Environment and Climate-smart Food Production 3030715701, 9783030715700

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Charis M. Galanakis Editor

Environment and Climate-smart Food Production

Environment and Climate-smart Food Production

Charis M. Galanakis Editor

Environment and Climate-smart Food Production

Editor Charis M. Galanakis Research & Innovation Department Galanakis Laboratories Chania, Greece

ISBN 978-3-030-71570-0 ISBN 978-3-030-71571-7 (eBook) https://doi.org/10.1007/978-3-030-71571-7 © Springer Nature Switzerland AG 2022, corrected publication 2022 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

Preface

Climate change is one of the biggest global challenges, affecting agriculture, food systems, forestry, and the marine and bio-based sectors. Among these sectors, climate change will affect farming first in line through changes such as rising temperatures, occurrence of droughts, rainfall regimes, heat waves, storms, and floods. Subsequently, farmers will need to adapt to climate change and develop farming systems resilient to fluctuating environmental and socio-economic conditions. Moreover, the well-being of citizens is directly affected by the way cities and rural regions are shaping food production. To this line, the challenge of providing consumers with safe, nutritious, and affordable food is more urgent than ever. Subsequently, there is a need for a new reference covering all these issues. Food Waste Recovery Group (www.foodwasterecovery.group) has developed a portfolio of numerous activities, including consultation reports, e-courses, webinars, workshops, reference modules, publications, and multiple books in the broad fields of food, nutrition, bioresources, and environment. Following this trend, the current book aspires to fill this gap in the existing literature by providing specific solutions for industrial sustainability in terms of fish processing, production, and waste management and discussing current trends. The present book addresses climate change and resilience in the food sector, contributing to the transition towards a circular bioeconomy, as well as fostering functional and sustainable food systems, boosting major innovations in agriculture, and finally developing smart and connected value chains in rural areas. The book consists of 11 chapters. Chapter 1 exemplifies climate-smart management schemes that can be applied in tropical production systems. Microclimate plays a determining role in the development of biotic and abiotic interactions within agriculture and livestock systems and the physiological and productive performance of plants and animals. Climate-smart agriculture can develop agroforestry-based production systems that contribute to soil water retention, soil and air temperature reduction, nutrient fixation, weed control, soil stabilization, and protection against wind and runoff in the improved physiological performance of crops and, therefore, higher productivity. Chapter 2 discusses sustainable, climate-smart, and agroecological farming systems in the face of adverse climatic variations and changes. v

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Particular emphasis is given to the agroforestry systems of smallholder farmers. Chapter 3 presents the telecoupling model to map out the causes and effects of each system and its relationship with others. The telecoupling model is applied in the Brazilian agri-food exportation to China case. It indicates implications of the high rate of meat production and export to long distances vis-à-vis the need to supply the increased demand for meat consumption due to economic prosperity. Chapter 4 discusses genetic resources that play a crucial role in fulfilling the five basic needs of human beings (food, feed, fodder, fiber, and fuel). The need for systematic conservation and sustainable utilization of plant genetic diversity is highlighted to ensure food and nutrition security. Chapter 5 aims to advance the understanding of the ability of plants to adapt to extreme conditions or to react to sudden changes in their environment. The chapter outlines the mechanisms used by plants to sense and signal abiotic stresses, morphological and physiological changes that take place in plants as they adapt to stressful conditions, and the associated alterations in metabolic and biological reactions. Chapter 6 provides innovations in plant variety testing and insights on crop-specific characters that support resource efficiency and resilience to challenging environments. The technical issues addressed and new features (entomology and statistics) are expected to mitigate climate change effects, enhance performance, and significantly improve existing criteria and methods. Chapter 7 discusses the global resource flows of agriculture and food production in the worldwide system that is now the focus of new circular economies and carbonneutral programs. Long-term data are revised together with existing applications that assess how nutrients flow through food supply chains to their eventual consumption as the food and beverage products that make up our diets. This analysis defines how applications, technologies, and models can be used to identify systemic interventions in the global marketplace so that they can deliver environmentally climate-smart food production. Chapter 8 introduces vertical farming by discussing energy and water consumption, yield, and scalability criteria under climate effects. Cultivation methods such as hydroponic, aeroponic, and aquaponic systems are discussed together with novel technologies like artificial intelligence. Chapter 9 discusses the challenges and opportunities of digital technologies in ensuring soil quality and optimizing land management. The “whole to part” mapping strategy to guide sustainable land management is revisited, and scientific techniques discussed include multi-criteria analysis intended to: (i) screen-out sensitive variables to any complex problem, (ii) hierarchically rank significant variables, (iii) investigate the interrelationships of the facets, and (iv) synthesize information at various scales to explore viable solutions. Chapter 10 presents the production of high-quality fertilizers from biogas digestion. Biogas, one of the most important renewable energy sources, is obtained by anaerobic fermentation of organic wastes. While energy is received at the end of biogas production processes, in addition to this, biogas digestates generated as a result of production can be considered as a quality fertilizer source due to their chemical content. Chapter 11 describes the impact of global food production and ultra-processed foods on sustainability and the importance of eating a plant-based diet. Food system

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transformation and the importance of citizen science as a catalyst for change are thoroughly discussed. Using the frame of Citizenshift, four examples of food-related approaches and engagement in cities to promote the food system’s sustainability are explored. Today, despite increased information demand from consumers and food chain players alike, Europe’s food businesses and farmers are slow at adopting digital technologies such as the Internet of Things, artificial intelligence, big data technologies, and remote and localized sensing. Chapter 12 engages the agri-food community in supporting the development of solutions to remove the barriers to adopting digital technologies, taking a multi-actor approach across different supply chains (conventional and organic) from farm to fork. Conclusively, the current book is assisting food scientists and technologists; environmental, agricultural, and chemical engineers; as well as researchers working in the edge of the food and ecological field. It also concerns agricultural or food engineers seeking to improve the efficiency of production systems and professionals and strategy developers working in the agro-food industry, from farm to fork. Moreover, university libraries and institutes could use it as a textbook in undergraduate and postgraduate level multi-discipline courses dealing with sustainable food systems and agricultural and environmental science. At this point, I would like to thank all authors for their fruitful collaboration and dedication to the editorial guidelines and timeline. I would also like to acknowledge the book’ manager Arjun Narayanan and the acquisition editor Daniel Falatko, and all colleagues at Springer’s production team for their assistance during this book’s preparation. Finally, I have a message for all the readers: those collaborative efforts contain hundreds of thousands of words and may contain errors. Subsequently, constructive comments and even criticism are always welcome, so please contact me to suggest any changes. Chania, Greece

Charis M. Galanakis

Contents

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Microclimate Management: From Traditional Agriculture to Livestock Systems in Tropical Environments. . . . . . . . . . . . . . . . . . . . . . . . . Manuel Jesús Cach-Pérez, Gilberto Villanueva López, José Armando Alayón Gamboa, José Nahed Toral, and Fernando Casanova Lugo Climate-Smart and Agro-ecological Farming Systems of Smallholder Farmers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nyong Princely Awazi, Martin Ngankam Tchamba, Lucie Felicite Temgoua, Marie-Louise Tientcheu Avana, Abubakar Ali Shidiki, Gadinga Walter Forje, and Barnabas Neba Nfornkah The Telecoupling Approach to the Global Food System and Climate Change Regime: The Pivotal Role of Brazil and China . Douglas de Castro, Daniele Arcolini Cassucci de Lima, and Caroline Romano

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Genetic Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Padmavati G. Gore, R. Gowthmi, Kuldeep Tripathi, Pavan Kumar Malav, Vandana Tyagi, Neeta Singh, and Veena Gupta

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Plant Adaptation to Environmental Stress: Drought, Chilling, Heat, and Salinity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Khayelihle Ncama, Oladapo Adeyemi Aremu, and Nkanyiso Justice Sithole

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Innovations in Plant Variety Testing with Entomological and Statistical Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Luke Chinaru Nwosu and Ugochinyere Ihuoma Nwosu

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Global Resource Flows in the Food System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 Wayne Martindale and Kate Lucas

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Vertical Farming: Under Climate Change Effect . . . . . . . . . . . . . . . . . . . . . . . 259 A. Teoman Naskali, Ozgun Pinarer, and A. Cagri Tolga

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Challenges and Opportunities of Digital Technology in Soil Quality and Land Management Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 Vincent de Paul Obade, Charles Gaya, and Paul Thomas Obade

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High-Quality Fertilizers from Biogas Digestate . . . . . . . . . . . . . . . . . . . . . . . . . 319 Mustafa Sürmen and Emre Kara

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Citizen-Driven Food System Approaches in Cities. . . . . . . . . . . . . . . . . . . . . . 349 Sue Booth, Christina M. Pollard, and Claire E. Pulker

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ICT-Enabled Agri-Food Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383 Pedro D. Gaspar, Vasco N. G. Soares, and João M. L. Caldeira

Correction to: Global Resource Flows in the Food System . . . . . . . . . . . . . . . . . .

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Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417

Chapter 1

Microclimate Management: From Traditional Agriculture to Livestock Systems in Tropical Environments Manuel Jesús Cach-Pérez, Gilberto Villanueva López, José Armando Alayón Gamboa, José Nahed Toral, and Fernando Casanova Lugo

Abstract Microclimate plays a determining role in the development of biotic and abiotic interactions within agriculture and livestock systems and the physiological and productive performance of plants and animals. Given that management practices determine the degree of microclimate modification within production areas, different agriculture and livestock management strategies can contribute to reducing the effects of climate change, a phenomenon that puts food sustainability at risk. Climate-smart agriculture can develop agroforestry-based production systems that contribute to soil water retention, soil and air temperature reduction, nutrient fixation, weed control, soil stabilization, and protection against wind and runoff in the improved physiological performance of crops and, therefore, higher productivity. Moreover, the implementation of silvopastoral systems contributes to the efficient use of water and space and forage production in livestock systems, making them

M. J. Cach-Pérez () CONACYT - El Colegio de la Frontera Sur Unidad Villahermosa, Departamento de Agricultura, Sociedad y Ambiente, Grupo de Agroecología, Villahermosa, Tabasco, Mexico e-mail: [email protected] G. Villanueva López El Colegio de la Frontera Sur Unidad Villahermosa, Departamento de Agricultura, Sociedad y Ambiente, Grupo de Agroecología, Villahermosa, Tabasco, Mexico e-mail: [email protected] J. A. Alayón Gamboa El Colegio de la Frontera Sur Unidad Campeche, Departamento de Conservación de la Biodiversidad Agroecología, Ganadería y Cambio Climático, Campeche, Mexico e-mail: [email protected] J. Nahed Toral El Colegio de la Frontera Sur Unidad San Cristóbal, Departamento de Agricultura, Sociedad y Ambiente, Grupo de Agroecología, San Cristóbal de las Casas, Chiapas, Mexico e-mail: [email protected] F. Casanova Lugo Tecnológico Nacional de México/Instituto Tecnológico de la Zona Maya, Juan Sarabia, Qunitana Roo, Mexico © Springer Nature Switzerland AG 2022 C. M. Galanakis (ed.), Environment and Climate-smart Food Production, https://doi.org/10.1007/978-3-030-71571-7_1

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more productive, profitable, durable, and resistant to climate change. This chapter exemplifies climate-smart management schemes that can be applied in tropical production systems. Keywords Climate-smart agriculture · Climate change · Milpa · Agroforestry systems · Silvopastoral systems · Sustainability

1.1 Introduction Since the discovery of agriculture little more than 12,000 years ago and the beginning of the domestication of plants and animals some 10,000 years ago, humankind has modified the environment to provide the ideal microclimatic conditions for the development of species that provide food, clothing, building materials, and other consumables (Vasey 2002). This has resulted in a constant increase in the productivity of crops and animals under management—a product both of natural selection and human selection—and the biotic and abiotic interaction within the spaces destined for food crops or livestock (FAO 1996). However, population increase brings with it a greater demand for food to satisfy this need. It has been necessary to develop different cultivation and management techniques, new varieties of plants and animals and allocate more space and supplies ever to agriculture and livestock, in turn creating huge environmental problems such as loss of biodiversity, soil loss and contamination, and water and air pollution (Keenan et al. 2015; FAOSTAT 2020b). Between 1997 and 2017, approximately 1.12% of the world’s forest area lost, while agricultural land use increased by an average of 0.54%, the only exceptions being some countries in Europe and Oceania (Table 1.1; FAOSTAT 2020a). Latin America has one of the highest annual deforestation rates, associated with global food demand. Between 1990 and 2015, around 200 million hectares of tropical forest were lost due to anthropogenic activities such as fires and the transformation of land for agriculture and livestock use (Keenan et al. 2015). Recent estimates suggest that around one million square kilometers of dry forest persist in the tropical regions of South America (54.2%), North and Central America (12.5%), Africa (13.1%), Eurasia (16.4%), and Australasia and insular Southeast Asia (3.8%) are seriously threatened, mainly by conversion to cropland and grasslands (Miles et al. 2006). Pasture for global livestock production occupies 3315 billion hectares of land and uses approximately 33% of surface area to raise grazing animals (Herrero et al. 2013; FAOSTAT 2020a; Lorenz and Lal 2018). In Latin America, land use is mainly for pasture reaching between 60% and 80% of total land area in some countries, including Brazil, Nicaragua, Colombia, and Mexico. In Mexico, the change in land use associated with agriculture and livestock has caused the fragmentation of ecosystems, creating complex landscapes with patches of transformed and non-transformed areas and causing the loss of more than 50% of vegetation cover. In some states of southeastern Mexico, land use for

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Table 1.1 Forest and agricultural land use and emissions volume change between 1997 and 2017 per region in the world Region/indicator Africa America Asia Europe Oceania

Forest land area (% of total land area) −2.1 −1.6 −0.8 −0.7 −0.4

Agricultural land area (% of total land area) 1.03 0.4 0.2 −1.2 −11.2

Emissions in agriculture (CO2 eq. Gg) 340,083 194,064 412,738 −101,030 7618

Source: FAOSTAT (2020a)

agriculture and livestock takes up between 60% and 90% of total land area, as is the case with the humid tropics, including Veracruz, Tabasco, Campeche, and part of Chiapas (Ochoa-Gaona et al. 2004; Flamenco-Sandoval et al. 2007; Nahed-Toral et al. 2013a; Villanueva-López et al. 2019). Food production is one of the primary sources of greenhouse gas emissions into the atmosphere and has accelerated global climate change, in turn, harming agriculture and livestock. Changes in rainfall patterns, extreme drought and atypical rains, higher temperatures, and lower air humidity, as well as more robust and more frequent hydrometeorological phenomena, are a severe threat to food sustainability in regions around the world (IPCC 2014). Rural and family agriculture are particularly vulnerable since they lack the technical and economic means to confront environmental changes that significantly affect small scale food production. However, while up to 85% of global food production is estimated to come from small scale producers, recent years have seen a systematic decrease in their production compared to big producers, accentuating the problem of food production for self-consumption (FAOSTAT 2020b). A search is needed for production alternatives that contribute to reducing climate variations caused by phenomena such as land-use change and climate change. These production schemes should be sustainable and accessible in technical, economic, and social terms for small-scale (family) producers, thereby benefiting food sustainability. One alternative is undoubtedly the search or rescue of practices that modify the microclimate in a way that is beneficial to plant and animal development, many of which were widely practiced before the green revolution. For this, it is necessary to evaluate the advantages such practices can offer, for example, water retention capacity, soil cover as protection against erosion, and excessive soil temperature, shade, and quality food source for animals, among other benefits. In this context, the dimension of the microclimate can be variable. Microclimate can be defined as the particular climatic conditions measured around an organism and includes factors such as air temperature and humidity, wind speed, light, precipitation, dew formation, soil temperature, and water content. An important fact about microclimate is that change in any of these factors has an effect on the others and on the organism itself. The delimitation of the microclimate depends on the

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capacity for mobility or dispersion of the organism it affects ((Jones 1985; Naiman et al. 2005; Mislan and Helmuth 2008). For a plant, for instance, the microclimate may be the climatic conditions in an area measured in centimeters or meters around it, while for an animal, the microclimate may be much broader and more variable, given its capacity for movement and its location at any given time. The microclimate, therefore, plays a vital role in the development of processes such as growth, reproduction, productivity, and even mortality of organisms (Naiman et al. 2005). This chapter addresses some practical examples of management strategies, firstly in agricultural systems that exemplify the advantages of positive impacts of microclimate modification on the physiology and productivity of maize and cocoa (as study cases), and secondly in livestock production management with a view to the development of climate-smart livestock. The aim is to identify practices that help to improve production processes through the more efficient use of resources and the potential reduction of possible effects of climate variation as a result of climate change and land-use change.

1.2 Microclimate Management in Agricultural Systems 1.2.1 Microclimate and Crops Cultivated plants, like any other group of plants, interact with different biotic and abiotic elements of their surroundings, provoking a response in physiological, biochemical, morphological, and molecular performance (Fig. 1.1; Qaderi and Reid 2009). Any variation in the behavior pattern of these factors will cause the plant to respond as a means to acclimatize to the new conditions, as long as the variation in biotic and abiotic elements remains within the plant’s tolerance threshold. In this way, crop areas undergo continuous biotic and abiotic changes that depend on the crop type and management strategies and the environment, determining factors in the ecological dynamics within the crop area (Hoy 2015). Crop production is, therefore, highly vulnerable to climate variation; for example, rainfall patterns, increased air temperature and CO2 concentration, drought, flooding, and interannual climate variation bring a considerable reduction in crop productivity (IPCC 2014; Mall et al. 2017; Siebert et al. 2017). The above is derived from the physiological stress that crops suffer as a consequence of climate variability. In general, high temperatures reduce photosynthesis, increase respiration and transpiration, and affect hormonal regulation and secondary metabolism of the cultivated plants. This leads to lower growth and production rates: in tomatoes, temperatures higher than 34 ◦ C reduce the formation of flowers, while in celery, lettuce, and spinach, temperatures above 35 ◦ C inhibit seed germination; wheat production can drop by up to 6% per degree Celsius of air temperature

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Fig. 1.1 Illustration of plant response to different biotic and abiotic factors. (Elaborated and modified from Qaderi and Reid (2009))

increase (Nascimento et al. 2000; Timsina and Humphreys 2006; Restrepo-Diaz et al. 2010; Jarma-Orozco et al. 2012; IPCC 2014). The combined effect of high temperatures and low water availability causes an increase in atmospheric evaporative demand, as well as a reduction in soil humidity. This, in turn leads crops to suffer from hydric stress and reduced photosynthetic efficiency due to damage to the photosynthetic apparatus (Oliver et al. 2010; Sunkar 2010). For example, high temperatures and hydric pressure could minimize wheat production by up to 61% in some regions of India by 2050 (Vashisht et al. 2013). Given the above, microclimate management within the crop area using different agricultural management practices can contribute to reducing or mitigating the possible effects of climate change, initially on a local scale, but which as a whole can mean the continuity of food production to meet growing demand worldwide.

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1.2.2 Monoculture Vs. Polyculture: The Case of Maize Rainfed agriculture represents around 75% of the world’s cropped land (Reddy and Syme 2014). The practice is carried out by small producers in reduced areas making it highly vulnerable to climate variability. The management practices used in rainfed agriculture, therefore, can play a determining role in the economy of natural resources within these systems. Cropped areas, small-holdings or any other production system on any scale can be considered a mini-ecosystem, in which interactions established between all its components are, in no small degree, influenced by the microclimate evolving from the various production and management practices put into use (Hoy 2015). In this way, water retention, soil and air temperature, among other factors, will not be the same in a monoculture system (which also implies more significant demand for resources such as water, nutrients, fertilizers, and pesticides) as in a polyculture system (Pino et al. 2000; Pérez-Hernández et al. 2020). Maize is an excellent example of this, cultivated in Mexico under a wide variety of highly contrasting environments in terms of altitude, rainfall, humidity, soil, and temperature, leading to the development of different management practices and, as a result, the development of 64 breeds (CONABIO 2011; Mariaca 2011; González-Merino and Ávila-Castañeda 2014; Weerarathne et al. 2017). One of the primary crop practices developed in rural agriculture is a polyculture system called “milpa”. In the milpa system, maize is grown in combination with beans (Phaseolus spp.) and squash (Cucurbita spp.), although these last two components may vary depending on the particular geographical region (Aguilar et al. 2003; Mariaca 2011). It is well known that this combination provides a varied and balanced diet for farmers; however, it also brings ecological advantages to the system. These advantages include: the fixation of atmospheric nitrogen through a symbiosis between Rhizobium spp. and the bean plant roots, as well as soil water retention and weed control by the squash. The maize, in turn, provides support for the beans, acts as a physical barrier that prevents the spread of some diseases, and promotes the conservation of local agrobiodiversity (Nigh and Diemont 2013; Wang et al. 2017). Despite this, little is known about the microclimatic modifications involved or their potential advantages for the physiological performance of maize and, ultimately, its productivity. Also, the practice has been losing ground to public policies that prefer and encourage the implementation of monoculture systems. Pérez-Hernández et al. (2020) tested microclimate variation in maize cultivation in the southeast of Mexico. They compared a monoculture system to the milpa system, which combined maize, bean, and squash (Fig. 1.2) and looked at the effect of microclimate variation on the physiological performance of the maize. Ninetynine days after sowing the maize (close to harvest) when the bean and squash plants were fully developed, they found that the microclimate conditions were more suitable for the physiological development of maize in the milpa system. The milpa registered lower air temperature and vapor pressure deficit than monoculture. The most significant difference was that in the milpa system, water in the soil was close to saturation with a volumetric water content approximately 45% higher than in the

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Fig. 1.2 Images that show (a), maize plants under monoculture treatment, and (b) under milpa treatment, which combines maize with bean and squash Table 1.2 Maximum microclimate conditions recorded at 14:00 inside the maize crop area under two management schemes Variable/management Air temperature (◦ C) Soil temperature (◦ C) Soil volumetric water content (m3 /m3 ) Vapor pressure deficit (kPa)

Monoculture 38.8 35.3 0.18 3.4

Milpa 37.5 30.9 0.33 3.0

Difference in the milpa −1.3 −4.6 +0.15 −0.4

Pérez-Hernández et al. (2020)

monoculture system. Air and soil temperatures were also close to 14% lower in the milpa system compared to monoculture (Table 1.2). It should be noted that the rainfall in the experiment zone was 135.1 mm over 4 months; however, the high humidity (100% for more than 12 h during the afternoon and night) allowed water condensation to form on the leaves, which drained off and moistened the soil. The cover provided by the squash leaves helped to reduce the direct impact of solar radiation on the ground, lowering its temperature and, consequently, water evaporation. The above is reflected in a 25% increase in total daily CO2 assimilated through photosynthesis in the maize plants under the milpa system compared to the monoculture. This may have been possible due to the milpa plants having sufficient water in the soil to compensate for the loss by transpiration, thereby maximizing stomatal opening time. Although transpiration in the milpa plants was 21.7% higher

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than in the monoculture, water use efficiency was the same in both cases; that is, maize plants under both management systems lost the same amount of water per CO2 molecule assimilated; however, higher assimilation of CO2 in the plants under the milpa system may represent a higher production of maize grain compared to the monoculture. Given the degree of microclimate modification produced by the milpa management system, this practice may be an excellent alternative to help mitigate the possible effects of climate change in rural agriculture. The milpa system contributes to the incorporation of nutrients, water retention even without rain, and lower soil temperature, which are more favorable conditions for the physiological performance of maize in comparison with the monoculture system. In addition, the wide variety of possible combinations of species grown with maize in the milpa system helps to promote the conservation of local biodiversity as well as enabling small producers to reach food sustainability by obtaining a balanced diet from the same plot.

1.2.3 Agroforestry in Maize and Cocoa Production Systems Although the milpa system for the cultivation of maize in combination with other vegetable species offers microclimatic advantages that enhance the physiological performance of the crop, for it to become established, small portions of the forest must first be leveled and in some cases burned. These areas are used for up to 5 years, after which period the land is abandoned for a varying length of time to encourage its natural regeneration before the next clearing and use, known in some areas as lying fallow (Nigh and Diemont 2013; Rodríguez et al. 2016). Under a good management scheme, contrary to what might be thought, the milpa can favor nutrient dynamics, natural regeneration, and increased biodiversity (Rodríguez et al. 2016). In this sense, agroforestry can contribute to the design of management schemes associated with the milpa and other maize and vegetable production systems and, in general, any surface destined for food production. Agroforestry systems are considered one of the most essential practices in the tropical regions of the world (Montagnini et al. 2015). The presence of trees can significantly and beneficially modify the microclimate within the growing area for the cultivated species. In addition to restoring and improving soil fertility through the incorporation of tree species with the capacity to fix atmospheric nitrogen, optimize water uptake and retention in soil, reduce air temperature and wind speed, and provide a habitat for organisms that contribute to biological control (such as birds and insects), among other benefits (Murgueitio et al. 2015; FAO 2017). Under a simple scheme of living fences or the delimitation of cropland (Fig. 1.3), tree species can be established in tropical regions that contribute not only to the aforementioned gains but also help to generate biomass with higher nutrient content for (1) human and animal nutrition, (2) incorporation into the soil by direct application or through composting processes, and (3) wood production. Depending on geographical location, fruit species such as Mangifera indica L.,

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Fig. 1.3 Illustration of an agroforestry system showing how growing space is leveraged through the establishment and combination of different tree species that contribute to the production of food for human (fruits) and animal (foliage, seeds) consumption, wood, as well as provide shade for the animals. Foliage obtained by pruning can be incorporated into soil to provide nutrients within the growing area; the simple presence of trees helps protect crops against wind, runoff, pests and diseases, stabilizes and helps retain water in the soil. The main growing area can be used for the establishment of different species, as in the milpa system, which combines maize, beans and squash

Psidium guajava L., and Tamarindus indica L. can be incorporated into the first and second group, while species such as Samanea saman (Jacq.) Merr., Enterolobium cyclocarpum (Jacq.) Griseb., Crescentia cujete L., Gliricidia sepium (Jacq.) Kunth ex Walp., Leucaena leucocephala (Lam.) de Wit or Guazuma ulmifolia Lam., Brosimum alicastrum Sw. can be incorporated for animal consumption. In all cases, these species offer shade and edible fruits for people and animals, forage with high nutrient content for livestock feeding or incorporation into the soil within the farming area, and in some cases, fruits of traditional use in different regions. Within the third group, species such as Swietenia macrophylla King, Cedrela odorata L., Tabebuia rosea (Bertol.) DC. can be found (Hiwale 2015a, b; Murgueitio et al. 2015). However, perhaps one of the best examples of agroforestry systems in the world is related to the production of cocoa (Theobroma cacao L.), a species commonly grown under the shade of other trees, mainly timber or fruit trees (Soto et al. 2008). Poor shade management of these trees, through the pruning (or absence of it), can lead to various problems such as the presence of fungal diseases; if the canopy is very closed (due to a combination of lack of pruning of shade trees and excess density of both cocoa and shade trees), less light will enter the growing area, causing

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poor air circulation, high humidity, and lower temperature than on the outside, conditions that create an extremely favorable habitat for the growth of fungi such as Moniliophthora roreri, one of the leading causes of depleted cocoa production. With this in mind, Jiménez-Pérez et al. (2019) tested the effect of canopy management variation on the microclimate within shaded cocoa plantations and how this is related to hydric status and cocoa productivity. The authors’ selected open canopy cocoa plantations (Leaf Area Index = 1.6) and closed canopy cocoa plantations (Leaf Area Index = 4.2) and characterized the microclimate inside the cultivation area, as well as the hydric state of the cocoa during two seasons (dry and rainy) in one year. They found that the amount of light received inside the canopy was up to 93.7% and 92.3% lower under closed canopy conditions during the dry and rainy seasons, respectively. This implies a decrease of up to 11% in vapor pressure deficit in the closed canopy cocoa plantations compared to open canopy (Fig. 1.4a), while air humidity increased by up to 8.6% in both seasons (Fig. 1.4b). Although the air temperature was similar under both canopy conditions (Fig. 1.4c), soil temperature was up to 2.9 ◦ C lower under closed canopy conditions (Fig. 1.4d). Despite the above, sap flow was higher throughout the day in cocoa trees under closed canopy conditions than open canopy, suggesting a higher water demand in closed-canopy conditions. Furthermore, it implies that cocoa trees under extreme shade keep stomata open for more extended periods in an attempt to assimilate

Fig. 1.4 Average microclimatic variables recorded between 11:00 and 16:00 in open canopy and closed canopy cocoa plantations and open space (reference). (a) vapor pressure deficit (VPD, kPa), (b) relative air humidity (%), (c) air temperature (◦ C), (d) soil temperature (◦ C)

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more CO2 , however, without sufficient light the photosynthetic process is less than optimal. This is evident in the production reported for each system, which was up to 56% lower in shaded cocoa plantations (2633 kg/ha/year−1 vs. 1133 kg/ha/year−1 for open and closed canopy plantations, respectively). The study by Jiménez-Pérez et al. (2019) demonstrates that proper management of canopy shade through pruning and thinning contributes to the development of microenvironmental conditions favorable for cocoa production, as observed by other authors who compared the physiological performance of cocoa under contrasting light conditions. Gómez (2002), for example, reported better photosynthetic performance of cocoa plants when they received between 40% and 60% of total incidental light, similar to the results recorded under open canopy conditions in the previous experiment.

1.3 Climate-Smart Livestock 1.3.1 Impact of Livestock on Climate Change The ecosystem diversity that once characterized Latin America has been replaced by vast expanses of monoculture pasture. This represents a threat to numerous species of wild animals and plants, prompting the invasion of generalist species and interrupting pollination and seed dispersal, as well as raising the threat of new zoonotic diseases and the reemergence of tropical diseases. Nevertheless, it is possible to reverse these effects through improvements to the sustainable management of grazing, incorporating variables aimed at organic livestock production (NahedToral et al. 2013a). Such measures can increase the diversity of flora and fauna species, improve the quantity and quality of forage, wildlife habitat, and lessen forest fires’ risk by reducing the amount of fire-susceptible biomass. For example, the browsing of ruminants over shrubs and tree species acts as biological control of undesirable species. It stimulates the maintenance of grasslands by reducing the shade from trees and scrubs (Bermejo et al. 2012). Moreover, ruminants function as seed dispersers through the deposition of feces on the land, which modifies the presence and distribution of certain plant species. In humid and sub-humid tropical conditions and under low to moderate grazing intensities—which predominate in the south-southeast of Mexico—it is possible to encourage greater plant species diversity in grasslands, as shown by the intermediate disturbance hypothesis (Gao and Carmel 2020). Furthermore, the transformation of natural vegetation into large areas of pasture for the development of extensive livestock farming, in addition to modifying the microbiological properties and macrofauna of the soil, reduces the content of organic matter (OM), nitrogen (N), phosphorus (P) and potassium (K), accelerates erosion and promotes compaction, and it is considered a significant cause of the depletion of soil organic carbon (SOC) reserves (Pulido et al. 2017; Angst et al.

