Genetic Enhancement in Major Food Legumes: Advances in Major Food Legumes 3030644995, 9783030644994

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Kul Bhushan Saxena Rachit K. Saxena Rajeev K. Varshney  Editors

Genetic Enhancement in Major Food Legumes Advances in Major Food Legumes

Genetic Enhancement in Major Food Legumes

Kul Bhushan Saxena • Rachit K. Saxena • Rajeev K. Varshney Editors

Genetic Enhancement in Major Food Legumes Advances in Major Food Legumes

Editors Kul Bhushan Saxena Pigeonpea Breeding International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) Patancheru, Telangana, India

Rachit K. Saxena Research Program- Genetic Gains International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) Patancheru, Telangana, India

Rajeev K. Varshney Research Program- Genetic Gains International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) Patancheru, Telangana, India

ISBN 978-3-030-64499-4 ISBN 978-3-030-64500-7 https://doi.org/10.1007/978-3-030-64500-7

(eBook)

© Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Foreword

“Legume,” a popular French word that was coined in 1676, represents a group of crops that play a significant role in nutritional security and agricultural sustainability in a number of Afro-Asian countries. The consumption of legumes in diets provides a valuable protein-energy balance that is necessary for normal growth of those living below the poverty line. Therefore, in 1972, the CGIAR very wisely assigned chickpea, groundnut, and pigeonpea, the three most important legumes, to ICRISAT’s mandate and gave responsibility for their quality research and development. These crops are also known for their high resilience against most common biotic and abiotic yield reducing stresses of semi-arid tropics (SAT) agriculture. As of now the worldwide demand of legumes is on the increase due to greater awareness of their nutritional and health benefits. Hence, keeping in view the increasing urbanization, rapid population growth, and looming effects of environmental changes, the genetic enhancement of productivity of legumes is obligatory. In the recent past our scientists have achieved remarkable accomplishments against the smart goals set by the legume community. For instance, in the upstream science, ICRISAT and its partner institutes have developed genome assemblies and largev

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scale genomic resources including a range of marker-genotyping platforms in ICRISAT mandate legume crops for advancing legume biology and breeding applications. By using both traditional and genomics-assisted breeding approaches, a number of early maturing, high yielding, disease-resistant, and drought-tolerant varieties have been developed in above-mentioned legume crops. In addition, some special technologies such as machine harvestable varieties in chickpea, high oleate varieties in groundnut, and hybrid technology in pigeonpea have also been developed. Similarly, in groundnut high yielding, drought-tolerant, disease-resistant, high oleic, and high/low oil content varieties have been developed. Similarly, through Tropical Legumes projects (funded by Bill & Melinda Gates Foundation), ICRISAT has worked with other CGIAR institutes like IITA and CIAT and national programs in about 15 countries in Africa and Asia and has contributed towards replacement of old varieties and to enhance legumes production. For this outstanding work and impact in African countries through the Tropical Legumes projects, ICRISAT has been awarded the 2021 Africa Food Prize. I am happy to see different areas of legumes including market demand, priority setting, genomics, genetic engineering, breeding, pathology, entomology, modeling, and agronomy included in the present book Genetic Enhancement in Major Food Legumes. This book elegantly encompasses some past achievements along with the latest research accomplishments and technologies. Besides, it also focuses on the future research areas in various legume crops. This book, in my opinion, will provide a useful reference to the present as well as future generations of legume scientists worldwide. I congratulate all the authors and the editors in particular for their hard work and quality vision in completing this task. I am sure readers of this book will be benefitted with the knowledge and experience of the authors. International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, Telangana, India

Arvind Kumar

Preface

For ages, legumes unknowingly, in the pre-historic era or knowingly, in the modern time, have contributed to the well-beings of human race by providing valuable proteins, minerals, vitamins, and fibers to achieve good growth and development. Legumes, in combination with cereals, make a perfect balanced diet, especially for those earning their livelihoods from subsistence agriculture in the arid, sub-tropical and tropical areas of the developing world. Globally, more than a dozen grain legumes including soybean, groundnut, cowpea, common bean, chickpea, faba bean, mung bean, pigeonpea, lentil, urd bean, and dry pea form a major component of rain-fed farming systems as a sole or intercrop. Considering the ever-increasing population and urbanization of agricultural lands, the present production level cannot meet the recommended (54 g/head/day) protein requirements of the masses. On the research and development front, it is pleasing to note that although an appreciable progress has been made in the recent past by achieving an annual production growth rate of 1.5%, it is limited to some crops like soybean, groundnut, and cowpea; and still a lot needs to be done to raise the production and productivity of legumes. Under this scenario, doubling of grain productivity in the shortest possible time appears to be the only way out. To achieve this objective, the present generation of scientists needs to review the situation not only with respect to identifying the major production constraints, but by designing and implementing some innovative crop improvement strategies and development plans. In this context, this book entitled Genetic Enhancement in Major Food Legumes covering diverse research aspects would be a great help. The team of authors has made tremendous efforts in compiling information about production trends, genetic enhancement technologies, widening crop adaptation, reducing crop losses, and application of innovating genomics tools. We believe that this book will help in understanding and solving some critical issues, besides planning research and development targets for the overall genetic enhancement of the legume crops.

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We are thankful to all the authors for contributing high-quality research, ideas, and plans in different chapters. We would like to thank several colleagues from Springer especially Vignesh Viswanathan, Anthony Dunlap, Eric Stannard, and Nicholas DiBenedetto for all their help and support during the entire course of the book project. Furthermore, the editors are grateful to their colleagues, collaborators, and family members for the support to them while working on this project. In this regard, K.B.S. is thankful to his wife Suman Saxena, who allowed her time to be taken away to fulfil editorial responsibilities. K.B.S. would also like to thank Amrit, Rajita, Sandeep, Aarti, Sanjana and Shuban. R.K.S. is grateful to his wife Shelly Patwar and two young sons (Aniruddha Saxena and Madhav Saxena) for their help and moral support in doing editorial responsibilities in addition to research duties at ICRISAT. R.K.V. is thankful to his wife Monika A. Varshney and children Prakhar Raj Varshney and Preksha Varshney for allowing their family time to go for editing this book. Finally, we very much hope that the book will be read and cited extensively. We look forward to receiving constructive criticism and suggestions on the book so that the editors can take them into consideration while preparing the next edition of the book, if any. International Crops Research Institute for the Semi-AridTropics (ICRISAT), Patancheru, India International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India

Kul Bhushan Saxena Rachit K. Saxena Rajeev K. Varshney

Contents

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Genetic Enhancement in Major Food Legumes: An Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kul Bhushan Saxena, Rachit K. Saxena, and Rajeev K. Varshney

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Trends in Legume Production and Future Outlook . . . . . . . . . . . . . Shyam Narayan Nigam, Sunil Chaudhari, Kumara Charyulu Deevi, Kul Bhushan Saxena, and Pasupuleti Janila

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Genomics: Shaping Legume Improvement . . . . . . . . . . . . . . . . . . . Abhishek Bohra, Uday C. Jha, S. J. Satheesh Naik, Swati Mehta, Abha Tiwari, Alok Kumar Maurya, Deepak Singh, Vivekanand Yadav, Prakash G. Patil, Rachit K. Saxena, and Rajeev K. Varshney

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Genetic Engineering of Grain Legumes: Their Potential for Sustainable Agriculture and Food and Nutritional Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sumita Acharjee and Thomas J. V. Higgins

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Hybrid Breeding in Food Legumes with Special Reference to Pigeonpea, Faba bean, and Soybean . . . . . . . . . . . . . . 123 Kul Bhushan Saxena, Vijay Dalvi, Rachit K. Saxena, and Rajeev K. Varshney

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Biotic Stresses in Food Legumes: An Update and Future Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Mamta Sharma, Avijit Tarafdar, Abhay Pandey, S. Ahmed, Vibha Pandey, Devashish R. Chobe, Raju Ghosh, R. M. Nair, Suneeta Pandey, M. Surya Prakesh Reddy, Fouad Maalouf, and Safaa G. Kumari

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Identification, Evaluation and Utilization of Resistance to Insect Pests in Grain Legumes: Advancement and Restrictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 Jagdish Jaba, Sanjay Bhandi, Sharanabasappa Deshmukh, Godshen R. Pallipparambil, Suraj Prashad Mishra, and Naveen Arora

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Using Crop Modelling to Improve Chickpea Adaptation in Variable Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 Yashvir Chauhan, Karine Chenu, and Rex Williams

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Recent Advances in the Agronomy of Food Legumes . . . . . . . . . . . 255 Aman Ullah, Muhammad Farooq, Mubshar Hussain, and Kadambot H. M. Siddique

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Scaling Up Food Legume Production Through Genetic Gain and Improved Management . . . . . . . . . . . . . . . . . . . . . . . . . . 303 Suhas P. Wani, Girish Chander, Mukund D. Patil, Gajanan Sawargavkar, and Sameer Kumar

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339

Contributors

Sumita Acharjee CSIRO Agriculture and Food, Canberra, Australia S. Ahmed International Center for Agricultural Research in the Dry Areas (ICARDA), Beirut, Lebanon Naveen Arora International Crops Research Institute for the Semi-Arid tropics (ICRISAT), Patancheru, India Sanjay Bhandi ICAR-Indian Institute of Pulses Research (IIPR), Kanpur, UP, India Abhishek Bohra ICAR-Indian Institute of Pulses Research (IIPR), Kanpur, UP, India Girish Chander International Crops Research Institute for the Semi-Arid tropics (ICRISAT), Patancheru, India Sunil Chaudhari International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India Yashvir Chauhan Department of Agriculture and Fisheries (DAF), Queensland Government, Kingaroy, QLD, Australia Karine Chenu The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Toowoomba, QLD, Australia Devashish R. Chobe International Crops Research Institute for the Semi-Arid tropics (ICRISAT), Patancheru, India Vijay Dalvi Shriram Agriculture Research Center, Shriram Chemicals and Fertilizers (An Unit of DCM Shriram)), Ludhiana, Punjab, India Sharanabasappa Deshmukh University of Agriculture & Horticultural Science, Shivmogga, Karnataka, India Muhammad Farooq Department of Crop Sciences, College of Agricultural and Marine Sciences, Sultan Qaboos University, Muscat, Oman xi

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The UWA Institute of Agriculture, The University of Western Australia, Perth, Australia Raju Ghosh International Crops Research Institute for the Semi-Arid tropics (ICRISAT), Patancheru, India Thomas J. V. Higgins CSIRO Agriculture and Food, Canberra, Australia Mubshar Hussain Department of Agronomy, Bahauddin Zakariya University, Multan, Pakistan Jagdish Jaba International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India Pasupuleti Janila International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India Uday C. Jha ICAR-Indian Institute of Pulses Research (IIPR), Kanpur, UP, India Sameer Kumar International Crops Research Institute for the Semi-Arid tropics (ICRISAT), Patancheru, India Kumara Charyulu Deevi International Crops Research Institute for the Semi-Arid tropics (ICRISAT), Patancheru, India Safaa G. Kumari International Center for Agricultural Research in the Dry Areas (ICARDA), Beirut, Lebanon Fouad Maalouf International Center for Agricultural Research in the Dry Areas (ICARDA), Beirut, Lebanon Alok Kumar Maurya ICAR-Indian Institute of Pulses Research (IIPR), Kanpur, UP, India Swati Mehta ICAR-Indian Institute of Pulses Research (IIPR), Kanpur, UP, India Suraj Prashad Mishra International Crops Research Institute for the Semi-Arid tropics (ICRISAT), Patancheru, India R. M. Nair World Vegetable Center (AVRDC), ICRISAT Campus, Patancheru, India Shyam Narayan Nigam International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India Godshen R. Pallipparambil NSF Center for Integrated Pest Management (CIPM), North Carolina State University, Raleigh, NC, United States Abhay Pandey World Vegetable Center (AVRDC), ICRISAT Campus, Patancheru, India Suneeta Pandey Jawaharlal Nehru Krishi Vishwa Vidalya (JNKVV), Jabalpur, India Vibha Pandey Jawaharlal Nehru Krishi Vishwa Vidalya (JNKVV), Jabalpur, India

Contributors

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Mukund D. Patil International Crops Research Institute for the Semi-Arid tropics (ICRISAT), Patancheru, India Prakash G. Patil ICAR-National Research Centre on Pomegranate (NRCP), Solapur, India M. Surya Prakesh Reddy Jawaharlal Nehru Krishi Vishwa Vidalya (JNKVV), Jabalpur, India S. J. Satheesh Naik ICAR-Indian Institute of Pulses Research (IIPR), Kanpur, UP, India Gajanan Sawargavkar International Crops Research Institute for the Semi-Arid tropics (ICRISAT), Patancheru, India Kul Bhushan Saxena International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India Rachit K. Saxena International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India Mamta Sharma International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India Kadambot H. M. Siddique The UWA Institute of Agriculture, The University of Western Australia, Perth, WA, Australia Deepak Singh ICAR-Indian Agricultural Statistical Research Institute (IASRI), New Delhi, India Avijit Tarafdar International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India Abha Tiwari ICAR-Indian Institute of Pulses Research (IIPR), Kanpur, India Aman Ullah Department of Crop Sciences, College of Agricultural and Marine Sciences, Sultan Qaboos University, Muscat, Oman Rajeev K. Varshney International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India Suhas P. Wani International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India Rex Williams Department of Agriculture and Fisheries (DAF), Queensland Government, Kingaroy, QLD, Australia Vivekanand Yadav ICAR-Indian Institute of Pulses Research (IIPR), Kanpur, UP, India

Chapter 1

Genetic Enhancement in Major Food Legumes: An Overview Kul Bhushan Saxena, Rachit K. Saxena, and Rajeev K. Varshney

1.1

Overview on Genetic Enhancement in Food Legumes

An overview of the vital production statistics related to seven prime legumes seems to be the right point for kick-starting the book. Dr. SN Nigam and his colleagues have provided the key statistics parameters, particularly on total area sown, gross production and mean productivity in the chapter “Trends in Legumes Production and Future Outlook”. The authors revealed that the combined production of these crops recorded 549% increase between triennium ending in 1961–1963 and 2014–2016. This increase is the consequence of a combined effect of both area expansion (153%) and productivity enhancement (86%). The annual growth rates with respect to cropped area, gross production and average yields were estimated at 1.7%, 3.5% and 1.2%, respectively (Nigam et al. 2021). They concluded that the developing countries need to play a greater role in meeting the future global demand of legumes. The available information also showed that, with the exception of soybean and groundnut, inadequate attention is still going on to the R&D of other legume crops. To meet the required protein needs, this scenario needs a drastic change as early as possible (Foyer et al. 2016). Chapter 3 entitled “Genomics: Shaping Legumes Improvement”, authored by Dr. Abhishek Bohra and collaborators, provides status and potential application of various genomics technologies for the genetic improvement of the legume crops. They describe new genomics tools that are available for identifying and locating genes of importance in different legumes. Such developments will help breeders in incorporating key trait(s) from even an unproductive genetic stock to elite breeding materials more rapidly and with greater precision (Bohra et al. 2021). The authors also decipher the role of genomics science for some important futuristic breeding

K. B. Saxena · R. K. Saxena · R. K. Varshney (*) International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India e-mail: [email protected] © Springer Nature Switzerland AG 2021 K. B. Saxena et al. (eds.), Genetic Enhancement in Major Food Legumes, https://doi.org/10.1007/978-3-030-64500-7_1

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programmes such as breeding of high yielding hybrids in pigeonpea. One such application is overcoming the most serious constraints related to hybrid seed quality control in pigeonpea. Overall, the genomics technologies will also help in integrating genomics selection procedures with traditional breeding methods (Bohra et al. 2020, Roorkiwal et al. 2020, Pandey et al. 2020). In this context, a sequence-based breeding procedure has also been proposed for combining population improvement with genomics selection and genome-wide association (Varshney et al. 2019a). Besides the above, the authors also summarize the major breakthroughs achieved so far in legumes using genomics and molecular breeding technologies. They have also very rightly highlight the limitations and difficulties often encountered in integrating genomics with traditional crop breeding procedures to develop high yielding widely adapted legume cultivars. When germplasm doesn’t have natural variation for a given trait, genetic engineering technologies are very powerful for trait improvement. Therefore, in Chap. 4 entitled “Genetic Engineering of Grain Legumes: Their Potential for Sustainable Agriculture and Food and Nutritional Security”, Drs. Sumita Acharjee and Thomas J. Higgins have summarized the potential role of genetic engineering in sustainable agriculture and nutritional security. They start with a very relevant statement related to the fate of protein-rich legumes. In order to tackle the issue of widespread hunger and the follow-up wave of “green revolution”, the policy makers put all the eggs in one basket and ignored the development of legumes, with the exception of soybean and groundnut, perhaps for their valuable oil component and diverse usage. Of the two, soybean always got the top billing for fulfilling the demands of high-protein food for animals. In order to reduce crop losses and enhance yield and stability in legumes, research related to alien gene transfer using transformation, etc. was given priority. These efforts resulted in the development of genetically modified soybean varieties that were tolerant to the herbicide or resistant to pod borers (Grossi-de-Sá et al. 2011). These successes led to the expansion of transgenic research in other legumes such as beans, pigeonpea, cowpea, pea, lentil and chickpea, and the results are awaited. The authors also draw attention towards some plus points in favour of transgenic soybeans, and these were related to their positive impact in farm income, lower carbon footprint and better sustainability of the farm environment. If things go well, it is expected that soon the transgenic cultivars would be available to farmers following approvals from the respective Government authorities. Overall, the authors, in this chapter, have covered a challenging subject in a concise and interesting form, and they deserve appreciation. Gene editing is another powerful technology that will supplement genetic engineering technology for crop improvement (Varshney et al. 2019b). “Hybrids in legumes” may give strange feelings to many, but it is true in case of pigeonpea. The hybrid breeding is a proven technology that has provided breakthroughs in yield in cereal and vegetable crops, but not in legumes. This discrimination is primarily attributed to the self-pollinating nature of the latter. Some enthusiastic breeders, however, tried to exploit the heterosis in crops like faba bean, soybean and pigeonpea which are blessed with partial natural crosspollination. Of these, significant progress has been achieved in the case of pigeonpea

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only. Dr. K. B. Saxena and his colleagues have compiled information about hybrid breeding efforts made in faba bean, soybean and pigeonpea. In faba bean, the stability of cytoplasmic male sterility was the prime issue, while in soybean, efforts are still ongoing to enhance the level of natural outcrossing. In pigeonpea, the insectaided natural outcrossing was found sufficient to produce large quantities of hybrid seeds, and the cytoplasmic male sterility is also highly stable (Saxena et al. 2018). Besides these, the realized heterosis is also significant. These basic components made the hybrid breeding a possibility in pigeonpea, and subsequently the world’s first cytoplasmic male sterility-based legume hybrid ICPH 2671 was released. This hybrid has broken the decades-old low-yield plateau in pigeonpea by recording 30–50% more yields over the local controls in farmers’ fields (Saxena et al. 2021). At present, however, all is not well with hybrids, and its technology transfer suffered a serious setback due to the inability to maintain high standards of seed quality. This constraint has now been ably addressed through the contribution of genomics scientists. The exceptionally high yields and greater resilience make pigeonpea hybrids an attractive alternative to pigeonpea farmers. At present, the pigeonpea breeders and genomics scientists are working together with some private seed companies for upscaling of hybrid technology, and soon the farmers will be able to reap the benefits of hybrid vigour in this legume (Sameerkumar et al. 2019; Bohra et al. 2020). Legumes suffer from a number of soil-borne and foliar pathogens and inflict severe losses globally. In the manuscript on diseases in legumes, prepared by Dr. Mamta Sharma and her colleagues, information on the research and development with special reference to emerging diseases under the backdrop of changing climatic parameters has been presented. High genetic diversity, short generation turnover time, dynamic evolution of pathogens and excessive usage of chemicals are considered potential threats in the breakdown of genetic resistances and ineffectiveness of chemicals in controlling diseases. The authors believe that such situations could be managed ably through proactive resistance breeding programmes. They also rightly recommend that new legume cultivars should be bred with high levels of genetic resistance to more than one disease (Sharma et al. 2021). To achieve this, they advocate the integration the wisdom of plant breeding, genomics and plant pathology. In this context, some genomics tools such as gene editing, identification of diagnostic markers and marker-assisted breeding can play a significant role. The authors also believe that since new diseases are appearing on the scene due to significant changes occurring in key climate parameters and cropping systems, therefore, close monitoring of diseases and their virulence should be carried out on a regular basis so that some anticipatory crop management activities could be formulated and implemented. Dr. Sharma and cooleagues also visualize that interdisciplinary and international institutional research approaches would help in saving the legume crops from the existing and emerging diseases and their new biotypes. Saving legumes from insects is the most challenging job in agriculture because a range of insects feed on the crop, insects which are dynamic in nature and can hibernate, migrate from place to place and survive on a range of alternate hosts. Besides this, it requires the highest level of skill at farming, research and crop

