is estimated to lead to a yield penalty of 74 kg ha− 1 in the tropics under irrigated conditions (White and Singh, 1991). Responsiveness of flowering to photoperiod (i.e. time to flowering) is a major target trait for common bean-breeding programmes as it would determine flowering at a given time of year, independent of sowing date, allowing the plant to take advantage of available resources (Beebe, 2012). Common bean is a facultative short-day species (Michelangeli et al., 2013), although the response varies dramatically (White and Laing, 1989). Wild accessions and Andean cultivars typically have a

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short-day response, whereas Mesoamerican cultivars are day neutral. Investigations into photoperiod sensitivity in common bean have identified a gene that regulates flowering time (see, Kwak et al., 2008), which has been tested within a QTLbased environmental linear mixed effect model accurately predicting time to flowering (Bhakta et al., 2017). Developments to understand photoperiod sensitivity and flowering time are a positive step forward allowing breeders to adapt elite varieties for the varying environmental conditions in which common bean is produced. A critical physiological trait determining yield is the abscission of reproductive organs during the reproductive stage. The abortion of reproductive tissues (flowers, buds, and young pods) occurs during the reproductive stage until a specific pod number has been established (Binnie and Clifford, 1981). Even under ideal conditions, approximately 60%–70% of flowers and young pods can be aborted (Boutraa and Sanders, 2001; Lizana et al., 2006). The process of abscission of reproductive material is not well understood, although it is likely a consequence of source–sink dynamics through competition between sinks, variation in sink strength or a loss of sink activity.

2.6  Strategies for adaptation to climate change Given the variation in growth characteristics, as mentioned above, and wide geographic range in which common bean is produced, different varieties may be chosen to adapt to abiotic stress including climate change. Additionally, agronomic management practices such as changes in sowing date may also be used. Earliness is a common mechanism for drought avoidance that also has market advantages; however, the yield potential is relatively low in these cases (Beebe, 2012). Heat currently limits production of common bean in lowland areas, and higher temperatures are the primary threat to production from climate change (Beebe et al., 2013). Temperatures over 20°C at night substantially reduce pollen viability and pollination (Porch and Hall, 2013). Increased temperature is also related to the rate of evapotranspiration and the physiological capacity of a plant to take up soil moisture further influencing the suitability of production areas. As described in Fig. 12.1, modelling suggests that the area suitable for common bean production will reduce in future due to increasing temperatures, particularly in lowland areas of Malawi, DR Congo, Tanzania, Uganda, and Kenya. Consequently, crop improvement efforts are targeting tolerance to an increase in maximum temperatures of 4°C (Ojara et al., 2019).

3  Growth and resources 3.1  Capture and efficiency in the use of radiation A critical driver of growth is the interception of radiation and its use. Common bean evolved in a very competitive light environment, and rapid canopy expansion is required for optimum growth (Beebe et al., 2008).

3.1.1  Canopy architecture Penetration of light through the canopy is dependent on the orientation and distribution of leaves, that is canopy architecture. The trifoliate leaf in common bean allows the plant to orient its leaves towards the incoming radiation maximising light interception (Barradas et al., 1999). Canopy architecture for common bean varies between determinate and indeterminate growth habits, leading to differences in light distribution and, therefore, higher yield in indeterminate types. For example, a bush bean canopy intercepts up to 95% of radiation with leaf area index of four or more, whereas in growth habit I or II that has a short duration, the crop cannot develop a large leaf area index prior to pod set, compromising light interception and growth. Nevertheless, this can be overcome by management practices such as increasing sowing density for growth habits I, II, and III to increase light interception to almost 100% at flowering (Ricaurte et al., 2016). A large leaf area index may be advantageous, where resources are limiting as canopy biomass is positively associated with grain yield under drought (Tanaka and Fujita, 1979; Polania et al., 2016b, 2017b). Processes that determine leaf area development, including leaf addition, expansion, and senescence, vary with growth habit, genotype, environmental and management factors for example, sowing density (Fig. 12.4). The number of nodes developed on the main stem and branches and the internode length are important measures for determining the addition and expansion of leaves in common bean. The successive generation of leaves at the shoot apical meristem results in the production of successive phytomers that are repeating units comprising a node with an attached leaf, a subtending internode, and an axillary meristem at the base of the internode. There is a direct relationship between the number of flowers/ inflorescences/branches with the number of nodes produced in a plant, and the temperature was found to be a major driving factor of node addition rate in common bean (Zhang et al., 2017). Typically, the number of main stem nodes is expressed as a function of phyllocron (thermal period between emergence of successive leaves) (Sinclair, 1984; Soltani et al., 2006).

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FIG. 12.4  Plant leaf area development (cm2 plant− 1) as a function of node number (nodes plant− 1) for three common bean genotypes of varying growth habits (CAL 96—Growth Habit I; DOR 364—Growth Habit II; and Carioca—Growth Habit III), grown at 5, 20, and 35 plants m− 2 at two locations (Darien and Palmira). Symbols represent observed values, whereas lines represent predicted values from the best model. Reproduced with permission from, Ricaurte, J., Michelangeli, J.A.C., Sinclair, T.R., Rao, I.M., Beebe S.E., 2016. Sowing density effect on common bean leaf area development. Crop Sci. 56, 2713–2721.

However, a recent study suggests that phyllocron differences may have no impact on leaf area development as (Ricaurte et al., 2016) found that for two contrasting genotypes (phyllocron differed by ~ 21°C) amounting to 1 day that is unlikely to be biologically meaningful. In common bean, higher sowing densities can limit branch formation, number of nodes and, therefore, the impact on leaf area and vegetative biomass (Nieunuis and Singh, 1985; Doust, 1992; Hampton et al., 1997). Ricaurte et al. (2016) found that differences in node addition and leaf development (resulting from different genotypes and growth habits) led to marked differences in leaf area indices and the estimated fraction of intercepted light at lower sowing densities (Fig. 12.4). At approximately 40 days after emergence, depending on the cultivar and growing conditions, leaf area declines as photosynthate and nitrogen are remobilised into developing pods. Senescence starts at lower nodes, moves up the main stem and then to the branches. Senescence not only leads to a decline in leaf area, thereby influencing the capture of radiation, but also involves the translocation of nutrients towards reproductive tissues, which is known to play an important role in legumes (Hocking and Pate, 1977), including common bean (Rao et al., 2017; Smith et al., 2019).

3.1.2  Photosynthesis at the leaf and canopy scale Photosynthesis is tightly regulated at many levels (including genetic and enzymatic) to balance energy needs with avoiding photodamage; this regulation coordinates light capture, photoprotection, metabolism, and physiology (Kramer and Evans, 2011). In common bean, photosynthetic capacity is high, although as in other species, photosynthetic rates are reduced by nutrient deficit and drought. In addition, a paraheliotropic response, the movement of leaves to avoid incident radiation, is induced under high irradiance, high temperature, and high vapour pressure deficit (Donahue, 1990; Yu and Berg, 1994; Pastenes et al., 2004, 2005). This response maintains leaf temperature below ambience and acts as a photoprotection mechanism (Pastenes et al., 2004). Radiation-use efficiency (RUE) is a measure of photosynthetic performance at the crop scale and is defined as the ratio of biomass accumulated per unit radiation intercepted (Monteith, 1977). RUE is influenced by environment (temperature, radiation, and humidity), the plant (nutrient status, water status, development stage, and source–sink dynamics) and also canopy architecture (varying with growth habit and genotype). Understanding RUE for common bean is particularly important due to its integration in cropping systems, where it may be shaded by cereal crops such as maize, and agronomic strategies must be used to minimise shading during critical phases of crop development to ensure adequate light interception (Worku et al., 2004). However, to our knowledge, RUE is not described for common bean. Grain legumes typically have a low RUE (Sinclair and Muchow, 1999) the reason for which is not fully understood but may be a consequence of source–sink dynamics.

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3.2  Capture and efficiency in the use of water Beans are sensitive to both drought and excess rainfall. Drought is the biggest abiotic stress impacting production with approximately 60% of production regions worldwide affected (Rao, 2014). Water requirements for maximum production of 60- to 120-day beans vary between 300 and 500 mm depending on the climate (Allen et al., 1998). The effect of drought on growth and yield depends on the timing, intensity and duration of drought that are in turn related to the amount of seasonal distribution of rainfall across the growing season, evaporative demand, and soil hydraulic properties (Rao, 2014). Drought stress is characterised by a reduction in water content, diminished leaf water potential, and turgor loss, closure of stomata, and decrease in cell enlargement and growth. The onset of drought interrupts photosynthesis and tissue expansion, as stomata closes restricting gas exchange (O’Toole et al., 1977; Walton et al., 1977). With continued drought stress, plant water potential declines, resulting in wilting and loss of the ability of the plant to orient its leaves. Tepary bean (P. acutifolius) is more drought resistant than common bean (Cory and Webster, 1984; Rao et al., 2013). Tepary bean has a number of adaptive mechanisms to reduce water loss including: increased leaf thickness and/or reduced cell size; increased stomatal sensitivity to humidity; increased cuticular wax; and variation in leaf pubescence, orientation, colour, or size. Tepary bean offers a unique source of genetic diversity that can be used in crop improvement efforts, particularly to adapt common bean to changing climatic conditions (Berny Mier et al., 2018).

3.2.1  Above-ground mechanisms Water deficit impacts photosynthesis and growth by the closure of stomata at low water potential. Amongst common bean, stomatal development, density, and distribution vary with genotype influencing stomatal conductance and, therefore, adaptation to abiotic stress such as drought (Lizana et al., 2006). Recent work has shown that stomatal control and low stomatal conductance were associated with drought adaptation, increasing intrinsic water use efficiency (Traub et al., 2017). Conservative water use strategies such as low stomatal conductance as approximated by low carbon isotope discrimination reduces yield potential, whereas temporal adjustments of stomatal conductance within the growing season and in response to environmental factors (such as vapour pressure deficit) help to optimise the trade-off between carbon gain and water loss (Blessing et al., 2018). Under terminal drought, water-conservative traits help to maintain soil moisture until the pod-filling period, but profligate traits, if tightly regulated, are important under conditions of transient drought to profit from short intermittent periods of available soil moisture (Blessing et al., 2018). Blum (2015) proposed crop ideotypes for targeting in plant breeding, according to agro-ecological zones and types of drought stress. The two ideotypes are isohydric (water saving) and anisohydric (water spending). The ‘water saving’ model has the advantage in harsh drought conditions, whereas the ‘water spending’ model may perform better under more moderate drought. In an effort to characterise water use strategies of common bean and the suitability of different plant ideotypes to different agroecological niches, Polania et al. (2017a) classified common bean into ‘water savers’ and ‘water spenders’ based on shoot and root traits (Table 12.2). Each group is characterised with a few morpho-physiological characteristics that confer adaptation to specific agro-climatic conditions: water savers are adapted to terminal drought and water spenders to intermittent drought (Polania and Rao, 2019). Drought-resistant lines showed greater ability to mobilise photosynthates to pod and grain production combined with an enhanced sink strength as reflected by superior number of pods and seeds per area. Polania and Rao (2019) suggested that PHI [(dry weight of seed at harvest/dry weight of pod at harvest)×100], pod number per area, seed number per area and grain carbon isotope discrimination could be useful selection criteria for breeding for improved drought resistance. Carbon isotope discrimination is a proxy for water use efficiency, effectively representing the balance between partitioning of assimilate to biomass and water conservation, as originally described by Farquhar et al. (1982). In common bean, carbon isotope discrimination measured from the leaf tissue correlated with root length density and yield in a number of environmental conditions (Sponchiado et al., 1989; White et al., 1990; White, 1993; Hall, 2004). In recent studies, carbon isotope discrimination measured from the leaf and phloem tissue has not consistently described the severity of water deficit experienced by the plant (Smith et al., 2016, 2018a). Similarly, White (1993) found that carbon isotope discrimination for common bean grown in acidic soil was not a reliable indicator of plant adaptation to drought. Notably, the carbon pool measured for determination of carbon isotope discrimination does influence assessments of water use efficiency in common bean (Smith et al., 2016). Polania et al. (2016c) found that carbon isotope discrimination determined from grain tissue more closely corelated with stomatal conductance and yield as it integrates the impact of terminal drought over yield development. Although carbon isotope discrimination has been recommended as a trait to be incorporated in common beanbreeding programmes, greater understanding of temporal and spatial variations of carbon isotope discrimination is required to effectively utilise this phenotyping technique.

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TABLE 12.2  Root and shoot traits related to the water saving ideotype and the water-spending ideotype for adaptation of common bean to drought-prone agroecological zones.

Root and shoot traits

Targeting to specific agro-ecological niches

Water savers’ ideotype

Water spenders’ ideotype

• Intermediate-to-shallow rooting system

• Vigorous and deep rooting system

• Intermediate root growth rate and penetration ability

• Rapid root growth rate and penetration ability

• Fine root system

• Thicker root system

• Lower SNF ability

• Moderate SNF ability

• Earliness

• Earliness

• High water use efficiency

• Effective use of water

• Reduced transpiration rate

• Moderate transpiration rate

• Less carbon isotope discrimination

• More carbon isotope discrimination

• Limited leaf area and canopy biomass development

• Moderate canopy biomass accumulation

• Reduced sink strength

• Moderate sink strength

• Superior photosynthate remobilisation to pod and grain formation

• Superior photosynthate remobilization to pod and grain formation

Zones with terminal drought stress and soils with lower capacity to store available water deep in the soil profile

Zones with intermittent drought stress and soils that can store greater amount of available water deep in the soil profile

Reproduced with permission from, Polania, J., Poschenrieder, C., Rao, I.M., Beebe, S., 2017a. Root traits and their potential links to plant ideotypes to improve drought resistance in common bean. Theor. Exp. Plant Physiol. 29, 143–154.

Drought stress in some genotypes improves remobilisation of photosynthates and nitrogen (Polania et al., 2016a,b; Rao et al., 2017), which could permit greater root growth and improve osmotic adjustment. Traub et al. (2017) found abscisic acid (ABA) to be especially drought responsive, and grafting studies indicated that shoot identity controlled ABA levels in stressed roots and that root identity had little or no effect on stomatal behaviour. Pollen viability and pollen germination were found to be reduced under drought stress (Shen and Webster, 1986), and this could affect pod set percentage if fewer viable pollen grains are deposited on the receptive stigma. Drought can induce pod abortion during flowering and pod set stages due to a reduction in pollen viability and pollen germination and impaired stigma/style function or early embryo abortion. Heat stress often occurs concurrently with drought stress and can increase the damage caused by drought stress (Beebe et al., 2013). Heat stress reduces pollen viability, which impacts seed set and limits seed yield (Porch and Hall, 2013). Gradually raising temperatures is a useful method for screening large number of bean entries for heat tolerance (Traub et al., 2018). When compared with common bean, tepary bean tolerates both heat and drought stress, and it serves as a source of genes for genetic improvement of common bean for both heat and drought stress conditions (Rao et al., 2013; Gaur et al., 2015; Polanía et al., 2016).

3.2.2  Below-ground mechanisms Root architecture, the spatial configuration of the root system over time, determines soil exploration and, therefore, the acquisition of water and nutrients (Lynch, 1995). Common bean root structure is typically described as a taproot system with several lateral roots. Large genetic variation for root growth and architecture has been described (Lynch and van Beem, 1993). The variation in root architecture of common bean is likely a consequence of selection for greater phosphorus efficiency during domestication leading to greater root biomass (Berny Mier et al., 2018). The influence of domestication on common bean root systems highlights that trade-offs exist in root architecture for the capture of resources as deep roots promote access to moisture under drought, and shallow roots favour acquisition of immobile nutrients such as phosphorus (Lynch, 2019). The integrated genetics and genomics approach to dissect molecular processes from genome to phenome is the key to achieve increased water capture and use efficiency through developing better root systems (Ye et al., 2018). Greater rooting depth is an important target for enhancing water uptake. For common bean, the root traits that have been modified to increase root foraging depth include; basal root growth angle, basal root whorl number, adventitious root

396  Crop Physiology: Case Histories for Major Crops

TABLE 12.3  Penetration ratio and root diameter of six common bean genotypes evaluated using a media impedance wax-layer system to simulate compacted soil layers under well-watered and drought conditions. Root penetration ratioa Genotypes

Well watered b

Mean root diameter (mm)

Drought

Well watered

Drought

0.15 (0.02)

0.52 (0.02)

0.61 (0.05)

A 774

0.18 (0.02)

ALB 91

0.46 (0.04)

0.41 (0.03)

0.65 (0.07)

0.72 (0.10)

BAT 477

0.23 (0.05)

0.20 (0.04)

0.56 (0.02)

0.71 (0.02)

CALIMA

0.25 (0.05)

0.23 (0.04)

0.52 (0.02)

0.60 (0.02)

DAB 295

0.34 (0.04)

0.36 (0.02)

0.68 (0.03)

0.69 (0.05)

SMC 140

0.32 (0.03)

0.30 (0.02)

0.59 (0.06)

0.80 (0.05)

Mean

0.30

0.28

0.59

0.69

CV

15.4

12.8

8.40

9.6

LSD0.05

0.08

0.06

0.09

0.12

a

Mean ratio of the number of roots penetrating the wax layer over the total number of roots.

b

Standard error.

Reproduced with permission from, Rivera, M., Polania, J., Ricaurte, J., Borrero, G., Beebe, S., Rao, I., 2019. Soil compaction induced changes in morphophysiological chracteristics of common bean. J. Soil Sci. Plant Nutr. 19, 217–227.

abundance, and lateral root branching density (Strock et al., 2019). Under drought, increases in rooting depth have been found to increase drought adaptation in the tropics, leading to higher aboveground biomass and yield (White and Singh, 1991; Polania et al., 2017a). However, it is important to note that the root characteristics to benefit yield under drought stress vary with the amount and distribution of precipitation; texture, depth, and water-holding capacity of the soil; and root system characteristics (Palta and Turner, 2019). In common bean, the response of root vigour to drought stress appeared to be related with ideotypes of water use (Polania et al., 2017a). The water-spender ideotypes presented deeper root system, whereas the water-saver ideotypes showed a relatively shallower root system. Polania et al. (2017a) identified seven common bean lines (SEA 15, NCB 280, SCR 16, SMC 141, BFS 29, BFS 67, and SER 119) with greater root vigour under drought stress in the glasshouse and higher grain yield under drought stress in the field. In a recent controlled-environment study, reproductive fitness in common bean under drought was found to be associated with root length and volume (Sofi et al., 2018). The study also noted a positive relationship between root volume and total root length with pod set percentage and pod weight per plant. Drought-tolerant genotype (Topaz) had a wider root angle, higher root biomass, root:shoot ratio, total root length, and root volume along with higher pod set percentage and pod weight under drought stress. Appropriate agronomic management practices are important to reduce the risk of drought stress. Soil compaction severely restricts root growth and is further aggravated by drought. Rivera et  al. (2019) tested soil compaction-induced changes in morpho-physiological characteristics of six bean genotypes and identified an interspecific line, ALB 91 as the one with thicker roots and the greatest root penetration ability when compared with A 774 that showed the lowest root penetration ability under both well-watered and drought stress conditions (Table 12.3).

3.3  Capture and efficiency in the use of nutrients Fertiliser requirements of common bean vary depending on the macro- and micronutrient and the fertility of the soil. Under well-managed field conditions (yield 2.5–4.0 t ha− 1), common bean may require 40 kg of N to be absorbed from the soil to produce each ton of grain, and this will result in an export of 29 kg of N through grain (see Table 12.4 for information on absorption and export of other nutrients). Soil constraints are a feature in common bean production as they typically occur in low fertile soils with minimal external inputs (Thung and Rao, 1999). Common issues are low availability of phosphorus and nitrogen, low soil organic matter, low calcium availability, and aluminium toxicity (Beebe, 2012; Rao et al., 2016). These constraints can be overcome with soil amendments, or by adapting roots to soils in which they are commonly grown with limited inputs.

Common bean Chapter | 12  397

TABLE 12.4  Macronutrients and micronutrients absorbed (content in shoot during pod filling) and exported (content in grain after harvest) for each ton of common bean grain grown under well-managed conditions in the field with grain yields ranging between 2.5 and 4.0 t ha− 1 from different field trials. Macronutrient

Absorbed (kg t− 1 of grain)

Exported (kg t− 1 of grain)

Nitrogen

40

29

Phosphorus

4.8

3.9

Potassium

34

13

Calcium

18

2

Magnesium

6

2

Sulphur

5

2

Micronutrient

Absorbed (g t− 1 of grain)

Exported (g t− 1 of grain)

Boron

48

22

Copper

17

8

Iron

350

100

Manganese

100

15

Zinc

55

30

Modified from, Oliveira, L.F.C., Oliveira, M.G., Wendland, A., Heinemann, A.B., Guimaraes, C.M., Ferreira, E.P., Quintela, E.D., Barbosa, F.R., Carvalho, M., Junior, M.L., Silveira, P.M., Silva, S.C., 2018. Conhecendo a Fenologia do Feijoeiro e Seus Aspectos Fitotécnicos 2ª edição revista e ampliada, Embrapa Santo Antonio de Goias, GO, Brazil.

3.3.1 Nitrogen The amount of N available to the plant has been quantitatively linked to seed yield in common bean as N is translocated from leaves and stems during reproductive development to support grain fill (Michelangeli et al., 2019). Saberali et al. (2015) found a linear relationship between seed yield and accumulated N. Lynch and White (1992) made detailed observations of N allocation patterns under field conditions in the tropics in several genotypes of high-yielding bush bean, highlighting the dominant N allocation to seeds and variations in allocation with phenology, which limits leaf photosynthesis in the tropics. Phenology influences N allocation patterns, for example an ideal genotype with late/fast seed fill would reduce the effects of declining N availability on leaf level photosynthesis (Lynch and White, 1992). The variability in phenology and allocation of N in common bean germplasm suggest that these traits could be considered for genetic improvement of N use efficiency in common bean. As a legume species, common bean can access atmospheric nitrogen through a symbiotic relationship with rhizobia to form nitrogen-fixing nodules. Symbiotic nitrogen fixation (SNF) involves the fixation of N2 by specialised bacteria, which is converted to ammonia and then reduced to ureides for long-distance transport through the xylem. Common bean nodule development is determinate, meaning that the nodules arise from the central cortex (Oldroyd et al., 2011). Common bean has a relatively low SNF capacity with an estimated value of nitrogen derived from the atmosphere of 39% when compared with ~ 54%–65% for other legumes (Peoples et al., 2009; Chapter 8: Soybean, Section 3.3). The reasons for low SNF are multifaceted and unclear (see, Beebe, 2012) and include the low competitiveness and SNF capacity of rhizobia typically associated with common bean (Muñoz-Azcarate et al., 2017). To overcome the shortfall, nitrogen fertilisation before sowing assists with early vegetative growth, and a top dressing may be provided later to meet the relatively high demand for N during pod fill (Sinclair and de Wit, 1976). SNF capacity varies with crop and rhizobia genotype and environment. Traits related to nitrogen fixation vary amongst both conventional and heirloom common bean genotypes (Wilker et al., 2019), which suggests that genetic variation may be exploited in breeding. Common bean can associate with a broad range of rhizobia (5 genera and 19 species nodulating common bean described to date) with the predominant microsymbiont across the world being Rhizobium etli bv. phaseoli (Muñoz-Azcarate et al., 2017). Despite the diverse range of rhizobia for nodulation, competitiveness for nodulation and N fixation capacity of most strains are typically low (Muñoz-Azcarate et al., 2017). Indeterminate bean cultivars with a longer growth cycle tend to have a higher yield potential and higher SNF capacity, when compared with bush-type

398  Crop Physiology: Case Histories for Major Crops

­cultivars (Barbosa et al., 2018). In temperate regions, SNF in common bean can be more than 100 kg N ha− 1, whereas in tropical regions, it is typically half (Hardarson et al., 1993). SNF is impacted by biotic and abiotic stress, including deficiency of phosphorus, potassium, and sulphur, drought, pests, and disease (Ramaekers et al., 2013; Polania et al., 2016a; Diaz et al., 2017; Barbosa et al., 2018). SNF is particularly sensitive to drought (see, Beebe, 2012; Devi et al., 2013; Polania et al., 2016a).

3.3.2 Phosphorus Low phosphorus is the single most important constraint on common bean yields affecting 50% of production worldwide (Beebe et  al., 2009; Rao et  al., 2016). Low P limits vegetative development and root growth, which in drought-prone areas overrides drought adaptation improvements (Rao et al., 2016). P efficiency can be improved through P acquisition efficiency (PAE) that is P uptake per unit root length (Ramaekers et al., 2010) and P use efficiency (PUE) that is gram of grain produced per gram of shoot P uptake (Veneklaas et al., 2012). Genetic variation is available within common bean germplasm to make these improvements (Lynch and Beebe, 1995), particularly as P acquisition is strongly linked to root architecture (Rao et al., 2016). Other mechanisms such as root exudation and mycorrhizal association also contribute to improved PAE (Jansa et al., 2011; Ramaekers et al., 2013). A comprehensive understanding is required on plant adaptation to low P availability in soil for simultaneous improvement of PAE and PUE. Research should focus on quantifying the magnitude of gains in improving PAE and PUE that may be obtained through different mechanisms and their variation associated with genetic and environmental factors (Bovill et al., 2013; Heuer et al., 2017; Bernardino et al., 2019; Lynch, 2019). Genetic approaches such as genome-wide expression (transcription), QTL analyses and genome-wide association using next-generation sequencing could help to identify loci related to plant PAE and PUE under varied environmental conditions (Oladzad et al., 2019). Some common bean genotypes can perform well in both low P and drought (Ho et al., 2005; Beebe et al., 2008, 2014; Cichy et al., 2009). Studies are needed to define the interactions between low P availability and water availability in soil (Margaret et al., 2014) and between aluminium toxicity in acidic soil and drought (Yang et al., 2013) prevalent in the tropics.

4  Yield and quality 4.1  Yield and related traits Yield can only be improved with the use of better varieties and agronomic practices (Sadras and Calderini, 2015). Common bean yields in production areas fall well-below recorded experimental yields, for example in bush bean, national average yields in SSA range from 500 to 700 kg ha− 1, whereas in experimental conditions, at CIAT-Popayan yields can reach 3–4 t ha− 1 (Beebe, 2012). The yield gap is a consequence of a range of biotic and abiotic constraints, in particular drought and low soil fertility. Efforts have focused on overcoming yield constraints, increasing the understanding of processes involved in yield development, and, thereby, also improving yield potential (Beebe et al., 2008). The breeding efficiency and the rate of genetic gain need to be enhanced by reducing time required in development of a cultivar through integration of genomics-assisted breeding approaches and rapid generation advancement (Ojiewo et al., 2017; Mukankusi et al., 2018; Oladzad et al., 2019). Following self-pollination, pods develop on the plant and then seeds develop from fertilised ovules within the pod. Reproductive structures at varying stages of development coexist, on the plant. Reproductive development in common bean also varies spatially with pods developing at nodes on the main stem or branches. Traits closely associated with yield are root and shoot biomass, pod number, seed number, harvest index, PHI and pod-partitioning index (PPI). Pod number is one of the most important determinants of yield in common bean (Fageria and Santos, 2008). Under drought, pod number per unit area and seed number per unit area are the most impacted yield components (Rao et al., 2007, 2013, 2017; Beebe et al., 2008; Assefa et al., 2014). Given the importance of these traits for yield improvement, it is critical to rapidly determine the physiological processes involved in determining the supply and utilisation of photoassimilate and N by pods and seeds (Rao et al., 2017). The relationships of grain yield with seed number per area, pod number per area, and 100 seed weight on a set of 36 bean genotypes grown under irrigated or rainfed conditions are shown in Fig. 12.5. As expected, the two very small seeded accessions of Tepary bean showed the highest number of seeds and pods per unit area, whereas the large-seeded Andean cultivar, ICA Quimbaya, showed the lowest number of seeds and pods per unit area under rainfed conditions (Fig. 12.5b and d). Drought stress decreased the mean seed number per area, pod number per area and 100 seed weight (Fig. 12.5a–f). However, across genotypes and environments, grain yield seems to be better associated with grain number per area, and

Common bean Chapter | 12  399

Irrigated

1600

1000 800 600 400

Mean: 381 LSD0.05: 107

(b)

G 40159

1400 1200

Rainfed

(a) SEA 15 Pinto Villa SEA 23 RAB 651 RAB 650 Tio Canela BAT 477 RAB 619 INB 39 ICA Pijao RAB 632 G 40068 SEA 17 INB 36 DOR 390 RAB 608 RAB 636 G 21212 ICA Quimbaya Apetito Mean: 526 G 1977 LSD0.05: 160

0

200

400

600

Mean: 960 LSD0.05: 261

G 40068 G 40159 RAB 650 SEA 15 SEA 23 SEA 18 RAB 651 BAT 477 SEA 5 Pinto Villa Tio Canela 75 SEA 20 DOR 390 ICA Quimbaya ICA Pijao

1000 1200 1400 0

800

200

400

600

800

Mean: 661 LSD0.05: 189

1000 1200 1400

Seed number per area (no. m -2) Grain yield (kg ha-1)

1600

(c)

1400 1200 1000 800 600 400

(d)

G 40159

Mean: 107 LSD0.05: 36

SEA 15 Pinto Villa SEA 23

RAB 651 RAB 650 Tio Canela 75 RAB 609 INB 35 SEA 18 SEA 5 RAB 632 DOR 390 RJB 7 G 40068 INB 36 RAB 609 SEA 20 Apetito ICA Quimbaya Mean: 137 G 1977 LSD0.05: 44

50

100

150

Mean: 960 LSD0.05: 261

200

250

300

G 40068 G 40159 RAB 650 SEA 15 SEA 23 18 SEA 16 RAB SEA 651 SEA 5 RAB 632 BAT 477 Tio Canela G 21212 G 1977 RJB 7 ICA Quimbaya DOR 390 ICA Pijao

350 50

100

150

Mean: 661 LSD0.05: 189

200

250

300

350

Pod number per area (no. m -2) 1600 1400 1200 1000 800 600 400

(e) SEA 15

RAB 651 RAB 650 SEA 23 BAT 477 Tio Canela 75 ICA Pijao SEA 21 G 40068 RAB 632 RAB 619 SEA 5 DOR 390 SEA 17 INB 36 G 21212 RAB 608 RAB 636 SEA 20 Apetito Mean: 19.2 G 1977 LSD : 1.7 0.05

10

Mean: 18.3 LSD0.05: 1.4

(f)

G 40159

15

20

25

Pinto Villa Mean: 960 LSD0.05: 261

ICA Quimbaya

30

35

G 40159 G 40068 RAB 650 SEA 23 SEA 15 SEA 18 RAB 651 Mean: 661 SEA 16 RAB 632 SEA 5 Pinto Villa LSD0.05: 189 G 21212 BAT 477 SEA 20 INB 38 RAB 608RJB 7 ICA Quimbaya DOR 390 ICA Pijao

40 10

15

100 seed weight (g)

20

25

30

35

40

FIG. 12.5  Identification of genotypes with greater grain yield and seed number per area (a,b), grain yield and pod number per area (c,d), and grain yield and 100 seed weight (e,f) under irrigated or rainfed conditions on a Mollisol at Palmira. Higher yielding genotypes with greater seed number per area, pod number per area and 100 seed weight were identified in the upper, right-hand quadrant. Reproduced with permission from, Rao, I., Beebe, S., Polania, J., Ricaurte, J., Cajiao, C., Garcia, R., Rivera, M., 2013. Can tepary bean be a model for improvement of drought resistance in common bean? Afr. Crop. Sci. J. 21, 265–281.

the relationship between grain yield and 100 seed weight is exceptional in the sense that seed size was less influenced by drought stress (Rao et al., 2013). Senescence and abscission of reproductive material (flowers and pods) are major determinants of final yield and dependent on source–sink relationships. Typically, there is a complete pod set from first-opened flowers and then abscission of both these young pods and later formed flowers. Modern cultivars typically develop a limited number of pods within a short time that allows for uniform pod set. However, pods may also abort later in development likely as a consequence of reduced resource availability. For instance, a recent study demonstrated that although heat stress did not directly affect photosynthesis, it disrupted source–sink relationships through changes in photosynthate transport from source leaves to developing reproductive sink tissues, which resulted in reduced seed set (Soltani et al., 2019).

400  Crop Physiology: Case Histories for Major Crops

Allocation of photosynthate from reserves in vegetative structures to seeds, measured by traits PHI and PPI, plays a significant role in yield under both drought and low phosphorus stress and also under nonstress conditions (Beebe et al., 2008, 2009; Klaedtke et al., 2012; Assefa et al., 2013; Rao et al., 2013). PHI varied from 67% to 82%, whereas PPI varied between 33% and 70% (Rao et al., 2013). Recently, the genetic relationship between PHI and yield has been confirmed (Berny Mier et al., 2019), and QTL for PPI under drought were identified (Dramadri et al., 2019). However, the mechanisms that determine the movement of photoassimilate from the pod wall and into the seed are not well understood, and further research is required to characterise the underlying causes of poor remobilisation often described as ‘lazy pod syndrome’. Yield is a consequence of both source and sink strength for photoassimilates over the course of reproductive development. The movement of photoassimilate from source to sink is controlled by a highly regulated signalling network elicited by resource availability (Paul et al., 2001; Rossi et al., 2015). Despite the importance of source–sink dynamics in determining growth and yield, we have limited mechanistic basis for source–sink relationships, including how changing environmental conditions have an impact on transport of photoassimilate, partitioning between heterotrophic tissues and remobilisation of carbohydrate into reproductive tissues (Smith et al., 2018b). The pathway between photoassimilate export and the corresponding demand by sink tissue have been studied as a potential target to improve yield but large gaps remain (Ainsworth and Bush, 2011; Lemoine et al., 2013; White et al., 2016).