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2018; Poulton et al. 2018; Fornara et al. 2020). In the tropics, 25% to 27% of SOC can be lost by transforming forests to cropland (McSherry and Ritchie 2013; Kopittke et al. 2017). It is also known that affecting the composition of biotic communities and microbial activity reduces soil organic matter (SOM) storage and the cycling of nutrients (N, P, K, sulfur (S)), affecting plant growth and productivity. These changes are exacerbated by the effects of climate change (CC). For example, higher environmental temperature reduced precipitation, and a decrease in soil moisture affects the efficiency of microorganisms to incorporate OM into the soil and store carbon (C) (Tardy et al. 2015; Geyer et al. 2016 Villanueva-López et al. 2019). The demand for water in livestock farming varies according to the production system, its productivity, and geographical region. The amount of water required to produce one kilogram of meat varies depending on the production system. Intensive systems (IS) have a higher demand than extensive systems (ES) due to the use of water for the intensive production of grain used in animal feed. In the humid and sub-humid tropics of Mexico, IS for calf rearing (cow-calf) and fattening demand more significant quantities of water compared to ES, due to the high impact (75%) caused by irrigation of the maize crop, the manufacture of fertilizers (14%) and the drying and transportation of maize (11%). Meanwhile, in ES, the highest impact on water depletion is the greater demand to generate electricity for water cooling (31%) and maize production (29%) (Rivera-Huerta et al. 2016). Extensive livestock farming based on monoculture pasture also contributes to water pollution due to the indiscriminate use of synthetic fertilizers on grasslands. Less than half of the N and P applied as fertilizers is absorbed and used by the grasses; the rest is removed through surface runoff or leaching into the subsoil, thus contaminating water sources. Also, water runoff from agricultural fields can result in excess sediments, nutrients, and pesticides in surface water bodies, making it a significant contributor to eutrophication. This is common throughout Latin America, where only 20% of the water used for cleaning excreta undergoes treatment for reuse (Rivera-Huerta et al. 2016). Adaptation and mitigation strategies are needed, therefore, to make rational and efficient use of water for the development of climatesmart livestock farming, not only to help farmers adapt to a changing climate but also to provide fresh water for human consumption. For example, organic livestock farming can be adapted to allow cattle to deposit 170 kg of N per hectare per year, which is achieved with no more than two dairy cows per hectare, or their equivalent, to avoid contamination of the soil and surface and groundwaters with N, which becomes nitrate, and with diverse microorganisms contained in manure (IFOAM 2009). The unprecedented transformation of forest cover into pasture since the midtwentieth century has contributed to a global rise in greenhouse gases (GHG) emissions from the livestock sector of 51% and 117% in developing countries (Herrero et al. 2013; Chang et al. 2015). The primary GHG from livestock are CO2 , methane (CH4 ), and nitrous oxide (N2 O); these gases are responsible for between 12% and 18% of total atmospheric emissions (Rosenzweig et al. 2016; Hu et al. 2019). The CH4 produced by enteric fermentation in ruminants is an estimated

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emission of 1.6 Pg CO2 eq, while manure management is 0.25 Pg CO2 eq (Herrero et al., 2013). In the case of N2 O, the highest contribution is from agricultural land, with around 65% of current emissions due to synthetic nitrogen fertilizers (WagnerRiddle et al. 2017). While there is little information about the effect of climatic conditions on N2 O emissions, it appears that the more humid the environment, the more N2 O is emitted.

1.3.2 Impacts of Climate Change on Livestock Climate variability has a direct and indirect impact on livestock. In Mexico, particularly in the south-southeast region, the existence of fragile ecosystems, high poverty levels, and development strategies in discord with local conditions, among other factors, have made the region one of the most susceptible to severe damage from the adverse effects of climate change. Large expanses of pasture in these regions have been affected and degraded by the impact of drought and flooding, causing an increase in animal mortality, a higher rate of disease, loss of pasture, forest fires, loss of production infrastructure, and diminished assets for farmers (Schroth et al. 2009). The Intergovernmental Panel on Climate Change (IPCC) warns that, if action is not taken to reduce GHG emissions, by 2100, a temperature increase of 1.8 ◦ C to 4 ◦ C (0.1 ◦ C per decade) can be expected, which will strongly affect current precipitation patterns. Similarly, sea levels are foreseen to rise by 10 cm to 80 cm, increasing the risk of flooding in low-lying areas, along with more frequent and prolonged droughts. Together, all of these changes can limit crop growth and yield and, at the same time, render the majority of living species unable to adapt to CC. Water scarcity exacerbates soil degradation and delays the establishment and development of grass species, contributing to desertification and biological diversity loss (Smith et al. 2016). On the other hand, the root biomass of grass species responds differently to the impacts of CC. While some grass species such as Brachiaria brizantha are unable to survive prolonged periods of drought, others (e.g., Cynodon nlemfuensis) succumb to floods caused by tropical storms and hurricanes, which are becoming more intense and frequent. In animals, lack of water decreases productivity (milk, meat) and increases their susceptibility to disease. CC is expected to increase the ambient temperature by 2 ◦ C to 4 ◦ C, depending on the biome; rainfall will decrease by 5% to 15%. The interaction of these conditions with ambient CO2 concentrations will cause, in tropical regions, an increase in pasture biomass production (aerial and underground) by up to 15%, due mainly to an increase (10%) in OM decomposition, C cycling, and storage (5%) and a reduction in N mineralization (10%) (Parton et al., 1995). This change in pasture production represents the availability of forage that could be used efficiently for animal production. Care must be taken to optimize the use of metabolizable energy (ME), which may eventually be available to the animals and which is high in the tropics (9.5–12.5 MJ ME/Kg dry matter) (Herrero et al. 2013). In production units,

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Fig. 1.5 (a) Livestock grazing system under the influence of shade from Cedrella odorata trees; (b) monoculture pasture livestock system (unshaded) in the tropical south-southeast region of Mexico

adaptation measures must be taken that allow changes and adjustments to pasture resting time and management of grazing intensity to avoid harvesting mature forage of low nutritional quality. One strategy is to strengthen hybrid (crop-livestock) production systems by implementing trees and scrubs in combination with the grass and which are used as food supplements. These systems generally allow higher productivity and forage quality than monoculture forage crops (Valladares and Niinemets 2008; Pang et al. 2019). Furthermore, to achieve better animal welfare conditions in the face of CC, it will be necessary to change the management of shadeless pasture, under which conditions animals are susceptible to heat stress, while pasture with tree cover improves animal welfare by reducing heat stress; besides, the cattle spend more time ruminating and resting, which has a positive effect on productive and reproductive indicators (Valladares and Niinemets 2008; Calle et al. 2012; Pang et al. 2019) (Fig. 1.5).

1.3.3 Climate-Smart Livestock: Opportunities to Increase Productivity and Sustainability Climate-smart livestock (CSL) is part of climate-smart agriculture (CSA). CSA is neither a new agricultural system nor a new set of practices. CSA is an approach to address the challenges implied by the interconnection of three areas: a) ensuring food security through productivity and income of the rural population; b) adapting to CC, and c) contributing to the mitigation of CC (FAO 2010). CSL faces the challenge of interconnecting these areas as a means of dealing with food security in a changing climate, with the aim of improving food security for the population, helping communities to adapt to CC and contributing to the mitigation of CC by adopting reasonable practices, developing policies, and mobilizing the necessary

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finances to build resilient systems (Nahed-Toral et al. 2013b). To that end, CSL shares principles and objectives with the concepts of sustainable development and a green economy. Its objective of food security and contributing to the conservation of natural resources also links it to the concept of sustainable intensification and with the agri-food chain approach (FAO 2010).

1.3.3.1

Principal Strategies for Achieving Climate-Smart and Resilient Livestock in the Tropical Region of Mexico

Farmers have already developed some local strategies to adapt to various environmental and climatic changes. However, the high demand for animal food products, population increase, and the substantial environmental impacts caused by agriculture and livestock have meant that such strategies are not always successful. For this reason, actions and mechanisms must be implemented to mitigate and adapt to CC and help to reverse the environmental impacts and reduce GHG emissions resulting from livestock activities, as well as restore the functionality of ecosystems in the medium and long term. Such actions must use natural resources more efficiently, prioritize biodiversity conservation and the preservation of native forest habitat within livestock landscapes, ensuring the goods and services they provide in the long term, maintaining or restoring tree diversity within the livestock systems, and retaining other types of tree cover in those systems to improve environmental sustainability, landscape connectivity and habitat availability (Balvanera et al. 2002; Barlow et al. 2016). Agroforestry Systems, which comprise a wide range of management strategies—including silvopastoral systems, the use, and management of acahuales and good livestock practices—are resilient and sustainable alternatives for agriculture and livestock production—understanding resilience in an ecological sense as the capacity of a system to respond to disturbances and recover through its processes and feedback mechanisms, which interact with decision-making and environmental processes (Holling 1973). In this context, silvopastoral systems (SPS) allow the production of food in a sustainable way. SPS effectively buffers the yield and quality of grasslands against the impacts of CC, especially in the reduction of water availability, since they integrate woody perennials (trees and shrubs) with grazing pasture under a comprehensive management system. They also provide more nutritious and easily accessible food sources to the livestock (branches, flowers, fruits, and shoots of native species), which helps to reduce excessive grazing and slow down soil degradation. These systems encourage the efficient use of water, electricity, and space, making them more productive, profitable, durable, and CC resistant, capable of guaranteeing food security and attenuating heat waves and the effects of hurricanes (Calle et al. 2012). This has caused silvopastoral systems to receive more attention from farmers and public policies in recent years (Murgueitio et al. 2011; Portela Lima et al. 2017). The most common SPS in the tropical regions of south-southeastern Mexico is, in order of importance: Living fences (LF), scattered trees in the pasture (STP), grazing

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Fig. 1.6 Most common silvopastoral systems in tropical regions of Mexico. (a) monospecific living fences with Gliricidia sepium; (b) multistrata living fences with G. sepium (lower stratum) Tabebuia rosea (upper stratum); (c) Elaeis guineensis; (d) grazing in forest plantations (Cedrela odorata, Swietenia macrophylla and Tabebuia rosea); (e) grazing in fruit plantations (Citrus limon, Citrus sinensis); (f) grazing in oilseed plantations (Cocus nucifera)

in plantations of forest species, fruit trees and oilseed species, alley cropping and protein banks (Figs. 1.6 and 1.7). From the point of view of CC mitigation, trees in SPS have the potential to improve the physical, chemical, and biological properties of soil due to atmospheric N fixation through symbiotic associations and the input of plant litter (fallen leaves, branches, stems, roots, and their exudates). Over time, the deposition of fallen

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Fig. 1.7 Most common silvopastoral systems in tropical regions of Mexico (continuation). (a) scattered trees in pasture; (b) alley cropping; (c) protein bank of Morus alba; (d) protein bank of Gliricidia sepium; (e) protein bank of Tithonia diversifolia; (f) protein bank of Hibiscus rosasinenis

material increases the surface and ground OM content, cation exchange capacity, and the availability and recycling of nutrients such as N, P, and K, among others, which help to decrease the rate of soil degradation (Angst et al. 2018; Poulton et al. 2018; Aryal et al. 2019). In that regard, Villanueva-López et al. (2015)—in a study carried out in SPS with live fences of G. sepium and in livestock systems in monoculture pasture (MP) in the humid tropics of southeastern Mexico—found that the soil of SPS had higher contents of OM (5.7% vs. 4.9%) and N (0.29% vs.

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0.25%), and lower pH (7.23 vs. 7.53) and apparent soil density (1.3 vs. 1.5 g cm−3 ) values compared to the MP livestock systems. In SPS with STP in the same tropical region, Aryal et al. (2019) also reported that soils had higher organic carbon content (5.7% vs. 3.7%) and electrical conductivity (72.7 vs. 48.9 ms m−1 ), and lower pH values (6.2 vs. 6.9) and apparent soil density (0.9 vs. 1.1 g cm−3 ) compared to MP livestock systems. Both studies indicate that live roots contribute with approximately 11% of C uptake by plants. SPS has also been recognized for its potential to mitigate CC, given that they can store large quantities of C compared to MP livestock systems. This is because the tree component plays a vital role in atmospheric CO2 absorption through photosynthesis and C storage in aerial biomass, plant litter, and soil reservoirs and functioning as CO2 sinks. In the humid and sub-humid tropics of Mexico, many examples demonstrate the advantage of SPS over MP livestock systems to mitigate the effects of CC by C capture. Along these lines, Morales et al. (2020) reported for the sub-humid tropics of southeastern Mexico changes in C storage above and below ground and its relationship with the production of fine roots in SPS with LF, STP, and MP livestock systems. The SOC content was significantly higher in SPS (LF: 2.4%, STP: 3.1%) than the MP livestock systems (1.6%). The fine roots production differed between the SPS (LF: 27.8, STP: 45.4, and MP livestock systems: 9.4 g m−2 year−1 ) and was positively correlated with SOC content. Similarly, Aryal et al. (2019), in the same tropical region, also quantified C storage above and below ground in SPS with STP and in MP livestock systems. They found that SPS with STP stored 104.82 Mg C ha−1 , compared to MP livestock systems (58.63 Mg C ha−1 ). C in aerial biomass in the SPS with STP varied from 11.53 Mg C ha−1 to 14.63 Mg C ha−1 . SOC concentrations were higher in SPS, while apparent soil density was higher in MP livestock systems. In a study under dry tropical conditions in the Yucatán Peninsula, CasanovaLugo et al. (2018) evaluated C concentration and storage in tree biomass above and below ground and in the soil in forage banks of Leucaena leucocephala (Lam.) de Wit, Guazuma ulmifolia (Lam.) and a combination of both species (Fig. 1.8). They found that C concentration was higher in stems (45.1%) and roots (44.9%) compared to foliage (43.4%); in addition, total C storage in soil was higher in the forage banks with only legumes than in the mixed-species banks (35.7 vs. 30.8 t C/ha). They concluded that, in tropical regions, forage banks are an essential option for improving C capture in plant biomass and in soil, thereby reducing CO2 emissions from the soil into the atmosphere and providing a high-quality diet that enhances the efficiency of the animal production system. A study by López-Santiago (2018) in the humid tropical region of the state of Tabasco in southeastern Mexico evaluated C storage in biomass in STP consisting of Brachiaria brizantha grasses and in a livestock system in a MP of Brachiaria decumbens (Fig. 1.9). It found that SPS with STP stored a total of 130.8 Mg C ha−1 , while the livestock system in MP stored a total of 96.9 Mg C ha−1 . In a parallel study in the same systems, the author found that the CO2 fluxes from the soil were different, with the MP showing higher values (36.01%) compared to the STP

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Fig. 1.8 Protein banks in dry tropical conditions in southeastern Mexico: (a) Leucaena leucocephala; (b) Guazuma ulmifolia

Fig. 1.9 (a) Silvopastoral system with trees scattered in pasture; (b) livestock system in monoculture grass (unshaded) in the humid tropics of southeastern Mexico

system. The highest releases presented in the rainy season with 36.98% and 38.84% for STP and MP concerning the early dry season. In the same region, VillanuevaLópez et al. (2015) also quantified the existence of C in the biomass of trees above and below ground and the soil in SPS with LF of G. sepium and MP livestock systems. The authors found that SPS with LF stored a more significant amount of C in the soil than MP livestock systems (119.82 vs. 113.34 Mg C ha−1 , respectively). This means that the presence of G. sepium trees in the LF contributed 6.48 Mg C ha−1 (5.7%) of the total C stored. In addition, they found no variations in CO2 between systems. However, fluxes were higher in the rainy season compared to the dry season. Similarly, Adame-Castro et al. (2020) also evaluated CO2 fluxes from the soil in two SPS: one with L. leucocephala and C. plectostachyus, and another with L. leucocephala and P. maximum, under sub-humid tropical conditions in the south of Quintana Roo, in southeastern Mexico. They found that CO2 fluxes from soil were similar in both systems with values of 6.0 ± 0.14 and 6.1 ± 0.12 μmol CO2 /m−2 /s, respectively. However, fluxes in the L. leucocephala and P. maximum system were 12.5% higher in the rainy season compared to the dry season.

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Table 1.3 Nutritional composition (%) of Leucaena leucocephala and grasses in two silvopastoral systems in the sub-humid tropics of southern Quintana Roo, Mexico Parameters (%) Crude protein Neutral detergent fiber Organic matter Ash

L. leucocephala + P. maximum cv. Mombasa L. leucocephala + C. plectostachyus Legumes Grass Legumes Grass 19.5

7.4

22.4

9.2

66.6

74.2

68.5

75.6

89.9

87.0

89.9

89.2

7.0

11.3

6.5

8.0

Table 1.4 Biomass yield (g DM/m−2 ) of Leucaena leucocephala (Ll), Panicum maximum cv. Tanzania (Pm) and Brachiaria brizantha (Bb), in pure and mixed crop systems Treatments Ll (pure) Pm (pure) Bb (pure) Ll + pm (mixed) Ll + bb (mixed) Ll + pm + bb (mixed) SE

Ll 691.4

Pm

Bb

368.4 a 557.0 a 596.6 520.7 684.1 81.3

115.7 b 152.0 b 32.2

115.8 b 191.4 b 29.7

Total 691.4 b 368.4 c 557 bc 712.3 b 636.5 b 1027.5 a 143.2

Means followed by different literals in the row indicate significant statistical differences (P ≤ 0.05). SE, standard error

Another alternative for the development of resilient livestock is to increase forage yield and quality by including tree species with potential forage in pastures, which in turn can help reduce overgrazing and slow down soil degradation. In this context, in the sub-humid tropics of southern Quintana Roo, Mexico, it was found that SPS of L. leucocephala with grasses such as P. maximum cv Mombasa and C. plectostachyus reported forage yields that fluctuate between 10 and 12 t DM/ha/year, under rainfed conditions and gleysol soil. The nutritional quality of these forage species varies based on the frequency of use by the animals, which is from 3 to 5 days of grazing in each paddock with rest periods ranging from 30 to 50 days (Montejo-Martínez et al. 2020) (Table 1.3). Another study carried out in this same tropical region by Aldava-Navarro et al. (2017) in a SPS of L. leucocephala with P. maximum cv Tanzania and B. brizantha grasses found that the various associations of L. leucocephala with the evaluated grasses had dissimilar agronomic behaviors (Table 1.4). However, the crude protein content of the grasses increased significantly in all cases (Table 1.5). It should be noted that the biomass yield and crude protein content of L. leucocephala remained uniform during the evaluation period (Tables 1.4 and 1.5).

1 Microclimate Management: From Traditional Agriculture to Livestock. . . Table 1.5 Crude protein content (%) of Leucaena leucocephala (Ll), Panicum maximum cv. Tanzania (Pm) and Brachiaria brizantha (Bb), in pure and mixed crop systems

Treatments Ll (pure) Pm (pure) Bb (pure) Ll + pm (mixed) Ll + bb (mixed) Ll + pm + bb (mixed) SE

Ll 21.6 a

Pm

21 Bb

11.7 c 10.5 c 22.0 a 22.1 a 22.7 a 0.4

15.0 a 14.1 b 0.2

13.0 a 12.0 b 0.3

Mean 21.6 a 11.7 d 10.5 d 18.5 b 17.6 b 16.3 c 0.3

Means followed by different literals in the row indicate significant statistical differences (P ≤ 0.05). SE, standard error

Another alternative for climate-smart livestock is the use of acahuales. In Mexico, Mayan livestock farmers living in sub-humid tropical conditions, with a dominance of deciduous forest, designate areas of secondary vegetation known as acahuales as reserve and grazing zones for multiple purposes. These acahuales provide enough protein for the diet of the animals during the dry season, and despite being subjected to foraging, they preserve a diversity of plant species, similar to the vegetation of the deciduous forest (Albores-Moreno et al. 2020). Furthermore, the menu of forage species consumed by the animals provides them with enough condensable tannins to reduce the emission of enteric methane (31%), thereby contributing to the mitigation of GHG (Albores-Moreno et al. 2018; AlboresMoreno et al., 2019). Furthermore, the use of acahuales influences animal health and welfare, providing shade during foraging, and some plant species consumed can potentially remove rumen protozoa (defaunation). In contrast, others have anthelmintic activity, such as Gymnopodium floribundum, Mimosa bahamensis (Castañeda-Ramírez et al. 2018), and Diospyros anisandra (Flota-Burgos et al. 2020). The decision to maintain acahuales avoids the transformation of larger areas used for pasture cultivation and with that the emission of CO2 due to changes in land use. Farmers also benefit from multiple ecosystem services: wood for the home, bushmeat for self-consumption, honey for commercialization, and conservation of water bodies for their animals (Ortiz-Colín et al. personal communication). Grazing management is another strategy for CSL in tropical regions. However, due to inefficient management practices and the complex interactions of variables such as temperature, rainfall, and soil pH, many pastures are degraded and instead of acting as C sinks become a source of CO2 emissions (McSherry and Ritchie 2013; Poulton et al. 2018; Wiesmeier et al. 2019). These issues can be addressed by managing the intensity and frequency of grazing, animal load, grass species, soil, and climate conditions (Smith et al. 2016). From a mitigation point of view, one of the more apparent benefits is soil C sequestration, which results from reducing grazing pressure as a means of stopping land degradation or rehabilitating degraded land. Similarly, with less grazing, animals tend to choose forage more nutritious and easily digestible, thereby reducing enteric emissions. Restoring degraded grasslands improves soil health and water retention, increasing the resilience of the grazing

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system to climate variability. The risk of lowering grazing pressure is that total meat production per hectare may drop. One strategy to address this risk and promote CSL is through rotational grazing, which adjusts the intensity and frequency of grazing by moving the cattle as pasture becomes available. This strategy also includes replacement of native grasses with a field of pasture of higher quality, productivity, digestibility, and more resistant to drought and disease. In addition to enhancing the nutritional value of pasture, rotational grazing can maintain and provide forage throughout the year, improving the quality and digestibility of forage, boosting system productivity, and reducing CH4 emissions (Kopittke et al. 2017; Hu et al. 2019). In addition, the more frequent rotation of cattle in the grazing paddocks can improve the resilience of these livestock systems to CC. This makes it possible to recover pastures and increase animal productivity (meat and milk). Furthermore, additional gains can be promoted from the rehabilitation of grasslands through payment schemes for environmental services as a possible CC mitigation strategy (Nahed-Toral et al. 2013b). From a CC adaptation viewpoint, SPS are recognized for their critical role in heightening the resilience of livestock productivity by regulating climate variability and modulating the exchange of energy and water between the ground surface and the atmosphere. This improves water quality, provides clean water and acts as a buffer against adverse weather conditions (Jose and Bardhan 2012; Liang et al. 2017). SPS also reduces the speed of runoff, promoting water infiltration, sediment deposition, and nutrient retention. They also decrease by 20% the movement of nutrients and contamination of ground and surface water, due to the capacity of trees to improve water quality since their roots can use the excess nutrients that have been leached below the rooting zone (Anderson et al. 2009; Balesdent et al. 2011). Further to the above, several studies point out that animals benefit from shade. The tree canopy improves the microclimate with more shaded areas, which can enhance grass growth and produce a large amount of combustible biomass, even having a higher fiber and protein content than grass growing in full sun. Likewise, the shade from woody plants in SPS helps to maintain higher humidity in the soil and environment, reducing water loss in the system by evapotranspiration (Rusch et al. 2014; Paciullo et al. 2014; Portela Lima et al. 2017). Finally, SPS can help to preserve biodiversity on both a small-holding and landscape-scale by providing habitat, food resources, connectivity between fragmented landscapes, and as biological corridors for a broad range of plant and animal species that inhabit livestock systems; this drives high animal and plant genetic diversity, essential for the conservation of a wide array of species, decreasing the pressure on forests and jungles and reducing deforestation in tropical regions (González-Valdivia et al. 2016; Barlow et al. 2016). Moreno-Calles et al. (2010) point out that agroforestry systems can contain on average from 31.28% to 97% (including perennials and herbaceous species) of the species in natural ecosystems and have a similar composition. González-Valdivia et al. (2011) found that in the tropical region of southern Mexico, despite agricultural and livestock dynamics having cleared the jungle for pastures, the pastures which contain surviving vegetation maintain half of the flora and fauna species of the original vegetation.

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Villanueva-López et al. (2019) evaluated soil macro-arthropod diversity and plant species richness in eight agroforestry systems (AFS): family garden (FG), shade trees in plantations (ShTP), STP, LF, alley cropping (AC), Taungya systems (TS), slash and burn agriculture (SBA) and grazing plantations (GP), in the humid tropics of southeastern Mexico. They found that plant richness varied among AFS in the following order: FG (108), ShTP (106), STP (32), LF (38), AF (30), TG (16), SBA (0), and GP (2). They conclude that AFS with higher plant species richness are essential agricultural production strategies that increase the diversity and conservation of soil macro-arthropods.

1.4 Conclusions Climate-smart agriculture, which includes the design of agroforestry and silvopastoral systems associated with the production of food from plant and animal origin, can contribute to providing suitable microclimatic conditions for the development of cultivated plants and animals. In addition, these practices offer excellent economy in the use of resources such as water. Also, management practices that include these systems can be quickly adopted by producers since small changes, such as correct pruning in cocoa agroforestry systems, the establishment of polycultures using local species, and the presence of multipurpose trees in pastures, contribute significantly to the retention of water in the soil, reduction of air and soil temperature, provide protection against natural phenomena such as hurricanes and torrential rains, heavy runoff, recuperate or increase soil fertility while generating a diversity of foods of high nutritional quality to improve the diet of both animals and farmers, firewood, etc., in the same area of land. Furthermore, the adoption of these strategies can promote the recovery of degraded ecosystems and improve the structural heterogeneity of the landscape, its connectivity, and function as biological corridors for wildlife, among other ecosystem services. It can undoubtedly become a reservoir of biodiversity. In this way, climate-smart agriculture can be a significant driver for the strategic recovery of food production lands. Similarly, these practices help to reduce greenhouse gas emissions associated with agricultural systems and could even become carbon sinks. This quality is an opportunity for producers to also benefit economically from the sale of environmental services due to carbon sequestration. Clearly, the adoption of climate-smart agriculture will help to reduce the possible effects of climate change that seriously threaten global food production. Simple, low-cost practices that promote microclimatic modification favorable to plants and animals will bring us closer to the goal of food sustainability, particularly in the rural environment.

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Chapter 2

Climate-Smart and Agro-ecological Farming Systems of Smallholder Farmers Nyong Princely Awazi, Martin Ngankam Tchamba, Lucie Felicite Temgoua, Marie-Louise Tientcheu Avana, Abubakar Ali Shidiki, Gadinga Walter Forje, and Barnabas Neba Nfornkah

Abstract The adverse effects of climatic variations and changes (CVC) are real as evidenced by the unprecedented cyclones that have battered several countries in the tropics in recent times, leaving discernible trails of destruction in their wake. Amongst those bearing the brunt of extreme weather events are smallholder farmers who in the majority lead subsistence lives. Within this backdrop, this study investigated sustainable, climate-smart and agro-ecological farming systems of smallholder farmers in the face of adverse climatic variations and changes, with particular focus on agroforestry systems (AFS). Socio-economic and biophysical data was collected from primary and secondary sources. Results revealed that climate parameters (temperature and rainfall) have been experiencing significant variations and changes in the past five decades. The main agroforestry systems practiced by smallholder farmers in the face of adverse climatic variations and changes were agrosilvicultural agroforestry system (44%), silvopastoral agroforestry system (11%), and agrosilvopastoral agroforestry system (25%). The main components of these agroforestry systems were food/cash crops, livestock, and trees/shrubs used for food, fuelwood, fodder, finance and soil fertility improvement. There was a statistically significant (p < 0.01; p < 0.05; and p < 0.10) cause-effect and noncause-effect relationship between smallholder farmers’ practice of agroforestry systems faced with climate change adversities and explanatory variables (number of farms, size of household, income of household, educational level of household head, vulnerability to climate change, resilience to climate change, information

Declaration: We declare as the authors of this study, that all tables and figures are self-developed and not published elsewhere or needing usage permission. Thus, the tables and figures are original. N. P. Awazi () Department of Forestry and Wildlife Technology, College of Technology, The University of Bamenda, Cameroon M. N. Tchamba · L. F. Temgoua · M.-L. T. Avana · A. A. Shidiki · G. W. Forje · B. N. Nfornkah Department of Forestry, Faculty of Agronomy and Agricultural Sciences, University of Dschang, Dschang, Cameroon © Springer Nature Switzerland AG 2022 C. M. Galanakis (ed.), Environment and Climate-smart Food Production, https://doi.org/10.1007/978-3-030-71571-7_2

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accessibility, credit accessibility, land accessibility, and access to extension services) indicating that the practice of agroforestry systems by smallholder farmers in the face of adverse climatic variations and changes is influenced by different environmental, institutional, and socio-economic variables. Drawing inspiration from these findings, it is recommended that decision-makers seeking to promote the practice of agroforestry and reduce the vulnerability of smallholder farmers to adverse climatic variations and changes take into consideration these determinants when implementing policies geared towards improving the livelihood of smallholder farmers. Keywords Smallholder farmers · Climate variations · Climate change · Agroforestry systems · Policy · Cameroon

2.1 Introduction The adverse effects of climatic variations and changes are real as evidenced by the unprecedented cyclones that have battered several countries in the tropics in recent times. Cyclone Idai and cyclone Kenneth that battered several parts of southern Africa, and Cyclone Fani that battered many areas in and around the Indian subcontinent have all left easily discernible trails of destruction in their wake. Mankind, therefore, needs to roll back the specter of global warming and climate change if these extreme weather and climate events are to be prevented from occurring regularly and wreaking mayhem on man and ecosystems (NAS and RS 2014; FAO et al. 2018; Niles and Salerno 2018; Paterson and Charles 2019). This is very much possible if green house gas emissions are reduced and temperatures stay below the 1.5 ◦ C threshold stipulated by the Paris COP21 climate change conference (UNFCCC’s COP 21 2015). For greenhouse gas emissions to stay below the 1.5 ◦ C threshold, bold and swift actions need to be taken by mankind as a whole (IPCC 2018). This implies that climate-smart and sustainable best practices need to be implemented by all stakeholders – stretching from stakeholders involved in the primary sector activities, passing through the secondary right up to the tertiary sector activities (Paterson and Charles 2019). With the agricultural sector bearing the brunt of changes in climate conditions, much needs to be done to alleviate this situation. Amongst the agricultural stakeholders most affected by climate change, are smallholder farmers who constitute over 80% of the farming population in the developing world (FAO 2016; FAO et al. 2018). A major reason for the high level of vulnerability experienced by smallholder farmers in the face of adverse climatic variations and changes is their continuous dependence on unsustainable agricultural practices (Deressa et al. 2009; FAO 2013). Climate-smart and sustainable best practices therefore, need to be taken up by smallholder farmers if their plight is to be alleviated in the face of adverse climatic variations and changes (Jerneck and Olsson 2014). Agroforestry is one

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of the climate-smart and sustainable agro-ecological practices that can contribute enormously to better the livelihood of smallholder farmers in the face of adverse climatic variations and changes (Newaj et al. 2015, 2016; Awazi and Tchamba 2019). Research has proven that African agriculture is dominated by smallholder farmers whose main goal is to achieve food self-sufficiency (FAO 2013; Tankou et al. 2017; Niles and Salemo 2018; Partey et al. 2018). Therefore, measures aimed at enhancing adaptation to and/or mitigation of climate change in smallholder farming systems need to demonstrate that they can increase food production and provide multiple benefits capable of reducing vulnerability and enhancing resilience (Jerneck and Olsson 2014; Appiah et al. 2018; Awazi and Tchamba 2019; Awazi et al. 2019a, b, c). Studies have shown that agroforestry could provide these multiple benefits that allow small-scale farmers to better mitigate and/or adapt to the adverse effects of climatic variations and changes (Bishaw et al. 2013; Nguyen et al. 2013; Amare et al. 2018). Agroforestry has emerged as one of the most successful practices with huge potentials to curb the effects of climate change while at the same reducing vulnerability and enhancing resilience especially in smallholder farming systems (Lasco et al. 2015; FAO et al. 2018; Awazi 2018; Awazi and Tchamba 2019; Awazi et al. 2019b, c). With most programs on climate change adaptation and mitigation across the world focusing on reforestation and forest protection of tropical forests, the necessity for a compromise arises between reducing tropical deforestation and expanding agricultural production to feed the ever-growing population (Mbow et al. 2013; Mbow et al. 2014). It is within this context that agroforestry comes in as a partial panacea capable of solving the reforestation and agricultural production conundrum, owing to its potential to act as a carbon sink while enhancing agricultural productivity simultaneously (Kumar and Nair 2012; Bishaw et al. 2013; Awazi and Tchamba 2019). Presently, the contributions of agroforestry systems to enhance mitigation and/or adaptation to climate change in small-scale farming systems has not been substantially evaluated although there is growing evidence that agroforestry can increase carbon storage, produce more livelihood assets and ecosystems services like food, fodder, fuelwood, soil fertility improvement, finance and many others, which go to improve the adaptive capacity of smallholder farmers in the face of adverse climatic variations and changes (Verchot et al. 2006; Mbow et al. 2013, 2014; Luedeling et al. 2014; Amare et al. 2018; Newaj et al. 2016; Negawo and Beyene 2017). Agroforestry systems’ contribution and potential contribution towards improving adaptation to and mitigation of climate change in small-scale farming systems cannot be overlooked (Noordwijk et al. 2011; Thorlakson and Neufeldt 2012; Bishaw et al. 2013; Awazi and Tchamba 2019). In Cameroon like other parts of sub-Saharan Africa, food-based agricultural systems are a quintessential part of the agricultural sector (Azibo and Kimengsi 2015; Azibo et al. 2016; Kimengsi and Botanga 2017; Awazi and Tchamba 2018; Awazi et al. 2019a). These largely food-based farming systems are primarily controlled by small-scale farmers who constitute above 80% of the farming population

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(Molua 2006). Small-scale farmers’ agroforestry systems make up part of these food-based farming systems (Asaah et al. 2011; Njongue et al. 2017; Munjeb et al. 2018). However, despite the seemingly widespread nature of agroforestry systems in Cameroon, limited research has been undertaken to examine the different components found in these systems as well as the determinants of smallholder farmers’ practice of agroforestry systems in the face of adversities resulting from climatic variations and changes. In this perspective, this examined the determinants and policy ramifications of small-scale farmers’ practice of agroforestry systems in the face of climatic variations and changes. More specifically, the study evaluated the degree of variation and changes in climatic variables (temperature and rainfall) in the past 50 years; identified the different agroforestry systems practiced by smallholder farmers in the face of adverse climatic variations and changes; identified and categorized the different components of agroforestry systems practiced by smallholder farmers in the face of climate change adversities; and examined the factors affecting the practice of agroforestry systems by smallholder farmers in the face of climate change adversities.