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management levels in selecting the right insecticide(s) and spraying technologies and recommends cultural practices that minimize insect losses. The chapter entitled “Identification, Evaluation and Utilization of Resistance to Insect Pests in Grain Legumes: Advancement and Restrictions” written by Dr. Jagdish Jaba and his co-authors highlights various insect-related issues including the present status, limitations and future approach. A range of insect pests damage the legumes at different growth stages in the field and during storage. Of these, pod borers, pod fly, blister beetles, etc. are important insects. The issue of insect control becomes more complicated when farmers resort to using both the recommended and non-recommended insecticides indiscriminately to protect their crops, and this may result in insects’ development of resistance to insecticides (Jaba et al. 2021). The authors discuss different insect screening technologies involving no-choice and dual-choice cages, detached leaf and diet incorporation assays. They also highlight the technologies needed for evaluating germplasm, mapping populations and genetically modified crops for resistance to insect pests under field as well as greenhouse conditions. The effective methods for mass insect multiplication and identifying lines with diverse resistance mechanisms are also discussed. These can help in phenotyping plants in segregating populations to achieve the target of gene pyramiding to develop cultivars with the stable resistances to a specific pest. Appreciable levels of genetic resistances for pod borers have been identified in wild relatives of chickpea, pigeonpea and cowpea, but their incorporation into the cultivated germplasm has yet to see the light of day. This aspect needs special consideration in the future, particularly in the light of emerging technologies. Drs. Chauhan, Chenu and Williams start the chapter “Using Crop Modelling to Improve Chickpea Adaptation in Variable Environments” by emphasizing the need for integrating crop modelling as a research component in chickpea research and development endeavours for different farming systems. This approach would help in optimising the relative contributions of genotype, environment and crop management practices towards realizing high yields. The authors believe that the chickpea genetic enhancement programmes can be enriched if specific environmental parameters such as drought, temperature, uniform agro-ecological regions, etc. are characterized and made an integral part of breeding goals and activities. This review also focuses on the importance of abiotic stresses and suggests the ways to tackle these for improved performance of chickpea. The crop modelling approach could also assist in defining appropriate selection environments for wide and specific adaptation and in identifying suitable adaptation domains for a specific variety. This will increase the heritability of the target trait and enhance the breeding value. The crop models for a given ecosystem, therefore, can play a key role in the overall chickpea improvement programmes. Research on agronomic components is a continuous process since the new varieties with diverse plant types and maturities are bred at regular intervals and their yield optimizing agronomy will be different from the existing cultivars. The field of agronomy research has assumed an even greater importance in view of the vagaries of climate changes. These may also bring some associated changes in the cultivation scenarios. The team of authors led by Dr. Aman Ullah suggests that an

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integrated approach should be adopted to optimize yield and minimize the losses caused by various biotic and abiotic stresses. The authors believe that adoption of some critical agronomic practices such as seed priming, early sowing, spatial planting arrangements, raised seeding beds and precise input management could help in increasing and stabilizing legume productivity (Ullah et al. 2021). They also emphasize that the conservation agriculture including minimum tillage, rainwater harvesting, integrated nutrient and insect pest management and diversification of cropping systems would also be of help in regulating the use of herbicides, fertilizers and pesticides, besides enhancing water use efficiency and soil fertility. The information presented by Dr. SP Wani and his colleagues is very relevant in today’s agricultural scenario. The productivity of most legumes is universally low, and the well-known constraints include the lack of high yielding varieties, inappropriate fertilizers, shortage of irrigation water and various biotic and abiotic stresses. To overcome these limitations, the authors suggest the formulation of an integrated approach in which due consideration should be given to overcome the widespread deficiencies of secondary and micro-nutrients that are prevalent in rain-fed areas. This can be done by soil health mapping and quality seed supply. By applying these approaches, they reported productivity enhancement by 20–50% in crops like pigeonpea, chickpea, soybean, green gram, groundnut and black gram. They also concluded that if the farmers spend a little amount on soil test-based fertility management, then they can fetch threefold benefits or even more in their incomes. Mission projects popularly identified as Bhoochetana which covered over 4.75 million ha in India conclusively demonstrated the success of the integrated developmental programmes (Wani et al. 2021). Overall, this book provides up-to-date information on several aspects of research and development in seven important legume crops, grown worldwide. These subjects covered by the authors include various technologies in the fields of genomics, genetic engineering, plant breeding, crop protection, agronomy and technology transfer. Hopefully, this compilation would be of help to both present and nextgeneration scientists to develop the road map of legume improvements. Acknowledgements Authors are thankful to the Department of Agriculture, Cooperation & Farmers Welfare, Ministry of Agriculture & Farmers’ Welfare, Government of India, for financial support. This work has been undertaken as part of the CGIAR Research Program on Grain Legumes and Dryland Cereals (GLDC). ICRISAT is a member of the CGIAR Consortium.

References Bohra A, Jha UC, Naik SSJ, Mehta S, Tiwari A, Maurya AK, Singh D, Yadav V, Patil PG, Saxena RK, Varshney RK (2021) Genomics: shaping legumes improvement. In: Saxena KB, Saxena RK, Varshney RK (eds) Genetic Enhancement in Major Food Legumes. Springer. https://doi. org/10.1007/978-3-030-64500-7_3 Bohra A, Saxena KB, Varshney RK, Saxena RK (2020) Genomics assisted breeding for pigeonpea improvement. Theor Appl Genet 133:1721–1737. https://doi.org/10.1007/s00122-020-03563-7

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Foyer H, Hon-Ming L, Nguyen HT, Siddique KHM, Varshney RK, Colmer TD, Cowling W, Bramley H, Mori TA, Hodgson JM, Cooper JW, Miller AJ, Kunert K, Juan V, Cullis C, Ozga JA, Wahlqvist ML, Liang Y, Shou H, Shi K, Yu J, Fodor N, Kaiser BN, Wong FL, Valliyodan B, Considine MJ (2016) Neglecting legumes has compromised human health and sustainable food production. Nature Plants 2:16112. https://doi.org/10.1038/nplants.2016.112 Grossi-de-Sá MF, Pelegrini PB, Fragoso RR (2011) Genetically modified soybean for insect-pests and disease control. In: Rijeka SA (ed) Soybean-Molecular Aspects of Breeding. In Tech, pp 429–452. https://doi.org/10.5772/16109 Jaba J, Bhandi S, Deshmukh S, Pallipparambil GR, Mishra SP, Arora N (2021) Identification, evaluation and utilization of resistance to insect pests in grain legumes: advancement and restrictions. In: Saxena KB, Saxena RK, Varshney RK (eds) Genetic Enhancement in Major Food Legumes. Springer. https://doi.org/10.1007/978-3-030-64500-7_7 Nigam SN, Sunil C, Kumaracharyulu D, Saxena KB, Janila P (2021) Trends in legumes production and future outlook. In: Saxena KB, Saxena RK, Varshney RK (eds) Genetic enhancement in major food legumes. Springer. https://doi.org/10.1007/978-3-030-64500-7_2 Pandey MK, Pandey AK, Kumar R, Nwosu CV, Guo B, Wright GC, Bhat SR, Chen X, Bera SK, Yuan M, Jiang H, Faye I, Radhakrishnan T, Wang X, Liang X, Liao B, Zhang X, Varshney RK, Zhuang W (2020) Translational genomics for achieving higher genetic gains in groundnut. Theor Appl Genet 133:1679–1702. https://doi.org/10.1007/s00122-020-03592-2 Roorkiwal M, Bharadwaj C, Barmukh R, Dixit GP, Thudi M, Gaur PM, Chaturvedi SK, Fikre A, Hamwieh A, Kumar S, Sachdeva S, Ojiewo CO, Tar’an B, Wordofa NG, Singh NP, Siddique KHM, Varshney RK (2020) Integrating genomics for chickpea improvement: achievements and opportunities. Theor Appl Genet 133:1703–1720. https://doi.org/10.1007/s00122-020-03584-2 Sameer Kumar CV, Ganga Rao NVPR, Saxena RK, Saxena KB, Upadhyaya H, Siambi M, Silim S, Reddy KN, Hingane A, Sharma M, Sharma S, Lyimo SD, Ubwe R, Makenge M, Gad K, Kimurto P, Amane M, Kanenga K, Obong Y, Monyo E, Ojiewo C, Nagesh Kumar MV, Jaganmohan P, Prasanthi L, Sudhakar C, Singh IP, Sajja S, Varshney RK (2019) Pigeonpea improvement: an amalgam of breeding and genomics research. Plant Breed 138:445–454 Saxena KB, Sharma D, Vales MI (2018) Development and commercialization of CMS pigeonpea hybrids. In: Plant breeding reviews. https://doi.org/10.1002/9781119414735.ch3 Saxena KB, Dalvi V, Saxena RK, Varshney RK (2021) Hybrid breeding in food legumes with special reference to pigeonpea, faba bean, and soybean. In: Saxena KB, Saxena RK, Varshney RK (eds) Genetic Enhancement in Major Food Legumes. Springer. https://doi.org/10.1007/9783-030-64500-7_5 Sharma M, Tarafdar A, Pandey A, Ahmed S, Pandey V, Chobe DR, Ghosh R, Nair RM, Pandey S, Reddy MSP, Maalouf F, Kumari SG (2021) Biotic stresses in food legumes: An update and future prospects. In: Saxena KB, Saxena RK, Varshney RK (eds) Genetic Enhancement in Major Food Legumes. Springer. https://doi.org/10.1007/978-3-030-64500-7_6 Ullah A, Farooq M, Hussain M, Siddique KHM (2021) Recent advances in the agronomy of food legumes. In: Saxena KB, Saxena RK, Varshney RK (eds) Genetic Enhancement in Major Food Legumes. Springer. https://doi.org/10.1007/978-3-030-64500-7_9 Varshney RK, Pandey MK, Bohra A, Singh V, Thudi M, Saxena RK (2019a) Toward the sequencebased breeding in legumes in the post-genome sequencing era. Theor Appl Genet 132:797–816 Varshney RK, Godwin ID, Mohapatra T, Jones JDG, McCouch SR (2019b) A sweet solution to rice blight. Nat Biotechnol 37:1280–1282 Wani SP, Chander G, Patil MD, Sawargavkar G, Sameer Kumar CV (2021) Scaling-up food legumes production through genetic gain and improved management. In: Saxena KB, Saxena RK, Varshney RK (eds) Genetic Enhancement in Major Food Legumes. Springer. https://doi. org/10.1007/978-3-030-64500-7_10

Chapter 2

Trends in Legume Production and Future Outlook Shyam Narayan Nigam, Sunil Chaudhari, Kumara Charyulu Deevi, Kul Bhushan Saxena, and Pasupuleti Janila

2.1

Introduction

Legumes have a special place in diverse diets all over the world and are especially important in developing countries as they are a rich source of protein, minerals (Ca, Fe, Cu, Zn, P, K, and Mg), vitamins (thiamine, riboflavin, niacin, vitamin B6, and folic acid), and water-soluble fibers and are affordable in price to poor communities (Reyes-Moreno and Paredes-López 1993). They are often labelled as the “poor man’s meat.” There is an inverse relationship between legumes consumption and income of the family (Messina 2014). Globally, more than a dozen grain legumes, viz., adzuki bean (Vigna angularis (Willd.) Ohwi & Ohashi), chickpea (Cicer arietinum L.), cluster bean (Cyamopsis tetragonoloba L.), common bean (Phaseolus vulgaris L.), cowpea (Vigna unguiculata L.), dry pea (Pisum sativum L.), faba bean (Vicia faba L.), grass pea (Lathyrus sativus L.), groundnut (Arachis hypogaea L.), hyacinth bean (Lablab purpureus L.), lentil (Lens culinaris Medik), lima bean (Phaseolus lunatus L.), mungbean (Vigna radiata L.), pigeonpea (Cajanus cajan (L.) Millsp.), soybean (Glycine max L. Merr.), tepari bean (Phaseolus acutifolius A. Gray), urdbean (Vigna mungo (L.) Hepper), and vetches (Vicia sativa L.), are commonly grown in different parts of the world as a component of subsistence farming in dry areas. In addition to being rich in protein, groundnut and soybean are also rich in fat and as such are sources of edible oil in many countries. They are treated both as food and oilseed crops. Consumption of legumes reduces the risk of several diseases such as cancer, diabetes, osteoporosis, and cardiovascular diseases (Hu 2003; Pihlanto and Korhonen 2003; Tharanathan and Mahadevamma 2003). As consumers are looking

S. N. Nigam (*) · S. Chaudhari · K. C. Deevi · K. B. Saxena · P. Janila International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Telangana, India © Springer Nature Switzerland AG 2021 K. B. Saxena et al. (eds.), Genetic Enhancement in Major Food Legumes, https://doi.org/10.1007/978-3-030-64500-7_2

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for a greater balance between plant- and animal-derived nutrition, legumes offer a practical solution for diet diversification between plant and animal food sources. Addition of legumes in crop rotation has a beneficial impact on growing concerns about the negative influence of agricultural practices on the environment, as they have the capacity to fix atmospheric nitrogen in the soil, thereby, reducing the need for chemical fertilizers and improving soil health.

2.2

Global Status of Legumes

Among the legumes, common bean, chickpea, cowpea, lentil, pigeonpea, groundnut, and soybean are the major legumes grown across the world. Common bean (126 countries), groundnut (114 countries), and soybean (89 countries) are widespread followed by chickpea (55 countries), lentil (52 countries), cowpea (37 countries), and pigeonpea (23 countries). These seven legumes together occupy 212.5 m ha area with a total production of 421.8 m t and an average productivity of 1240 kg/ha (FAOSTAT 2016) (Table 2.1). Among these, soybean contributes largest (56.5%) followed by common bean (14.2%), groundnut (12.8%), chickpea (6.0%), cowpea (5.8%), pigeonpea (2.5%), and lentil (2.2%) to the total global area of these major legumes during 2014–2016. Soybean (76.2%) is also the largest contributor to the total global production of these legumes followed by groundnut (10.6%), common bean (6.4%), chickpea (2.3%), lentil (1.3%), cowpea (1.2%), and pigeonpea (1.1%). There is large variation in the average productivity of these legumes across the world. The highest average yield was recorded in the case of soybean (2678.7 kg/ha) and the lowest in cowpea (499.9 kg/ha) during 2014–2016. The average yields in the remaining five legumes were as follows: groundnut (1652.3 kg/ha), lentil (1154.1 kg/ha), chickpea (948.5 kg/ha), common bean (899.9 kg/ha), and pigeonpea (847.6 kg/ha) (Table 2.1). Globally, these major legumes have experienced a total production gain of 548.6% (356.77 m t) during the past five and a half decades (1961–1963 to 2014–2016) due to a combined effect of area expansion by 152.6% (128.37 m ha) and yield enhancement by 85.9% (573.2 kg/ha). However, the gains vary among these legumes across the regions (Tables 2.2a and 2.2b). The maximum gain in production is observed in the case of soybean followed by cowpea, lentil, groundnut, pigeonpea, common bean, and chickpea. But the maximum gain in yield was observed in soybean followed by lentil, groundnut, common bean, chickpea, cowpea, and pigeonpea. Among these legumes, the highest increase in area is recorded in soybean followed by cowpea, lentil, pigeonpea, groundnut, common bean, and chickpea (Tables 2.2a and 2.2b).

Area (m ha) 30.13 (14.2%) 12.82 (6.0%) 12.27 (5.8%) 27.15 (12.8%) 4.77 (2.2%) 5.37 (2.5%) 119.99 (56.5%) 212.50

Production (m t) 27.11 (6.4%) 12.18 (2.3%) 6.13 (1.2%) 44.84 (10.6%) 5.50 (1.3%) 4.55 (1.1%) 321.49 (76.2%) 421.80

1240.3

2678.7

847.6

1154.1

1652.3

499.9

948.5

Yield (kg/ha) 899.9

Major countries for Area Production India, Myanmar, Myanmar, India, Brazil Brazil India, Pakistan, India, Australia, Australia Myanmar Niger, Nigeria, Nigeria, Niger, Burkina Faso Burkina Faso India, China, China, India, Nigeria Nigeria Canada, India, Canada, India, Turkey Turkey India, Myanmar, India, Myanmar, Tanzania Malawi USA, Brazil, USA, Brazil, Argentina Argentina

Figures in parentheses indicate % share in the column totals Source: FAOSTAT 2016

Total/ average

Soybean

Pigeonpea

Groundnut with shell Lentil

Cowpea

Crop Common bean Chickpea

Saint Vincent and the Grenadines, Trinidad and Tobago, Puerto Rico Turkey, Italy, Georgia

New Zealand, Croatia, China

Israel, Malaysia, Palestine

Iraq, Palestine, Macedonia

China, Israel, Moldova

Yield Mali, Ireland, Barbados

Table 2.1 Global distribution of seven major legumes across the world during 2014–2016

89

23

52

114

37

55

Number of countries grown in the world 126

2 Trends in Legume Production and Future Outlook 9

East Africa

Africa

West Asia

Southeast Asia

East Asia

South Asia

Central Asia

Region Asia

Year 1961–1963 2014–2016 % Changeb 1992–1994 2012–2014 % Change 1961–1963 2014–2016 % Change 1961–1963 2014–2016 % Change 1961–1963 2014–2016 % Change 1961–1963 2014–2016 % Change 1961–1963 2014–2016 % Change 1961–1963 2014–2016 % Change

0.02 0.08 232.4 6.99 10.30 47.3 3.72 1.07
71.33 0.82 3.64 346.0 0.12 0.11
13.5 1.72 7.28 322.0 1.04 4.72 355.4

0.02 0.11 425.9 1.91 4.55 137.8 2.90 1.50
48.1 0.61 5.55 807.5 0.14 0.26 84.9 1.03 6.51 533.1 0.72 4.51 521.8

Common bean Aa Pa 11.65 5.57 15.27 12.10 31.1 117.4 918.3 1459.5 58.9 273.6 441.8 61.4 778.6 1413.1 81.5 750.9 1526.1 103.2 1148.6 2453.0 113.6 596.4 894.7 50.0 699.0 954.4 36.5

Ya 477.4 792.6 66.0 0.01 0.03 128.0 10.73 10.29
4.0 – – – 0.12 0.37 221.3 0.13 0.46 247.5 0.37 0.61 64.1 0.19 0.50 156.1

0.01 0.02 139.5 6.55 8.89 35.7 – – – 0.05 0.57 1002.8 0.12 0.58 399.3 0.20 0.76 270.6 0.13 0.65 415.9

Chickpea A P 10.98 6.72 11.14 10.05 1.5 49.7 721.6 789.8 9.5 610.8 861.4 41.0 – – – 438.1 1527.4 248.6 872.5 1252.4 43.5 546.0 1234.7 126.2 652.2 1313.3 101.4

Y 612.2 901.3 47.2

Y 743.1 922.3 24.1 – – – – 1346.8 – 1042.7 1072.7 2.9 622.0 867.4 39.4 – – – 304.2 489.2 60.8 568.9 522.5
8.2

P 0.03 0.15 323.3 – – – – 0.01 – 0.01 0.01
5.4 0.02 0.12 648.0 – – – 0.85 5.88 589.9 0.09 0.48 403.7

Cowpea A 0.05 0.16 240.8
c – – – 0.01 – 0.01 0.01
8.3 0.03 0.13 434.8 – – – 2.88 12.03 318.0 0.17 0.91 448.7 0.01 0.00
57.5 7.04 5.16
26.8 1.48 4.61 211.2 1.08 1.69 56.3 0.02 0.05 169.3 6.27 14.25 127.3 1.06 3.39 219.4

0.02 0.01
16.9 5.15 7.19 39.7 1.43 16.60 1058.5 1.02 2.72 167.2 0.05 0.19 322.0 5.38 13.54 151.6 0.61 2.62 332.7

1505.6 2937.1 95.1 731.7 1412.0 93.0 964.5 3600.6 273.3 940.6 1606.7 70.8 2423.6 3800.4 56.8 859.0 950.2 10.6 569.2 762.1 33.9

Groundnut with shell A P Y 9.63 7.65 794.2 11.52 26.72 2324.5 19.6 249.5 192.7

Table 2.2a Region-wise area (m ha), production (m t), and yield (kg/ha) of seven major legumes during the 1961–1963 and 2014–2016 periods

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Caribbean

North America

South America

Central America

Americas

West Africa

Southern Africa

North Africa

Middle Africa

1961–1963 2014–2016 % Change 1961–1963 2014–2016 % Change 1961–1963 2014–2016 % Change 1961–1963 2014–2016 % Change 1961–1963 2014–2016 % Change 1961–1963 2014–2016 % Change 1961–1963 2014–2016 % Change 1961–1963 2014–2016 % Change 1961–1963 2014–2016 % Change

0.45 1.72 280.9 0.01 0.04 226.1 0.07 0.07 1.3 0.15 0.72 373.8 5.70 6.84 20.0 1.95 2.39 22.6 3.14 3.67 16.7 0.60 0.78 28.6 0.19 0.31 61.5

0.20 1.11 448.3 0.01 0.14 847.6 0.05 0.07 47.4 0.04 0.69 1567.2 3.84 7.24 88.7 0.85 1.77 107.6 2.07 3.90 88.1 0.91 1.57 72.5 0.09 0.27 189.76

447.6 644.4 44.0 1076.2 3121.2 190.0 639.7 912.8 42.7 273.7 964.1 252.2 666.8 1049.1 57.3 437.0 738.4 68.9 659.3 1060.4 60.8 1512.0 2027.7 34.1 478.7 859.0 79.4