4.2  Nutritional quality Common bean is a major staple providing protein and minerals to complement starchy cereal or tuber and root crops. Research has focused on protein, iron, and zinc availability due to their importance in human diets (Broughton et al., 2003). Common bean seed contains 20%–25% protein mostly from phaseolin, the major globulin storage protein. Phaseolin is a determinant of protein quality (Bliss and Brown, 1983; Gepts and Bliss, 1984). Phaseolin has a low-sulphuric amino acid content, although when beans are eaten with cereals such as maize, all essential amino acids requirements are met. Phaseolin is poorly digested by humans. Despite a desire to improve protein content and to incorporate more digestible proteins within the seed, minimal attention has been given to this area by breeding, likely as a consequence of a reported negative correlation between protein concentration and yield (Beebe, 2012). Common bean is an important source of iron, phosphorus, magnesium, manganese, and also zinc, copper, and calcium (Broughton et al., 2003). Biofortifying common bean, by breeding for improved nutrient content, is an efficient delivery system to address malnutrition, where beans are consumed daily. Biofortification of common bean through the HarvestPlus initiative (https://harvestplus.org/) was advantageous due to the relatively high baseline seed content for iron and also high genetic variability for the trait (Beebe et al., 2000; Beebe, 2012). Iron biofortified beans have recently been released in several bean-producing countries (Fig. 12.6). A nutritional study in Rwanda found significant improvements in the iron status of women who consumed iron biofortified beans (Haas et al., 2016). Antinutritional factors, including α-amylase inhibitors, arceline, lectins, phytate, phenolics, and tannins, are present in the seeds of common bean, often conferring protection to biotic or abiotic stress (Broughton et al., 2003). Genetic variability has been found for seed tannins and anthocyanins (Díaz et  al., 2010), traits closely associated with seed colour (Broughton et al., 2003; Beebe, 2012). Common bean is high in sugars, raffinose, stachyose, and verbascose that are fermented by microflora in the intestine leading to flatulence. Broughton et al. (2003) proposed that this could be overcome by targeting the genotypic variation within the species. Despite consumers expressing a desire for ‘low-flatulence’ beans, no efforts have been made in this direction to date (Beebe, 2012). Phytic acid is a major storage molecule for phosphorus in common bean seeds, required for development and germination but is detrimental for the absorption of iron, zinc, calcium, magnesium, and manganese (Petry et al., 2015; Raboy, 2001). Polyols, including myo-inositol, a precursor to phytic acid, increase under drought due to the important role they play during osmoregulation (Lockhart et al., 2016; Dumschott et al., 2017). Although further studies are required, drought may have significant implications for nutritional composition of common bean as a significant increase in myo-inositol in the grain of 12 common bean lines grown in the field under drought has been detected (Smith et al., 2019).

5  Concluding remarks: Challenges and opportunities Common bean plays an important role in agricultural systems and human diets. Major advances have been made in the last decade towards understanding physiological mechanisms of abiotic stress tolerance and improving germplasm for adaptation to climate change, particularly drought and heat. Nevertheless, average yields for common bean remain low. Collaborative research between crop physiologists, agronomists, breeders, and soil scientists is required to improve the

Common bean Chapter | 12  401

FIG. 12.6  Biofortified bush and climbing beans released under HarvestPlus of the CGIAR Research Program on Agriculture for Nutrition and Health. Data from, Andersson, M.S., Saltzman, A., Virk, P.S., Pfeiffer, W., 2017. Progress update: crop development of biofortified staple food crops under Harvest Plus. Afr. J. Food Agric. Nutr. Dev. 17, pp. 11906–11935; see the Annex of the same article.

resource use efficiency of common bean and achieve yield improvements in regions, where climates are shifting and large numbers of consumers rely on common bean as a staple crop.

Acknowledgements We are grateful to Dr. Jose Polania for his comments and suggestions to improve the manuscript. We acknowledge the financial support from the CGIAR Research Program on Grain Legumes and Dryland Cereals. We would also like to thank all donors who supported this work through their contributions to the CGIAR Fund.

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Plant. 167, 391–403. Smith, M.R., Rao, I.M., Merchant, A., 2018b. Source-sink relationships in crop plants and their influence on yield development and nutritional quality. Front. Plant Sci. 9 (1889). Smith, M.R., Veneklaas, E., Polania, J., Rao, I.M., Beebe, S.E., Merchant, A., 2019. Field drought conditions impact yield but not nutritional quality of the seed in common bean (Phaseolus vulgaris L.). PLoS One 14, 1–18. Sofi, P., Djanaguiraman, M., Siddique, K.H.M., Prasad, P.V.V., 2018. Reproductive fitness in common bean (Phaseolus vulgaris L.) under drought stress is associated with root length and volume. Indian J. Plant Physiol. 23, 796–809. Soltani, A., Robertson, M.J., Mohammad-Nejad, Y., Rahemi-Karizaki, A., 2006. Modeling chickpea growth and development: leaf production and senescence. Field Crop Res. 99, 14–23. Soltani, A., Weraduwage, S.M., Sharkey, T.D., Lowry, D.B., 2019. Elevated temperatures cause loss of seed set in common bean (Phaseolus vulgaris L.) potentially through the disruption of source-sink relationships. BMC Genomics 20, 312. Souter, J.R., Gurusamy, V., Porch, T.G., Bett, K.E., 2017. Successful introgression of abiotic stress tolerance from wild tepary bean to common bean. Crop Sci. 57, 1160–1171. Sponchiado, B.N., White, J.W., Castillo, J.A., Jones, P.G., 1989. Root growth of four common bean cultivars in relation to drought tolerance in environments with contrasting soil types. Exp. Agric. 25, 249–257. St. Clair, S.B., Lynch, J.P., 2010. The opening of Pandora’s Box: climate change impacts on soil fertility and crop nutrition in developing countries. Plant Soil 335, 101–115. Strock, C.F., Burridge, J., Massas, A.S.F., Beaver, J., Beebe, S., Camilo, S.A., Fourie, D., Jochua, C., Miguel, M., Miklas, P.N., Mndolwa, E., NchimbiMsolla, S., Polania, J., Porch, T.G., Rosas, J.C., Trapp, J.J., Lynch, J.P., 2019. Seedling root architecture and its relationship with seed yield across diverse environments in Phaseolus vulgaris. Field Crop Res. 237, 53–64. Tanaka, A., Fujita, K., 1979. Growth, photosynthesis and yield components in relation to grain yield of the field bean. J. Fac. Agric. Hokkaido Univ. 59, 145–238. Thung, M.D., Rao, I.M., 1999. Integrated management of abiotic stresses. In: Singh, S.P. (Ed.), Common Bean Improvement in the Twenty-First Century. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 331–370.

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Tohme, J., Gonzalez, D.O., Beebe, S., Duque, M.C., 1996. AFLP analysis of gene pools of a wild bean core collection. Crop Sci. 36, 1375–1384. Toro, O., Tohme, J., Debouck, D.G., 1990. Wild Bean (Phaseolus vulgaris L.): Description and Distribution. International Board for Plant Genetic Resources (IBPGR), Centro Internacional de Agriculture Tropical (CIAT), Cali, Colombia. Traub, J., Kelly, J.D., Loescher, W., 2017. Early metabolic and photosynthetic responses to drought stress in common and tepary bean. Crop Sci. 57, 1670–1686. Traub, J., Porch, T., Naeem, M., Urrea, C.A., Austic, G., Kelly, J.D., Loescher, W., 2018. Screening for heat tolerance in Phaseolus spp. using multiple methods. Crop Sci. 58, 2459–2469. Veneklaas, E.J., Lambers, H., Bragg, J., Finnegan, P.M., Lovelock, C.E., Plaxton, W.C., Price, C.A., Scheible, W.R., Shane, M.W., White, P.J., Raven, J.A., 2012. Opportunities for improving phosphorus-use efficiency in crop plants. New Phytol. 195, 306–320. Voyset, O., 1998. Major bean-growing environments for integrated crop and resource management research. In: Voyset, O. (Ed.), An Ecoregional Framework for Bean Germplasm Development and Natural Resource Management. CIAT, Cali, Colombia, pp. 1–29. Walton, D.C., Galson, E., Harris, M.A., 1977. The relationship between stomatal resistance and abscisic acid levels in leaves of water-stressed bean plants. Planta 133, 145–148. White, J.W., 1993. Implications of carbon isotope discrimination studies for breeding common bean under water deficits. In: Ehleringer, J., Hall, A., Farquhar, G. (Eds.), Stable Isotopes and Plant Carbon-Water Relations. Academic Press, San Diego, CA, pp. 387–398. White, J.W., Laing, D.R., 1989. Photoperiod response of flowering in diverse genotypes of common bean (Phaseolus vulgaris). Field Crop Res. 22, 113–128. White, J.W., Montes, R.C., 1993. The influence of temperature on seed germination in cultivars of common bean. J. Exp. Bot. 44, 1795–1800. White, J.W., Singh, S.P., 1991. Sources and inheritance of earliness in tropically adapted indeterminate common bean. Euphytica 55, 15–19. White, J.W., Castillo, J.A., Ehleringer, J.R., 1990. Associations between productivity, root growth and carbon isotope discrimination in Phaseolus vulgaris under water deficit. Aust. J. Plant Physiol. 17, 189–198. White, A.C., Rogers, A., Rees, M., Osborne, C.P., 2016. How can we make plants grow faster? A source-sink perspective on growth rate. J. Exp. Bot. 67, 31–45. Wilker, J., Navabi, A., Rajcan, I., Marsolais, F., Hill, B., Torkamaneh, D., Pauls, K.P., 2019. Agronomic performance and nitrogen fixation of heirloom and conventional dry bean varieties under low-nitrogen field conditions. Front. Plant Sci. 10, 952. Worku, W., Skjelvåg, A.O., Gislerød, H.R., 2004. Responses of common bean (Phaseolus vulgaris L.) to photosynthetic irradiance levels during three phenological phases. Agronomie 24, 267–274. Yang, Z.B., Rao, I.M., Horst, W.J., 2013. Interaction of aluminium and drought stress on root growth and crop yield on acid soils. Plant Soil 372, 3–25. Ye, H., Roorkiwal, M., Valliyodan, B., Zhou, L., Chen, P., Varshney, R.K., Nguyen, H.T., 2018. Genetic diversity of root system architecture in response to drought stress in grain legumes. J. Exp. Bot. 69 (13), 3267–3277. Yu, F., Berg, V.S., 1994. Control of paraheliotropism in two Phaseolus species. Plant Physiol. 106, 1567–1573. Zhang, L., Gezan, S.A., Eduardo Vallejos, C., Jones, J.W., Boote, K.J., Clavijo-Michelangeli, J.A., Bhakta, M., Osorno, J.M., Rao, I.M., Beebe, S., Roman-Paoli, E., Gonzalez, A., Beaver, J., Ricaurte, J., Colbert, R., Correll, M.J., 2017. Development of a QTL-environment-based predictive model for node addition rate in common bean. Theor. Appl. Genet. 130, 1065–1079.

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Image source: Lachlan Lake

Chapter 13

Lentil Akanksha Sehgala, Kumari Sitab, Abdul Rehmanc, Muhammad Farooqd,e,f, Shiv Kumarg, Rashmi Yadavh, Harsh Nayyari, Sarvjeet Singhj, and Kadambot H.M. Siddiquef a

Department of Plant and Soil Science, Mississippi State University, Starkville, MS, United States, bInstitute of Himalayan Bioresource Technology (IHBT), Palampur, India, cDepartment of Crop Sciences and Biotechnology, Dankook University, Cheonan-si, Korea, dDepartment of Crop Sciences, College of Agricultural and Marine Sciences, Sultan Qaboos University, Muscat, Oman, eDepartment of Agronomy, University of Agriculture, Faisalabad, Pakistan, fThe UWA Institute of Agriculture and School of Agriculture & Environment, The University of Western Australia, Perth, WA, Australia, gInternational Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco, hICAR-National Bureau of Plant Genetic Resources, New Delhi, India, iDepartment of Botany, Panjab University, Chandigarh, India, jDepartment of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, India

1 Introduction Lentil (Lens culinaris Medik.) is a cool-season legume and one of the oldest edible crops, having been cultivated since 7000 BC in southwestern Asia (Alexander, 2015), North America, and North Africa (Erskine et al., 2016). Cultivated lentil is the third-most important cool-season grain legume in the world after chickpea and pea, and ranks sixth in grain legume production after dry bean, pea, chickpea, faba bean, and cowpea (FAO, 2015). Yet, global lentil production comprised only 6% of total dry pulse production from 2010 to 2015, with an average yield of 926 kg ha− 1 (FAO, 2015). India, Turkey, and Canada contribute around ~ 70% of global production (Alexander, 2015). Globally, lentil is grown in three distinct agro-ecological zones: Mediterranean, subtropical savannah, and northern temperate (Tullu et al., 2011). Table 13.1 outlines the diverse cropping systems for lentils. The success and adoption of the crop are influenced by agroclimatic, socio-economic, and technological factors (Sekhon et al., 2007). Lentil intercropping or mixed cropping with cereals (e.g. wheat and barley) increases total grain yield (Carr et al., 1995; Akter et al., 2004; Wang et al., 2012), economic returns (Akter et al., 2004; Loïc et al., 2018), and lodging resistance (Carr et al., 1995; Wang et al., 2012; Loïc et al., 2018), mainly due to the N supply from lentil and low competition of lentil with wheat (Naudin et al., 2009). Likewise, intercropping of lentil with sugar-beet produced more yield, better quality, and economic benefits when compared with sugar-beet intercropping with oilseed or cereals (Usmanikhail et al., 2012). The seeding rate is critical for economic lentil production in a sole crop, intercropping, or mixed cropping as it influences crop establishment, radiation absorption, canopy development, insect, pest and disease infestation, crop lodging, and ease of harvesting (Siddique et al., 1998a). In the Mediterranean-type environment of southwestern Australia, Siddique et al. (1998b) determined an optimal plant density of 150 plants m− 2 on coarse-textured acidic soils. In Nepal, lentil is grown on residual soil moisture as a relay crop in paddy before rice harvesting or immediately after rice in rainfed conditions. Bandyopadhyay et al. (2018) studied the effect of residual moisture after rice using different tillage practices on lentil production. They concluded that no-till or medium tillage after the preceding long-duration monsoon rice improved lentil production, relative to conventional tillage, by enhancing water availability at the reproductive stage. Rainfall variation is the source of variation in yield (Shrestha, 1997, 1998). The yield of a subsequent nonleguminous crop after lentil is higher due to enhanced soil fertility, N2 fixation, and breakdown of insect, pest, or disease cycles (Table 13.1). In Indian Himalayas, soybean–lentil rotation improved physiochemical properties of sandy clay loam soil in comparison with soybean–wheat rotation (Prakash et al., 2004; Bhattacharyya et al., 2006). Sugarcane–lentil intercropping increased land equivalent ratio and cane growth relative to sugarcane alone due to enhanced N supply by lentil and no shading effect (Rana et al., 2006). Liu et al. (2019) introduced lentil in an oilseed–cereal cropping system in three rotations: (1) fallow–lentil–spring wheat (year 1), canola–oriental mustard–camelina (year 2), and durum wheat (year 3); (2) fallow (year 1), fallow–lentil–spring wheat (year 2), canola–oriental mustard–scamelina (year 3), and durum wheat (year 4); and (3) fallow (years 1 and 2), fallow–lentil–spring wheat (year 3), and canola–­oriental ­mustard– camelina (year 4). The complete rotation cycles at three sites across western Canada showed that the drought ­reduced system productivity from 47% to 3%, which was lowest in the lentil–oilseed–durum wheat system. The ­introduction of lentil Crop Physiology: Case Histories for Major Crops. https://doi.org/10.1016/B978-0-12-819194-1.00013-X Copyright © 2021 Elsevier Inc. All rights reserved.

409

410  Crop Physiology: Case Histories for Major Crops

TABLE 13.1  Effect of lentil on the productivity and other outcomes of cropping systems. Cropping system

Treatments

Effects

References

Intercropping

Lentil–wheat (organic farming)

↑ Grain yield, ↓ lodging

Loïc et al. (2018)

Lentil–sugarbeet

↑ Grain yield, ↑ quality, ↑ economic benefits

Usmanikhail et al. (2012)

Sugarcane + lentil

↑ Cane growth, ↑ land equivalent ratio, ↑ nitrogen fixation

Rana et al. (2006)

Lentil–wheat (3:1) (organic farming)

↑ Grain yield, ↑ land equivalent ratio, ↓ weed biomass, ↓ lodging

Wang et al. (2012)

Lentil–barley (3:1) (organic farming)

↑ Grain yield, ↑ land equivalent ratio, ↓ weed biomass, ↓ lodging

Wang et al. (2012)

Oilseed–cereal–lentil rotation

Canola–wheat–lentil–wheat

↓ Input, ↓ environmental effect, ↑ economic benefits

MacWilliam et al. (2014)

Maize-based cropping system

Maize–lentil–mung bean (conservation tillage)

↑ Soil physiochemical properties, ↑ yield, ↑ profitability

Prasai et al. (2018)

Oilseed–cereal cropping systems

Lentil–oilseeds–durum wheat

↑ Systems productivity, ↓ yield variation

Liu et al. (2019)

Lentil as preceding crop

Lentil–lentil–lentil–wheat

↑ Biological nitrogen fixation, ↑ yield of following crop

Niu et al. (2017)

Mixed cropping

in an oilseed–cereal system increased system productivity and reduced yield variation in semiarid environment (Liu et al., 2019). In the temperate climate of western Canada, replacing spring wheat with lentil in an oilseed–cereal-based rotation (canola–spring, wheat–spring, and wheat–spring wheat) reduced the environmental effect on ecosystem quality by 1%–24% and resource use by 17%–25% with increased economic benefits. A metaanalysis by MacWilliam et al. (2018) reported that inclusion of dry beans or lentil in cereal or oilseed crop rotations reduced GHG emissions by 489–1185 kg CO2 e ha− 1 over 2 years by offsetting the synthetic N requirement in the subsequent crop.

2  Crop structure, morphology, and development Domesticated lentil (Lens culinaris sp. culinaris) is an annual, herbaceous, self-pollinating, and true diploid (2n = 2x = 14) species with an estimated genome size of 4063 Mbp/C (Arumuganathan and Earle, 1991). It is one of the first domesticated grain legume species, originating from the Near East centre of origin (Zohary, 1999) and the most appreciated grain legume of the Old World (Smartt, 1990). Seed indehiscence (intact pericarp at seed maturity), rapid germination due to low seed dormancy and larger seed were key in the domestication of wild progenitors for Neolithic farmers (Ladizinsky, 1979, 1985; Fuller et al., 2011). Khazaei et al. (2016) estimated the genetic structure and diversity of 352 accessions of lentil from 54 countries using 1194 polymorphic single nucleotide polymorphism markers and grouped them based on geographical origin (world ecological zones): (i) South Asia (subtropical savannah), (ii) the Mediterranean, and (iii) northern temperate. Canadian and South Asian germplasm exhibited narrow genetic diversity. Wild relatives of lentil have high genetic diversity but are yet to be explored (Singh et al., 2014). Shrestha et al. (2005) tested lentil genotypes from West Asian (6) and South Asian (6) origin and crossbreds (5) under rainfed conditions; South Asian and crossbreds yielded 285% and 370% higher than West Asian genotypes, respectively. This was associated with rapid ground cover, high total dry matter, high water use efficiency, early flowering, and early maturity with longer reproductive duration, and higher seed and pod numbers.

2.1  Crop structure: height and branching Lentil plants are short and highly branched with slender stems (Erskine and Goodrich, 1991; Heath et al., 1994; Whitehead et al., 2000). Seasonal and genetic sources of variation affect plant height and branch number (Erskine and Goodrich, 1991). Erskine and Goodrich (1991) assessed 25 lentil genotypes at a commercial planting density of 200 seeds m− 2 and reported

Lentil Chapter | 13  411

variation for branching and plant height associated with average internode length from 1.5 to 1.9 cm and branch number. In a comparison of 28 genotypes, Singh (1977) found that highly branched plants produced more pods on the plant periphery, leaving the central part barren, which lead to a lower harvest index. Fewer branches allow better radiation penetration through the canopy and improved pod formation (Ouji et al., 2016). Singh (1977) and Kusmenoglu and Muehlbauer (1998) suggested selecting taller plants with reduced branching to increase yield and harvest index. In contrast, Erskine and Goodrich (1991) reported that plant height was positively correlated with straw yield and lowest pod-bearing branches (secondary and tertiary branches) and negatively associated with seed yield and harvest index.

2.2  Phenological development 2.2.1  Sowing to emergence Germination and emergence of lentil are influenced by temperature, sowing depth, soil moisture and seed size; salinity also affects germination and emergence (Section 2.2.2). Kamakar and Nabizadeh (2017) used field emergence data of six lentil cultivars to derive the base (4.5°C), optimum (22.9°C), and maximum (40.0°C) temperatures for seedling emergence. Hojjat and Galstayan (2012) exposed 24 lentil genotypes to ambient temperatures from 4°C to 24°C to find maximum emergence at 20°C. During lentil germination, α-galactosidase activity increases rapidly during first 3 days reaching maximum between 23°C and 29°C, followed by a gradual decline. Likewise, activity of proteinase peaks at the third day, followed by decline on the fourth day, and then a further rise is seen due to decrease in the proteinase inhibitory activity. The amylolytic enzymes show uniform activity during germination (Petrova et al., 2010). Farooq et al. (2019) demonstrated that osmopriming of lentil (1% CaCl2) improved the α-amylase activity and total soluble sugars resulting in early and better seedling emergence and biomass production under both normal and water deficit conditions. Al-Karaki (1998) studied the effect of seed size and water potential (0, − 0.5, and − 1.0 MPa, as PEG 8000) on water uptake and emergence of lentil cultivars and found that decrease in soil water potential reduced the germination, and seedling emerged from bold seeds exhibited higher shoot and root length and biomass production than seedlings grown from medium or small seeds. Bold seeds produced healthy plants under both normal and water stress conditions. In southwestern Australia, Siddique and Loss (1999) found an optimum sowing depth for crop establishment at 4–6 cm. Soil tilled with horizontal axis rotary tiller and vertical axis rotary tiller with roller hastened the emergence and improved the seedling emergence percentage when compared with conventional tillage (Altikat and Celik, 2011).

2.2.2  Emergence to flowering Lentil plants complete their life cycle in 90–120 days under optimal conditions usually in spring, but the growth duration delays up to 30–60 days in winter-sown crop, particularly at initial growth stages due to low temperature (Oktem et al., 2008). Photoperiod, radiation intensity, and light quality all affect lentil development. Erskine et al. (1994) evaluated the model of photothermal flowering responses of 369 lentil accessions in two field environments in Pakistan and Syria and concluded that lentil crop dissemination following domestication in West Asia to the lower latitudes (e.g. India and Ethiopia) has depended on selection for inherent earliness and less photoperiod sensitivity. Moreover, lentil dissemination and domestication from West Asia to the higher latitudes complemented by spring sowing has slightly decreased sensitivity to photoperiod and increased sensitivity to temperature. In Nepal, Shrestha et al. (2005) found that West Asian genotypes had later flowering (43 days) and shorter reproductive period when compared with South Asian and crossbred genotypes. South Asian genotypes and crossbred exhibited rapid ground cover and more biomass production. Light intensity influences flower initiation (Runkle and Heins, 2006) and radiation spectral composition. Lentil plants receiving red/far-red R:FR ≤ 3.1 flowered 10 days earlier than those with R:FR of 5.6 in the study of Mobini et al. (2016). Yuan et al. (2017) showed that low R/FR reduced the response to flowering of most of the wild lentil genotypes but accelerated flowering in the cultivated lentil genotypes and three wild genotypes.

2.2.3  Flowering to maturity Lentil has indeterminate growth habit. In Canada, Zakeri and Bueckert (2015) found that more than 85% of biomass and nitrogen (N) were accumulated after flowering. Biomass allocated to pod, stem, and leaf was in a 58:26:15 ratio during pod filing. Partitioning of N at maturity between pod, stem, and leaf was 75:15:10. The medium-maturing cultivar CDC Milestone allocated proportionally more dry matter and N to pods than late-maturing cultivars. Zakeri et al. (2013) found that N application at podding increased the reproductive growth period, delayed the maturity and increased biomass and seed yield.

412  Crop Physiology: Case Histories for Major Crops

Lentil grown with an extended photoperiod of 22/2 h at 25–28°C and 12–18°C light/dark temperature reduced the days to maturity (84 days) with higher growth rate than plants grown at 10/14 to 12/12 h day/night photoperiod and 15/32°C [172 days (Idrissi, 2020)]. Kumar and Srivatava (2015) found that long reproductive duration negatively affected yield but increased seed size. Thavarajah et al. (2015) studied the nutrient distribution in developing lentil seeds at two temperature regimes in Canada (decreasing temperatures) and India (rising temperature) and found that high temperature increased phytate, Zn, and Fe concentrations in seeds. The cooler temperatures of temperate summers may lower phytate concentration in seed. Reproductive development is further discussed in Section 4.1.

2.3  Development and adaptation to stress Lentil is mostly grown as dry land crop and often faces water deficit during key growth stages; hence, the importance of adaptation to drought. Lentil also faces temperature extremes during its life cycle particularly heat stress during reproductive growth. Altering crop phenology to avoid water and heat stress during critical stages of development is the focus of much research in lentil (Fig. 13.1; Shrestha et al., 2006b).

2.3.1  Elevated temperature Lentil has an optimum temperature range of 18–30°C from flowering to maturity (Sinsawat et al., 2004; Roy et al., 2012; Barghi et al., 2012) and is highly susceptible to high temperatures. Heat stress affects pollen viability, seed set, and grain filling (Barnabás et al., 2008). Temperature above 32/20°C at reproductive stages drastically limits lentil yield (Delahunty et al., 2015). In 2009, a heat wave (35°C for 6 days) across southeastern Australia reduced the yield in lentil crop by 70% (Delahunty et al., 2015). In India, Bhandari et  al. (2016) tested heat-stress tolerance in three lentil genotypes (LL699, LL931, and LL1122) sown at the optimal time (November) or later (February). The optimal sowing ensured ≤ 32/20°C during the reproductive phase, in comparison with temperature above 32/20°C for late-sown counterparts. Late-sowing accelerated plant phenology, reduced duration of flowering and pod filling, biomass, and yield. Supra-optimal temperatures (> 32/23°C) during the reproductive phase reduced relative water content and stomatal conductance and impaired reproductive function. In another study, heat stress alone or in combination with intermittent drought (50% field capacity from seed filling to maturity) drastically damaged cell membranes, impaired photosynthetic machinery and reduced seed set in heat- and drought-sensitive genotypes (Table 13.2; Sehgal et al., 2017).

ZĞĚƵĐĞĚŐƌĂŝŶĮůůŝŶŐ ĚƵƌĂƟŽŶ

>ŽǁƉŽůůĞŶǀŝĂďŝůŝƚLJ ĂŶĚĞĂƌůLJŇŽǁĞƌŝŶŐ

WŽŽƌĂƐƐŝŵŝůĂƚĞ ƚƌĂŶƐůŽĐĂƟŽŶƚŽ ĚĞǀĞůŽƉŝŶŐŐƌĂŝŶƐ

ZĞĚƵĐĞĚƉŚŽƚŽƐLJŶƚŚĞƐŝƐ

/ŶĐƌĞĂƐĞĚƉŽĚĂďŽƌƟŽŶ

WŽŽƌƌŽŽƚĚĞǀĞůŽƉŵĞŶƚ͕ ŶƵƚƌŝĞŶƚƵƉƚĂŬĞ

^ŵĂůůĂŶĚƐŚƌŝǀĞůĞĚƐĞĞĚ͕ ůŽǁĞƌĐƌŽƉLJŝĞůĚ

ZĞĚƵĐĞĚǁĂƚĞƌŝŵďŝďŝƟŽŶ͕ ĂŵLJůĂƐĞĂĐƟǀŝƚLJ͕ ƐƵŐĂƌŵĞƚĂďŽůŝƐŵĂŶĚ^ĞĞĚ ŐĞƌŵŝŶĂƟŽŶ Abiotic stresses (Salinity, drought, heat) FIG. 13.1  Effect of abiotic stresses on phenological development of lentil.

Lentil Chapter | 13  413

TABLE 13.2  Effect of heat and drought stress on lentil growth and yield sowing date on drought and heat tolerance in lentil. Genotypes

Stress

Effect on plant

References

Gachsaran and Landrace (drought tolerant)

(− 1.2 MPa) drought stress at vegetative and reproductive stage

↑ Antioxidant activities, ↑ grain yield

Allahmoradi et al. (2013)

Simal (South Asian cultivar)

Drought stress at flowering

↓ Leaf area, ↓ dry matter production, ↓ flower number, ↓ pod set, ↓ grain yield

Shrestha et al. (2006a,b)

Khajura 2 (South Asian genotype), Cassab (West Asian genotype) droughttolerant genotypes

Drought stress at flowering and podding

↑ Grain yield, ↑ flower number, ↑ seed set

Shrestha et al. (2006a,b)

LL699, LL931, LL1122

Heat stress (>32/23°C) at flowering and pod filling

Short duration of flowering and pod filling, ↓ RWC, ↑ membrane damage, ↓ sucrose synthesis, ↓ biomass production, ↓ seed yield

Bhandari et al. (2016)

IG2507, IG3263, IG3745, IG4258, FLIP2009

>32/20°C during reproductive stage

↑ Sucrose, ↑ anthers, ↑ photosynthetic function, ↑ cellular oxidising ability, ↑ RLWC, ↑ stomatal conductance, ↑ expression of antioxidant, ↓ oxidative damage

Sita et al. (2017)

IG2821, IG2849, IG4242, IG3973, IG3964

>32/20°C during reproductive stage

↓ Sucrose, ↓ anthers, ↓ photosynthetic function, ↓ cellular oxidising ability, ↓ RLWC, ↓ stomatal conductance, ↑ expression of antioxidant, ↑ oxidative damage

Sita et al. (2017)

72578 (India), 71457 (Jordan), 73838 (Albania), 70549 (Argentina) droughttolerant genotypes

Heat stress during reproductive stage

↓ Reduction in seed setting and grain yield

Delahunty et al. (2015)

ILL 2150, ILL 4345 (drought sensitive) 1G 3973 and 1G 3964 (heat sensitive)

Heat + drought stress at reproductive stage (> 30/20°C + 50 field capacity)

↓ Duration of flowering and podding, ↓ grain yield, ↑ membrane damage, ↓ sucrose concentration

Sehgal et al. (2017)

Genetic variation in response to heat stress offers the opportunity to improve plant adaptation (Table 13.2). Bhandari et al. (2016) reported a decline in stomatal conductance, relative leaf water content, and biomass in lentil genotypes subjected to heat stress, suggesting transient water stress in addition to heat stress. The decline in vapour pressure deficit associated with high temperature may be important for lowering stomatal conductance and relative water content. Bhandari et al. (2016) found that stomatal conductance of well-watered plants was diminished at 38/23°C. Heat-tolerant genotypes set pods at 40/30°C, whereas heat-sensitive genotypes did not produce pods even at 38/28°C (Sita et al., 2017). Lower leaf temperatures were noticed in heat-tolerant genotypes than in heat-sensitive genotypes, which helped to protect the plants by transpirational cooling (Sita et al., 2017). Sehgal et al. (2017) recorded that stomatal conductance in lentil increased with heat stress but decreased with drought stress and the combined stresses in lentil. Screening 38 lentil genotypes of diverse origin (India, Morocco) for heat tolerance by delaying sowing to impose heat stress during the reproductive stage revealed that the heat-tolerant genotypes had better pollen viability, germination, pollen tube growth, and pod set than the heat-sensitive genotypes (Sita et al., 2017). The heat-tolerant genotypes had 1.8- to 22-fold higher nodulation, 35%–78% more sucrose in anthers, 65%–73% more sucrose in leaves, and higher antioxidant enzyme activity than their heat-sensitive counterparts. In another study, 50 lentil genotypes were exposed to heat stress (unshaded) and control (shaded) conditions (Delahunty et al., 2015), and yields declined from 100% to 20% in response to heat stress when compared with the control,

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but temperature and radiation were confounded in these experiments. Genotypes from India (72 578), Jordan (71 457), Albania (73 838), and Argentina (70 549) were more heat tolerant and produced 65%–80% more grain yield than the controls. Heat stress reduced seed numbers by 50% in the commercial cultivars but only by 18% in heat-tolerant genotypes.

2.3.2  Water stress Water stress, either individually or in combination with high temperature during the reproductive phase, reduces lentil yield (Sehgal et al., 2017; Sita et al., 2017). Although lentil is well adapted to dry conditions, water stress can reduce yield up to 54% (Shrestha et al., 2006b; Moradi et al., 2013; Mishra et al., 2016). Lentils use a typical drought-avoidance strategy at the reproductive stage when high temperature and water deficit induce rapid senescence and early maturity (Erskine et  al., 1994; Siddique et  al., 2003; Shrestha et  al., 2006a). In the Mediterranean environment, lentil genotypes from South Asia and Ethiopia have early phenology and, thus, escape drought, whereas genotypes from northern latitudes are late maturing and hence poorly adapted to drought (Hamdi et al., 1992). Drought-tolerant lentil genotypes have deeper roots than drought-sensitive genotypes that allow plants to extract more at Tel Hyada, Syria both in field and pots (Sarker et al., 2005). Lentil responds well to irrigation, usually on light-textured soils (Erskine and Saxena, 1993; Shrestha, 1996; Shrestha et al., 2006b). Lentil is cultivated in India on residual soil moisture or current rainfall; in both cases, it is usually subjected to terminal drought that reduces seed yield (Panwar and Srivastava, 2012). Adopting practices that enhance the availability of stored soil moisture and developing varieties that are drought tolerant (Oweis and Hachum, 2006) can help in improving yield of rainfed lentil. Breeding drought-tolerant cultivars require the identification and transfer of morpho-physiological and biochemical traits that impart drought tolerance in high-yielding cultivars (Morgan, 1984; Singh and Choudhary, 2003). Lentil breeding for drought tolerance has progressed through selection for suitable phenology and enhanced water use efficiency (Yadav et al., 2003), defined as the biomass or grain produced per unit of water used by the crop (Hatfield and Dold, 2019). Genotypes with the lowest tolerance against stress, stress susceptibility index and with the highest mean productivity, geometric productivity, and stress tolerance index were superior in terms of yield and drought tolerance (Somarian and Mohmoodabad, 2011).