2.1.1 Classification of and Major Trade-Offs in Agroforestry Systems 2.1.1.1

Classification of Agroforestry Systems

Different definitions have been given to agroforestry. According to Nair (1989) and Nair (1993) agroforestry can be as a technology or land-use system where shrubs, trees, bamboos, palms (woody perennials) are integrated intentionally on the same unit of land as livestock and/or crops, following different temporal sequence or spatial arrangement. Agroforestry according to Leakey (1996) is defined as a system of managing natural resources in a dynamic and ecologically-based fashion where trees/shrubs are integrated on farms and croplands to increase socio-economic and environmental benefits for all land users through the diversification and sustenance of production. Thus, the essential characteristic of an agroforestry system is the intentional integration of shrub/tree species with other components like livestock and crops. And the components of the agroforestry system should be interacting a lot with each other. The criteria used for classifying agroforestry systems are broad. These criteria include: structural basis (temporal arrangement of the different components; vertical stratification of all the components, and spatial arrangement of the woody component); socio-economic basis (commercial goals and intensity of management); functional basis (the main function of the system, especially the role of the woody (tree/shrub) component); Ecological (ecological suitability of systems, and environmental condition) (Nair 1993; Atangana et al. 2013). Based on these criteria emerge three major agroforestry systems: silvopastoral agroforestry system, agrosilvopastoral agroforestry system, and agrosilvicultural agroforestry system.

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These three major agroforestry systems are made up of several different practices (Rao et al. 2007). The agrosilvicultural system for example, is made up of practices like improved fallows, Taungya, alley cropping, tree gardens, multipurpose trees on croplands, home gardens, live fences (Viswanath et al. 2018). The silvopastoral system on its part is made up of practices like estate crops with pasture, fodder banks, and trees on rangelands. The agrosilvopastoral system consists of practices like home gardens with livestock, multipurpose woody hedgerows, aquaforestry. In sub-Saharan Africa, all these agroforestry systems and practices are ubiquitous (Amonum et al. 2009; Mbow et al. 2013; Awazi and Tchamba 2019).

2.1.1.2

Major Trade-Offs in Agroforestry Systems

Food-based farming systems are a major part of the agricultural setup in subSaharan Africa. As the population grew very rapidly in sub-Saharan Africa, there is increasing urgency to increase food production to meet the growing food needs. It is for this reason that most smallholder farmers – who constitute a bulk of the farming population, have resorted to unsustainable but high food yielding agricultural systems. This therefore, means that sustainability is compromised in favour of high crop yields. It is within this backdrop that agroforestry systems come in. Agroforestry systems are sustainable, agro-ecological and climate-smart, providing the different ecosystem services defined by the MEA report (Millennium Ecosystem Assessment – MEA 2005). These include supporting services (photosynthesis, nutrient cycling, soil formation); cultural services (aesthetics, recreation, spiritual); provisioning services (fiber, food, fodder, fuelwood, timber, water, finance); and regulating services (climate regulation; water quality, disease, flood, and waste control) (Kumar 2016; Mkonda and He 2017). Jose (2009), found that on a spatial scale ranging from local/farm, regional/landscape, and global, agroforestry systems provide a diversity of ecosystem services. Following Jose’s synthesis study, the ecosystem services provided by agroforestry systems at the local/farm scale include control of pests, dispersal of seed/pollination, Net Primary Production, clean water, enrichment of the soil, erosion control/stabilization of the soil, clean air, flood control, biodiversity, cultural/aesthetics, and carbon sequestration. At the regional/landscape level, the principal ecosystem services provided by agroforestry systems are flood control, carbon sequestration, clean air, clean water, cultural/aesthetics, and biodiversity. And at the global scale, agroforestry systems provide ecosystem services like aesthetics/cultural, carbon sequestration, and biodiversity. Thus, the study of Jose (2009) identifies eleven (11) ecosystem services provided by agroforestry systems at different spatial scales, and these services fall within the four major categories of ecosystem services defined by the Millennium Ecosystem Report of 2005. Agroforestry systems therefore, have the potential to ensure the sustainability of farming systems while at the same time enhancing food production (Atangana et al. 2013). There is however some trade-off to be made between the conflicting goals of food production and attaining sustainability (Mbow et al. 2013; Vaast and Somarriba

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2014). This is because while agroforestry systems can ensure the sustainability of agricultural systems, their role in the enhancement of food production is not too significant (Awazi and Tchamba 2019). Nevertheless, agroforestry’s collective role in balancing out both food production and sustainability is far greater than many other farming systems (Andreotti et al. 2017) – thus the necessity for smallholder farmers to practice agroforestry, especially in the present situation of adverse climatic variations and changes.

2.1.2 Tree Diversity and Density in Agroforestry Systems The tree/shrub component is the main component in an agroforestry system. A system can only be considered an agroforestry system if the tree/shrub component is present. Thus, tree diversity and density in agroforestry systems are very important especially in the present condition of adverse climatic changes. Studies undertaking in sub-Saharan Africa have shown that tree densities in agroforestry systems range from as low as 5% tree cover in the Sahelian regions to above 45% tree cover in the humid tropics where oil palm, cocoa and coffee-agroforestry systems dominate (Mbow et al. 2013). Similarly, it was found that in sub-Saharan Africa, 15% of farms have at least a 30% tree cover. Endale et al. (2017) in a study carried out in the semiarid East Shewa region of Ethiopia found that agroforestry systems are characterized by varying levels of tree diversity. According to the findings of this study, 77 tree species belonging to 32 families were identified. The study however, found that tree diversity varied across different land uses (woodlots, home gardens, croplands and line plantings), with the highest diversity occurring in line plantings and the lowest in woodlots. Ajake (2012) in a study conducted in the Cross River State of Nigeria found that a diversity of non-forest and forest tree species was integrated into the farming systems of the indigenous population. In total, 19 indigenous tree species were commonly found in the farming systems of the local population. Zomer et al. (2009) found that tree cover in agricultural lands in sub-Saharan Africa, increased by 2% between the years 2000 to 2010. This increase is however slow when compared to South America (12.6% increase), South Asia (6.7% increase), East Asia (5% increase), Oceania (3.2% increase), and Southeast Asia (2.7% increase) (Zomer et al. 2009). Bishaw et al. (2009) in studies conducted in Ethiopia and Kenya found high levels of tree diversity and density in different agroforestry systems like home gardens, rotational woodlots, alley cropping, live fences, and fodder banks. However, the highland home garden agroforestry system of the Gedeo region of Ethiopia was found to have a higher tree diversity and density than the arid pastoral agroforestry system in the Afar region located in North-Eastern Ethiopia. In Kenya, agroforestry systems in the Meru highlands were found to have more tree diversity and density than their counterparts in the Kibwezi district which is arider. In the same light, Negawo and Beyene (2017) equally conducted a study in the Eastern part of Uganda which demonstrated that coffee-based agroforestry systems were

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characterized by high levels of tree diversity which contributed enormously to tree resources conservation in Eastern Uganda. Although some research has been carried out across sub-Saharan Africa on agroforestry systems and the tree/shrub component integrated there in, little or nothing has been done so far to assess all the different components (trees/shrubs, crops and livestock) found in agroforestry systems, and the determinants and policy ramifications of smallholder farmers’ practice of different agroforestry systems in the face of climate change adversities – the knowledge gap the study filled.

2.2 Agroforestry for Mitigation of and Adaptation to Climate Change Adversities in Smallholder Farming Systems Studies have shown that in the face of climate change adversities, agroforestry systems like scattered trees on croplands, improved fallows, home gardens, cocoabased, coffee-based and banana-based agroforests constitute sustainable, environmentally benign and climate-smart adaptive options practiced mostly by smallholder farmers in the tropics (Latin America. Asia, and Africa) (FAO 2010; Thorlakson 2011; Rao et al. 2011; Mbow et al. 2013; Bishaw et al. 2013; Mbow et al. 2014; Kabir et al. 2015; Awazi and Tchamba 2019). Based on the findings of the aforementioned studies, agroforestry systems can provide several ecosystem services to smallholder farmers’ raising their adaptive capacity to climatic variations and changes. The four ecosystem services laid out by the MEA report (Millennium Ecosystem Assessment report) of 2005 i.e. regulating, provisioning, supporting and cultural services) (MEA 2005), can all be obtained from agroforestry systems. The provisioning services obtained from agroforestry systems (fiber, medicines, food, building materials, wood) can assist smallholder farmers to increase their sources of income – improving their adaptive capacity to climate change (Zomer et al. 2009; Syampungani et al. 2010; FAO 2010; Thorlakson 2011; Noordwijk et al. 2011; Nguyen et al. 2013; Mbow et al. 2013; Bishaw et al. 2013; Kabir et al. 2015). Sub-Saharan Africa has experienced a major decline in soil fertility caused principally by extreme weather/climate events like droughts, floods, and desertification, all triggered by climate change (Mbow et al. 2013). Sustainable agriculture is highly affected by declining soil fertility levels – as such agricultural practices depend largely on the natural fertility of the soil. Top soil erosion is the main cause of soil degradation and this is worsened by the washing away of crop residues and surface litter (Mbow et al. 2013). In sub-Saharan Africa, poor agricultural policies, as well as high cost and scarcity of mineral fertilizers have made sustainable and climatesmart practices like agroforestry to come to the limelight (Mbow et al. 2014). Studies have shown that agroforestry has huge potentials to improve soil fertility (Charles et al. 2013, 2014; Mbow et al. 2013, 2014). Based on the findings of these studies, the practice of agroforestry on farms enhances biological nitrogen fixation by leguminous trees/shrubs and increase soil organic matter. Also, they state that the

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presence of trees on farms improves the cycling of nutrients thereby enriching the soil with organic matter and nutrients while simultaneously contributing towards the improvement of soil structural properties. Thus, trees contribute towards improving organic matter in the soil, soil moisture conservation through the tapping of water from deeper layers of the soil and leaching prevention, as well as nutrients recovery. All these go to maintain soil fertility while enhancing smallholder farmers’ adaptive capacity faced with climate change adversities. In the present dispensation of recurrent extreme weather/climatic events caused by climate change, studies have shown that the yield gap can be reduced tremendously through the practice of agroforestry (Nguyen et al. 2013; Ekpo and Asuquo 2012; Kabir et al. 2015; Awazi and Tchamba 2019). These studies demonstrated the existence of a plethora of successful agroforestry technologies some of which included: rapidly growing trees/shrubs for fuelwood, medicinal trees/shrubs, local fruit trees/shrubs providing added income and nutrition, and trees/shrubs contributing towards soil fertility improvement. Following these studies, it is important to distinguish between simple or less complex agroforestry practices (like intercropping practices, hedgerow, and alley cropping) and complex agroforestry practices functioning more or less like natural forests ecosystems, found in agricultural management systems. This is because in the face of climate change adversities, trade-offs need to be made between crop yields and the sustainability of the system. Faced with adverse climatic variations and changes, simple agroforestry practices are generally less sustainable than complex agroforestry (Awazi and Tchamba 2019). Nevertheless, complex agroforestry systems produce far lesser crop yields than simple agroforestry practices. Thus, in the face of climatic variations and changes, simple agroforestry practices produce more crop yields but are less sustainable while complex agroforestry practices produce fewer crop yields but are more sustainable (Awazi and Tchamba 2019). This therefore, calls for a trade-off in both systems. From the foregoing, agroforestry practices/systems therefore, have the potential and capacity to increase the diversity of agro-ecosystems, provide several assets to farmers, and improve sustainable agricultural production which helps to enhance smallholder farmers’ adaptation efforts in the face of climate change adversities (Charles et al. 2014).

2.3 Materials and Methods 2.3.1 Location of the Study Area The study was carried out in the north western part of Cameroon (Fig. 2.1). North western Cameroon is located between longitude 9◦ 30 E to 11◦ 15 E and latitude 5◦ 4 N to 7◦ 15 N. It covers 17,910 km2 . The climate is the humid tropical highland type with an average annual rainfall of 1500 mm and a mean annual temperature

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Fig. 2.1 Map of the study area

of 20 ◦ C. The vegetation consists of savannah grassland and the soils are ferralitic, vertisols and andosols. Agriculture is the main economic activity with over 90% of the farmers involved being smallholders.

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2.3.2 Sampling Procedure The multiple-phase sampling procedure was adopted for the study: In the first phase, north western Cameroon (the study area) was selected on purpose owing to the existence of diverse agroforestry systems. Non-governmental and governmental authorities working in the ministries of livestock; the environment and agriculture in north western Cameroon as well as some related studies have generally reported the existence of diverse agroforestry systems in north western Cameroon. In the second phase, with the assistance of agricultural extension officials, ten villages were selected in north western Cameroon. Selected villages were Mendakwe, Mankon, Nkwen, Bambui, Bambili, Kedjom Keku, Awing, Akum, Njong and Mbei. The villages were selected based on their socio-economic, biophysical and agro-ecological attributes to get a representative cross-section of all the villages in the study area. In the third phase, focus group discussions were held in the study area with smallholder farmers. Five focus group discussions were held. The ten villages selected were stratified into five strata (based on their socio-economic, agroecological and biophysical attributes) and one focus group discussion was held in each of the five strata, making five focus group discussions in total. Key informant interviews were equally conducted with thirty resource persons. These resource persons included village heads, agricultural extension agents, and officials in the ministries of agriculture, environment, and livestock. Key informant interviews and focus group discussions were conducted just to get a general picture of the study area and to know the targeted group of smallholder farmers for household and field surveys. In the last phase (the fourth phase), random sampling of smallholder farmers was done in the selected villages. In this light, forty (40) smallholder household heads were sampled randomly in the villages of Mankon, Nkwen, Bambui, Bambili, and Akum i.e. 200 smallholder farmers sampled for the five villages; while 30 smallholder household heads were sampled in the Kedjom Keku, Mendakwe, Mbei-Santa, Njong-Santa and Awing villages i.e. 150 smallholder farmers sampled for the five villages. Thus, 350 smallholder household heads were sampled in the ten selected villages. Although, the villages were selected based on their socio-economic, agro-ecological and biophysical characteristics, some villages had a larger population of smallholder farmers than others. This explains why 40 smallholder farmers were sampled in the villages with a larger population of smallholder farmers, while 30 smallholder farmers were sampled in the villages with a smaller population of smallholder farmers.

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2.3.3 Secondary Data Collection Climate data was the main secondary data collected for the study. Data for two main variables of climate was collected (rainfall and temperature). This data was collected from the meteorological station of the regional department of transport for north western Cameroon. Several books and book chapters, articles and research papers having a link with the study’s subject matter were sourced offline and online during the review of the literature.

2.3.4 Primary Data Collection The collection of primary data was done using quantitative and qualitative methods. Household surveys and field surveys (inventories) were used to collect most of the quantitative data. Key informant interviews (KIIs), field observations (FOs), and focus group discussions (FGDs) were also used to complement and confirm the data collected through household and field surveys. Thus, both biophysical and socioeconomic data were collected. During household surveys, semi-structured questionnaires were administered to smallholder farmer household heads – who made up the target population. Household surveys were conducted in December 2018 and January 2019 in the ten selected villages. The survey was conducted by the principal investigator and some team members, with assistance from agricultural extension agents found in each of the ten selected villages. During the administering of questionnaires, mainly the face-to-face interview approach was employed. The household surveys were conducted in December and January because it was the dry season, which facilitated movements to the different study sites; and considering that it was the off-season for farming, which meant that most farmers were at home, thus facilitating the household survey proper. Key informant interviews (KIIs) with purposely selected resource persons and focus group discussions with smallholder farmers in the study area provided general information to complement and confirm the data obtained through household surveys. A semi-structured interview guide approach was employed during key informant interviews (KIIs) and focus group discussions to obtain some salient data. Field surveys (inventories) were also undertaken using data collection sheets for the inventory of trees, crops and livestock in smallholder farmers’ agroforestry plots. Field surveys (inventories of smallholder farmers’ agroforestry plots) provided vital biophysical data necessary for the study.

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Sample Frame, Study Population and Sample Size

For this study, the sample frame consisted of ten villages in north western Cameroon: Mankon (Latitude 6o 00 N; Longitude 10o 06 E; Altitude 1200 meters above sea level – masl), Nkwen (Lat. 5o 98 N; Long. 10o 21 E; Alt. 1350masl), Bambui (Lat. 6o 02 N; Long.10o 21 E; Alt. 1250masl), Bambili (Lat. 6o 01 N; Long. 10o 27 E; Alt. 1500masl), Kedjom Keku (Lat. 5o 97 N; Long. 10o 31 E; Alt. 1350 masl), Mendakwe (Lat. 5o 92 N; Long. 10o 20 E; Alt. 1900 masl), Mbei-Santa (Lat. 5o 78 N; Long. 10o 14 E; Alt. 1700 masl), Akum (Lat. 5o 88 N; Long. 10o 16 E; Alt. 1900 masl), Njong-Santa (Lat. 5o 79 N; Long. 10o 17 E; Alt. 1700 masl), and Awing (Lat. 5o 83 N; Long. 10o 25 E, Alt. 1650 masl). With the help of agricultural extension agents, these villages were selected considering the environmental, agroecological and socio-economic characteristics of the zone of study. A household survey was then undertaken in the selected sites using semi-structured questionnaires. In the villages selected, smallholder household heads constituted the study population. The study population was sampled in such a way as to ensure an adequate sample size (≥30) for each of the selected villages. As a general rule of thumb, in random sampling, a minimum of 30 respondents should be sampled in a given study site to obtain sufficient information that can be used for analysis. It was in this light that a minimum of 30 smallholder household heads was sampled randomly in each of the villages selected.

2.3.4.2

Questionnaire Design for the Household Survey

The study made use of the semi-structured questionnaire approach in order to obtain quantitative information from smallholder farmers. Both open-ended and close-ended questions were incorporated to acquire sufficient information from the respondents (smallholder farmer household heads). The questions were designed to provide answers to the specific objectives of the study. The questionnaire was sub-divided into three main sections. In the first section, the socio-economic and demographic attributes of the respondents (smallholder household heads) were examined. These attributes included a degree of vulnerability and resilience to climatic variations and changes, number of farms, household size, age, marital status, educational level, gender, and household income. The second section emphasized the perceptions of smallholder farmers about variations and changes in climate. And the third section examined smallholder farmers’ agroforestry systems faced with climate change adversities and the components of these systems. Thus, the questions were tailored to attain the study’s secondary objectives. To ensure that the questions posed on the questionnaire were good enough, the questionnaire was tested with non-respondents outside the study area. The reason for the pre-testing of the questionnaire was to gauge smallholder farmers’ comprehension and response to each of the questions posed. The responses from the pre-testing helped the principal investigator and his team to refine and restructure

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the final questionnaire to make it more comprehensible to smallholder farmers’ in the selected villages.

2.3.4.3

Inventory in Agroforestry Plots of Smallholder Farmers

Between May and June 2018, field inventories were carried out to identify the livestock, crop, and tree/shrub species integrated into the agroforestry and nonagroforestry plots of smallholder farmers. These inventories were done by the principal investigator with the help of some team members (including botanists and experts in livestock, fisheries and animal husbandry). The inventories were undertaken to acquire vital biophysical data needed for the study. Field inventories were undertaken in May and June because it was the rainy season, which was the peak period of growth for most crops, and trees/shrubs. Thus, it was relatively easier to identify the different crops and trees/shrubs on the agroforestry farm plots of smallholder farmers at this time. A total of two hundred (200) agroforestry plots of smallholder farmers were surveyed in the 10 villages under study. Garmin GPS 60 was used to take the geographical coordinates (latitude, longitude) as well as the altitude of the surveyed plots. A map was then established showing the spatial distribution of the agroforestry plots of smallholder farmers (Fig. 2.2). Inventories were done on the agroforestry plots of smallholder farmers to identify the trees/shrubs, livestock and crops species integrated into the different agroforestry systems.

2.3.4.4

Characterizing the Agroforestry Practices of Smallholder Farmers Faced with Adverse Climatic Variations and Changes

There exists a scholarship (although few) that have used different data collection methods to characterize smallholder farmers’ agroforestry practices in the face of adverse climate variations and changes. For this study, household and field surveys (inventories) were conducted. During the household surveys, smallholder farmer household heads practicing agroforestry were asked to identify the different agroforestry practices they practiced faced with climate change adversities. Smallholder farmer household heads that practiced agroforestry faced with adverse climate variations and changes usually practiced a combination of agroforestry practices simultaneously. The different agroforestry practices cited by smallholder farmer household heads were then classified into three broad categories of agroforestry systems: agrosilvopastoral agroforestry system, agrosilvicultural agroforestry system, and silvopastoral agroforestry system. Information gotten from household surveys was complemented with that gotten from field inventories of smallholder farmers’ agroforestry plots. Both the agroforestry practices identified by smallholder farmer household heads and the classified agroforestry practices (agroforestry systems) were then coded and imputed into SPSS version 20.0 for descriptive and inferential

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Fig. 2.2 Map showing the distribution of agroforestry plots surveyed

statistical analysis. Little or no research has been done that applies this data collection procedure to characterize smallholder farmers’ agroforestry practices faced with climate change adversities. Most studies conducted across Africa have applied other data collection approaches which are mostly qualitative or based on a

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review of previous literature (Atangana et al. 2013; Bishaw et al. 2013; Negawo and Beyene 2017).

2.3.4.5

Examining the Contribution of Agroforestry Practices to Climate Change Adaptation Efforts in Smallholder Farming Systems

Different data collection methods have been used by various studies to examine the contribution of agroforestry practices towards improving adaptation to climate change in smallholder farming systems. For this study, household surveys were conducted during which smallholder farmers’ perceptions were obtained. During the household surveys, smallholder farmers were asked if they practiced agroforestry or not in the face of climatic variations and changes. Smallholder farmers who said they practiced agroforestry were asked to cite the agroforestry practices they practiced and the different ecosystem services they derived from agroforestry and how the ecosystem services affected their adaptation to climate change adversities. Data collected through household surveys were complemented with information gotten from field inventories, key informants, focus group discussions and direct field observations. The responses of smallholder farmers were then coded and imputed into SPSS version 20.0 for descriptive and inferential statistical analysis. Few or no studies have applied the data collection method explained here, to assess the factors affecting farmers’ adoption of agroforestry as an adaptation measure to climate change adversities. Most studies have been based entirely on a qualitative or review of the literature approach (Negawo and Beyene 2017; Mkonda and He 2017).

2.3.5 Variables of the Study Household income, gender of household head, age of household head, number of farms, household size, level of education, vulnerability to climate variations and changes, resilience to climate variations and changes, information accessibility, credit accessibility, land accessibility, location, and access to extension services were the independent or explanatory variables of the study (Table 2.1). The dependent variables were agrosilvicultural agroforestry systems, silvopastoral agroforestry systems, agrosilvopastoral agroforestry systems, and “no agroforestry” (Table 2.1).

2.3.6 Analysis of Data Collected data was imputed into software packages for descriptive and inferential statistical analysis. Bar charts and percentage indices were the main descriptive statistics computed while the chi-square, t-test, correlation (Spearman rank), and

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Table 2.1 Variables of the study Independent variables Number of farms Household size Age of household head Household income Education level

Description Quantitative Quantitative Quantitative Quantitative Takes value of 0, if none, 1 if Primary, 2 if Secondary, and 3 if Tertiary. Gender Takes value of 1, if male and 0, if No. Vulnerability to climate change Takes value of 1, if Yes and 0, if No. Resilience to climate change Takes value of 1, if Yes and 0, if No. Acc_info (information accessibility) Takes value of 1, if Yes and 0, if No. Acc_credit (credit accessibility) Takes value of 1, if Yes and 0, if No. Access_land (land accessibility) Takes value of 1, if Yes and 0, if No. Acc_exten_svs (access to extension services) Takes value of 1, if Yes and 0, if No. Location Takes values of 1 to 10 for the ten villages under study. Dependent variables Description Agrosilvicultural Agroforestry Systems (AFS) Takes value of 1, if Yes and 0, if No. Silvopastoral Agroforestry Systems (AFS) Takes value of 1, if Yes and 0, if No. Agrosilvipastoral Agroforestry Systems (AFS) Takes value of 1, if Yes and 0, if No. No Agroforestry Takes value of 1, if Yes and 0, if No.

logistic regression were the main inferential statistics computed. As a general rule, before choosing the suitable inferential statistic for the analysis, the normality of the independent and dependent variables was tested using: Qauntile Quantile (QQ) and Probability Plot (PP) diagrams, the histogram with a normal curve, and in particular the one-sample Kolmogorov−Smirnov test. The independent samples t-test (Eq. 2.1) and chi-square test statistic (Eq. 2.2) were used to determine if there was a significant non-cause-effect relationship between smallholder farmers’ practice of agroforestry systems and the continuous and discontinuous explanatory variables respectively. The t-test statistic and chisquare test statistic are computed thus:  Y −X t − test statistic =  where Sp = Sp (m+n) mn

2   2  X−X + Y −Y m+n−2 (2.1)

Where: Y : is the mean of variable Y; X: is the mean of variable X; m: is the sample size of variable X; n: is the sample size of variable Y;

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Sp : is the pooled estimate of the common standard deviation of both variable X and Y Chi − square test statistic (X2) =

(a.d − b.c)2 .N (a + c) . (b + d) . (a + b) . (c + d) (2.2)

Where: a: is the frequency of agroforestry practitioners affected by climate change; b: is the frequency of agroforestry practitioners who are not affected by climate change; c: is the frequency of non-agroforestry practitioners who are affected by climate change; d: is the frequency of non-agroforestry practitioners who are not affected by climate change; N: is the total frequency of all observations. Spearman rank correlation (Eq. 2.3) was run to determine the relationship between smallholder farmers’ practice of different agroforestry systems faced adverse climatic changes, and explanatory variables. The Spearman rank correlation which is the non-parametric equivalent of the Pearson correlation coefficient is computed thus: Spearman rho = 1 −

6(di)2   n n2 − 1

(2.3)

Where: n: is the numbers of pairs of values of variables X and Y; di: is the difference obtained from subtracting the rank of Yi from the rank of Xi; (di)2 : is the sum of the squared values of di. The binary logistic (BNL) regression (Eq. 2.4) was used to assess the cause-effect relationship between smallholder farmers’ practice of agroforestry systems faced with climate change adversities and different explanatory variables. The model permits the analysis of decisions across two categories and predicts the odds of having made one decision or the other.  BNL = ln

ˆ Y ˆ 1−Y

 =∝ +βX

(2.4)

Where: ˆ is the predicted probability for a farmer to practice agroforestry faced with Y: climate change adversities; ˆ is the predicted probability of a farmer not to practice agroforestry faced with 1 − Y: climate change adversities;

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X: is the independent or explanatory variable e.g. age, the income of the household, land accessibility, access to extension services. Microsoft Excel 2007 and the Statistical Package for Social Sciences (SPSS) version 20 were the main data analysis software used. After an in-depth literature review, it was found that little or no research had been carried out adopting the data analysis procedure explained in this study. This is one of the first studies to apply an inferential analysis procedure to assess the causeeffect and non-cause-effect relationship existing between independent variables and smallholder farmers’ practice agroforestry systems in the face of climate change adversities.

2.4 Results 2.4.1 Variation and Changes in Climate Elements 2.4.1.1

Temperature

Temperature fluctuations have been statistically significant in the past five decades (Fig. 2.3). Analysis of temperature data revealed that, in the past five decades, many years have experienced positive anomalies than negative anomalies. The highest positive temperature residuals occurred in the years 1986–1990, 1996–2000, 2011– 2015, and 2016–2018. The highest negative temperature residuals occurred in the years 1981–1985, and 2001–2005.

2.4.1.2

Rainfall

Rainfall on its part has equally fluctuated tremendously in the past five decades (Fig. 2.4). Analysis of rainfall data for the past five decades indicates that many more years have recorded negative anomalies than positive anomalies. The years which recorded the highest negative residuals were 1971–1975, 1991–1995, and 2016– 2018. The years which recorded the highest positive residuals were 1961–1965 and 1966–1970.

2.4.1.3

Rainy Days

Concerning rainy days, high levels of fluctuation were equally noticed (Fig. 2.5). Many of the years recorded negative rainy days residuals than positive rainy days residuals. Negative rainy days residuals were most especially recurrent between the years 1986–1990, 2011–2015, and 2016–2018. Positive rainy days residuals were most especially high between the years 1966–1970, 1976–1980, and 2001–2005.

Five years average temperature residuals

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1.50

1.00

0.50

0.00

-0.50

-1.00

-1.50 1961- 1966- 1971- 1976- 1981- 1986- 1991- 1996- 2001- 2006- 2011- 20161965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2018

Years

Five years average rainfall residuals

Fig. 2.3 Temperature anomalies 1961–2015

500.0

250.0

0.0

-250.0

-500.0

1961- 1966- 1971- 1976- 1981- 1986- 1991- 1996- 2001- 2006- 2011- 20161965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2018

Years

Fig. 2.4 Rainfall anomalies

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Five year average rainy days residuals

20

10

0

-10

-20

-30

-40 1961- 1966- 1971- 1976- 1981- 1986- 1991- 1996- 2001- 2006- 2011- 20161965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2018

Years

Fig. 2.5 Rainy days anomalies Table 2.2 Smallholder farmers’ agroforestry systems faced with adverse climatic variations and changes System Agrosilvicultural agroforestry system Silvopastoral agroforestry system Agrosilvopastoral agroforestry system No agroforestry Total

Frequency 153 40 86 71 350

Percentage 44 11 25 20 100

2.4.2 Agroforestry Systems of Smallholder Farmers in the Face of Adverse Climatic Variations and Changes In the face of adverse climatic variations and changes, smallholder farmers practice different agroforestry systems (Table 2.2). The most recurrent agroforestry system practiced by small-scale farmers in the face of adverse climatic variations and changes was the agrosilvicultural agroforestry system (44%). Next was the agrosilvopastoral agroforestry system (25%). The least agroforestry system practiced by small-scale farmers in the face of adverse climatic variations and changes was the silvopastoral agroforestry system (11%). It must however, be said that some smallholder farmers (20%) faced with climate change adversities did not practice any agroforestry system. Thus, of the 350 smallholder farmers sampled, 80% practiced agroforestry in the face of adverse

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climatic variations and changes, while 20% did not practice agroforestry faced with climate change adversities.