– – – 0.18 0.11
36.6 – – – – – – 0.19 0.28 52.5 0.14 0.08
37.9 0.05 0.06 23.4 – 0.14 – – – –

– – – 0.08 0.10 32.1 – – – – – – 0.15 0.43 191.2 0.12 0.14 19.3 0.03 0.06 139.6 – 0.22 – – – –

– – – 431.4 915.8 112.3 – – – – – – 796.9 1519.4 90.7 891.3 1720.1 93.0 546.0 1059.3 94.0 – 1609.4 – – – – 0.05 0.38 605.4 – 0.25 – 0.02 0.01
40.7 2.63 10.48 298.0 0.05 0.03 243.5 – – – – 0.02 – 0.05 0.01
73.8 0.07 0.04 236.54

0.03 0.25 718.3 – 0.11 – 0.01 0.01
51.0 0.71 5.04 607.9 0.03 0.04 41.8 – – – – 0.02 – 0.03 0.02
23.1 0.03 0.03 5.88

580.9 674.4 16.1 – 418.8 – 503.7 425.7
15.5 278.0 481.0 73.0 516.3 1065.7 106.4 – – – – 1324.4 – 617.3 1808.1 192.9 437.0 729.3 66.9

0.72 2.31 222.7 0.31 2.07 569.4 0.30 0.05
83.0 3.88 6.42 65.6 1.37 1.32 23.6 0.08 0.11 41.4 0.73 0.61
15.6 0.57 0.60 5.7 0.07 0.05 227.48

730.9 1040.0 42.3 1247.3 876.8
29.7 824.6 1001.5 21.5 935.5 1032.3 10.4 1356.9 3316.3 144.4 1250.8 2694.7 115.4 1361.5 2666.9 95.9 1421.2 4319.5 203.9 936.5 947.5 1.2 (continued)

0.52 2.41 360.0 0.38 1.83 380.2 0.25 0.05
78.2 3.62 6.63 83.0 1.89 4.50 138.3 0.09 0.29 203.9 0.99 1.63 64.7 0.80 2.58 221.1 0.07 0.05 226.73

2 Trends in Legume Production and Future Outlook 11

Year 1961–1963 2014–2016 % Change 1961–1963 2014–2016 % Change 1961–1963 2014–2016 % Change 1961–1963 2014–2016 % Change 1961–1963 2014–2016 % Change 1961–1963 2014–2016 % Change 1961–1963 2014–2016 % Change

Common bean Aa Pa 4.10 1.03 0.39 0.96 290.5 27.2 1.84 0.26 0.21 0.45
88.8 77.2 0.10 0.10 0.01 0.01
94.3
87.2 2.17 0.67 0.06 0.10
97.0
84.5 – – – – – – – – 0.04 0.03 – – 23.37 11.56 30.13 27.11 28.9 134.6 Ya 251.6 2443.9 871.5 140.4 2211.7 1475.3 1077.4 2430.8 125.6 308.0 1609.2 422.5 – – – – 941.9 – 494.4 899.9 82.0

Chickpea A P 0.42 0.22 0.25 0.25 238.9 13.3 – – 0.19 0.19 – – – – – – – – 0.41 0.22 0.06 0.06
85.4
71.7 – – – – – – – – 0.54 0.69 – – 11.95 7.29 12.82 12.18 7.3 67.0 Y 530.6 1006.0 89.6 – 1019.2 – – – – 528.9 1028.0 94.3 – – – – 1279.3 – 610.1 948.6 55.5

Cowpea A 0.01 0.01 218.2 – – – – – – 0.01 0.01
18.2 – – – – – – 3.05 12.27 302.0 P 0.04 0.03 224.7 – – – – – – 0.04 0.03
24.7 – – – – – – 0.98 6.13 523.9

b

a

A ¼ area, P ¼ production, Y ¼ yield Percent change in 2014–2016 over the 1961–1963 period c – indicates the crop is either not grown or grown in 1000-fold) to pyrethroid insecticide (Kranthi et al. 2001). Integrated pest management (IPM) strategies are needed that focus on the optimum combination of insect-pest management options to minimize yield losses caused by insect pests (Siddique et al. 2012). In northern Bangladesh, an IPM approach for chickpea includes (1) mixed cropping chickpea with barley, linseed, and coriander to encourage enemies of pod borers such as wasps and discourage oviposition by Helicoverpa moths, (2) regular inspection of chickpea before flowering for pod borer, and (3) spray of insecticide HNPV at a rate of 250 larvae per hectare (Harris et al. 2008a). Food legumes are prone to soil-borne and foliar diseases (Allen and Lenné 1999). There is evidence of high levels of resistance in host plants, and resistant varieties have been developed against diseases such as Fusarium wilt in chickpea (Haware et al. 1992) and pigeon pea (Reddy et al. 2012). Host plant resistance is often unstable or partial, which requires alternate management options such as integrated disease management (IDM) in both smallholder and large-scale commercialized agriculture (Siddique et al. 2012). In chickpea, Ascochyta blight is the major yield-limiting disease—the IDM package for this includes (1) use of quality seed (blight-free), (2) use of partially resistant varieties, (3) burning or burying of chickpea stubble, (4) crop rotation, (5) fungicide spray at seedling stage or before disease occurrence, (6) management of canopy to minimize conditions favorable for disease development, (7) adjusting row spacing and sowing time, and (8) intercropping with non-legume crops (Gan

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et al. 2006). The IDM package for chickpea botrytis gray mold (BGM) is similar to the above (Bakr et al. 2002). In Bangladesh and Nepal, these packages can effectively control BGM in chickpea (Pande et al. 2005; Johansen et al. 2008). In India, using IPM to control insect pests in mungbean and soybean—seeds treated with imidacloprid (5 ml kg 1) + one spray of NSKE (5%) at 30 days after sowing + chemical insecticide application [triazophos 40 EC (0.04%)]—significantly reduced the attack of jassids, thrips, and whitefly, reduced pod damage by 7.82 and 11.47%, and increased grain yield by 0.21 and 0.14 t ha 1 and net profit by US$ 560 and 367 ha 1, relative to farmers’ practices, in 2012 and 2013, respectively (Singh and Singh 2015). In conclusion, diversifying cropping systems with legumes will minimize insect pests and diseases by breaking the pest and disease cycle due to genetic dissimilarity.

9.8.2

Integrated Weed Management

Weeds are a major problem for legume production in both developing and developed countries. In early growth stages, most legumes grow slowly and are prone to competition by the faster-growing weeds (Siddique et al. 2012). Weed competition reduces grain legume yields by 25 to 40% (Pandey et al. 1998). To maximize yields, weeds should be controlled at early stages of growth (50 to 70 days after sowing) (Diaz and Penaloza 1995).Weed–crop competition is complex and is affected by crop establishment, crop variety, weed density, seeding rate, and crop rotation (Siddique et al. 2012). Chemicals play an important role in weed management. In the late 1980s and early 1990s, the introduction of selective herbicides in developed countries along with herbicide-tolerant and high-yielding cultivars facilitated the adoption of minimum tillage and boosted production of food legumes (Siddique et al. 2012). In developing countries, legumes are grown using the broadcast method, and weeds are controlled manually, which requires intensive manpower. Manual methods of weed control are losing their value due to labor shortages and crop damage (Siddique et al. 2012). When legumes are grown on residual soil moisture, due to non-availability of water for pre-soaking irrigation or late harvest of previous crop, traditional cultural methods of weed control methods may not be feasible. As a result, pre-seeding herbicide application to control weeds is important. However, unregistered herbicide use with limited or no knowledge can cause serious crop damage (Siddique et al. 2012). There is a need to develop an integrated system to control weeds. Recently, innovative chemical and non-chemical weed control methods have been developed that are useful for controlling weeds in legume (Siddique et al. 2012). An increase in plant density increased the competitive ability of lentil and suppressed the weed effect using chemical and mechanical control (Paolini et al. 2003; McDonald et al. 2007). Mechanical treatment had good weed control potential when combined with weed harrowing and hoeing. Similarly, in inter-rows, mowing weeds followed by a knockdown spray provided effective control of broadleaf

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weeds but did not affect grass weeds (as annual ryegrass) (Hashem et al. 2004). Likewise, in non-chemical control, trimming the heads of weeds provides effectively reduced weeds in lupin and chickpea (Riethmuller et al. 2009). Moreover, weeds were controlled by crop-topping and spraying with paraquat close to maturity to prevent seed set (McMurray 2010). Avola et al. (2008) evaluated the use of plant lodging (narrow, wide, and twin rows) combined with mechanical weed treatment to control weeds in chickpea, field bean, and pea. The study found that the different crops had different competitive behaviors—in field bean, the wide-row treatment reduced the different weeds to 70% and narrow rows to 30%, and the mechanical treatment had a similar effect in the narrow and wide rows, while in chickpea, the mechanical weed treatment combined with wide rows reduced weeds. In another study, the combination of hand weeding (30 days after sowing) + pendimethalin (0.45 kg ha 1) + ridging (50 days after sowing) had the best weed control efficiency (Singh and Sekhon 2013). In India, a 3-year study of IWM in chickpea under rainfed conditions found that pendimethalin 38% CS + hand weeding at 30–35 DAS and pendimethalin 30% EC + imazethapyr 2% + one hoeing at 30–35 DAS recorded the highest grain yields (1.19 t ha 1), net returns (US$341 ha 1), and benefit–cost ratios (2.10), lowest weed dry weights (11.3 g m 2), and highest weed control efficiency (83%) relative to the other integrated treatments (Rathod et al. 2017). In a recent study of IWM in lentil, 50 g ha 1 quizalofop-ethyl + one hand weeding at 45 days after sowing significantly reduced weed density and dry matter and increased grain yield (1.48 t ha 1), net return (US$378 ha 1), and benefit–cost ratio (2.53) relative to the other integrated treatments (Kumar et al. 2018). In conclusion, an integrated system approach should be used to control weeds. Such approaches include efficient cropping systems and non-chemical, mechanical, and agronomic methods of weed control.

9.9

Integrated Crop Management

Major threats to food legume productivity include growing crops on marginal lands, lack of improved and certified seed, lack of improved cultural practices, weed pressure and disease attack, biotic and abiotic stresses, and neglected research. Crop research tends to focus on various levels of a single factor, or perhaps a few by keeping other factors constant, and usually involve major yield-limiting factors. For farmer benefit, these factors should be part of an overall crop management strategy or integrated crop management (ICM) at both the temporal and spatial level, which incorporates ecosystem management and non-crop factors (Siddique et al. 2012; Johansen and Siddique 2018). Biotic and abiotic stresses are a major threat to grain legume cropping. Many studies have been undertaken, but often the research output does not reach smallholder farmers in resource-poor areas. Therefore, to improve grain legume

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production and the income of resource-poor and smallholder farmers, an integrated approach is needed that includes (1) visits to on-farm trials to determine major yieldlimiting factors, (2) conduct of surveys across farmers’ fields, (3) environmental characterization, (4) on-farm trials for each factor, (5) multi-location evaluation of improved treatments, (6) comparisons of improved ICM packages with conventional packages, (7) calculation of ICM packages, and (8) adoption and impact analysis with solution to bottlenecks (Siddique et al. 2012). In Nepal and Bangladesh, ICM packages have been used for the rehabilitation of chickpea against BGM and to enhance food legume production (Pande et al. 2005; Johansen et al. 2008). These packages included i) monitoring BGM under IPM and managing BGM, (2) monitoring pod borer under IPM and managing these pod borers, (3) applying triple superphosphate (100 kg ha 1) and boron (1 kg ha 1), and (4) using quality seed and fungicide treatments. In a 2-year study, chickpea yields using the ICM package were double those using normal farmer practices (Pande et al. 2005). In another study, the ICM package resulted in more profitability for chickpea than irrigated cereal crops and 50% higher net returns than conventionally grown chickpea (Johansen et al. 2008). Being hardy in nature, legumes can be grown on marginal lands to get higher economic returns with ICM systems compared to cereals (Al-Tawaha et al. 2010; Fig. 9.1). In southern Ethiopia, faba bean was grown with ICM approaches to increase yield. The row planting +100 kg DAP ha 1+ twice weeding approach increased soil fertility (N by 0.268%, P by 17.79 mg kg 1, organic C by 2.95%, and OM by 5.08%) relative to the other ICM packages. The same approach produced the highest grain yield (3.86 t ha 1), fewest weeds (30 m 2), and highest gross (US$595 ha 1) and net (US$461 ha 1) benefits (Gebremariam et al. 2018). In India, mungbean was grown under different ICM approaches, being fertilizer application, weed and pest management, and rhizobia, organic manure, and ZnSO4 application. The recommended fertilizer dose (20:40:00 NPK kg ha 1) + weed management (pendimethalin +1 hoeing at 20–15 days after sowing) + pest management (metasystox 625 ml 25 EC ha 1 at 14–21 days after sowing) produced the highest pods per plant (24), seeds per pod (10), and seed yield (1.50 t ha 1) compared to control (no application of fertilizers, weed and pest management) (Jitender et al. 2016). In crux, low grain legume production is due to the non-adoption of ICM approaches. Extensive research has been undertaken on individual yield-limiting factors such as resistance to abiotic stresses (drought, salinity, chilling, and heat); availability of efficient, certified, and short-duration varieties; and fertilizer application to improve fertility. There is a need for integrated approaches such as IPM, IDM, INM, IWM, and ICM to improve grain legume production.

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Toward an Innovation Systems Approach

Over the coming decades, global food security will need a major re-think in terms of plant sciences, production agronomy, and crop improvement to identify climateresilient varieties/cultivars of grain legumes with good grain characters to increase food production (Considine et al. 2017). In the past 50 years, global cereal production has almost tripled, while that of food legumes has increased by 60% (Foyer et al. 2016). Low yield improvements in grain legumes are due to their low genetic potential (Cowling et al. 2017). To improve grain yields in food legumes on a sustainable basis, an innovation systems approach is needed. The success of sustainable innovation depends on the environment and the innovation system. For sustainable economic growth and development, innovation is a key component (Alkemade et al. 2006). Theoretical and practical approaches for promoting agriculture innovation have evolved from a “linear” mode of innovation, which includes production and knowledge exchange, to a “system” approach (Sumberg 2005). One approach involves the design of cropping systems for ecosystem services that include minimal soil disturbance and a mixture of grain crops and winter cover crops (e.g., red clover and annual rye) to conserve and increase soil fertility. Cover crops are components of long-term ecological agricultural research systems as they support ecosystem services such as soil conservation, nutrient cycling, organic matter buildup, and improved water quality (Snapp et al. 2005). In Malawi, “doubled-up legume intercropping” is practiced to boost productivity, of which groundnut–pigeon pea intercropping is most successful (Smith et al. 2016). To boost grain legume productivity, innovation systems approaches include improved cultivation practices, raised beds, row spacing adjustments, seed treatments, biofertilizer application, adoption of efficient integrated management approaches (e.g., ICM), and development of better productivity cropping systems which will ensure food quality, sustainability, and environmental protection. In conclusion, adoption of an innovation system approach is needed to improve grain legume production to meet food, nutrition, and protein security. Development of such a system to that incorporates grain legumes requires the interaction of researchers, farmers, processors and end users.

9.11

Conclusion

Grain legumes are an important part of the human diet, particularly in the vegetarian populations of developing countries. Major threats to the productivity of food legumes include being grown on marginal land, the lack of improved cultural practices, biotic and abiotic stresses, weed pressure, and disease attack. Thus, grain legume production should focus on integrated approaches that minimize these threats. The sustainable production of grain legumes requires diversification

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of cropping systems, seed enhancements (agronomic and genetic approaches), use of microbes/PGPR, and production systems such as a doubled-up intercropping system. Legumes are grown on marginal soils under suboptimal conditions. Seed enhancements can improve stand establishment, growth, and yield and help plants to cope with stresses (biotic and abiotic) by strengthening plant defense/escape mechanisms. The adoption of various agronomic strategies including early planting, spatial planting arrangements, raised bed planting, and the precise management of each input will substantially increase and stabilize yields in food legumes. Conservation agriculture is helping to overcome the excessive use of synthetic chemicals such as herbicides, fertilizers, and pesticides, increase water use efficiency, and sustain soil fertility, which will result in more balanced ecological systems. The adoption of an organic farming approach that incorporates legumes in different cropping systems is recommended.

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Chapter 10

Scaling Up Food Legume Production Through Genetic Gain and Improved Management Suhas P. Wani, Girish Chander, Mukund D. Patil, Gajanan Sawargavkar, and Sameer Kumar

10.1

Tropical Legumes: Major Food Crops and Current Status

Pulses are important part of cropping systems and food systems in Asia, Africa, and Latin America and occupy about 5.8% of the world’s arable land area (Joshi and Parthasarathy Rao 2017). Pulses are unique largely due to their ability to grow on marginal soils as they are able to fix most of their nitrogen requirement through biological nitrogen fixation (BNF), are main source of proteins for vegetarian people, and are also able to withstand stress situations such as drought. In 2011–2013, pulses accounted for 80 million ha of global crop area and produced 72 million metric t of grain. With respect to production globally, dry beans account for 32%, chickpea 17%, dry peas 14.6%, cowpea 8.9%, lentils 7%, pigeon pea 6.2%, and broad bean 5.8%. During the years 2005–2007, total production was around 60 million metric t, and so there is a significant increase in production in Canada and Australia, the area expansion under pulses in Africa, and the export-oriented production in Myanmar (Parthasarathy Rao et al. 2010). Developing countries account for 70% of the global pulse production, but there is huge yield gap for pulses between developed (1640 kg ha
1) and developing countries (765 kg ha
1). The differences are apparently due to differences in inputs, technology, and infrastructure. S. P. Wani (*) · G. Chander · M. D. Patil · G. Sawargavkar International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Research program Asia, Patancheru, Telangana, India S. Kumar International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Research program Asia, Patancheru, Telangana, India Professor Jayashankar Telangana State Agricultural University (PJTSAU), Hyderabad, India © Springer Nature Switzerland AG 2021 K. B. Saxena et al. (eds.), Genetic Enhancement in Major Food Legumes, https://doi.org/10.1007/978-3-030-64500-7_10

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In the semi-arid tropics, most legumes, in general, are grown in marginal lands under rain-fed conditions with minimal inputs, using seeds of low-yielding cultivars available with farmers. With increasing water scarcity and increased water demand for growing crops due to impacts of climate change, legumes would be main food crops to replace cereals grown currently. Legumes with low crop yields, to be grown with minimum inputs on marginal soils, largely by the small farm holders and poor market linkages resulted in these crops to be referred as neglected and underutilized species (NUS). In the search of climate-resilient agriculture, international bodies are promoting and popularizing climate-resilient/smart agriculture with smart crops. ICRISAT has termed the dryland legumes as smart crops as these are good for the farmers, good for the planet, and also good for the people (ICRISAT, 2017a). However, to address the sustainable development goals (SDGs) particularly the SDG 2 of achieving zero hunger and SDG 3 of good health and wellbeing during the climate change era, there is an urgent need to diversify the food systems as well as promote locally grown nutrient-rich food crops which were NUS (Li et al. 2018). The Food and Agriculture Organization (FAO) along with several national and international partners has recommended Future Smart Food (FSF) concept to address the problems of climate change, food security, and malnutrition in Asia, and several food legumes have been prioritized by a number of countries for positive interventions using FSFs (Li and Siddique, 2018). Large yield gaps up to fivefold between the current farmers’ yield and the achievable potential yields for almost all the crops exist in Asia and Africa (Rockstrom et al. 2010; Wani et al. 2012b & c) largely due to existence of death valley of impacts as a large number of small farm holders are deprived of extension support about the new technologies as well as improved cultivars and inputs (Wani et al. 2012a and 2018b). Many technologies and improved cultivars don’t see the light of the day on farmers’ fields largely due to existence of lack of synergy among various actors involved in different phases from discovery to outcomes and impacts. Most of the actors including the scientists who develop the technologies and improved products work in compartments/silos, and integrated and holistic solutions are not provided to the farmers, and the technologies/products fall in the death valley of impacts (Wani et al. 2018b). For achieving the impact, the researchers must engage in action/development research to develop appropriate solutions together with resource users as well as various actors involved in the impact pathway (Wani et al. 2018b; Hagmann et al. 2002) (Fig. 10.1).

10.2

Enhancing Productivity of Dryland Legumes

As the current farmers’ crop yields are lower by two- to five fold that of achievable potential yields in rain-fed agriculture which are largely due to knowledge gap and not the technology gap, there is an urgent need to undertake development research for scaling up the impacts to bridge the yield gaps. However, it’s a new branch of science, so to achieve the impacts, farmers look for the holistic and integrated

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Fig. 10.1 Death valley of impact (Source: Wani and Raju, 2016)

solutions in contrast to the compartmental solutions provided at present. This approach involves science-based solutions integrating backward and forward linkages using new technologies such as information and communications technology (ICT), artificial intelligence (AI), machine learning (ML), remote sensing (RS), crop and water budgeting simulations, market information, as well as inputs supply. Such a model needs partnerships with various sectors, and initial transaction costs are higher, but the impacts are far larger than expected (Wani et al., 2018). A consortium approach involving partnerships among technology/knowledge-generating institutions, knowledge sharing institutions, and public and private institutions along with government departments is found the most appropriate to benefit the farmers (Wani and Raju, 2016).