2.3.3 Salinity Lentil is sensitive to salinity, that is magnified in arid and semiarid regions (Singh et al., 2017). Salt stress can compromise seed germination and early seedling growth (Kitajima and Fenner, 2000). Singh et al. (2017) screened 162 lentil accessions for salinity in a hydroponic study and concluded that salinity tolerance associated with restricted Na+ and Cl− movement was coupled with a thick epidermis and increased vascular bundles, decreased H2O2 production, increased K+ accumulation, proline accumulation, antioxidant enzyme activity, seedling growth, biomass, and seedling survivability. Furthermore, salt-tolerant lines and wild accessions of lentil were better able to regulate Na+ and Cl− in roots and shoots than the saltsensitive cultigens (Singh et al., 2017). The uptake of K+ by roots and further translocation and distribution within plant organs were better controlled and integrated in the salt-tolerant cultivars than the salt-sensitive cultigens. Bandeoğlu et al. (2004) studied the effects of salt stress (100 and 200 mM NaCl) on antioxidant responses in shoot and root of 14-day-old lentil seedlings in chambers. Salt stress decreased the length, dry weight and increased the proline content of both shoot and root tissues. Salinity increased H2O2, lipid peroxidation content and electrolyte leakage in leaves, whereas root tissues were less damaged than leaves. Root tissues of lentil are protected better from NaCl stress-induced oxidative damage due to enhanced total SOD activity together with higher APX activity under salinity stress. In another chamber study, lentil plants were grown with sufficient water, NaCl, and drought conditions (Muscolo et al., 2015). The effects of drought and salt stress on metabolic and phenotypic traits were transient and depended on the type and severity of the stress; stress tolerance at the seed stage did not ensure the establishment and growth of the seedling under stress conditions. Exploring lentil genotypes for nodulation and N2 fixation and the compatible salt-tolerant Rhizobium strains is important for improving their performance in saline environments. Two Rhizobium strains (ND-16 and TAL-1860) and four lentil genotypes (DLG-103, LC-50, LC-53, and Sehore 74-3) were found to be suited to sodic soils (Rai and Singh, 1999). Interactions between salt-tolerant lentil genotypes and Rhizobium strains under field conditions were significant and increased nodulation, N2 fixation (nitrogenase activity), total nitrogen, plant height, root length, and grain yield in sodic soils than uninoculated controls. The plants grown in normal soils had significantly more nodulation, nitrogenase activity, glutamine synthetase (GS), and NADH-dependent glutamate synthase (NADH-GOGAT) activities than in soils supplemented with 4% or 8% of NaCl. Salt stress inhibited nitrogenase, GS, and NADH-GOGAT activities. However, nitrogenase activity in nodules was more sensitive to salt stress than GS and NADH-GOGAT activities (NH4 assimilation) (Bougeais-Chaillou et al., 1992). These studies revealed that agronomic traits including pod number and seed yield per plant can be used to

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distinguish salt-tolerant lentil genotypes from salt-sensitive ones during the reproductive stage under saline field conditions (Rai and Singh, 1999).

3  Growth and resources 3.1  Capture and efficiency in the use of radiation Crop yield is a function of growth rate, period of growth, and allocation of growth to seed (Azam et al., 2002). Crop growth depends on the amount of photosynthetic active radiation (PAR) intercepted by the canopy and the radiation use efficiency (RUE). However, there are limited data in lentil that compare the components of radiation interception, leaf area index (LAI), canopy geometry, and RUE.

3.1.1  Leaf area index and extinction coefficient Radiation interception varies during the season (Sivakumar and Virmani, 1984; Watiki et al., 1993) with the dynamics of LAI and canopy extinction coefficient (k), which depend on leaf size, shape, the elevation of the sun and the amount of direct and diffused solar radiation (Thomson and Siddique, 1997; Jeuffroy and Ney, 1997). For lentil at high plant density in Canterbury New Zealand, LAI of seven was intercepted as 95% of incident solar radiation (McKenzie and Hill, 1991). The k reported for lentil is 0.26 (McKenzie and Hill, 1991), in comparison with 0.36–0.48 for field bean (Husain et al., 1988), 1.06 for pea and 0.91 for lupin (Thomson and Siddique, 1997). In Faisalabad (Pakistan), early sowing enhanced radiation interception by 80% when compared with late sowing, and radiation interception in fully irrigated crop was enhanced by 89%, relative to the controls.

3.1.2  Radiation use efficiency RUE has been defined as the slope of the zero-intercept linear regression between biomass and PAR intercepted by a crop (Monteith, 1977; Muchow and Sinclair, 1994; Ceotto and Castelli, 2002). RUE is affected by abiotic factors, such as drought (Jamieson et  al., 1995) and nutrient availability (Sinclair and Horie, 1989). Legumes have lower RUE than nonleguminous C3 species (Gosse et  al., 1986; Sinclair and Muchow, 1999; Tesfaye et  al., 2006). McKenzie and Hill (1991) in New Zealand reported lentil RUE falling from 1.40 to 1.25 g MJ− 1 PAR with plant density increasing from 100 to 500 plants m− 2 and from 1.52 to 1.32 g MJ− 1 with sowing delayed from April to October. There were season-dependent variations with genotype, whereby Titore was superior to Primera in one season (1.45 versus 1.10 g MJ− 1), but cultivars had similar RUE in another season.

3.1.3 Lodging Severe plant lodging reduces yield, seed quality, harvest efficiency and increases disease pressure (McPhee and Muehlbauer, 1999), and the extent of yield losses depends on the timing and severity of lodging. Four stiff-stem unadapted lentil genotypes differing in leaf size and canopy openness [FLIP 96-47L (open canopy and very early maturity), FLIP 2000-6L (narrow leaf and early maturity), FLIP 96-25L (narrow leaf and early maturity), and FLIP 2000-7L (bushy canopy)] were compared with two large green market class (CDC Grandora and CDC Plato) and other locally adapted cultivars [Milestone (small green type, a smaller plant with higher HI and greater yield than large green types) and Crimson (red lentil prone to basal lodging)] at three population densities in the field (Hanlan et al., 2006). The adapted cultivars had high biomass (525–700 g m–2), seed yield (96–130 g m–2), maximum radiation interception (61%–80%), final branch number (17–19), and plant height (0.3–0.44 m), but the large green cultivars were prone to lodging. The unadapted genotypes produced less biomass and lodged the least. To enhance lodging resistance, plants are selected for stem stiffness, for example, field pea (McPhee and Muehlbauer, 1999) that allows plants to withstand a heavy load without decreasing plant height.

3.2  Capture and efficiency in the use of water Lentils growing in the Mediterranean and semiarid zones often face terminal drought (Ghanem et al., 2017). Drought tolerance could be partly the consequence of constitutive traits that affect water use earlier in the growing season when water does not limit transpiration (Vadez et al., 2012). Partial stomatal closure at higher soil water content during the soil drying cycle enhances the availability of water during reproductive development (Ludlow and Muchow, 1990; Sinclair and Rufty, 2012). However, stomata closure prevents evaporative cooling (Mahrookashani et al., 2017). Under water scarcity late in the season, genotypes with conservative

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water behaviour could use conserved soil water during seed filling and increase yield (Sinclair et al., 2005). In Western Australia, lentil seed yield has been positively correlated with postflowering water use but not total water use or water use before flowering (Siddique et al., 2001). In northern Syria, in 3 experiments across 12 seasons, seasonal variation lead to changes in water use, which affected the production of chickpea and lentil (Zhang et al., 2000). Seasonal evapotranspiration (ET) correlated with seasonal rainfall, and both chickpea and lentil had lower water use efficiency and potential transpiration efficiency than cereals (Zhang et al., 2000). Carbon isotope discrimination Δ13C, a measure of the 13C/12C ratio in the plant relative to the air (Farquhar and Richards, 1984), is associated with transpiration efficiency in C3 species (Condon et al., 2002). When stomata are open, C3 plants discriminates against 13C, a heavy isotope of C that represents about 1% of the carbon in the biosphere. The Δ13C of lentil germplasm from 15 countries varied with genotype but not with water regime, and negatively correlated with WUE demonstrating the usefulness of this marker for selection (Johnson et al., 1995; Matus et al., 1996).

3.2.1  Patterns of water supply and demand Lentil in India, West Asia, and Australia is frequently water limited, with unpredictable rainfall and variable soil water combined with increasing frequency of heat events. Gross and Kigel (1994) observed that nonlimiting water increased lentil yield by 74% in Mediterranean-type climates, where seed filling is typically restricted by elevated temperature and water scarcity. The combined abiotic stresses can further reduce seed yield (Zhang et al., 2010) and are considered synergistic (combined stress is more severe than either individual stress), antagonistic (combined stress is less severe than either individual stress) or hypoadditive (combined stress is higher than distinct effects but less than their sum) effects on seed filling, growth, and yield traits (Mahrookashani et al., 2017). The indeterminate growth habit of lentil may provide a recovery mechanism to sustain seed set when water stress is relieved by subsequent rainfall or through supplementary irrigation (Wang et al., 2006; Vadez et al., 2012). High daily temperatures could shorten crop growing seasons at rates proportional to accumulated heat units (Evans and Sadler, 2007). Hydrological uncertainties would be compounded because precipitation and temperature changes could have disproportionately larger effects on crop ET and the volume and timing of snowmelt, especially in arid and semiarid areas. These factors combined would change where and which crops are grown (Evans and Sadler, 2007). Irrigators need to improve productivity per unit of water consumed through cultural, managerial, engineering, and institutional changes (Evans and Sadler, 2007; Muchara et al., 2018). This must be supported by system-wide enhancement of water delivery systems, advanced site-specific irrigation technologies that include self-propelled sprinklers and micro-irrigation systems, and other monitoring, modelling and control technologies.

3.2.2  Root system The lentil root system comprises slender taproot with a mass of fibrous lateral roots of variable depth. Root size, morphology, depth, length, density, and hydraulic conductance influence the uptake of water (Passioura, 1982; Subbarao et al., 1995; Turner et al., 2001; Gahoonia and Nielsen, 2004). During early growth, with moisture available in surface soil, lateral roots capture water, and nutrients. With the termination of rain and increased temperature and evaporation, the plants rely on water from deeper soil strata through their taproots (Turner, 1986; Sarker and Erskine, 2006). Ideally, lentil varieties must have both deep roots and roots that can spread across large soil volumes to capture surface water (Gorim and Vandenberg, 2017a). However, deep rooting does not always reflect the ability of a genotype to extract extra soil water, especially if the wet profile is shallow (Bandyopadhyay, 2014). Lentil genotypes with more root length may have a slight advantage in soils with shallow water tables (Loss and Siddique, 1994). The root system of lentil has been compared with those of oilseeds, other pulses and wheat (Gan et al., 2009, 2011; Liu et al., 2010). The distribution pattern of fine root traits, such as root length, root surface area, root volume, and root diameter in cultivated lentil were different from those of other crops with implications for water and nutrient uptake. Gorim and Vandenberg (2017b) assessed the root systems of five wild lentil species and Lens culinaris under fully irrigated conditions. Considerable differences were recorded for root traits and fine root distribution between and within species, and the proportion of root biomass partitioned into each soil layer was unique for each genotype. The number of root tips is an indicator of root function, from water uptake to regulating plant growth (Aiken and Smucker, 1996; Bandyopadhyay, 2014). Well-watered lentil plants had more root tips than their drought-stressed counterparts (Bandyopadhyay, 2014). The uptake of water and extraction from soil depend on whether the soil has stored water or the water supply is driven by in-season rainfall (Tron et al., 2015; Gorim and Vandenberg, 2017a,b). Water stress may upregulate dry matter allocation to roots (Leport et al., 2006). The root to shoot ratio in lentil subjected to drought at the reproductive stage increased from 14% to 100%, relative to well-watered lentil (Bandyopadhyay, 2014),

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which was a consequence of the relatively greater decline in shoot growth than root growth, rather than an increase in absolute root weight (Shrestha et al., 2005; Bandyopadhyay, 2014). Aquaporins are water channel proteins that are expressed in various membranes of plant cells. Plant aquaporins are encoded by a large multigene family, with 35 members in Arabidopsis thaliana; many of these aquaporins show a cell‐ specific expression pattern in roots (Javot and Maurel, 2002). Plant aquaporins have been identified in both plasmalemma (PIP) and tonoplast (TIP) and are thought to regulate water flow through membranes during growth, development, and stress responses (Harvengt et al., 2000). Two TIP isoforms have been isolated from the PSV membrane of lentil seeds (Harvengt et al., 2000). Chemical cross-linking experiments revealed that lentil TIP isoforms exist as hetero-oligomers in the membrane. A 52-kDa magnesium-dependent protein kinase, capable of phosphorylating each isoform, was also identified. Further studies are needed to determine the functionality of aquaporins in lentil under various environments.

3.3  Capture and efficiency in the use of nutrients 3.3.1 Nitrogen Lentil nodules are indeterminate, and reduced nitrogen is exported as amides. Indeterminate nodules grow and form elongated or lobed structures with distinct zones. With maturity, the section of nodules closest to the plant root loses its pink coloration, turning grey, or green. As long as the section of the nodule furthest from the root retains some pink tissue, the nodule remains active (GRDC, 2018). In favourable conditions, lentil can obtain most of its nitrogen through N2 fixation (Kurdali et al., 1997), but N2 fixation is reduced by water stress (Cowell et al., 1989), lack of efficient Rhizobia (Gan et al., 2005), high level of inorganic N in soil (Bremer et al., 1989), and P deficiency (see below). The amount of N2 fixed by lentil in semiarid areas ranged from 10 to 129 kg N ha− 1, depending on the site and rainfall (Zahran, 1999). Maximum N2 fixation in lentil occurs during early flowering under dry conditions (Kurdali et al., 1997), and up to pod formation under irrigated conditions (van Kessel, 1994). The carry over of nitrogen from lentil is low at 23–45 kg N ha–1 (Erskine et al., 2011). Nevertheless, cereals grown in rotation with lentil and other grain legumes increase yield and protein content when compared with mono-cropping cereals (Quinn, 2009). Rates of N assimilation generally decline, whereas N allocation to roots increases with increasing temperature (DeLucia et al., 1992; Rachmilevitch et al., 2006). Although there is no information on lentil, heat stress reduces the level and activity of nitrate reductase, GS, and glutamate synthase and the synthesis of ureides (Hungria and Vargas, 2000). Lentil cultivars vary in their ability to fix N2 (Rennie and Dubetz, 1986). Both biomass and seed yield rely on the nitrogen-fixing capability of the genotype as N requirements are high during reproductive growth (Sinclair and de Wit, 1975). During later pod filling, the N2 fixation rate decreases contributing to leaf senescence (Kurdali et al., 1997). The mobilisation of N from vegetative organs can account for as much as 70% of the N in seed at maturity (Kurdali et al., 1997; Whitehead et al., 1998). A rainfed field experiment used 15N isotopes to measure the source of nitrogen (N2 fixation, soil, and fertiliser), N assimilation, partitioning, and mobilisation at various growth stages in five lentil cultivars (Kurdali et al., 1997). There were strong cultivar effects for N2 fixation and soil nitrogen uptake beyond flowering, and the plants were characterised by either current N2 fixation/soil N uptake or by minor N assimilation from either source. Net mobilisation of N from shoot and root to pod varied from 43% to 94%. When the soil and atmospheric nitrogen sources were restricted, vegetative tissues became the main source of nitrogen during pod filling. The reproductive organs had higher percentages of N derived from the atmosphere (% Ndfa) than vegetative plant parts (van Kessel, 1994). Nodules are strong sinks for P (Hart, 1989), and low availability of P reduces nodulation and N-fixation (Sepetoglu, 2002; Sharma and Sharma, 2004; Gahoonia et al., 2006; Singh et al., 2011; Divito and Sadras, 2014). Increasing P dose from 20 to 40 kg P2O5 ha–1 increased the number, dry weight, and leghaemoglobin content in lentil nodules (Jindal et al., 2008). A positive relationship between leghaemoglobin content and N2 fixation was noticed (Lakhmanrao et al., 1983). Nodule number declined at the highest P dose (80 kg ha–1), but nodule dry weight increased with each increment from 40 to 80 kg ha–1 (Rasheed et al., 2010). Source-to-sink partitioning of nitrogen is influenced by nitrogen uptake and metabolism in source organs (Tegeder and Masclaux-Daubresse, 2018). Amino acid transport in the shoot regulates root nitrogen uptake, source metabolism, and allocation to sinks. In pea, augmented amino acid phloem loading positively affected nitrogen root uptake and, subsequently, nitrogen availability for assimilation and usage in the source and sink (Zhang et al., 2015; Perchlik and Tegeder, 2017). Enhanced nitrogen export from leaves and concomitant changes in leaf nitrogen concentration may induce a shoot-to-root signal upregulating nitrogen uptake and delivery to leaves. Increased nitrogen availability in leaves can improve photosynthesis and speed up the phloem loading of carbon (Zhang et al., 2015). Studies on this aspect are lacking in lentil and need attention.

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3.3.2 Phosphorus Phosphorus influences lentil root and shoot growth, N2 fixation (Section 3.3.1), and resistance to plant diseases (Fig. 13.2). Increased availability of P increased lentil height and branching (Rasheed et al., 2010; Datta et al., 2013), LAI and crop growth rate (Rasheed et al., 2010), pod numbers (Maqsood et al., 2000; Togay et al., 2008), grain number per pod (Choubey et al., 2013), 1000-grain weight-mediated by the effect of P on cell division (Togay et al., 2008), harvest index (Fatima et al., 2013), seed yield and protein concentration (Niri et al., 2010), seed P content, and the formation of fat and albumin (Maqsood et al., 2000). Phosphorus mainly enters the roots as H2PO4 through the soil solution (Hendrixs, 1967) and impacts root length and root hairs (Singh and Singh, 2016). Under severe deficiency of P in the soil, lentil roots secrete organic acids that solubilise P (Singh and Singh, 2016). P deficiency increases phenolic compounds in roots that chelates Al3 + and Fe3 +: the increased solubility of Fe–P and Al–P chelates favours P acquisition (Sarker and Karmoker, 2011a). P deficiency stimulates the production of anthocyanins in leaves, which may act as an indicator of P stress (Sarker and Karmoker, 2011a). Phosphorus deficiency decreased the accumulation of reducing sugars in lentil leaves and stems but increased accumulation in the roots, and increased proline in root and shoot (Sarker and Karmoker, 2011b). Phosphorus application modulates root and shoot traits [root hairs, root and stem diameters, leaf thickness, cortical zone of stems and roots, vascular area, and stem pith area (Sarker et al., 2015)] that may improve yield under drought (Singh et al., 2005). Root length and lentil yield improved by phosphorous application (30 kg P2O5 ha− 1) and inoculation with P-solubilising bacteria (PSB) on medium phosphorous alluvial soil belt of northern India (Alagawadi and Gaur, 1988). High phosphorous content and yield due to inoculation with PSB in lentil was chiefly due to higher mobilisation of native soil phosphorous.

3.3.3 Micronutrients Very fine roots (diameter < 0.5 mm), fine roots (diameter 0.5–2.0 mm) and root hairs determine the root surface area that is available for nutrient uptake (Zobel et al., 2007; Zobel and Waisel, 2010). Genotypes with prolific root hairs were better able to take up micronutrients with low soil availability and limited transport to roots by diffusion, Fe, Mn, Cu, Zn, Mo, K, and P (Gahoonia et al., 2006). Zinc plays a role in the biosynthesis of plant hormones, particularly indoleacetic acid, and is a cofactor of several enzymes (Islam et al., 2018). Zinc deficiency inhibits protein synthesis, reduces water uptake, N2 fixation, pollen fertility, and seed yield (Pandey et al., 2006; Ahlawat et al., 2007). Iron deficiency, which depends upon the soil pH and redox state, occurs due to inadequate accessibility of iron for root uptake (Mahmoudi et al., 2005). Iron-related chlorosis is a major limitation for most legumes, particularly those for seed production (Saxena et al., 1990; Zaiter and Ghalayini, 1994). It affects the mineral composition of leaves and flowers (Tagliavini et al., 2000) and leads to severe yellowing of young leaves, reduced chlorophyll concentration, and a substantial decline in plant biomass (Mahmoudi et al., 2005). Boron is one of the most important essential trace elements for plants (Islam et al., 2018). It can affect the absorption of nitrogen, phosphorus, and potassium, and its deficiency alters the equilibrium of those three macronutrients. Boron also plays a role in sugar translocation, N2 fixation, protein synthesis, sucrose synthesis, cell wall composition, membrane stability, and K+ transport (Singh et al., 2015). Boron deficiency results in plant sterility by deforming reproductive tissues affecting pollen germination, which increases flower drop and reduces fruit set (Subasinghe et al., 2003; Islam et al., 2018). Molybdenum (Mo) is essential for the nitrogenase of Rhizobium bacteria. It is also the cofactor for the nitrate reductase involved in nitrogen assimilation (Hänsch and Mendel, 2009). Mo, as a constituent of nitrate reductase and nitrogenase enzymes, is associated with ammonia reduction and N2 fixation, and its deficiency can reduce crop growth and yield (Singh et al., 2015). Mo-deficient lentil plants produce fewer and smaller flowers, with most failing to open or mature, which ultimately lead to lower seed yields (Islam et al., 2018). Supplementation of various micronutrients increased pod set, seeds per pod, and seed yield in lentil (Islam et al., 2018). The combined use of Zn, B, and Mo had a significant effect on the protein content of seeds (Islam et al., 2018).

4  Yield and quality 4.1  Reproductive development Flowering in lentil is indeterminate, occurring from axillary buds on the main stem and branches. It proceeds acropetally from lower to higher nodes. A single plant may have open flowers at high nodes and full pods in lower nodal positions at the same time; many of the early flowers abort. Although the main stem and basal primary branch carry the same number

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FIG. 13.2  Role of soil phosphorous in legumes’ growth, development, and production.

of pods, on an average, podding on the primary branches is concentrated on fewer nodes per branch than the main stem (Erskine and Goodrich, 1991). Nodes 11 and 12 on the basal primary branch are the most commonly podded (Erskine and Goodrich, 1991). As a reference point spanning the most commonly podded nodes, the most advanced reproductive structure within nodes 10–13 on the basal primary branch will determine the reproductive stage. The node of the first flower and the period between two successive nodes are altered depending on the genotype and environment. During winter, the duration between nodes is generally long during vegetative and early reproductive development but shorter during summer. With suitable moisture and temperature, crop growth, node formation, and flowering continue until flowering ceases. With water stress or temperature above 30°C, flowering and crop growth cease (GRDC, 2017). The time from sowing to maturity is affected by temperature, rainfall, and ranges from 75 to 180 days (Saxena, 2009). Although, longer days to maturity may increase biomass and yield (Thomson et al., 1997; Whitehead et al., 2000), it can also lower HI and increase frost risk in Canada (Saskatchewan Pulse Growers, 2002). Lentil requires from 944°C to 1270°Cd (base temperature 5°C) from seeding to maturity. This requirement is generally achieved in most parts of the Great Plains (Miller et al., 2002). In low rainfall conditions of Australia, early flowering and pod set help lentil to escape terminal drought; however, there is a trade-off between preflowering duration and yield (Thomson et al., 1997). On an average, lentil flowered 31–106 days and matured 26–66 days after flowering in a study of 287 genotypes and 15 registered cultivars in Pullman, Washington (Tullu et al., 2001). In drier conditions, early flowering increased HI by increasing the length of pod filling and avoiding late-season drought (Erskine et al., 1989; Silim et al., 1993). In Washington and Idaho, USA, each day of delayed flowering and delayed maturity increased straw yield and decreased seed yield (Kusmenoglu and Muehlbauer, 1998). Increased temperatures during reproduction accelerate development and decrease flower number and size, and pod and seed set (Morrison and Stewart, 2002; Barghi et al., 2012; Kaushal et al., 2013; Bhandari et al., 2016; Kumar et al., 2016; Sehgal et al., 2017; Sita et al., 2017, 2018). Water stress in lentil leads to flower and pod abortion, unfilled pods, reduced seed size, yield, and quality (Sehgal et al., 2017, 2018). Drought during pod formation was most damaging to lentil, followed by flower initiation, than drought stress at the mid-vegetative stage (Mishra et al., 2016). Time to flowering accounted for 49% of the variation in seed yield (Silim et al., 1993). Early flowering is important for drought escape, but

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it involves a trade-off with yield under favourable conditions (Turner et al., 2001). In Syria, the medium-flowering lentil, ILL4400 and ILL4401, had higher seed yields in most seasons than other landraces (Erskine and Goodrich, 1991), but they were intolerant to drought in a separate study (Silim et al., 1993). Similarly, early flowering and maturing genotypes were high yielding in low rainfall, low-yielding environments in Australia, but these genotypes were low yielding when compared with the best medium rainfall genotypes over eight sites for 5 years (Materne, 2003). Wild lentils, particularly L. culinaris sp. orientalis, are often found in habitats with low average rainfall (Erskine and Saxena, 1993; Erskine et al., 1994), but they produce less biomass and seed yield than cultivated lentils under dry conditions (Hamdi and Erskine, 1996). Hamdi and Erskine (1996) found that L. odemensis (Ladizinsky) had the lowest reduction in seed yield when drought and nondrought treatments were compared. Shrestha et al. (2006a) conducted a glasshouse experiment to determine the sensitivity of reproductive development to water deficit in three lentil genotypes of different origins. Genotypes did not vary in vegetative growth or seed yield under well-watered or water-deficit conditions but differed in flower number, fruiting nodes, pods, seeds, and HI. ‘Cassab’ had 61% larger seeds than ILL 7979 and 105% larger seeds than ‘Simal’. The small-seeded genotypes produced the most fruiting nodes and, hence, more flowers, pods, and seeds. Seed size correlated with seed growth rate (r = 0.77) and seed filling duration (r = 0.45). Water deficit reduced total dry matter by about 60% and flower number by 35%–46% and increased seed abortion (empty pods) from 17% to 46% when compared with well-watered plants. Water deficit had no effect on maximum seed growth rate, seed filling duration or final seed size in the three genotypes. Therefore the 70% reduction in seed yield induced by water deficit was primarily due to a reduction in pod and seed numbers (59%–70%), rather than seed size. Shrestha et al. (2006b) showed the effect of water deficit at two growth stages (flowering and podding) on the physiology and growth of lentil genotypes. Under water deficit, seed yield decreased by up to 60% in the crossbreds and the South Asian cultivar, ‘Simal’. However, seed yield increased with water deficit at flowering and at podding in the West Asian genotype, ‘Cassab’, and the South Asian genotype, Khajura 2, respectively. In the other genotypes, withholding water at flowering or podding reduced total dry matter (32%–50%), flower production (22%–55%), and pod and seed numbers (27%–66%), with more flower drop and empty pods when water was withheld. Osmotic adjustment at flowering and podding was unrelated to seed yield. In a comparison of eight lentil genotypes—two drought tolerant (DPL53 and JL1), two drought sensitive (ILL 2150 and ILL 4345), two heat tolerant (1G 2507 and 1G 4258) and two heat sensitive (1G 3973 and 1G 3964) subjected to terminal drought stress, seed weight per plant and individual seed weight declined, more so in stress-sensitive genotypes, due to a decline in the rate and duration of seed filling (Sehgal et al., 2017).

4.1.1  Yield components Yield can be analysed in terms of biomass and harvest index and its numeric components, that is plants per unit area, pods per plant, seeds per pod and seed weight (Çiftçi et al., 1998; Anjam et al., 2005; Salehi et al. 2006; Younis et al., 2008; Tsigie et al., 2011). Luthra and Sharma (1990) reported a positive relationship amongst biomass, number of pods, and the number of seeds/plant and a negative correlation between yield and the number of seeds in pod and 1000 seed weight. Seed yield per plant was positively associated with biomass and harvest index (Yadav et al., 2003; Biçer and Şakar, 2008). Lentils have a highly variable HI, ranging from 0.01 to 0.60 (Kirthisinghe, 1986), from 0.25 to 0.59 (Pandey, 1980), and from 0.53 to 0.59 (Shrestha et al., 2006a,b) under normal conditions and from 0.36 to 0.45 under drought stress during pod formation (Shrestha et al., 2006a). Seed filling is modulated by source–sink relationships and hormones and is sensitive to environmental factors (Plaut et al., 2004; Yang and Zhang, 2006; Barnabás et al., 2008; Ochatt, 2015; Sehgal et al., 2018).

4.1.2  Seed quality and composition Lentils are a source of nutrients, including protein, fibre, and minerals (Thavarajah et al., 2015). Lentils seeds have approximately 28.6% protein, 3.1% ash, 4.4% fibre, and 63.1% total carbohydrates (nitrogen-free extract) (Sehgal et  al., 2018). Both heat and drought stress have an adverse effect on seed quality mainly due to their impact on nutrient uptake, assimilate partitioning, and nutrient mobilisation (Prasad et al., 2008). Heat stress strongly affects the seed composition of lentil especially at flowering and early pod filling stages (Sita et al., 2018; Sehgal et al., 2018), especially proteins and their various fractions (Sita et al., 2018; Parvin et al., 2018). Reductions in starch, protein, and oil content were attributed to the inhibited activity of sucrose synthase and soluble starch synthase (Sehgal et al., 2018). Lentil maintained high seed protein content under elevated CO2. (Rogers et al., 2009). Starch, seed proteins, and soluble sugars declined under drought stress along with fat, crude fibre, and ash contents under combined heat and water stress. Seed storage proteins such as albumins, globulins, glutelin, and prolamins were severely reduced by both stresses (Sehgal et al., 2018). Grain protein content is positively correlated with P availability

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in lentil (Togay et al., 2008) and this may be due to superior nodulation that promotes N2 fixation (Rasheed et al., 2010). Application of 40 kg ha− 1 SSP to lentil improved albumins, globulins (subfraction, i.e. legumins and vicillins), prolamins and glutelins relative to no fertilisation and increased nitrogen, crude protein, sulphur, and total protein content (Sital et al., 2011). In contrast, in dryland areas, application of 60 kg ha− 1 P reduced seed protein, relative to 40 kg ha− 1 (Niri et al., 2010), suggesting variations in P requirements under stress environments. Potential trade‐offs exist between seed yield and seed quality in legumes, especially when exposed to abiotic stresses, but their underlying causes remain poorly explored. In lentils grown in western Canada between 1998 and 2003, the annual mean crude protein content ranged from 25.8% to 27.1%. Samples for crude protein content submitted by producers ranged from 21.4% to 30% (Wang et al., 2003). Although the overall average crude protein content did not vary greatly from year to year, the deviation amongst samples within a year suggests the influence of a combination of environmental situations, agronomic practices, and genetic factors (Wang and Daun, 2006). Studies on yield-protein trade-off in lentil are lacking.

5  Concluding remarks: Challenges and opportunities Lentil yield is affected by biotic and abiotic stresses (drought, salt, heat, and mineral deficiency). Current cultivars lack resistance to these stresses partially due to a narrow genetic base. Hence, efforts are required to assess genetic variation in lentil germplasm for tolerance to specific stresses, based upon agro-ecological regions. Considering climate change, drought, and heat would prevail, both individually and in combination, in most lentil-growing regions. Hence, more attention is needed to design cultivars and practices with adaptation to combined stresses. Genetic variability in flowering may be useful for the development of early maturing cultivars to escape terminal stress. Wild relatives have not been explored much, despite their high genetic diversity and may provide leads in this context. Effective management is needed to enhance yields under stress. Mineral deficiencies, such as phosphorus, reduce lentil yield, which needs to be addressed, along with other nutrients, including Zn, Fe, Mo, and B. Nitrogen-fixing ability is drastically affected by stress, especially drought, salt, and heat; less attention has been paid to improve this trait. Seed quality traits need to be investigated in the existing germplasm. Bio-fortification (both agronomic and genetic) to enhance the nutritive value of seeds with vital minerals such as Fe, Zn, and Se would be helpful in this regard.

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Image source: Uta E. from Pixabay

Chapter 14

Lupin Alejandro del Pozoa and Mario Merab a

Plant Breeding and Phenomics Center, Faculty of Agricultural Science, University of Talca. Talca, Chile, bDepartment of Agricultural Production, Faculty of Agricultural and Forestry Sciences, University La Frontera, Temuco, Chile

1 Introduction The genus Lupinus L. (Fabaceae) is highly diverse and comprises about 270 annual and perennial species, distributed mostly in North and South America and a small number (~  12 species) in the Mediterranean region of Europe and northern Africa (Drummond et al., 2012). Four species have been domesticated and are cultivated: white lupin (L. albus L.), narrowleafed lupin (L. angustifolius L.), yellow lupin (L. luteus L.), and sweet pearl lupin (L. mutabilis Sweet.). The three first ones originated in the Mediterranean Basin, and L. mutabilis originated in the Andean region of Peru. The more widely grown species are L. albus and L. angustifolius (Walker et al., 2011). The four domesticated species are annuals but differ in morphology and chromosome number (Naganowska et al., 2003; Susek et al., 2016): L. albus (2n = 50) is 60–180 cm tall and presents white or blue flowers; L. angustifolius (2n = 40) is normally shorter than L. albus, presents narrow leaves and purple or blue flowers but white flowers in Australian cultivars; L. luteus (2n = 52) is also smaller than L. albus and has yellow flowers; and L. mutabilis (2n = 48) can be taller than L. albus and flowers are blue or light blue. There is also a semidomesticated species, L. cosentinii (2n = 32) of blue flowers, but white in the Australian cultivar Erregulla and with a protein content of 25% (Pastor-Cavada et al., 2009). Because of their protein and energy value, lupins are widely used in livestock feeds for ruminants and monogastrics, including fish (Edwards and van Barneveld, 1998). In aquaculture diets, lupins are highly nutritious protein and energy sources with some unique functional properties to contribute to aquafeed pellets and few nutritional problems (Glencross, 2008). Lupin can be a source of high-quality protein for food and novel lupin-based foods such as bakery products, pasta, breakfast cereal, among others (Lucas et al., 2015; Johnson et al., 2017). The world cropping area of lupins in 2018 was 984 894 ha, and the average yield was 1.21 t ha−  1; the main producer was Australia (612 014 ha), followed by Poland (95 639 ha), Morocco (88 941 ha), Russian Federation (71 163 ha) and Chile (24 968 ha) (FAOSTAT, 2020). However, cropping area has varied in Europe and Australia since the 1960s (Fig. 14.1a). From 1960s to 1990s the acreage in Europe progressively decreased, whereas in Australia it increased till a maximum of 140 000 ha in 1998 and then declined till 2014. The strong increase in acreage in Australia after the 1970s occurred after the release of the first cultivars of L. angustifolius, the predominant cultivated species (Walker et al., 2011). Among the reasons for the reduction in the cropping area in Australia were the rise of herbicide resistant weeds, the availability of herbicidetolerant oilseed rape, and frequent droughts (Meldrum, 2015). The world average grain yield has increased linearly from 0.5 t ha−  1 in 1960 to about 1.8 t ha−  1 in 2018 (Fig. 14.1b). Yield potential is much higher; 5–6 t ha−  1 in L. angustifolius (Ayaz et al., 2004a; Palta et al., 2004; Prins and Nuijten, 2015) and L. albus (Espinoza et al., 2012; Mera and Alcalde, 2019). In general, northern Europe cultivates L. angustifolius and some countries, such as Poland, also cultivate L. luteus because these species have a shorter life cycle than L. albus that is grown in southern Europe (France, Spain, Italy). Since the 1980s, the cropping area of L. albus and L. luteus has declined in Europe due to the risk of anthracnose and the increasing use of imported soybeans, but the area of L. angustifolius has become more prevalent. Australia grows L. angustifolius and L. albus (Walker et al., 2011) in a 4:1 ratio. Lupins can play a role on agricultural sustainability. As legume crops, lupins can meet up to 90% of their nitrogen needs through symbiotic fixation (Unkovich et al., 2010; Espinoza et al., 2012), which explains the general lack of response to nitrogen fertilisation. Part of the N fixed by lupins is left in the soil as shoot residues, roots, and nodules and constitutes an important N input for the following crop (Unkovich et al., 2010; Espinoza et al., 2012). In addition, lupin crops reduced greenhouse gas emissions from a wheat crop by 56% (on a per hectare basis), primarily by lowering nitrogen fertiliser inputs (Barton et al., 2014). Because of root adaptations to P-deficient soil, lupins show little or no yield response to phosphate fertilisers. L. albus and L. luteus, in particular, exudate short-chain organic acids (carboxylates) from specialised proteoid Crop Physiology: Case Histories for Major Crops. https://doi.org/10.1016/B978-0-12-819194-1.00014-1 Copyright © 2021 Elsevier Inc. All rights reserved.