2.4.3 Components of Agroforestry Systems of Small-Scale Farmers Faced with Adverse Climatic Variations and Changes Faced with adverse climatic variations and changes, findings revealed that the most widespread food/cash crops integrated in the agrosilvicultural agroforestry systems of smallholder farmers’ (N = 100) included Zea mays (96%), Phaseolus vulgaris (93%), Vigna unguiculata (87%), Colocasia spp (82%), Manihot esculenta (76%) and Ipomoea batatas (59%). The most widespread tree/shrub species integrated within the agrosilvicultural agroforestry systems of smallholder farmers (N = 100) included Magnifera indica (79%), Vernonia amygdalina (88%), Psidium guajava (99%), Persea americana (98%), Elaies guineensis (53%), Dacryodes edulis (67%), Eucalyptus spp (55%), and Albizia spp (62%). It is therefore noticed that smallholder farmers in north western Cameroon mostly integrate a combination of food crops and fruit trees within their agrosilvicultural agroforestry systems (Table 2.3). About the silvopastoral agroforestry system (N = 35) with two main components (livestock and trees/shrubs), it was found that the main domestic animal species integrated within smallholder farmers’ silvopastoral agroforestry systems were Bos taurus (88.6%), Equus caballus (77.1%), Ovies aries (68.6%), Gallus gallus domesticus (97.1%), and Capra aegagrus hircus (65.7%) while the main tree/shrub species integrated in the silvopastoral agroforestry systems of smallholder farmers faced with climate change adversities were Albizia spp (57.1%), Psidium guajava (88.6%), Eucalyptus spp (54.3%), Persea americana (51.4%) and Calliandra calothyrsus (42.9%). Thus, a variety of livestock and tree species are integrated into silvopastoral agroforestry systems by smallholder farmers in the face of climate change adversities (Table 2.3). Concerning the agrosilvipastoral agroforestry system (N = 65) characterized by three components (crops, domestic animals and trees/shrubs), findings revealed that the most common crop species integrated with smallholder farmers’ agrosilvipastoral agroforestry systems were Zea mays (93.8%), Colocasia spp (84.6%), Phaseolus vulgaris (95.4%), Vigna unguiculata (63.1%), Musa spp (95.4%), Dioscorea spp (72.3%), Manihot esculenta (40%) and Ipomoea batatas (47.7%). Principal domestic animals reared by smallholder holder farmers practicing the agrosilvipastoral agroforestry system were Gallus gallus domesticus (95.4%), Sus domesticus (87.7%), Capra aegagrus hircus (70.8%), Ovis aries (50.8%) and Carvia porcellus (49.2%). And common tree/shrub species integrated in the agrosilvipastoral agroforestry systems of smallholder farmers were Psidium guajava (93.8%), Dacryodes edulis (80%), Carica papaya (90.8%), Mangifera indica (70.8%), Elaies guineensis (47.7%), Cola anomala (44.6%), Vernonia amygdalina (89.2%) and Persea amer-

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Table 2.3 Crop, tree/shrub and livestock components of different agroforestry systems practiced by smallholder farmers faced with climate variability and change Agroforestry system Agrosilvicultural agroforestry system (N = 100 smallholder farmers agroforestry plots)

Components of the system

Crops Manihot esculenta Colocasia spp Ipomoea batatas Vigna unguiculata Musa spp Zea mays Phaseolus vulgaris Coffea arabica Dioscorea spp Arachis hypogaea Solanum sp Market gardening crops Trees/shrubs Cola anomala Persea americana Psidium guajava Vernonia amygdalina Prunus africana Mangifera indica Elaies guineensis Dacryodes edulis Canarium shweinfurtii Eucalyptus spp Albizia spp Citrus spp Ficus spp Rauvolfia vomitoria Myrianthus arboreus Adansonia digitata Antiaris africana Milletia courauri Uapaca guineensis Spondianthus preussii

Frequency of occurrence

76% 82% 59% 87% 41% 96% 93% 22% 63% 18% 1% 3% 43% 98% 99% 88% 35% 79% 53% 67% 41% 55% 62% 19% 7% 19% 15% 9% 28% 14% 6% 29% (continued)

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Table 2.3 (continued) Agroforestry system Silvopastoral agroforestry systems (N = 35 smallholder farmers agroforestry plots)

Agrosilvipastoral agroforestry system (N = 65 smallholder farmers agroforestry plots)

Components of the system

Livestock Bos tuarus Equus caballus Ovis aries Equus asinus Gallus gallus domesticus Capra aegagrus hircus Trees/shrubs Albizia spp Calliandra calothyrsus Leucaena spp Gliricidia sepium Psidium guajava Pinus spp Casuarina equisetifolia Eucalyptus spp Persea americana Carica papaya

Crops Zea mays Manihot esculenta Colocasia spp Phaseolus vulgaris Vigna unguiculata Ipomoea batatas Musa spp Coffea arabica Solanum sp Dioscorea spp Arachis hypogaea Market gardening crops Livestock Carvia porcellus Oryctolagus cuniculus Sus domesticus Bos tuarus Equus caballus Ovis aries Equus asinus Gallus gallus domesticus Capra aegagrus hircus

Frequency of occurrence

88.6% 77.1% 68.6% 20% 97.1% 65.7% 57.1% 42.9% 31.4% 22.9% 88.6% 8.6% 25.7% 54.3% 65.7% 51.4%

93.8% 40% 84.6% 95.4% 63.1% 47.7% 95.4% 30.8% 35.4% 72.3% 23.1% 21.5% 49.2% 43.1% 87.7% 24.6% 18.5% 50.8% 12.3% 95.4% 70.8%

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Table 2.3 (continued) Agroforestry system

Components of the system Trees/shrubs Psidium guajava Dacryodes edulis Carica papaya Mangifera indica Elaies guineensis Cola anomala Vernonia amygdalina Prunus africana Citrus spp Persea americana Leucaena spp Calliandra calothyrsus Gliricidia sepium

Frequency of occurrence 93.8% 80% 90.8% 70.8% 47.7% 44.6% 89.2% 36.9% 29.2% 80% 24.6% 33.8% 9.2%

icana (80%). This clearly shows that in the face of climate change adversities, smallholder farmers integrate a diversity of food/cash crops, livestock as well as trees/shrubs within their agrosilvipastoral agroforestry systems (Table 2.3).

2.4.4 Non-cause-Effect Relationship Between Explanatory Variables and Smallholder Farmers’ Practice of Agroforestry Systems Faced with Adverse Climatic Variations and Changes The statistics of the Pearson chi-square (X 2 ), Spearman rank correlation, and the independent sample t-test revealed the existence of a non-cause-effect relationship between explanatory variables and the practice of agroforestry systems (agrosilvicultural agroforestry systems, silvopastoral agroforestry systems, and agrosilvopastoral systems) by smallholder farmers faced with climate change adversities (Tables 2.4 , 2.5 , and 2.6).

2.4.4.1

Agrosilvicultural Agroforestry Systems

A non-cause-effect relationship was found to exist between explanatory variables and the practice of agrosilvicultural agroforestry systems by smallholder farmers faced with adverse climatic variations and changes. The variables which had a statistically significant non-cause-effect relationship with smallholder farmers’ practice of the agrosilvicultural agroforestry system included: farms owned, size of

Agrosilvicultural AFS t p-level 9.87*** 0.000 4.21*** 0.007 0.89ns 0.341 11.96*** 0.000

Silvopastoral AFS t p-level ns 0.75 0.432 2.46* 0.061 0.73ns 0.415 12.99*** 0.000

***, **, *; Significant at 1%, 5% and 10% probability levels respectively; ns not significant

Number of farms Household size Age of HHH Household income

Continuous variables

Agrosilvopastoral AFS t p-level 10.24*** 0.000 3.47** 0.038 0.68ns 0.532 10.91*** 0.000

No agroforestry t −11.42*** −4.67*** −2.69* −14.52***

p-level 0.000 0.008 0.084 0.000

Table 2.4 T-test statistic showing the non-cause-effect relationship between continuous independent variables and smallholder farmers’ agroforestry systems faced with adverse climatic variations and changes

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Agrosilvicultural AFS X2 p-level 4.27* 0.058 0.53ns 0.471 24.02*** 0.000 19.17*** 0.000 1.64ns 0.142 4.59* 0.052 14.04*** 0.000 7.94*** 0.005 1.97ns 0.153

Silvopastoral AFS X2 p-level 3.34* 0.075 0.48ns 0.497 20.48*** 0.000 18.01*** 0.000 1.40ns 0.156 0.96ns 0.645 12.65*** 0.000 0.88ns 0.748 3.81ns 0.142

***, **, *; Significant at 1%, 5% and 10% probability levels respectively; ns not significant

Education level Gender Vulnerable Resilient Acc_information Acc_credit Access_land Acc_exten_svs Location

Discontinuous variables

Agrosilvopastoral AFS X2 p-level 4.72* 0.067 0.41ns 0.423 18.22*** 0.000 14.95*** 0.000 2.14* 0.071 2.39* 0.067 11.72*** 0.000 4.16*** 0.004 0.85ns 0.981

No Agroforestry X2 p-level 5.37** 0.029 0.58ns 0.457 15.55*** 0.000 17.49*** 0.000 3.58* 0.058 4.11* 0.051 14.35*** 0.000 38.68*** 0.000 3.67ns 0.457

Table 2.5 Chi-square test statistic showing the non-cause-effect relationship between discontinuous independent variables and smallholder farmers’ agroforestry systems faced with adverse climatic variations and changes

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Agrosilvicultural AFS r p-level 0.76*** 0.000 0.55*** 0.000 0.14ns 0.465 0.82*** 0.000 0.41** 0.046 0.09ns 0.871 0.92*** 0.000 −0.87*** 0.000 0.11ns 0.473 0.33* 0.077 0.85*** 0.000 0.37* 0.079 0.01ns 0.793

Silvopastoral AFS r p-level 0.19 0.384 0.31** 0.046 0.09ns 0.879 0.86*** 0.000 0.29* 0.097 0.04ns 0.489 0.89*** 0.000 −0.75*** 0.000 0.09ns 0.639 0.03ns 0.982 0.82*** 0.000 0.05ns 0.922 0.03ns 0.611

***, **, *; Significant at 1%, 5% and 10% probability levels respectively; ns not significant

Number of farms Household size Age of HHH Household income Education level Gender Vulnerable Resilient Acc_weather_info Acc_credit Access_land Acc_exten_svs Location

Variables

Agrosilvopastoral AFS r p-level 0.75*** 0.000 0.91*** 0.000 0.09 0.980 0.93*** 0.000 0.34* 0.058 0.03ns 0.521 0.82*** 0.000 −0.74*** 0.000 2.06* 0.084 0.27* 0.085 0.94*** 0.000 0.31* 0.080 0.01ns 0.785

No Agroforestry r p-level 0.97*** 0.000 0.69*** 0.001 0.24* 0.086 0.96*** 0.000 0.31* 0.059 0.04ns 0.448 0.95*** 0.000 0.89*** 0.000 0.43** 0.036 0.31* 0.052 0.86*** 0.000 0.96*** 0.000 0.05ns 0.921

Table 2.6 Correlation (r) between independent variables and smallholder farmers’ agroforestry systems faced with climate change adversities

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household, the income of the household, educational level, vulnerability to climate change, resilience to climate change, credit accessibility, land accessibility, and access to extension services. Variables that had no statistically significant noncause-effect relationship with smallholder farmers’ practice of the agrosilvicultural agroforestry system included gender, age, information accessibility, and location. Therefore, 09 independent variables were having a statistically significant noncause-effect relationship with the practice of the agrosilvicultural agroforestry system by smallholder farmers faced with the adversities of climate change. Four (04) explanatory variables were having no statistically significant non-causeeffect relationship with the practice of the agrosilvicultural agroforestry system by smallholder farmers, faced with adverse climatic variations and changes.

2.4.4.2

Silvopastoral Agroforestry Systems

There equally existed a non-cause-effect relationship between explanatory variables and smallholder farmers’ practice of silvopastoral agroforestry systems faced with the adverse effects of climate variability and change. Variables that had a statistically significant non-cause-effect relationship with smallholder farmers’ practice of silvopastoral agroforestry systems faced with adverse climatic variations and changes included the size of the household, the income of the household, educational level, vulnerability to climate change, resilience to climate change, and land accessibility. Variables that had no statistically significant non-cause-effect relationship with smallholder farmers’ practice of silvopastoral agroforestry systems in the face of adverse climatic variations and changes included gender, number of farms, age, information accessibility, credit accessibility, access to extension services, and location. Therefore, six (06) variables had a statistically significant non-causeeffect relationship with smallholder farmers’ practice of silvopastoral agroforestry systems faced with the adverse impacts of climate variability and change, while seven (07) variables had no statistically significant non-cause-effect relationship with smallholder farmers’ practice of agroforestry faced with the adversities of climate variability and change.

2.4.4.3

Agrosilvopastoral Agroforestry Systems

Similarly, a non-cause-effect relationship was found to exist between explanatory variables and smallholder farmers’ practice of agrosilvopastoral agroforestry systems in the face of adverse climate variations and changes. The variables that had a statistically significant non-cause-effect relationship with smallholder farmers’ practice of agrosilvopastoral agroforestry systems faced with adverse climatic variations and changes included number of farms, size of the household, income of the household, educational level, vulnerability to climate change, resilience to climate change, information accessibility, credit accessibility, land accessibility, and access to extension services. Variables that had no statistically significant non-

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cause-effect relationship with smallholder farmers’ practice of agrosilvicultural agroforestry systems included gender, age, and location. Thus, ten (10) variables had a statistically significant non-cause-effect relationship with smallholder farmers’ practice of agrosilvicultural agroforestry systems faced with climate change adversities, while three (03) variables had no statistically significant non-cause-effect relationship with smallholder farmers’ practice of agrosilvicultural agroforestry systems faced with adverse climatic variations and changes.

2.4.4.4

No Agroforestry

Likewise, a non-cause-effect relationship was found to exist between explanatory variables and smallholder farmers’ practice of “no agroforestry” faced with the adverse effects of climate variability and change. Variables that showed a statistically significant non-cause-effect relationship with smallholder farmers’ practice of “no agroforestry” faced with adverse climatic variations and changes included number of farms, size of the household, age, income of the household, educational level, vulnerability to climate change, resilience to climate change, information accessibility, credit accessibility, land accessibility, and access to extension services. Variables that had no statistically significant non-cause-effect relationship with smallholder farmers’ practice of “no agroforestry” included gender, and location. Thus, 11 explanatory variables were having a statistically significant non-causeeffect relationship with the practice of “no agroforestry” by smallholder farmers faced with the adversities of climatic variations and changes. Two (02) explanatory variables had no statistically significant non-cause-effect relationship with the practice of “no agroforestry” by smallholder farmers faced with adverse climatic variations and changes.

2.4.5 The Cause-Effect Relationship Between Explanatory Variables and Smallholder Farmers’ Practice of Agroforestry Systems Faced with Adverse Climatic Variations and Changes Parameter estimates of the logistic regression model revealed a cause-effect relationship between explanatory variables and smallholder farmers’ practice of agroforestry systems (agrosilvicultural agroforestry systems, silvopastoral agroforestry systems, and agrosilvopastoral systems) faced with climate change adversities (Table 2.7).

Agrosilvicultural AFS Coeff. β p-level 7.62*** 0.000 6.71*** 0.000 0.64ns 0.582 3.15** 0.049 1.93* 0.071 0.14ns 0.843 9.08*** 0.000 −8.92*** 0.000 0.28ns 0.877 1.65* 0.096 8.48*** 0.000 1.73* 0.091 −29.32 0.000 350 87.1% 194.89 141.40 0.000 0.614

Silvopastoral AFS Coeff. β p-level 0.17ns 0.734 2.35** 0.038 −0.04ns 0.997 1.99** 0.042 1.57* 0.076 −0.24ns 0.896 6.76*** 0.003 −4.51*** 0.005 0.19ns 0.992 0.15ns 0.994 7.19*** 0.000 −0.41ns 0.593 −32.47 0.000 350 71.9% 177.24 126.91 0.000 0.758

***, **, *; Significant at 1%, 5% and 10% probability levels respectively; ns not significant

Number of farms Household size Age of HHH Household income Education level Gender Vulnerability Resilience Acc_info Acc_credit Access_land Acc_exten_svs Intercept Num. observations Number of cases correctly classified Log likelihood Likelihood ratio X2 Pseudo R2

Variables

Agrosilvipastoral AFS Coeff. β p-level 4.36*** 0.008 2.58** 0.047 −0.21ns 0.893 2.43** 0.035 2.67** 0.044 0.38ns 0.817 7.89** 0.000 −6.92*** 0.000 2.48** 0.043 1.71* 0.093 8.54*** 0.000 2.57** 0.033 −27.54 0.000 350 64.8% 186.72 154.83 0.000 0.947

No Agroforestry Coeff. β p-level −3.36** 0.041 −2.78* 0.057 −1.13* 0.092 −4.69*** 0.001 −1.34* 0.085 0.21ns 0.974 −6.95*** 0.000 7.37*** 0.000 −1.99* 0.075 −2.18** 0.048 −7.27*** 0.000 −1.31* 0.082 −47.64 0.000 350 98.6% 191.31 137.19 0.000 0.833

Table 2.7 Parameter estimates of the binary logistic regression model predicting smallholder farmers’ practice of agroforestry systems faced with climate change adversities from independent variables

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2.4.5.1

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Agrosilvicultural Agroforestry Systems

A cause-effect relationship was found to exist between explanatory variables and smallholder farmers’ practice of agrosilvicultural agroforestry systems faced with adverse climatic variations and changes. The variables which had a statistically significant cause-effect relationship with smallholder farmers’ practice of agrosilvicultural agroforestry systems included farms owned, size of household, the income of a household, educational level, vulnerability to climate change, resilience to climate change, credit accessibility, land accessibility, and access to extension services. Variables that had no statistically significant cause-effect relationship with smallholder farmers’ practice of agrosilvicultural agroforestry systems included gender, age, and information accessibility. Nine (09) explanatory variables had a statistically significant cause-effect relationship with smallholder farmers’ practice of agrosilvicultural agroforestry systems faced with adverse climate variations and changes, while three (03) variables had no statistically significant cause-effect relationship with smallholder farmers’ practice of agrosilvicultural agroforestry systems faced with adverse climatic variations and changes.

2.4.5.2

Silvopastoral Agroforestry Systems

A cause-effect relationship existed between smallholder farmers’ practice of silvopastoral agroforestry systems faced with climate change adversities and the explanatory variables of the study. Explanatory variables having a statistically significant cause-effect relationship with smallholder farmers’ practice of silvopastoral agroforestry systems faced with the adverse effects of climatic variations and changes were educational level, the size of household, land accessibility, vulnerability to climate change, the income of the household, and resilience to climate change. Variables that had no statistically significant cause-effect relationship with smallholder farmers’ practice of silvopastoral agroforestry systems in the face of adverse climatic variations and changes included gender, number of farms, age, information accessibility, credit accessibility, and access to extension services. Thus, six (06) variables had a statistically significant cause-effect relationship with smallholder farmers’ practice of silvopastoral agroforestry systems faced with the adverse impacts of climate variability and change, while six (06) variables had no statistically significant cause-effect relationship with smallholder farmers’ practice of agroforestry faced with adverse climatic variations and changes.

2.4.5.3

Agrosilvopastoral Agroforestry Systems

In the same light, a cause-effect relationship was found to exist between explanatory variables and smallholder farmers’ practice of agrosilvopastoral agroforestry systems in the face of adverse climatic variations and changes. The variables that had a statistically significant cause-effect relationship with smallholder farm-

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ers’ practice of agrosilvopastoral agroforestry systems in the face of climatic variations and changes included number of farms, size of household, income of household, educational level, vulnerability to climate change, resilience to climate change, information accessibility, credit accessibility, land accessibility, and access to extension services. Variables that had no statistically significant cause-effect relationship with smallholder farmers’ practice of agrosilvicultural agroforestry systems faced with climate change adversities included gender and age. Thus, ten (10) explanatory variables had a statistically significant cause-effect relationship with smallholder farmers’ practice of agrosilvicultural agroforestry systems faced with adverse climatic variations and changes, while two (02) explanatory variables had no statistically significant cause-effect relationship with smallholder farmers’ practice of agrosilvicultural agroforestry systems faced with the adverse effects of climatic variations and changes.

2.4.5.4

No Agroforestry

Similarly, a cause-effect relationship was found to exist between explanatory variables and smallholder farmers’ practice of “no agroforestry” in the face of adverse climatic variations and changes. Variables that showed a statistically significant cause-effect relationship with smallholder farmers’ practice of “no agroforestry” faced with climate change adversities included income of household, number of farms, age, educational level, size of household, vulnerability to climate change, resilience to climate change, information accessibility, credit accessibility, land accessibility, and access to extension services. The variable that had no statistically significant cause-effect relationship with smallholder farmers’ practice of “no agroforestry” was gender. Hence, eleven (11) variables had a statistically significant cause-effect relationship with smallholder farmers’ practice of “no agroforestry” faced with climate change adversities, while two (02) variables had no statistically significant cause-effect relationship with smallholder farmers’ practice of “no agroforestry” faced with climate change adversities.

2.5 Discussion 2.5.1 Climate Variations and Changes Variations and changes in climate have been significant in the past five decades evidenced by the extreme positive and negative anomalies noticed in the two main climate elements (temperature and rainfall). Many more years of positive anomalies for temperature indicated that temperature has been higher than normal in the past five decades. Many more years of negative rainfall and rainy days anomalies indicated that rainfall was more scanty and erratic in the past five decades. These

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extreme variations and changes in rainfall and temperature are therefore indicators of climate change in north western Cameroon. Although some studies undertaken in the north western part of Cameroon (Azibo and Kimengsi 2015; Azibo et al. 2016) have reported that climate variability is real, these studies made use of just a few decades of climate data. This is one of the first studies to use over five decades of climate data to analyze the climate change phenomenon in north western Cameroon.

2.5.2 Agroforestry Systems of Smallholder Farmers Faced with Climate Change Adversities Three principal agroforestry systems were practiced by smallholder farmers faced with the adversities of climate change, with the agrosilvicultural agroforestry system being the most recurrent. The predominance of the agrosilvicultural agroforestry system in smallholder farming systems faced with climate change adversities could be due to the low cost, less labour intensive nature and ease of practicing and managing the system when compared to the silvopastoral and agrosilvopastoral systems which are difficult to set up and cumbersome to manage. Other studies (Nair 1985, 1993; Nair et al. 2004; Pandey 2007; Lin 2007; Nair and Garrity 2012 ; Mbow et al. 2013, 2014; Lasco et al. 2014, 2015; Viswanath et al. 2018; Amare et al. 2018; Awazi and Tchamba 2019), have generally found that most small-scale farmers prefer agrosilvicultural systems because agrosilvicultural agroforestry systems need less land for their establishment when compared to silvopastoral and agrosilvopastoral agroforestry systems which need more land for their establishment. This study is however, one of the first to prove that faced with climate change adversities smallholder farmers mainly practice agrosilvicultural agroforestry systems (and not silvopastoral and agrosilvopastoral agroforestry systems) owing to the less complex, low cost, less labor-intensive and easy-to-manage nature of the agrosilvicultural system.

2.5.3 Components of Smallholder Farmers’ Agroforestry Systems Faced with Climate Change Adversities Of the components integrated into small-scale farmers’ agroforestry systems; it was found that three main components (crops, trees/shrubs and livestock) were present. Crops and trees/shrubs were integrated into the agrosilvicultural agroforestry system; livestock and trees/shrubs were integrated into the silvopastoral agroforestry system; while crops, trees/shrubs and livestock were integrated into the agrosilvopastoral agroforestry system. Mostly food crops and fruit trees were integrated into the agrosilvicultural and agrosilvopastoral agroforestry systems of smallholder farmers which could be because smallholder farmers’ prime goal is

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to achieve food self-sufficiency in the face of adverse climate change impacts. In the silvopastoral and agrosilvopastoral agroforestry systems, the trees/shrubs planted and the livestock raised enjoyed a mutually beneficial relationship as the trees/shrubs were used as feed for the livestock and the manure from the livestock used to fertilize the trees/shrubs. It could therefore, be said that, faced with climate change adversities, smallholder farmers resort to agroforestry systems owing to the diversity of crop, tree/shrub and livestock species present there in, which helps them to spread risk, reduce vulnerability and enhance resilience to climate change adversities. Although some research works have reported the existence of a diversity of tree/shrub, livestock, and crop species in agroforestry systems (Pandey 2007; Nair and Garrity 2012; Amare et al. 2018; Awazi and Tchamba 2019; Awazi et al. 2019c), limited research has been done assessing the different components of smallholder farmers’ agroforestry systems faced with the adverse effects of climatic variations and changes – the knowledge gap the finding of this study has filled.

2.5.4 Determinants of Smallholder Farmers’ Practice of Agroforestry Systems Faced with Climate Change Adversities Variables influencing smallholder farmers’ practice of agroforestry systems faced with climate change adversities were wide-ranging with the major factors being number of farms, level of education, degree of vulnerability to climate change, size of household, degree of resilience to climate change, credit accessibility, land accessibility, income of household, and access to extension services.

2.5.4.1

Number of Farms (or Farms Owned)

The number of farms owned had a statistically significant positive cause-effect relationship with smallholder farmers’ practice of agrosilvicultural and agrosilvopastoral agroforestry systems faced with the adverse impacts of climate variability and change. This implies that as the number of farms increases, smallholder farmers’ propensity to practice agrosilvicultural and agrosilvopastoral agroforestry systems increases, which could be because farmers with more farms can practice agroforestry easily, for the land is a major asset needed for the practice of agroforestry. The number of farms had a statistically significant negative causeeffect relationship with the practice of “no agroforestry”, implying that as the number of farms increases, the practice of “no agroforestry” diminishes. This goes to show that more smallholder farmers would practice agroforestry faced with adverse climatic variations and changes if they had more farms.

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Household Size

Household size on its part equally had a statistically significant positive cause-effect relationship with smallholder farmers’ practice of all three agroforestry systems (agrosilvicultural silvopastoral and agrosilvopastoral) faced with the adverse effects of climate variability and change. This means that as household size increases, the tendency of smallholder farmers’ to practice agrosilvicultural, silvopastoral and agrosilvopastoral agroforestry systems equally increases, which could be attributed to the fact that more labor is needed to successfully practice agroforestry. Household size however, had a statistically significant negative cause-effect relationship with the practice of “no agroforestry”, signifying that as household size increases, the practice of “no agroforestry” decreases. This indicates that the bigger the household size the greater the practice of agroforestry faced with climate change adversities, which could be due to the extra labour force supplied by the persons in the bigger household.

2.5.4.3

Educational Level

There was equally a statistically significant positive cause-effect relationship between the level of education and smallholder farmers’ practice of the three main agroforestry systems faced with climate change adversities. This shows that as the educational level of the farmers increase, there is a higher possibility of the farmers practicing any of the three main agroforestry systems faced with climate variability and change. This could be because more educated farmers have a broader perspective and easily take to best practices that could assist them to cope with the adverse effects of climate change. A statistically significant negative cause-effect relationship was found to exist between education and the practice of “no agroforestry” which is vivid proof that the higher the level of education, the lesser the practice of “no agroforestry” in the face of adverse climatic variations and changes.

2.5.4.4

Degree of Vulnerability and Resilience to Climate Change Adversities

The degree of vulnerability and resilience to climate change adversities had a statistically positive and statistically negative cause-effect relationship with smallholder farmers’ practice of the three main agroforestry systems respectively faced with adverse climatic variations and changes. This implies that the greater the vulnerability of smallholder farmers to the adverse impacts of climate variability and change, the greater their propensity to practice the three main agroforestry systems. This could be because the many products and services supplied by agroforestry systems help to reduce smallholder farmers’ vulnerability to climate change adversities. The greater the resilience of smallholder farmers faced with

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climate change adversities, the lesser their propensity to practice the three main agroforestry systems faced with adverse climate changes. Equally, the degree of vulnerability and resilience of smallholder farmers faced with the adverse effects of climate variability and change had a statistically significant negative and positive cause-effect relationship respectively with smallholder farmers’ practice of the three main agroforestry systems, indicating that as vulnerability increases, the practice of “no agroforestry” reduces, while as resilience increases, the practice of “no agroforestry” increases. This because agroforestry provides various ecosystem services that help farmers to reduce vulnerability and improve resilience faced with climate change adversities.

2.5.4.5

Credit Accessibility

Credit accessibility also had a statistically significant positive cause-effect relationship with smallholder farmers’ practice of agrosilvicultural and agrosilvopastoral agroforestry systems faced with adverse climatic variations and changes. This means that as access to credit services increases, smallholder farmers’ propensity to practice agrosilvicultural and agrosilvopastoral agroforestry systems increases, which could be attributed to the fact that credit services permit smallholder farmers to buy farm inputs necessary for the agroforestry system. Credit accessibility however, had a statistically significant negative cause-effect relationship with the practice of “no agroforestry”, implying that as credit services increases, the practice of “no agroforestry” falls drastically. This goes to show that more smallholder farmers would practice agroforestry faced with adverse climatic variations and changes if more credit services are provided.

2.5.4.6

Access to Land and Extension Services

These two explanatory variables all had a statistically significant cause-effect relationship with smallholder farmers’ practice of agroforestry systems faced with climate change adversities. This is a glaring indication that more land and extension services are needed to permit smallholder farmers to practice different agroforestry systems, faced with adverse climatic variations and changes. A significant negative cause-effect relationship existed between access to land and access to extension services, and smallholder farmers’ practice of agroforestry systems faced with adverse climatic variations and changes. This goes to show that as land and extension services increase, smallholder farmers’ practice of “no agroforestry” reduces tremendously. As unearthed by this study, the determinants of smallholder farmers’ practice of agroforestry systems faced with adverse climatic variations and changes are wideranging. This is one of the first studies to assess the determinants of smallholder farmers’ practice of agroforestry systems faced with climate change adversities. Most studies (Zomer et al. 2009; Lott et al. 2009; Syampungani et al. 2010;

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Thorlakson and Neufeldt 2012; Bishaw et al. 2013; Mbow et al. 2013, 2014; Luedeling et al. 2014; Amare et al. 2018) have focused mainly on the contributions and potential contributions of agroforestry systems towards the adaptation to and mitigation of climate change. According to Verchot et al. (2006), Pandey (2007), Rao et al. (2007), Snelder and Lasco (2008), Noordwijk et al. (2011), Asaah et al. (2011), Nguyen et al. (2013), Sobola et al. (2015), Viswanath et al. (2018) and Awazi et al. (2019c), agroforestry systems provide several products and services including food, fodder, fuelwood, soil fertility improvement, finance and many other ecosystem services which enable smallholder farmers practicing agroforestry to cope with climate extremes than their counterparts involved in monoculture and other unsustainable agricultural practices. Therefore, a relatively large number of studies have examined the contributions and potential contributions of different agroforestry systems towards mitigation of and adaptation to climate change. However, limited research has been undertaken to assess the determinants of smallholder farmers’ practice of agroforestry systems faced with adverse climatic variations and changes, which is the knowledge gap the findings of this study have filled.

2.6 Conclusion and Policy Ramifications This study assessed agroforestry systems, its components and the factors influencing smallholder farmers’ practice of different agroforestry systems faced with climate change adversities. It was found that three major agroforestry systems were dominant (agrosilvopastoral, silvopastoral, and agrosilvicultural agroforestry systems); crops, trees/shrubs and livestock were the main components of these agroforestry systems; and the factors influencing smallholder farmers’ practice of agroforestry systems faced with adverse climatic variations and changes included: vulnerability to climate change, resilience to climate change, size of household, level of education, credit accessibility, income of household, land accessibility, number of farms, and access to extension services. The policy ramifications of the study’s findings are far-reaching especially for decision and policy makers seeking to advance the cause of agro-ecological farming systems like agroforestry faced with the adverse impacts of climatic variations and changes. Based on these findings therefore, policies should be put in place that will encourage reticent smallholder farmers to adopt agroforestry systems like agrosilvicultural, silvopastoral and agrosilvopastoral. Equally, policies that help to ease access to farm ownership, improve smallholder farmers’ income, build smallholder farmers’ capacity through training workshops, attenuate vulnerability and improve resilience, and increase access to credit, land and extension services will encourage smallholder farmers to practice different agroforestry systems faced with the adverse effects of climatic variations and changes. All these will contribute towards encouraging the practice of sustainable agricultural systems – in this case agroforestry systems.