10.2.1 Integrated Watershed Management Model For upgrading rain-fed agriculture, a holistic integrated watershed/catchment-level management approach is the desired, preferred, and proven strategy for sustainable food production including legume production to meet growing food demand. This approach also addresses issues of water scarcity, land degradation, and minimizing the impacts of climate change (Wani et al. 2018a 2012b, c, 2014, Wani et al. 2002b). Inclusive market-oriented development (IMOD) approach in watershed management meets the multiple objectives of zero hunger, no poverty, good health and wellbeing of people, climate action, gender equality, and building partnerships contributing to several sustainable development goals (SDGs). In rain-fed agriculture major risk of water scarcity can be addressed through integrated watershed development model which also contributed to crop diversification and sustainable intensification and water and soil conservation along with improved crop cultivars and management. This approach/model is built on the principles of four ICEs, i.e., Innovative, Inclusive, Intensification, and Income (4Is); Consortium, Convergence, Collective action, and Capacity building (4Cs); and Equity, Economic gain, Empowerment, and Environment protection (4Es) (Wani et al. 2002, 2012a). This model was developed

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based on the comprehensive assessment (CA) of watershed programs in India undertaken by the ICRISAT-led consortium that revealed that 99% of watershed projects were economically remunerative and were silently revolutionizing rain-fed agriculture with a benefit-cost ratio of 2.14 while reducing runoff by 45% and soil loss by 2 to 5 tons/ha/y increasing agricultural productivity by 50% to 400% and cropping intensity by 35% (Joshi et al. 2008; Wani et al. 2008). However, large scope existed for improving the performance of 68% of the watershed projects as only 32% of the projects performed above average based on the detailed case study of 622 watershed projects implemented and published in India, which were performing below average. In addition, the changes due to impacts of climate change also need to be taken into account as the number of rainy days during the season has reduced and high-intensity rainfall events have increased (Rao et al. 2013) while choosing crops, cultivars, and drought-proofing measures.

10.2.1.1

Water Management for Drought Proofing

Most important aspect in rain-fed agriculture is the efficient management of rainwater and other available resources in the watershed. The foremost intervention to be undertaken in any watershed development is efficient management of green water, i.e., soil moisture. Efforts must be to store as much rainwater in soil during the rainy season as possible by adopting in situ water and soil conservation measures such as contour planting, adoption of ridges and furrows/broad bed and furrows (BBF)/tied ridges/basins, conservation furrows, etc. Appropriate landform applications and adoption minimize the risk of waterlogging and provide longer opportunity time for rainwater to infiltrate in Vertisols. Once the rainwater is stored in soil, the next step is to minimize unproductive evaporation losses of soil moisture and increase productive evapotranspiration (ET) producing crop growth by adopting measures like mulching with organic residues or plastic film, minimizing tillage, zero tillage, intercropping with appropriate crop, weeding, etc. Effectiveness of land and water management techniques is influenced by soil characteristics and climate. For example, heavy textured soils with clay content ranging from 40 to 60% or more have high water holding capacity, which makes Vertisols ideal soils for rain-fed/irrigated dryland agriculture. In India, one fourth of semi-arid region is covered by Vertisols. Infiltration rate when the soil is dry can be as high as 50–80 mm h
1, through the bypass/preferential flow through cracked Vertisols may be much higher. But after wetting, swelling of the soil closes the cracks leading to extremely low infiltration rates (less than 1 mm h
1) (Pathak et al. 2013). Increased occurrence of heavy rainfall events with reduced frequency of low rainfall events is the characteristic impact of climate change in the SAT (Rao et al. 2013). The knowledge of soil characteristics and climate helps in selecting the appropriate land management practices. Soil moisture availability is directly related to crop productivity generally in rainfed agriculture except in case of heavy rainfall events in Vertisol areas where waterlogging could become an issue affecting crop productivity adversely. Extended

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dry spell during critical crop growth period significantly reduced crop yield, and supplemental irrigation using harvested rainwater significantly enhances crop productivity. Thus, avoiding waterlogging situation during high rainfall event and conserving soil moisture during the dry spell are essentials for any crop to avoid yield loss. The proper withdrawal of excess water through furrow and increased storage of soil moisture in broad bed by providing more opportunity time for rainwater infiltration take care of waterlogging due to excess rainfall and water scarcity due to extended dry spells, respectively. Furrows made at a specified gradient (0.2 to 0.4%) carry runoff water into waterways slowly and then releasing in the main waterways to carry along the slope in the farm pond. Runoff from entire field may be captured by constructing water harvesting structures. Long-term micro-watershed experiments at International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India, showed that implementation of soil and water conservation practices during 1976 to 2012, intercropping/sequential cropping, and integrated nutrient management produced average crop yield of 5.1 t ha
1 y
1 (sorghum/pigeon pea intercropping) compared to 1.1 t ha
1 y
1 (sole sorghum) with farmer’s practice (Wani et al. 2011). Much emphasis must be given to rainwater management and harvesting at farm level as a drought-proofing strategy in the drylands (Wani et al. 2002b). Pilot studies in drylands of Andhra Pradesh, Telangana, Maharashtra, and other sites indicated that small low-cost farm ponds provide access to water for critical irrigation during drought and check yield losses up to 20–60% (Chander et al. 2018a). Conservation agriculture (CA) is another important in situ intervention considered for practicing resilient and climate-smart agriculture. The basic components of CA are (1) zero or minimum tillage; (2) retention of crop residues on the soil surface; and (3) crop diversification. Minimal tillage reduces quantity and velocity of surface runoff and reduces soil erosion and nutrient loss; incorporation of crop residue enhances soil moisture availability and reduces evaporation losses (Jat et al. 2012, 2015; Araya et al. 2012; Araya et al. 2011; Potter et al. 1995; Gilley et al. 1986; Massee and Cary, 1978), improves the infiltration by restricting surface runoff (Yule et al. 1990), and reduces surface sealing from raindrop impact (Potter et al. 1995). Crop diversification reduces risk of the crop failures and is recognized as a costeffective solution to build resilience into agricultural production system (Rusinamhodzi et al. 2012; Lin, 2011). Diversification brings stability in soil fertility through cultivating legumes with cereals in rotation or with intercropping system (Myaka et al. 2006; Aslam et al. 2003; Chamango, 2001). Recent studies have reported that CA improved crop productivity by 20–120% and water use efficiency by 10 to 40% (Ngwira et al. 2012; Rockstrom et al. 2009; Ito et al. 2007; Li et al. 2007; Wang et al. 2007). But adoption rate of CA among the farmers is constrained by economic conditions of small and marginal farmers as well as lack of appropriate machinery. Also, the increased crop yields are not evident in initial years although CA reduced runoff and soil loss in long-term experiment at ICRISAT (Jat et al. 2015). In the SAT regions, crop residues are primarily used for animal feeding and fetch high value, plus after harvest fields are open for common grazing; also as a

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result farmers are hesitant to adopt crop residue retention in field. Moreover, yield advantage of CA over farmers’ practices is not much clear. Some of the studies reported no improvement or negative effects on crop yield by adopting such techniques (Baudron et al. 2012; Van den Putte et al. 2010).

10.2.1.2

Soil Health Mapping and Balanced Nutrients Application

Soil health is a critical factor in crop production, but unfortunately farmers are not aware about their soil health. Even though knowledge about soil analyses is available with the scientists, farmers apply blindly NPK fertilizers only as advised by the fertilizer dealers/neighbors resulting in imbalanced use of nutrients and low crop yields with increased cost of cultivation (Wani et al. 2017). Healthy soils are a must for good crop production and healthy foods which are must for healthy people. When the world is facing severe problem of malnourished people, legumes grown on healthy soils could contribute significantly to meeting SDG 2 & 3. However, analysis of farmers’ fields across different states in India as well as in China, Thailand, and Vietnam as well as in Africa showed widespread deficiencies of multiple nutrients including secondary and micro- along with macronutrients. In India across the states, widespread deficiencies of essential nutrients including secondary and micronutrients along with the primary nutrients were recorded which were found to be directly associated with low crop yields in farmers’ fields (Table 10.1; Wani et al. 2018b, 2017, 2016, 2015a,b, 2013, 2012a, 2011; Sahrawat et al. 2016, 2007; Rego et al. 2005; Rao et al. 2014; Chander et al. 2018b, 2016, 2014, 2013a,b, 2012, GoI, 2017). Response studies to balanced nutrients in on-farm trials have recorded significant productivity benefits varying between 20 and 50% in crops like pigeon pea, chickpea, green gram, and black gram (Tables 10.2, 10.3, 10.4 and 10.5; Fig. 10.2).

10.2.2 Improved Cultivars with High Genetic Gain Genetic gain is the amount of increase in performance mainly yield that is achieved annually through artificial selection (Xu et al. 2017) which results in development of stress-tolerant improved cultivars. Defined as the rate of increase in yield over a given period, the real genetic gain is estimated against potential yield but can also be assessed under defined stress conditions. Genetic gain in legumes, like other crops, is mostly based on pedigree and performance-based selection over the past halfcentury. Monotonous breeding with the less appreciated interdisciplinary approach, resulting in inefficient selection criteria and extended breeding cycles, failed to unlock stagnant genetic gains in legumes. Faster genetic gains in legumes could be achieved through multidisciplinary approach where breeders, molecular biologists, physiologists, plant nutritionist, plant protection scientists, and data scientists work together. The integration of modern genomics, high-throughput phenomics,

District All 13 districts

Bengaluru, Bidar, Bijapur, Chamrajnagar, Chikballapur, Chitradurga, Davangere, Dharwad, Gadag, Gulbarga, Hassan, Haveri, Kolar, Raichur, Tumkur, Yadgir

State Andhra Pradesh

Karnataka

Mandal/taluk/block Kollur, Sattenapalli, Kanigiri, Konakana Mitta, Ongole, Indukurpeta, Podalakur, TP Gudur, Konduru, Ghantasala, Akividu, Kamavarapu Kota, Gangavaram, Yeleswaram, Penukonda, Raptadu, Kothacheruvu, Santhipuram, V Kota, Porumamilla, B Mattam, Veeraballe, Sambepalle, Banaganapalli, Devanakonda, Parvathipuram, Pusapatirega, Polaki, Ranasthalam, Seethampeta, Butchayyapeta, Chintapalle, Padmanabham All taluks in 16 districts 52

% samples deficient in soil org C 58

41

P 23

23

K 06

52

S 47

Mg 03

–

Ca 29

–

55

Zn 52

62

B 32

% fields deficient in plant nutrients

Table 10.1 Percentage of deficient farmers’ fields supplying inadequate levels of available nutrients across various states in India

–

Fe 02

–

Cu 05

–

Mn 01

Scaling Up Food Legume Production Through Genetic Gain and Improved. . . (continued)

92,864

No. of samples 5319

10 309

Mayurbhanj, Keonjhar

Basavan Bagewadi Pulkal, Sangareddy Rajgarh, Kushalgarh, Jahajapur, Hindoli, Bichiwara, Jhalarapatal, Khandar, Deoli, Newai, Girwa Raidih, Saraikala Badwani, Devas, Madusudangarh, Samer, Silwani, Rajgarh, JC Nagar, Sehore, Agar, Vidisha, Lateri, Meghnagar, Niwas Mayurbhanj, Harichandanpur

Bijapur Medak Alwar, Banswara, Bhilwara, Bundi, Dungarpur, Jhalawar, Sawai Madhopur, Tonk, Udaipur

Odisha

Bethamcherla

Kurnool

Gumla, Kharsawan Badwani Dewas, Guna, Indore, Raisen, Rajgarh, Sagar, Sehore, Shajapur, Vidisha, Jhabua, Mandla

Mandal/taluk/block Patancheru Khandala Sandur Jawhar Wanaparthy Penukonda

District Medak Satara Bellary Palghar Mahabubnagar Anantapur

Jharkhand Madhya Pradesh

State Telangana Maharashtra Karnataka Maharashtra Telangana Andhra Pradesh Andhra Pradesh Karnataka Telangana Rajasthan

Table 10.1 (continued)

18

42 22

49 71 38

50

% samples deficient in soil org C 59 52 35 05 81 87

73

65 74

89 28 45

15

P 10 26 30 43 46 69

10

96

77 64

71 55 71

– 06 15

50 01

76

S 35 80 55 57 83 77

08

K – 03 – 03 14 15

0 0 –

– –

–

– –

–

0

Mg 0 0 – 0 01 0

– 06 –

80

Ca 01 – – – 38 29

07

71 66

94 66 46

75

Zn 62 76 67 27 81 94

99

97 79

16 45 56

35

B 19 67 23 57 73 77

% fields deficient in plant nutrients

–

– –

08 – –

04

Fe 01 05 15 – 10 07

–

– –

0 0 –

0

Cu 0 0 08 0 0 0

–

– –

0 02 –

12

Mn 0 0 0 0 39 44

177

115 341

187 246 422

169

No. of samples 189 324 879 95 192 190

310 S. P. Wani et al.

10

Scaling Up Food Legume Production Through Genetic Gain and Improved. . .

311

Table 10.2 Effects of soil test-based application of micronutrients and secondary nutrients in pigeon pea crop in Karnataka

District Bagalkot

Crop Pigeon pea Pigeon pea

Bellary Bidar

Pigeon pea

Bijapur

Pigeon pea

Davanagere

Pigeon pea

Gulbarga

Pigeon pea

Kolar

Pigeon pea Pigeon pea Pigeon pea

Raichur Ramanagara Yadgir

Pigeon pea

Year 2011

Grain yield kg ha
1 Farmers’ practice Balanced (FP) fertilization 1080 1440

% increase over FP 33

2011 2012 2010 2011 2010 2011 2011 2012 2010 2011 2011

620 310 1230 790 920 740 470 530 1380 1240 1360

920 460 1700 1030 1160 980 560 670 1870 1850 1850

48 48 38 30 26 32 19 26 36 49 36

2010

960

1280

33

2011 2012 2010 2011 2012

1010 650 1630 660 1580

1430 860 2230 850 1960

42 32 37 29 24

simulation modeling, crop improvement, and appropriate agronomy enhances genetic gains. Selection intensity, generation interval, and improved operational efficiencies in breeding are too expected to elevate the genetic gain. Improved seed access to farmers, combined with appropriate agronomic packages in farmers’ fields, will deliver higher genetic gains. Enhanced genetic gains, including not only productivity but also nutritional and market traits, will increase the profitability of farming and the availability of affordable, nutritious food, especially in developing countries (Varshney et al. 2018). Once the improved cultivars are released, the next and most important step is to popularize the improved cultivars on farmers’ fields.

10.2.2.1

Development of Improved Cultivars

As indicated earlier conventional breeding alone cannot enhance the genetic gain and integrated breeding for higher and faster genetic gain through a multidisciplinary team is essential. Worldwide legumes constitute 16% of 5.55 million a large number (5.55 million) of plant accessions assembled (FAO, 1996; Upadhyaya, et al. 2007). First and foremost, the need is to broaden genetic diversity using large germplasm

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Table 10.3 Effects of soil test-based application of micronutrients and secondary nutrients in chickpea crop in Karnataka

District Bagalkot Bellary Bidar Bijapur

Crop Chickpea Chickpea Chickpea Chickpea

Chitradurga Davanagere

Chickpea Chickpea

Dharwad Gadag Gulbarga Haveri Raichur Yadgir

Chickpea Chickpea Chickpea Chickpea Chickpea Chickpea

Year 2011 2011 2010 2010 2011 2009 2010 2011 2009 2011 2011 2011 2010 2011

Grain yield kg ha
1 Farmers’ practice (FP) 1550 450 1660 1200 1200 1240 1400 1300 1070 710 1040 400 1340 560

Balanced fertilization 2010 620 2310 1560 1560 1520 1780 1590 1430 1100 1440 540 1700 750

% increase over FP 30 38 39 30 30 23 27 22 34 55 38 35 27 34

Table 10.4 Effects of soil test-based application of micronutrients and secondary nutrients in green gram crop in Karnataka District Bidar

Bijapur Dharwad Gadag Gulbarga Yadgir

Year 2010 2011 2012 2010 2011 2011 2010 2011 2010 2011 2010 2011 2012

Grain yield kg ha
1 Farmers’ practice (FP) 870 810 890 330 240 950 280 760 460 480 540 570 850

Balanced fertilization 1200 1120 1190 480 300 1380 440 1080 590 690 710 810 1100

% increase over FP 38 38 34 45 25 45 57 42 28 44 31 42 29

Table 10.5 Effects of soil test-based application of micronutrients and secondary nutrients in black gram crop in Karnataka District Bidar Gulbarga

Year 2010 2011

Grain yield kg ha
1 Farmers’ practice (FP) 930 410

Balanced fertilization 1260 560

% increase over FP 35 37

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Fig. 10.2 Productivity benefits in chickpea with balanced nutrition under Bhoochetana, Andhra Pradesh, during post-rainy season 2012

collection (20,602 accessions of chickpea and 13,771 in pigeon pea), and it’s a challenging task. The concept of core/mini core collection of the germplasm lines for effective utilization in breeding program is proposed (Frankel, 1984, Upadhyaya et al., 2006, 2016). Pre-breeding is one of the potential genetic diversity-enhancing approaches. Introgression of desired genes from rich wild/exotic species to current-day cultivars is today’s necessity for broadening the genetic base of legumes (Kumar et al. 2003). Selection intensity and satisfactory heritability are an outcome of a robust breeding profile which can be further improved through field phenotyping or application of molecular markers, shuttle breeding, and multilocation testing/selection at national and international sites. A success story of pigeon pea hybrid has set an example where partnership between ICAR and ICRISAT resulted in release of world’s first commercial pigeon pea hybrid ICPH 2671 in Madhya Pradesh (Saxena et al. 2018). Genomic approaches play important and critical role in enhancing the genetic gain process. Unraveling the genome sequence of soybean (1115 Mb), pigeon pea (833.07 Mb), chickpea (738 Mb), common bean (587 Mb), mung bean (548 Mb), adzuki bean (612 Mb), and cowpea (323 Mb) was done using high-throughput genotyping platform. This has generated sufficient genomic resources with associated phenotypic data to discover target traits and breeding of superior varieties (Varshney et al. 2018). Diversity array technology (DArT)-seq, restriction siteassociated DNA sequencing, and high-throughput SNP approaches are used for

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developing high-density genetic maps, refining the QTL mapping, and identifying trait-linked markers in legumes in affordable costs in chickpea, pigeon pea, and soybean (Varshney et al. 2018). Marker-assisted selection (MAS), marker-assisted backcrossing (MABC), and marker-assisted recurrent selection (MARS) are used to breed climate-resilient legume crops (Varshney et al., 2018). LeasyScan phenotyping platform at ICRISAT; semi-hydroponic phenotyping system, Australia; GLO-Roots, USA; and X-ray computed tomography, UK, are few highthroughput phenotyping platforms used in crop improvement. Integration of modern phenotyping tools in breeding programs accelerates wider adaptability, resilience, increased productivity, and quantum jump in genetic gains (Varshney et al. 2018).

10.2.2.2

Farmer Participatory Evaluation of Improved Cultivars

Balancing productivity, profitability, and environmental health is a key challenge for today’s agriculture for ensuring long-term sustainability (Foley et al. 2011; Robertson and Swinton, 2005). However, most crop production systems in the world are characterized by low species and management diversity, high use of fossil energy and agrichemicals, and large negative impacts on the environment. Therefore, there is urgent need to focus our attention toward the development of crop production systems with improved resource use efficiencies and more benign effects on the environment (Foley et al. 2011; Tilman et al. 2002). Cropping system design provides an excellent framework for developing and applying integrated approaches to management because it allows for new and creative ways of meeting the challenge of sustaining the agricultural productivity. The participatory varietal evaluation program on different varieties of legume crops was started by ICRISAT, along with NARS partners, toward increasing farmer productivity by facilitating the delivery of drought-tolerant, high-yielding, profitable variety of groundnut which is well adapted to a wide range of soil types, environments, and farming systems in different parts of India. The suitability of this variety was assessed by providing accredited, unbiased information to farmers on better adapted different crop varieties, or new and better cultivars of legumes, at the earliest opportunity. Secondly, this farmers’ participatory varietal evaluation program with legume cultivars was conducted with an objective to initiate the process to scale up the adoption of suitable cultivars, having suitable traits for better adaptation to biotic and abiotic stresses to enhance or sustain productivity, and further scale up the spread of these varieties to satellite villages/taluks/districts. The layout of this demonstration comprised of approximately half to 1 acre of farmers’ field with adjoining field as control with his/her traditional variety. Similarly, emphasis was given on best-bet management practices comprising application of balanced nutrition, viz., 50 kg DAP, 10 kg borax, 50 kg zinc sulfate, and 200 kg gypsum ha
1, and also other management practices, viz., weed and pest control. With these trials, farmers were exposed to benefits of improved legume varieties grown in their area and had the option of evaluating the performance of legume

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315

cultivar crop more or less in the same climatic and soil conditions with different levels of input management. The layout of participatory varietal trial was designed to assess the performance of local legume variety with traditional way of input management (FP) and improved cultivar + best-bet inputs (IP) as shown in Layout 1. Layout 1. Participatory varietal selection cum yield maximization trial with legume variety Traditional/local cultivars + farmers’ inputs (FP) HY cultivar + best-bet management (IP)

The activity is promoted through ICRISAT’s project which is being supported by different state governments in India with the active involvement of state agriculture departments. The program collects and delivers the data which not only assist farmers with the best choice of suitable variety available but also facilitate the registration and commercialization of this variety by plant breeders. The experimental protocol has been established to evaluate the performance of improved varieties/ cultivars under balanced nutrition against a common set of traditional varieties of legumes to characterize their yield, quality, disease resistances/tolerances, and agronomic characteristics. The information on yield performance of both the practices, viz., improved practice and farmer’s practice, are collected through crop cutting experiments by ICRISAT staff, FFs, and agriculture department staff/officials along with staff of Department of Statistics and Economics.