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(a)

(b)

FIG. 14.1  Cropping area (a) and average grain yield in Australia, Europe, and the world. Data from FAOSTAT (www.fao.org/faostat).

roots (Gardner et al., 1983) that solubilise soil phosphorus. In crop rotations, lupins may enhance the yield of cereals and the profitability of the whole system by allowing a better control of grass weeds and by interrupting the cycle of some diseases. The global demand for protein is increasing (Henchion et al., 2017), and whether for feed or food, efficient protein crops are needed to help meeting this demand. Lupin crops are a sound option to produce plant protein in climates that are too cold for soybeans. The main drawbacks to be solved are improved yield potential and stability, resistance to anthracnose, the most threatening disease of lupins and tolerance to alkaline soils, where required, while maintaining alkaloids in the grains at safe levels for feed and food. Plant development, crop growth (CG) and yield potential of lupins are affected by various of environmental factors, such as temperature, photoperiod, and incident photosynthetic active radiation (PAR) and the availability of water and nutrients. In this chapter, we analyse lupin structure and morphology, plant development and growth, and the capture and efficiency in the use of radiation, water, and nutrients. We emphasise the two species with larger grown areas, L. angustifolius and L. albus.

2  Crop structure, morphology, and development Plant height varies considerably within each species, particularly in L. albus. In general, L. albus and L. mutabilis are taller and can accumulate larger biomass than L. angustifolius and L. luteus. Cultivars of sweet L. albus are usually between 60 and 120 cm, but cultivated forms of bitter L. albus and L. mutabilis may exceed 180 cm. Excessive plant height is hazardous because in fertile soils and with good water supply, pods become heavy far-off the soil surface and make the plant susceptible to lodging. Cultivars of L. angustifolius are commonly in the range of 50–90 cm height, and most cultivars of L. luteus are shorter but still susceptible to lodging. The number of leaf primordia on the main stem and other vegetative apices decreases as sowing is delayed; therefore, autumn-­ sown L. albus produces on average more leaves than spring-sown ones (Huyghe, 1997), with further variation associated with genotype and growing conditions (Cowling et  al., 1998a). Because of the greater number of leaves on the mainstem (MS), autumn-sown lupins maintain a characteristic rosette through the winter. Nevertheless, early sowings may induce a large number of MS nodes and, thus, excessively tall MSs, more prone to lodging. In contrast, early spring-sown L. angustifolius retards flowering by several days and reduces plant height (Kurlovich, 2002), which reduces risk of lodging in highly productive environments. The four cultivated species have soft palmate compound leaves, most of them with 5–12 leaflets. Seeds of L. albus are the largest among the cultivated species, usually ranging 280–400 mg; however, cultivated bitter L. albus genotypes may

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exceed 1000 mg (Mera and Galdames, 2007). Seed weight range is 150–190 mg in L. angustifolius and 110–150 mg in L. luteus. The range for L. mutabilis is large, but most types are between 200 and 300 mg (Gross, 1982).

2.1  Crop development The life cycle of lupin can be divided into three phases: vegetative, floral, and pod and seed growth (Fig. 14.2). Temperature influences the duration of phenological stages. For germination, base, optimum, and maximum temperatures of L. angustifolius are 1.3°C, 25.7°C, and 35.4°C, respectively (Tribouillois et al., 2016). During the vegetative phase, leaves, roots, main stem, and branches grow actively. Leaf initiation and emergence on the main stem of L. angustifolius were at the rate of 25.6°Cd per primordium (plastochron; base temperature 0°C) and 34.5°Cd per leaf (phyllochron), respectively (Dracup and Kirby, 1993). The maximum area of leaves increases from the lower to the upper part of the main stem and in the first-order branches (Dracup and Kirby, 1993). The length of the main stem and branches (first-, second-, and third-order; Fig. 14.3) increases almost linearly with thermal time (up to 1050°Cd); the stem length is maximum for the main stem and decreases for branches of different orders (Dracup and Kirby, 1993). The floral phase starts with flower initiation, that is when the flower spike bud of the main stem is clearly separated from the base of the highest leaf and the spike bud starts producing flowers. Each flower spike is made up of many individual flowers. Lupin flowers are similar to those of other species of the Fabaceae family; the corollas have five sepals (fused to form the calyx), five petals, five stamens, and a single carpel. The number of flowers per flower spike is highly variable in the cultivated species and depends mostly on branching position and growing conditions. The number of days from emergence to flowering is genetically controlled but strongly modulated by temperature, and photoperiod. For instance, wild population of L. angustifolius collected across the Mediterranean basin showed large genetic differentiation and differences in flowering time resulting from climatic adaptation (Mousavi-Derazmahalleh et al., 2018). Time to flowering is reduced when plants are exposed to increasing photoperiod (from 10 to 16 h) in both L. angustifolius (Reader et al., 1995) and L. albus (Christiansen and Jornsgard, 2002), indicating that these two species have long-day response. Indeed, a model incorporating average temperature and photoperiod explained up to 83% of the variance of time to flowering in some cultivars of L. angustifolius (Reader et al., 1995). But, there is little information on genotypic variation in the response of flowering to photoperiod. Vernalisation, the induction of flower initiation by the exposure to cold environment, also influences the flowering time of lupins. The response of plants with two to three fully developed leaves to 0–4 days at 6°C in L. albus L. (6 genotypes),

FIG. 14.2  Lupin growth cycle indicating the vegetative, floral, and pod and seed phases. The main growth stages are: germination (G), leaf emergence, floral initiation, anthesis (A, opening the first flower), pod ripening (pod density), seed ripening, and physiological maturity (PM). The decimal code scale is indicated in bracket. More detail about the growth stages of lupin is in Walker et al. (2011). The horizontal arrows represent the period of crop response to temperature (orange), photoperiod (yellow), and vernalisation (blue). The sketch of the lupin plants is from: http://www7.uc.cl/sw_educ/ cultivos/legumino/lupino.htm.

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L. luteus L. (7), and L. mutabilis (4) revealed that all genotypes reached flowering, but days to flowering and the number of internodes were significantly reduced in some late flowering genotypes of all three species in relation to nonvernalised plants; only one genotype of L. mutabilis failed to flower without vernalisation, indicating the obligate nature of vernalisation of this genotype (Adhikari et al., 2012). Most wild genotypes of L. angustifolius are late flowering and require a vernalisation period to promote flowering, but modern Australian cultivars have a dominant, early flowering gene Ku that removed the vernalisation requirement (Gladstones and Hill, 1969; Boersma et al., 2007). The Ku allele is a flowering locus T (FT) homologue, named LanFTc1a, that has a deletion in the promoter region causing the loss of vernalisation response (Nelson et al., 2017). This Ku mutation has allowed to expand the growing zone of lupins to warmer areas or to environments with short growing seasons. Other early flowering genotypes of L. angustifolius contain the efl gene that reduced but did not eliminate the vernalisation requirements (Taylor et al., 2020). More information about the phenotypic and genetic variation for flowering time in lupins is found in Taylor et al. (2020). In field conditions, sowing time affects days to flowering and maturity due to the influence of temperature, photoperiod, and vernalisation on plant development (Reader et al., 1995; Keeve et al., 2000). For instance, in five cultivars of L. albus established under irrigated conditions at Potchefstroom, South Africa, time flowering and maturity were strongly reduced (up to 50 and 100 days, respectively) as the sowing was delayed from April to November (Keeve et al., 2000). Similarly, for L. angustifolius in New South Whales, Australia, time to flowering was shortened with delayed sowing from May to late June (Walker et al., 2011). Therefore agronomists look for location-specific combinations of sowing date and cultivar to ensure flowering, and the pod-fill period captured the trade-off between risk of frost, heat, and drought; see for example Chapter 17: Canola, Section 2.3. The pod and seed growth phase stars at pod set, when a fertilised ovary stars to grow into a pod, and end up at PM. Pods develop first on the primary flower spike and then on lateral flower spikes. Seed growth starts after the pod has reached its maximum length and width. The pod wall acts as a storage organ and can supply assimilates to seed filling (Dracup and Kirby, 1996b). The growth of individual seeds follows a sigmoidal curve, and PM corresponds to maximum seed dry weight. The rate of seed growth and the duration of the grain filling period are sensitive to air temperature and soil water availability (Downes and Gladstones, 1984; Dracup and Kirby, 1996b). In L. angustifolius, temperatures above the optimum (~  25°C) after flowering reduced the time from flowering to maturity and the seed number and seed weight (Downes and Gladstones, 1984).

2.2  Branching patterns In contrast to other legumes that produce flower buds on leaf axils, lupins produce terminal inflorescences on the main stem and branches (Fig. 14.3). Cultivated lupin is ‘indeterminate’ with vegetative development after the onset of the reproductive stage (Fig. 14.4). Floral initiation (FI) on the MS releases its apical dominance and triggers the activation of nodes immediately below, at the base of the upper leaves of the MS. First-order or primary branches develop from these nodes, usually three (Fig. 14.3). In turn, each branch produces a terminal inflorescence, and the release of apical dominance allows for second-order branches in the nodes immediately below the inflorescence. Following this hierarchy, third- and fourth-order branches may be produced, with fewer flowers in higher-order inflorescences. Pods that contribute to grain yield are borne on the terminal racemes of the main stem, first-order, and second-order branches and rarely on third-order branches. In ‘determinate’ types, all vegetative buds become floral at a given time of the reproductive stage (Fig.  14.3). This habit reduces or cancels the overlapping between vegetative and reproductive phases. When the vegetative phase ceases completely after FI, lupins form only an inflorescence on the main stem, fully restricting the development of apical branching. Fully restricted genotypes have no branch in the axil of any leaf on the main shoot (Dracup and Kirby, 1996a). This nonbranching habit has been occasionally called ‘epigonal’ (Mikolajczyk et  al., 1984). The restriction of branching or ‘determinacy’ is genetically controlled, and recessive genes responsible have been identified in the four cultivated species (Huyghe, 1998), but the environment plays a role. Even when the main stem bears the vast majority of the pods in fully restricted branching types, some underdeveloped first-order branches may develop, yet with little or no contribution to yield. Nevertheless, in some lupin genotypes, vegetative growth is precluded not only immediately after the flowering in the main stem but also when this occurs after the flowering of first-order branches and even after the flowering of secondorder branches. As a consequence, the main stem and one or two branch orders bear pods, and the terms ‘determinate’ and ‘semideterminate’ have been used to describe these plants (Milford et al., 1993). Polish plant breeders have used the term ‘self-completing’ to describe highly restricted branching types in the three cultivated Mediterranean species (Cowling et al., 1998a). Clearly, the terminology for branching patterns is confusing. After attempting to clarify the terms, this chapter maintains the terms used by the authors to avoid misinterpretations.

Lupin Chapter | 14  435

FIG. 14.3  Plant architecture of lupin. Branches arising from the main stem were referred to as first order of primary branches (u, u1, etc.); branches that developed on the first-order branches are named second-order or secondary branches (u/u, u/u-1, etc.); and similarly for the third-order or tertiary branches (u/u/u, u/u/u1, etc.). Modified from Dracup, M., Kirby, E.J.M., 1996a. Lupin Development Guide. University of Western Australia Press, Nedlands, Western Australia.

FIG. 14.4  Five branching patterns in Lupinus albus: indeterminate, restricted branching (self-completing), fully restricted branching (epigonal), indeterminate dwarf, and restricted branching dwarf (self-completing dwarf). Inflorescences on the mainstem (M), primary (P), secondary (S), tertiary (T), basal (B), and basal primary branches are indicated. Modified from Noffsinger, S.L, Huyghe, C., van Santen, E., 2000. Analysis of grain-yield components and inflorescence levels in winter-type white lupin. Agron. J. 92, 1195–1202.

436  Crop Physiology: Case Histories for Major Crops

Recently, selecting plants with shorter MS and branches has improved the architecture of indeterminate L. mutabilis in Bolivia (Gulisano et al., 2019). Tarwi plants may show a prominent MS taller than the lateral branches, but plants with shorter MSs, a more desirable trait, are relatively easily found (Gulisano et al., 2019). Another architectural trait of lupins is basal branching, whereby branches arise from the axils of the lowest leaves of the MS (Fig. 14.4). All four cultivated species feature some degree of basal branching, particularly L. angustifolius and L. luteus, whereas this trait may be absent in some genotypes of L. albus and L. mutabilis (Falconí, 2012). Autumn-sown L. albus Chilean cv. ‘Alboroto-INIA’ is indeterminate with compact architecture and lacks MS basal branching, except for occasional branching in border-row plants (Mera et al., 2015). In contrast, L. albus cv. ‘Rumbo-Baer’ develops one or two basal branches even in normally populated stands. In compensation for the absence of basal branches, ‘Alboroto-INIA’ develops four and even five first-order lateral branches, whereas the average of most genotypes is three. The absence of basal branching is thought to allow a more efficient use of assimilates (Noffsinger et al., 2000). Anyhow, the contribution of basal branching to yield is low (Harzic et al., 1996).

2.3  Use of restricted branching in lupin breeding Restricted branching is sought for two main reasons: to reduce the competition for assimilates between vegetative and reproductive growth and to reduce the life cycle for adaptation to areas with short growing season. Spontaneous and induced mutants have been found that restrict branching in all cultivated lupin species (Cowling et al., 1998a), and cultivars with some degree of determinacy have been released in several countries (Kurlovich and Kartuzova, 2002). The recessive gene epI (Mikolajczyk et al., 1984), that confers a nonbranching type, has been used extensively in L. albus. For instance, in the early maturing Russian L. albus cv. ‘Deter 1’, pods are set only on the MS (Gataulina et al., 2008), and the Chilean winter cultivars ‘Typtop’ that is fully determinate and ‘Pecosa’, with short primary branches and early maturing (von Baer et al., 2009). Likewise, partial determinacy to restrict branching has been used to develop early cultivars of L. ­albus that can mature in the central region of the European part of Russia; cv. ‘Gamma’, for example, forms short primary branches and sets 89% of the pods on the MS (Gataulina et al., 2008). The degree of determinacy in L. albus is influenced by the environment and genotype-by-environment interactions, but its heritability is high (Julier et al., 1995). Determinate types are crucial for the adaptation of spring L. albus (Tyutyunov et al., 2011) and L. angustifolius (Kurlovich et al., 2011) to the short summer in northern Europe. Also, L. angustifolius cultivars with restricted branching and higher rates of MS pod set were pursued in the breeding programme of Western Australia, where plants were exposed to a severe terminal drought and high temperature (Cowling et al., 1998a; Atkins, 2004). The reduced branching was attributed to a single, incompletely dominant gene (Gladstones, 1994), but other dominant and recessive genes have been found (Cowling et al., 1998a). The first Australian restricted branching cv. ‘Tallerack’ was released in 1977 (Cowling et al., 1998a). However, the need for genetic options to restrict branching in L. angustiflius has been questioned because this trait has not contributed to improve grain yield for two reasons (Palta et al., 2008). First, branches would become autonomous in their carbon economy early on in development. Second, terminal drought in Western Australia reduces branch development per se (Palta et al., 2008). In Germany, branching cultivars out yielded nonbranching cultivars on most soil types (Gresta et al., 2017). Nevertheless, nonbranching implies early ripening, an important trait in Germany and particularly further north. Even when up to five reproductive modules are observed in some cultivars under favourable growing conditions, normally only the lower three levels bear pods that add to grain yield and just the lower two modules (main stem and primary branches) when soil humidity becomes limiting or air temperature induces early senescence. Fully determinate L. luteus cultivars such as ‘Taper’ have been developed in Poland. According to Wolko et al. (2011) the gene rb, for restricted branching, discovered in Hungary, was introduced in several self-completing yellow lupins including cultivars ‘Manru’, ‘Borselfa’, ‘Radames’, ‘Markiz’, and ‘Legat’. When is branching restriction required and to what degree are issues that most lupin breeders have had to deal with during the last decades. Restricted branching is considered an essential trait to shorten the growing period and take advantage of the opportunity for crop production in northern Europe with climate change. Fully determinate lupins may be desirable for earliness in Mediterranean environments with severe terminal drought, at the expense of biomass and yield potential in comparison with indeterminate types. The determinate architecture is supposed to allow the separation of vegetative and reproductive growth resulting in more stable yields (Julier et al., 1993) but not necessarily better yields. In indeterminate types, the successive orders of branching decrease in size, vigour, and productivity (Dracup and Kirby, 1993). Nevertheless, indeterminate growth allows a compensation among the production of main stem and branches that stabilise yield when deficient plant density and adverse

Lupin Chapter | 14  437

(a)

(b)

FIG. 14.5  (a) Proportion of pod number, seed number, and seed mass on MS, primary, secondary, and tertiary branches in L. albus cv. Alboroto-INIA grown in the temperate region of southern Chile in 2016–17 cropping season. (b) Proportion of seed mass produced by L. albus cvs. Alboroto-INIA and Rumbo-Baer on four productive modules at two sowing times in four locations in the temperate region of southern Chile during 2016–17 cropping season. Error bars are two standard errors. Data from Mera, Mario (unpublished).

climatic conditions affect pod set. Low temperatures or occasional frosts during FI may cause flower abortion, abscission of young pods, and reduce grain production on the MS. In Poland, traditional white lupins cultivars out yield determinate or self-completing ones (Borowska et al., 2015). In Polish trials with good water supply, traditional cultivars of yellow lupin out yield self-completing ones (Borowska et al., 2015) and the indeterminate cv. ‘Mister’ out yielded the determinate cv. ‘Perkoz’ (Szymańska et al., 2017). The distribution of yield on the MS and branches vary with genotype (Julier et al., 1995), temperature and water availability during pod set. In southern Chile, where high rainfall and soils with high water-holding capacity buffer dry spells between rainfall events during pod set and seed filling, final yield of L. albus is concentrated in the primary branches (Fig. 14.5). High-yielding cultivars of L. angustifolius released in Australia have had higher production on the branches than on the main stem (Palta et al., 2008). In L. luteus, the yield usually depends more on the MS pods; indeed, this species develops a high number of pods on the main stem (17) when compared with L. mutabilis (5), L. albus (6), and L. angustifolius (10) (Buirchell and Cowling, 1998). Mean seed weight on the MS and primary branches is similar and normally higher than that on secondary and basal branches (Harzic et al., 1996).

2.4 Dwarfism Dwarfism shortens the internodes and reduces plant height in both determinate and indeterminate plants (Fig. 14.4). In autumn-­sown L. albus, dwarfism shortened the MS by 41% and the first-order branches by 22% (Harzic and Huyghe, 1996). In combination with determinacy, dwarfism has delivered successful architectural ideotypes in France, and several winter L. albus cultivars (‘Lumen’, ‘Magnus’, ‘Clovis’, and ‘Orus’) have been released. In indeterminate autumn-sown

438  Crop Physiology: Case Histories for Major Crops

genotypes, dwarf-types had similar intercepted radiation (Harzic and Huyghe, 1996) and improved allocation of assimilates to pods at the expense of allocation to stem when compared with nondwarf controls (Harzic et al., 1995). This disparity in allocation was small but consistent over years and did not create difference in grain yield (Huyghe, 1998). Excessive dwarfism may compromise harvest because the lowest pods may lie too close to the soil surface.

3  Growth and resources 3.1  Capture and efficiency in the use of radiation Crop dry matter derives from the photosynthesis of leaves, stems, and pods. Leaves of lupins have higher photosynthetic capacity than leaves of other grain legumes and wheat, with maximum rate of photosynthesis at light saturation (Amax) of about 30 μmol m−  2 s−  1 (Henson et al., 1989, 1990; Leport et al., 1998). Amax is positively correlated with the leaf N content per unit area (del Pozo et al., 2000; Adams et al., 2018). With high N supply (and leaf N content per unit area), lupins and chickpea seem to have higher Amax than wheat and barley (Adams et al., 2018). Before pod set, photosynthesis supports plant growth and the accumulation of water-soluble carbohydrates (WSC) in stems, roots, and leaves. During grain filling with active leaf senescence, seed growth is supported by the photosynthesis of green stems and pods and mobilisation of WSC (Carvalho et al., 2004). CG can be expressed as the product of the intercepted PAR (IPAR) by the canopy and RUE (Monteith, 1977; Russell et al., 1989): CG  IPAR  RUE

(14.1)

The IPAR is the result of the fraction of radiation intercepted (fi) and the incoming PAR (PARt), thus: IPAR  fi  PARt

(14.2)

The fraction fi depends on the space–time distribution of the leaf area index (LAI) and on an extinction coefficient (k), thus: fi    1  exp  k  LAI  

(14.3)

where α is the maximum IPAR. A wide range of maximum LAI has been reported for L. angustifolius (Table 14.1) with lower LAI (1.0–3.3) in rainfed Mediterranean environments in Australia and higher ones (3.7–6.0) in a temperate environment in Canterbury, New Zealand. The critical LAI at which the crop intercepts 90%–95% PAR have been estimated at 3.1–3.5 in Canterbury, New Zealand (Ayaz et al., 2004b). For L. albus, the maximum LAI in a Mediterranean environment (Córdoba, Spain) depended on plant density and seasonal precipitation and ranged between 1.8 and 7.5 (López-Bellido et al., 2000). The coefficient of extinction depends upon the architecture of the canopy and the transmission of radiation through individual leaves (Gallagher and Biscoe, 1978; del Pozo and Dennett, 1999) and ranged between 0.77 and 0.91 for L. angustifolius; a single study reports k = 0.99 for L. albus (Table 14.1). The crop yield (Y) is related to the shoot dry weight at harvest (W), which depends on the amount of IPAR from emergence (e) to harvest (h) and the partitioning of the plant biomass to the grain, that is the harvest index (HI = GY/W). Therefore: h

Y  HI  RUE    fi  PARt  e

(14.4)

The total shoot dry weight, total IPAR, and RUE vary widely in both L. angustifolius and L. albus depending on environmental conditions (Table 14.1). The linear relationship between total shoot dry weight and cumulative IPAR from emergence to PM returned an average RUE of 1.35 g MJ−  1 for L. angustifolius (Fig. 14.6). In Canterbury, New Zealand, total shoot dry weight, total IPAR, and RUE was higher for L. angustifolius than for chickpea (Cicer arietinum), lentil (Lens culinaris), and peas (Pisum sativum) (Ayaz et al., 2004b). In general, RUE, biomass, and yield of grain legumes are lower than in C3 cereals (Sadras et al., 2016a; e.g. Chapter 8: Soybean, Section 3.1; Chapter 15: Faba bean, Section 3.1.4). Berger et al. (2012) reported a linear regression between shoot dry weight and seed yield of L. angustifolius across environments in Australia from 1997 and 2007, indicating that lupin productivity was in most cases limited by biomass production. Significant differences on the slope of the relationships were found among cultivars suggesting different HI in

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TABLE 14.1  Ranges of maximum (LAImax) and critical leaf area index (LAIcritical), extinction coefficient (k), the maximum fraction of radiation intercepted (fi max), total intercepted photosynthetic active radiation, shoot dry weight, grain yield, radiation use efficiency, and harvest index for L. angustifolius and L. albus. L. angustifoliusa

L. albusb

Range

N

Range

N

LAI max

1.0–6.1

17

0.9–7.5

14

LAI critical

3.1–3.5

7

–

–

k

0.77–0.91

10

0.99

2

0.32–0.98

17

–

–

531–1042

11

–

–

207–1600

33

170–2308

34

0.69–1.49

15

0.81–1.54

16

Grain yield (t ha )

0.40–6.50

32

0.80–5.37

35

Harvest index

0.16–0.59

16

0.15–0.50

24

fi max −  2

Total IPAR (MJ m ) −  2

Total shoot dry weight (g m ) −  1

RUE (g MJ ) −  1

a

Data from Gregory and Eastham (1996), Thomson and Siddique (1997), Ayaz et al. (2004a,b), Kang et al. (2008), Espinoza et al. (2012), Sulas et al. (2016), and Denton et al. (2017).

b

Data from Thomson and Siddique (1997), López-Bellido et al. (2000), Shield et al. (2002), and Espinoza et al. (2012).

FIG. 14.6  Relationship between the total shoot dry weight and total intercepted photosynthetic active radiation for L. angustifolius in different environments and growing conditions. Data from Gregory, P.J., Eastham, J., 1996. Growth of shoots and roots, and interception of radiation by wheat and lupin crops on a shallow, duplex soil in response to time of sowing. Aust. J. Agric. Res. 47(3), 427. https://doi.org/10.1071/ar9960427; Ayaz, S., McKenzie, B. A., Hill, G. D., McNeil, D.L., 2004a. Variability in yield of four grain legume species in a subhumid temperate environment I. Yields and harvest index. J. Agric. Sci. 142(1), 9–19. https://doi.org/10.1017/s0021859604004101; Ayaz, S., McKenzie, B.A., McNeil, D.L., Hill, G.D., 2004b. Light interception and utilization of four grain legumes sown at different plant populations and depths. J. Agric. Sci. 142(3), 297–308. https://doi.org/10.1017/ s0021859604004241; Kang, S., McKenzie, B.A., Hill, G.D., 2008. Effect of irrigation on growth and yield of Kabuli chickpea (Cicer arietinum L.) and narrow-leafed lupin (Lupinus angustifolius L.). Agronomy 38, 11–32.

L. angustifolius (Berger et al., 2012). Comparison between L. angustifolius and L. albus indicates that the shoot dry weight could be higher in L. albus but not the grain yield (Table 14.1; Fig. 14.7). The relationship between shoot dry weight and grain yield had a higher slope in L. angustifolius than in L. albus (Fig. 14.7). Indeed, both species feature large variability for shoot dry weight, grain yield and HI depending on the environmental conditions: lower in rainfed Mediterranean and higher in temperate climate (Table 14.1). The highest reported HI (0.59) and grain yield (6.5 Mg ha−  1) was for L. angustifolius at 400 plants m−  2 in Canterbury, New Zealand (Ayaz et al., 2004a). In L. albus in Córdoba, Spain, the HI was higher in 20 plants m−  2 when compared with 40–60 plants m−  2 and ranged between 0.24 and 0.50 in four growing seasons (LópezBellido et al., 2000). Under controlled conditions, the HI in L. angustifolius was reduced at higher temperatures (30/25°C, 33/28°C, and 36/31°C day/night) after flowering (Downes and Gladstones, 1984).

440  Crop Physiology: Case Histories for Major Crops

FIG. 14.7  Relationship between total shoot dry matter and grain yield in L. angustifolius and L. albus. Data are from references indicated in Table 14.1. The linear regression (in red) obtained for L. albus in experiments conducted in 25 farmers’ fields in Pays de la Loire is included for comparison (Cheriere, 2016).

3.2  Capture and efficiency of use of water Grain yield depends on crop transpiration, that is the water use (WU), the WU efficiency (WUE, biomass per unit transpiration) and HI (Passioura, 2006): Y  WU  WUE  HI

(14.5)

In dryland Mediterranean environments, where precipitation decreases and reference evapotranspiration increases during spring, lupins are exposed to terminal drought that reduces the leaf water potential (Ψleaf), shoot dry weight, HI, and grain yield (Palta et al., 2004; Carvalho et al., 2004; Berger and Ludwig, 2014). In controlled conditions, terminal drought reduced plant WU exponentially over time in both wild and domesticated accessions of L. luteus and L. angustifolius collected in Mediterranean environments (Berger and Ludwig, 2014; Berger et al., 2020). The evaluation of 21 landraces and 3 cultivars of L. albus in an outdoor phenotyping platform showed that severe drought stress reduced grain yield by 79% (Annicchiarico et al., 2018); two landraces from Jordan and Algeria presented the highest grain yield under drought and both exhibited high HI under drought and irrigated conditions. Root distribution, architecture, and functioning influence WU, but these traits are difficult to phenotype in field conditions. A glasshouse study conducted in large plastic grow bags showed that root distribution and architecture vary widely between species; for instance, roots of L. angustifolius presented a dominant taproot with primary lateral roots concentrated in the upper part and few secondary roots, whereas L. mutabilis, L. pilosus, L. atlanticus, and L. palaestinus had a more developed lateral root system with primary, secondary, and tertiary roots (Clements et al., 1993). Large variation for root traits (especially total root length, branch length, and branch number) has been found among 111 wild genotypes of L. ­angustifolius in a semihydroponic setting (Chen et al., 2012). L. albus had a deep root system dominated by a taproot with lateral root branches that were more extensive and thinner than in L. angustiflolius. It also develops cluster roots that are short lateral roots (brush-like root formations) that increase the root surface area. Physiological traits related to water uptake and plant water status include relative leaf water content (RWC), Ψleaf, osmotic adjustment (OA), stomatal conductance (gs), and carbon (δ13C) isotope composition. The gs of lupin species (L. angustifolius, L. albu, and L. luteus) is very sensitive to drying soil and Ψleaf (Palta et al., 2012). Glasshouse experiments with potted plants showed that leaf photosynthesis (An) and gs were more sensitive to water deficit in L. cosentinii than those in wheat; the Ψleaf for stomatal closure was −  0.85 to −  0.90 MPa in L. cosentinii and −  1.4 and −  1.5 MPa in wheat (Henson et al., 1989). In L. albus, gs was more sensitive to water deficit during flowering than RWC or Ψleaf; indeed, gs began to decline after a small reduction in the available soil water (ASW), whereas RWC and Ψleaf started to decrease at 50% and 60% of the ASW, respectively (Rodrigues et al., 1995). In L. luteus, the response of RWC and Ψleaf to ASW simulating drought after pod set in a glasshouse revealed more sensitivity in higher-rainfall ecotypes than in low-rainfall ecotypes (Berger and

Lupin Chapter | 14  441

Ludwig, 2014). Also, the response of RWC and Ψleaf to the progressive decline in ASW was lower in L. angustifolius than in L. luteus (Berger et al., 2020). OA occurs when solutes accumulate actively within the plant in response to water stress; genotypic variation in OA has been reported in many species (Turner, 2018). OA has two major functions in water-limited conditions: (a) it enables leaf turgor maintenance, thus supporting stomatal conductance under lower leaf water potential (Ali et al., 1999) and (b) it improves root capacity for water uptake (Chimenti et al., 2006; Chapter 16: Sunflower, Section 3.2.3). L. angustifolius, L. cosentinii, and L. luteus showed low OA (0.0–0.23 MPa) in response to drought (Turner et al., 1987; Palta et al., 2007) in comparison with L. pilosus and L. atlanticus (Palta et al., 2012). The OA of L. albus was lower when compared with other grain legumes, such as lentil, chickpea, or field peas (Leport et al., 1998). Carbon isotope composition (δ13C) can be used as a selection criterion for high WU efficiency (Condon et al., 2004) and provides an indirect determination of the effective water used by the crop (Blum, 2009). In L. angustifolius, drought stress reduced the photosynthetic rate, gs and the ratio of intercellular to atmospheric CO2 concentration (Ci/Ca), but the δ13C of phloem sap was less negative when compared with fully irrigated plants (Merchant, 2012). In L. angustifolius, δ13C varied between plant parts; it ranged from −  27.7‰ in leaves to −  24.5‰ in seeds (Cernusak et al., 2002). Genotypic differences in δ13C have been reported for various crop species under Mediterranean conditions (Araus et al., 2013 in durum wheat; del Pozo et al., 2016 in bread wheat; Sadras et al., 2016b in chickpea) and also among 10 cultivars of Lupinus angustifolius (ranging between −  22.7‰ and −  21.1‰) under well-watered conditions in a glasshouse (Turner et al., 2007). No information exists for other Lupinus species. Ecophysiological studies of landraces and ecotypes of L. albus (Annicchiarico et al., 2018), L. angustifolius (Berger et al., 2020), and L. luteus (Berger et al., 2008; Berger and Ludwig, 2014) collected in Mediterranean environments indicated that early flowering is an important mechanism to escape water stress. Ecotypes from environments with low rainfall and stronger terminal drought exhibited lower leaf node number, shoot dry weight, and WU but higher root:shoot ratio when compared with those from higher rainfall environments (Berger et al., 2008, 2020; Berger and Ludwig, 2014). Important genetic variability exists for WU and HI in lupin, that can be exploited by breeding programmes to improve adaptation to terminal drought.