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Nguyen, Q., Hoang, M. H., Öborn, I., & van Noordwijk, M. (2013). Multipurpose agroforestry as a climate change resiliency option for farmers: An example of local adaptation in Vietnam. Climatic Change, 117, 241–257. Niles, M. T., & Salerno, J. D. (2018). A cross-country analysis of climate shocks and smallholder food insecurity. PLoS One, 13(2), e0192928. https://doi.org/10.1371/journal.pone.0192928. Njongue, Y. N., Avana, T. M. L., Fotsing, E., & Muderhwa, P. M. (2017). Assessing farmers’ interest in agroforestry practices around the Mount Cameroon national park (MCNP) in Cameroon. International Journal of Geomatics, Planning and Resources Management, 1(2), 1–15. Noordwijk, V. M., Hoang, M. H., Neufeldt, H., Öborn, I., & Yatich, T. (Eds.). (2011). How trees and people can co-adapt to climate change: Reducing vulnerability through multifunctional agroforestry landscapes. World Agroforestry Centre (ICRAF): Nairobi. Pandey, D. N. (2007). Multifunctional agroforestry systems in India. Current Science, 92(4), 455– 463. Partey, S. T., Dakorah, A. D., Zougmoré, R. B., Ouédraogo, M., Nyasimi, M., Nikoi, G. K., & Huyer, S. (2018). Gender and climate risk management: Evidence of climate information use in Ghana. Climatic Change. https://doi.org/10.1007/s10584-018-2239-6. Paterson, B., & Charles, A. (2019). Community-based responses to climate hazards: Typology and global analysis. Climatic Change. https://doi.org/10.1007/s10584-018-2345-5. Rao, K. P. C., Verchot, L. V., & Laarman, J. (2007). Adaptation to climate change through sustainable management and development of agroforestry systems. SATe Journal, 4(1), 1–30. Rao, K. P. C., Ndegwa, W. G., Kizito, K., & Oyoo, A. (2011). Climate variability and change: Farmer perceptions and understanding of intra-seasonal variability in rainfall and associated risk in semi-arid Kenya. Experimental Agriculture, 47, 267–291. Snelder, D. J., & Lasco, R. D. (2008). Smallholder tree growing for rural development and environmental services: Lessons from Asia (Advances in agroforestry, series, Vol. 5, 1st ed, 494 pages). https://doi.org/10.1007/978-1-4020-8261-0. Springer Netherlands. Sobola, O. O., Amadi, D. C., & Jamala, G. Y. (2015). The role of agroforestry in environmental sustainability. IOSR Journal of Agriculture and Veterinary Science, 8(5), 20–25. Syampungani, S., Chirwa, P. W., Akinnifesi, F. K., & Ajayi, O. C. (2010). The potential of using agroforestry as a win-win solution to climate change mitigation and adaptation and meeting food security challenges in Southern Africa. Agricultural Journal, 5(2), 80–88. Tankou, C. M., de Snoo, G. R., Persoon, G., & de Iongh, H. H. (2017). Evaluation of smallholder farming systems in the Western Highlands of Cameroon. IOSR Journal of Engineering (IOSRJEN), 7(1), 01–11. Thorlakson, T. (2011). Reducing subsistence farmers’ vulnerability to climate change: The potential contributions of agroforestry in western Kenya. Occasional Paper 16. Nairobi: World Agroforestry Centre. http://www.worldagroforestry.org/downloads/Publications/PDFS/ OP11183.pdf Thorlakson, T., & Neufeldt, H. (2012). Reducing subsistence farmers’ vulnerability to climate change: Evaluating the potential contributions of agroforestry in western Kenya. Agric Food Security, 1, 1–13. UNFCCCs Conference of the Parties Twenty-first session (COP21) Paris, 30 November–11 December 2015. Adoption of the Paris Agreement, Draft decision -/CP.21, 12 December 2015. http://www.c2es.org/docUploads/cop-21-paris-summary-02-2016-final.pdf Vaast, P., & Somarriba, E. (2014). Trade-offs between crop intensification and ecosystem services: The role of agroforestry in cocoa cultivation. Agroforest System, 88, 947–956. Verchot, L. V., Noordwijk, M. V., Kandji, S., Tomich, T., Ong, C., Albrecht, A., Mackensen, J., Bantilan, C., Anupama, K. V., & Palm, C. (2006). Climate change: Linking adaptation and mitigation through agroforestry. Mitigation and Adaptation Strategies for Global Change. https://doi.org/10.1007/s11027-007-9105-6. http://www.worldagroforestry.org/downloads/ Publications/PDFS/ja07159.pdf.

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Viswanath, S., Lubina, P. A., Subbanna, S., & Sandhya, M. C. (2018). Traditional agroforestry systems and practices: A review. Advanced Agricultural Research & Technology Journal, 2(1), 18–29. Zomer, R. J., Trabucco, A., Coe, R., & Place, F. (2009). Trees on farm: Analysis of global extent and geographical patterns of agroforestry. ICRAF Working Paper no. 89. Nairobi: World Agroforestry Centre. http://worldagroforestry.org/sites/default/files/WP89_text_only.pdf.

Chapter 3

The Telecoupling Approach to the Global Food System and Climate Change Regime: The Pivotal Role of Brazil and China Douglas de Castro, Daniele Arcolini Cassucci de Lima, and Caroline Romano

Abstract The global food system and the climate change regime deal with highly complex natural and social issues. Despite some advances in both systems, the world and its population face existential threats as poverty and hunger are not eradicated, and adverse effects due to climate change are on the rise. Also, the negative feedback that each system has in the other makes addressing the issues even harder. This chapter presents the telecoupling model to map out the causes and effects of each system and their relationship with others. The telecoupling model is applied in the Brazilian agri-food exportation to China case, which should indicate implications of the high rate of meat production and exported to long distances vis-à-vis the need to supply the high demand of meat consumption due to economic prosperity. The Brazilian carbon neutral meat subcase is presented as the axis of the global food system and the climate change regime, which points out a possible way to address the challenges. However, it presents itself with additional ones. Keywords Telecoupling model · Climate change · Global food system · Brazil and China

D. de Castro () The School of Law, Lanzhou University, Lanzhou, China D. A. C. de Lima Ambra University, Orlando, FL, USA Strategic Business Management, UNIFEOB, São Paulo, Brazil PUC – Minas, Belo Horizonte, Brazil C. Romano UNIP, São Paulo, Brazil Anhanguera University, Valinhos, Brazil EBRADI, São Paulo, Brazil © Springer Nature Switzerland AG 2022 C. M. Galanakis (ed.), Environment and Climate-smart Food Production, https://doi.org/10.1007/978-3-030-71571-7_3

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3.1 Introduction The dualistic view of the natural and social worlds has been producing challenges in managing the circular effects. The modernistic project in which the idea that humankind must dominate the forces of nature is in check nowadays due to the harmful effects we have been experiencing, thus, forcing us to interpret the signs uttered by nature. As such, we are treating the environment as a separate thing from the social realm provides a meaning to the world that is not sustainable any longer (Argyrou 2005). An empirical instance of the challenges posed by this dualistic approach is represented by the interactions (sometimes the lack of) between the climate change regime and the international food system. While increasing the output of food has not proven itself as the best practice for improving food security, it generates local externalities that include water pollution and loss of biodiversity. Simultaneously, transporting large amounts of food to distant markets has negative impacts on climate change as emissions increase (Clapp and Cohen 2009). Considering the complex interaction between the global food system and the climate change regime, we argue in this chapter the need to adopt an analytical model that (1) considers both the natural and social worlds and (2) captures micro causes, processes, and agents that are neglected by other qualitative and quantitative models (Scartozzi 2020). The methodological approach is deductive par excellence by pointing out the telecoupling model developed by Liu et al. (2013), which will set the conditions for within the case study. However, before the model is conceptualized and applied to the selected case, exploratory analysis is made using descriptive statistics and epistemic networks to design the background conditions and external boundaries of the case (Tukey 1977; Yin 2017). Considering that the main objective is to identify the implications of the interaction between the global food system and climate change international regime, the selection of the case possess a two-fold strategy: first, to look into a larger picture, which involves the interdependence generated between Brazil and China on regards to food commodities, and second, the production and exportation of the carbon-neutral meat by Brazilian producers. These two stances of analysis we call from now on “the case,” which is representative of the theorytesting strategy adopted in the chapter (Gerring 2008). In this sense, this chapter is designed as follows: (1) the exploratory analysis presenting the gap in the literature in dealing with the complexity between the global food system and climate change regime; (2) the operationalization of the telecoupling model, build upon the existing literature, and inferences made by the authors; and (3) the application of the telecoupling model to the Brazilian exportation of food commodities to China and the producing of the carbon-neutral meat as the initiative to decrease impact to climate change. We expect that present enough empirical evidence and implications to shed more light on the subject, thus contributing to the advancement of knowledge.

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3.2 Building the Bridge Between the Global Food System and Climate Change Regime Dealing with two complex systems such as the global food system and climate change regime, which are embedded in both social and natural realms implies necessarily the identification of the level of connection between them, along with the title of the firm and weak ties, and the incompatibilities (Johnson 2010a, b). In other words, the challenge is finding a balance between them to achieve the sustainable use of natural resources involving food production.

3.2.1 Exploratory Analysis: Identifying the Gap Addressing society’s concerns about hunger and climate change effects are concerns of the states in formulating local and global public policies and how these policies are diffused and the layers of governance (Marsh and Sharman 2009). Addressing both concerns are in line with the most basic functions of the modern state that might be summarized in the core human rights to life and dignity. However, there is a strong correlation between food production and damage to the environment. In other words, the increase of food production brings negative impacts to the environment, mainly climate change and loss of biodiversity (Gonzalez 2012). Also, the interplay of other agents in both systems provide an extra layer of complexity as interests often collide as the scope of transnational food corporations and climate or biodiversity NGOs. Considering the high complexity of the issues related to increasing food security by increasing food output and addressing the existential threats posed by climate change, policymakers often rely on epistemic communities1 to provide the necessary scientific knowledge to formulate, adjust or remove public policies in the interest of the society at large (Will 2020). One particular group that receives special attention from decision-makers are the academics of a broad array of areas involving environment-related issues.2 The exploratory analysis in this section focuses on the production of knowledge about the relationship between the global food system and climate change regime,

1 According

to Haas (2008, p. 794): “Epistemic communities are networks—often transnational— of knowledge-based experts with an authoritative claim to policy relevant knowledge within their domain of expertise. Their members share knowledge about the causation of social or physical phenomena in an area for which they have a reputation for competence, and a common set of normative beliefs about what actions will benefit human welfare in such a domain. Members are experts with professional training who enjoy social authority based on their reputation for impartial expertise.” 2 For the purpose of this research, we assume the causal direction OBSERVED PHENOMENA - > ACADEMIC OUTPUT - > DECISION-MAKING ON PUBLIC POLICY.

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which points out to a disconcerting and growing absence of debates, which we infer to be one of the causes for the existing gap between local public policies and international initiatives to tackle the environmental problems. As posed by Stebbins (2001, p.3), the exploratory analysis is [ . . . ] a broad-ranging, purposive, systematic, prearranged undertaking designed to maximize the discovery of generalizations leading to description and understanding of an area of social or psychological life. Such exploration is, depending on the standpoint taken, a distinctive way of conducting science—a scientific process—a special methodological approach (as contrasted with confirmation), and a pervasive personal orientation of the explorer. The emergent generalizations are many and varied; they include the descriptive facts, folk concepts, cultural artifacts, structural arrangements, social processes, and beliefs and belief systems normally found there.

The exploratory analysis is a two-fold strategy composed of bibliometric and network analysis, as we shall explain as follows.

3.2.1.1

Bibliometric Analysis

The first step in the exploratory analysis is to find academic outputs related to the subject, or in other words, conduct a bibliometric analysis of the relationship between the food system and climate change. As posed by Thanuskodi (2010, p.77): Bibliometrics is a type of research method used in Library and information sciences. It is an emerging area of research in the LIS field. The quantitative analysis and statistics to describe patterns of publication within a given field of the body of literature are utilized. Researchers use bibliometric methods of evaluation to determine the influence of a single author or to describe the relationship between two or more authors or works. Bibliometric studies can be used to study regional patterns of research, the extent of cooperation between research groups, and national research profiles. The main derivatives of bibliometrics are publication counts, citation counts, co-citation analysis, co-word analysis, scientific ’mapping,’ and citations in patents.

To assist the bibliometrics analysis, we have used Harzing’s Publish or Perish application.3 The parameter for searching in the Google Scholar database was the term “food system*climate change” in the title, keywords, and abstracts.4 The search resulted in the following output (Fig. 3.1): As observed, the number of papers is 980 out of 1.000, in which citations between 1977 and 2020 amount to 267.332. Considering the deep complexity of the world

3 In

https://harzing.com/resources/publish-or-perish. Last Access: Nov 30, 2020.

4 “The Google Scholar search pane gives access to all Google Scholar search parameters, accessible

through a pop-up on the Google Scholar homepage. This is not the same as a standard Google Scholar search (i.e., from the standard Google Scholar search box). A standard Google Scholar search is equivalent to performing an Keywords query, which matches the search terms anywhere in the searched documents (author, title, source, abstract, references etc.) and usually provides too many irrelevant results for an effective citation analysis. Hence it is normally recommended to use a more specific query;” Publish or Perish User’s Manual, in https://harzing.com/resources/publishor-perish/manual. Last Access: Nov 30, 2020.

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Fig. 3.1 Publish or Perish output. (Made by the Authors)

in terms of producing knowledge, the output is representative and essential as the research restricted the search to the specific time “food system * climate change.”

3.2.1.2

Network Analysis

The second step in the exploratory inquiry was to test how strong were the connections between the specific query with other terms found in the papers found in the bibliometrics analysis. To visualize the core term with the related terms that appear in the documents, we used the VOSviewer application.5 The parameters were adjusted (1) to extract the terms from the title and abstract of the 980 papers; (2) to make a full counting; (3) at least 5 times of the occurrences of the term; and (4) only 60% of the most relevant connections were considered. The more massive clusters in the networks mean more aggregated the terms are and how strong are the ties among them. Considering the high degree of optimism during and shortly after the Paris Agreement’s conclusion in 2015 would be expected an increase in the debate on the effects of other regimes in climate change, especially considering the voluntary and nationally determined targets assumed by the countries.6 However, as observed in Fig. 3.2, from 2015, the connections among terms related to “food system*climate change” were becoming weaker to the point that no one has passed the 5-occurrence

5 In

https://www.vosviewer.com/. Last Access: Nov 30, 2020. https://www.climatechangenews.com/2016/11/04/its-official-the-paris-climate-deal-is-nowinternational-law/. Last Access: Nov 30, 2020.

6 In

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Fig. 3.2 VOSviewer network. (Made by the Author)

threshold in part of 2019 and 2020 (no yellow cluster or term is found in the network). The tracking of the “climate change scenario” cluster (Fig. 3.3) reveals that the concern about climate change had a strong connection with the term “rice” in 2007, or other words, the peak for the food crises that led millions around the world under severe hunger (Clapp and Cohen 2009).7 As stated by Bello (2009, p.1): Alarmed by massive global demand, countries like China and Argentina resorted to taxes or quotas on their rice and wheat exports to avert local shortages. Rice exports were simply banned in Cambodia, Egypt, India, Indonesia, and Vietnam. South-South solidarity crumbled in the crisis, a victim of collateral damage.

The end of the acute effects of the global food crisis in 2009 provides the empirical justification for the increasing lack of studies connecting two existential threats for humanity. As observed in Fig. 3.3, the connection between “climate change scenario” and other essential concepts related to the global food system starts to dissolve towards 2020, even though the paradigm of exporting large sums of commodities worldwide increased, which according to Clapp and Fuchs (2009, p.6): The globalization of food and agricultural systems may have produced some benefits, such as increased varieties of foods available to consumers and new markets for producers. But critical thinkers and a number of NGOs have also raised concerns about the impacts of

7 See

also: http://www.fao.org/3/i0876e/i0876e00.htm. Last Access: Nov 30, 2020.

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Fig. 3.3 “Climate change scenario” cluster. (Made by the Authors)

corporate control of “food globalization” on socioeconomic and environmental outcomes. In particular, there is a growing critique of the effects that corporate concentration in a globalized food system is having on food security, small-farmer livelihoods, environmental quality, food safety, and consumer sovereignty. These concerns have been driving forces behind international efforts to establish rules, norms, and institutions to govern the global food and agricultural systems.

As such, the current paradigm of food production relies on increasing food production by large corporations. However, such fact does not possess a positive correlation with eradicating hunger. It would require more vigorous participation of the states as its goals are better perceived in the long run and related to its citizens’ wellbeing vis-à-vis the corporate short term goals related to the wellbeing of investors. In this sense, Pogge (2016, p.17) present a picture that in theory would be a feasible one to address the problem; however, in the real world, a more crude reality: As another justification for the asymmetry, one might want to argue that overcoming poverty and hunger is much harder and more expensive than defeating the axis. But this, too, is untrue. At the height of the Second World War, the belligerent powers spent about half of their Gross National Incomes on fighting the war – the Russians, the British, and even the U.S. did so. Overcoming poverty and hunger, by contrast, would require only around 2 % of the rich countries’ Gross National Incomes and could be done relatively quickly. This latter fight would also be much easier because it would not really be a fight. Nobody would be opposing us; no one would stand up to defend hunger and poverty.

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Therefore, the current global food system at the corporate and international institutions level does not provide a proper response to eradicate hunger or address related issues to it. The challenge is located into the corporate discursive, instrumental, and structural dimensions that permeate the relationship between the state and corporate agents, which, according to Clapp and Fuchs (2009, p.8): The understanding of corporate power employed in much of the literature on globalization, including that in the food sector, tends to equate corporate power with market share (e.g., MacMillan 2005). While the economic dimension of corporate influence is important, our approach is to go deeper to uncover the different political facets of corporate power and its sources. A multifaceted approach to corporate power reveals the many ways this power is being employed, which together constitute corporations’ ability to influence the governance of the global food system. Specifically, these different facets of power enable corporations to have a say in what is on the list and what is not and to shape the distribution of the costs and benefits of the resulting rules and regulations. Market power influences political power, but one cannot assume that it translates into political power in a one-to-one relationship. Accordingly, it is important to unpack corporate power and to look at its different political facets as well as to consider important additional sources besides market power such as access to information and the policy process or the perceived political legitimacy of corporate actors.8

However, the intrinsic contradiction between eradicating hunger and the current global food system is not the only one. Considering the environmental dimension of food production, we might look at the critical implications for climate change and biodiversity. As for the climate change implications, according to Vermeulen et al. (2012), the food system contributes to an overall 19% to 29% anthropogenic greenhouse gas emissions. The authors present a comprehensive list of agents involved in the food production chain that provide us the scope of the challenge: Food chain activities are the manufacturing and distribution of inputs (seed, animal feed, fertilizers, pest control); agricultural production (crops, livestock, fisheries, wild foods); primary and secondary processing, packaging, storage, transport, and distribution; marketing and retail; catering; domestic food management; and waste disposal. In some cases, this supply is linked through a “cold chain” in which continuous refrigeration is used to extend and ensure the shelf life of fresh and processed foods. Importantly, food systems encompass not only food chain activities but also the outcomes of these activities and their governance. All humans participate in food systems and, in doing so, have multiple objectives: livelihoods, profit, and environmental stewardship, as well as securing food (for nutrition, pleasure, and social functions). Food systems worldwide are in flux, owing to

8 In

this same sense: “Capitalist economies are geared first and foremost to the growth of profits, and hence to economic growth at virtually any cost—including the exploitation and misery of the vast majority of the world’s population. This rush to grow generally means rapid absorption of energy and materials and the dumping of more and more wastes into the environment—hence widening environmental degradation. Just as significant as capitalism’s emphasis on unending expansion is its short–term time horizon in determining investments. In evaluating any investment prospect, owners of capital figure on getting their investment back in a calculable period (usually quite short) and profits forever after.”, FOSTER, J. B. Ecology Against Capitalism. Monthly Review. Retrieved on November 1, 2020 from http://monthlyreview.org/2001/10/01/ecologyagainst-capitalism/

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demand-side drivers (population growth, shifting patterns of consumption, urbanization, and income distribution) and trends in the food supply, which are related to climate change, to competition (for water, energy, and land), and the interactions between food production and other ecosystem services Vermeulen et al. (2012, p.197).

Although not directly related to our study’s subject, the high impact of food production on the erosion of biodiversity is worth mentioning, especially the serious adverse effects of intensive monocultures over water supplies and biodiversity erosion (Shiva 1993). Donald (2004, p. 32) presents the scenario just for soybean, which for the other cultures is not far from regarding the effects: External costs of soybean production include displacement of former land occupiers, chemical pollution, soil erosion and exhaustion, extreme income concentration, social disparity, and the diversion of government subsidies that could otherwise be directed to education and health. Em- ployment on soybean farms is minimal (as low as one worker per 200 ha of soybeans). Growth in the soybean industry has slowed the development of the babassu palm (Attales spp.) industry. The babassu palm, a rainforest tree, sustainably produces oil and products for fodder and building. It represents a sustainable form of tropical forest use (Balick 1987). Another environmental implication of soybean production is soil erosion and depletion. Hoogmoed and Derpsch (1985) documented soil erosion on land recently converted from coffee to soybeans after the coffee plants were killed by frost. Soil erosion was estimated at over 400 tons/ha/year, and soybean production generates more soil erosion than most other crops. Fertilizers are leached from soybean production systems into local water supplies. Increases in nitrogen and phosphorus in the Mississippi River Basin are likely the result of an increase in soybean production in the river’s watershed (Donner 2003). Conversion of uncultivated pasture to soybeans results in a fall in the diversity of economically important rhizobia.

Thus, it provides empirical evidence on the vulnerability of the global food system that is subject to speculation9 and does not alleviate hunger at the same time that produces important implications on climate change in the production and transportation through long distances phases, which Denny et al. (2017, p.140) conclude: The fight to eradicate hunger is an ethical challenge structural change that can only be overcome with systemic changes in the current economic model. Mitigation measures shy, condemn millions to death, disease, or lack of quality of life and therefore need to be replaced and fostered by more effective policies and bold. The goals model adopted by the Agenda of the Millennium and now through the 2030 Agenda generate positive results, but much less than necessary.

9 According

to the UN World Economic Situation and Prospects: “Speculation in the actual, physical exchange of commodities certainly influenced prices as speculators bought and stored commodities, betting on price increases. Such positions have temporarily reduced the supply of goods and have no doubt affected price movements. The impact of speculation in futures markets (that is to say, where speculators do not physically trade any commodities) on price trends is much more difficult to determine, however. Futures trades are bets on buying or selling goods entitlements which are continuously rolled over. It is therefore not clear whether such trading does more to commodity prices other than increase their volatility. In http://www.un.org/en/ development/desa/policy/wesp/. Last Access: Nov 30, 2020.

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In the same vein, the Report Actions to Transform Food Systems Under Climate Change states: Recent decades have been characterized by rapid changes: increasing globalization; increasing inequality, the rise of consumer power and of social media; shifts in consumption; advances in technology; and rapid urbanization. Then there is climate change. Increases in climate variability are already having effects on agricultural systems, and these will intensify in the future—rising CO2 concentrations are being linked to decreases in micronutrient densities of some staple crops20—and increasing frequencies of floods, droughts, and extreme heat are already having serious repercussions for human wellbeing and health (Figure 4). These challenges are particularly problematic for many lower-income countries whose rural populations are largely reliant on agriculture and associated value chains. Many of these people are already food insecure and poor, and these are the people likely to be most affected by climate change (CCAFS 2020, p.10).10

In this scenario, how to reconcile the increasing lack of debates on the subject at both national and international level with International Law, in which the obligation on sustainable development reaches the status of jus cogens.11 It becomes a matter of global justice that imposes a reflection on the existing international institutions to adequately address the challenges, which, as we have been seeing, is far from being resolved while the structures remain the same. As stated by Lemos (2019, p.122): It should be noted that the search for the moral responsibility of the individual is not an accusatory quest or of guilty assessment: many of those collaborators of situations of injustice throughout the centuries did not have the necessary discernment to visualize the misconception in their conduct, even though that nowadays such actions are seen as acts of violation of third party rights. However, if they had the necessary discernment, such individuals would have a compulsory moral responsibility to act in pursuit of institutional reforms to reverse situations of violation of rights. (POGGE 1989, p., 278).

Therefore, breaking down complexity in analyzing the interaction between the global food system and climate change seems to be vital to find full implications not only to the directly involved systems but also to understand the spillover effects in other systems such as trade and human rights. With that in mind, the next section of the chapter builds the telecoupling model to be applied in the study case with the primary objective to shed some light on this critical research agenda, as well as to point out a parsimonious analytical tool that can extrapolate the boundaries of the case in search of more implications.

10 In

addition, see The EAT-Lancet Commissions on Food, Planet and Health, in https://eatforum. org/eat-lancet-commission/. Last Access: Nov 14, 2020. 11 The Article 53 of the Vienna Convention on the Law of Treaties (1969) states: “A treaty is void if, at the time of its conclusion, it conflicts with a peremptory norm of general international law. For the purposes of the present Convention, a peremptory norm of general international law is a norm accepted and recognized by the international community of States as a whole as a norm from which no derogation is permitted and which can be modified only by a subsequent norm of general international law having the same character.”

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3.2.2 The Telecoupling Framework The basic premise to justify the importance of the telecoupling framework is outlined by Liu et al. (2019, p.19) as follows: Human and natural systems around the world are becoming increasingly connected through distant processes, such as international trade, migration, foreign investment, flows of ecosystem services, and species invasion. The speed, scale, complexity, and consequences of these interactions have profound implications for global challenges such as biodiversity conservation, food security, energy security, water security, environmental protection, and human wellbeing.

The relevance of the framework is that it provides analytical tools to explain and understand the feedback between distant systems and subsystems in an increasingly globalized world. By no means do we refer only to the distance in the geographical dimension but especially in the ontological one, such as socioeconomic and environmental interactions across the globe in multiple scales (Meyfroidt 2019). To that end, the model helps to increment visibility of as many implications as possible considering the interactions between complex systems, thus, facilitating policy coordination that for Lindblom & Woodhouse (1992, p.51), is “[...] a set of decisions is coordinated if adjustments have been made in them, such that the adverse consequences of anyone decision for another decision are to a degree and in some frequency avoided, reduced, or counterbalanced or overweight.” As such, the telecoupling model considers three interlocking systems that produce and receive at the same time influences from each other, which for analytical purposes we call “flows” as it encompasses material and non-material exchanges among sending, receiving, and spillover systems. Note in the model below that each system is composed of agents, which relationship produces causes and effects with significant implications (Fig. 3.4). The empirical dimension of our study is to present these implications emerging from the relationship between the global food system and climate change regime beyond the disconnected data produced, considering them two separate systems with distinct ontological and epistemological stances, as their discursive construction have been occurring in specialized forums globally in the last decades (Litfin 1994). As such, the application of the telecoupling model in our study will take into consideration a couple of premises based on the existing literature on the global food system and climate change regime and the exploratory analysis conducted in the first part of this chapter. The basic assumptions are: 1. Causal Direction: the epistemic community seems to agree that both systems produce effects on each other. However, the most significant set of impacts comes from food production to the environment, thus, to climate change. Therefore, the causal Direction adopted in the study is the food system → climate change; 2. Case Study: the application of the model is made in the exportation of Brazilian agri-food to China, in which the production of Carbon Neutral Meat possesses both explanatory and normative dimensions. To this end, the operationalization of the model follows 1) SENDING SYSTEM: Brazil as the source of agri-food

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Fig. 3.4 Telecoupling Framework. (Made by the Authors)

production and exportation that is in line with its developmental strategy based on immediate exportation of commodities; 2) RECEIVING SYSTEM: China as the largest buyer of food commodities from Brazil, seeking food security to its population due to increasing demand as the result of economic growth and improvement of quality of life; SPILLOVER SYSTEM: Climate change regime as the primary affected system due to the increment of agri-food production and long-range transportation (of course, the spillover system might be international trade, biodiversity, or another country, for instance). To a more normative dimension, the application of the telecoupling model will help us to indicate the empirical evidence that is consistent with or not with what Chasek et al. (2013, p. 252) points out as factors for regime success: First is regime design, particularly the strength of the key control provisions aimed at addressing the environmental threat, but also the provisions on reporting, monitoring, regime strengthening, noncompliance, and financial and technical assistance. Second is the

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level of implementation, the extent to which countries (and to a lesser extent, international organizations) adopt formal legislation and other regulations to enact the agreement. The third is compliance, the degree to which countries and other actors actually observe these regulations, and the extent to which their actions conform to the explicit rules, norms, and procedures contained in the regime.

This is especially true if we consider the positioning of China in the international affairs arena and the entanglement between national and international interests, along with the interdependence generated with other countries, along with the disruption in regards to the United States stand. For that reason, Jiang Shixue contests the growing narratives of decoupling, calling them as pseudo-propositions as the praxis does not allow such propositions. Shixue adds that: The rapid development of China’s economy did widen the gap among emerging countries, leading to significant changes in the global economic pattern. However, due to the particular national conditions, such as its large population, the state will remain a developing country for the foreseeable future. President Xi Jinping once said it is a world where countries are linked with and dependent on one another at a level never seen before. Mankind, by living in the same global village within the same time and space where history and reality meet, has increasingly emerged as a community of shared destiny in which everyone has in himself a few others.12

3.3 The Case of the Brazilian AGRI-Food Exportation to China 3.3.1 Setting the Background Conditions Brazil is an important developing country, which policies and activities produce necessary implications in the food production system and environment. As for his commitment to International Law, the country adopts the United Nations Goals for Sustainable Development (SDGs), which establishes the guidelines for formulating public policies to achieve balanced development. For our inquiry, among the 17 SDGs, SDG 1 (No Poverty); 2 (Zero Hunger); and 12 (Responsible Consumption and Production) gain importance as they project effects at both international and local levels. In dealing with CO2 emissions in Brazil, we have to isolate livestock production from other significant sources of emission such as agriculture and high concentration of burnings in the Cerrado and Amazon regions, which in addition to increasing the emissions, decreases the capacity of absorption of CO2. The FAO provides data pointing out that the production of cattle in 2017 was 38.723.000 heads in 2017 and 39.602.000 heads in 2018.13

12 In

https://global.chinadaily.com.cn/a/202007/15/WS5f0e4421a310834817259784.html. Last Access: Nov 14, 2020. 13 In http://www.fao.org/faostat/en/#data. Last Access: Nov 30, 2020.

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On the other hand, or in other words, the buyer side, China has become an essential global player in global affairs. As so, the Chinese president indicated in the 2015 High-Level Forum on Poverty Reduction and Development the need for a new type of international cooperation and exchange.14 This positioning of China has necessary implications for the fulfillment of the U.N. Sustainable Development Goals (SDGs) 1- No Poverty, and 2- Zero Hunger.15 To acquire robust normativity in the international arena as a diplomatic principle, China had to do the homework first in terms of working towards decreasing poverty and hunger in the country by an institutional, systematic approach that includes accountability, policy, investment, assistance, social mobilization, multichannel/general supervision, and assessment. As a result, for instance, the World Banks points out that in the last decade the poverty has been reduced to 0.7%, 7%, and 27.2% of the population living respectively with US$ 1,90, US$ 3,20, and US$ 5,50/day; thus, contributing to a global poverty reduction of staggering 70%.16 Castro and Denny (2020, p.240) found that the “[ . . . ] relationship between Brazil and China is consistent with the Bandung Spirit and that the two countries considered the Brazilian vocation to concentrate exports on commodities; it was not something imposed by asymmetrical relations.” Or in other words, there is a coupling of interests between Brazil and China in terms of food production that leads to economic and food security, respectively (Castro and Denny 2020). This trend is confirmed by Jank et al. (2020, p. 327–8) in presenting an outline of the relationship between the two countries in these words: In recent decades, both China and Brazil have become important global agricultural players. China has proposed various partnership modalities (such as the Belt and Road Initiative – BRI) and trade agreements with many different countries, as trade and food security policies are major Chinese concerns. International trade is central to the Chinese development strategy, and due to its huge population and extensive rural migration, food security is a strong social priority. In this sense, trade is strategic to guarantee food supply in China [...] The comparison of the trade balances of the Brazilian and Chinese agri-food sectors in Figure 2 illustrates the substantial volume of Chinese trade, as well as its clear dependence on agri-food products, which has risen rapidly during the last 10 years. Notwithstanding the spike in Chinese production and export of agricultural products, the country is increasingly dependent on international supply. With its growth in GDP per capita, China will likely continue to be highly dependent on the external market to meet its food security needs.