Details of Evaluation of Legume Cultivars in Karnataka During 2012 to 2016 Field trials for improved legume crop cultivars were evaluated in Karnataka during 2012 to 2016–2017, and details are presented in Table 10.6. The efforts were made to make available climate-smart crop cultivars which are tolerant of mid-season and end-of-season drought, and are high yielding were made available to farmers for their evaluation. The results revealed that there has been 12 to 24 percent increase in the legumes’ productivity compared to local popular cultivar (Fig. 10.3).

10.3

Agronomic Innovations for Enhancing Productivity and Production

Greater efforts are needed to popularize the best agronomic management practices among the farmers to improve the productivity and profitability of rain-fed system. Therefore, emphasis should be given on popularization of the climate-smart legume crops, to support farmer seed-sharing networks to ensure availability of diverse crop varieties and to encourage a diverse farming economy at landscape (if not always farm) level.

Lakshmi (ICPL85063), Asha (ICPL87119), Puskal (ICPH2671), ICPH 2740 Lakshmi (ICPL85063), Puskal (ICPH2671)

Chikmagalur

Chitradurga

Tumkur

Bangalore 2

Gadag

Chamarajanagar

Bijapur

Lakshmi (ICPL85063), Puskal (ICPH2671), Asha (ICPL87119) Laxmi (ICPL85063), Asha (ICPL87119), Puskal (ICPH2671) Lakshmi (ICPL85063)

Lakshmi (ICPL85063), Asha (ICPL871119), ICPH 2740, Puskal (ICPH2671) Asha (ICPL87119), Lakshmi (ICPL85063), Puskal (ICPH2671), ICPH 2740 Asha (ICPL87119), Lakshmi (ICPL85063)

Davanagere

Haveri

Pigeon pea ICPL87119 (Asha), hybrid (Puskal) ICPH2671

District name Belgaum

HG 563

HG 563

– –

ICGV 91114 – ICGV 91114, ICGV 02266, ICGV 00308, ICGV 00351 ICGV 91114

SML 668 – – – –

SML 668

ICGV 91114

HG 563

–

SML668

–

–

HG 563

HG 563

–

–

–

–

Cluster bean HG 563

ICGV 91114

Groundnut

–

Soybean JS 9560, JS 335, DSB 21

Groundnut ICGV 91114

Green gram SML 668

Table 10.6 List of crop cultivars demonstrated in farmer’s fields in different districts of Karnataka

JG 11, JAKI 9218

JG 11, JAKI 9218

KAK 2, ICCC 37, JG 11JAKI 9218 JG 11, JAKI 9218

KAK 2, ICCC 37, JG 11JAKI 9218 ICCC 37, JG 11, JAKI 9218

ICCC 37, JG 11, JAKI 9218

JG 11, JAKI 9218, ICCC 37

Chickpea JG 11, JAKI 9218

316 S. P. Wani et al.

Raichur

Bellary

Bidar

Gulbarga

Yadgir

Puskal (ICPH2671), Asha (ICPL87119), Laxmi (ICPL85063) Puskal (ICPH2671), Asha (ICPL87119), Laxmi (ICPL85063), ICPH 2740 Puskal (ICPH2671), Asha (ICPL87119), Laxmi (ICPL85063), ICPH 2740 Puskal (ICPH2671), Asha (ICPL87119), Laxmi (ICPL85063), ICPH 2740 Puskal (ICPH2671), Asha (ICPL87119), Laxmi (ICPL85063), ICPH 2740

Laxmi (ICPL85063), Asha (ICPL87119), Puskal (ICPH2671), ICPH 2740

SML 668

SML 668

SML 668

SML 668

–

ICGV 91114,ICGV 02266,ICGV 00308, ICGV 00351

ICGV 91114

–

–

–

JS 9560, JS 335, DSB 21

ICGV 91114 ICGV 91114

HG 563

HG 563

HG 563, N 87, RGE-986

HG 563

–

JG 11, JAKI 9218, KAK 2

JG 11, JAKI 9218, KAK 2

JG 11, JAKI 9218

JG 11, JAKI 9218 JG 11, JAKI 9218

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Fig. 10.3 Comparison of grain yield of legumes under farmer participatory varietal evaluation

10.3.1 Cropping Systems Management and Length of Growing Period (LGP) Choice of the crops grown under rain-fed conditions should be made based on length of the adequate moisture availability during the crop-growing season. The length of the growing period (LGP) varies as per the soil type, rainfall pattern, and temperature. Based on the soil moisture holding capacity soils like Vertisols which can hold 200 to 250 mm soil water, post-rainy season crops also could be grown even with 700 to 800 mm annual rainfall. In sub-humid areas like Odisha, where humid period is more than 12 weeks’ duration and the rainfall is twice that of PET, rice-based cropping systems are suitable, as other crops cannot tolerate water stagnation. Choice of post-rainy season crops is related to the moisture regime that plays a major role. Medium-deep Alfisols provides greater potential for cultivation of sole paddy during rainy season with the cultivars of 120 to 130 days’ duration. Similarly, in upland areas of Odisha, intercropping with short- to medium-duration crops, viz., pigeon pea, is best suited to make better use of soil water availability.

10.3.2 Land Resources Inventory for Selection of Legumes-Based Cropping System The land resources inventory (LRI) helps for the classification and mapping of soil characteristics from the LRI database. The LRI contains several attributes describing physical, chemical, and biological soil characteristics and other database. The database can be used to list the cropping pattern details within each of the

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physiographic sub-zones of the country. Details include the approximate planting and harvesting dates for each crop, the inundation land type on which it is grown, and whether the crop is irrigated. The system can be used to include a component that permits the evaluation of crop suitability. First, individual crop suitability ratings need to be analyzed, and then suitability for various cropping patterns is rated using a database of known and potential cropping patterns (rotations). This suitability modeling takes into account individual crop characteristics, input/management levels, soil physical characteristics, hydrologic and climatic conditions, and seasonal variability. Extrapolations of existing cropping system technologies can also be made to delineate suitable areas on a national scale.

10.3.3 Selection of Cropping Systems Depending on the normal rainfall and type of soil, crops and the cropping systems are generally evolved over the years by farming communities in an agro-ecoregion. Other considerations that determine the choice of cropping systems include food and fodder requirements, commodity markets, crop rotational requirements, and endemic pests and diseases affecting productivity. Depending on the possible length of growing season as estimated from seasonal rainfall, potential evapotranspiration, and soil characteristics, a double cropping system either a sequential system or an intercrop system could be adopted to enhance crop intensity and annual productivity (Table 10.7). While selecting sequential systems, duration of each crop and suitability of sowing windows in each cropping season are more critical. Sequential system requires short-duration crops/cultivars to fit into possible crop-growing season and to improve productivity. In areas receiving >1000 mm rainfall and 30 weeks of effective growing season, only paddy-based cropping system is possible in red soils, shallow black soils, deep Aridisols, and Entisols. In deep black soils (Vertisols), sequential post-rainy season Table 10.7 Information on crop critical stages, water requirement, and sensitivity to weather anomalies of important legume crops

Crop Pigeon pea Green gram/ black gram Groundnut Chickpea

Critical growth stages Emergence, flowering, pod formation, pod development Flowering, pod formation, pod development Emergence, flowering, pegging, pod development Emergence, flowering, pod development

Source: FAO irrigation and drainage paper 33, 56

Water requirement (mm) 500–800

Duration (days) 140–180

350–400

65–80

500–700

90–140

300–500

85–130

Crop sensitivity Frostgermination Frost Frostgermination Frostflowering

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crops, viz., chickpea/black gram/maize/green gram, are possible. Intercropping is possible in regions having 20–30 weeks of effective growing season and having medium to black soils. With the availability of improved rain-fed technologies like rainwater management, choice of crops, and agronomic practices, a greater proportion of rain-fed lands can be brought under intensive cropping system.

10.3.4 Choosing Appropriate Sowing Window and Seed Rate Farmers choose a sowing window, mainly depending on the rainfall, in situ soil moisture, and normal timing in the season. Their considerations include sufficient or excess soil moisture to affect seed germination, expected dry spells in the season, planning for second season crop, and crop productivity. Informed decision-making to increase cropping intensity in a favorable season using skill of probabilistic rainfall forecast and crop modeling to help farmers improve crop productivity by increased use of nutrient inputs efficiently. In rain-fed systems, managing required population is a critical issue. It is evident that sufficient seed rate in case of groundnut, soybean, and chickpea can significantly enhance crop yields; however, due to higher seed costs as well as prospects of low rainfall or soil moisture, farmers tend to adopt low seed rate resulting in sparse population and low productivity especially with rain-fed crops. Maintaining optimum seed rate and plant population significantly improves crop productivity. Intercropping with grain legumes is one of the key strategies to improve productivity and sustainability of rain-fed agriculture. Productive intercropping options identified to intensify and diversify rain-fed cropping systems are: • Groundnut with pigeon pea. • Pigeon pea with maize. • Pigeon pea with soybean. Some of the other interventions are ridge planting systems; seed treatment; integrated pest management (IPM); and adoption of improved crop varieties and production technologies, promoting community-based seed production groups and market linkages. Farmers need to be encouraged to practice seed treatment with Trichoderma spp. and fungicides for managing seedling diseases and IPM options for controlling pod borer in chickpea and pigeon pea. Improved water use efficiency through integrated water management (IWM) is the key in rain-fed agriculture. Alternative sources of irrigation water are the carefully planned reuse of municipal wastewater and drainage water. ICRISAT assessed several sequential and intercrop systems on different soil types and recorded a yield advantage ranging between 20 and 35% with maize/ pigeon pea and green gram/black gram/pigeon pea intercrop systems and yield advantages ranging from 20 and 50% with maize-chickpea, paddy-chickpea, and paddy-black gram/green gram sequential systems compared to sole crop traditional systems in different years. On Alfisols, groundnut/millet and groundnut/pigeon pea

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intercrop systems were evaluated for enhancement of productivity and recorded yield advantages ranging between 10 and 25% in long-term experiments.

10.3.5 Seed Treatment Seed treatment with fungicide and insecticide is desirable to avoid damage to germinating tender seedlings from seed-borne or soil-borne fungi and insects. If seed treatment is done with systemic fungicides or systemic insecticides, seedlings will be protected from diseases or insects for a month. Generally, seeds are treated with imidacloprid at the rate of 2 mg kg
1 to control sucking insects like jassids and aphids and chlorpyrifos at the rate of 4 ml kg
1 of seed to control soil-borne insects. Mancozeb at the rate of 3 gm kg
1 or carbendazim at the rate of 1 gm kg
1 of seed will be sufficient to control fungal diseases. Combination of fungicides is also recommended in seed treatment module, where seed treatment with thiram and carbendazim (1:1) at the rate of 2.5 g kg
1 was found to be the effective component in groundnut IDM. Seed of legume crops should be treated with crop-specific efficient biological nitrogen-fixing bacterial (Rhizobium) cultures. In order to enhance fixation, appropriate tillage and balanced nutrient management methods should be adopted for surface soil to provide good aeration. Leveled fields with gentle slope no water stagnation even after high rainfall events are desirable to facilitate good aeration and higher N fixation in the root zone. Seed priming is another technique used to improve seeding establishment at sowing and for good germination and also exerts drought tolerance in crops.

10.3.6 Crop Water Requirement and Water Management Dryland crops vary widely in their water requirement for crop growth and maturity. Besides soil type, rainfall, and temperature in the region, which determine length of crop-growing period, crop water requirement is critical to plan crops and cropping systems appropriate for a region. Knowledge on critical growth stages of crops, those that can be affected by water deficit resulting in varying degree of crop yields, is very important to effectively use available water in rain-fed situations.

10.3.7 Weeding and Intercultural Operations Weeding and intercultural operations are most important in dryland farming, as higher-density weed population compete and efficiently steal the valuable scarce soil nutrients and moisture affecting cultivated crops. It is estimated that weeds on an average cause 20% crop production loss in India. Interculture for inter-row weeding

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and soil mulching to prevent moisture loss from lower layers, which is very important for rain-fed crops frequently affected by long dry spells. Initially slowgrowing and low-population crops like pigeon pea, green gram, black gram, etc. are more prone to weed infestation. Besides intercultural operations, control measures include crop rotation, of crop holidays are some cultural measures. Although chemical control measures are expensive, they are effective, and some chemicals are selective in timing and crop specific also. Pre-emergence herbicides and postemergence crop-specific herbicides are also available.

10.3.8 Crop Diversification The main objective is to enhance the farm income by targeting crop diversification and intensification through suitable cropping systems management. The diversification will be targeted by two ways: first by crop diversification and second by agricultural diversification. In India, crop diversification is generally viewed as a shift from traditionally grown less remunerative crops to more remunerative crops. It is intended to give a wider choice in the production of a variety of crops in a given area so as to expand production-related activities on various crops and also to help in reducing risk in agriculture. The legumes are best fit to crop diversification. The introduction of new compatible crop as well as improved varieties of selected crop with appropriate production technology enables the farmers to diversify their systems. The aim is to enhance plant productivity, quality, health, and nutritional value and/or build crop resilience to diseases, pest organisms, and environmental stresses. Agricultural diversification is a process of a gradual movement out of subsistence food crops (particularly staple foods) toward diversified market-oriented crops that have a larger potential for return. This process is triggered by the availability of improved rural infrastructure, rapid technological advancements in agricultural production, and changing food demand patterns. Hence, this process of diversification toward high-value crops is likely to accelerate agricultural growth and usher in a new era of rural entrepreneurship and generate employment opportunities.

10.3.9 Crop Intensification Large areas with Vertisols like in Madhya Pradesh are kept fallow during rainy season or following paddy cultivation in Indo-Gangetic Plains (IGPs) in spite of availability of soil moisture largely due to poor adoption of land and water management technologies as well as short-duration high-yielding cultivars. In Madhya Pradesh alone, two million ha area with Vertisols and assured rainfall is kept fallow during the rainy season due to anticipated crop losses because of waterlogging and delayed sowing of post-rainy season crop (Wani et al. 2002a). However, BBF and

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use of short-duration cultivars of soybean and adoption of minimum tillage for the rabi (post-rainy) season crop have enabled cultivation of these Vertisols with two crops (soybean + wheat or chickpea) (Wani et al. 2002a, Wani et al. 2012c). Enhancing the cropping intensity through managing the existing cropping system either through vertical or horizontal expansion will be focused in both the regions. Basically the crop intensification has been done with the introduction of legumes in the existing cropping system either through vertical integration or horizontal integration. The major constraints include lack of short-duration cultivars, soil fertility decline, and poor agronomic practices. Diversification/intensification should be taken place either through area augmentation or by crop substitution. If carried out appropriately, it can be used as a tool to augment farm income, generate employment, alleviate poverty, and conserve precious soil and water resources. Major driving forces for crop diversification/intensification targeted are increasing income on small farm holdings; mitigating effects of increasing climate variability; balancing food demand; improving fodder for livestock animals; conservation of natural resources; minimizing environmental pollution; reducing dependence on off-farm inputs; depending on crop rotation; decreasing insect pests, diseases, and weed problems; and increasing community food security.

10.3.9.1

Crop Intensification Through Rainy Season Fallow Management

Rainy season fallow management Vertisols and associated soils, which occupy large areas globally (approximately 257 m ha; Dudal, 1965), are traditionally cultivated during post-rainy season on stored soil moisture due to waterlogging-associated risks during the rainy season caused by poor infiltration rates. The practice of fallowing Vertisols and associated soils in Madhya Pradesh, India, was perceived to be decreased after the introduction of soybean; however, 2.02 m ha of cultivable land is still kept fallow in central India, during the kharif season (Wani et al. 2002; Dwivedi et al. 2003). However, the survey also indicated that rainy season soybean area expansion only replaced sorghum areas and fallows remained fallow because rainy season crop delays the sowing of post-rainy (rabi) crop, forcing the farmers to keep the cultivable lands fallow, thus reducing WUE and enhancing soil erosion. Through watershed on-farm participatory research, ICRISAT demonstrated the avoidance of waterlogging during initial crop growth periods on Vertisols by preparing the fields as BBF along with grassed waterways. Simulation studies using the SOYGRO model showed that early sowing of soybean in 7 out of 10 years was possible by which soybean yields can be increased threefold along with appropriate nutrient management. Hence, evolving timely sowing with shortduration soybean genotypes could pave the way to successful post-rainy season crop where the moisture-carrying capacity is sufficiently high to support it. On-farm soybean trials conducted by ICRISAT involving improved land configuration (BBF) and short-duration soybean varieties along with fertilizer application (including micronutrients) showed a yield increase of 1300–2070 kg/ha compared with

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790–1150 kg/ha in Guna, Vidisha, and Indore districts of Madhya Pradesh. Increased crop yields (40%–200%) and incomes (up to 100%) were realized with landform treatment, new varieties, and other best-bet management options (Wani et al. 2008). On-farm trials on conservation tillage were conducted with shortduration soybean in Madhya Pradesh (Guna, Vidisha, and Indore districts) to intensify the kharif fallow areas using suitable landform management (broad bed furrow system). The trials then adopted zero-till planters to sow the succeeding rabi chickpea with minimum tillage to enhance the cropping intensity. The results revealed increased crop yields (40–200 percent) and incomes (up to 100 percent) using landform treatments, new varieties, and other best-bet management options (Wani, Joshi and Raju, 2008) through crop intensification. So, for better utilization of residual soil moisture, practices such as zero/minimum tillage and relay planting are recommended. Specially designed machinery, such as the zero-till multi-crop planter, can be used effectively to sow in paddy fallow without severely affecting soil moisture.

10.3.9.2

Rice-Fallow Management for Crop Intensification

In Southeast Asia, paddy is mostly grown in the kharif season. A substantial part of this area (15 million ha) remains fallow during the rabi (post-rainy) season, primarily due to limited soil moisture availability in the topsoil layer for crop establishment (Subbarao et al. 2001). Paddy fallow is the land used to grow paddy in the kharif season but is left uncropped during the following rabi season. Of the total paddy fallow area in South and Southeast Asia, 2.11 million ha (33 percent of the kharif paddy-growing area) is in Bangladesh, 0.39 million ha (26 percent) is in Nepal, and 11.65 million ha (29 percent) is in India. Since paddy is grown on some of the most productive lands in this region, there is scope for increasing the cropping intensity by introducing a second crop during the rabi season using appropriate technologies. The exact area under paddy fallow per country in Southeast Asia is not available but is needed to plan sustainable intensification. In South Asia, there is approximately 15 million ha of paddy fallow, which is nearly 30 percent of the paddygrowing area. In India, nearly 82 percent of the paddy fallow is located in the states of Assam, Bihar, Chhattisgarh, Madhya Pradesh, Orissa, and West Bengal. GIS analysis of this fallow land identified diverse soil types and climatic conditions (Kumar Rao et al. 2008). The available soil water holding capacity (1 m soil profile) for most of this land ranges from 150 to 200 mm (Singh et al., 2010). If we assume that these soils are fully saturated during most of the paddy-growing season, then there will be residual moisture in the soil at paddy harvest that could be used by the following crop. Wani et al. (2009a) reported that these paddy fallows offer a potential niche for legume production due to the considerable amount of available green water after the monsoon, which could be used by a short-duration legume crop after simple seed priming and micronutrient amendments (Kumar Rao et al. 2008; Singh et al. 2010).