3.3  Capture and efficiency of use of nutrients 3.3.1 Nitrogen Lupin species fix atmospheric N2 in symbiotic association with Bradyrhizobium lupini. The bacteria penetrate the cells at the junction between the root hair base and an adjacent epidermal cell, causing an infection that becomes a nodule. After inoculation, the infected cells divide to form the central infected zone of the young nodule. The rate of N fixation depends on factors such as the presence and effectiveness of rhizobia, soil attributes including pH, content of mineral N, availability of other nutrients (P, K, and Mo), temperature, and water content (Chalk et al., 2010; Denton et al., 2017). The percentage of the legume N derived from atmospheric N2 (%Ndfa) can be determined by comparing the 15N natural abundance (expressed as δ15N or parts per thousand [‰] relative to the 15N composition of atmospheric N2) of the legume (δ15Nleg) with that of nonlegume reference plant (δ15Nref), using the following equation (Köhl and Shearer, 1980):



 

%Ndfa  100  [  15 N ref   15 N leg /  15 N ref   

(14.6)

where β represents the 15N abundance of the legume plant totally reliant upon N2 fixation for growth; the reference plant is used to provide a measure of the 15N abundance of plant-available soil N (Unkovich et al., 2008). The %Ndfa and the amount of shoot N fixed for L. angustifolius, L. albus, and L. luteous showed large variability depending on the inoculation method and the environmental conditions (Table 14.2; Unkovich et al., 2010). The highest amount of N fixed and shoot N reported in the literature were 225–287 kg N ha−  1 for L. angustifolius and 359–375 kg N ha−  1 for L. albus (Table 14.2). There is a close relationship between shoot dry weight and the amounts of nitrogen fixed by L. angustifolius and L. albus across environments and management; for both species, around 21 kg N was fixed for every tonne of shoot dry weight produced by the legume (Fig. 14.8). This slope is higher than that was previously reported (14.2 kg N t−  1) for L. angustifolius by Unkovich et al. (2010). However, data of shoot N fixed do not include the N associated with roots; thus, the relationship shown in Fig. 14.8 would underestimate the contribution of lupin N fixation to the production system. Considering the percentage of total N in roots and the shoot:root N ratios, Unkovich et al. (2010) calculated a ‘root factor’ for grain and annual pasture legumes, to estimate the total N (shoot N + root N); in the case of lupins, the root factor was 1.33, 1.71, and 1.45, for L. angustifolius,

442  Crop Physiology: Case Histories for Major Crops

TABLE 14.2  Range of percentage of legume N derived from air (%Ndfa), amount of shoot N fixed, and shoot N in L. angustifolius and L. albus, reported in the literature. L. angustifoliusa

L. albusb

L. luteusc

Range

N

Range

N

Range

N

%Ndfa

15–87

17

45–95

8

70–97

2

Shoot N fixed (kg N ha−  1)

16–225

17

63–359

8

103–152

2

Shoot N (kg ha−  1)

54–287

17

53–375

8

147–157

2

a

Data from Espinoza et al. (2012) and Denton et al. (2017).

b

Data from Espinoza et al. (2012) and Sulas et al. (2016).

c

Data from Espinoza et al. (2012).

FIG. 14.8  Relationships between shoot dry weight and amounts of shoot N fixed by L. angustifolius and L. albus. Data from Espinoza, S., Ovalle, C., Zagal, E., Matus, I., Tay, J., Peoples, M.B., del Pozo, A., 2012. Contribution of legumes to wheat productivity in Mediterranean environments of central Chile. Field Crop Res. 133, 150–159. https://doi.org/10.1016/j.fcr.2012.03.006; Sulas, L., Canu, S., Ledda, L., Carroni, A.M., Salis, M., 2016. Yield and nitrogen fixation potential from white lupine grown in rainfed Mediterranean environments. Sci. Agric. 73(4), 338–346. https://doi.org/10.1590/0103-9016-2015-0299; Denton, M.D., Phillips, L.A., Peoples, M.B., Pearce, D.J., Swan, A.D., Mele, P.M., Brockwell, J., 2017. Legume inoculant application methods: effects on nodulation patterns, nitrogen fixation, crop growth and yield in narrow-leaf lupin and faba bean. Plant Soil 419(1–2), 25–39. https://doi.org/10.1007/s11104-017-3317-7.

L. albus, and L. luteus, respectively. This means that total N input by N fixation of lupins is much higher than that was calculated in Fig. 14.8, particularly in L. albus, highlighting the importance of lupin crops as a source of N in farming systems. The number of root nodules, N fixation rates, and %Ndfa are sensitive to any form of N in the soil (Streeter, 1988; Pampana et al., 2016; Guinet et al., 2018). In L. albus, the rate of N fertiliser from 0 to 160 kg ha−  1 (applied as urea) reduced nodule biomass at a linear rate of 30 mg m−  2kg−  1 of applied N and reduced N fixed at 60 mg N m−  2kg−  1 of applied N (Pampana et al., 2016). The rate of N fertiliser from 0 to 300 kg ha−  1 (applied as NH4NO3 at sowing) reduced linearly the %Ndfa in L. albus (Guinet et al., 2018). In both studies (Pampana et al., 2016; Guinet et al., 2018), the reduction in N fixed in lupins was smaller than that for other grain legumes, such as lentil (Lens culinaris), faba bean (Vicia faba), or pea (Pisum sativum), revealing that the N fixation in L. albus is less sensitive to soil N availability.

3.3.2 Phosphorus Lupins are well adapted to low soil fertility, particularly N and P. A special adaptation for P deficiency is the formation of proteoid (cluster) roots that are not only common in members of the Proteaceae family but also in some Lupinus species, such as L. albus and L. cosentinii, but not in L. angustifolius (Lambers et al., 2013; Uhde-Stone, 2017). Comparison of the response to P deficiency between L. albus and L. angustifolius in hydroponic culture revealed different strategies for P acquisition; the former invested in production of cluster roots, whereas L. angustifolius allocated more biomass to roots (Funayama-Noguchi et al., 2015). The cluster root formation was induced by P deficiency and inhibited at high P availability (> 50 μM) in the nutrient solution (Neumann, 2000; Abdolzadeh et al., 2010; Ma et al., 2011; Thuynsma et al., 2014).

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This specialised root structure of L. albus releases large quantities of carboxylate acids such as malate and citrate, as well and acid phosphatase, increasing the availability of P in the rhizosphere (and also of iron, manganese, and zinc) and P uptake by the plant (Lambers et al., 2013). The exudation of carboxylates is induced by both P deficiency and aluminium toxicity (Neumann, 2000; Wang et al., 2007). Experiments using a split-root system demonstrated that cluster root formation is induced by low P concentration in the shoot (Shen et al., 2005). The release of carboxylates, which are organic anions, is accompanied by the release of protons and other cations, leading to a strong acidification of the rhizosphere (Neumann, 2000; Lambers et al., 2013). The carboxylate, and citrate in particular, in the rhizosphere increases the availability of P to plants by mobilising inorganic and organic P bound to cationic sorption sites in the soil particles. Phosphatases hydrolyse organic P compounds, once these have been mobilised by carboxylates (Lambers et al., 2013). More information about cluster root formation and functioning of L. albus can be found in two comprehensive reviews (Lambers et al., 2013; Uhde-Stone, 2017). Although L. angustifolius, L. luteus, and L. mutabilis do not form cluster roots, they are able to release large quantities of carboxylates (mainly citrate but also malate) to the rhizosphere, especially under P deficiency (Pearse et al., 2006). The above events explain why lupin species usually exhibit higher productivity under P deficiency and lower sensitive to P supply than other grain legumes and temperate cereals (Bolland and Brennan, 2008; Sandaña and Pinochet, 2016). The responses of L. albus, L. atlanticus, and L. micranthus to four P concentrations (1, 10, 50, and 150 mM) in hydroponic culture showed that the plant growth rate and P uptake increased up to 10 and 50 mM, respectively; the P concentration in leaf, stem, and roots increased up to 50 mM (Abdolzadeh et al., 2010). The responses of L. angustifolius, wheat and oilseed rape to P supply (17 levels from 0 to 1.2 g P pot−  1) were studied in a pot experiment (Bolland and Brennan, 2008); the relationship between shoot dry weight and P concentration in shoots followed a rescaled exponential (Mitscherlich) equation, but the parameters were different in the three species. The P concentration to reach 90% of the maximum shoot DW was lower in lupin than in wheat but higher when compared with oilseed rape. The P uptake (mg P pot−  1) at maturity, across the whole range of P supply, was similar in L. angustifolius and wheat but lower than in oilseed rape, indicating similar P acquisition efficiency (kg P uptake kg−  1 P available) of lupin and wheat. Sandaña and Pinochet (2016), compared the P response of L. angustifolius, peas and wheat growing in glass-walled boxes at two P supplies (0 and 50 mg P kg−  1 soil) and showed that the P utilisation efficiency (kg dry weight kg−  1 P uptake) was higher in wheat than in lupin (and pea) at high P supply but not at low P supply. However, the P uptake per unit of root length was much higher in lupin at both P supplies (Sandaña and Pinochet, 2016).

4  Yield and quality 4.1 Yield The yield of lupin species varies with environmental conditions and agronomical practices (Table 14.1). Comparison of Mediterranean cultivated species in temperate environments of southern Chile showed a yield ranking L. albus > L. angustifolius > L. luteus (Table 14.3). In Italy, L. albus out yielded L. angustifolius except on late-winter sowing (Annicchiarico and Carroni, 2009). Despite the recognised value of its protein content, the low yield of L. luteus is a major drawback and is the reason for discontinued breeding in Germany and Denmark; breeding in this species is now limited to Poland (Gresta et al., 2017). The gap between experimental and farmer yields of lupins is significant. For example, the yield of L. mutabilis in experimental plots was 3–5 t ha−  1 (Cowling et al., 1998b) when compared with 0.13–0.22 t ha−  1 in small-holding farms in the Andes (Falconí, 2012). Achieving a lupin field with proper and uniform plant population and relatively free of weeds is not easy, whereas these factors can be easily managed in trials. During the last years, the dry seasons have been more frequent in Chile (Garreaud et al., 2017) and yields have decreased, but yields of 1.8 t ha−  1 are achieved even in the more affected areas.

4.2  Yield components We have analysed yield in terms of biomass and HI (Section 3.1) and with a focus on MS and branches (Section 2.2). Here, we focus on the yield of lupins as the product of three components: number of pods per unit square metre pod density (PD), number of seeds per pod (NSP), and seed weight (SW). PD depends on plant density and the number of pods per plant. A plant density experiment with L. albus during four growing seasons in Cordoba, Spain, showed that pods per plant and PD were the most sensitive and variable yield component, with an average of 5.2 pods per plant and 289 pods m−  2 at 60 plants m−  2 when compared with 9.7 pods per plant and 197 pods m−  2 at 20 plants m−  2 with a correlation of 0.78 between pod

444  Crop Physiology: Case Histories for Major Crops

TABLE 14.3  Grain and protein yield of the best cultivar within three species of Mediterranean lupins established in autumn (A) and winter (W), in a temperate environment in southern Chile, in 2015 and 2016. Grain yield (t ha−  1) 2015

Protein yield (t ha−  1) 2016

2015

2016

Best cultivar

A

W

A

W

A

W

A

W

L. albus indeterminate

4.83

3.64

6.22

5.65

1.55

1.06

1.87

1.70

L. albus determ. dwarf

3.91

2.60

4.67

4.03

1.04

0.66

1.24

1.10

L. angustifolius

–

–

3.97

4.28

–

–

1.03

1.04

L. luteus

2.13

2.21

3.43

3.00

0.75

0.77

1.21

1.10

Source: Mera, M., Alcalde, J.M., 2019. Lupinus albus is the species that achieves greatest grain and protein yields in Chile. In: Developing Lupin Crop Into a Modern and Sustainable Food and Feed Source, 44 Abstracts Book of the 15th International Lupin Conference, Cochabamba, Bolivia, 18–21 March 2019. Fundación PROINPA, Cochabamba, Bolivia.

density and grain yield (López-Bellido et al., 2000). At high plant density, the number of branches is reduced and plants produce fewer pods per plant and on the main stem. NSP and SW vary less (3.1–3.8 and 404–572 mg, respectively) across plant densities and growing seasons (LópezBellido et al., 2000). Among 59 accessions of L. angustifolius collected in Spain, the majority produced 5–6 seeds per pod, and only few had 3–4 larger seeds (Lema et al., 2005). In 55 accessions of L. luteus from northwestern Spain, NSP ranged 3.8–5.2 (Lema and Lindner, 2010). NSP can be reduced as the number of branch order increases (Walker et al., 2011). The pod and seed growth phase are sensitive to abiotic stresses leading to pod and seed abortion and reduction in yield. For instance, heat waves of 34–36°C during seed development can reduce weight per seed and hence yields of L. angustifolius (Reader et al., 1997). Sequential 15-day shading periods showed that the most critical period for L. angustifolius was between 10 days before anthesis and 50 days after anthesis (Sandaña and Calderini, 2012); the largest reduction in PD and grain yield was attained around 20–30 days after anthesis. This critical period was similar to that of pea (Sandaña and Calderini, 2012), and other pulses (Chapter 10: Chickpea; Chapter 15: Faba bean). Severe source reduction during grain filling in L. angustifolius reduced SW and yield by 90% (Sandaña et al., 2009). Increased air CO2 from 350 to 700 ppm during pod-filling increased seed number per plant by 34%–45% and yield by 44%–66% (Palta and Ludwig, 2000). These results revealed that lupin is limited by carbon during grain filling, and the amount or rate of mobilisation of reserve WSC to the grain is limited.

4.3  Pod wall Pod wall of lupins can represent a third or more of the total pod weight that is higher than that for other grain legumes (Mera et al., 2006). A reduced pod wall proportion (PWP) may contribute to improving the HI of lupins and, thus, grain yield. Large genetic variation has been reported for the proportion of pod walls in lupins. In 325 ecotypes of L. albus from the Mediterranean region and North Africa, the proportion of pod wall ranged 0.21–0.37 (Lagunes-Espinoza et al., 2000); the genetic variance for PWP accounted for 44% of the total variance (broad-sense heritability). In 14 genotypes of L. albus grown in 8 temperate environments (4 sites and 2 years), PWP ranged 0.23–0.43 (mean 0.32), and the broad-sense heritability was 0.46 (Mera et al., 2006); PWP was negatively correlated with seed number per pod (r = −  0.61) seed weight per pod (r = −  0.58). A similar study of 14 genotypes of L. angustifolius grown in 8 temperate environments revealed that PWD ranged 0.32–0.36 with an average of 0.33 (Mera et al., 2004); the broad-sense heritability 0.27 and 0.44 for pods from MS and branches. The negative correlation between PWD and seed number and seed weight per pod (Mera et al., 2004, 2006) indicate that selecting for low PWP could lead to cultivars with higher seed number and weight per pod and therefore HI. Similar conclusions were reached for field pea (Chapter 9: Field Pea).

4.4  Grain protein Lupin grain is rich in protein and is a source of good-quality oil. Oil concentrations (dry matter basis) are 5%–6% in L. angustifolius and L. luteus, 9%–13% in L. albus and 15%–17% in L. mutabilis (Chiofalo et al., 2012). Common protein

Lupin Chapter | 14  445

contents are 34%–39% for L. albus, 28%–32% for L. angustifolius, 37%–43% for L. luteus and 38%–50% for L. mutabilis. Nevertheless, protein contents vary with the environment and, for example, means from 30.7% to 36.6% were recorded for 11 L. angustifolius cultivars in four seasons in Germany (Böhm et al., 2008). The relationship between grain yield and protein content (%) was analysed using data from field experiments shown in Table 14.3, indicating a significant and positive correlation (r = 0.41; N = 64) for L. albus but not for L. luteus (r = 0.15; N = 32) and L. angustifolius (r = 0.01; N = 30). This is different to the common negative relationship observed in wheat (Oury and Godin, 2007). The protein yield of L. albus was on average 1.55 t ha−  1, whereas in L. angustifolius and L. luteus it was 1.03 and 0.96 t ha−  1, respectively (Table 14.3). In Germany, protein yields of over 1.2 t ha−  1 have been reported for some cultivars of L. angustifolius (Böhm et al., 2008) but common yields are below 1 t ha−  1. As the seed-coat proportion in L. albus, L. angustifolius, and L. luteus is high (about 18%, 24%, and 25% of the whole seed dry weight respectively) and the protein content of the seed coat is low (3%–4%), the protein content of dehulled grain may reach 41%–45%, 38%–42%, and 50%–57%, respectively. L. mutabilis has only about 11%–13% seed coat proportion, most likely the result of selection by the ancient inhabitants of the Andes highlands. As this grain is mainly for human consumption, it is seldomly dehulled.

5  Concluding remarks: Challenges and opportunities Five branching patterns have been described for lupins, ranging from indeterminate to restricted branching, dwarf-growth habit. Cultivars of L. angustifolius with restriction of branching or ‘determinacy’, where most of the vegetative buds becoming flowers at a given time of the reproductive stage have been selected for environments with severe terminal drought; however, this grow habit could limit yield, particularly with good water supply. The advantage of restricted growth habit in temperate environments is unclear and more information is needed for both L. angustifolius and L. albus. The time to flowering is modulated by temperature, photoperiod, and vernalisation. There is little information about the genotype variability in the response of flowering to photoperiod in the four lupin species. The effect of vernalisation and the genes involved in flowering responses have been studied mainly in L. angustifolius. There is less information about the genetic regulation of flowering time in the other lupin species. The leaf photosynthetic capacity of lupin and its genetic variability have not yet been studied. The total shoot dry weight, total IPAR, and RUE vary in L. angustifolius, but the information is scarce for L. albus, L. luteus, and L. mutabilis. The relationship between the shoot dry weight and grain yield shows a higher slope in L. angustifolius than in L. albus, indicating higher HI particularly in high-yielding environments. The critical period for yield has been determined for L. angustifolius (between 10 days before anthesis and 50 days after anthesis), but there is no information for other lupin species and how the growth habit influences the critical period. Lupins fix atmospheric nitrogen through symbiotic association with Bradyrhizobium lupini. L. angustifolius and L. albus fix around 21 kg N per tonne of shoot dry weight. Proteoid (cluster) roots are developed in L. albus and L. cosentinii but not in L. angustifolius. These cluster roots release large amounts of carboxylate acids such as malate and citrate, as well and acid phosphatase, increasing the availability of P, Fe, Mn, and Zn in the rhizosphere. Most comparative studies about P responses of lupin species have been conducted under controlled conditions (pots or hydroponics in glasshouses) that cannot be extrapolated to field conditions. Abiotic stresses have an impact on yield, particularly water deficit. In Mediterranean environments, where lupins are exposed to terminal drought, early flowering is an important mechanism to escape water stress. Lupins present a rapid response of stomatal conductance to drying soil, which help to reduce water loss thought transpiration. Direct comparisons between L. angustifolius, L. albus, and L. luteus in Mediterranean and temperate environments are needed to elucidate differences in productivity, and yield responses to heat and drought during grain filling. Lupins can play a role on agricultural sustainability and are a source of plant proteins for food and feed in Mediterranean and temperate environments. Part of the N fixed by lupins is left in the soil (as crop residues, roots, and nodules) and constitutes an important input for the following crop. As a part of crop rotations, lupins may enhance the productivity of cereals and the profitability of the whole system by allowing a better control of grass weeds and breaking the cycle of some diseases.

Acknowledgement We thank Josefa del Pozo for drawing the figures of architecture and branching patterns of lupins using BioRender.

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Carbon isotope discrimination is not correlated with transpiration efficiency in three cool-season grain legumes (pulses). J. Integr. Plant Biol. 49 (10), 1478–1483. https://doi.org/10.1111/j.1672-9072.2007.00557.x. Tyutyunov, S.I., Voronin, A.N., Navalnev, V.V., Tsygutkin, A.S., 2011. A review of prospects for production and utilization of white lupin (Lupinus albus L.) in the Belgorod region of Russia. In: Naganowska, B., Kachlicki, P., Wolko, B. (Eds.), Lupin Crops—An Opportunity for Today, a Promise for the Future. Proceedings of the 13th International Lupin Conference, Poznan, Poland, 6–10 June 2011. International Lupin Association, Canterbury, New Zealand, pp. 149–151. Uhde-Stone, C., 2017. White Lupin: a model system for understanding plant adaptation to low phosphorus availability. In: Legume Nitrogen Fixation in Soils With Low Phosphorus Availability. Springer, Cham, pp. 243–280, https://doi.org/10.1007/978-3-319-55729-8_13. Unkovich, M., Herridge, D., Peoples, M.B., Cadisch, G., Boddey, B., Giller, K., Alves, B., Chalk, P., 2008. Measuring plant associated nitrogen fixation in agricultural systems. In: ACIAR Monograph No. 136. 258 p. Unkovich, M.J., Baldock, J., Peoples, M.B., 2010. Prospects and problems of simple linear models for estimating symbiotic N2 fixation by crop and pasture legumes. Plant Soil 329 (1–2), 75–89. https://doi.org/10.1007/s11104-009-0136-5. von Baer, E., von Baer, I., Riegel, R., 2009. Pecosa-Baer: a new cultivar of white lupin with determinate bushy growth habit, sweet grain and high protein content. Chilean J. Agric. Res. 69 (4), 577–580. Walker, J., Hertel, K., Parker, P., Edwards, J., 2011. Lupin Growth and Development. PROCROP Series Book, New South Wales Department of Industry & Investment, ISBN: 978 1 74256 059 5. 84 p. Wang, B.L., Shen, J.B., Zhang, W.H., Zhang, F.S., Neumann, G., 2007. Citrate exudation from white lupin induced by phosphorus deficiency differs from that induced by aluminum. New Phytol. 176 (3), 581–589. https://doi.org/10.1111/j.1469-8137.2007.02206.x. Wolko, B., Clements, J.C., Naganowska, B., Nelson, M.N., Yang, H., 2011. Lupinus. In: Kole, C. (Ed.), Wild Crops Relatives: Genomic and Breeding Resources, Legume Crops and Forages. Springer-Verlag, Berlin, pp. 153–206.

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Image source: Authors

Chapter 15

Faba bean M. Inés Míngueza,b and Diego Rubialesc a

Department of Agricultural Production, School of Agricultural Engineering, Food Technology and Biosystems, and Research Centre for the Management of Agricultural and Environmental Risks, Madrid, Spain, bTechnical University of Madrid (UPM), Madrid, Spain, cInstitute for Sustainable Agriculture, CSIC, Córdoba, Spain

1 Introduction Faba bean (Vicia faba L., Fabaceae), also known as broad bean, field bean and horse bean, is a cool-season grain legume or ‘pulse’. It belongs to the subfamily Faboideae, tribe Fabeae (Cubero, 2011). The species has been traditionally divided in four types or varietal groups: (1) major (broad beans with very flattened seeds, 1.0 to more than 2.0 g seed− 1), (2) equina (horse beans, field beans, flattened seeds between 0.6 and 1.0 g seed− 1), (3) minor (tic beans, ellipsoidal seeds 0.3–0.6 g seed− 1), and (4) paucijuga (Cubero and Nadal, 2004). The pauciga type is a rare form, likely close to the wild hypothetical form (Duc et al., 2011). It is grown mainly for seed that is characterised by high content of protein and fibre and low in fat (www.pulses.org). It is cultivated widely on all continents, although areas have diminished in recent decades and are small when compared with major crops. The crop is mainly cultivated as a staple food for humans and for animal feed. It is most commonly grown in rotations with cereals to supply N and provide disease breaks. In Europe there is a current interest, as with other grain legumes, to intercrop faba bean with cereals to reduce application of N fertiliser and assist in controlling diseases to which legume crops are prone. A very small proportion is grown as a horticultural crop and harvested for fresh seeds or pods for human consumption. Antinutritive factors that limit the value of faba grain for livestock fodder are removed by cooking before consumption by humans. Faba bean is highly valued in countries on both sides of the Mediterranean Sea. Falafel, medames and habitas con jamón are widely appreciated and nutritious dishes. Faba bean is susceptible to a number of foliar fungal diseases (e.g. Chocolate spot, Botrytis fabae; Ascochyta blight, Ascochyta fabae; Cercospora leaf spot, Cercospora zonata; Downy mildew, Peronospora viciae; Rust, Uromyces viciae-­ fabae) that may require fungicide treatment for control. It is also susceptible to the root parasitic weed, broomrape (Orobanche crenata), that deserves much attention in crop improvement programmes (Stoddard et al., 2010; Sillero et al., 2010). This chapter will address the crop grown for dry grain production, dealing with physiological bases of yield and adaptation to environment and agronomic practice, and the role of breeding and genetic diversity for crop improvement.

1.1  Origin of the crop The region of origin remains unknown, although it seems likely that domestication may have taken place in the Near East, where it has been grown for 8–10 000 years (Cubero, 1974; Ladizinski, 1975; Zohary and Hopf, 2000). It then followed different routes of human migration (Cubero and Nadal, 2004): (a) the European route crossing Anatolia, Greece, following the Danube Valley and reaching Central Europe and then the rest of the continent; (b) towards the west following the African Mediterranean coast until the Mediterranean west (Maghreb and the Iberian Peninsula); (c) extending from lower Egypt and Mesopotamia heading southward, reaching Abyssinia. Faba bean in India could have reached from Mesopotamia or from Abyssinia through the Sabean route; and (d) finally, from the Near East, crossing the Caucasus and reaching the Euroasiatic plains (Cubero, 1974). China was probably reached during the first millennium AD, as the Chinese faba landraces are only of major type. It was taken to the Americas in the 16th century and to South Africa and Australia in the 18th century. The crop is now grown in the Americas but only recently, and to a small extent, in North America.

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454  Crop Physiology: Case Histories for Major Crops

1.2  Cropping environment and production Faba bean is well adapted to Mediterranean-type climates, where crops are sown in autumn and to cold-temperate regions, where crops are sown in autumn with cold resistant cultivars or in spring to avoid severe damage from winter frosts. It is also grown in warm high-elevation subtropical climates. When supplemental irrigation is available, sowing can be delayed to late winter or beginning of spring to avoid low temperatures that slow crop establishment, increase weed competition and disease development on more rapidly growing plants. World crop area sharply decreased from 5.5 Mha in 1960 to around 2.2 Mha in 1990 with a subsequent slight recovery that has stabilised around 2.5 Mha during the last decade to 2017 (Fig. 15.1a). Stabilisation of world area is dominated by Asia, where most of the crop is grown but also by increasing area in Africa. China, for example, produces ca. 40% of global total, whereas the increased area in Africa is in the northern (Mediterranean) regions and also in the East (Ethiopia, high elevation subtropical). Since 1960, world production has been relatively stable, between 3.5 and 4.5 Mt (Fig. 15.1b) because average global yield has increased steadily from 0.9 to 1.9 t ha− 1 (Fig. 15.1c). It is likely that the least productive areas were predominantly lost to the crop, so the contribution of actual yield improvement is unknown. Current average yields vary widely amongst countries (Table 15.1). Highest yields are reported from Germany (3.9 t ha− 1), UK (3.8 t ha− 1), and Egypt (3.5 t ha− 1), the latter probably irrigated. The relatively low yields in Spain (1.4 t ha− 1) and Australia (1.7 t ha− 1) reflect the impact of water shortage in these two Mediterranean-type environments. A summary of rotations used before the decline in faba bean cultivation is presented by Bond et al. (1985) with a detailed bibliography from 1960 to 1985, including extension services. Inclusion of faba beans in double-cropping systems, with rice, maize, sorghum, or rapeseed, in China, Egypt, and Sudan or in mixed cropping (intercropping) with cereals for grain, or with peas and vetches for fodder in Egypt, Ethiopia, China, and Afghanistan was relatively extended. Faba beans were a valued rotational crop that fixed large amounts of N. Water availability reflected on farmers’ yields; soils with high water-holding capacity were chosen in low-rainfall environments, although cropping was successful on sandy-loam soils in temperate areas of higher rainfall. The decline in faba bean area during a period of major world population growth and food demand since 1960 must be a result of not only the increasing affordability of N fertiliser but also farmers’ small margins and competition from other pulses and in particular soybean. Soybean has expanded from 23.8 to 123.6 Mha during the period and, with an increase in average yield from 1.13 to 2.85 t ha− 1, has increased annual production from 27 to 353 Mt (see Chapter 8: Soybean). The

FIG. 15.1  World faba bean production and yield 1960–2017. (a) Total harvested area and in three continents, (b) production, and (c) average yield. Based on data from: FAOSTAT, 2019. http://www.fao.org/faostat/en/#data/QC.

Faba bean Chapter | 15  455

TABLE 15.1  Mean harvested area, yield and production of faba bean (2013–17) in various countries within its worldwide distribution. Country

Area (1000 × ha)

Yield (t ha− 1)

Production (1000 × t)

Australia

219.5

1.72

357.2

China

779.3

2.01

1563.0

Egypt

36.7

3.50

128.7

Ethiopia

460.0

4.60

915.0

Germany

32.0

3.89

124.6

Spain

34.9

1.41

46.9

UK

74.4

3.79

282.1

Based on data from: FAOSTAT, 2019. http://www.fao.org/faostat/en/#data/QC.

evident reduction in current research and crop improvement in faba bean is a reflection of the diminution of its sown area. Perhaps, there can be a renewal in production and scientific interest because the high protein and fibre content of faba bean are attractive to the incipient market for ‘fake’ meat. Variability in the content of vicine and convicine antinutritional factors amongst cultivars (Skylas et al., 2019) points to possibilities for improving faba bean nutritional value for livestock.

2  Crop structure, morphology, and development Faba bean, as other legumes, has an ‘architectural plasticity’ from leaf to crop level that is, in general, positive for stabilising growth and yield in variable environments. Plants show a range of morphological responses to both cultivar and environment, especially water supply, temperature, and photoperiod.

2.1  Crop structure 2.1.1 Canopy Faba beans are annual herbaceous plants that have robust, erect, and hollow stems. Branching occurs from basal leaf axils and is highly variable with final number depending on cultivar, sowing density, nutrient supply, and water availability. Leaves are alternate, one per node, pinnate with two to six oval leaflets that can vary with orientation becoming more vertical at higher densities. Canopy height varies greatly with environment in the range 0.50–1.80 m. The maximum was achieved in a yield-potential experiment for cv. Alameda, where plants needed staking (Sau and Mínguez, 2000). Canopy development is dominated by successive production of stem–leaf units. Emergence and expansion of leaves in a spring-sown crop in UK was shown to be highly correlated with temperature (Fig. 15.2). In this study, the leaf appearance interval (phyllochron) was proportional with weekly maximum temperature (R2 = 0.93). Often, the phyllochron is described by thermal units (TU = 58°Cd with Tbase = 0°C) (Ruiz-Ramos and Mínguez, 2006) (see Section 2.3.1).

2.1.2 Roots Four traits well define crop root systems, that is depth, total root length (TRL) per unit crop area, root diameter (d), and in more detail, the vertical profile of root length density (RLD) and the length of root per unit soil volume. Depth, along with physical characteristics of the soil, defines root-zone water-holding ability, and TRL defines root water uptake capacity, whereas d and RLD define the distance that water must move to the root system for uptake. This becomes increasingly important as soils become dry and hydraulic conductivity decreases, rapidly. Each trait varies during the crop cycle. Some data are available for faba bean that comprises a taproot with abundant secondary lateral roots. Nodules develop in the root hairs of upper lateral roots. When there is no soil impairment, roots can exceed 1.0 m depth, as has been measured for an autumn-sown indeterminate Mediterranean cv. Alameda (Sau and Mínguez, 2000) and in cv. Fiord (Turpin et al., 2002b). These cultivars had roots that reached deeper soil layers than those found in other studies such as ICARDA cultivars (Manschadi et al., 1998a with Syrian landrace IBL1814) sown in early-to-late autumn or with European cultivars (Sprent et al., 1977). In general, around 60% of roots are in the top 0.15–0.20 m of soil (Husain et al., 1990; Rengasamy

456  Crop Physiology: Case Histories for Major Crops

FIG. 15.2  Canopy development in faba bean. Progressive formation and expansion of leaves 3–17 on the mainstem of spring-sown cv. Maris Bead. Composed from: Redrawn from Dennett, M.D., Elston, J., Milford, J.R., 1979. The effect of temperature on the growth of individual leaves of Vicia faba in the field. Ann. Bot. 43, 197–208.

and Reid, 1993; Manschadi et al., 1998a; Sau and Mínguez, 2000). Cessation of root growth was recorded during flowering or beginning of pod filling in cultivars Herz Freya and Maris Bead (Sprent et al., 1977) and in Maris Bead (Husain et al., 1990). RLD of faba bean appears much smaller than that of cereals. Müller et al. (1986) compared spring-sown cv. Diana with oats (Avena sativa L.) and highlighted that, although RLD in the first 0.8 m depth was only 10%–15% of that of oats, total water transpired by faba bean was 86% of the amount transpired by the cereal. The authors attribute faba bean with substantially higher water conductivity per unit of root length than oats. Fig. 15.3 presents an example of vertical profiles of RLD in response to irrigation and N fertiliser. Others have measured TRLs under faba bean crops. Sau and Mínguez (2000) reported 5.6 and 7.9 km m− 2 at bloom and harvest, respectively, for the autumn-sown indeterminate cv. Alameda. In contrast, Manschadi et al. (1998a) reported only 2.7 km m− 2 for landrace IBL1814 at ICARDA.

2.1.3  Flowers and fruits Flowers, once initiated, form progressively in racemes of 1–5 in axils of leaves, although in minor European types, they can reach 10 flowers. Many more flowers are produced than are pods set or that mature. It is argued that excessive flowers are important to attract pollinators (bombicids and bees, Hymenoptera), and yet, pod set has been shown to be improved by their activity (Nayak et al., 2015). Even though faba bean is partially allogamous, it is highly self-fertile in particular landraces and modern cultivars. Despite an unknown wild progenitor of faba bean, there is useful variability within the domesticated gene pool (Duc et al., 2011) for increasing yield potential. Partial cross-pollination represents both challenges and opportunities for population development and breeding and for crop structure, morphology, and development. Currently existing cultivars are either open pollinated populations or synthetics (Torres et al., 2011). Pods have usually 2–4 seeds and up to 6–10 seeds in some landraces. Seed size varies from 0.2 g (paucijuga types) to 2.0 g (major types: Aguadulce and Mucha Miel). Seed size gave rise to the species traditional groups as described in Section 1. Total pod dry weight increases rapidly during seed filling (Fig. 15.4).

2.1.4  Flowering types The progressive flowering of faba bean produces an overlap in vegetative and reproductive growth. It distinguishes three growth types: indeterminate, semideterminate, and determinate. In indeterminate types, vegetative growth continues if conditions are suitable after initiation of the final flower, providing continuing competition for assimilation between vegetative and reproductive growth. Breeding has produced semideterminate types with reduced vegetative growth after flowering that reduces competition and also determinate types in which stem growth ceases with a terminal inflorescence. These distinct types are an important part of the discussion of crop adaptation, management, and improvement.

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FIG. 15.3  Root depth and density (RLD) of faba bean crops (cv. Alameda) at two stages of growth under combinations of rainfed or irrigated and N2 fixing (− N) or fertilised (+ N with 300 kg N ha− 1 to inhibit N2 fixation). Modified from: Sau, F., Mínguez, M.I., 2000. Adaptation of indeterminate faba beans to weather and management under a Mediterranean climate. Field Crop Res. 66, 81–99.