The mere narrative that there is an imperium relationship in which China is seeking to review the world order and implement some sort of colonial venture towards Latin America is baseless of empirical evidence. As stated by Castro in Baisotti (2020, p.141):

14 In

https://www.chinadaily.com.cn/china/2015-10/16/content_22204202.htm. Last Access: Nov 30, 2020. 15 In https://www.undp.org/content/undp/en/home/sustainable-development-goals.html#:~:text= The%20Sustainable%20Development%20Goals%20(SDGs,peace%20and%20prosperity%20by% 202030. Last Access: Nov 30, 2020. 16 In https://data.worldbank.org/country/china. Last Access: Nov 30, 2020.

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The contribution of this chapter is to lend a social perspective to the relationship between China and Latin America, in which the ideational variables are present and which have important implications for the regions and the world. We found that the Bandung Spirit that provides the axes of solidarity and resistance permeates the relationship, even though in some respects there might be a prevalence of material conditions, which is the only dimension investigated by mainstream academics in International relations. In this sense, as representative of this trend, we point out that the “[...] significance of China’s example is not that it provides an alternative, but the demonstration that alternatives exist” beyond the realist and liberal worlds.

3.3.2 Brazil as the “Sending System” – The Meat Production for Exportation We have been presenting a scenario which points out to Brazil as an agri-food exporter as part of its national strategy and China as an essential buyer for food security reasons. In this section of the chapter, we present the basic features of meat production as part of the said strategy. This is important to conceptualize the country’s role as the “sending system” in the telecoupling model proposed by the authors. Livestock activity is hugely relevant for Brazil. It has internal and external implications on the global food system and climate change as two hyper-connected systems just to be constricted to our study’s dimension. In a broader spectrum, we might cite the trade, intellectual property, and biodiversity systems as those that suffer direct and indirect implications on national, regional, and international levels. It’s crucial for generating a large scale of jobs and sustaining the Brazilian GDP within positive levels (IPEA 2020). As Brazil has the largest cattle herd in the world and is one of the main responsible for the export of meat in the international scenario and, as Olivo (2008) points out, the country has numerous advantages for meat production, such as favorable soil conditions and of climate. However, every country has its ups and downs in the economy, sometimes in accelerated growth, sometimes in economic crisis, with Brazil, it is no different. In surveys carried out since 2001 (Brazil 2020), the country has gone through several phases of expansion and growth on the international stage. As posed by André Souto Maior Pessoa & Débora da Costa Simões in Jank et al. (2020, p.155): In the last two decades, agricultural and livestock production in Brazil has undergone extraordinary growth. From 2000 to 2019, the gross value of production of these sectors more than doubled in real terms1, rising from R$ 262,43 billion to R$ 609,52 billion (Mapa 2019). In the same period, grain production nearly tripled, from 83.0 to 242.1 million tons (Conab 2019), and meat production – including beef, chicken, and pork – almost doubled from 14.8 to 28.5 million tons. As a result of this performance, agribusiness enhanced its strategic position within the Brazilian economy. The sector contributed significantly to the country’s development by increasing food production and its affordability, generating trade surpluses, promoting food safety and security, and improving standards of living in the countryside.

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The international scenario and the performance of countries in global society have a direct impact on their economy. As a result, international trade translated, in summary, by the set of commercial operations carried out by countries operating in a global society, has existed since the beginning of civilization, being one of the most significant evidence of globalization (Portela 2019). The expansion of international trade contributes directly to mitigate the impacts of countries’ internal economic crises in the face of the diversity of markets and activities. (Aguillar 2012). In history, people already used exchange or barter to supply each other’s needs. To better illustrate: When it was discovered that a condition could be satisfied or when a market was found for a product; when the primitive man understood that he could make an exchange with advantage, then the commercial spirit was born (Meirelles 1983, p. 29). Trade, whether domestic or international, is directly linked to the development of nations; that is, countries that have more significant commercial transactions have more considerable growth. When countries’ growth affects the international economy, the globalized economy is leveraged, and all peoples benefit directly or indirectly. The international trade regime plays a vital role in this sense. The World Trade Organization is the global body responsible for enabling equal conditions for trade between countries. (Aguillar 2012). Also critical is the approach on the General Agreement on Tariffs and Trade (GATT) signed in 1947 (Decree 313, of 07/30/1948), of which Brazil is one of the original signatories. (Portela 2019). The objective of GATT is to encourage international trade by promoting freedom in the commercial field, with the strategy of removing customs and non-customs barriers. However, GATT did not promote trade in services, which only took place after the 1994 Marrakesh minutes.17 Later, in the 90s, with the well-known Uruguay Round, the WTO was born to regulate not only trade but also services and investments. It has its legal personality, has permanent bodies and some Member States, and is recognized as one of the most important multilateral institutions globally, as it provides decisions that govern international trade. (Portela 2019, p. 459) In general, the basic principles that govern international trade, from the facilitation of global trade flows of goods and services, non-discrimination, which equates to the same conditions for all countries (Mello 2004), and transparency, allow Brazil to play an essential role in the international trade scene. Agribusiness has been growing steadily and plays a vital role in Brazil’s economy, balancing its trade balance. Through agribusiness, Brazil has been active in the world export scene. Minervine (2012), products manufactured in Brazil, in general, can be seen as “quality” in many countries. For this to happen, it is necessary to remain active

17 The

entire content of the ““Ata de Marrakech “, also called” Ata de Marrakesh “, can be found on the website of Palácio do Planalto, in the legislation section, at the link http://www.planalto. gov.br/ccivil_03/decreto/1990-1994/

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in the international market and take actions that contribute to the negotiations’ sustainability. Considering that livestock is one of the main activities in Brazil, international trade in agricultural products is intense, and goods from this practice represent more than 50% of the country’s exports, totaling more than US$ 120 billion (Brazil 2020). Following the line of reasoning outlined, exports from the slaughterhouse sector increased the collection of foreign currency. Brazil appears as one of the largest exporters of beef in the world. It is second place, second only to the United States due to pork exports (Brazil 2020). The performance of Brazil in the scenario of beef export in international trade, according to points out Olivo (2008), is due to the advantages that the country has for the production of meat, such as favorable climatic conditions, a vast dimension of land and systems with the capacity to do the conversion of vegetable protein to animal. According to the annual report of (Abiec 2020), Pasture in Brazil represents ~90% of the cattle-raising process for beef. The other 10% is due to the final confinement and slaughter of cattle. This puts Brazil in better conditions for the activity and, therefore, increases meat exports. The green ox, raised in the pasture, means that Brazil has a low production cost since cattle feed on pastures, while in other countries, the absence of vast ranges means that cattle have to provide with silage and hay, significantly increasing the cost of meat production. As pointed out by the Agri Benchmark (2015), cattle ranching on farms in Brazil has a lower production cost than the value practiced in European countries (Ireland, Spain, and France) and the United States and Mexico. Therefore, the mechanism that places Brazil in the world export ranking is the primary ratio of the lowest cost, the lowest price. Brazil’s meat is more attractive than that of its competitors due to its low-cost offer. And, in addition to the advantages presented, the format of feeding and cattle breeding, directly linked to climate change, as shown in the text, also puts Brazil at an advantage over other countries because the feeding of cattle with pastures decreases the development of cattle diseases, which directly interferes with meat exports. In this regard, the “mad cow disease,” in other countries, was triggered by the feeding of cattle with bone feed (Bbc 2005; Scot Consultoria 2020). Still, to leverage meat exports once and for all, taking advantage of the mentioned advantages, Brazil took care to equip the slaughterhouses to adopt product quality certificates. It is noticed that Brazil, for a considerable time, has been structuring itself to sell quality meat in the international market. When doing this retrospective, it is easy to arrive at the considerations made in the present text: the lower cost for the livestock activity allows more significant export of meat, but, to remain in the international market, mainly to reach some niche markets and consumers, Brazil needed to adapt to quality meat production, which directly reflects on the environment, which is one of the main problems that concern humanity. This is because, as already portrayed, as he ponders (Portela 2019), all human activity has an impact on the environment, which triggers changes in the conditions

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in which we live, which is why all efforts are required so that the development of the economy can also reflect changes favorable to sustainable development and preservation of the environment. Alertness and concern for the environment have been considered for a long time (Soares 2002); however, nowadays, climate changes are no longer subtle and are directly and continuously affecting humanity. Daily, it is possible to come across news related to various forms of environmental degradation. The greater the preservation of the environment, the lesser the impacts caused by climate change, which culminates in the proximity of Brazil meeting the targets set for reducing the emission of greenhouse gases. In continuity, through the table below (ABIEC 2020), it is possible to see the evolution of beef exports (Fig. 3.5): The growing export activity is easily noticed through the table above, materializing, in practice, the advantages that Brazil has in livestock, cattle raising, and meat exports. Further on, it is essential to check the leading destinations for beef from Brazil (Fig. 3.6): A critical aspect of the livestock industry is the Brazilian Agricultural Research Corporation (EMBRAPA), connected to the Ministry of Agriculture,18 with farmers ranging from small to transnational corporations. This relationship should be explained in carbon-neutral meat later in this chapter. As noted in Figure 3.5, China is among the largest buyers of beef from Brazil. For that reason, in the next part of the chapter, we will look into the role of China as the “receiving system” in the telecoupling model. China plays a vital role in keeping the Brazilian economy at flow, especially during the adverse effects of the Covid19 pandemic (Frischtak et al. 2015). The relationship between the two countries is complementary in terms of economic security for Brazil and food security for China (Rodrigues and Campos 2017). However, Brazil faces severe challenges in keeping agricultural production growth to supply the demands on trade, especially beef. One of the reasons is because livestock is responsible for 70% of the emission of greenhouse gases, making the country move towards optimizing measures to contain the degradation of the environment. There is a growing concern worldwide, especially in parts like Europe and Asia, regarding sustainable production of agri-food products, which places extra pressure on Brazil to find better ways to sustain exportation (Sabadin 2006). The situation has become so critical that in 2020, a group of transnational companies (some Brazilian and other foreign operating in Brazil) get together to put pressure on the Brazilian government to act upon the burnings in the Amazon Forest, demanding, among other things: In particular, this group has been increasingly attentive to and concerned about the impact on businesses of the current negative perception of Brazil’s image internationally when it comes to the social and environmental issues in the Amazon. Such a negative perception

18 For

more information see: https://www.embrapa.br/en/international.Last access: Last Access: Nov 30, 2020.

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Fig. 3.5 Graph of the Evolution of Brazilian Beef Exports (available for public use at the Ministry of Agriculture, Livestock, and Supply) (In https://www.gov.br/agricultura/pt-br/assuntos/camarassetoriais-tematicas/documentos/camaras-setoriais/carne-bovina/2019/53a-ro/cenario-da-carne. pdf. Last Access: Nov 30, 2020.)

Fig. 3.6 Top 10 Brazilian beef destinations in 2018. (available for public use at Ministry of Agriculture, Livestock, and Supply) (In https://www.gov.br/agricultura/pt-br/assuntos/camarassetoriais-tematicas/documentos/camaras-setoriais/carne-bovina/2019/53a-ro/cenario-da-carne. pdf. Last Access: Nov 30, 2020.)

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D. de Castro et al. is highly prejudicial to Brazil, not only from a reputational perspective but also to the development of businesses and indispensable projects for the country. In this context, this group makes itself available to the Amazon Council to contribute to solutions focusing on: • Unconditional, comprehensive combat to illegal deforestation in the Amazon and other Brazilian biomes • Social and economic inclusion of local communities to ensure the preservation of forests • Minimizing the environmental impact from the use of natural resources, and seeking efficiency and productivity in the economic activities that use natural resources • Valuing and preserving biodiversity in business strategies • Adopting mechanisms for the trading of carbon credits • Directing funding and investments to a circular, low-carbon economy • Stimulus packages for economic recovery from the COVID-19 pandemic effects conditional upon a circular, low-carbon economy.19

As an empirical manifestation of this trend, the protocol signed between the General Administration of Quality Supervision, Inspection and Quarantine (AQSIQ) and the Brazilian Ministry of Agriculture in 2010 allows the exportation of meat to China upon the fulfillment of some requirements, such as that the animal should be born, raised and slaughtered in the country, free from diseases, and submitted to residue control (CANAL RURAL 2010; Cateora et al. 2013). Even considering several regulatory factors that hinder exports, there is no decrease in exports, not at least because China is continually growing. However, there are no prospects for an increase in domestic production in the country that will cause the need to export Brazilian beef to decrease. On the contrary, since the signing of the protocol mentioned above, surveys have indicated that the export of meat to China would have considerable growth. Ten years later, the surveys were confirmed.

3.3.3 China as the “Receiving System” Most of all, the economic or social indicators in China tend to be astronomical and controversial due to preconceptions and misunderstandings of the civilizational path in the country. Of course, we might point out some wrongdoings that are tattooed in China’s history, considering the governance system adopted after the Cultural Revolution seems unlikely to happen again. China is the most populous country in East Asia, therefore, presents the need for diverse sources of food (USDA, apud MONTE, et al. 2017). This is what Pei Guo in Jank et al. (2020, p.45) refers to: Between 1979 and 2018, the period investigated in this chapter, the rate of economic growth in China has averaged 9.4%, and more than 770 million people have been lifted out of poverty. After 40 years of reforming and opening up, China has now miraculously

19 In

https://cebds.org/publicacoes/comunicado-do-setor-empresarial-brasileiro/#.X72fnGij_IU. Last access: Nov 18, 2020.

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accomplished more in the past 40 years than what any country or civilization has accomplished in history, which makes the country vital, and permits a rich and affluent lifestyle for a large part of its population.

As such, dealing with food security has been a challenge for China since 1976, the year in which the Cultural Revolution has ended and the dismantlement of the agricultural collectives started to ramp up the quality of life in the countryside. An additional perspective is added by Lardy (1983, p. xi), for whom the turning point had more to do with economics: Mao’s successors understand the costs of rejecting the use of prices and markets in agriculture. Thus they have sought to enhance the use of the market and reduce the interference of the state and Party bureaucracy in agricultural production. Initial efforts to raise productivity through price incentives and increased trade have met with some success.

Since then, China presents staggering growth rates in agricultural productivity, leading to actual increments in economic and food security to the point of reaching poverty eradication in 2020.20 According to Rudolph & Szonyi (2019), this is due to three key elements: data, development, and welfare. Considering the meat consumption per se, China faces serious challenges such as the high cost of meat production and not enough output for securing the daily protein intake of its citizens. According to the think-tank China Power: With regard to meat products, China has witnessed an astronomical increase in consumption. In 1975, China consumed a mere 7 million tonnes of meat. That figure had grown to 86.5 million tonnes by 2018, making it the largest meat consumer in the world. At 55.2 million tonnes consumed, pork was China’s top meat source in 2018 by a wide margin. On a per-capita basis, China consumed 48.9 kilograms (kg) of meat per person in 2018 – roughly half as much as the U.S. (99 kg per capita) and Australia (93 kg), but slightly higher than Japan (43 kg). This exploding demand for meat in China can be largely attributed to changing demographics. The emergence of China’s urban middle class has corresponded with a shift away from a grain-oriented diet to an increasingly meat-heavy intake. More affluent urban residents have likewise developed an appetite for other resource-intensive foods, such as dairy products.21

The solution is to import from other countries as the expectation by the U.S. Department of Agriculture is continued growth through 2023 (Gale et al. 2014). Brazil is the leading partner for meat and other agri-food products imported by China, according to Carvalho (2018); however, the high and growing Chinese internal demand for beef and the Brazilian need to sustain the economy up and running, especially during the Covid-19 crisis, brings essential implications to the climate change regime “spillover system” of our modeling. President Xi Jinping’s speech at the 2017 World Economic Forum stressed the need to address climate change globally and that China is willing to implement

20 In

https://news.cgtn.com/news/2020-11-23/China-eliminates-absolute-poverty-one-monthbefore-schedule-VEp8VAJJS0/index.html. Last access: Nov 26, 2020. 21 In https://chinapower.csis.org/china-food-security/. Last access: Nov 26, 2020.

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the necessary steps to decrease the environmental footprint.22 Looking into China’s exportation plan, we observe enough evidence that the speech’s remarks were not mere rhetoric but a call for action (Dollar et al. 2020). According to Rosito (2020, p.61): The adoption of high environmental standards embedded in the new Chinese strategies of circular economy and sustainability represent new development opportunities. Still, they will also have a cost in the transition to the new model. China is already the world’s largest producer and supplier of renewable energy technologies and usually exceeds its goals faster than expected. In 2018, China launched a carbon tax and recently converted to a bidding system in renewable energies. In recent years there were massive campaigns for adopting stricter environmental standards by Chinese companies, with negative results for economic activity in some regions, which then had to be reversed. In general, the Chinese authorities’ commitment to new sustainability standards is high, and, despite excesses, there is a strong alignment of incentives in this area.

The dimension of the importation side should be of concern as environmental awareness is on the rise in China; thus, sooner or later, the entrance of meat from Brazil should receive resistance within the society (Rudolph and Szonyi 2019). According to Diálogo Chino (2020, p.2): Data from 2017 analyzed by Trase show that China imported beef from more than 1200 cities, but just 25 were responsible for half the risk of CO2 emissions linked to deforestation. A company’s deforestation risk is assessed by looking at the annual rate of deforestation in the municipalities it purchases. The complexity of beef supply chains makes it difficult to identify precisely where cattle bought by large meatpacking companies are sold on.

Therefore, as the “receiving system” of the Brazilian meat industry, the for China to fulfill the obligations assumed in international fora to address climate change will soon be a problem for Brazil, which has been receiving intense pressures from other countries, especially from Europe due to the Mercosul-European union trade agreement that might be not ratified by member-countries due to the dangerous dismantlement of internal structures to take care of the environment as the lack of regard to the assumed international obligations. In this sense, Rodrigo Carvalho de Abreu Lima and Laura Barcellos Antoniazzi in Jank et al.(2020, p.403) pose the challenge: Brazil has a key role to play in tackling global food security. In the meantime, there are social and environmental issues challenging the development of the agricultural systems, including land-use changes and deforestation, soil degradation, pollution, biodiversity loss, gaps in productivity, lack of rural extension, and poor infrastructure, especially for small farmers, as well as other social impacts. The evolution of the multilateral environmental agendas linked to sustainable development positions agriculture at the center of a multifaceted debate about ending hunger, promoting nutritional improvement, producing food more efficiently, adopting good agricultural practices, recovering degraded areas, promoting native vegetation conservation, and restoring and fostering low carbon agriculture and resilience.

22 In

https://america.cgtn.com/2017/01/17/full-text-of-xi-jinping-keynote-at-the-world-economicforum. Last access: Nov 26, 2020.

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For that matter, economic and food security are internal demands for the countries that create important feedbacks in the climate change regime, thus, presenting the need to explore the carbon-neutral meat in this study.

3.3.4 The Carbon Neutral Meat – The Implications to the “Spillover System” Within the climate change legal framework,23 which Brazil is part of, a strategy for offsetting the emission of greenhouse gases would be the planting of forests that makes it possible to neutralize carbon that is removed from the environment and placed in the organic material in the trees, called ‘biomass.’ This carbon neutralization occurs in the proportion of 1 (one) ton of gas for every 7 (seven) trees in their first twenty years of age. From there, it is possible to determine the number of trees that will be needed to neutralize GHG emissions.24 Between the years 2006 and 2009, the image of the Brazilian livestock industry was unsatisfactory. Brazil applied techniques that no longer are accepted worldwide, such as the rapid deforestation of the Amazon due to cattle ranching, degraded pastures, and high gas emissions, such as methane, contributing to the greenhouse effect. If Brazil kept applying such techniques, it would face serious market issues, with significant trade barriers, reducing meat exports, a robust market for the country. To change the situation, the Brazilian government has set bold goals, creating aggressive actions based on brand new developments and in-depth research. At the United Nations conference in 2009, COP 15, Brazil proposed to reduce gas emissions by 40%, which challenged even the most developed countries, setting new goals such as to reduce the deforestation rate in the Amazon by 80% and 40% in the Cerrado. To successfully meet those goals, they launched the LCA plan (Low Carbon Agriculture). Based on the LCA plan, the Brazilian Agricultural Research Corporation (Embrapa) started researching what would make sustainability possible. Embrapa, a Brazilian state-owned company, was created in April 1973 by the Ministry of Agriculture, Livestock, and Supply (MAPA), and its biggest challenge is the development of sustainable agriculture and livestock model, respecting the environment. The studies on greenhouse gases in livestock and how to neutralize them brought up an innovative method, where it is possible to integrate agriculture, livestock, and forest. And that is because, during photosynthesis, the vegetation increases

23 https://www.mma.gov.br/clima/convencao-das-nacoes-unidas/acordo-de-paris.Last

Access: Nov 30, 2020. 24 https://www.ibflorestas.org.br/conteudo/compensacao-de-co2-com-plantio-de-florestas. Last Access: Nov 30, 2020.

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the quantity of oxygen in the atmosphere due to the exchange between carbon and oxygen, neutralizing the methane gas produced by ruminants, in addition to improving the landscape, controlling the temperature, and giving comfort to the cattle, achieving a higher quality of the produced meat. Due to several possibilities for countries’ services to minimize the devastation of the environment and prevent the chaos of climate change, Embrapa – Brazilian Agricultural Research Corporation – created and developed the concept brand “Carne Carbono Neutro” (Neutral Carbon Meat – NCM), whose technology is 100% Brazilian and innovative.25 One of the premises of Embrapa is the concern with sustainability that appears as a priority in the mission, values, and vision of the referred company; that is, all actions of the company concentrate efforts aimed at satisfying and maintaining the needs of humanity without compromising the future of generations to come. Given the relevance, it is essential to mention that Embrapa moves to stimulate agriculture and livestock, emphasizing new technologies that are passed on to national producers to maintain their growth together with it and its partners. Currently, Embrapa has 38 research units and remains to invest in a team. Its performance is so expressive that it reflects in the international arena; even some countries have already suggested the possibility of creating company units to develop research of their interests.26 Cooperation on Embrapa’s international scene is broad and is present on several continents through partnerships; it is also coordinated by the Secretariat of Intelligence and Strategic Relations (Sire), which, in turn, have significant repercussions with the Brazilian government’s cooperation program for the use of national technologies for countries with similar needs. Regardless to say the importance of the livestock activity for Brazil, which along with agriculture, represents 21,4% of the Brazilian GDP, being necessary for economic security.27 The whole chain of production depends on it at local and international levels.28 It turns out that the exercise of livestock activity is responsible for 69% of greenhouse gas emissions in Brazil, according to the Climate Observatory. These gases are in the form of methane (CH4), resulting from the digestive process of the ruminant nitrous oxide (N2O, due to the use of nitrogen fertilizers). The CO2 emission is also verified. Given the concern with the impacts of climate change and the verification of the amount of greenhouse gas (GHG) emissions in the exercise of agriculture, the need

25 https://www.embrapa.br/busca-de-solucoes-tecnologicas/-/produto-servico/3488/marca-

conceito-carne-carbono-neutro.Last Access: Nov 30, 2020. https://www.embrapa.br/atuacao-internacional. Last Access: Nov 30, 2020. 27 In https://www.cnabrasil.org.br/cna/panorama-do-agro#:~:text=Em%202019%2C%20a%20so ma%20de,R%24%20494%2C8%20bilh%C3%B5es. Last Access: Nov 30, 2020. 28 In https://agenciabrasil.ebc.com.br/pesquisa-e-inovacao/noticia/2016-10/setor-agropecuario-eresponsavel-por-69-das-emissoes-de-gases. Last Access: Nov 30, 2020. 26 In

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Fig. 3.7 The NCM Seal (available for public use at Embrapa’s website) (In https://www.embrapa.br/ busca-de-publicacoes/-/ publicacao/1056155/carnecarbono-neutro-um-novoconceito-para-carnesustentavel-produzida-nostropicos. Last Access: Nov 30, 2020.)

arose to create practices to mitigate these gases’ emission, such as research and creation “sustainable meat” with carbon sequestration. Since 2010, the Federal Government has instituted the ABC Plan (Low Carbon Emission Agriculture)29 to leverage the implantation of crop-livestock-forest integration systems (ILPF) as a strategy to mitigate GHG emissions in agriculture. As mentioned elsewhere, Embrapa started research on the possibility of selling meat sustainably, thus creating the NCM. This brand aims to confer the Neutral Carbon Meat seal to show that the beef meat had its GHG emission volumes neutralized in the production process due to silvopastoral integration systems (livestock-forestry – IPF) or agro-silvopastoral (crop-livestock-forest – ILPF).30 The NCM seal has been trademarked within the Brazilian Intellectual Property Office (INPI)31 under 907078982, 907079156, and 907079270.32 This seal can be used in frozen, fresh, or processed meats, which are preserved in curing, salting, smoking, or adding chemical preservatives; thus, attaching objective and subjective levels of sustainability meanings to products (Fig. 3.7).33 Alves et al. (2015) provide a detailed description of the symbolism within the seal: The circular arrow symbolizes the fixation, neutralization, and recycling of carbon, alluding to the letter “C .” The green color symbolizes the neutralization of GHG emissions from beef production through sequestration and carbon fixation provided by the tree component (represented, in a stylized way, by a branch with two leaves). The black color symbolizes the GHG emissions of the system (represented, in a stylized way, by the termite of a bovine, Bos indicus).

29 In https://ainfo.cnptia.embrapa.br/digital/bitstream/item/203141/1/Carne-carbono-neutro-1.pdf. Last Access: Nov 30, 2020. 30 in https://ainfo.cnptia.embrapa.br/digital/bitstream/item/203141/1/Carne-carbono-neutro-1.pdf. Last Access: Nov 30, 2020. 31 in https://www.gov.br/inpi/pt-br/@@search?SearchableText=carne+carbono+neutro.Last Access: Nov 30, 2020. 32 In https://www.embrapa.br/busca-de-publicacoes/-/publicacao/1056155/carne-carbono-neutroum-novo-conceito-para-carne-sustentavel-produzida-nos-tropicos. Last Access: Nov 30, 2020. 33 https://cancer-code-europe.iarc.fr/index.php/pt/12-formas/regime-alimentar/1447-o-que-seentende-por-carne-vermelha-e-produtos-de-carne-transformados. Last Access: Nov 30, 2020.

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The NCN relies on the creation of cattle and the planting of trees in the middle of the pasture. A recent study carried out by Embrapa in Campo Grande shows that approximately 200 (two hundred) trees per hectare would be sufficient to neutralize the methane emitted by 11 (eleven) cattle in either IPF or ILPF approaches (ALVES et al. 2015). Worthwhile to mention the Embrapa’s trijunction project, which started in 2017 in the city of Cocos, Bahia state, increased production in sandy soils that are susceptible to degradation.34 Also, Embrapa developed the SisILPF software to assist producers in planting trees and to simulate planting performance according to the intended objectives.35 The scenario reveals that the trees are fundamental for livestock and livestock activity, in general, because the carbon removed from the atmosphere is stored in the tree trunks, neutralizing greenhouse gas emissions (GHGs). Also, trees contribute to the environment’s temperature, absorb CO2, reduce winds, and provide animal welfare. There is the challenge of using the seal as a benefit for everyone involved in the production chain – from the producer to the consumer -, preventing the ‘seal’ from bringing any burden, which will undoubtedly carry difficulties to the practice of sustainable meat. This is also justified by the fact that livestock is a primary activity that, since ancient times, requires simplicity in its process. Cattle farmers, in general, have always practiced the activity with the least amount of input and investment possible, which is why, for sustainable meat production to be used and come to become habitual practice, there is a need to be advantageous and simplified. Tax exemptions, as occurs to increase and encourage all production, would be tax incentives such as, for example, the collection of sales taxes over meat with the NCM seal. However, it is necessary to point out that the tax incentives might negatively affect the international trade regime. As noted, the production of carbon neutralized meat production is feasible, which corroborates Brazil’s race to meet the goals for reducing greenhouse gas emissions by 37% below 2005 levels in 2025 and 43% in 2030.36 Marfrig Global Foods (MARFRIG) is the first Brazilian company to receive the NCM seal. MARFRIG is a Brazilian animal protein-based food company founded in 2000 with operations worldwide, focusing on sustainability and acceptable practices.37 Since 2000, MARFRIG has stood out for actions related to the environment and sustainability in general, being a pioneer in sustainable beef. Attesting its

34 https://www.embrapa.br/busca-de-noticias/-/noticia/56078722/projeto-trijuncao-produz-carne-

com-baixa-emissao-de-carbono?p_auth=WQ1nyE2b. Last Access: Nov 30, 2020. https://www.cnpf.embrapa.br/software/. Last Access: Nov 30, 2020. 36 in https://www.mma.gov.br/informma/item/12978-noticia-acom-2015-09-1163.html. Access: Nov 30, 2020. 37 In https://www.marfrig.com.br/marfrig/quem-somos. Last Access: Nov 30, 2020. 35 in

Last

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commitment to sustainability, MARFRIG, after investing almost R$ ten million, launched the Viva brand – an NCM with the Embrapa’s seal.38 The Viva line products come from livestock and forest integration so that the emission of greenhouse gases is reduced in the livestock process. The consequence is a sustainable beef capable of meeting not only the internal goals of Brazil, but also of leveraging exports, especially for companies that seek the difference in a product as raw as meat, but that is concerned with climate change and its strengths consequences for the environment and humanity. The NCM has the advantage of being the right product, especially for the public of conscious consumers who have become aware of the constant concern with the environment, with climate change, deforestation, and with too many greenhouse gas emissions. Considering the need for Brazil to increase the sustainable production of meat due to the international demands from buyers, the NCM option, although in the first stages, seems to be an essential contribution to achieving the U.N. SDGs. However, a closer look at the production stages points out plenty of actions to present a more robust and sustainable international food system. For instance, according to the Brazilian Association of the Meat Export Companies (ABIEC), the meat exportation to China and Hong Kong reached in the year 2020 (even with the Covid-19 pandemic) the sum of 800.000 tons.39 As one of the largest buyers of meat products from Brazil, China possesses a strong and growing demand to increase food security. As mentioned before, the internal growth of China’s economic activity and the increasing living standards of its citizens present high pressure on the food system. On the other hand, Brazil is a historical producer of commodities for exportation, which relies on this activity to sustain a large chunk of its economy.40 As such, the NCM is just a part of the puzzle in which other sources of pollution should be considered: (1) road transportation from farms to exportation port that accounts for 60% of all cargo transported according to Wanke (2010), and (2) the sea transportation from Brazil to China that for geographical reason should be thru the Atlantic, thus, a longer distance to cover (Huang et al. 2017; Garcia et al. [s.d.]).

38 In

https://agroemdia.com.br/2020/08/27/em-parceria-com-a-embrapa-marfrig-lanca-linha-decarne-carbono-neutro/. Last Access: Nov 30, 2020. 39 In http://abiec.com.br/exportacoes/. Last Access: Nov 30, 2020. 40 In https://www.pwc.com.br/pt/publicacoes/setores-atividade/assets/agribusiness/2013/pwcagribusiness-brazil-overview-13.pdf. Last Access: Nov 30, 2020.