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Paddy fallow is an underutilized resource of poor farmers with subsistence agricultural practices largely due to biophysical, production, and socio-economic constraints to cultivate the second crop in paddy fallow. Biophysical constraints comprise the persistence of rain-fed ecology, high runoff and low moisture storage, water stagnation/excessive moisture in coastal regions, and low residual moisture in dry regions which are the main biophysical limitations. Development of deep cracks during drying of soil, compaction of topsoil layer due to puddling in paddy fields, low soil organic matter content, and poor microbial activity are other factors. Narrow sowing window for second crop, lack of short-duration and high-yielding varieties, poor plant stands due to poor soil-seed contact in relay sowing, lack of fertilizers/ chemicals, severe weed infestations including parasitic weeds, high incidence of diseases, moisture stress, and terminal drought are important production constraints. Socio-economic constraints include letting loose animals to open graze after the harvest of paddy, resource-poor farmers, lack of credit and market infrastructure, non-availability of critical inputs such as suitable machinery, and scarcity of human labor after paddy harvest due to migration to urban areas. As global warming sets in, agricultural production worldwide is projected to fall by 2 percent per decade, as food demand increases by 14 percent. Global bodies are pushing for climate-smart farming with smart crops in a bid to reduce the carbon footprint of agriculture. Dryland grain legumes are branded as smart food crops (ICRISAT, 2017a) in which consumers, farmers, and the planet benefit as they diversify farming systems and help smallholder farmers adapt to climate change. Climate change is already affecting crop production, which will impact farmer livelihoods and food availability. So climate-smart crops and management offer sustainable options to farmers to both adapt to and mitigate climate change (FAO, 2017), and such locally produced, nutrient-rich, climate-smart/climate-resilient crops are referred as Future Smart Foods (FSFs) by the FAO (2017). The FSFs include a variety of warm-season legumes (e.g., black gram, groundnut, mungbean, pigeon pea, soybean) and cool-season legumes (e.g., chickpea, faba bean, Lathyrus, lentil, pea). A considerable amount of green water is available after the monsoon, especially in rice-fallow systems, which could easily be utilized by introducing a short-duration legume crop with simple seed priming and micronutrient amendments (Subbarao et al. 2001; Kumar Rao et al. 2008; Wani et al. 2009a; Singh et al. 2010). Taking advantage of sufficient available soil moisture in the soil after harvesting rice crop during the cool season in eastern India and growing of early maturing chickpea in rice-fallow areas with best-bet management practices (minimum tillage for chickpea, seed priming of chickpea, 4–6 h with the addition of sodium molybdate to the priming water at 0.5 gL/kg seed and Rhizobium inoculation at 5 g/kg seed, micronutrient amendments, and use of short-duration rice cultivars during rainy season) resulted in chickpea yields of 800–850 kg/ha (Harris et al. 1999; Kumar Rao et al. 2008). An economic analysis has shown that growing legumes in rice-fallows is profitable for the farmers with a B/C ratio exceeding 3.0 for many legumes. Also, utilizing rice-fallows for growing legumes could result in the generation of 584 million person-days of employment for South Asia.

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Table 10.8 Evaluation of chickpea cultivars in paddy fallows in Jharkhand during 2010–2013 District Gumla

Block Raideh

Crop Chickpea

Seraikella-Kharsawan

Saraikeala

Chickpea

Variety KAK 2 JG 11 KAK 2 JG 11

Yield (kg ha
1) 1520 1340 1490 1280

The scaled-up on-farm research showed that short-duration pulses are suitable for cultivation in paddy fallow and yield well, provided that suitable varieties and technologies (including mechanization for crop establishment) are available. Participatory trials in Jharkhand state with the purpose of demonstrating and evaluating chickpea cultivars (JG 11 and KAK 2) in post-rainy fallow yielded 1490–1520 kg ha
1 for KAK 2 and 1280–1340 kg ha
1 for JG 11 (Table 10.8), which indicates that chickpea is a suitable crop to grow after paddy with the benefit of additional income and enhanced rainwater use efficiency. An economic analysis showed that growing legumes in paddy fallows is profitable for farmers, with a benefit-cost ratio of >3.0 for many legumes. Such systems could generate 584 million person-days of employment for South Asia and make the region self-sufficient in pulse production. In a number of villages in the states of Chhattisgarh, Jharkhand, and Madhya Pradesh in India, on-farm farmers’ participatory action research trials sponsored by the Ministry of Water Resources, GoI, showed significantly enhanced RUE through cultivation of rice-fallows with a total production of 5600–8500 kg/ha for two crops (rice + chickpea), benefiting the farmers with increased average net income of Indian rupees 51,000–84,000 (USD 1130–1870/ha) (Singh et al. 2010). Similarly, Parthasarthy et al. (2010) observed that cultivation of legumes improves soil fertility and has follow-on beneficial effects on paddy performance. Soil-building integrated approach promoted in study sites emphasized recycling of local materials and reduced reliance on external inputs. In Chhattisgarh, the on-farm participatory research trials sponsored by the Ministry of Water Resources revealed that the introduction of best management practices, viz., zero-till sowing of rabi crops, seed priming, etc., in paddy-based cropping systems enhanced rabi crop productivity and thereby total system productivity. Early sowing of paddy along with good management practices increased paddy productivity by 8–29% (Table 10.9) with scope for cultivation of rabi crops on the residual moisture. In an initiative supported by the Department of Agriculture, Co-operation and Farmers Welfare (DAC&FW) in India, ICRISAT focused on crop intensification in paddy fallows through the introduction of chickpea, bringing in three million ha of paddy fallow from the eastern state under FSF crops. DAC&FW along with ICRISAT conducted a national-level workshop at Bhubaneswar for scientists, researchers, farmers, and policymakers on the introduction of FSF crops to existing single cropping of paddy. In 2016/2017, a DAC&FW-led consortium introduced chickpea to almost 1.8 million ha along with best management practices, including seed priming and mechanized sowing with zero-till multi-crop planters with minimal

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Table 10.9 Percent increase in paddy and chickpea yields with improved management from 2007–2008 to 2008–2009 Number of farmers District involved Paddy (kharif season) Ambikapur 48 Kanker 36 Bastar 18 Chickpea (rabi season) Ambikapur 28 Kanker 80 Bastar 41

Biomass yield (kg ha
1)

Grain yield (kg ha
1)

Traditional

Improved

Traditional

Improved

% increase in grain yield

15 15 15

11,110 12,930 8260

12,460 14,880 10,100

5520 6090 3910

5970 7370 5060

8.1 20.9 29.4

4.8 19.7 14.3

– – –

480 1980 1020

– – –

220 1140 540

– – –

Area sown (ha)

tillage. The farmers harvested 650–800 kg per hectare of chickpea with net economic benefits ranging from INR 15000 to 20,000 per hectare.

10.3.10

Weed Management

Weeds are one of the major biological constraints which can cause up to 33 percent crop losses and also act as alternate hosts for pathogens, insects, and nematodes. Weeds compete with crops for land, water, and light as well as added inputs and reduce yield and quality. Suitable integrated weed management strategies are a must for enhancing productivity of legumes.

10.4

Empowering Farmers Through Knowledge, Science, and Technology

Application of digital tools such as remote sensing, geographical information systems, telemetric sensors, and several decision support systems in agriculture is, often, not making cross-over beyond academics or used only in planning process at country or state level. Among these, however, communication technologies are strengthening the agricultural extension system as both government department and private companies have invested in business of agriculture knowledge dissemination services. The key advantage of all these technologies is the solution to large-scale farming community, for example, identifying moisture stress area in a country using remote sensing technology for preparing plan to assist farmer in supplementary irrigation or even declaring drought. The following are the few examples of digital tools in agriculture.

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10.4.1 Identifying Suitable Land for Crop Cultivation Soils, weather, and water availability have great influence on crop productivity. Soilsite suitability studies have provided the criteria to select the suitable crops for given piece of land. This helps to find out specifically the suitability of the land resources like soil-site characteristics, water, weather, climate, and other resources and the type of constraints that affect the yield and productivity of the selected crop. The National Bureau of Soil Survey and Land Use Planning (NBSS&LUP), India, has prepared manual on soil-site suitability criteria for major crops (Naidu et al. 2006). In this assessment, the specific requirements of a crop are compared with the characteristics of land, and suitability of the area for the crop is arrived based on the matching. If the land characteristics of an area match the requirements of the selected crop, then the area is considered as suitable for the crop; otherwise it is grouped as not suitable for the crop. The site-specific land resources database helps to establish the suitability of the resources to any selected crop for the area in a very objective manner, which was not possible earlier with general datasets. The applicability of this approach to large extent is limited due to unavailability of high-resolution spatial information of soil properties. However, this methodology is being piloted at micro-watershed scale in selected district of Karnataka state under a World Bank-supported project. The high-resolution maps provide more detailed information, but it may not be feasible to analyze a soil profile per 1 hectare area. Digital soil mapping (DSM) – or predictive soil mapping – provides option to generate soil property surfaces at fine resolution with the uncertainty of prediction. Digital soil mapping uses field and laboratory observation method such as proximal soil sensing (Viscarra Rossel et al. 2011) and soil spectroscopy (Nocita et al. 2015) as input to predictive model that provides soil maps. A global consortium is working together to make a new digital soil map of the world using state-of-the-art and emerging technologies for soil mapping and predicting soil properties at fine resolution (GSM, 2017). Although the DSM product has some prediction uncertainties, it provides the spatial information at much higher resolution and at less cost. The crop suitability maps at large extent have been successfully implemented by Tasmania using digital soil mapping to generate high-resolution soil maps (Kidd et al. 2014a; Kidd et al. 2014b) (Fig. 10.4). The web application is available in public domain.1

10.4.2 Weather-Based Agro-advisories The information required for giving weather-based advisories are historic weather data, current observed weather data, forecast weather, soil characteristics, and crop management details. An algorithm for moisture adequacy index (MAI) will consume 1

http://maps.thelist.tas.gov.au/listmap/app/list/map?bookmarkId¼216124

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Fig. 10.4 Enterprise suitability maps of Tasmania

required data from data cloud and provide probable moisture status. Based on moisture status and weather forecast, suitable advisories are being given to farmers regarding crop sowing window and irrigation requirement. ICRISAT in collaboration with Microsoft has piloted weather-based advisories to farmers in selected villages in Andhra Pradesh (ICRISAT, 2017b). The advisories have helped farmers achieve optimal harvests by suggesting the best time to sow crops depending on weather conditions, soil, and other indicators. This algorithm utilizes extensive data including soil characteristics, rainfall over the last 45 years, as well as 10 years of groundnut sowing progress data for Kurnool district of Andhra Pradesh. This data is then downscaled to build predictability and guide farmers to pick the ideal sowing week. This advisory is being scaled up in selected districts of Andhra Pradesh, Telangana, and Karnataka covering more than 3500 farmers.

10.4.3 Irrigation-Based on Crop Water Requirement Inappropriate management of water resources and irrigation methods results in low crop yields and poor water use efficiency (WUE). The irrigation methods and irrigation strategies are important factors for improving WUE. Despite water scarcity in most farmers’ fields in semi-arid tropic locations, water is carried through open channels, which are usually unlined and, therefore, a significant amount of water is lost through seepage. In India, farmers irrigate land rather than crops. For example, for Alfisols and other sandy soils with more than 75 percent sand, Improved method

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of irrigation system practices may include the lining of open field channels and use of drip/sprinkler systems for irrigation. A number of tools are available for simulation of water balance, crop growth, and yield. Moreover, some of the tools/models are exclusively developed for designing irrigation scheduling. But use of these tools is mainly limited to the scientific community due to complex parameterization. ICRISAT has developed a simple MS Excel-based tool “water impact calculator” using strategic research data collected at ICRISAT and validated at three pilot sites situated into three Indian states (Rajasthan, Gujarat, and Andhra Pradesh). This tool needs very basic data as an input which users easily can provide and provides simple actionable information as how much water is needed to be applied on what date. Moreover, this tool allows user to update rainfall and applied irrigation values so that the future irrigation scheduling gets adjusted as per the estimated soil moisture storage. Availability of a decisionmaking tool which is simple to use and technically robust will help farmers for applying irrigation as per need rather than adapting the calendar-based irrigation schedule. Current water use efficiency (WUE) in agriculture (rain-fed and irrigated) can be doubled from 35–50 percent to 65–90 percent with large-scale interventions of scientifically proven management (land, water, crop, and pest) options. The Pradhan Mantri Krishi Sinchai Yojana (PMKSY) scheme of the Government of India enables the handling of green and blue water resources together by adopting holistic and integrated water management approaches (Wani et al., 2012, Wani et al., 2016). It is important that all components of the PMKSY scheme be implemented together in rain-fed or irrigated areas with micro-watersheds as an implementing unit in the districts. Measures to enhance WUE are discussed elsewhere in this chapter and are reiterated here for continuity: • • • • • • • • • • • • • •

Efficient use of rainwater stored in soil as soil moisture (green water). Conjunctive use of blue water through rainwater harvesting in farm ponds. Improved landform for efficient irrigation and water management. Protected cultivation of high-value crops. Soil test-based integrated nutrient management. Improved crop management practices. Efficient irrigation using micro-irrigation (zero-flood irrigation). Water balance-based irrigation scheduling in place of calendar-based irrigation scheduling. Crop rotations and intercrops. Improved crop cultivars (drought tolerant and water efficient). Integrated pest and disease management. Enabling policies and innovative institutional mechanisms. Organic matter amendments through in situ generation of green manuring and composting (vermicomposting and aerobic composting). Minimum tillage, channels with some hard cementing material, covering of channels with solar panels as in Gujarat, or using irrigation pipes to reduce high seepage and evaporation water losses and enhance productivity and profitability.

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The use of closed conduits (plastic, rubber, metallic, and cement pipes) should be promoted (Pathak, Sahrawat and Wani, 2009) to achieve high WUE. Micro-irrigation, in general, is practiced for high-value and horticulture crops. Similarly, microirrigation in field crops, including paddy-based cropping systems, should be promoted on a large scale to address the issue of groundwater depletion and water scarcity. Some field trials undertaken in Raichur under the Bhoosamruddhi program on drip irrigation in paddy revealed that growth parameters (plant height, tiller number, soil plant analysis development, and leaf area) improved significantly under sub-surface and surface drip irrigation with laterals spaced 60 cm apart. The highest grain yields of paddy of 10.1 and 9.0 tonnes per hectare were recorded in direct-seeded paddies compared with transplanted paddy under surface drip irrigation with laterals placed 80 cm apart and 60 cm, respectively (Bhoosamruddhi Annual Report, 2016). Similarly, drip irrigation trials in wheat at Tonk and Udaipur, Rajasthan, and Mota Vadala, Gujarat, showed that 40–50 percent of water could be saved using improved irrigation techniques. For water-loving crops, including sugarcane and banana, it is necessary to popularize water-saving technologies, such as drip irrigation, by making them mandatory. In Jharkhand, to use the available water efficiently, drip irrigation was promoted by ICRISAT for vegetable cultivation in Teleya village in Gumla district, which increased the net profit to farmers from Rs 8000 to Rs 10,000 per acre.

10.4.4 Information and Communication Tools for Information Dissemination Dissemination of agricultural knowledge up to the small and marginal farmers has been weak link in agriculture. Farmers require actionable information at right time to take decision and plan for action. Moreover, market players also require similar information so that market will be ready to cater farmers’ demands. In fact, few input providers have initiated the advisory services on subscription basis to provide information as well as promote their product. Similar to subscription model, various ways are being implemented to increase the outreach of extension system. In India, the government has Kisan Call Center (KCC) facility to provide information as per farmers’ demand in 22 local languages. The government has brought all the tools for disseminating agriculture knowledge under one umbrella mKisan.2 The services provided through this website are Unstructured Supplementary Service Data (USSD) and SMS-based dissemination, pull and push SMS, interactive voice response system (IVRS), KCC, and android-based applications. In addition to the government, private companies are also providing innovative solutions for agriculture extension. For example, IFFCO Kisan Sanchar Limited 2

www.mkisan.gov.in

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has introduced voice messages for agro-advisory system, and Thomson Reuters introduced mobile-based integrated agro-advisory system “Reuters Market Light.” One of the best ways to convince farmers regarding the improved farming practices is through demonstrating those technology in their fields – “seeing is believing.” But this may not be the solution for outreaching millions of farmers. An alternative solution for this initiated by a private organization Digital Green is sharing farmer’s experience of improved farming practices to group of farmers through video documentation. The advantage of this farmer to farmer (F2F) information dissemination method is the fact that farmers trust fellow farmers to adopt improved management practices. Farmers can easily understand these farming practices as they explain in their languages. Farmer shares his/her experience about the technology on camera. These short videos are screened to small gathering (20–30 farmers) in villages using battery-operated small projectors.

10.5

Legumes: Key Component in Doubling Farmers’ Incomes

Dryland grain legumes branded as Smart Food Crops (ICRISAT, 2017a) are good for consumers, farmers, and the planet as they diversify farming systems and help smallholder farmers adapt to climate change. As we know unpredictable and erratic climatic patterns resulting from climate change are affecting crop production. This will have an impact on farmer livelihoods and food availability. So climate-smart crop and management provides sustainable options to farmers to both adapt to and mitigate climate change. Such climate-smart/climate-resilient crops are referred as Future Smart Foods (FSFs) by the FAO (2017). The pulse production is facing an enormous underutilized resource base with subsistence agricultural practices. To be precise, there are different constraints in harnessing the potential yield of pulses which are characterized into three main heads, viz., biophysical, production, socio-economic. Looking at the situational analysis of the above aspects, our focus needs to be on bringing in vertical integration in the existing legumes-based cropping systems to meet the increasing food demand. Efforts need to be made to analyze the current status of legumes’ productivity, assess the potential for intensifying the cropping system, and propose a new paradigm to enhance agricultural productivity per unit area through introducing bestbet agronomic management practices with a holistic management approach and operationalize the integrated genetic natural resource management (IGNRM) strategy. Based on our hands-on experiences in India for harnessing the untapped potential of rain-fed legume areas, it can be proposed that legumes can play an important role in doubling the farmers’ income through increase in total food production and thereby improvement in the livelihoods of people with finite and scarce resource through enhanced resource use efficiency.

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Under National Food Security Mission in paddy fallow areas of Kalaghatagi taluk popularization of improved green gram variety IPM 02–14 (Shreya) was demonstrated during rabi season of 2015–2016. Introduction of high-yielding variety, transfer of improved production package (seed treatment with bioagents, viz., Rhizobium, Trichoderma, phosphate solubilizing bacteria (PSB), 2% DAP (diammonium phosphate) spray at flowering and pod initiation stage), and use of integrated pest management practices proved beneficial. The improved variety IPM 02–14 (Shreya), being a short-duration (75 days) variety resistant to yellow mosaic virus and crinkling disease, helped the farmers to plan the third crop even in the summer season, improving the economy of the farming community. The adoption of new variety through the intervention of Krishi Vigyan Kendra, Saidapur Farm, Dharwad, in the cluster approach enhanced the productivity leading to sustainable income annually. This has not only resulted in socio-economic security but also helped in attaining food and nutrition security of the community along with the fodder requirement of farm animals (www.kvkdharwad.org.) To empower farmers in the pigeon pea and chickpea production during 2007, IIPR (Indian Institute of Pulses Research), Kanpur, implemented a project “Model Seed System(s) in district Fatehpur.” The interaction between scientists and farmers heightened the ability to grow mung bean after rice-wheat cropping system. Field demonstration of successful summer mungbean at IIPR farm encouraged farmers to opt for it. The farmers of Mauhar and Alipur villages of Malwan block of Fatehpur district came forward to start mung bean cultivation in summer. In 2008 mungbean was grown after mustard under the guidance of the IIPR scientists. The farmers harvested 12–14 q/ha mung bean in 65 days and earned Rs 50–60 thousand/ha. The mungbean variety Samrat yielded 13.5 q/ha and Meha 14.0 q/ha. From the total produce of 48q, Mr. Patel earned Rs 1,76,000 with an investment of Rs 28,000 only. The farmers of Mauhar and Alipur villages have opted cultivation of summer mungbean as they are fully confident of bonus yield and monetary gains from summer mungbean without affecting their current rice-wheat cropping system (https://www.icar.org.in/node/250.)

10.6

Legumes and Sustainable Development

Legumes have an important role in sustainability of cereal-based cropping system through N fixation in the soil. Cropping systems, in which legumes are component, are found to sequester huge quantities of atmospheric CO2. Long-term studies at ICRISAT (Wani et al., 2003) showed that improved system comprising landform management (broad bed and furrow cultivation), soil test-based balanced fertilization, and crop management significantly increased soil organic C content. In this historical study, an additional quantity of 7.3 tons C per ha (335 kg C per ha per year) was sequestered in soil under the improved system compared with the traditional system over the 24-year period. Leguminous plants are considered to have a competitive advantage under global climate change because of increased rates of

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symbiotic N fixation in response to increased atmospheric CO2 (Serraj, 2003; Wani et al. 2003). Acknowledgments We sincerely acknowledge the state governments of Andhra Pradesh, Karnataka, and Madhya Pradesh and Ministry of Water Resources and Department of Agriculture, Farmers’ Welfare and Co-operation, Governments of India, and Sir Dorabji Tata Trust and Sir Ratan Tata Trust, Mumbai, for financial support. We also acknowledge the help of implementing partners in the consortium in different states as well as the farmers who conducted the trials.