FIG. 15.4  Crop and pod biomass in faba bean cv. Alameda with time after sowing under irrigation. Composed from: Boote, J., Mínguez, M.I., Sau, F., 2002. Adapting the CROPGRO legume model to simulate growth of faba bean. Agron. J. 94, 743–756.

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2.2  Vegetative and reproductive responses Temperature and photoperiod affect vegetative development, that is node and leaf appearance rate and longevity as well as hardening. They also affect flowering along with vernalisation. Hardening is a response to cold that increases tolerance to ensuing deeper cold (Stoddard et al., 2006). Vernalisation is the effect of low, yet above-zero, temperature that removes inhibition of flowering. Flowering in faba bean is generally induced by increasing photoperiod, that is they are longday plants, but there are some day-neutral forms. These responses establish three types of cultivars: winter, spring, and Mediterranean (Patrick and Stoddard, 2010): ●

●

●

Winter cultivars have strong vernalisation requirements, and hardening responses, that allow autumn sowing in coldtemperate areas by assured delay of flowering and vegetative survival until the following spring. Spring cultivars have no vernalisation requirement or hardening response and so are an option for spring sowing in various environments, including cold areas and high-altitude subtropical areas. Mediterranean cultivars have mild vernalisation requirements, some hardening capacity and, as the name suggests, are adapted to autumn sowing in warm-temperate (Mediterranean) areas.

In all areas, cultivars are chosen to optimise adaptation for yield and yield stability in response to variable weather. The challenge is great because of the difficulty, as pointed out by Evans (1959), to evaluate the specific effect of one environmental condition in the presence of other controlling factors. It is almost certain that current cultivars are not, even when sown at appropriate times, optimised for their particular requirements of winter survival, avoidance of flower damage by spring frost or water shortage during grain filling (Flores et al., 2012, 2013).

2.2.1 Temperature Temperature drives growth and reproductive development. The relationship to growth is seen in the cardinal temperatures that define approximate environmental ranges for individual species. Faba bean is a cool-season species with minimum, optimum, and maximum temperatures for growth of ca. 3°C, 27°C, and 35°C as proposed by Boote et al. (2002) relying on various sources. Growth responds positively over a wide range of temperature in cool-season environments as reported for leaf emergence and expansion in Fig. 15.2. Reproductive development is distinct also depending directly on photoperiod and vernalisation but with large differences in responses. Reproductive development of day-neutral cultivars and others that have no vernalisation response can be explained by temperature alone. But so also can cultivars at sites, where requirements of photoperiod and vernalisation are met by the passage of weather. In many cases, however, reproductive development requires attention to photoperiod and vernalisation.

2.2.2 Photoperiod Faba beans are mostly long-day plants flowering with increasing photoperiod. The review by Evans (1959) presented the complexity of faba bean responses and provided a critical photoperiod (Lc) within the range of 9–13 h, which matches with that of 9.5–12 h proposed more recently by Patrick and Stoddard (2010). Catt and Paull (2017) based on Evans (1959), Ellis et al. (1990), and McDonald et al. (1994) expanded the description by referring to day-neutral types as those that eventually flower regardless of photoperiod and to photoperiod-insensitive types that flower in the same thermal time regardless of photoperiod. In this way, other spring-sown cultivars, such as those studied by Stützel (1995a,b) in Germany and Grashoff and Stokkers (1992) in The Netherlands, are day neutral and eventually flower regardless of photoperiod.

2.2.3 Vernalisation Vernalisation of both presown seed and plants accelerates time to flowering. Seed vernalisation decreased the time to flower in almost all cultivar × environment combinations tested in controlled environment by Ellis et al. (1988) on one cultivar and five accessions from various origins (UK, Spain, Ethiopia, and Sudan). The vernalisation requirement of winter faba bean is considered quantitative, rather than absolute, as unvernalised plants do eventually flower on a higher node. There is, however, no systematic analysis of vernalisation requirement. Exposure of seed to 4°C during 30 days has been used to evaluate flowering response of several cultivars and accessions (McDonald et al., 1994). Vernalisation of crops of spring and Mediterranean types accelerates flowering in line with the accumulated photothermal growing time above the base temperature of 0°C as described by Ellis et al. (1988, 1990) and Soja and Steineck (1986), and reviewed by Link et al. (2010). Vernalisation can lead to flowering at a lower node requiring lowering the height of the cutting bar at harvesting, which may not be possible in irregular or stony soils. Vernalisation is a complex response because warm temperatures (23°C) during the process can diminish (even cancel) accumulated response (devernalisation) (Link et al., 2010).

Faba bean Chapter | 15  459

2.2.4 Hardening Winter faba bean is an attractive crop for the expected benefits of higher grain yield over that of spring beans. Winter hardiness exists in faba bean cultivars, and there is potential for expanding the range of winter faba bean through breeding for improved hardiness (Flores et al., 2012). Winter hardiness has allowed faba beans to survive European (− 15°C) and Mediterranean winters (− 10°C) (Schill et al., 1998; Elzebroek and Wind, 2008a,b) and mild winters in Germany (Herzog, 1989). Faba bean shows increased frost hardiness after a few days of exposure to low nonfreezing temperature, achieving a maximum hardening in 2–3 weeks (Herzog and Saxena, 1988). Hardening has been found even in spring faba beans (Link et al., 2010). Arbaoui et al. (2008), and working with a representative set of frost-tolerant and frost-susceptible faba bean germplasm indicated that fatty acid composition, proline content, and electrolyte leakage correlated with winter-hardiness. Hardening is a crucial adaptation that allows faba bean cultivars to match their development to the cool rainy seasons in Mediterranean environments when water is available and will allow faba beans to be cultivated in a wider area than at present (Stoddard et al., 2006; Link et al., 2010).

2.3  Quantifying phenological development There are two steps. First is to establish scales for measurement of phenological stages and second to explain the durations between successive stages in terms of responses to temperature and photoperiod. The latter serve as definitions of cultivar response and as inputs to the prediction of phenological development in crop simulations studies.

2.3.1  A phenological scale Development stages of faba bean are presented in Table 15.2 based on description by Knott (1990). Principal stages or major phases are germination and emergence (stages 0–1), vegetative (1–2), reproductive (2–3), pod senescence (3–4), and stem senescence (4). Substages dissect them according to nodes and stems. Stage 1 refers to the main stem, and stage 2 refers to the most advanced fertile node. These secondary stages allow more precise description and can be linked to farming and crop operations. Because most cultivars are indeterminate, vegetative growth, flowering, pod filling, and ripening can happen concurrently. Even so, Knott’s phenological stages are sufficiently simple to use in extension services or technical notes.

2.3.2  Phenological indices for simulation of faba bean development Current simulation models deal with simpler scales than those presented in Table 15.2. For example, durations between the following stages. 2.3.2.1  Sowing–emergence–first flower–last flower–physiological maturity Durations are calculated as thermal (TU, °Cd) and photothermal (PTU, °Cdh) times from time-of-sowing experiments, mostly at single locations but sometimes over multiple years. They establish the best-fit thermal or photothermal durations required for passage through individual stages or groups of consecutive stages under the various combinations of temperature and photoperiod. These values can then be used in models to calculate the development rate (dr, d− 1) for new crops at the same site as: dr  TU /(max  0,  Td  Tb   or PTU / max  0,  Td  Tb    max  0,  Ld  Lc    where Td is the daily average temperature above a base temperature (Tb), Ld is photoperiod and Lc is the critical photoperiod to initiate response. The corresponding phenophases are completed when ∑ dr = 1. The first simulation models built for faba bean were by Stützel (1995a,b) in Germany and Manschadi et al. (1998a,b) in Syria. Subsequently, further models were added to major crop-modelling platforms (CROPGRO) by Boote et al. (2002) for studies in Spain and APSIM by Turpin et al. (2003) to cover the range of environments for expanding faba bean production in Australia. Each uses TU and PTU to quantify duration of phenophases that, although summarised here in four phenophases, differ with application of subphases between the models (Table 15.3). The dominant control on development is temperature with some calculation against Tb = 0°C and others that use 1.5°C. Photoperiodic response is restricted to the vegetative phase immediately after crop establishment. All cultivars are long day but with differing Lc. Once the photoperiodic response is established, further development depends on temperature. Currently, no model specifically includes vernalisation.

460  Crop Physiology: Case Histories for Major Crops

TABLE 15.2  Phenological scale for faba bean development. Development phase

Growth stage (GS)

Description

00 Germination and emergence

GS000

Dry seed

GS001

Imbibed seed

GS002

Radicle apparent

GS003

Plumule and radicle apparent

GS004

Emergence

GS005

First leaf unfolding

GS006

First leaf unfolded

GS101

First node (i.e. first leaf fully unfolded with one pair of leaflets)

GS10(x)

x = node (x leaf fully unfolded with more than one pair of leaflets)

GS1(n)

n = last recorded node

10 Vegetative

n—any number of nodes on main stem with fully unfolded leaves 20 Reproductive

30 Pod senescence

40 Stem senescence

GS201

Flower buds visible and still green

GS203

First open flowers on first flower cluster

GS204

First pod visible at first fertile node

GS205

Green pods fully formed and small immature seeds within

GS207

Pod-fill and pods green

GS209

Seeds rubbery, pods still pliable, and turning black

GS301

Pods dry and black, seeds dry

GS301

10% of pods dry and black

GS305

50% of pods dry and black

GS308

80% of pods dry and black

GS309

90% of pods dry and black

GS310

All pods dry and black

GS401

10% stems brown/black of stems green

GS405

50% stems brown/black of stems green

GS410

All stems brown/black, all pods dry and black, and seed hard

Based on: Knott, C.M., 1990. A key for stages of development of the faba bean (Vicia faba). Ann. Appl. Biol. 116, 391–404.

TABLE 15.3  Application of thermal (TU) and photothermal (PTU) units to model phenological development in faba bean.

Model

Sowing– emergence (adequate water)

First flower

Last flower

Maturity (grain filling)

Cultivar

References

Faba bean

–

TU

TU

TU

Herz Freya, Ticol

Stützel (1995a,b)

FAGS

TU

TU

TU

TU

ILB 1814

Manschadi et al. (1998a,b)

CROPGRO-Faba bean

–

PTU

PTU

TU

Alameda, Brocal

Boote et al. (2002)

APSIM-Faba bean

TU

PTU

TU

TU

Fiord and others

Turpin et al. (2003)

Faba bean Chapter | 15  461

Despite the progress so far, phenological models of faba bean are limited to fully capture environmental and cultivardependent developmental processes, including vernalisation and photoperiodic requirements. Guidance could be obtained from the more systematic work on soybean.

3  Growth and resources 3.1  Capture and efficiency in the use of radiation Leaf area and leaf display determine the interception and distribution of irradiance on leaf surfaces. These, together with the photosynthetic response of individual leaves, determine the production of assimilates then available for growth of crop organs. Important issues are not only adequacy of leaf area to intercept available radiation but also the production of excessive leaf area that competes with growth of pods and seeds and also shades leaves supporting growth of pods low in the canopy.

3.1.1  Canopy development The indeterminate growth habit of faba bean produces new foliage (leaves and stems) over most of the entire crop cycle. At sowing, there is usually available water, so early vigour depends upon temperature and soil fertility (Fig. 15.5).

3.1.2  Radiation capture Interception of radiation by faba bean canopies has been measured in temperate regions for autumn and spring sowings (Confalone et al., 2010; Fasheun and Dennett, 1982; Del Pozo and Dennett, 1999; Stützel and Aufhammer, 1991) and for autumn sowings in Mediterranean environments (e.g. Loss et al., 1997; Mínguez et al., 1993; Siddique et al., 2001). Some studies include specific comparisons of indeterminate and determinate cultivars and sowing patterns and densities. The analyses use the exponential-extinction profile of radiation, I/Io = exp (− kL), where I/Io is the proportion of incident radiation, usually expressed as PAR, penetrating below a cumulative leaf area index (LAI), LAI = L, to define canopy structure as an extinction coefficient (k). This exponential model is most suited to full cover crops in which foliage is randomly distributed and leaf angle largely determines k. In the early stages of row crops, as faba bean is commonly sown, foliage is strongly underdispersed (clumped) so other models that include row geometry are often useful (Stützel and Aufhammer, 1991). The available studies establish considerable variation between cultivars and sowing densities that emphasise the architectural plasticity of faba bean canopies. Turpin et al. (2002a) measured radiation penetration to ground level throughout the growth of various experimental crops, returning a generally appropriate k = 0.73 (Fig. 15.6). On the other hand, Del Pozo and Dennett (1999) who measured radiation penetration at 10 cm vertical intervals in crops of cv. Tina (determinate) established k = 0.68 and 0.93 for crops sown at 20 and 70 plants m− 2, respectively. This difference was the result of more vertically displayed, smaller leaves in the densely sown crop. The result is a more even distribution of irradiance within the canopy. The data do not reveal differences in k with depth in the canopy. In another experiment, Fasheun and Dennett (1982) made continuous measurements of k with sensors above and permanently below crops (cv. Maris Bead) sown on three occasions (Fig. 15.5). Row spacing was 45 cm, and sowing densities for the three crops were 24, 42, and 36 plants m− 2. Averaged for the canopy, k decreased from 0.5 to 0.35 as LAI increased

FIG. 15.5  Leaf area index (LAI) of Maris Bead crops in the UK for two spring (May) sowings and one summer (July) sowing. Modified from: Fasheun, A., Dennett, M.D., 1982. Interception of radiation and growth efficiency in field beans (Vicia faba L.). Agric. Meteorol. 26, 221–229.

462  Crop Physiology: Case Histories for Major Crops

FIG. 15.6  Interception of radiation by faba bean crops, cv. Fiord, under various treatments in Australia. Composed from: Turpin, J.E., Robertson, M.J., Hillcoat, N.S., Herridge, D.F., 2002a. Faba bean (Vicia faba) in Australia’s northern grains belt: canopy development, biomass, and nitrogen accumulation and partitioning. Aust. J. Agric. Res. 53, 227–237.

from 2 to 6, after which it remained stable. These values are much smaller than others reported, suggesting much more horizontal leaves as the canopy developed. Taken together, the results overall emphasise the need for various values of k for effective simulation of interception of radiation by faba bean crops. There is no information on variability of k with depth in canopies, which is an interesting issue for discussions and studies of crop photosynthesis and efficiency of use of intercepted PAR.

3.1.3  Photosynthesis: Leaf to canopy Rates of leaf photosynthesis of faba bean cultivars that have been reported from growth chamber (Avola et  al., 2008), greenhouse (Kropff, 1987; Yan et al., 2013) and in field studies (Del Pozo and Dennett, 1999; Leport et al., 1998) place it well within the C3 group. It is disappointing that so few measurements have been made on field-grown plants. Some responses are presented in Fig. 15.7. Recorded maximum rate of assimilation (Pmax) for one cv. was 25 μmol CO2 m− 2 s− 1 at 350 μmol mol [CO2] and PAR 1200–1400 μmol m− 2 s− 1 at 25°C and yet for another was 15 μmol CO2 m− 2 s− 1 (Fig. 15.7a). Responses of Pmax to specific leaf nitrogen (SLN, g N m− 2) measured under the same conditions are presented in Fig. 15.7b, whereas comparative leaf photosynthetic characteristics of two faba bean types are presented in Table 15.4. Avola et al. (2008) and Del Pozo and Dennett (1999) both show the positive short-term response of leaf photosynthesis to [CO2] at 700 μmol mol− 1. These days there is more interest in acclimatisation to continuing high [CO2], which is common in C4 but not in C3 species.

FIG. 15.7  Responses of faba bean leaf photosynthesis to (a) temperature at saturating irradiance of two lines from massive selection and (b) to specific leaf nitrogen at maximum photosynthesis for cv. Tina grown at two plant densities. Composed from: Avola, G., Cavallaro, V., Patane, C. Riggi, E., 2008. Gas exchange and photosynthetic water use efficiency in response to light, CO2 concentration and temperature in Vicia faba. J. Plant Physiol. 165, 796–804; Del Pozo, A., Dennett, M., 1999. Analysis of the distribution of light, leaf nitrogen, and photosynthesis within the canopy of Vicia faba L. at two contrasting plant densities. Aust. J. Agric. Res. 50, 183–189.

Faba bean Chapter | 15  463

TABLE 15.4  Optimised parameters of leaf photosynthesis response of faba bean to irradiance at 350 μmol CO2 mol− 1 and 25°C. Faba bean line or cv. Parameter

‘116 Is’ − 2 − 1

Dark respiration (μmol m  s ) − 2 − 1

Pmax (μmol CO2 m  s ) − 2 − 1

Light compensation point (μmol m  s ) − 2 − 1

Light saturation (μmol m  s ) − 1

Quantum efficiency (mol mol )

‘45NsVt’

Sicania

Mean

Coeff. variation (%)

0.71

0.77

0.71

0.73

3

25.9

16.1

23.4

21.8

13

16

19

14

16

10

1520

1150

1180

1280

9

0.045

0.041

0.053

0.046

7

Composed from: Avola, G., Cavallaro, V., Patane, C. Riggi, E., 2008. Gas exchange and photosynthetic water use efficiency in response to light, CO2 concentration and temperature in Vicia faba. J. Plant Physiol. 165, 796–804.

Crop photosynthesis, the sum of contributions from all leaves, responds to these and additional factors including demand from growing tissue (sink strength), the strongest of which is grain. Experiments that excise pods reveal the large feed-back effect that depresses photosynthesis and the mechanisms involved (Yan et al., 2013). Nitrogen is a key nutrient because it is a major component of chloroplasts and their enzymes that determine Pmax. Continuing leaf production (Fig. 15.2) is associated with leaf ageing and senescence and decreasing PAR, in the lower canopy. In this way, crop canopies develop profiles of decreasing SLN as leaves age. Examples for faba bean provided by Del Pozo and Dennett (1999) are exponential with cumulative LAI and, hence, closely related to PAR. Pmax of leaves in both canopies was similarly related to SLN (Fig. 15.7b). This raises the issues of distribution of SLN in crop canopies for maximum photosynthesis and the optimal pattern of withdrawal of N from leaves during grain filling to maximise productivity. Responses to 550 ppm CO2 (eCO2) reflected on 28% greater shoot biomass (g plant− 1) than under control atmospheric (aCO2) conditions, in well-watered crops in a FACE experiment (Parvin et al., 2019); this could probably reflect greater leaf photosynthetic rates due to (eCO2).

3.1.4  Growth rates and RUE Crop growth rates (CGR, g m− 2 d− 1) are maximised when cover is complete, incident PAR is intercepted completely and photosynthesis is not limited by temperature, water supply, or nutrition. This can occur in the life cycle of arable crops when they have established complete soil cover reaching critical LAI (LAIc) when ca. 95% PAR is intercepted. This is a critical stage in development and is attained faster in temperate and Mediterranean regions when crops are sown in early autumn, whereas the soil remains warm in summer and in spring-sown crops that are commonly sown more densely. Knowledge of RUE sets benchmarks for discussion of the efficiency of resource use in crop production, crop breeding, and management. Key parameters are shorter term (e.g. biweekly or monthly) estimates of radiation-use efficiency of intercepted radiation (RUE, g MJ− 1 iPAR) useful for intracrop comparisons and gram CHO equivalent MJ− 1 iPAR for intercrop comparisons, other than vegetative biomass, that include grains with largely varying proportions of energy-rich components (proteins and lipids). The latter accounts for the respiratory energy required to build tissues and, in the case of legumes, to support rhizobial symbiosis for N2 fixation. These adjustments are needed for fair comparisons of intercrop productivity in accordance with photosynthetic productivity. In commerce, these differences in yield of various crops are taken into account in setting prices per unit mass. The range of maximum CGR, critical and maximum LAI and RUE are the result of experiments with variable sowing dates and densities, cultivars and the architectural plasticity of faba bean crops. In the highest-yielding crops of Sau and Mínguez (2000), LAImax was eight to nine, and maximum CGR was 25 g m− 2 d− 1 when LAI reached five. In Fasheun and Dennett (1982), LAImax was 8.5 and CGR reached 30 g m− 2 d− 1 during 30 days. Seasonal data from Sprent et al. (1977) averaged 13–24 g m− 2 d− 1 in spring and summer sown plots. Corresponding maximum for RUE are 1.85 g MJ− 1 (Loss and Siddique, 1997); 1.79 g MJ− 1 (Mínguez et al., 1993); and 1.7 g MJ− 1 (Confalone et al., 2010). It is interesting to consider these CGR and RUE in terms of the additional load for N2 fixation. Estimates of the impact of N2 fixation v/v NO3 − are to increase CHO equivalent by 1.2 for peanut, 1.3 for soybean and 1.4 for chickpea (Connor et al., 2011). On that basis, perhaps a conservative 10%–15% loading on growth could be considered for faba bean when 50% of legume N is from fixation.

464  Crop Physiology: Case Histories for Major Crops

3.2  Capture and efficiency in the use of water Crop demand for water depends upon LAI and environmental conditions. Supply to maintain internal water content within tolerable limits depends upon root traits (Section 2.1.2) and soil water content. The latter is recharged regularly by irrigation but intermittently and incompletely by rainfall. Faba beans are sown so that harvest can proceed in late spring–summer during periods without rain. In many rainfed crops, terminal water deficits occur during pod filling, and in Mediterranean environments, during flowering also.

3.2.1  Crop water balance Crops respond indirectly to rainfall and irrigation through their effect on soil water content. An open stomatal pathway for CO2 assimilation by foliage provides an inevitable pathway for the loss of water by transpiration from the moist internal tissues of the leaf to the drier external environment. Crops also lose water by evaporation from wet soils and drainage below the root system. Internal drying of foliage establishes a water potential gradient to roots and adjacent soil up to which water flows. This flow requires that crop water potential decreases below that of drying soil and is limited by decreasing soil conductivity, root density and distribution, and the capacity of individual crops to sustain internal water stress. Control over water loss is provided by stomatal closure that reduces leaf and crop conductance to water transfer, and the latter is also dependent on crop cover. Faba bean has the capacity to produce much foliage that is supported in terms of water balance by relatively shallow root systems (Day and Legg, 1983; Müller et al., 1986). Root growth is rapid before flowering (Sprent et al., 1977; Husain et al., 1990) and can continue until maturity if conditions are favourable. It does, however, have physiological and morphological responses to decreasing internal water status that maintain high internal water content, although at the expense of continuing growth. The consequence is that as a crop, it can suffer limited internal water status and reduced growth under rainfed conditions, whereas responding positively in growth under irrigation. Periods of water shortage can have a greater effect on reproductive yield than on biomass production if they occur at sensitive times of crop development. The impact of water shortage is seen in late-sown spring crops in temperate areas and is most marked in Mediterranean climates, where the crop is most widely grown and invariably subjected to stress during flowering and to terminal water stress. Nevertheless, both seed yield and harvest index (HI, ratio of grain yield to total aboveground biomass) are positively correlated with water use postflowering so that an excess of water use during preflowering reduces grain growth later if no rainfall occurs. Crops and cultivars with more transpiration postanthesis are considered advantageous in low-rainfall Mediterranean environments (Loss et al., 1997; Siddique et al., 2001).

3.2.2  Adaptation to water shortage Faba bean crops are sown in water-short areas with short-season cultivars that offer the best opportunity to escape the most serious effects of water shortage. The crop has physiological and morphological responses to decreasing internal water status that adjust water demand (LAI and leaf conductance) to water uptake capacity (root depth and RLD) that have the effect of avoiding internal water stress. The latter can be impaired if root growth stops during grain filling and/or because there is no available water in the soil. The FACE experiments by Parvin et al. (2019) showed that elevated CO2 induced slower rate of soil drying due to lower stomata conductance and helped to conserve soil water up to flowering, improved water use efficiency, and could mitigate the negative effects of water deficit on yield and N2 fixation. Nevertheless, prolonged terminal water deficits during pod filling reduced the improvement of yield grain N concentration when compared with the control treatments. 3.2.2.1 Phenology Farmers select cultivars with phenological development best suited to seasonal rainfall, especially water availability during flowering and pod filling. In Mediterranean environments, indeterminate cultivars may recover from late radiation frosts, flower abortion or failure in pod setting if late rains occur and flowering resumes. 3.2.2.2  Stomatal responses Stomatal closure limits transpiration adjusting it more closely to root water uptake, thus maintaining high internal water content (Fig. 15.8a). This is done at the expense of photosynthesis, but the effect is reversible diurnally (Fig. 15.8b). Leaf water potential (Ψleaf) has been monitored in the field under nonlimiting and water limiting conditions and shown to fall to − 1.7 MPa in the field when water deficits are established slowly (Elston et al., 1976; Dantuma and Grashoff,

Faba bean Chapter | 15  465

FIG. 15.8  (a) Leaf conductance and (b) photosynthesis of faba bean cv. Fiord and chickpea accession T1587 in response to leaf water potential that varied with water supply. Composed from: Leport, L., Turner, R.J. French, N.C., Tennant, D., Thomson, B.D., Siddique, K.H.M., 1998. Water relations, gas exchange and growth of cool-season grain legumes in a Mediterranean-type environment. Eur. J. Agron. 9, 295–303.

1984; Sau and Mínguez, 2000). Müller et al. (1986) monitored the daily course of leaf conductance of faba bean and oats and established a smaller value (25%–50%) for faba bean than oats under water deficit. Stomatal closure occurred at relatively high Ψleaf (− 1.2 MPa) in N2 fixing (− N fertiliser) plants and lower but more varied Ψleaf when fertilised (+ N with 300 kg ha− 1) (Sau and Mínguez, 2000). Drought tolerance to low Ψleaf through osmotic adjustment, although found in many other legumes including chickpea and pea, has not been demonstrated in faba bean (Sau and Mínguez, 2000; Khan et al., 2010). 3.2.2.3  Canopy responses Water deficit can restrict leaf emergence and expansion (De Costa et al., 1997a) so that critical LAIc of four to five may not be reached and overall productivity is reduced. This occurs in both in temperate and Mediterranean environments: Mínguez et al. (1993) for indeterminate Mediterranean cv. Alameda; De Costa et al. (1997a) for determinate cultivar Tina and the indeterminate cultivar Gobo; Manschadi et al. (1998b) with ICARDA’s large-seeded landrace IBL1814. Mild terminal water deficits can control vegetative growth in indeterminate cultivars avoiding decrease in HI and yielding 3.95 t ha− 1 (Sau and Mínguez, 2000). On the other hand, reduction in LAI, recorded by various authors (e.g. Husain et al., 1990), along with stomatal closure, decreases the demand for transpiration by adjusting water demand more closely to that of root water uptake and, therefore, maintaining high Ψleaf. In that way, it complements responses that increase root depth and RLD. Such adaptation is most important for yield during flowering and pod filling. 3.2.2.4  Root systems Adaptation to water shortage requires roots that can explore depth to gain access to maximum water possible and use it efficiently. Available data reveal that faba bean roots, although often shallow, can explore well-structured soil profiles to 1.2 m depth (Sau and Mínguez, 2000) or 1.8 m (Turpin et al., 2002b) but are seriously impeded by compacted layers (Husain et al., 1990). Adaptation to water stress requires deep roots to provide access to otherwise inaccessible water. Uptake rate is determined by RLD that if small imposes a conservative water use strategy that can preserve water for late-season use. On the other hand, an increase in RLD would assist access to currently available water. In the experiment by Sau and Mínguez (2000), RLD at bloom increased in rainfed relative to irrigated treatments, whereas indirect estimations of root/shoot ratio also supported an increase in RLD in rainfed treatments. More research is needed on root depth and RLD of faba bean cultivars including explanation of the uptake ability of the small RLD of faba bean when compared with cereals (Müller et al., 1986). The question of the assimilate cost of deeper root systems and/or greater RLD under variably water-short conditions is of great relevance. Elevated CO2 increased root biomass and TRL to a greater extent under drought at flowering. Similarly, (eCO2) increased root–shoot ratio, and this increase was greater under water deficit (57%) than in well-watered conditions (9%) both at flowering and maturity (Parvin et al., 2019).

466  Crop Physiology: Case Histories for Major Crops

3.2.2.5  Options for future progress Genotypic variation in the response of faba bean to water deficits is available (Abdelmula et al., 1999; Amede et al., 1999; Maalouf et al., 2015). Some studies have reported physiological traits associated with water stress, such as carbon isotope discrimination, leaf temperature and stomatal conductance (Khan et al., 2010; Khazaei et al., 2017), which could be used, together with spectral indices (Maalouf et al., 2015) to select for resistance to water stress. It is not yet reported that any faba bean breeding programme is using selection for high transpiration efficiency. Breeding for drought tolerance in grain crops should be as much as possible location specific because periods of water deficit or/and drought vary in length, timing and intensity, and different traits are important with different types of drought (Passioura, 2012), for example the investment in root systems should aim for soils that still can have water available during terminal stress. Indeed, the search for generic drought tolerance using single-gene transformations has been disappointing (Passioura, 2012). Breeding for cold tolerance to allow crop cycle to match the autumn–winter rainy season could also be considered. In the Mediterranean climates with mild winter and hot and dry summer, faba bean is traditionally sown in autumn, profiting from the winter rains and escaping the usual terminal drought. To expand autumn sowing in cooler areas, winter hardiness and frost tolerance are needed (Landry et al., 2016). Genetic variation for these traits is available in faba bean germplasm, and significant breeding progress has been made (Link et al., 2010; Arbaoui et al., 2008).

3.3  Capture and efficiency in the use of nutrients Nutritional issues with faba bean include the requirements of mineral nutrients for growth and yield, the replacement of nutrients extracted by harvest from soil and the contribution of N fixation to current yield of sole legume or intercrop and longer-term contribution to N fertility of cropping systems.

3.3.1  Mineral nutrients The content of macro nutrients in a faba bean crop (Table 15.5) provides guidance for discussion of the nutrient requirement and replacement by fertiliser. Large amounts of P, K, and Ca are required to support yield, in this case by 5 t ha− 1; a large proportion of P and K (83% and 33%, respectively) is contained in the seed. In contrast, the high content of Ca is found dominantly in stems and leaves. Extraction of nutrients in harvested grain provides guidance to fertiliser requirement. Faba bean has higher requirements for most nutrients when compared with cereals (Fig. 15.9). Particular attention is required to provide the high requirements for P and K, which otherwise would reduce grain production, despite the ability of the crop to provide N by fixation. Micronutrients involved in N2 fixation are molybdenum (Mo), cobalt (Co), copper (Cu), and zinc (Zn) (the last two shown in Fig. 15.9), so their addition in fertilisers is recommended in soils with low availability (reviewed by Weisany et al., 2013). Micronutrient content of grain contributes to their nutritional value (Section 4.4). Baloch et al. (2014) assessed faba bean germplasm from 129 landraces and 4 cultivars from diverse geographic regions of Turkey for seed micronutrient content and provided the following ranges: Fe (29.7–96.3 g t− 1), Mn (15.5–29.2 g t− 1), Cu (10.3–33.0 g t− 1), and Zn (10.4–49.3 g t− 1). High boron (B) content such as that found in subsoils of northwest Victoria in Australia can limit faba bean production. Excess B results in yellowing of mature foliage that restricts N translocation to seeds, followed by marginal necrosis and death of the whole plant. Poulain and Almohammad (1995) showed that although B accumulation in faba bean tissues can be held to be a passive mechanism, they found genotypic differences amongst two cultivars and six pure lines suggesting the possibility of a genetic control and selection for tolerance.

3.3.2 N2 fixation mechanism and rates

Faba bean, with measured fixation rates of ca. 150 kg N ha− 1, is amongst legumes with the highest recorded N2 fixation rates (Jensen et al., 2010). The symbiosis operates with the fast-growing Gram-negative rod bacterium Rhizobium leguminosarum and as recently shown also with R. fabae sp. nov. (Tian et al., 2008) and R. laguerreae sp. nov. (Saïdi et al., 2014). Fixed N is transported to stems and leaves as amide. N accumulation by legumes cannot be attributed entirely to fixation because roots also access available mineral N in the soil. The matter is resolved by measurement of natural abundance of N isotopes. A common feature of legume N2 fixation is that it is depressed by increase in soil mineral N (SMN) content ( NO3 − and NH 4 + ). This severely limits the contribution of legume N2 fixation in high-yielding cropping systems. Legumes are more efficient at low N but still require considerable quantities of macronutrients, especially P and K (Table 15.5). Contribution

TABLE 15.5  Dry matter (DM), nutrient concentration and content in plant organs of faba bean cv. Diana. Nutrient content (kg ha− 1)

Nutrient concentration (%) Plant part

DM (t ha− 1)

N

P

K

Na

Ca

Mg

N

P

K

Na

Ca

Mg

Seed

5.07

4.56

0.58

1.24

0.01

0.09

0.12

231

29

63

1

5

6

Pods

1.24

1.29

0.07

3.28

0.05

0.07

0.19

16

1

41

1

1

2

Stems and leaves

5.38

1.28

0.22

1.60

0.16

1.80

0.17

68

5

87

9

97

10

Roots

0.75

1.01

0.07

0.92

0.25

0.57

0.08

8

1

7

2

4

1

Total (no roots)

11.69

315

35

191

11

103

18

Harvest proportion (%)

43

73

83

33

9

5

33

Composed from: Jensen, E.S., Peoples, M.B., Hauggaard-Nielsen, H., 2010. Faba bean in cropping systems. Field Crop Res. 115, 203–216.

468  Crop Physiology: Case Histories for Major Crops

FIG. 15.9  Nutrient extraction by faba bean and wheat in 1 t of grain. Data from GRDC (2018). Grownotes. Faba Bean. Plant Growth and Development. Sections 5 and 6. Grains Research and Development Corporation, Canberra p. 3 of Section 6.

to longer term N fertility cannot be achieved when faba bean is harvested for grain. For that, as with other grain legume crops, it must be used as green or brown manure so that all fixed N is returned to soil. In a 2-year field intercropping maize–faba bean experiment, Li et al. (2016) and Li (2019) demonstrated that decline of soil nitrate concentration caused by uptake by maize roots is not the sole mechanism by which maize promotes faba bean nodulation and increased total N2 fixation. There was also a twofold increase in exudation from maize roots of flavonoids that are signalling compounds that promote nodulation and N2 fixation in faba bean. Parvin et al. (2019) showed that (eCO2) induced stimulation of nodulation and nodule density helped to maintain N2 fixation under drought. Nevertheless, prolonged water deficit during pod filling decreased nodule activity and could affect N concentration in the grain. They relate this decrease to decreased carbohydrate and increased amino acid concentrations in nodules, indicating a downregulation of N2 fixation.