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3.3.5 The Forgotten Dimension: Land and Sea Transportation Impacts in the “Spillover System” Despite the lack of structure in the transport sector, the transportation of the meat to the ports causes high costs and tons of emissions. According to reports issued by the Brazilian statistics bureau-IBGE, the leadership of the domestic market production is in the state of Mato Grosso, which caused the migration of many slaughterhouses to the Midwest region; however, under a logistical perspective for exportation promotes higher costs in terms of production. The challenges range from long distance, precarious infrastructure, a reduced fleet of specific vehicles for meat transportation, and unusual containers. The prices are controlled by a handful of companies (Bezerra 2005). Logistics is a strategic activity for managing the processes necessary to obtain, move inventory, raw materials, semi-finished materials, aiming at optimizing activities, cost, maximizing your profit, and bringing greater competitiveness. Transportation itself is responsible for a large share of operating expenses, on average, 59% of the whole logistics sector in Brazil. The exportation sector in a given country needs the necessary development and investment to be competitive. In other words, the availability of choice of an efficient and low-cost transportation system can ultimately influence the development of a nation (Silva Neto and Caixeta-Filho 2009). The logistical and transportation cost is directly linked to the displacement of the product, creating economic bottlenecks, and such recognition seeks efficiency in competition for the Brazilian market. For this reason, the Brazilian Association of Meat Exporting Industries (ABIEC) created a sustainability project, the Beef Report 2020, where it demonstrates in an annual report the development of meat exports and new goals where one of the stages brings a logistics distribution project, to identify possible expectations of distribution and international consumers, establishing objectives for product distribution, to achieve greater efficiency by strengthening connections in the production and distribution chain (ABIEC 2020). The absence of railways makes road transportation the only option for transporting meat production to the port; in addition to the lack of railway structure, meat production requires specific refrigerated transport and the greater distance between production and the demand port higher transport costs, in fuel, also include the emission of greenhouse gases, the logistical issue and the lack of government investment is one-day obstacles that can bring difficulties to leverage the Brazilian economy. With all the logistical challenges, Brazil still manages to stand out in the export of meat, so it is worth noting that infrastructure investment would place the country on much higher levels. Air pollution from the emission of greenhouse gases released by the operation of vehicles has an impact on climate change, causing acid rain or even the fog resulting from the mixture of gases in the atmosphere, the famous smog effect. In the surveys carried out, as pointed out in this work, livestock is responsible for 69% of greenhouse gas emissions, and the transport sector accounts for at least 20%

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of global CO2 emissions. According to information from the Ministry of Science and Technology (MCT), the transport sector remains a segment with high emissions of these gases. As pointed out by Angelo (2019), the increase in emissions was due to two factors: the rise in mileage traveled by cars and the fall in the share of alcohol consumption, with the consequent rise in demand for gasoline, in the middle of the year. 2010 due to the difference in prices for the final consumer. The road transportation mode predominates in Brazil due to its reach and accessibility compared to the railway network, airports, and waterways; in other words, the country’s investment is concentrated in transport by road. Another issue that promotes road transport, especially foreign trade, is the ease of clearing goods, which can wait months for clearance. When transporting by road, the bureaucracy with the procedures and release of goods is higher. Corroborating with the fact that road transport is the most used, in surveys conducted, as pointed out by the Automotive Business website, the fleet of trucks in circulation in Brazil grew, reaching two million units in 2019, making a total of 2,3% higher than the previous year’s records. The effects of pollutants emitted by motorized transport affect the environment, causing, in addition to climate change, about three thousand deaths per year in the city of São Paulo alone, according to studies carried out by the Experimental Air Pollution Laboratory. (Sabadin and Michels 2006). The problem reported is a long-standing one, and measures to contain or mitigate the impact of greenhouse gas emissions from transportation are not sufficient. As an example, in the city of São Paulo, where the problem is evident, one of the measures adopted in an attempt to minimize the impact of pollutants was the rotation of vehicles through the plates, implemented in 1997 by the city hall. It should also be said that maritime transport is also responsible for a large part of the transportation of goods and, since 2018, it has been the target for the reduction of greenhouse gas emissions. The International Maritime Organization (IMO) has proposed reducing greenhouse gas (GHG) emissions for international maritime transport. By 2050, maritime transport needs to reduce pollutant emissions by 50%, and this action is of paramount importance considering that international maritime transport transports around 80% of global trade in volume.41 In this context, it should be noted that, before and after the 1997 Kyoto Protocol, maritime transport had not been the target of reducing greenhouse gas emissions. Likewise, in 2015, with the Paris Agreement, there was also no reference to maritime transport and measures for ships to be concerned about any action to minimize GHG emissions.

41 In

United Nations Conference on Trade and Development (UNCTAD), Review of Maritime Transport (UNCTAD/RMT/2018, United Nations Publications 2008) 23. The largest share of deadweight tonnage is carried by dry bulk carriers (iron ore, coal, grain and similar cargo), followed by oil tankers (crude oil and by-products) and container ships.

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The International Maritime Organization, however, since the 1990s is dealing with environmental issues, taking care to establish some measures to reduce the emission of pollutants by ships42 and the European Parliament, in turn, adopted the Regulation on Monitoring, Reporting, and Verification of Carbon Dioxide Emissions from Maritime Transport (EU-MRV),43 which consists of checking the CO2 emission by ships with more than 5000 gross tons calling at European ports (Garcia et al. 2020). However, with the intense flow of road or sea transport, these measures, even of extreme necessity for breaking the impacts of pollution on the environment, affect international trade in general, because, to reduce, some measures are necessary. Changes that, inevitably, will influence the distribution of the products transported. Therefore, there is chaos: on the one hand, the practice of measures to preserve the environment and contain the impact of climate change that the world has been suffering, and on the other, the increase in services and more significant embarrassment for products to reach their destination (Garcia et al. 2020). Also, one of the most simplified measures to reduce the emission of greenhouse gases is the creation and implementation of taxes that will be responsible for inflating tax revenues. As a consequence of all the above, the expression “spillover effect” appears, which can be classified as contamination that arises through a considerable change in the relations between markets. Such a situation can be compared to the crisis that, upon reaching one country, is overflowing to another, causing economic instability, introducing changes in the perspectives of investors, contaminating other countries. (Forbes and Rigobon 2001). Several economists say that if a shock from one country is overflowed to another, even if there is no change in the cross-market effects, this overflow can be understood as contagion. Some theories claim that in a crisis context, the expansion of the contagion effect between markets causes economic instability, inducing changes in investors’ expectations about other needs, which would not happen in a situation of financial equilibrium. In this sense, a shock to a nation’s economy can be spilled over to other countries and significantly impact markets(Forbes and Rigobon 2001). In international trade, it is easy to see a connection network; that is, several international relations directly and indirectly link the countries. The financial market is a branch of these relations. Thus, everything in one country can be felt in another, as there is a connection given the uniqueness of interests and demand alone. Breaking the impacts of climate change, however, has a high price; that is, practically all measures to minimize the reduction of greenhouse gas emissions in

42 In

Convention on the International Maritime Organization, 6 March 1948, 289 UNTS 3, arts 37–41 43 In Regulation (EU) 2015/757 of the European Parliament and of the Council of 29 April 2015 on the monitoring, reporting and verification of carbon dioxide emissions from maritime transport (EU-MRV Regulation) para 19, Official Journal of the European Union, 19 May 2015, L 123/55.

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Table 3.1 Framing the Brazilian meat exportation to China case into the telecoupling model Telecoupling Framework Systems Sending Receiving Spillover Flows Material/energy

Information

Agents

State Intergovernmental organizations Non-states

Causes

Economic

Political Technological Environmental Effects

Environmental

Socioeconomic

Carbon Free Meat Brazil China Climate Change regime Meat Money Fossil fuels – Land and sea transportation Massive T.V. adds Prices Cattle production techniques Embrapa United Nations International maritime organization Marfrig Transportation companies Need to sustain Brazilian GDP – Economic security China’s demand for food International commitments – China and Brazil (zero hunger and cut CO2 emissions) Improving cattle production techniques Pressures by foreign investors for sustainable development Decrease local CO2 emissions Increase of local forestry coverage Low impact on local biodiversity Increase of CO2 emissions in transportation New effects on the local environment yet to be measured Help compliance with SDGs Local development New local preservationist mindset Intensive land use in Brazil Increase in food security in China Yet to be measured real effects in the spillover system – Overall contribution of the NCM Impact of the NCM model on the international trade system

Made by the Authors based on Liu et al. (2013)

transport – which is used for everything – transcend the barriers of every country that puts in place means to protect the environment, especially about the emission of greenhouse gases whose effects are devastating for the planet.

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It is concluded that the international financial market connects the countries to move the domestic and foreign economy, being certain that the implementation of measures to reduce the emission of greenhouse gases generates consequences that will undoubtedly have, from any angle, the contamination for all countries in the international scenario, so the expression “spillover effect” has never made so much sense.

3.4 Final Remarks and Findings After reviewing the relevant literature on the complicated systems and describing the parameters of the case study, the final remarks point out a promising dimension in which both countries -Brazil and China – towards compliance with international obligations related to eradicate hunger and protect the environment. However, the application of the telecoupling model suggests that some causes and effects need to be dealt with, especially considering that land and sea transportation are significant sources of pollution, or in other words, the relationship between the sending and receiving systems in the food system dimension causes a severe impact in the spillover system that is climate change. The findings of this chapter are summarized below, representing the empirical stance of the telecoupling analysis.

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Brasileira de Economia, Administração e Sociologia Rural (SOBER). https://ideas.repec.org/p/ ags/sobr06/145669.html Scartozzi, C. M. (2020). Reframing climate-induced socio-environmental Conflicts: A Systematic Review. International Studies Review.https://doi.org/10.1093/isr/viaa064. Shiva, V. (1993). Monocultures of the mind: Perspectives on biodiversity and biotechnology. Atlantic Highlands: Zed Books. Silva Neto, W., & Caixeta-Filho, J. (2009). Logística da exportação de carne bovina: Uma aplicação em programação linear. Revista de Economia Mackenzie, 7, 59–77. Stebbins, R. (2001). Exploratory research in the social sciences: What is exploration? In Exploratory research in the social sciences (pp. 2–18). Thousand Oaks: SAGE. Thanuskodi, S. (2010). Journal of social sciences: A bibliometric study. Journal of Social Sciences, 24(2), 77–80. https://doi.org/10.1080/09718923.2010.11892847. The Rise of State-Transnational Capitalism in the Xi Jinping Era: A Case Study of China’s International Expansion in the Soybean Commodity Chain. (n.d.). Retrieved August 19, 2020, from http://www.mattersburgerkreis.at/site/de/shop/jepartikel/shop.item/1934.html Tukey, J. W. (1977). Exploratory Data Analysis. (1a Edição). Newbury Park: Pearson. Vermeulen, S. J., Campbell, B. M., & Ingram, J. S. I. (2012). Climate change and food Systems. Annual Review of Environment and Resources, 37(1), 195–222. https://doi.org/10.1146/ annurev-environ-020411-130608. Wanke, P. F. (2010). Logística E Transporte De Cargas No Brasil: Produtividade E Eficiência No Século Xxi. (1a edição). São Paulo: Atlas. Will, A. (2020). Epistemic communities and the creation of the intergovernmental panel on climate Change. München: GRIN Verlag. Yin, R. K. (2017). Case study research and applications: Design and methods (6th ed.). Los Angeles: SAGE.

Chapter 4

Genetic Resources Padmavati G. Gore, R. Gowthmi, Kuldeep Tripathi, Pavan Kumar Malav, Vandana Tyagi, Neeta Singh, and Veena Gupta

Abstract Nature has bestowed with abundant wealth of genetic resources (plants, animals, microorganisms, forests). Genetic resources play a crucial role in the fulfilment of five basic needs of human beings (food, feed, fodder, fiber, and fuel). Due to climate change and over-exploitation of genetic resources in their natural habitat, at a global level, about 13.49% of the world’s vascular plants (3,00,000), totalling approximately 40,468 species are under varying degrees of threat, extinction rate has increased to hundred or thousand-fold compared to background extinctions, thus led to the ‘sixth mass extinction’ crisis and plant diversity is threatened at the habitat, species, and genetic levels to a degree never seen previously in our planet’s history. Our ability to estimate the exact reaction of biodiversity to climate change is limited by the lack of long-term data on parameters that might be affected by climate change. Thus, climate change may result in the extinction of many populations and species. Further, COVID-19 has also reemphasized on the role of strengthening Food Security Systems on long-term basis. Since GRs are the building blocks and insurance for developing new crop varieties and modern breeds. More significant investment in the conservation and utilization of GR is of utmost importance. Therefore, there is an urgent need to plan and implement complementary conservation strategies for different genetic resources to ensure safe conservation. This chapter contains an introduction and overview of genetic resources, systematic conservation and sustainable utilization of plant genetic diversity to attain food and nutrition security. Keywords Biodiversity · Climate change · Conservation · Genebank · Plant genetic resources

P. G. Gore () · R. Gowthmi · K. Tripathi · P. K. Malav · V. Tyagi · N. Singh · V. Gupta ICAR – National Bureau of Plant Genetic Resources, New Delhi, India © Springer Nature Switzerland AG 2022 C. M. Galanakis (ed.), Environment and Climate-smart Food Production, https://doi.org/10.1007/978-3-030-71571-7_4

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4.1 Introduction Biological diversity refers to the variability among living organisms including, inter alia, terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part (Article 2, CBD). Biological diversity is measured at three levels at the ecosystem level (ecological diversity), at the species level (taxonomic diversity), and the population level (genetic diversity) (Fig. 4.1). Biodiversity is the basis of life on the planet earth. Morton and Hill (2014) described the importance of biodiversity in several terms viz., (i) Economical- livelihoods of the farmers, fishers and timber workers and many others are dependent on biodiversity. It gives raw materials for consumption and production. (ii) Ecological life support- Ecosystem services (ESS) like, supply oxygen, clean air and water, pollination of plants, pest control, wastewater treatment are important for functioning of the planet earth, all those are provided by biodiversity. (iii) Recreation- many recreational activities depend upon our unique biodiversity, such as birdwatching, hiking, camping and fishing. Our tourism industry also depends on biodiversity. (iv) Cultural- our culture is closely connected to biodiversity through the expression of identity, through spirituality and aesthetic appreciation. (v) Scientific- biodiversity represents a wealth of systematic ecological data that help us to understand the natural world and its origins. Tough biodiversity is the support system of life. It is under serious threat as a result of population growth and resource consumption, climate change and global warming, habitat conversion and urbanization, invasive alien species, overexploitation of natural resources and environmental degradation (Fig. 4.2). Biologists estimate that species extinctions are currently 500–1000 times the normal as compared to the rate seen previously in Earth’s history.

Fig. 4.1 Three levels of biodiversity

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Fig. 4.2 Main threats to biodiversity

Genetic resources (GRs) are the reservoir of useful genes for adaption in the era of climate change (Tripathi et al. 2020). Genetic resources refer to the genetic material of actual or potential value and genetic material is any material of plant, animal, aquatic, microbial or other origin containing functional units of heredity. They are the central for achieving zero hunger, good health and wellbeing on planet earth as well the key for sustaining future plant breeding. Genetic resources diversity for food and agriculture (plants/crops, animals, aquatic resources, microorganisms, forests, and invertebrates) plays a key role in fulfilling the basic needs of human beings concerning food and health. In addition to the food and nutrition, genetic resources are essential for pollination in plants, water purification, soil health maintenance, food processing industries, protection against extreme weather events and many other vital services. It also includes wild species that are harvested for food and other purposes.

4.2 Type of Genetic Resources Genetic Resources includes plants, forests, animals, aquatic and microorganisms. These are the primary material upon which the world relies to maintain a healthy life. The enormous diversity of plants, animals and the microorganism is present on Earth, and an estimated ~8.7 million eukaryotic species are present on Earth (Mora et al. 2011). However, only about 1.2 million species have so far been documented (Shivanna 2020). A total of 4,00,000 plant species, 60,000 tree species (FAO), ~7,770,000 species of animals, ~611,000 species of fungi (molds, mushrooms), over 1,60,000 aquatic species (fish, aquatic molluscs, crustaceans, and plants), ~36,400

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Fig. 4.3 Types of genetic resources

species of protozoa, ~27,500 species of chromista (including, eg. brown algae, diatoms, water molds), 9680 species of bacteria are reported on Earth (Mora et al. 2011). However, 99% of bacteria and protista species remain unknown (Fig. 4.3).

4.3 Animal Genetic Resources Animal Genetic Resources (AnGR) are one of the important components of agricultural biodiversity and plays a crucial role in achieving agricultural sustainability. It contributed mostly for ecosystem services (ESS) by their complex relations with their respective environments. AnGR represent the collection of domesticated birds and mammals (40 species) with more than 8800 breeds (including 7000 distinct local and 1000 transboundary livestock breeds) of currently used for food and agriculture. Locally adapted breeds have developed specific adaptive features for harsh environments. It plays an essential role in the provision of ecosystem services and is an integral part of many agroecosystems by (1) transforming feeds unsuited for human consumption into nutritious foods and other useful products; (2) interacting directly with ecosystems through grazing, browsing, trampling and the production of dung and urine; and (3) they can respond to fluctuations in resource availability and climate by moving around.

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Table 4.1 Diversity of aquatic species identified in the wild and the number of farmed and fished species or species items and families Wild species (marine Taxon Finfish 18,768 Molluscs 47,844 Crustaceans 52,412 Aquatic plants 12,128 Total 131,152

Wild species (freshwater) 12,834 4998 11,990 2614 32,436

Number of farmed species 344 95 60 40 554

Number of farmed families 80 27 13 21 151

Number of captured species 1452 151 181 29 1839

Number of captured families 237 37 34 14 335

Source: the state of the world’s aquatic genetic resources for food and agriculture, 2019

4.4 Aquatic Genetic Resources Aquatic Genetic Resources includes, DNA, genes, chromosomes, tissues, gametes, embryos and other early life-history stages, individuals, stains, stocks and communities of aquatic organisms. They strengthen the productivity and sustainability of the aquaculture and capture fisheries and the essential services provided by aquatic ecosystems in marine, brackish and freshwaters. Table 4.1 represents the diversity of aquatic species.

4.5 Microbial Genetic Resources Microorganisms and invertebrates are the most numerous groups of species on Earth. Invertebrates are a highly diverse group, ranging from tiny insects to giant squids, and account for more than 95 per cent of total animals. Microorganisms comprise the vast and diverse range of organisms that are too small to be seen by the human eye and are vital to food and agriculture. Various kinds of microorganisms establish mutually beneficial symbiosis with agricultural plants (e.g. colonizing roots and improving nutrient uptake) or animals (e.g. living the rumens of species such as cattle, sheep, and goats and enabling them to digest fibrous foods). It also provides vital services in food processing industries through fermentation by yeasts and bacteria in the production of bread, yogurt, etc. Many crops depend on invertebrate pollinators, most commonly bees for its fruit set. Both microorganisms and invertebrates play significant roles as biological control agents, and are indispensable in nutrient cycling, in the decomposition and the recycling of organic matter in soils. For conservation of filamentous fungi, actinomycetes, yeasts and bacteria, various method like short term and long term are used (Fig. 4.4). Culture and storage conditions vary with the type of AIMs like fungi are preserved under mineral oil storage, or freeze-drying/lyophilization or slants.

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Fig. 4.4 Conservation of microbes

4.6 Forest Genetic Resources Thirty per cent of the global land area are covered under forests ecosystems that providing habitat for numerous terrestrial species. Forests are essential part of livelihoods, economic and social development of human beings. They are also valuable for environment protection and conservation of natural resources. Forests contain more carbon than the atmosphere. With climate change, forests, with their dual roles as both producers and absorbers of carbon, take on a new importance. Forests are the world’s important and most valuable renewable natural resource repositories of terrestrial biological diversity. Forest genetic resources are the genetic materials of actual or potential economic, environmental scientific or societal value possess within and among trees and other woody plant species. Diversity in forest genetic resources is the basic need for the evolution and adaptation of forests and trees to cope up with changing climate. Forests and trees enhance and protect landscapes, ecosystems, and production systems as well as provide goods and services which are crucial to the continued existence and wellbeing of all humanity. Till now approximately 80,000 to 1, 00,000 tree species have been described (Oldfield et al. 1998; Turok and Geburek 2000) together with larger woody shrubs, they likely represent about 50 per cent of all vascular plant species. Tree species possess high levels of genetic variability that helps in adapting tree species to adopt with changing climate as well can be utilized in improving the forest production and environmental services. Trees and other woody species are long-lived, perennial organisms. For longterm survival at a particular site, they need to be able to endure environmental extremes and changes to persist in the soil. The high genetic variability associated with stress tolerance and disease resistance help them to persist and thrive for long periods. Table 4.2 represents life spans longest-lived conifers on planet earth. Around 1.6 billion people (more than 25 per cent of the world’s population) depend on forest resources for their livelihoods (FAO 2013).

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Table 4.2 Life span of some of the longest-lived conifers > 1000 years Xanthocyparis nootkatensis, Cryptomeria japonica, Juniperus scopulorum, Larix lyalli, Pinus albicaulis, Pinus edulis, Pinus flexilis, Pseudotsuga menziesii, Taxodium distichum

> 2000 years Juniperus occidentalis, Lagarostrobus franklinii, Pinus aristata, Pinus balfouriana, Sequoia sempervirens

> 3000 years Fitzroya cupressoides, Sequoiadendron giganteum

> 4000 years Pinus longaeva, Picea abies

4.7 Plant Genetic Resources (PGR) Although domesticated plant species represent only a small proportion of the Earth’s total biodiversity, they are of fundamental importance to humankind. Human beings are selecting plants from the wild, domesticating them, and adapting them to our needs for around 10,000 years. This process of domestication has led to the existence of an enormous number of different cultivars (product of plant breeding) and landraces (product of farmer-based selection and breeding). IPGRI (1993) defined PGR as “the taxonomic and genetic diversity of plants that is of value as a resource for the present and future generations of people”. According to an estimate, so far, a total of 2, 50,000 higher plant species described taxonomically (Ungricht 2004) out of that about 1, 15,000 are PGR (46%) and 35,000 (14%) species are cultivated (Khoshbakht and Hammer 2008). Plant Genetic Resources include the landrace and their wild relatives, improved and released cultivars and genetic stocks, wild and weedy species. PGR that has the valuable socio-economic importance other than food inclde ornamental species, medicinal species and fuel species (Fig. 4.5). Crops may here be broadly defined as any cultivated species, so including those used for food, food additives, feed (animal food), fiber, fuel, feedstocks, bio-based materials, fun (ornamentals and turfgrass), medicine, environmental uses, poisons and gene sources. Different components of plant genetic resources are represented in Table 4.3.

4.8 Threats to Plant Genetic Resources Plant biodiversity has economic, social, and ethical value for humankind, it is a finite natural resource and it is currently being eroded or lost by over-exploitation and unsustainable human practices. According to an estimate of FAO (2010), human activities contributed more to biodiversity loss. More alarmingly, up to 75% of the genetic diversity of crops has already disappeared. The main threats to PGR are (a) introduction of modern varieties; (b) overexploitation of natural resources; (c) gradual social change; (d) development interventions affecting habitat and

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Diversity of Plant Genetic Resources

PGR for food and agriculture

1. Landraces 2. Released cultivars 3. Genetic stock

Wild and Weedy species

1. Crop Wild relatives 2. Weedy races

Non food socio economic PGR

1. Ornamental species 2. Medicinal species 3. Fuel species

Fig. 4.5 Types of plant genetic resources Table 4.3 Different components of genetic resources and its importance Genetic Resources Landraces:

Obsolete cultivars

Released cultivars: Advanced breeding lines

Genetic stocks: Crop wild relatives: Weedy races:

Non-food socioeconomic species: Mutants

Definition and importance Genetically diverse crop varieties that are the product of traditional seed saving systems, commonly associated with local adaptation, and traditional agricultural practices in more marginal agricultural environments. Obsolete cultivars comprise of genotypes that are out of large-scale cultivation, generally replaced by higher performing genotypes. These varieties may be still cultivated in small pockets. Improved varieties of of the past are known as obsolete cultivars. They are the varieties which were popular earlier and now have been replaced by new varieties. Genetically uniform or clonal crop varieties bred by plant breeders and currently sown by farmers. Pre-released plants which have been developed by plant breeders for use in modern scientific plant breeding are known as advanced lines. They include advanced cultures which are not yet ready for release to farmers. Sometimes advanced breeding lines and stocks are not very much productive, but constitute a valuable part of gene pool for various economic characters. Material used by plant breeders to develop modern cultivars by means of crossbreeding or use of biotechnology tools Wild species that are relatively closely related to a crop and may be crossed with the crop either using conventional or genetic engineering techniques to introduce desirable traits from the wild species to the crop. Wild species that occur as part of crop-weed complexes either as result of hybridization between the crop and wild species, the crop and wild species being evolved from the same ancestor or as the crop’s progenitor, often found in gene centers but which hybridize freely with the crop and may introgress useful genes from wild species. Species whose value is associated with non-agricultural exploitation, such as species with medical, forestry, recreational or ornamental value.

Mutants refer to a product of mutation. It may be a genotype, a cell or a polypeptide.

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production environment (e) lack of appropriate policy support for the conservation and utilization of local PGR.

4.9 Conservation of Plant Genetic Resources Conservation of PGR aims to sustain the taxonomic and genetic diversity of plants, the habitats or ecosystems in which they live and the interrelationships between plants, other organisms and their environment and genetic erosion. Conservation is not just about maintaining alleles or individual plant populations but includes all levels of biodiversity from ecosystems (a community of organisms and its abiotic environment), through communities (collection of species found in a common environment or habitat), species and populations to genetic diversity within populations. A conservationist needs the knowledge of genetics, ecology, geography, taxonomy and many other disciplines for sustainable conservation of biodiversity. The benefits of germplasm conservation include (a) increase in productivity and food security (and consequently economic returns); (b) reduce the pressure of agriculture on fragile areas, forests and endangered species; (c) build stability, robustness, and sustainability of farming systems; (d) contribute to sound pest and disease management; (e) diversify products and income opportunities from farms; (f) help maximize effective use of resources and environment (restore ecological health); (g) reduce dependency on external inputs and (h) increase nutritional value and provide a source of pharmaceutical products.

4.10 Conservation Strategies CBD (1993) defines two strategies of conservation namely, ex situ conservation and in situ conservation. The two general strategies may be subdivided into several specific techniques. Figure 4.6 represents the detailed techniques used for the conservation of plant diversity.

4.11 In Situ Conservation In situ conservation refers to the conservation of ecosystems in their natural habitats where they have developed their distinctive properties, while maintaining viable populations of the species. Thus in-situ conservation refers to protection zones and areas of high biological diversity. In-situ conservation areas are known as natural ecosystems and protect the species with minimum or no human interference. In this method, the genetic resources are protected in the form of biosphere reserves, forest reserves, botanical gardens, national parks, on-farm conservation.

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Fig. 4.6 Techniques used for the conservation of plant genetic resources

4.12 On-Farm Conservation In on-farm conservation, diverse set of populations, i.e., locally developed traditional crop varieties, with associated wild and weedy species or forms are under continuous cultivation and management by farmers in the agro-ecosystems where a crop has evolved. It conserves a range of diverse traditional varieties that are suited to low input farming and adaptable to changing climate.

4.13 Ex-Situ Conservation Ex situ conservation is the conservation of plants and animals away from their native ecosystem. Ex situ conservation methods conservation of orthodox seeds at low temperature (−18 ◦ C to −20 ◦ C), recalcitrant seeded species, forest crops and clonally propogated species as whole plant in the field genebanks. For those species that do not produce seed or have recalcitrant seeds are conserved in the in vitro genebanks. In the cryo genebanks also recalcitrant seedee species, pollens and DNAs are preserved under ultra-low temperature (−196 ◦ C).

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4.14 Field GeneBank (FGB) In the Filed genebank germplasm collected from different places is planted in the field as a permanent living collection. Field genbank is away from the original natural habit of plant species so, natural evolution and adaptation processes are either temporarily halted or altered that suppressed the selection pressures. Field genebank is generally used for conserving species that produce non-orthodox (recalcitrant) or intermediate seeds, species produce very few seeds or no needs, species that are clonally propagated, or species with long juvenile periods (Pandey et al. 2019).

4.15 Seed Genebank Nature has conserved plant species through inbuilt in seeds during millions of years of evolution. Seed conservation was the oldest conservation strategy followed by our ancestors to store seeds for next sowing, for consumption in offseason etc. Conservation of plant species with orthodox seeds (seeds that can be dried to low moisture contents i.e., 3–7%) at low temperatures (~ -20 ◦ C), is the most commonly used method of ex situ conservation of plant genetic resources. It is reported that ~90% of the 7.5 million accessions stored in genebanks belong to orthodox seeds and it is estimated that only 25–30% of them are distinct, bringing the number of unique accessions maintained around the world to around two million. Tables 4.4 and 4.5 represents the number of accessions held at different genebanks. Figure 4.7 represents genebank across the globe.

Table 4.4 Plant germplasm conserved at CGIAR genebanks CGIAR Genebank International crop research Institute for the Semi-Arid Tropics, India International Rice research institute, the Philippines International Centre for agricultural research in dry areas, Syria International institute of tropical agriculture, Nigeria Centro Internacional de Agricultura tropical, Colombia Centro Internacional de Mejoramiento de Maíz y Trigo, Mexico Africa rice Centre International livestock research institute, Ethiopia Centro Internacional de la papa, Peru Information and communication division, International Center for Research in agroforestry, Kenya

Total no. of accessions conserved 1,28,155 1,32,140 1,54,405 28,000 66,787 2,09,277 26,098 20,229 16,061 2005

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Table 4.5 Plant germplasm conserved in National Genebanks National Genebank USDA Genebanks, USA National bureau of plant genetic resources, India N.I. Vavilov all-Russian scientific research Institute of Plant Industry, Russia Leibniz Institute of Plant Genetics and Crop Plant Research, Germany Plant breeding and acclimatization institute, Poland Asian vegetable Research and Development Center, Taiwan Department of applied genetics, John Innes Centre, Norwich Research Park, UK Millennium seed Bank project, seed conservation department, Royal Botanic Garden, Kew, UK Division of genetics and plant breeding, research Institute of Crop Production, Czech Republic

Fig. 4.7 Genebanks around the World

Total no. of accessions conserved 6,25,112 4,60,000 3,46,415 1,37,010 67,980 60,883 26,669 46,689 43,151

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4.16 In Vitro Conservation and Cryopreservation of Plant Genetic Resources Plant genetic resources can be conserved by in situ or ex situ methods. In in situ conservation method, the genetic resources are protected within their natural habitat in the form of biosphere reserves, forest reserves, botanical gardens, national parks, on-farm conservation. In ex situ conservation, the genetic resources have been collected from their natural habitats and conserved away from its natural habitats in seed genebanks, field genebanks, in vitro gene banks, cryo genebanks. The most effective and convenient way to conserve genetic diversity is by way of seed banks, wherein storage of desiccated seeds is carried out at low temperature. This strategy is not applicable to crops that do not produce seed (e.g. bananas, garlic) or those (many tropical trees) which produce recalcitrant seeds which are intolerant to low moisture and low temperature (considered suitable for storage of orthodox seeds) or the ones (fruit, timber and ornamental trees) which are vegetatively propagated (Engelman 2011; Reed et al. 2013). A valuable alternative to complement such collections and widen the genetic base of crops and endangered plant species is in vitro conservation in in vitro genebanks (Pandey et al. 2015).

4.17 In Vitro Conservation for Short- to Medium-Term Conservation The objectives of in vitro conservation of germplasm are maintenance and exchange of germplasm in disease-free and genetically stable state using different tissue culture techniques. With an aim to conserve germplasm in vitro, several laboratories were established worldwide and have been engaged in developing in vitro propagation protocols and in vitro conservation protocols. The main aim of in vitro conservation programmes is to reduce frequent demand for subculture and this can be achieved in two ways i. maintaining cultures under normal growth under standard culture room conditions (SCC) (normal growth) ii. Maintaining cultures under growth limiting strategies (slow growth strategies) (Rajashekaran and Sahijram 2015).

4.18 In Vitro Conservation by Slow Growth The main objective of in vitro conservation by slow growth is to decrease the subculture duration without causing any adverse effects to the cultures. In vitro conservation by slow growth can be achieved by a single method or in combination with different methods of the following (a) physical growth limitations (low temperature, low light or photoperiod, minimal containment, reduced oxygen concentration,

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osmotic adjustment, modification of gaseous environment), (b) chemical growth limitations (growth regulator reduction, growth retardants), (c) nutrient limitations (low macronutrient levels, low micronutrient levels). The storage temperature is species-specific, as the tropical species are cold-sensitive, 15–20 ◦ C or even higher is required for stored, while temperate species can be stored 1 and 10 ◦ C, whereas tropical species are stored in the range of 10-22 ◦ C. The light intensity can be reduced by 60% from standard requirement (Rajashekaran and Sahijram 2015). Inclusion of osmotic, growth retardants, removal of growth regulators in the medium has also proved to be an important technique to prolong subculture period. These methods have been quite effective in restricting the growth of many plant species. However, most of the successful in vitro conservation strategies were achieved by using combinations of different methods of slow growth strategies.