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Index

A Abhaya (ICPL 332), 219 Abiotic constraints, 233 Abiotic stress resistance, 233 Abiotic stresses, 57, 92, 93 Acetolactate synthase, 109 Acyrthosiphon pisum Harris, 198 Affymetrix Axiom technology, 52 Affymetrix GeneChip, 53 Agricultural diversification, 322 Agricultural systems food security and sustainability, 49 Agriculture innovation, 289 Agrobacterium-mediated methods chickpeas, 103, 105 common bean, 98 cotyledonary, 95 cowpea, 102 in vitro regeneration, 109 leaf disc, 109 lentils, 108 peas, 106 soybean, 95 transformation protocol, 101 Agroecosystems, 264 Agronomic packages, 92 Agronomic practices, 5 choosing seed rate, 320, 321 choosing sowing window, 320, 321 crop diversification, 322 crop intensification (see Crop intensification) cropping system management, 318 cropping systems selection, 319, 320 LGP, 318

LRI, 318 seed treatment, 321 water requirement/management, 321 weed management, 327 weeding and intercultural operations, 321 Agronomic strategies, 290 Agronomy genetic enhancement, 260–261 innovation systems approach, 289 integrated crop management, 287–288 microbes use, 266–272 nutrient management, 264–266 plant protection, 285–287 productivity (see Production systems) seed enhancement, 256–257 water use efficiency, 261–264 Agronomy research, 4 ahas gene, 99, 101 Alfisols, 40 Allium sativum (garlic) leaf agglutinin (ASAL), 105, 215 Amol (BDN 708), 219 Amplified fragment length polymorphism (AFLPs), 52 Amylase inhibitor protein (αAI-1), 104 Andmolybdenum, 264 Annual growth rates (AGRs), 16 Antibiosis, 208, 209 Anti-nutritional factors, 212 Antixenosis mechanism, 203 Aphids (Aphis craccivora), 105 Apis cerana Fabricius, 128 APSIM-chickpea, 248 Arabidopsis (AtBAG4), 106

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339

340 Arabidopsis gene encoding acetohydroxyacid synthase, 96 Arabidopsis thaliana, 101 Arbuscular mycorrhizal fungi (AMF), 266, 269 Artificial chemical barrier, 181 Artificial intelligence (AI), 305 Artificially managed environments, 246 Ascochyta blight, 73, 150, 172, 174, 232 Ascochyta lentis, 174 AtSSU-regulated cry2Aa gene, 104

B BAC library, 72 Bacillus thuringiensis (Bt), 101, 285 Bacteria, 93 Bacterial leaf spot (Xanthomonas axonopodis), 153 Bacterial strains, 164 Balancing productivity, 314 BALB/c mice, 104 Bar gene, 99 Barley HVA1 gene, 100 Bayesian LASSO, 74 Bayesian least absolute shrinkage and selection operator (LASSO), 74 Bayesian models, 74 Bayesian Reproducing kernel Hilbert spaces regression (RKHS), 74 Bean common mosaic necrosis virus (BCMNV), 99 Bean common mosaic virus (BCMV), 99 Bean cultivars, 209 Bean golden yellow mosaic virus (BGYMV), 99 Bean weevil (Acanthoscelides obtectus), 98 Bee-aided pollinations, 128 Begomovirus, 153 Best-bet management practices, 314 BGMV-resistant bean, 99 Bhoochetana, 5 Bhoosamruddhi program, 331 Bio-agents, 163 Biochemical traits MAS, 213–215 nutritional factors, 211 secondary metabolites, 212–213 Biocontrol agents, 163 Biolistic method, 99, 101 Biological management antagonistic effects, 164 bacterial strains, 164 bio-agents, 163

Index foliar spray, 164, 165 P. aeruginosa strain PNA1, 164 Pant L-639A, 164 pigeonpea diseases, 164 plant extracts, 164 T. harzianum, 163 Biological nitrogen fixation (BNF), 303 Biological nitrogen-fixing bacterial (Rhizobium) cultures, 321 Biopriming faba bean, 257 Bio-rational pesticides, 159 Biotic stresses, 57, 93, 248 biological control agents/botanical products, 180 diseases (see Emerging diseases) fungal pathogens, 149 genetic gains, 180 productivity limiting factor, 150 resistance/tolerance, 169 wild Cajanus spp., 169 Bi-parental QTL mapping abiotic stresses, 57 biotic stresses, 57 quality traits, 68 seed and seed yield-related traits, 69 Black gram cultivars, 211 Botrytis gray mold (BGM), 150, 152, 286 Breeder Seed Stage 2, 142 Breeding programs, 233 Breeding trials, 240 Broad bed and furrows (BBF), 306 Bruchid-resistant chickpea, 104 Bruchids, 102, 104 Bt-chickpeas, 104, 112 BT-cowpea, 101

C Cajanus cajan (pigeonpea), 125 Cajanus cajan gene expression atlas (CcGEA), 53 Cajanus cajanifolius (Haines), 140 California Blackeye No. 46 (CB46), 214 CAPS/dCAPS markers, 68 Caribbean island, 41 Central Queensland and Dawson Callide regions, 243 Cercospora leaf spot cultivar SML 668, 178 in faba bean, 153 fungicide hexaconazole 5 EC, 161 genetic inheritance, 174 QTL mapping, 175

Index in mungbean, 158 resistant lines, 168 treatment, 161 yield losses, 153 Cereal-based cropping system, 264, 266, 333 Cereal grains, 94 Cereals, 144 Chemical and non-chemical weed control methods, 286 Chemical fungicides, 160 Chemical hybridizing agents (CHA), 130, 131 Chemical management bactericides, 162 foliar application, 162 fungicides, 159, 162 innovative technologies, 159 insecticides, 162 pathogen evolution, 159 pesticide groups, 159 seed treatment, 159, 161 Chickpea, 57, 72 abiotic stresses tolerance, 105, 106 Aphids resistance, 105 biotic and abiotic stresses, 103 bruchids resistance, 104 crop improvements, 232 cultivation, 102 environmental characterisation approaches, 235–237 environmental characterisation needs, 234, 235 environmental characterisation relevance, 244–246 export crop, 231 food legume, 231 genetic transformation, 103 GxExM interactions, 233, 246–248 hybridization barriers, 103 Indo-Gangetic Plain Zone, 232 large-scale adoption limitation, 232 low yield, stresses, 233, 234 nitrogen fixation, 231 pest, 102 pod borers resistance, 104, 105 production, 232 rich proteins, 102 yield potential, 232 Chickpea agro-ecological regions, Northern Australia agricultural resource management issues, 240 agroclimatic conditions, 240 breeding trials, 240

341 clustering, 239 crop failure, 238 crop simulation modelling, 240 environmental attributes, 240 GxE interactions, 240 model-based characterisation, 240 potential yields, 238, 239 seasonal variation, 239 winter rainfall, 240 yield percentile, 239 Chickpea cropping systems characterisation agro-ecological regions, 238–241 Ascochyta blight, 238 drought environments, 241–243 modelling approaches, 237 temperature, 241–243 Chickpea genetic enhancement programmes, 4 Chickpea genotype ICCV 92944, 260 Chickpea genotypes, 209 Chickpea transformation, 103 Chocolate spot, 153 Cicer arietinum gene expression atlas (CaGEA), 53 Cicer arietinum L. environmental characterisation approaches agroclimatic conditions, 237 biophysical attributes, 236 crop modelling approach, 236 environmental classification, 235 gravimetric soil water measurements, 236 GxE interactions, 235 in-season climatic variability, 236 mega-environments, 235 modelling approaches, 237 multilocation trials, 235 pedo-climatic classification, 236 temperatures/nitrogen limitation, 237 TPEs, 235 Cicer bijugum, 207 Cicer reticulatumtum, 207 Cleistogamous legume flowers, 125 Climate changes, 48 atmospheric CO2 levels elevation, 156, 157 chickpea, disease, 155, 156 environmental conditions, 155 pathogens, 154 temperature, 156 variables, 154 Climate-smart farming, 325 Climate variability, 154 Climatic elements, 179 Climatic parameters, 3 CLIMEX species distribution model, 181

342 CMS-based hybrid, 138 CMS systems, 133 Coating seeds, 257 Cold-tolerant varieties, 93 Colletotrichum-Stylosanthes pathosystem, 156 Commercial hybrid system, 145 Common bean (Phaseolus vulgaris) cultivation, 98 genetic transformation, 98, 99 golden mosaic virus resistance, 99 herbicide and drought tolerance, 99, 100 pests, 98 phosphorous deficiency, 98 production constraints, 98 Comprehensive assessment (CA), 306 Conservation agriculture (CA) adoption, 273, 278 advantages, 290 components, 307 conventional tillage, 278 crop productivity, 307 in situ intervention, 307 legumes, 273 minimum soil disturbance, 278 no-till practices, 273 reduced runoff, 307 soil health and crop productivity, 273, 274 soybean, 273 types, 273 volumetric soil moisture, 278 yield and grain quality, 273 Consortium, Convergence, Collective action, and Capacity building (4Cs), 305 Contemporary chickpea variety, 241 Conventional breeding method BGM resistance, 167 biotic stress-resistant germplasm, 168 disease-resistant varieties, 166 Fusarium screening, 166 genotypic differences, 167 horizontal resistance exploitation, 166 ICARDA programme, 168 lentil resistance varieties, 167 MYMD-resistant lines, 168 national programmes, 169 parasitic weed-infested highlands, 169 pigeonpea genotypes, 167 resistance selection, 166 wild Cicer spp., 167 Conventional germplasm, 182 Conventional methods, 220 Cool-season legumes, 75 Corn–soybean cropping system, 284

Index Cotyledonary nodes, 101 Cover crops, 289 Cowpea (Vigna unguiculata) bruchids resistance, 102 cultivation, 100 genetic transformation, 100, 101 germplasm, 100 herbicide-tolerance, 102 pod borer resistance, 101 production constraints, 100 Cowpea Select Consortium Array, 52 Cowpea trypsin inhibitor gene, 110 Cowpea weevil (C. maculatus), 106 Crop breeding cycle, 75 Crop diversification, 322 Crop intensification area augmentation, 323 cropping system management, 323 rainy season fallow management, 323, 324 rice-fallow management, 324–327 targets, 323 water management technologies, 322 Crop management practices, 4 Crop modelling approach, 4, 236 Crop models, 247 Crop research, 287 Crop simulation modelling, 240 Crop species, 54 Crop water requirement-based irrigation closed conduits, 331 direct-seeded paddies, 331 drip irrigation trials, 331 open channels, 329 PMKSY scheme, 330 simulation tools, 330 soil moisture storage, 330 water impact calculator, 330 WUE, 329 Cropping system design, 314 Cropping systems, 3, 5 Crossover interactions, 247 Cross-pollination A-line multiplication, 140 faba bean, 127 groups, 125 legumes, 126 natural out-crossing, 138 nectar-hunting insects, 125 nectarivore and pollenivore insects, 124 pigeonpea, 127 seeds, 144 soybean, 127 vectors, 127

Index wind-supported, 134 Cry1Ab transgenic pigeonpea lines, 110 Cry1Ac transgenic lines, 105 Cry1E-C gene, 110 Cultivated soybean, 125 Cultural controls, 158 Cultural management crop rotation, 158 crop sanitation/deep ploughing, 158 fertilizer, 158 hot water seed treatment, 158 infected plant materials removal, 159 late sowing, 158 others, 159 row spacing, 158 volunteers/crop residues removal, 159 Cytoplasmic male sterility (CMS), 3, 76, 77, 130, 132, 133, 140

D DAC&FW-led consortium, 326 DArT-based genetic map, 54 Days after pollination (DAP), 75 Dense plant canopy, 210 Digital Green, 332 Digital soil mapping (DSM), 328 Diversified cropping systems decreased environmental impacts, 284 description, 284 eco-friendly, 284 faba bean, 284 high economic returns/sustainability, 285 improved soil fertility/crop yields, 285 legumes, 284 rainfed areas, 284 types, 284 Diversity array technology (DArT)-seq, 52, 313 DNA marker technologies, 51, 52 DNA sequencing technologies, 72 Dominant nuclear (MsMs) alleles, 130 Donor-resistant sources, 168 Doubled-up intercropping system, 290 Doubled-up legume intercropping, 289 DRED1A gene, 108 Drip irrigation, 331 Drought environments, chickpea drought patterns, 241, 242 individual patterns clustering, 241 multilocation trials, 241 pre-flowering droughts, 242 severe terminal stress, 241 supply-demand ratio, 241

343 terminal drought, 242 TPE, 241 transpiration demand, 241 types, 241 yield partitioning, 241 yield variation, 241 Drought proofing water management CA, 307, 308 conservation measures, 306 ET, 306 infiltration rate, 306 rainwater harvesting, 307 soil moisture availability, 306 vertisols, 306 water harvesting structures construction, 307 waterlogging, 307 Drought-responsive genes, 105 Dry root rot (DRR) bio-agents, 163 biological traits alterations, 177 chickpea, 158 and collar rot, 159 fungicide carbendazim, 164 L. nudicaulis and palmarosa, 164 mungbean and urdbean, 157 rain-fed environments, 155 RAPD, 172 resistant lines, 168 severity, 164 soil-borne disease, 150 soil moisture delpletion, 155 Dryland grain legumes, 325 Dryland legumes productivity enhancement consortium approach, 305 genetic gain (see Genetic gain) management model (see Integrated watershed management model) rain-fed agriculture, 304 science-based solutions, 305

E Early leaf spot (ELS), 68 Early-maturing cultivars use, 260 Early-maturing varieties, 245 Economic analysis, 325 Eco-physiological processes, 181 Ecosystem-based approach, 265 Elite genotypes, 246 Emerging diseases anthracnose, 153 Ascochyta blight, 150, 153

344 Emerging diseases (cont.) BGM, 150, 152 black root rot, 154 chocolate spot, 153 climate change (see Climate change) DRR, 150 FBG, 153 fungal diseases, 153 Fusarium wilt, 150, 152 MYMD, 153 PB, 152 rust, 152 SMV, 152 Stemphylium blight, 152 viruses, 154 wilt/root rot, 154 Empoasca kerri (Ruth), 211 Enamovirus, 107 Environment characterisation abiotic stress patterns, 245 advantage, 244 breeders, 245 chickpea phenology, 245 early-maturing varieties, 245 GxE interactions, 245 heat tolerance, 246 managed environments, 246 multi-environment trials, 245 possibilities, 244 scope, 245 soil water effects, 246 stored soil water, 246 traits, 245 varieties identification and development, 245 Environmental change, 154 Environmental parameters, 4 Environment-sensitive male sterility (EMS), 130, 132 Equity, Economic gain, Empowerment, and Environment protection (4Es), 305 Essential amino acids, 107 EST-SSRs, 53 Ethyl methanesulfonate (EMS), 176 Evapotranspiration (ET), 306 Expressed sequence tags (EST), 176

F Faba bean, 127 stability, 3 transgenic, 178

Index Faba bean gall (FBG), 153 Fabaceae (Leguminosae), 49 Farmer participatory evaluation best-best management practices, 314 experimental protocol, 315 farming systems, 314 ICRISAT’s project, 315 legume crop cultivars, Karnataka, 315, 316 legume crops, 314 varietal trial layouts, 315 Farmer to farmer (F2F) information dissemination method, 332 Farmers empowerment communication technologies, 327 crop water requirement, 329–331 digital tools, 327 identifying moisture stress, 327 information and communication tools, 331, 332 soil-site suitability studies, 328 weather-based agro-advisories, 328, 329 Farmers’ incomes doubling components fField demonstration, 333 IGNRM, 332 IIPR, 333 improved production package, 333 legumes-based cropping systems, 332 Smart Food Crops, 332 Fertile transgenic soybean lines, 95, 96 Fertility restorers, 140 Fertility restoring nuclear gene (FrFr), 130 Fertilizer use efficiency, 265 Field-based managed environments, 246 Field screening, 173, 199 Flavones/flavonol, 126 Flower initiation, 75 Foliar fungicides, 161 Food and Agriculture Organization (FAO), 149, 304 Food legume seeds, 180 Food legumes production constraints abiotic stresses, 92, 93 biotic stresses, 93 Fungal and bacterial biocontrol agents, 164 Fungal diseases, 233 Fungi, 93 Fungicide sprays, 162 Fungicides, 159, 162, 320 Fusarium oxysporum, 156 Fusarium wilt, 73, 150 Fusarium wilt resistance, 156, 175 Future Smart Food (FSF), 304, 325

Index G Galanthus nivalis agglutinin (GNA), 105 GBLUP models, 74 GE crops, 112 GE soybean biotic and abiotic stress tolerance, 95 cultivation, 94 epsps gene, 96 impacts, profitability and sustainability, 97, 98 incomes and environmental benefits, 110 Gene editing, 2 Genetic diversity, 3 Genetic engineering (GE), 2, 5, 94 Genetic gains, 50, 51, 77 agronomy, 311 conventional breeding, 311 genomic approaches, 313 multidisciplinary approach, 308 participatory evaluation (see Farmer participatory evaluation) pedigree and performance-based selection, 308 pre-breeding, 313 satisfactory heritability, 313 selection intensity, 313 stress conditions, 308 yield performance, 308 Genetic male sterility, 129, 131 Genetic markers, 171 Genetic resistance, 3, 156 Genetics, 1 Gene-trait associations, 69 Genome duplication, 72 Genome sequencing genomics resources (see Genomics resources) Genome-wide association (GWAS), 69–71 Genome-wide gene expression profiles, 53 Genome-wide SNPs, 52 Genomic selection (GS), 73, 74 Genomics resources, 50 next-generation mapping, 51, 52 Genomics science, 1 Genomics technologies, 1 Genomic-wide association studies (GWAS), 50, 77 Genotype x environment (GxE), 234 Genotype x environment x management (GxExM), 232 Genotypic diversity study, 175 Glasshouse, 75 Glasshouse screening, 201 Glycemic index (GI), 94

345 Glycine soja genotypes, 52 GMS hybrids, 138 Grain legumes, 49, 57, 58 cereals, 91 chickpea, 102–106 common bean, 98–100 composition, 94 cowpea, 100–102 crop production, 255 cropping systems, 91 dietary protein, 91 GE technology, 94, 111 genetic and agronomic improvements, 93 global biological nitrogen fixation, 91 human diet, 110, 289 lentils, 108–109 nutritional benefits/improvement scope, 93, 94 pea (Pisum sativum), 106–108 pigeonpea, 109–110 production, 289 proteins complement, 93 soybean, 95–98 yield potential, 91 Gravimetric soil water measurements, 236 Green revolution, 2, 92, 248 Greenhouse gas (GHG) emission, 49 Groundnut, 75, 256 Grow-out testing (GOT), 141 GUS-positive transgenic chickpea plants, 103 GxE interactions adaptation, 240 agro-ecological region, 240, 245 breeders, 246 crossover, 235, 236, 248 environmental characterisation, 234 genotypes, 234 impact, 237 unravel, 245 variability, 235, 240 GxExM combinations, 247 GxExM conditions, 247 GxExM interactions, 233 branching pattern, 247 chickpea adaptation, 248 drought-prone environments, 247 drought-tolerant genotypes, 247 genotypes impact studies, 247 optimum G and M combinations, 247 physiological basis, 247 physiological framework, 248 sensitivity analyses, 248 GY gamma radiation treatments, 178

346 H Halo blight (Pseudomonas savastanoi), 153 Hand weeding, 261, 287 Health benefits, 49 Helicoverpa armigera antixenosis, 207 Cicer spp., 204 host plant selection, 212 larval survival, 209 leaf feeding, 204 neonate larvae, 210 pest, 198 pigeonpea wild relatives, 203 recuperation, 209 reduced susceptibility, 204 resistance, 207 single largest yield-reducing factor, 199 Herbicide (imazapyr), 96 Herbicide-tolerant soybean, 96 Heterosis, 136 Heterotic groups, 143 High seed quality, 256 High yielding varieties, 5 High-density genetic linkage/QTL maps, 213 High-density linkage mapping, 54, 55 High-throughput genotyping platform, 313 High-throughput phenotyping, 165 High-throughput technologies, 53 High-yielding varieties, 92 Homoclimes, 237 Homozygosity, 75 Honey bees, 128 Host-plant resistance (HPR), 165, 167, 170 advantages, 217 breeding problems, 217, 218 eco-friendly measures, 217 identification and utilization, 202 HT soybean, 97 Hybrid breeding advantages, 144 ICRISAT, 124 male sterility (see Male sterility systems) pigeonpea (see Hybrid pigeonpea) reproductive biology, 124–125 yield enhancement, 144 Hybrid development, 76, 77 Hybrid ICPH 2671, 3, 138 Hybrid ICPH 2740, 139 Hybrid ICPH 3762, 139 Hybrid pigeonpea A4 CMS system, 139, 140 breeding, 137 high-yielding hybrid release, 137–139

Index large-scale seed production, 140 resilience, 3 seed quality control, 141–143 Hybrid seed production, 140, 141 Hybrid vigour agronomy, 135 faba bean, 136, 137 field-scale exploitation, 134 floral morphology, 135 high levels, 145 pigeonpea, 135 soybean, 137 Hydropriming, 257 Hygromycin resistant lines, 100

I ICC 4958, 53 ICPL 332WR, 219 ICPL 87119, 53 IFFCO Kisan Sanchar Limited, 331 Illumina Infinium BeadChip technology, 52 Illumina Infinium II technology, 52 Imidazolinones, 109 In silico analyses, 247 In vitro germination, 75 Inclusive market-oriented development (IMOD), 305 Independent chickpea lines, 104 Indian Council of Agricultural Research (ICAR), 123 Indian Institute of Pulses Research (IIPR), 333 Individual yield-limiting factors, 288 Indo-Gangetic Plains (IGPs), 322 Information and communications technology (ICT), 305 Innovation, 289 Innovative system approach, 289 Insect control, 4 Insect multiplication, 4 Insect pests constraints, 199 pod borers, 199 types, 198 yield losses, 199 Insect pests resistance HPR (see Host-plant resistance (HPR)) identification and utilization, 201–206 mechanism, 208–209 phenological and morphological traits, 210–211 transgenic, 215–217 wild relatives, 207–208