3.3.3  Soil acidification and root–root interactions in intercropping Soil acidity can not only be caused by fertilisers but also by differential cation–anion extraction by harvested crops. Growth of N2 fixing plants involves excess uptake of nutrient cations over anions from soil solution resulting in the net efflux of H3O+ ions from plant roots into the rhizosphere (Haynes, 1983; Wallace, 1994). Intercropping experiments of faba bean with wheat (Zhang et al., 2016) and maize using root barriers (Li et al., 2007; Li, 2019) have highlighted the mechanisms by which rhizosphere acidification increased P availability by chelation of P-fixing Ca, Fe, and Al compounds. Intercropped maize benefitted directly from greater P availability. There is also the possibility of greater phosphatase activity in the rhizosphere that decomposes soil organic P into inorganic form, shown for wheat–chickpea and maize–faba bean intercrops (Li et al., 2003, 2007; Li, 2019). These responses are interesting and provide a means to access P fixed in unavailable form from added fertiliser. But they operate through soil acidification that itself can reduce N fixation, and the amounts of P provided are small by the large amounts that crops, faba bean especially, require.

3.3.4  N uptake, storage, and mobilisation Legume crops do not always display the gradual dilution of N content during growth that is observed in many crops (Lemaire et al., 2007). Available data on N balance of faba bean under irrigation (Fig. 15.10a), for example, reveal continuous uptake on N until maturity in contrast with a diminishing pattern of durum wheat. Rather, provided conditions support continuing N2 fixation, faba bean and other legumes can continue to meet the demand for vegetative and reproductive growth independently of dwindling of SMN. Large amounts of N are required to build faba bean canopies that have high concentrations of N in stems and leaves. For example, a faba bean canopy of LAI = 4 and average SLN = 1.5 g m− 2 contains 60 kg N ha− 1. Mobilisation of internal N plays an important role in the growth of seed as presented in Fig. 15.10b. Mobilisation of canopy N to meet the requirements of grain growth raises important issues concerning the potential impact of senescing leaves on canopy photosynthesis and crop yield. Analyses have revealed an optimal pattern in which N should be withdrawn from individual leaf canopies to maximise crop photosynthesis, for example sunflower (Connor et al., 1995).

Faba bean Chapter | 15  469

FIG. 15.10  (a) N content as a function of biomass during growth of faba bean (cv. Sciliana) and durum wheat. (b) Dynamics of nitrogen concentration in organs of faba bean cv. Fiord. Composed from: Giunta, F., Pruneddu, G., Motzo, R., 2009. Radiation interception and biomass and nitrogen accumulation in different cereal and grain legume species. Field Crop Res. 110, 76–84; Turpin, J.E., Robertson, M.J., Hillcoat, N.S., Herridge, D.F., 2002a. Faba bean (Vicia faba) in Australia’s northern grains belt: canopy development, biomass, and nitrogen accumulation and partitioning. Aust. J. Agric. Res. 53, 227–237.

Large differences in withdrawal patterns have been identified in soybean (Spaeth and Sinclair, 1983; Martignone et al., 1987), so it seems likely that similar differences, and opportunities to exploit them, exist in faba bean.

4  Yield and quality 4.1  Crop yield 4.1.1  Yield progress Yield trends in Fig. 15.11 reveal important aspects of yield progress. Highest and most stable gains have been achieved in temperate Germany (27.5 kg ha− 1 y− 1, R2 = 0.69) and in the largely irrigated Mediterranean environment of Egypt (33.3 kg ha− 1 y− 1, R2 = 0.76). Although the UK is comparably productive to Germany, yield gain has been smaller (13.1 kg ha− 1 y− 1) and with more variability (R2 = 0.21). Spain and Australia have the most variable yield gains (R2 = 0.34 and 0.45, respectively), but yield gain has been faster in Australia (21.2 kg ha− 1 y− 1) than in Spain (8.0 kg ha− 1 y− 1).

4.1.2  Benchmarking yield and yield gaps Country statistics are interesting and valuable for discussions of trade and global food supply, but they fall far short of the data needed to guide research and development for continuing progress in yield gain. For that, actual farmer yields (Ya) are required for agro-environmental regions along with corresponding potential yields Ypot and water-limited potential yield

FIG. 15.11  Yield progress of faba bean in various countries. Based on data from FAOSTAT, 2019. http://www.fao.org/faostat/en/#data/QC.

470  Crop Physiology: Case Histories for Major Crops

Yw to benchmark irrigated and rainfed crops respectively (Van Ittersum et al., 2013; Fischer et al., 2014). Yield potential is the yield that can be achieved by the application of best technologies and practices to management of the most productive cultivars as defined by Evans and Fischer (1999). The calculation of the appropriate yield gap (Ypot − Ya or Yw − Ya) provides a goal for research and development, even though farmers may be best advised to set a yield goal below Ypot or Yw that is most economic under current market conditions. Such analyses of yield potential and yield gap have been made for major cereals (Fischer et al., 2014) and start on soybean, but little information is available for faba bean and other pulses (Yield Gap Atlas, www.yieldgap.org). In maximum yield experiments (yield potential) with autumn-sown indeterminate types sown at 33 plants m− 2 under Mediterranean conditions with irrigation, Ypot reached 6560 kg ha− 1 (Sau and Mínguez, 2000) and 7700 kg ha− 1 in a temperate region (Confalone et al., 2010).

4.2  Yield components The yield components of faba bean are ● ● ● ● ●

number of branches/stems per unit area proportion of branches/stems with harvestable pods number of harvestable pods per branch/stem average seed number per pod average seed weight

Yield expressed in these terms is the outcome from combinations of multiple interactions between cultivar, sowing date, density, spatial arrangement, and mainly water and nutrient availability. A simpler more commonly used version, for example harvestable pods per unit area, average seed number per pod and average seed mass misses the impact of unproductive branches/stems, which, when significant, should be recorded for completeness. It is the case, however, that across experiments carried out with the three faba bean types, at various densities, in temperate and Mediterranean regions, most authors conclude, as was highlighted by Sprent et al. (1977), that the major component affecting yield is the number of harvestable pods. Recent work has confirmed the critical periods and components for yield determination in faba bean. Lake et al. (2019) used sequential biweekly periods of shading (one per treatment) throughout the growth cycle of various locally adapted cultivars (two per site, six in total) at three sites in Australia and one in Chile to measure the impact of reduced growth on yield and its components. This technique has been widely used to capture the effects of periodic growth restriction on yield development that in practice is usually the result of deficiencies of water or nutrients. In this case, all cultivars showed the same general proportional response to the yield of unshaded controls, which ranged 2.3–6.8 t ha− 1, reflecting local conditions including water availability. Deviation of yield from controls commenced at 450°Cd before flowering and reached a maximum effect 100°Cd later. Shading from 700°Cd to 800°Cd after flowering had no effect on the yield. The major effect of shading at the critical stage was the reduction of seed number, whereas seed size was largely unaffected. Reduction in pod number played the major role in reduction in seed number in the response to shading before flowering. After flowering, the effect was produced by reductions in both pod number and seeds per pod. Some relatively small variation between cultivars offers opportunities for crop improvement. An example of the effect of sowing time on the yield components of faba bean cv. Alameda (intermediate major-equina) sown at 35 plants m− 2 in rows 0.35 m apart is presented in Table 15.6. This experiment was undertaken in an Atlantic temperate region of the Iberian Peninsula. It was irrigated, so the results cannot be extrapolated to Mediterranean regions, generally. The highest yields were attained from early spring sowing (end of March), when an equilibrium between LAI and leaf area duration, iPAR (data not shown, see Confalone et al., 2010) and competition between pods and leaves was achieved. The earliest sowing was subjected to high precipitation that reduced total solar irradiation, whereas late sowings reveal a reduction in grain yield associated with smaller grains. It seems that the crop cycle ended before attainable grain size was reached. HIs of early sown crops are within the ranges found in most experiments (Loss and Siddique, 1997; Sau and Mínguez, 2000). In yield-potential experiments, also irrigated, under Mediterranean conditions with autumn sown indeterminate types (cultivars Alameda and Brocal) sown at 33 plants m− 2, greater biomass production was not associated with greater yield with a consequent decrease in HI (Sau and Mínguez, 2000).

4.2.1  Grain size Grain size of major and equina cultivars is significantly affected by water supply and does not reach their maximum size with strong terminal water deficits. In a comparison of rainfed and fully irrigated experiment assessing cv. Alameda

Faba bean Chapter | 15  471

TABLE 15.6  Biomass, yield, yield components, and HI of faba bean cv, Alameda under five sowing dates repeated over 3 years in a temperate region. Sowing date

Biomass (kg ha− 1)

Yield (kg ha− 1)

Pods (m− 2)

Seed (pod− 1)

Seed (mg)

HI (prop)

End Oct.

9160 c

4281 d

208 d

2.16 bc

954.3 a

0.47 c

Mid Dec.

13 099 a

6738 b

308 b

2.19 bc

999.6 a

0.51 b

Mid Feb.

12 457 a

7733 a

367 a

2.11 c

975.3 a

0.62 a

End March

10 866 a

5466 c

267 c

2.53 a

799.3 b

0.50 b

Beg. May

9936 bc

3831 d

228 d

2.37 ab

708.4 c

0.39 d

Crops were sown at 35 plants m− 2 in rows 0.35 m apart. Different letters indicate significant effect of sowing date (P = .05). Based on Confalone, A., Lizaso, J.I., Ruiz-Nogueira, B., López-Cedrón, F-X., Sau, F., 2010. Growth, PAR use efficiency, and yield components of field-grown Vicia faba L. under different temperature and photoperiod regimes. Field Crop Res. 115, 140–148.

(an intermediate between major and equina), Sau and Mínguez (2000) found a 28% reduction in seed weight from 1.22 g in both N-fertilised and N2-fixing (− N) treatments versus 0.88 g in the corresponding rainfed treatments. The number of seeds per pod (average 2.25) was not affected by water availability. Seeds per pod and pods m− 2 were diminished 16% and 14%, respectively, in rainfed treatments when compared with irrigated treatments.

4.3  Indeterminate, determinate, and semideterminate cultivars In the 1980s ICARDA launched a project aimed at increasing yield and diminishing yield instability in faba beans. Indeterminate and determinate growth types were compared, including a cultivar with independent vascular supply to each pod that was promoted as a trait to improve translocation of assimilate to pods and reduce abortion (Silim and Saxena, 1992a,b). The goal was to increase assimilate partitioning to grain, rather than to otherwise unproductive stems. Following this objective, the project targeted breeding cultivars with determinate habit, fewer late branches and more reproductive nodes on the early formed main branches (ICARDA, 1987). All crops received supplemental irrigation and results showed that the indeterminate types yielded more grain and dry matter (DM) than the determinates but with a smaller HI. Branching was always greater in determinate types, including from the penultimate node. Yield was strongly correlated with biomass, followed by seed weight and less with HI across the experiments. There were significant negative correlations between grain yield and pods m− 2, seeds m− 2, and seeds per pod; they suggested the need for selection of determinate types for fewer, larger seeds. Subsequent studies with spring-sown crops in temperate regions (UK) have also compared determinate and semideterminate cultivars, including under irrigation regimes. Pilbeam et al. (1992) established a proportionally smaller yield of cv. Ticol (determinate) than cv. Minica (semideterminate) in all irrigation treatments, despite the production of more reproductive structures. The authors concluded that neither cultivar had a particularly water-sensitive phase. Lack of vegetative growth after flowering reduced biomass production to which HI was positively correlated. De Costa et al. (1997a,b) add a comparison between cv. Tina (determinate) and cv. Gobo (indeterminate) under four water regimes. The study concluded that the determinate cultivar yielded more under irrigation when sown in late spring but that the indeterminate cultivar yielded more under all other conditions. Thus although breeding programmes envisaged that determinate types and semideterminate types would partition photosynthate to yield components with greater efficiency, this has not been realised in practice. The reason it seems is that small LAI during grain fill prevents yields matching those of the indeterminate types (Gates et al., 1983; Hebblethwaite, 1984). Indeed, when mild or strong terminal water deficits occur, vegetative growth is restricted, so indeterminate types usually perform better than determinates. A positive linear relationship between pod yield and postflowering leaf area duration has been found in most field experiments (De Costa et al., 1997b; Sau and Mínguez, 2000; Confalone et al., 2010). The result then is that, after 50 years of attention, few determinate cultivars of faba bean have emerged from breeding programmes and their use is currently restricted to irrigated production. In contrast, semideterminate cultivars dominate production in rainfed production. Indeterminate and semideterminate types are mostly used at densities between 22 and 35 plants m− 2 for autumn sowings, whereas determinate and semideterminate types are sown at densities of 30–70 plants m− 2 in spring. Semideterminate types are mostly sown in Australia combining the advantages of indeterminate and determinate types. GRDC (2017, 2018) represent a way forward to help with the choice of cultivars and agronomic practices in Australia that could be applied to Mediterranean regions.

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4.4  Nutritional issues and grain quality Faba bean grain is consumed by humans in various forms. Young pods and green seeds can be consumed as vegetable (e.g. seeds with a length  25°C) stimulates the development of rhizomes and rhizome branching, increasing the potential number of tuber sites per plant (Struik et al., 1989a,b). Optimum temperatures for tuber development are 15°C for induction, 22°C for initiation and 15°C for setting. These are low temperatures when compared with other phenophases; therefore, the critical period for tuber yield formation is sensitive to heat (Struik, 2007). The tuberbulking phase, whose duration depends on tuber initiation onset (TIO) and the rate of leaf senescence, can be affected by moderately high temperature in two ways: extending the period of leaf growth and, hence, prolonging the tuber growth phase (Marinus and Bodlaender, 1975) or increasing leaf senescence rate (Menzel, 1985) and shortening this phenophase. The effect will depend on the timing and duration of heat events. In irrigated field experiments, an increase in average air temperature from 16°C to 20°C for 20 days at the beginning of tuber bulking, delays leaf senescence, probably due to extending the period of leaf growth (Lizana et al., 2017), whereas an increase in temperature from 17°C to 24°C for 40 days accelerated senescence and shortened the tuber bulking phase (Ávila-Valdés et al., 2020). Temperatures above 30°C accelerate senescence and, therefore, reduce the life span of canopy (Fahem and Haverkort, 1988; Midmore, 1990; Kooman et al., 1996b).

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2.1.1.1  Tuber yield response to temperature Tuber yield integrates development, total growth, and partitioning processes; the latter mainly driven by development. As each of these processes responds to temperature, the definition of optimum temperature for yield is elusive; thus, reported optimal temperatures for tuber yield range from 14°C to 22°C (Bodlaender, 1963; Marinus and Bodlaender, 1975; Timlin et al., 2006; Burton, 1981; Yandell et al., 1988). Night temperature affects potato growth and yield (Gregory, 1965; Sale, 1979; Levy and Veilleux, 2007), but differential mechanisms of potato yield loss under high day and night temperatures have been reported. Kim and Lee (2019) demonstrated similar tuber yield in response to high night (−17.2%) and high day temperature (−17.1%), with effect of high night temperatures, associated with delayed tuber development and lower early harvest index by reduced proportion of large tubers (>100 g) without interfering with photosynthesis. High day temperature, in change, decreased early tuber yield by reducing photosynthetic sources, probably attributed to decreased photosynthetic efficiency through a feedback inhibition. Optimum canopy net photosynthetic rates have been measured at around 24°C (Timlin et al., 2006; Hancock et al., 2014) with higher temperatures reducing photosynthesis by decreasing the efficiency of photosystem II (Prange et al., 1990) and reducing stomatal conductance (Dwelle et  al., 1981; Ku et  al., 1977). Conversely, in well-irrigated crops moderate heat stress (30/20°C day/night) increases stomatal conductance, thereby improving net carbon assimilation rate (Hancock et al., 2014). The optimum for starch synthesis in tuber is 25°C, with higher temperatures decreasing the activity of sucrose synthase and ADPG pyrophosphorylase, involved in sucrose–starch conversion to starch biosynthesis (Gaigenberger et al., 1998). Temperature also modulates the source:sink ratio and the partitioning of biomass to tubers, with temperatures above 18°C usually favouring aboveground growth in detriment of tuber yield, reducing harvest index (HI). High temperature (> 15–22°C) inhibits TIO and stimulates stem elongation, with a decline in leaf:stem ratio (Wheeler et al., 1986; Ewing and Struik, 1992). The relative partitioning rate of tubers is higher at cooler temperatures between 12°C and 20°C (Wolf et al., 1990; Lafta and Lorenzen, 1995; Kooman et al., 1996b). For example, 66% of assimilates were allocated to tubers at 18°C, in comparison with 50% at 28°C (Randeni and Caesar, 1986).

2.1.2 Photoperiod Photoperiod plays a key role in induction of tuberization and TIO. Potato is a quantitative short-day plant, as tuberization is faster when the photoperiod is below 12 h for S. tuberosum spp. andigena and 16 h for S. tuberosum spp. tuberosum (Bodlaender, 1963). The response is mediated by phytochrome, with Phytochrome B (Phy B) preventing tuberization of S. tuberosum spp. andigena and delaying tuberization of S. tuberosum spp. tuberosum, under long days. In addition, photoperiod has an effect on earliness of potato because of the relationship between the duration of the period between emergence and tuberization and the following crop phenophases. Thus an early TIO and subsequent early partitioning of dry matter (DM) into the tubers leads to earlier maturity (Kooman et al., 1996b). A coordinated action of Phytocrome A (PhyA) and Phy B in the repression of tuber induction was suggested (Fernie and Willmitzer, 2001). Phy B is involved in the production of a graft-transmissible inhibitor of tuberization (Jackson et al., 1998), and Phy A is involved in resetting the circadian clock of potato, delaying tuber formation under noninducing conditions (Yanovsky et al., 2000). The photoperiodic response interacts with temperature, accelerating the rate of development between emergence and TIO at higher temperatures. The response is also dependent on the sensitivity of the genotype mainly to temperature (Kooman et al., 1996a,b). As shown in Fig. 18.6, simulated and experimental responses of TIO to temperature at different latitudes, exhibited similar patterns, with warmer temperatures resulting in earlier maturity for all sites (Kooman, 1995; Fleisher et al., 2017). Experimental data including different locations and planting dates evidence the effect of photoperiod in the length of the emergence-TIO period (Kooman, 1995; Streck et al., 2007).

2.1.3  Light quality Phytochrome B mediates photomorphogenic responses in potato, by inducing stem elongation and increasing the number of nodes on main stems and branches and decreasing the leaf:stem ratio (Demagante and Vander Zaag, 1988; Menzel, 1985). As a consequence, tuberization is negatively affected by the change in biomass partition and the low signal of sucrose to tuberization (Bodlaender, 1963; Gray and Holmes, 1970; Sale, 1973a,b; Menzel, 1985). High level of sucrose is a key factor to tuber induction; the evidence from both in vitro and plant systems shows that an inhibition in sucrose transporter activity reduces tuber formation (Viola et al., 2001). Transgenic plants overexpressing Phytochrome B gene (PHYB) increased tuber number and tuber yield at high plant population density (20 plants m− 2), by altering the ability of plants to respond to light signals and also modifying the light

558  Crop Physiology: Case Histories for Major Crops

FIG. 18.6  (a) Temperature effect on planting-tuber initiation onset period at different locations. (b) Photoperiodic effect on the length of emergence to tuber initiation (Em-TI) phase. DAP, days after planting. Data from (a) Fleisher, D.H., Condori, B., Quiroz, R., Alva, A., Asseng, S., Barreda, C., Bindi, M., Boote, K.J., Ferrise, R., Franke, A.C., Govindakrishnan, P.M., Harahagazwe, D., Hoogenboom, G., Naresh Kumar, S., Merante, P., Nendel, C., Olesen, J.E., Parker, P.S., Raes, D., Raymundo, R., Ruane, A.C., Stockle, C., Supit, I., Vanuytrecht, E., Wolf, J., Woli, P., 2017. A potato model intercomparison across varying climates and productivity levels. Glob. Chang. Biol. 23, 1258–1281. https://doi:10.1111/gcb.13411, Bolivia, Burundi, Denmark, and US, based on 30-year simulations with seven models; Kooman P.L., 1995, Yielding Ability of Potato Crops as Influenced by Temperature and Daylength (Ph.D. thesis). Wageningen University, The Netherlands, Wageningen, Kinigi, Rubona, and Tunisia at spring, autumn and winter, experimental data of different locations and seasons, with different mean temperature during crop cycle and (b) Kooman P.L., 1995, Yielding Ability of Potato Crops as Influenced by Temperature and Daylength (Ph.D. thesis). Wageningen University, The Netherlands, different locations; Streck, N.A., de Paula, F.L.M., Bisognin, D.A., Heldwein, A.B., Dellai, J., 2007. Simulating the development of field grown potato (Solanum tuberosum L.). Agric. For. Meteorol. 142, 1–11. https:// doi:10.1016/j.agrformet.2006.09.012, different planting dates.

environment itself when compared with wild type (Fig.  18.7). This combination resulted in larger effects of enhanced PHYB expression on crop photosynthesis of all strata of the canopy due to increased leaf stomatal conductance at high planting densities. Boccalandro et al. (2003) proposed that enhanced PHYB expression could be used in breeding programmes to increase optimum planting densities; investigation of genetic variability for PHYB expression is of interest.

2.1.4 Hormones Hormones involved in TIO include cytokinin, jasmonic acid and related compounds, and abscisic acid (ABA). Gibberellic acid (GA) is an unequivocal inhibitor of tuberization, with declining GA levels during tuber induction. Both overexpression of the GA oxidase and exogenous application of GA inhibited tuberization in inductive environments (Carrera et al., 2000).

FIG. 18.7  Yield, morphological, and physiological responses of transgenic plants overexpressing PHYB, relative to wild type (WT) at two plan densities (10 or 20 plants m− 2). TY, tuber yield; TN, tuber number; LS, final stem length; SC, soil cover; PPDF, PPFD in medium canopy strata; PN, actual net CO2 uptake at medium canopy strata; SC, stomatal conductance. Data from: Boccalandro, H., Ploschuk, E.L., Yanovsky, M.J., Sánchez, R.A., Gatz, C., Casal, J.J., 2003. Increased phytochrome B alleviates density effects on tuber yield of field potato crops. Plant Physiol. 133, 1539–1546. https:// doi:10.1104/pp.103.029579.

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3  Growth and resources 3.1  Capture and efficiency in the use of radiation Crop yield is the product between the amount of intercepted radiation (IR), radiation use efficiency (RUE), and harvest index (HI) (Monteith, 1977): Yield  IR  RUE  HI

(18.1)

Tuber yield is mainly explained by accumulative IR under favourable conditions. Under stress, RUE is as important as IR, and under high temperature all three components (IR, RUE, and HI) contribute to tuber yield variation (van Der Zaag and Doornbos, 1987).

3.1.1  Radiation capture DM production correlates linearly with IR during the crop cycle. Cumulative IR is determined by the proportion of the aboveground biomass, the length of the growing period, and global incident radiation [of which about half is photosynthetic active radiation (PAR)]. Length of the crop cycle is the main determinant of cumulative IR across environments and genotypes, considering a sufficiently long, frost-free growing season, temperature range of 5–30°C, water availability during tuber bulking and lack of rain at harvest (Kooman, 1995; MacKerron and Haverkort, 2004). For example, Demagante et al. (1996) reported 740, 900, and 945 MJ PAR m− 2 IR for early, medium, and late maturing potato varieties, respectively, in the Philippines. At high latitudes, a late maturing variety can intercept more than 700 MJ m− 2 (van Der Zaag, 1992). For a combination of potato varieties and seasons, IR varied from 548 to 758 MJ m− 2 in the Netherlands and from 399 to 501 MJ m− 2 in Tunisia spring season (Kooman et al., 1996b). In southern Chile, late varieties intercepted up to 740 MJ PAR m− 2 during the crop season (Sandaña and Kalazich, 2015b). Kooman (1995) showed a direct correlation between cumulative IR and thermal time to T50 (when ground cover drops to 50% of maximum) and found a close correlation between tuber yield and cumulative intercepted PAR (Fig. 18.8). Field experiments in temperate environment of southern Chile showed that tuber yield was positively associated with solar IR between flowering and maturity, when thermal treatments modified the length of this stage (Lizana et al., 2017; Ávila-Valdés et al., 2020). Potato is a shade-tolerant plant suitable for intercropping or agroforestry systems (Schulz et al., 2019). Photosynthesis saturates at 400 mol PAR m− 2 s− 1, which corresponds to 15 MJ m− 2 d− 1 (Pleijel et al., 2002). In tropical and subtropical zones with radiation up to 30 MJ m− 2 d− 1, radiation does not limit potato production (Mariana and Hamdani, 2016; Nadir et al., 2018). At high latitudes, the average PAR in spring–summer is close to saturation. The amount of radiation available during crop season is between 10 and 20 MJ m− 2 d− 1 in the temperate zone of Europe (NASA, 2017). In southern Chile, radiation during crop season averages 20 MJ m− 2 d− 1 but varies from 2 to 32 MJ m− 2 d− 1 (Sandaña and Kalazich, 2015b). Incident light on the top of canopy is extinguished following a coefficient (k) that can change throughout the crop cycle, as a function of leaf size, leaf angle, and canopy architecture. Besides, high variability of canopy growth and architecture is observed between genotypes. Khurana and McLaren (1982) reported lower k at lower LAI at the beginning of the crop

FIG. 18.8  (a) Relationship between cumulative intercepted PAR and thermal time when ground cover reaches 50% of full ground cover (T50). (b) Association between tuber yield and cumulative IR. Data from: Kooman P.L., 1995. Yielding Ability of Potato Crops as Influenced by Temperature and Daylength (Ph.D. thesis). Wageningen University, The Netherlands, data from experiments combining genotypes, locations and seasons; Ávila-Valdés, A., Quinet, M., Lutts, S., Martínez, J.P., Lizana, X.C., 2020. Tuber yield and quality responses of potato to moderate temperature increase during tuber bulking under two water availability scenarios. Field Crop Res. 251, 107786. https://doi:10.1016/j.fcr.2020.107786 combining genotypes, irrigation, and thermal treatments; Sandaña, P., Kalazich, J., 2015b. Ecophysiological determinants of tuber yield as affected by potato genotype and phosphorus availability. Field Crop Res. 180, 21–28. https://doi.org/10.1016/j.fcr.2015.05.005. combining seasons and P availability.

560  Crop Physiology: Case Histories for Major Crops

cycle and higher k as LAI increased. Variation in the light extinction coefficient has been found, due to the precocity of the genotypes, availability of N, amongst others, with an average of approximately 0.4 in potatoes (Jongschaap, 2006). Canopy growth can be divided into three phases (Fig. 18.9). Phase 1 is from emergency to full canopy cover, where leaf appearance and expansion, stems elongation, and lateral branching occur (Vos, 1995; Fleisher et al., 2006; Khan, 2012). TIO normally coincides with Phase 1. Phase 2 spans from full canopy cover to the onset of canopy senescence, and Phase 3 is active canopy senescence. Most of tuber bulking occurs during Phases 2 and 3. The length of the phases depends on maturity type of genotypes (Haverkort and MacKerron, 1995). To achieve high yields, critic LAI (LAIc, i.e. when fractional IR is 95%) must be reached as soon as possible, and the canopy should remain closed as long as possible. The LAIc in potato is close to 3.0 (MacKerron and Waister, 1983; Burke, 2003; Wu et al., 2007); over this threshold, the fraction of intercepted radiation (FIR) stabilises with little or no response to agronomic management (Jefferies and Heilbronn, 1991; de la Casa et al., 2007). Under LAIc, the maximum FIR is directly associated with tuber yield (Mera et al., 2015 and Fig. 18.10). Because a curvilinear relationship between FIR and the fraction of soil covered by foliage applies to potato, the grid method (Haverkort et al., 1991b) and photograph method (de la Casa et al., 2007) have been validated to estimate FIR up to full canopy closure. Canopy size is affected by agronomic management, including tillage, irrigation, and fertilisation (Fig. 18.11). Under drought, fast attainment of exponential growth and maximum canopy cover has negative effects on tuber formation and tuber bulking. In a study by Aliche et al. (2018), growth rate, maximum canopy cover, and area under the curve of canopy growth under drought (representing photosynthetic capacity over the growing season) affected tuber bulking to a greater extent than tuber formation. Soil compaction also constrains canopy growth. For shallow compaction, above 30 cm depth,

FIG. 18.9  Canopy development dynamics in potato (solid line). Red dotted line represents tuber bulking dynamic, and green arrows show the time to full canopy cover and onset of canopy senescence. LAI, leaf area index.

FIG. 18.10  Ground cover of four potato genotypes 82 days after planting. (a) Native genotype Chona Negra, (b) crossing M29, (c) Rodeo, and (d) Patagonia INIA. (e) Relationship between tuber yield and the maximum fraction of radiation interception, from field experiments in Valdivia, Chile, evaluating different temperatures (Desiree Temp, Karu INIA Temp, and Yagana INIA Temp), shadow, and UV-B treatments in Desiree and Yagana INIA; Desiree cropped at different hill volume treatments (Desiree ridges). (a–d) courtesy of Carolina Lizana and (b) from Mera, M., Lizana, C., Calderirni, D.F., 2015. Cropping systems in environments with high yield potential of southern Chile. In: Sadras, V., Calderini, D. (Eds.), Crop Physiology, Applications for Genetic Improvement and Agronomy, second ed. Academic Press, San Diego, CA, USA, pp. 111–140.

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FIG. 18.11  (a) Canopy growth under irrigated and nonirrigated conditions. (b) Interaction between irrigation and soil compaction at 10 cm depth on canopy cover. Data from: Stalham, M.A., Rosenfeld, A.B., 1996. Soil compaction and canopy growth Cambridge University Potato Growers Research Association Annual Report 1996. CUPGRA, Cambridge, pp. 46–52.

an early restriction in canopy growth was followed by an increase in the rate of canopy growth once the roots had penetrated the compacted layer (Rosenfeld, 1997; Stalham et al., 1997). Deeper compaction did not affect early canopy growth but resulted in earlier senescence. These studies showed that canopy growth is affected as soon as root growth is restricted (Stalham and Allen, 2001), and that watering did not alleviate completely the negative effect of soil compaction (Stalham and Rosenfeld, 1996). Biotic factors can affect IR by reducing leaf area or indirectly, altering plant function. Late blight disease (Phytopthora infestans), one of the most destructive diseases in potato, induces circular lesions in leaves and reduces IR but not RUE (Haverkort and Bicamumpaka, 1986; van Oijen, 1991). Viral diseases caused by PVX, PVY, PVA, PVM, PVS, aucuba virus, mop-top virus, PLRV and APLRV, induce mosaic, stunting, necrosis, leaf roll, and generally reduce canopy size. The cyst nematode (Globodera rostochiensis and G. pallida) affects plant growth from emergence, decreases LAI in parallel to the size of the root system affecting water relations, nutrient uptake, RUE, and partition of assimilates (Trudgill et al., 1975; Haverkort et al., 1992; Shah et al., 2004; Fasan and Haverkort, 1991; Evans et al., 1975, 1977; Fatemy and Evans, 1986).

3.1.2  Radiation-use efficiency RUE of potato is higher than in other C3 crops and even higher than in some C4 crops, partially due to the low energy content of starch, the main component of potato tubers (Murchie and Reynolds, 2013). High RUE (calculated from PAR) has been reported in temperate regions: 3.5–3.7 g MJ− 1 in England (Allen and Scott, 1980), 3.9 MJ− 1 in Scotland (Jefferies and MacKerron, 1989), 3.2–3.8 MJ− 1 in Japan (Nishibe et al., 1989), and 2.9–3.0 MJ− 1 in Denmark (Zhou et al., 2017). A RUE of 5.4 g MJ− 1 was reported under foggy desert conditions of central coast of Peru dominated by diffuse radiation (Quiroz et al., 2017). SUBSTOR-Potato model uses a RUE value of 3.5 g MJ− 1 before tuberization and 4 g MJ− 1 after tuberization to estimate the potential growth rate (Griffin et al., 1993). Genotypic differences on RUE, especially comparing commercial and native genotypes, have been associated with different canopy size and/or architecture (Sandaña and Kalazich, 2015b) because RUE varies with the proportion of LAI at nonsaturating irradiance (Stöckle and Kemanian, 2009). A comparison of three varieties (Bondi, Fraser, and Russet) showed variation in RUE under nonlimiting conditions (Fig. 18.12), directly associated with tuber yield (Oliveira et al., 2016). The authors suggest a sink limitation for potato tuber yield, where the larger sink was associated with shorter rhizomes in high and middle node positions and the higher average tuber weight per plant. It was evident in Bondi, the variety with the highest tuber yield, when compared with Fraser and Russet. On the contrary, Fraser and Russet had a lower rate of canopy senescence during tuber bulking period, allocating the highest DM to leaves at the end of the tuber filling, in detriment of tuber yield (Oliveira et al., 2016). RUE is negatively correlated with total solar radiation between 10 and 25 MJ m− 2 d− 1, with increasing mean daily temperature between 13°C and 26°C and with increased vapour pressure deficit between 0.4 and 1.5 kPa (Manrique et al., 1991). In irrigated crops in Denmark, high temperature reduced RUE and DM production (10% per °C) from emergence to the end of tuber initiation. Studies in Hawaii showed that lower temperatures associated with higher elevation increased RUE from 2.2 g MJ− 1 at 91 m elevation to 2.8 g MJ− 1 at 1097 m (Manrique et al., 1991) because higher air temperatures favour aboveground growth in detriment of tubers. Night temperatures from 0–20°C increased potato root length, whereas temperature above 25°C sharply reduced the number and weight of tubers (Nyawade et al., 2018). These changes in biomass partitioning and plant architecture normally have consequences not only on IR but also for RUE.

562  Crop Physiology: Case Histories for Major Crops

FIG. 18.12  (a) Radiation-use efficiency, (b) average rhizome length, and (c) average tuber length at different node positions (1–14) on the below ground main stem of three potato varieties, 82 days after planting. Data from: Oliveira, J.S., Brown, H.E., Gash, A. Moot, D.J., 2016. An explanation of yield differences in three potato cultivars. Agron. J. 108, 1434–1446. https://doi:10.2134/agronj2015.0486.