Index Insect resistance screening techniques field screening, 199 glasshouse, 201 Insect toxicity, 179 Insect visitors, 128 Insecticides, 4, 162 Insect-related issues, 4 Insect-resistant soybean, see Pod borer-resistant soybean Insects, 100 Integrated crop management (ICM) biotic and abiotic stresses, 287 extensive research, 288 increased yield, 288 integrated approach, 288 mungbean growth, 288 packages, 288 rehabilitation, 288 temporal and spatial level, 287 Integrated disease management (IDM), 179, 180, 285 biological management, 163–165 chemical management, 159–163 cultural management, 158–159 mechanical management, 157 Integrated genetic natural resource management (IGNRM), 332 Integrated insect-pest and disease management adverse impacts, 285 Ascochyta blight, 285 B. thuringiensis, 285 genetic dissimilarity, 286 imidacloprid, 286 minimize yield losses, 285 resistance, 285 Integrated nutrient and crop management approaches, 265 Integrated nutrient management (INM) biofertilizers, 266 cropping system yields, 265 India, 266 lentil, 266 recommended fertilizers, 265 rice–wheat cropping system, 265 soil fertility indicators, 266, 267 yield maximization, 266 Integrated pest management (IPM), 320 HPR, 217 Integrated water management (IWM), 320 Integrated watershed management model balanced nutrients application, 308 311, 312 crop diversification, 305 drought proofing (see Drought proofing water management)

347 IMOD approach, 305 principles, 305 rain-fed agriculture, 305 soil health mapping, 308 watershed projects, 306 Integrated weed management chemicals, 286 control methods, 286 efficiency, 287 manual methods, 286 mechanical treatment, 286 plant lodging, 287 pre-seeding herbicide application, 286 rainfed conditions, 287 Inter-and intra-row spacing, 260 Interactive voice response system (IVRS), 331 Intercropping adoption, 281 description, 280 diversified cropping systems, 280 grain yield improvements, 281, 282 legumes, 281 meta-analysis, 281 P deficiency, 281 permutations, 280 phenologies and vegetative patterns, 280 reduced crop failure risk, 281 resource facilitation, 281 resource partitioning, 280 smallholder cropping systems, 280 sorghum grain yields, 281 International Atomic Energy Agency (IAEA), 176 International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), 307 International Food Policy Research Institute (IFPRI), 16 International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT), 16 International Year of Pulses by the United Nations (UN) General Assembly, 92 Intron-spanning regions (ISRs), 53 Intron-targeted primers (ITPs), 53 Irrigation management, 264

K KASP assay, 73 kharif season, 323 Kisan Call Center (KCC) facility, 331 Kosheli, 220 Kunitz trypsin inhibitor (KTI)-free soybean, 73

348 L Laboratory bioassays, 105 Land resources inventory (LRI), 318 Large-scale SSR, 54 L-arginine, 211 Late leaf spot (LLS), 68 L-Canavanine, 211 lea gene, 98 Leaf crinkle, 157 Leaf hairs, 210 Legume-based cropping systems, 284 Legume crops, 49–51, 53, 54, 68, 72, 73, 75, 77 Legume–microbe interactions, 266 Legumes abiotic and biotic stresses, 256 AGRs, 16–18 average yields, 1 bi-parental QTL mapping (see Bi-parental QTL mapping) consumption, 255 diet diversification, 8 diseases, 151 environmental sustainability, 48 GE, 2 germplasm, 2 global demand, 1 global distribution, 9 global production, 198 global status, 8 grain (see Grain legumes) high-protein vegetarian food, 123 hybrid breeding, 2 insects, 3 mutants, 177 performance (see Region-wise crop performance) production, 1 productivity constraints, 5 protein rich, 7 risk reduction, 7 soil-borne and foliar pathogens, 3 traits, 94 yield and production regions, 10, 13 Lentil (Lens culinaris) drought and salinity stresses, 108 genetic improvement, 108 global production, 108 pest, 108 sulfonylurea herbicides, 109 weed management, 109 Light-emitting diode (LED) systems, 75, 103 Linkage group (LG), 175 Low grain yield, 255

Index M MABC schemes, 73 Machine learning (ML), 305 Macrophomina blight, 179 Macrophomina phaseolina, 157 Macrophomina stem blight, 156 Maize–faba bean intercropping, 281 Male sterile (A-) line, 141 Male sterility systems biological event, 129 contribution, 129 faba bean, 133 field crops, 130, 131 gene transfers, 129 pigeonpea CMS, 132 EMS, 132 genetic male sterility, 131 research activities, 131 soybean, 134 types, 129–130 Marker-assisted backcross breeding (MABC) scheme, 57 Marker-assisted backcrossing (MABC), 214, 314 Marker-assisted breeding, 3 Marker-assisted recurrent selection (MARS), 314 Marker-assisted selection (MAS), 51, 73, 77, 314 aphid screening resistance, 214 bruchid resistance, 214 complex traits mapping, 213 cultivated gene pool, 213 genetic characterization, 214 genetic improvement programme, 213 QTLs, 214 resistance breeding programme, 215 undiscovered genes utilization, 213 wide pertinency, 213 Maruca pod borer resistance, 101 Matural cross-pollination, 129 Mechanical mangement, 157 Mega-environment, 236 Mexican dry bean weevil (Zabrotes subfasciatus), 98 Microarrays, 53 Micro-irrigation, 331 Million metric tons (MMT), 264 Model-based characterisation, 240 Moderate-density markers, 74 Modern breeding methods, 110 Moisture adequacy index (MAI), 328

Index Moisture-carrying capacity, 323 Molecular breeding, 50, 51 Molecular breeding method application, 176 Ascochyta blight resistance, 174 C 104 and WR315, 172 CA 2139, 172 dominant gene theory, 173 fertility restorers, 173 Foc races, 171 Fusarium wilt resistance, 175 GE, 170 gene actions, 173 genetic markers, 171 genotypic diversity study, 175 inbred genotypes, 176 MAS, 170 monogenic inheritance, 175 phenotypic selection, 170 QTLs, 172–175 reliable greenhouse, 173 resistance inheritance, 172, 174 SSR and RAPD, 172 transcriptome and genomic sequence, 176 Molecular mapping, 57 Molecular marker systems, 57 Molecular markers, 52, 53, 171 Molecular markers-based seed quality control female A-line, 141, 142 genetic properties, 141 GOT, 141 hybrid seeds, 142 male (R-) parent, 143 objectives, 141 Monocrop-specific problems, 232 Monotonous breeding, 308 Multi-environment field testing, 167 Multi-environment trials, 245 Multi-parent advanced generation inter-cross (MAGIC) populations, 51 Multiple-trait models, 74 Mung bean yellow mosaic Indian virus (MYMIV), 215 Mung bean yellow mosaic virus (MYMV), 214 Mungbean yellow mosaic disease (MYMD), 153 Mutagenic treatments, 177 Mutation breeding, 176–178 MYMD resistance, 174 MYMV-resistant mungbean varieties, 178

349 N Narrow sowing window, 325 National and international germplasm programmes, 180 National Biosafety Management Agency (NBMA), 101 National Bureau of Soil Survey and Land Use Planning (NBSS&LUP), 328 National Food Security Mission, 333 Natural cross-pollination, 125 Natural out-crossing cross-pollinated group, 125 cross-pollination, 126 environmental conditions, 127 faba bean, 127 heterosis exploitation, 126 insect-aided, 145 pigeonpea, 126 Neglected and underutilized species (NUS), 304 Neomycin phosphotransferase II (npt II), 95 Nested association mapping (NAM), 51, 52 New technologies, 304, 305 Next-generation mapping resources, 51, 52 Next-generation sequencing (NGS), 50, 53, 54, 175 N-fixing microbes, 272 NGS-based approaches, 52 Nitrogen, 272 Nitrogen-use efficiency (NUE), 49 Non-communicable diseases, 197 Nonprotein/unusual amino acids, 211 North Africa TPEs, 235 Nuclear Institute for Agriculture and Biology (NIAB), 178 Nutrient use efficiency (NUE), 265

O Oil cake meal, 35 Oil sprays, 163 On-farm farmers’ participatory action research trials, 326 On-farm seed priming, 256 On-farm trials, 308, 324 Optimum plant density, 260 Organic farming, 279, 280, 290

P Paddy-based cropping systems, 326 Paddy fallow, 325 Papilionaceous flower, 125

350 Participatory varietal evaluation program, 314 Particle bombardment-mediated gene transfer, 95 Pathogens, 159 Pathogen-suppressing organisms, 179 PCR-based, medium-throughput markers, 52 Pea (Pisum sativum) cultivation, 106 genetic transformation, 106, 107 nutritional value improvement, 107, 108 pesticides, 106 production constraints, 106 viral disease-resistance, 107 Pea enation mosaic virus (PEMV), 107 Pea pod borer (Etiella zinckenella Triet.), 198 Pea seed protein, 107 Pedigree method, 166 Pedo-climatic classifications, 236 Pesticide-resistant pathogen, 180 Pests, 93 Phaseoleae, 125 Phaseolus vulgaris gene expression atlas (PvGEA), 53 Phenological and morphological traits genotypes, 210 leaf hairs, 210 plant growth types/maturity, 210 pubescence, 210 seed texture and colour, 210 trichomes, 210, 211 Phosphorus mobilization, 49 Photosynthetic active radiation (PAR) lamps, 75 Phytophthora blight (PB), 152, 156 Pigeonpea (Cajanus cajan L.) biotic stresses, 109 buds, 124 genetic transformation, 109, 110 heterotic group, 143 papilionaceous flowers, 124 photo-sensitive species, 124 pod borer-resistance, 110 production, 109 second-grade crops, 123 tropics and sub-tropics, 123 yield plateau, 124 Pigeonpea genome, 54 Pigeonpea genotypes, 167 Plant extracts, 165 Plant growth-promoting rhizobacteria (PGPR) application, 257, 272 legumes growth and yield, 272 micronutrient bioavailability, 272

Index mineralize and decompose, 269 nutrients uptake and availability, 266 sustainable crop production, 266 use of, 269, 270 Plant lodging, 287 Plant quarantine, 180, 181 Pod borer (H. armigera)-resistant cultivar, 219 Pod borer-resistant pigeonpea, 110 Pod borer-resistant soybeans, 96, 97 Pod borers (Maruca vitrata), 100 Pod borers resistance mechanisms antibiosis, 209 categories, 208 genotypes, 209 H. zea, 209 IAC-Harmonia, 209 larval survival and weights, 209 pea varieties, 209 repellence and disruption, 209 tolerance, 209 Pod wasp (Tanaostigmodes cajaninae), 198 Pollinating insects faba beans flowers, 128 flavones/flavonol, 127 pigeonpea flowers, 127 soybean flowers, 128 Polymorphic markers, 74 Potyvirus pea seed-borne mosaic virus (PSbMV), 107 Powdery mildew, 153 Pradhan Mantri Krishi Sinchai Yojana (PMKSY), 330 Pre-breeding, 313 Precision agriculture (PA), 278, 279 Pre-flowering droughts, 242 Pre-flowering stress, 242 Proactive resistance breeding programmes, 3 Production systems CA, 273–278 conventional farming systems, 273 crop diversification, 284–285 intercropping, 280–281 organic farming, 279, 280 PA, 278, 279 Productivity limiting factors, 149 Protease inhibitors (PI), 212 Protein-coding genes, 72 Protein-rich legumes, 2 PSbMV replicase gene, 107 Pseudomonas aeruginosa strain PNA1, 164 P-solubilizing bacteria, 272 Pubescence, 210

Index Pulses chickpea/bengal gram, 197 cropping systems, 303 Indian agriculture, 197 insect pests, 198, 199 lentil, 198 pigeonpea, 198 soil and climatic conditions, 197 Pure-line breeding methods, 124 Pure-line cultivars, 144 Pure-line selection, 166

Q Quality traits, 68 Quantitative trait loci (QTLs), 173, 214 controlling gene resistance, 175 hotspot, 57 mapping, 57–67 Quarantine policies, 180

R Rabi crops, 326 Rain-fed agriculture, 305 Rain-fed cropping systems, 320 Rainwater management and harvesting, 307 Rainy season fallow management, 323, 324 Random amplified polymorphic DNA (RAPD), 52, 175 Rapeseed–mustard (Brassica spp.), 232 Rapid generation technology (RGT), 75 Recessive fertility nuclear alleles ( frfr), 130 Recombinant inbred lines (RILs), 51 Region-wise crop performance chickpea Africa, 24 Americas, 24 Asia, 23, 24 Australia, 25 contribution, 23 Europe, 24 global production, 23 projected demand/supply, 25, 26 common bean Africa, 20 AGRs, 16 Americas, 20, 21 Asia, 19 Europe, 21 global production, 16 projected demand/supply, 21, 22 South Asia, 19

351 cowpea Africa, 27, 28 AGRs, 27 Americas, 28 Asia, 27 Europe, 28 global production, 26 projected demand/supply, 28, 29 groundnut Africa, 31, 32 AGR, 30 Americas, 32, 33 Asia, 30, 31 global production, 29, 30 projected demand/supply, 33–35 lentils Africa, 36 AGRs, 35 Americas, 36, 37 Asia, 35, 36 Europe, 37 global production, 35 Oceania, 37 projected demand/supply, 37, 38 pigeonpea Africa, 40, 41 AGRs, 39 Asia, 39, 40 Caribbean island, 41 global production, 39 projected demand/supply, 41, 42 soybean Africa, 44, 45 AGRs, 43 Americas, 45 Asia, 43, 44 Europe, 46 global production, 43 Oceania, 46 projected demand/supply, 46, 47 Remote sensing (RS), 305 Resistance breeding programme conventional approach, 165 conventional method, 166–169 GE, 178 molecular approach, 165, 170–176 mutation breeding, 176–178 plant quarantine, 180, 181 policy-making, 179–180 wild resistant resources, 169–170 Resistance identification and utilization black grams evaluation strategies, 206

352 Resistance identification and utilization (cont.) pink pod borer, 206 chickpea aphids, 205 genotypes, 205 genotypes screening, 204 germplasm accessions, 204 H. armigera, 204 pod borer damage, 205 cowpea cultivars, 206 cultivated accessions, 206 Maruca sp., 205 sap-sucking jassid infestation, 205 wild green gram, 206 economic and eco-friendly approaches, 201 pigeonpea breeding programme, 203 H. armigera, 203 long-duration genotypes, 204 resistance rating, 204 positive selection, 201 Resistance, Avoidance, Elimination and Remedy (RAER), 182 Resistant cultivars, 150, 180 Restriction fragment length polymorphisms (RFLPs), 52 Rhizobial inoculation, 264 Rhizobium inoculation, 325 Rice-based cropping systems, 318 Rice-fallow management biophysical constraints, 325 climate change, 325 economic analysis, 326 follow-on beneficial effects, 326 FSFs, 325 GIS analysis, 324 global warming, 325 green water, 325 kharif season, 324 narrow sowing window, 325 national-level workshop, 326 on-farm research, 326 paddy, 324 residual moisture, 324 Rhizobium inoculation, 325 socio-economic constraints, 325 RNAi-mediated methods, 94 RNA-Seq, 53 Roundup Ready™ (RR) soybean, 96 rrBLUP models, 74 RuBisCO small subunit (rbcS), 215

Index S Salinity stress, 57 Salinity-tolerant varieties, 92 Sanger sequencing technique, 72 Sap-sucking jassid infestation, 205 Sarla, 178 Sclerotium rolfsii, 155 Seasonal variability, 235 Secondary metabolites amylase, 212 defensive compounds, 212 flavonoids, 212 gut proteinases, 212 impacts palatability, 212 malic acid, 212 PI, 212 polar solvent, 212 quercetin-3-methyl ether, 212 Seed and seed yield-related traits, 69 Seed dressing, 159 Seed enhancement coating, 257 hydropriming, 257 improved germination, 256 PGPR, 257 priming, 256, 257 quality improvement, 256 seed treatment, 257, 258 stand establishment, 257 Seed priming, 257 Seed quality, 256 Seed treatment, 159, 321 Self-/cross-pollinated crops, 166 Semi-hydroponic phenotyping system, 314 Sequence-based breeding, 2, 77 Shared socioeconomic pathway (SSP-2), 16 Simple sequence repeat (SSR), 52, 142, 143 Single dominant gene theory, 173 Single nucleotide polymorphism (SNP) markers, 52 Single recessive gene theory, 173 Single-seed descent (SSD) method, 51 Sitona weevil (Sitona crinitus Herbst), 208 Small micro-RNAs, 105 Smart Food Crops, 332 SMD-resistant cultivars, 173 SMS-based dissemination, 331 SNPs markers, 54 Socio-economic situations, 181 Soil fertility, 49 Soil health, 308 Soil health mapping, 5 Soil moisture availability, 306

Index Soil salinity, 92 Soil test-based balanced fertilization, 333 Soybean (Glycine max) cultivation, 95 GE, 95 genetic transformation, 95, 96 herbicide-tolerant, 96 nitrogen fixation ability, 95 pod borer-resistant, 96, 97 self-pollinated crop, 125 weed management, 95 Soybean projected utilization pattern, 47 Soybean-resistant genotypes, 211 Soybean transformation, 112 SOYGRO model, 323 Speed breeding, 50, 51, 75, 77 SSR markers, 54 SSR/SNP markers, 141 Stable cowpea transgenic lines, 101 Stemphylium blight, 152 Sterile/fertile plants, 132 Sterility mosaic disease (SMD), 173 Sterility mosaic virus (SMV), 152 Streptomyces hygroscopicus, 96 Support vector regression (SVR), 74 Sustainable agriculture production, 181 Sustainable development goals (SDGs), 304 Sustainable food supply, 256

T Target population of environments (TPEs) abiotic stresses, 247 agroclimatic conditions, 240 agro-ecological regions, 245 agronomic practices and environments, 246 breeding programs, 248 drought and thermal regimes, 248 generic classes, 235 genetic gains, 246 GxExM combination, 247 mean temperature regimes, 243 seasonal variability, 236 selection environment, 235 trials, 245 Temperatures, 243 Terminal drought priming, 257 Terminal water stress, 100 Three-parent hybrid seed production technology, 140 Tice-based cropping systems, 284

353 Tolerant cultivars, insect pests chickjpea H. armigera, 220 ICCV 6, 220 ICCV 7, 219 pigeonpea Abhaya (ICPL 332), 219 H. armigera-resistant cultivar, 219 ICPL 332WR, 218 Transgenic chickpeas, 104 Transgenic plants, 103, 104 Transgenic resistance ASAL gene, 215 bioassay, 216 Bt formulation, 215 cowpea genotypes, 217 cowpea hereditary change, 216 Cry1Ab, 216 Cry1Ac gene, 215 H. armigera, 215 insect-resistant gene, 216 pigeonpea plants, 216 rbcS promoters, 215 Transgenic virus-resistant beans, 99 Trichoderma spp., 164, 320 Trichomes, 210, 211 Tripogon loliiformis (T1BAG), 106 Tropical legumes BNF, 303 NUS, 304 production, 303 pulses, 303 semi-arid tropics, 304 yield gaps, 304

U Unstructured Supplementary Service Data (USSD), 331 Urdbean leaf crinkle virus (ULCV), 157

V Vegetative insecticidal protein (Vip), 101 Vertisols, 40, 306 Vicia faba (faba bean), 125 Vigna unguiculata gene expression atlas (VuGEA), 53 Vigna vexillata, 216 Vigna vexillate, 102 Viral disease-resistant pea, 107 Viruses, 93, 154

354 W Water impact calculator, 330 Water stress, 92 Water use efficiency (WUE), 329 agronomic approach, 264 biochar and phosphorus application, 262 desi chickpea, 262 early planting treatments, 261 enhancing measures, 330 flatbed planting, 262 food legumes, 261 grain yield, 261 hand weeding and herbicide use, 261 optimum row spacing, 262, 263 rainfed areas, 262 short-season Mediterranean environment, 261 supplemental irrigation, 262 transpiration efficiency, 261 Waterlogging, 322 Watershed on-farm participatory research, 323 Weather-based agro-advisories, 328, 329 Weed–crop competition, 286 Weedicides, 217 Wheat–soybean intercropping, 281 Whitefly Bemisia tabaci (Gennadius), 153 Whole-genome sequencing, 72, 73 Wild accession, 215 Wild relatives accessions, 207 biotic and abiotic stresses, 208 bruchid species, 208 C. bijugum, 207 C. reticulatumtum, 207

Index chickpea lines, 207 cultivated germplasm, 207 draft genome sequence, 208 H. armigera, 207 pea, 208 pod powder, 207 post-embryonic development period, 207 resistance genes, 207 Sitona weevil, 208 wild species, 208 Wild resistant resources cultivated genepools, 169 genetic variability, 169 genotypes, 170 interspecific sources, 170 lentil collection, 169 MYMV, 170 YMV, 170 World Trade Organization (WTO), 180

X Xylocopa spp., 127

Y Yield trends, 25, 37, 44, 45 Yields, 3 YMV-resistant mungbean variety, 178

Z Zn biofortification, 272 Zn-solubilizing strains, 272