Several studies reported the effect of drought on RUE (Trebejo and Midmore, 1990; Demagante et al., 1996; Jefferies and MacKerron, 1989; Camargo et al., 2015), allowing for linear associations between DM and RUE. Under water deficit, potatoes leaves curl, leading to reduced IR, water uptake, and RUE (Trebejo and Midmore, 1990). It has been hypothesised than in cool regions, DM accumulation is decreased mainly by reductions in IR as results of reductions in canopy size, but in hot and dry regions, RUE also affects the DM production (Midmore, 1992). Nitrogen deficiency can reduce leaf photosynthesis and RUE. Hu et al. (2014) reported up to 31% lower RUE associated with N rates from 0 to 300 kg N ha− 1, depending on the phenological phases of the crop. Zhou et al. (2017) found no effect of N on RUE, except for a lower RUE in nonfertilised crops. Two studies reported reductions in potato growth with P deficiency related to IR and independent of RUE (Jenkins and Ali, 1999; Sandaña and Kalazich, 2015b). Biotic stress can reduce leaf photosynthesis and RUE; RUE declined linearly from 3.1 to 1.9 g MJ− 1 with increasing density of cyst nematode causing leaf chlorosis and acting as a sink for carbohydrates (Shah et al., 2004). Reduced root size may have also reduced water uptake, leading to increased water stress and a reduction in leaf turgidity (Evans et al., 1977; Haverkort et al., 1991a,b).

3.1.3  Radiation interception and RUE in intercrops Intercropping aims at increasing production and efficiency in the use of resources when compared with single crops. Intercropping of potato and sulla (Hedisarium coronarium L.) in Tunisia increased IR by 18%–22% and total DM by 13%–15% with respect to the potato as a sole crop. Higher productivity was explained by RUE enhancement in potato (11.2%–11.4%) and sulla (19.4% in one experiment) under intercropping (Rezing et al., 2013a). Intercropping potato with bean (Phaseolus vulgaris L.) in the low valley of Medjerda River in Tunisia reduced IR of both crops (4.8%–20.9% in potato and 44.8%–58.7% in bean) but increased RUE from 7.7% to 23.6% in potato. Thus the intercropping was positive for biomass in potato but negative for bean (Rezing et al., 2013b). Intercropped potato with lima bean (Phaseolus lunatus L.) and dolichos (Lablab purpureous L.) in the tropical environment of Kenia showed increased LAI by 26%–57% relative to sole potato stands. Higher LAI reduced top-soil (0–30 cm) temperature by up to 7.3°C, inducing a soil water increase by up to 38% and RUE by 56%–78% (Nyawade et al., 2019).

3.2  Capture and efficiency in the use of water Drought scenarios, defined as the combination of timing, duration, and intensity of water restriction, cause different responses in tuber yield, with water shortage at the early developmental stage as the most yield-depressing period in potato (Ramírez et al., 2016). The selection of traits for drought tolerance must take into consideration the drought scenario. In a common water scenario in agriculture, mild water stress (Tardieu, 2012), opportunistic traits as higher transpiration and carbon assimilation or lower WUE take advantage of water pulses during crop season (Ramírez, 2014). On the contrary, under terminal or severe drought, a reduction in stomatal conductance to save water and increases in the intrinsic WUE are more effective to cope with water stress. A resilient genotype is one that has a high diversity of traits to respond to different drought scenarios. For example, genotype Sarnav (CIP code: 397077.16) featured adaptation to drought including high WUE, osmotic adjustment, and detoxication-related enzymes synthesis, which allowed it to take advantage of water pulses under drought stress (Legay et al., 2011; Ramírez et al., 2015a).

Potato Chapter | 18  563

FIG.  18.13  (a) Temporal dynamics of leaf greenness measured with SPAD in potato cv. Désirée under control (open circles) and water-restricted conditions (black circles). Red arrow shows maximum difference between water treatments. DAP, days after planting. (b) Green normalised difference vegetation index (NDVIg) 64 days after planting in four potato genotypes under two water regimens: weekly irrigation to field capacity (C) and irrigation when average maximum stomatal conductance reached 0.15 mol H2O m− 2 s− 1 (S). Genotypes were ‘Sarnav’-S (CIP code: 397077.16), ‘Tacna’-T (CIP code: 390478.9), ‘Unica’-U (CIP code: 392797.22), and Désirée-D (European cultivar). (c) Relationship between DTI (yield base drought tolerance index) and the slope of NDVIg decline between for each genotype assessed in (b). Modified from (a) Rolando, J.L., Ramírez, D.A., Yactayo, W., Monneveux, P., Quiroz, R., 2015. Leaf greenness as a drought tolerance related trait in potato (Solanum tuberosum L.). Environ. Exp. Bot. 110, 27–35. https:// doi:10.1016/j.envexpbot.2014.09.006 and (b,c) Ramírez and Loayza (unpublished).

Leaf greenness increases in association with the reduction in leaf expansion with water deficit, and chlorophyll amplitude (ChlAmp) is defined as the maximum difference in leaf greenness between stressed and control treatment (Fig. 18.13a). Chl Amp  XChl D  XChl C

(18.2)

where XChlD and XChlC are average chlorophyll content under drought and control conditions at the time of maximum difference (Fig. 18.13a). This trait is negatively related to drought and salinity tolerance and has been related to tuber yield (Li et al., 2019; Ramírez et al., 2014, 2019; Rolando et al., 2015). Further studies confirmed the conservative behaviour of ChlAmp under both long-term and short-term (cycling) water-deficit scenarios, whereas other traits related to senescence delay and antioxidant defence/damage depend on drought type mentioned above (e.g. Li et al., 2019). Screening for ChlAmp with remote sensing has used normalised difference vegetation index (NDVIg) or chlorophyll reflectance index (Fig. 18.13b and c). The use of vegetation indexes based on air-borne canopy reflectance for high-throughput phenotyping in potato could be incorporated to crop growth models for multienvironment yield predictions. In comparison to other staple crops, potato fresh yield is high per unit of land area and water use (Renault and Wallender, 2000). However, WUE defined as DM production per unit evapotranspiration ranges between 1.3 and 11.8 kg m− 3 (Doorenbos and Kassam, 1979; Renault and Wallender, 2000; Liu et al., 2006; Nagaz et al., 2007; Fleisher et al., 2008; Steduto et al., 2012; Yactayo et al., 2013). Potato has been considered as a drought susceptible crop because of its shallow root system (Stalham et al., 2007). However, some studies have highlighted the potential of some genotypes to generate deep roots (Iwama, 2008). A deeper

564  Crop Physiology: Case Histories for Major Crops

root system has been considered adaptive, but the role of these deeper roots for whole-plant water status is unclear. Hydrogen and oxygen isotopic analysis can help answer this perspective, for example labelling water with deuterium or analysing O16/ O18 concentration. Root systems are further discussed in relation to nutrient uptake (Section 3.3). The heterogeneity of soil water dynamics and the unknown role of deeper roots compromise the value and interpretation of measurements of soil water status, shifting the emphasis to crop-based indicators for irrigation in potato. Maximum, light-saturated stomatal conductance (gs_max) has been considered as a physiological surrogate of water status in plants (Medrano, 2002), with more relevance than relative water content and leaf water potential (Flexas et al., 2004, 2006). Some studies in potato have established thresholds of gs_max for irrigation (gs_max > 0.3 mol H2O m− 2 s− 1; Ramírez et al., 2016; Rinza et  al., 2019). Remote and proximal-sensing methods allow for scaling up from stomatal conductance to canopy temperature (Fig. 18.14). The crop water stress index (CWSI) is the standardised difference between canopy temperature (TCanopy) in relation to the atmospheric temperature and a valuea (dry temperature Tdry) divided by the difference between a wet temperatureb (Twet) in relation to Tdry: CWSI 

T T

dry

 TCanopy 

dry

 Twet 

(18.3)

CWSI for potato canopies has shown significant correlations with gs_max, carbon isotope discrimination (Δ) and tuber yield under drought. A threshold CWSI ~ 0.3–0.4 has been proposed for irrigation, saving water with tuber yield penality (Ramírez et  al., 2016; Rinza et  al., 2019; Cucho-Padín et  al., 2020). Because stomatal behaviour is subjected to circadian rhythm, the timing during the day for the assessment of gs_max and CWSI is critical. In humid environments, Rinza et al. (2019) proposed to measure these traits at 2–3 pm, when plants have accumulated enough heat, to detect differences amongst water regimes. Ramírez et al. (2014) proposed the assessment of carbon isotope discrimination (Δ) in leaves before the onset of tuber initiation (Fig. 18.15). Although leaf Δ has been considered as a surrogate for WUE, the relationship between both variables is complex because it depends on vapour pressure deficit, mesophyll conductance, which is affected by leaf morphology and biochemistry, and carbon fractionation caused by day respiration, photorespiration and postphotosynthetic reactions (Seibt et al., 2008; Resco et al., 2011). In tubers, Δ has been related to translocation of carbohydrates and respiration energy use showing significant correlations with tuber production under water stress (Ramírez et al., 2015a,b, 2016; Rinza et al., 2019).

FIG. 18.14  Thermal and RGB images (1) using camera FLIR E60 through the following steps: (2 and 3) data acquired for both RGB and IR bands. (4) IR image is filtered to avoid false detection of plants. (5) RGB and IR images are aligned to process plant temperature in a specific part of the plot. Surface reference for calculation of wet temperature is marked in the red square in 1. The alignment process was carried out using TIP_CIP open source software. Based on Cucho-Padín et al. (2020). Photo source: Chapter authors.

a. This value depends on the crop and reflects the maximum warming of leaves in relation to the atmospheric temperature. It is calculated in nontranspiring leaves covering their surfaces with petroleum jelly. In potato, it ranges from 7°C (Rud et al., 2014; Ramírez et al., 2016) to 13°C (Rinza et al., 2019) for Tdry calculations. b. Empirically (sensu Rud et al., 2014), this is the infrared temperature of a humid white cotton. This temperature mimics the maximum capacity of refrigeration through transpiration.

Potato Chapter | 18  565

24.0 23.5

∆leaf (‰)

23.0 22.5 22.0 21.5

C IP -7 0 C 35 IP 06 C 720 IP 2 -7 01 C 07 IP 12 -7 9 C 05 IP 35 -7 2 C 05 IP 8 -7 34 C 04 IP 4 -7 0 C 05 6 IP 06 C -7 IP 00 8 39 9 2 C 169 1 IP 38 1.9 6 C 038 IP -7 9.1 0 C IP 352 C -70 0 IP 59 37 5 C 40 2 IP 80 -7 . C 02 5 IP 8 -7 53 C 04 IP 05 C 706 7 IP 0 -7 50 C 067 IP 76 C 706 IP 7 -7 1 C 04 3 IP 3 -7 9 C 02 3 IP 3 -7 43 C 03 IP 97 -7 1 C 04 IP 50 -7 06 1 84 5

21.0

FIG. 18.15  Discrimination against carbon 13 sampled in leaves (Δleaf) of potato clones from CIP genebank. Samples were collected 79 days after planting at CIP experimental station in Lima, Perú. Modified from Silva-Díaz, C., Ramírez, D.A., Rinza, J., Ninanya, J., Loayza, H., Gómez, R., Anglin, N.L., Eyzaguirre, R., Quiroz, R., 2020. Radiation interception, conversion and partitioning efficiency in potato landraces: how far are we from the optimum?. Plants 9 (6), 787. https://doi.org/10.3390/plants9060787.

3.3  Capture and efficiency in the use of nutrients Fertilisers are critical to increase crop yields and maintain nutrient balance in soil (Jobbágy and Sala, 2014), but underfertilisation (Angus and Grace, 2017) and overfertilisation may have negative environmental impacts (Tilman et al., 2001, 2011; van Evert et al., 2012; Haverkort et al., 2014). Therefore sustainable intensification will require effort to improve nutrient-use efficiency, which implies to improve fertilisation management and/or higher nutrient-use efficiency. In this section, we review the critical N and phosphorus (P) dilution curves and discuss N and P-use efficiency (PUE) for potato crops.

3.3.1  Critical nutrient dilution curves For each nutrient, there is a critical nutrient concentration (%Nc) defined as the minimum to achieve maximum crop mass (Greenwood et al., 1990; Justes et al., 1994; Gastal et al., 2015; Zamuner et al., 2016). This %Nc declines with increasing crop biomass (W, t ha− 1) according to a negative power function—the critical nutrient dilution curve (Lemaire et al., 2008, 2019; Gastal et al., 2015; Lemaire and Gastal, 2018): %Nc  ac W  b

(18.4)

where ac is the plant nutrient concentration for W = 1 t ha− 1, and b is the dimensionless ratio between the relative decline in plant nutrient concentration and the relative crop growth rate (coefficient of dilution). In potato, several critical N dilution curves have been reported with ac from 4.50 to 5.53 and b from 0.25 to 0.58 (Fig. 18.16a and Table 18.2). These differences have been mainly attributed to water availability and cultivar differences in the partitioning of biomass between tuber and shoots (Duchenne et al., 1997; Bélanger et al., 2001; Giletto and Echeverría, 2012, 2015; Giletto et al., 2020). However, some of the curves are very similar, and slight differences could be attributed to the precision and methods for sampling and data analysis (statistical procedures to determine critical N). This is reinforced by similarity in critical N dilution curves for major crops within each metabolic groups C3 and C4 (Gastal and Lemaire, 2002; Lemaire and Gastal, 2018). Due to the uncertainty associated with the method, we propose to use the average of the parameters in Table 18.2. There are fewer critical P dilution curves for potato with differences attributable to methods (Table 18.2 and Fig. 18.16b). Zamuner et al. (2016) developed a critical P dilution curve for the cv. Innovator (group Chilotanum) grown under irrigated conditions in southern Argentina (37° 45′, 38° 22′ S), whereas Gómez et al. (2019) reported two curves for two Andigenum group cultivars (Diacol Capiro and Pastusa Suprema) grown in Colombia (4° 49′, 5° 6′ N). Soratto et al. (2020) tested these curves and demonstrated that the curve reported by Zamuner et al. (2016) was the best to capture variation between cultivars, P fertilisation rates, and environments in tropical Oxisols of Brazil (Fig. 18.17).

566  Crop Physiology: Case Histories for Major Crops

FIG. 18.16  (a) Critical N dilution curves for potatoes from different sources and (b) critical P dilution curves reported by Zamuner et al. (2016) and Gómez et al. (2019). Table 18.2 shows curve parameters and references.

TABLE 18.2  Coefficients ac (%) and b (dimensionless) of critical dilution curves for N and P for different potato cultivars and environmental conditions. Nutrients

Cultivars

ac

b

R2

References

Nitrogen

Bintje and Kaptah Vandel

5.21

− 0.56

–

Duchenne et al. (1997)

Shepody (irrigated)

5.04

− 0.42

–

Bélanger et al. (2001)

Russet Burbank (irrigated)

4.57

− 0.42

–

Bélanger et al. (2001)

Shepody (rainfed)

5.36

− 0.58

–

Bélanger et al. (2001)

Russet Burbank (rainfed)

4.50

− 0.58

–

Bélanger et al. (2001)

Innovator

5.30

− 0.42

0.92

Giletto and Echeverría (2012)

Gem Russet

5.32

− 0.36

0.76

Giletto and Echeverría (2015)

Umatilla Russet

5.19

− 0.25

0.63

Giletto and Echeverría (2015)

Dannock Russet

5.30

− 0.25

0.75

Giletto and Echeverría (2015)

Markies Russet

5.53

− 0.25

Giletto and Echeverría (2015)

Bintje

5.37

− 0.45

Abdallah et al. (2016)

Average

5.17

− 0.42

Innovator

0.39

− 0.30

Zamuner et al. (2016)

Diacol Capiro (group Andigenum)

0.52

− 0.19

Gómez et al. (2019)

Pastusa Suprema (group Andigenum)

0.53

− 0.18

Gómez et al. (2019)

Average

0.48

− 0.22

Phosphorus

3.3.1.1  Nitrogen and phosphorus nutrition indexes Lemaire and Gastal (1997) proposed the N nutrient index (NNI) calculated as the ratio between the actual and the critical plant N concentration. This NNI could be used to quantify the nutritional status of a crop (Bélanger et al., 2001; Abdallah et al., 2016; Giletto et al., 2020). NNI close to 1 indicates nonlimiting N, NNI > 1 indicates luxury N status and NNI  200 genes in the large intestine, which control insulin sensitivity, satiety, lipid oxidation, gut motility, and many other aspects of metabolism. Gao et al. (2019) concluded that RS supplementation can ameliorate insulin resistance in Type 2 diabetes mellitus (T2DM), especially for T2DM patients with obesity but not in simple obesity. Potato and other starch products can play a role in the prevention of hyperglycaemia if starch-derived glucose is slowly released into the circulation. RS attenuates hyperglycemic, hyperinsulinemic, and hyperlipidemic response in various subjects by restricting gluconeogenesis, bolstering glycogenesis, maintaining glucose, and lipid homeostasis and ameliorating pancreatic dysfunction (Meenu and Xu, 2018). Potato nutritional characteristics are affected by the storage time and conditions, influencing dehydration, formation of reducing sugars, mechanical damage, and rot and food quality. To prevent losses of quality, the storage conditions should first minimise moisture and weight loss, maintaining the content of glucose, ascorbic acid, and other nutrients and vitamins. The response to storage temperature is genotype dependent. Casañas et al. (2003) observed that the amylose/amylopectin ratio increased during the first 6 weeks of storage. The concentration of vitamin C decreased markedly during the storage, and, after the 20th week, the content had decreased by more than 50%.

5  Conclusion: Challenges and opportunities Food security in a context of climate change and sustainability is the main challenge for the potato crop. The broad distribution of potato, its adaptation to different cropping systems, and the increasing consumption in developing countries positions the potato as a major crop in human food. Climate change will impact global potato yield as predicted either in a slight or positive manner at high latitudes in the short term. Long-term effects do not seem promising, despite the positive effects of the projected CO2 increase in this crop. Thus implementation of adaptation strategies to climate change as shifts in time of planting, the use of later-maturing cultivars, irrigation, and a shift of the location of potato production are essentials to maintain crop productivity. The implementation of these strategies depends on our knowledge of crop phenology and physiology in response to environmental conditions. There is a wealth of information regarding crop growth and development under controlled conditions, although the number of field experiments evaluating the crop responses are limited. Genetic and environmental variability in potato response make it necessary to increase efforts related to field research and phenomics. Additionally, the calibration and validation of simulation models for potato require experimental data for more accurate predictions and to test adaptation strategies in different regions.

578  Crop Physiology: Case Histories for Major Crops

Despite the variability in the architecture and canopy size of potato genotypes, capture of radiation is highly dependent on the length of the crop cycle. This imposes a challenge for regions, where crop windows could be reduced, due to warmer or dryer conditions. An increase in the growth rate and efficiency in the use of resources could be obtained improving the agronomic management and varieties. Native germplasm features variability in traits that can contribute to this goal. The potato crop is highly sensitive to water deficits; therefore, irrigation strategies aimed to improve its WUE will increase both tuber yield and efficiency other resources. Potato cultivars with higher WUE will be required to improve yields and sustainability of rainfed potato production systems. Several crop traits associated with drought tolerance have been identified. The gap in the knowledge regarding nutrient-use efficiency, critical nutrient dilution curves, and diagnostic tools are different amongst primary macronutrients (N, P, and K). Substantial information exists for N and P but less so for K, Ca, and Mg. Yield gap in potatoes should be addressed considering ecophysiological aspects mentioned above, but the large variation of tuber yield observed around the world also depends on socio-economic aspects. The maintenance of local germplasm and ecotypes together with the crop of commercial varieties should be promoted, with the aim of increasing the stability of production systems and exploring new market alternatives that benefit farmers and consumers. In this regard, the nutritional properties, and particularly the high concentration of antioxidants in coloured potatoes, must be used to further encourage potato consumption and make its healthy properties available to a larger population.

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Image source: CIAT Flickr

Chapter 19

Cassava James H. Cocka and David J. Connorb a

Emeritus, The International Center for Tropical Agriculture (CIAT), Palmira, Colombia, bDepartment of Agriculture and Food, The University of Melbourne, Melbourne, VIC, Australia

1 Introduction Cassava (Manihot esculenta Crantz, Euphorbiaceae) (yuca, manioc, mandioc, mandioca, and tapioca) is a perennial vegetatively propagated shrub grown throughout the lowland tropics for its starchy tubers that are thickened roots. It may be harvested as early as 7 months after planting (MAP) but is generally harvested from 8 to 12 months. In some areas with monomodal rainfall and a long dry season, the crop is established in the first wet season and then left to bulk during the second for harvesting after 18 months or so from planting. In the subtropics with cool winters, the crop is often grown for about 18 months before harvesting.

1.1  Origin of the crop Cassava was most likely domesticated on the southwestern Amazonian Rim (Léotard et al., 2009) some 5000–7000 years ago (Allem, 2002) and was cultivated throughout the Neotropics when the first Europeans arrived. After the arrival of the Spanish ‘conquistadores’, the population was decimated by the introduction of new diseases, and the production of cassava appears to have declined. However, with the opening of trade between Brazil and Africa, Portuguese traders introduced cassava to the Congo Basin in the 16th century and at about the same time to Guinea. Two centuries later it was independently introduced to East Africa and Madagascar. The introduction to Asia is not clear. It may have been taken from Mexico to the Philippines in the Manila galleon in the 17th century and is known to have been grown in Indonesia by 1740. In 1786, it was registered in the Royal Botanical Garden in what is today Sri Lanka. The spread of the cassava outside its centre of origin has important implications for its productivity because the crop not only left behind many of the important pests and diseases but also their own natural enemies. Thus until recently, Asia and to a lesser extent Africa were relatively free of pests and diseases, although in Africa, the crop is infected by African Mosaic Disease and Brown Streak Disease, important diseases not known in their centre of origin. Furthermore, the Portuguese traders who first took cassava to Africa almost certainly selected cuttings from the humid coastal areas of Brazil for planting in comparable areas of the Congo and Guinea. Thus it is likely that the initial cassava germplasm taken to Africa was not specifically adapted to semiarid areas with a long dry season into which it has spread. Cassava was originally domesticated as a food crop. Although most ‘sweet’ cultivars can be safely eaten as a boiled vegetable, many of the ‘bitter’ cultivars require processing to reduce the concentration of cyanide produced by hydrolysis of the glycosides, linamarin, and lotaustralin. Amerindian tribes developed an array of methods by peeling and grating tubers, followed by squeezing the mass to express the cyanide. The basic features of these processes underlie the common methods used around the world today. Cassava is, however, no longer just a food crop. It is also grown for animal fodder, as a source of starch for multiple uses and more recently, in Asia, for fermentation to (bio)ethanol as a liquid fuel. In the past, cassava leaves were consumed by humans in the Amazon and in parts of Africa, particularly the Congo basin, but that is now decreasing. Thailand previously exported cassava leaves as a source of carotene until prohibited by government decree due, at least partially, to the potential impoverishment of soils associated with harvesting of the nutrient-rich leaves.

1.2  Production environment Most cassava is grown between ~ 30° latitude and is not generally found, where the mean average temperature is less than about 20°C. Near the equator, however, where seasonal temperature fluctuations are small, it can be found in areas with mean temperature as low as 17°C, currently corresponding to an upper limit of about 2000 m a.s.l. Crop Physiology: Case Histories for Major Crops. https://doi.org/10.1016/B978-0-12-819194-1.00019-0 Copyright © 2021 Elsevier Inc. All rights reserved.

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Most cassava is grown, where annual average rainfall exceeds 1000 mm but is also found in areas with as little rainfall as 750 mm and less frequently 600 mm distributed over as few as 5 months. Farmers in areas with erratic rainfall favour cassava over other crops. The crop also extends to areas with annual rainfall as high as 3000 mm, where it is restricted to well-drained soils, or grown on ridges, because even a few hours of standing water kills the crop. Cassava is well adapted to low fertility soils that predominate in large areas of the tropics and so is frequently cultivated on highly weathered and leached Oxisols, Ultisols, and Alfisols, with smaller areas on Inceptisols (particularly in India) and Entisols. It is, however, poorly adapted to alkaline (high pH) and saline soils, where it is rarely grown. Light textured and lateritic soils are attractive for cassava production because they are well drained and facilitate physical harvest of tubers. These soils are, however, intrinsically drought prone and infertile, often with a low pH and with associated problems of aluminium toxicity that cassava tolerates more than most crops (Howeler, 1991a). The cassava-growing areas of the world have been classified into mega environments or edapho-climatic zones: Lowland humid; acid soil subhumid; nonacid soil, subhumid; lowland semiarid; tropical highlands; subtropical lowland; and subtropical highland (Fig. 19.1). With global warming, it can be expected that cassava production will expand to higher latitudes and, in the equatorial belt, to higher altitudes.

1.3  Cassava production World cassava production has now reached 292 Mt of fresh tubers (35% dry weight) from 26.3 Mha (www.faostat.fao.org) that is equivalent, in energy terms, to more than 100 Mt of cereals. It is one of the most important sources of basic plant energy in tropical countries. Over the past 30 years, global production has more than doubled. Africa is the largest producer with 178 fresh Mt in 2017 destined almost entirely for human consumption. In Africa, production has doubled over the past 30 years due to increased area planted but with little change in yield. In Asia, production has also doubled during the past 30 years, with average yield increasing from 12 to 21 fresh t ha− 1 with several countries, including Vietnam, Laos and Cambodia, increasing planted area from a very small base. Cassava has decreased in importance in the Neotropics with small increases in yield being insufficient to compensate for loss of area. Nevertheless, cassava continues to be an important crop in Brazil and Paraguay, increasingly used for starch production. The top 10 world producers in 2017 are listed in Table 19.1 together with harvested area and yield. Nigeria is the largest producer with 59.5 fresh Mt on 6.8 Mha, whereas Thailand and Congo both harvested over 30 fresh Mt. The interplay between area harvested and yield presents interesting contrasts. Whereas Nigeria, with the largest area, has one of the smallest average yields (8.8 fresh t ha− 1), Thailand is a major producer on a small area (1.3 Mha) but at high average yield

FIG. 19.1  Edapho-climatic zones for cassava production. Source: Map modified from the original by Glenn Hyman and reproduced with permission from Hyman, G., Hodson, D., Jones, P., 2013. Spatial analysis to support geographic targeting of genotypes to environments. Front. Physiol. 4, 1–13, 40 under a Creative Commons License CC BY 3.0.

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TABLE 19.1  Top 10 world producers of cassava in 2017 with area harvested and fresh cassava yield. Country

Production (Mt)

Area (Mha)

Fresh cassava yield (t ha− 1)

Nigeria

59.5

6.8

8.8

Democratic Republic of the Congo

31.6

3.9

8.2

Thailand

31.0

1.3

23.1

Brazil

19.1

0.8

24.5

Indonesia

18.9

1.3

14.4

Ghana

18.5

1.0

19.1

Angola

11.8

1.0

11.6

Cambodia

10.6

0.39

27.0

Vietnam

10.3

0.53

19.3

8.8

1.07

8.2

Mozambique

Source: Compiled from www.faostat.fao.org.

(23.1 fresh t ha− 1). The highest average yield (27 fresh t ha− 1) is achieved by Cambodia, but on an smaller area (0.39 Mha) with much recent expansion of area in relatively fertile soils (Sopheap et al., 2012).

1.4  Role in the rural economy Cassava is frequently, and we suggest wrongly, described as a crop of subsistence agriculture which ‘farmers grow to feed themselves and their families’ such that production is targeted to survival and local requirements with little or no surplus for trade. Cassava has long ceased to be such a subsistence crop, even in Africa, where substantial trading of the crop was reported more than 60 years ago (Jones, 1959). More recently the Collaborative Study of Cassava in Africa (COSCA) study (Nweke et al., 2002) dismissed the myth that cassava is a subsistence crop produced by and for rural households. Rather, it is increasingly being transformed into a cash crop and marketed as an urban food staple in many African countries. The result is a significant contribution to rural development largely through increased incomes in marginal rainfed areas where cassava yields better than most alternative crops. Cassava has, however, long played the role as a famine-reserve crop due to its ability to withstand infertile soils, drought, and uncertain rainfall, coupled with the possibility to delay harvest of tubers until needed. Under threat of drought, Ministries of Agriculture in countries where maize is the main food staple often mount crash cassava production programmes. This strategy was pursued in several southern and central African countries during the drought of the mid-1980s and the El Niño of the 1990s. Unfortunately, these programmes have usually been abandoned soon after the drought. Cassava served as a food reserve during civil strife (1961–97) in Angola and Mozambique and in the Biafran war. This commonly perceived role as a famine fighter has tended to lessen its contribution to raising farmers’ incomes (Jones, 1959, p. 281). Labour productivity for cassava is low when compared with many other starch crops. For example, to produce and harvest each ton of cassava required 58 man-days in Madagascar (Okoye et al., 2016) and 17–22 man-days in African countries (Nweke et al., 2002). For this reason, despite its ability to yield well, cassava will struggle to compete in markets for starch-based products without greater labour productivity, now becoming increasingly possible through mechanisation and use of herbicides for weed control. However, in some regions, cassava-based foods such as ‘farinha’ in Brazil and ‘pan de yuca’ in Colombia are popular local food items that command relatively high prices and hence do not compete directly with other ‘commodity’ starch sources.

1.5  Cassava in cropping systems Amerindian tribes often planted cassava late in slash-and-burn culture when the fertility of their gardens declined such that other crops would not thrive. The crop could be left untended for long periods and then harvested as required. Cassava frequently played a similar role in shifting-culture systems in Africa. The ability to grow and produce on acid infertile soils and

592  Crop Physiology: Case Histories for Major Crops

to survive under uncertain rainfall has led many farmers to choose cassava as a crop in such areas. These are not optimum conditions for cassava production. As with all crops, farmers choose those crops that yield well and are most profitable under the conditions that prevail on their farms. Although it is well adapted to low soil fertility and is relatively drought tolerant, cassava responds to fertiliser and irrigation. When grown with traditional practices, it may exacerbate soil erosion (Reining, 1992) and if cropped continuously will exhaust soils. When farmers grow cassava on already depleted soils with little or no fertiliser, the crop continues to deplete the soil and its slow crop establishment and slow ground cover favour erosion. Slow establishment of cassava provides opportunity for inter and relay-cropping (Pypers et al., 2011) with shorter-cycle crops such as maize or grain legumes, with advantages to both diversification and control of soil erosion. As cassava growing has intensified and labour costs have risen in SE Asia and the Americas, intercropping has declined in importance and this tendency is likely to spread to Africa in the future.

2  Crop structure, morphology and development Until recently, almost all cassava cultivars or clones were landraces originally selected by growers from naturally occurring seedlings. Many local selections from South America, especially Brazil, were taken to Africa and Asia, where they became established. As modern breeding methods are applied to cassava, much of the genetic variation is in danger of loss. Currently, there is a large range of material with specific traits suitable for local environments and quality preferences. Breeders should maintain vigilance to ensure that breeding for high yield under favourable conditions does not lead to the loss of those traits that make cassava robust and productive under adverse conditions. Several agencies have collected and maintain collections of cassava germplasm, especially The International Center for Tropical Agriculture (CIAT) in Colombia.a

2.1  Crop structure The wide diversity complicates the overall description of crop structure, development, and morphology as no single plant type predominates. This contrasts remarkably with modern wheat and rice cultivars that differ little in overall crop architecture. Cassava plants comprise: (i) variably branching stems consisting of successive nodal units, each bearing a leaf lamina subtended by a long petiole and an associated axillary bud; (ii) fibrous feeder roots; and (iii) tubers, fleshy storage roots (Fig. 19.2). Flowers may be produced but are not commercially important. The branching habit determines the architecture of the canopy that develops in a hierarchical series with newly formed leaves at the top and ageing and senescing leaves towards the base.

2.2  Stem cuttings The crop is propagated from cuttings (stakes) taken from lignified stems. They are commonly about 20 cm long with at least four nodes and are planted either horizontally, vertically or inclined. Horizontally planted stakes are completely covered by soil, whereas vertical and inclined stakes are normally left with about one-third exposed. Stakes absorb water and, as internal nutrients are mobilised, axillary buds sprout to produce new roots and shoots as early as 5 days after planting. Roots are formed first from nodal axillary buds (nodal roots,) later followed by others from a callus that forms at the base of the cutting (basal roots). Stakes generally do not germinate below about 13°C (Cock and Rosas, 1975) that appears to be the base temperature for most of the developmental processes in cassava (Keating et al., 1982a).

2.3  Flower induction and branching Stems produce a succession of nodal units, principally from apical meristems, to form the above-ground structure of the plant. Apical dominance is strong so that axillary buds on the lower stem rarely develop into side branches, commonly known as suckers, unless the crop is planted at low density or lodges. Branching increases the scope for node production in multiplicative fashion. In cassava forked branching is always associated with flower initiation, although flowers frequently do not develop, leaving only scars as visible vestiges in the centre of the fork. The axillary buds immediately below the apex develop into two to four approximately equal-sized branches (Tironi et al., 2015) providing the basis for the large differences in plant structure between cultivars (Cock et al., 1979). a https://ciat.cgiar.org/what-we-do/crop-conservation-and-use/cassava-diversity/Development.

Cassava Chapter | 19  593

FIG. 19.2  Growth habit of cassava showing the original cutting, forking branches comprising nodal units, older nodes from which leaves have fallen, the canopy composed of the most recently formed leaves and tubers formed in surface soil at the base of the plant.

Some genotypes were thought to be nonbranching, but experiments with long-day treatments suggest that most genotypes can be induced to flower and branch (Cock and Rosas, 1975; Veltkamp, 1985; H. Ceballos personal communication). Recently, Ceballos et al. (2017) have shown that grafting of nonflowering with flowering types induces flowering, implying a hormonal effect which is consistent with long-day photoperiod stimulation. Observation of branching habits in the field suggest that flowering is temperature sensitive with an optimum of about 24°C (Personal observations and Irikura et al., 1979). Once flowering is induced, branching occurs at regular chronological intervals when average temperature remains constant (Tan and Cock, 1979b; Veltkamp, 1985). Stresses tend to increase the time between branching and reduce the number of branches at each branch point.

2.4  Production of nodal units The rate of appearance of nodal units on the most recent apical meristems is initially fast but declines as the crop ages (Fig. 19.3a). This tendency is observed in all cultivars, although at different absolute rates, and is less marked in nonbranching types or when branch number is experimentally reduced (Fig. 19.3a) (Tan and Cock, 1979a,b). This suggests that the declining rate of appearance of nodal units is modulated by competition for substrate. And yet, when plants were shaded, with presumably less available assimilate, the rate of node appearance was little affected (Cock et al., 1979), whereas with low nitrogen supply, it was reduced (Fig. 19.3b) (Orioli et al., 1967). This suggests that substrate limitation may be plantnutrient related. The phyllochron interval, taken as the rate of leaf appearance, is about 22°Cd with a base temperature of 13°C (no data approximately