4.19 Cryopreservation for Long-Term Conservation of Germplasm Cryopreservation is the only method presently available for long-term conservation of germplasm, where viable biological resources are conserved at ultra-low temperatures at −196 ◦ C in liquid nitrogen (LN) or at −150 ◦ C in the vapour phase, at this ultra-low temperature, all the cellular, metabolic and biochemical events were arrested without changing its genetic structure (Reed 2017). Cryopreservation can be achieved by using diverse type of explants viz., seed, embryo, embryonic axes, shoot tips, nodal segments, meristems, dormant buds, budwoods, cell suspension cultures, and pollen (Engelmann 2011; Sakai and Engelmann 2007). Cryopreservation can be attempted using classical (freeze induced) or new cryopreservation (vitrification based) techniques. Table 4.6 represents the examples of plant species conserved using in vitro and cryopreservation techniques.

4.20 How Can Plant Genetic Resources Help in the Climate Change Scenario? Through the process of evolution and intensive agriculture, the ancient cultivars have been completely replaced by the present high-yielding varieties and hybrids, in addition, monoculture system of crop production has resulted in the loss of genetic diversity. Plant genetic resources are the source for novel traits, genes of interest in the present climate change scenario. Better utilization of plant genetic resources in the crop improvement or direct introduction into the cultivation may help to combat the adverse effect of climate change on crop production.

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Table 4.6 Examples of plant species conserved using in vitro and cryopreservation techniques Conservation strategy and species 1. In vitroconservation Low temperature storage (3–2.5 years) Malus pumila, Mentha spp., Musa spp., Solatium tuberosum, Corylus sp., Malus domestica, Musa spp., Encapsulation (3–6 months) Rauvolfia serpentina, Phoenix dactylifera, Dendrobium spp., Psidium guajava, Zingiber officinale Minimal media (4–36 months) Chlorophytum borivilianum, Curcuma longa, Dioscorea alata, Elettaria cardamomum, Garcinia indica, Ipsea malabarica, Solatium tuberosum Minimal medium + cold storage (4–96 months) Allium sativum, Castanea sativa, Colocasia esculenta, Drosophyllum lusitanicum, Fragaria ananassa, Lilium hybrid Minimal medium + growth retardant (3-24 months) Ananas comosus, Coffea arabica, Dierama luteoalbidum, Ipomoea batatas, Vitis heyneana Low temperature + low light (1.5 to 12 months) Rauvolfia serpentine, Solanum tuberosum Low temperature + mannitol (12–22 months) Musa spp. Low-cost media using market sugar, isabgol (12–14 months) Musa AAB cv. Karpura Chakkarakeli, Curcuma longa Mineral oil overlay (12 months) Bacopa monnieri 2. Cryopreservation In vitro explants using vitrification Bacopa monnieri, D. deltoidea, Dianthus sp., Dioscorea bulbifera, Dioscorea alata, Dioscorea opposita, Kaempferia galanga, Malm domestica, Musa ABB subgroup Monthan, Picrorhiza kurroa, Prunus dulcis

References

Negri et al. (2000), Pedroso de Oliveira et al. (2000), Pruski et al. (2001), Reed and Chang (1997) and Banerjee and de Langhe (1985) Ray and Bhattacharya (2008), Hamed et al. (2003), Devi et al. (1998), Rai et al. (2008) and Sundararaj et al. (2010) Chauhan et al. (2016), Tyagi et al. (2007), Acedo and Arradaza (2012), Tyagi et al. (2009), Malik et al. (2005), Martin and Pradeep (2003) and Gopal and Chauhan (2010)

Hasan et al. (2007), Capuana and Di Lonardo (2013), Bessembinder et al. (1993), Gongalves and Romano (2007), Hassan and Bekheet (2008) and Bonnier and Tuyl (1997)

Canto et al. (2004), Naidu and Sreenath (1999), Madubanya et al. (2006), Jarret and Gawel (1991) and Pan et al. (2014)

Rajasekharan et al. (2007) and Pruski (2001)

Bhat and Chandel (1993)

Agrawal et al. (2010), Tyagi et al. (2007) and Agrawal et al. (2008a) Sharma et al. (2012)

Sharma et al. (2017), Sharma et al. (2011), Sharma and Pandey 2015, Sekizawa et al. (2011), Hong et al. (2009), Mukherjee et al. (2009), Preetha et al. (2013), Niino et al. 1992, Agrawal et al. (2008b), Sharma and Sharma (2003) and Chunnuntapipat et al. 2000 (continued)

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Table 4.6 (continued) Conservation strategy and species In vitro explants using droplet vitrification Ananas comosus, Colocasia esculenta (taro), Dioscorea spp. (yams), Elaeis guineensis, Fragaria sp., Musa spp., Vanilla planifolia

In vitro explants using encapsulation-dehydration D.bulbifera, Malus spp., Prunus dulcis, Pyrus communis, Pyrus syriaca, Vitis spp., Dioscorea bulbifera, Zingiber officinale,

In vitro explants using encapsulation-vitrification Dioscorea bulbifeml, Dioscorea opposite, Malus domestica Embryonic axes - rapid drying and rapid cooling Aesculus glabra, Aesculus hippocastanum, Aesculus octandra, Camellia sinensis, Euphoria longan, Fagus grandiflora, Fagus sylvatica, Hevea brasiliensis, Juglans nigra, Juglans regia, Landolphia kirkii, Quercus robur Seeds- desiccation (0–100% germination/survival) Abies alba, Larix decidua, Abies concolor, Pinus ponderosa, Acacia acuminata, Acer saccharinum, Aesculus pavia, Betula pendula, Bossiaea ornata, Bursaria occidentalis, Cassia venusta, Citrus maxima cv. Feizhouyou, Citrus maxima cv. Mansailong, Elaesis guineensis, Eucalyptus burracoppinensis, Eucalyptus Ioxophleba v. gratiae, eucalyptus lantyoolei, Fagus sylvatica, Glandulosa, Populus Mimusops elengi, Manilkara zapota, Jatropha curcas, Melaleuca cuticula, Melaleuca huegelii, Melaleuca uncinata, Pinus sylvestris, Pirns echinata, Populus nigra, Qualea parviflora, Salix hallaisanensis, S.gracilistyla, Templetonia retusa, Ulmus pumila, Viminaria juncea, Zygophyttum aurantiacum

References Souza et al. (2016), Hu et al. (2015), Sant et al. (2008), Leunufna and Keller (2003), Gantait et al. (2015), Pinker et al. (2009), Agrawal et al. (2014a, b) and Gonzalez-Arnao et al. (2009)

Mandal 1999, Mandal et al. 2009, Wu et al. 1999, Zhao et al. 1999, Shatnawi et al. 1999, Dereuddre et al. 1990, Scottez et al. 1992, Tahtamouni and Shibli 1999, Wang et al. 2000 and Yamuna et al. (2007)

Yin and Hong (2010), Zhao et al. (2016) and Paul et al. 2000

Pence (1992), Pence (1990), Chaudhury et al. (1990), Wesley-Smith et al. (1992), Fu et al. (1990), de Boucaud et al. (1991), Vertucci et al. (1991), Hor et al. (1990) and Radhamani and Chandel (1992)

Stanwood and Bass (1978), Connor and Bonner (2001), Chmielarz (2010), Yan et al. (2014), Grout et al. (1983), Ahuja (1986), Wen et al. (2013), Prada et al. (2015), Touchell and Dixon (1993), Suszka et al. (2014), Michalak et al. (2015), Wetzel et al. (2003), Popova et al. (2013) and Engstrom (1966)

(continued)

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Table 4.6 (continued) Conservation strategy and species Seeds- direct freezing (23–100% germination/survival) Acacia farnesiana, Bauhinia acuruana, Bletilla striata, Byrsonima basiloba, Cariniana estrellensis, Cariniana legalis, Cedrela fissilis, Chamaecrista desvauxii, Chorisia pubiflora, Crotalaria cf. spectabili, Dalbergia miscolobium, Dioscorea sp., Eriotheca gracilipi, Kielmeyera coriaeea, Magonia pubescens, Melanoxylum brauna, Mimosa sp., Pinus canariensis, Pinus echinata, Platypodium elqgans, Roupala montana, Tabebuia serratifolia, Tocoyena formosa, Triplaris gardneriana, Zeyheria montana

References

Salomao (2002), Hirano et al. (2005), Pita et al. (1998) and Wetzel et al. (2003)

4.21 Millet Genetic Resources Millets possess several morpho-physiological, molecular and biochemical characteristics which confer better tolerance to environmental stresses than major cereals. Millet is a common term for a various forage grasses known for their small “coarse’‘ grains viz., barnyard millet (Echinocloa crusgalli), black fonio (Digitaria iburua), finger millet (Eleusine coracana), foxtail millet (Setaria italica), guinea millet (Brachiaria deflexa), Job’s tears (Coix lacrymajobi), kodo millet (Paspalum scrobiculatum), little millet (Panicum sumatrense), pearl millet (Pennisetum glaucum), proso millet (Panicum miliaceum) and tef (Eragrotis tef ) and white fonio (Digitaria exilis). As the result of the green revolution, over 50% of the global food requirement is met by only three crops viz., maize, wheat and rice. Millets are the abundant source of vitamins, minerals, sulphur-containing amino acids and phytochemicals, hence commonly called as ‘nutritious millets or nutricereals. Millets are distributed in all types of agro-climatic conditions and adopted to diverse climatic adversities like water scarcity, high and low temperature, poor soil fertility. Short maturity duration of millets facilitates utilization of these crops in multiple cropping systems. These crops are very hardy and after harvesting grains can be preserved for a long time without any treatment, this quality makes them “Famine Reserves Crops”and “Climate Resilient Crops”. Besides, in the past few decades, millets gaining importance due to the increase in diabetes, gluten allergy, obesity etc. due to human diet as millets are low glycemic index crops. Millets are also reported to have greater racial diversity within the species. In barnyard millet, two cultivated species viz., Echinocloa colona (Indian barnyard millet) and E. crusgalli (Japanese barnyard millet) with two subspecies crusgalli and utilis. Echinocloa colona has two subspecies ie., colona and frumentacea with no races in subspecies colona and four races stolonifera, intermedia, robusta, and laxa

126

P. G. Gore et al.

in subspecies frumentacea. E. crusgalli has two subspecies crusgalli and utilis with two races crusgalli and macrocarpa in subspecies crusgalli and utilis and intermedia in susspecies utlis. In finger millet, a total of five races (coracana, which resembles the subsp. africana, vulgaris, compacta, plana, and elongata) (Dida and Devos 2006) and 10 subraces (laxa, reclusa, and sparsa in elongata; seriata, confundera, and grandigluma in plana; liliacea, stellata, incuriata, and digitata in vulgaris). In foxtail millet, three races i.e., moharia, maxima, and indica were reported. Race moharia has three subraces viz., aristata, fusiformis, and glabra, race maxima has three subraces viz., compacta, spongiosa, and assamense and race indica has four subraces viz., erecta, glabra, nana, and profuse. In kodomillet three races (regularis, irregularis and variabilis) were reported. In fonio, white and black fonio, differentiated by seed color is reported (Dwivedi et al. 2012). Apart from species and racial diversity, greater diversity occurs even within species level. Millets are grown in 90 countries across the world. Among different methods of conservation, ex situ seed conservation is the most commonly used method for conservation of millet genetic resources. To date, approximately 1, 61,708 accessions of millets are conserved in the seed gene banks across the globe, 98.1% of those conserved species are cultivated and 1.9% is of wild types. Among the different species of millets, finger millet, foxtail millet, pearl millet, and proso millet represents the largest collection of cultivated millets germplasm conserved in the seed genebanks. Globally, 816 accessions of cultivated species and 27 accessions of wild species in barnyard millet, 33,596 accessions of cultivated species and 1079 accessions of wild species in finger millet, 285 accessions of cultivated species in Fonio, 45,761 accessions of cultivated species and 552 accessions of wild species in foxtail millet, 4252 accessions of cultivated species in kodo millet, 159 accessions of cultivated species and 9 accessions of wild species in job’s tears, 1017 accessions of cultivated species in little millet, 41,910 accessions of cultivated species and 1369 accessions of wild species in pearl millet, 1017 accessions of cultivated species in little millet, 24,844 accessions of cultivated species in proso millet, 6001 accessions of cultivated species and 31 accessions of wild species in tef (Dwivedi et al. 2012). Table 4.7 represents the collection of millets germplasm conserved globally.

4.22 Underutilized Fruit Crops Genetic Resources “Underutilized fruit crops” are those fruit species, which has the potential to improve livelihood of people, food security and sovereignty. Still, these are not being fully realized because of their limited competitiveness with commodity crops in mainstream agriculture. Though their potential; may not be utilized at the national level, but they are efficiently utilized locally. Underutilized fruits are highly adapted to marginal, complex, and difficult environments, no need of intensive agriculture practices, usually grows in waste, barren lands and contributing significantly to diversification and resilience of agro-ecosystems. In recent past, due to the climate change scenario and to meet the needs of growing population, several

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Table 4.7 Global germplasm collections of millets Crop Barnyard millet

Fonia Finger millet

Institute ICAR-National Bureau of Plant Genetic Resources (ICAR-NBPGR) International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) National Institute of Agrobiological Sciences (NIAS) USDA Agricultural Research Service (USDA-ARS) Tropical Crops & Forages Collection, Australian Plant Genetic Resource Information Service Laboratory of Genetics and Biotechnology, Univ, Aboney-Calvi ICAR-National Bureau of Plant Genetic Resources (ICAR-NBPGR) International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) All India Coordinated Minor Millet Project (AICMMP) Kenya Agricultural Research Institute (KARI) Institute of Biodiversity Conservation (IBC) USDA Agricultural Research Service (USDA-ARS) Serere Agricultural and Animal Production Research Institute (SAARI) SADC Plant Genetic Resource Centre Central Plant Breeding and Biotechnology Division, Nepal Agricultural Research Council (CPBBD) National Center for Genetic Resources Preservation National Institute of Agrobiological Sciences (NIAS) Mt. Makulu Central Research Station Institute of Crop Germplasm Resources, Chinese Academy of Agricultural Sciences (ICGR-CAAS) Seed Conservation Unit, Plant Genetic Resources Centre

Country India

No. of accessions 1,956c

India

749a

Japan

159b

USA

232b

Australia

67a

Benin

261a

India

11,378c

India

6,804b

India

6,257a

Kenya

2,875a

Ethiopia USA

2,156a 1,452b

Uganda

1,231a

Zambia Nepal

1,037a 869a

USA

702a

Japan

565a

Zambia China

390a 300a

Sri lanka

295a (continued)

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P. G. Gore et al.

Table 4.7 (continued) Crop Foxtail millet

Jobs tear Kodomillet

Little millet

Institute Chinese National Genebank (CNGB) ICAR- National Bureau of Plant Genetic Resources (ICAR-NBPGR) ORSTOM-MONTP All India Coordinated Minor Millet Project (AICMMP) National Institute of Agrobiological Sciences (NIAS) International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) North Central Regional Plant Introduction Station, USDA-ARS Biologie Vegetale Appliquee, Institut Louis Pasteur (IUT) Kenya Agricultural Research Institute (KARI) USDA Agricultural Research Service (USDA-ARS) Estacion de Iguala, Instituto Nacional de Investigaciones Agricolas (INIA) Plant Germplasm Institute, Faculty of Agriculture, Kyoto University (KYOPGI) Plant Genetic Resources Institute, Natl. Agric. Res. Centre National Institute of Agrobiological Sciences (NIAS) ICAR-National Bureau of Plant Genetic Resources (ICAR-NBPGR) All India Coordinated Minor Millet Project (AICMMP) International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) USDA Agricultural Research Service (USDA-ARS) Tropical Crops & Forages Collection, Australian Plant Genetic Resource Information Service ICAR-National Bureau of Plant Genetic Resources (ICAR-NBPGR) All India Coordinated Minor Millet Project (AICMMP) International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) USDA Agricultural Research Service (USDA-ARS)

Country China India

No. of accessions 26,670b 4,665c

France India

3,500a 2,512a

Japan

2,450a

India

1,535b

USA

1,000a

France

850a

Kenya USA

772a 762b

Mexico

350a

Japan

274a

Pakistan

138a

Japan

140a

India

2,376c

India

1,111a

India

656b

USA

336b

Australia

227a

India

2,125c

India

544a

India

466b

USA

212b (continued)

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Table 4.7 (continued) Crop Pearl millet

Institute ICAR-National Bureau of Plant Genetic Resources (ICAR-NBPGR) Embrapa Milho e Sorgo (CNPMS) ORSTOM-MONTP, Montpellier Cedex Plant Genet. Resour. of Canada, Saskatoon Research Centre, Agr. and Agri-Food Canada ICRISAT Serere Agric. & Animal Prod. Res. Inst.,(SAARI) USDA Agricultural Research Service (USDA-ARS) Institut National de la Recherche agronomique du Niger (INRAN) National Plant Genetic Resources Center, National Botanical Research (NPGRC) Institute Plant Genetic Resources Institute, Natl. Agric. Res. Centre SADC Plant Genet. Resour. Centre Natl. Gene Bank of Kenya, Crop Plant Genet. Resour. Centre (KARI-NGBK) Fodder Research Institute Zambia Agriculture Research Institute (ZARI) Australian Tropical Crops & Forages Collection, Australian Plant Genetic Resource Information Service Station de Recherche Agronomique de Cinzana (SRAC) Inst. Biodiversity Conserv (IBC). Addis Ababa Embrapa Recursos Gen_eticos e Biotechnologia (CENARGEN) Centre National des Resources Phytog_en_etiques, Ministere de l’agriculture et du Developpement Rural (CNRF) National Institute of Agrobiological Sciences (NIAS) Centre de Recherches Agricoles de Farako-Ba (CRA) Chinese National Genebank (CNGB)

Country India

No. of accessions 8,566c

Brazil France Canada

7,225a 4,059a 3,764a

Niger Uganda

2,817a 2,142a

USA

2,063a

Niger

2,052a

Namibia

1,416a

Pakistan

1,377a

Zambia Kenya

785a 499a

Pakistan Zambia

333a 323a

Australia

252 a

Mali

243a

Ethiopia

166a

Brazil

161a

Angola

135a

Japan

133a

Burkina Faso

112a

China

103a (continued)

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P. G. Gore et al.

Table 4.7 (continued) Crop Institute Proso millet N.I. Vavilov All-Russian Scientific Research Institute of Plant Industry International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) Institute of Crop Germplasm Resources, Chinese Academy of Agricultural Sciences (ICGR-CAAS) Ustymivka Experimental Station of Plant Production Yuryev Plant Production Institute UAAS ICAR-National Bureau of Plant Genetic Resources (ICAR-NBPGR) Botanical Garden of the Plant Breeding and Acclimatization Institute USDA Agricultural Research Service (USDA-ARS) North Central Reg. Plant Introd. Station, USDA-ARS All India Coordinated Small Millet Project Estacion de Iguala, Instituto Nacional de Investigaciones Agricolas (INIA) National Institute of Agrobiological Sciences (NIAS) Australian Tropical Crops & Forages Collection, Australian Plant Genetic Resource Information Service Bangladesh Agriculture Research Institute Research Institute for Crop Production Tef Institute of Biodiversity Conservation Centro de Pesquisa Agropecuaria dos Cerrados (CPAC) USDA Agricultural Research Service (USDA-ARS) ICAR-National Bureau of Plant Genetic Resources (ICAR-NBPGR) National Institute of Agrobiological Sciences (NIAS) a Dwivedi

Country No. of accessions Russian Federation 8,778a India

8,429a

China

6,517a

Ukraine

3,976a

Ukraine India

1,046a 1,035c

Poland

721a

USA

719b

USA

713a

India

577a

Mexico

400a

Japan

302b

Australia

245a

Bangladesh

209a

Czech Republic Ethiopia Brazil

162a 4,741a 400a

USA

368a

India

253a

Japan

137a

et al. 2012; b Goron and Raizada (2015); c http://genebank.nbpgr.ernet.in

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organizations and researchers associated with biodiversity conservation are in search of alternatives for the presently cultivating main crops. Hence, neglected and underutilized species (NUS) are assuming vital importance and being recognized as “Future Smart Foods (FSF)” (Padulosi et al. 2011; Prakash et al. 2019). Despite with the vast diversity in fruit crops, our food basket is filled with only 25 major fruit crops, left out hundreds of minor fruits which are confined to specific regions local consumption (underutilized fruits). Distribution of underutilized edible fruits and nuts is given for the 12 regions of diversity of cultivated plants by Zeven and de Wet (1982) viz., Chinese-Japanese (222), African (131), Indochinese-Indonesia (226), European-Siberian (62), Australian (57), South American (263), Hindustani-Indian (344), Central American and Mexican (122), Central Asian and NearEastern (38), NorthAmerican (255) and Mediterranean (30). The actual number of underutilized fruits is unknown and every day new species have been identified and added to the diversity of these fruits. Few selected list of underutilized fruits are Aegle marmelos (Bael), Anacardium occidental (Cashew nut), Annona muricata (Soursop), Annona spp (Custard apple), Artocarpus heterophyllus (Jackfruit), Artocarpus hirsutus (Wild jack), Averrhoa carambola (Carambola), Castanea spp. (Chestnut), Citrus maxima (Pumello), Cordia dichotomagürke (Indian cherry), Diospyrus peregrina (Indian Persimmon), Emblica officinalis (Aonla), Elaeagnus conferta (Wild Olive), Elaeocarpus tectorius (Jew’s plum), Eriobotrya japonica (Loquat), Flacourtia indica (Batoko palm), Flacourtia montana (Indian plum), Garcinia spp. (Mangosteen), Feronia limonia (Wood apple), Ficus carica (Fig), Manilkara achras (Sapota), Mimusop selengi (Spanish cherry), Moringa oleifera (Drumstick), Morinda citrifolia (Noni), Morus spp. (Mulberry), Passiflora edulis (Passion fruit), Phyllanthus acidius (Star gooseberry), Pithocellobium dulce (Manila tamarind), Rubus racemosus (Black raspberry), Syzygium cumini (Jamun), Tamarindus indica (Tamarind), Trapa bispinosa (Water chestnut), Zizipus mauritiana (Ber).

4.23 Crop Wild Relatives Climate change is making major new demands on crop diversity as well as creating new opportunities for using diversity to mitigate its adverse impacts on agricultural systems. Crop wild relatives can help adapt cultivated crops to changing climatic conditions. The wild relatives of many crops have already proven to be important sources of genes for improving agricultural production. Crop wild relatives make a considerable contribution to plant breeding. Wild relatives have provided genes for various biotic and abiotic stresses like, disease resistance, tolerance of extreme temperatures, tolerance of salinity and resistance to drought. For example, wild relatives of the potato have offered resistance to late blight, Colorado potato beetle and various viruses. Three different wild relatives of cultivated peanuts have been used to develop varieties resistant to root knot nematodes. A wild tomato has facilitated an increase in the production of cultivated tomato of worth US$250

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P. G. Gore et al.

million annually to farmers in California (USA). Outbreak of grassy stunt virus in the 1970s transmitted by the brown plant hopper, devastated the rice fields in South and South-East Asia. After screening more than 17,000 samples of cultivated and wild rice, one wild relative, Oryza nivara of rice growing in Gonda, Uttar Pradesh, India found to have a gene for resistance to the grassy stunt virus. This gene is now routinely incorporated in all new varieties of rice grown across more than 100,000 km2 of Asian rice fields. Wild relatives have also helped increase the nutritional value in different cultivated crops like breeders crossed cultivated broccoli with a wild Sicilian relative the resulted variety contains 100 times more sulphoraphane a cancer-fighting chemical, improved cultivated tomato contains more vitamin C and beta-carotene. Report revealed that use of crop wild relatives improved the production and the nutritional contents of crops that resulted in enhanced people’s livelihoods and their health. Taking action now to rescue endangered crop wild relatives is the only way to ensure that this value will continue to be available to future generations. Table 4.8 represents the health-related problem caused due to the nutrient deficiency and the wild food sources to combat that deficiency.

Table 4.8 Nutrient deficiency, health related problem and the wild food sources to combat that deficiency Nutrient deficiency Protein–energy malnutrition

Vitamin A deficiency

Zinc deficiency

Related health problems Reduced growth, susceptibility to infections, changes in skin, hair and mental ability Impaired vision and immune function, blindness and death in extreme cases Slowed growth and development, suppressed immunity, increased complications in pregnancy

Iron deficiency

Anaemia, weakness and increased susceptibility to disease

Folate deficiency

Anaemia, neural tube defects Increased susceptibility to disease and impaired iron status

Vitamin C deficiency

Wild food sources to combat deficiency Energy-rich food, such as nuts, seeds, oil-rich fruit and tubers and wild animals such as snails Forest leaves and fruits, palm oil, bee larvae and other animal foods Animal-sourced foods, particularly red meat, along with certain types of nuts, including pine nuts, pecans and Brazil nuts Wild animals, including insects such as the tree ant, mushrooms, forest leaves, baobab fruit pulp Leafy and other vegetables and many fruits Forest fruits and leaves

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4.24 Medicinal Genetic Resources Though allopathic structure of medicine is well developed around the globe, most of the rural areas depend on plant based medicines. In the present scenario nature therapy has gained a significant thrust resulting in the increasing use of herbal medicines. This can be endorsed to the awareness of the self-concern of the human being for better health. The traditional use of the plant based treatments are well spread across the world and plants are the important part of many of the rural populations. Along with healing the health related problems medicinal plants also support in alleviating socio-economic status of the rural people. In technologyrich developed countries, research laboratories are working on utilization of a large number of plants as probable remedies to imminent diseases. Medicinal genetic resources are mostly found in the wild, very few efforts were made in the past for their collection and conservation. Hence, to utilize its full potential concrete efforts are required. NBPGR conserve medicinal plant germplasm in national genebank for long term storage. Table 4.9 represents medicinal germplasm conserved in the national genebank of India.

4.25 Ornamental Genetic Resources Nature has given a treasure of ornamental plants, that count approximately 85,000– 99,000 species including trees, shrubs, climbers and creepers, palms, ferns, succulent cacti and, grasses, bamboos, orchids, and reeds, bulbs, and other flowering crops. In the present scenario, ornamental has tremendous opportunities for employment generation, higher returns per unit area and fulfilling the pleasant pursuits of the people. Ornamental crops are used as raw material for manufacturing of essence, perfumes, medicines and confectioneries. Jaenicke (2013) reported that the production and marketing of ornamental crops contribute to the national GDP of many countries and thus can play an important secondary role in enhancing food security and nutrition. Breeder utilizes ornamental plants genetic resources for transferring important traits like new and multi colours, altered forms, enhanced fragrance and increased flower longevity commercial varieties (Heywood 2003; Tay 2007). The ornamental plant germplasm has been conserved in various genebank around the world. Some Important ornamental genetic resources presented in the Table 4.10. Till now, a total of 633 accessions of different ornamental plants have been conserved in National Genebank of India presented in detail in Table 4.11.

Cinquefoil Babchi Pala indigo / Wrightia Withania Birthworts/Indian birthwort Hogweed Milk-weed Carob tree

Crop name Indian Liquorice Country Mallow Indian mallow King of Bitters/ False waterwillow Mexican prickly poppy Kemuk Vetches Safed Musli/ Chlorophytum Datura/ Indian Thorn Apple/ Tree Dhatura Makhana/Fox nut Mehndi/Henna Cowhage / Kewanch Lemon Basil/ Sacred Basil/ Shrubby Basil/ Sweet Basil/ Basil Opium poppy/ Blue Poppy Psyllium/ Isabgol / Indian Plantain

Botanical name Abrus precatorius Abutilon indicum Abutilon persicum Andrographis paniculata, A. echinoides Argemone Mexicana Costus speciosus Asparagus adscendens, A. officinalis, A. racemosus Chlorophytum borivilianum, C. glauca, C. glaucoides, C. nimmonii Datura alba, D. metel, D. bernhardii, D. discolor, D. fastuosa, D. ferox, D. inoxia, D. quereifolia, D. straminium, D. suaveolens Euryale ferox Lawsonia vasica, L. alba, L. inermis Mucuna bracteata, Mucuna nigricans Ocimum americanum, O. xcitriodorum, O. sanctum, O. tenuiflorum, O. gratissimum, O. basilicum, O. canum, O. kilimandscharicum Gurke, O. viridis Papaver somniferum Plantago arenaria, Plantago hookeriana, Plantago lanceolata, Plantago major, Plantago coronopus, Plantago ovata, Plantago indica Potentilla fulgens Psoralea corylifolia Wrightia tinctoria, W. arborea, W. tomentosa Withania somnifera Aristolochia bracteata, A. grandiflora, A. indica, A. tagala Boerhavia diffusa Calotropis gigantea, C. procera Ceratonia siliqua

Table 4.9 medicinal germplasm conserved in the National Genebank, ICAR-NBPGR, India

3 87 36 428 35 22 23 1

531 95

108 113 5 624

Number of accessions 133 58 1 127 26 31 85 41 82

134 P. G. Gore et al.

Nux-vomica Oregano Periwinkle

Seabuckthorn

Broken bones plant Burra Gokhru Himalayan Rhubarb/ Bhootkesh/ Bhutkesh Chebulic Myrobalan/ Indian Laurel/ Arjun Gokharu, Punctine Vine Prickly-ash /Zanthoxylum Desmodium

Malaysian apple Entada Fagonia East Indian Screw Tree Holarrhena Touch-me-not Morinda/ Noni

Ballon Vine Cassia/ Indian barberry / Indian Laburnum/ Indian Senna/ Luvunga

Cardiospermum halicacabum Cassia alata, C. auriculata, C. frondosa, C. hirsuta, C. italica, C. mimosoides, C. obtusa, C. obtusifolia, C. occidentalis, C. rotundifolia, C. sophera, C. tomentosa, C. toria, C. angustifolia, C. fistula, C. senna, C. renigera, C. pumila, C. floribunda Diplocyclos palmatus Entada monostachya, E. pursaetha, E. rheedii, E. scandens Fagonia cretica Helicteres isora Holarrhena antidysenterica, H. arnottiana Mimosa pudica Morinda angustifolia, M. pubescens, Cassytha filiformis, M. citrifolia, M. tinctoria, Morinda tomentosa Oroxylum indicum Pedalium murex Rheum australe, R. emodi, R. spiciforme Selinium elatum, S. wallichianum, S. tenuifolium, S. vaginatum Terminalia chebula, T. alata, T. tomentosa, T. bellirica, T. catappa, T. muelleri, T. pallida, T. paniculata, T. arjuna Tribulus pentandrus, T. rajasthanensis, T. terrestris Zanthoxylum armatum, Z. rhetsa, Zanthoxylum alatum Desmodium elegans, D. gangeticum, D. gyrens, D. heterocarpon, D. intortum, D. motorium, D. triquetrum, D. sp. Hippophae rhamnoides L., Hippophae rhamnoides var. turkestanica, Hippophae salicifolia Strychnos nux-vomica Origanum vulgare Catharanthus roseus 40 34 22

181

44 16 52

30 20 10 11 145

29 23 4 30 26 29 168

15 192

(continued)

4 Genetic Resources 135

Jyotismati Leadwort

Indigofera

Nirgundi Hemp, Bhang Hogweed

Yarrow

Soapberry Spider flower Spurge Tephrosia Vidanga/False Black Pepper Wormwood

Serpentine Shivlingi Sida

Crop name Persimmon Tree Rohida Sage/Chia

Table 4.9 (continued) Botanical name Diospyros chloroxylon, D. D. oocarpa, Diospyros tomentosa, D. sp. Tecomella undulate Salvia deserta, S. hians, S. hispanica, S. horminum, S. moorcroftiana, S. nemorosa, S. officinalis, S. sclarea, S. tesquicola, S. verbenaca Rauvolfia beddomei, R. canescens, R. serpentina, R. tetraphylla Bryonopsis laciniosa, Pongamia uliginosa Sida cordata, S. cordifolia, S. ovata, S. acuta, S. rhombifolia, S. rhombifolia ssp. Retusa Sapindus emarginatus, S. laurifolius, S. mukorossi, S. trifoliatus, S. sp. Cleome icosandra, C. viscose Euphorbia cyathophora, E. hirta, E. nivulia, E. thymifolia Tephrosia candida, T. purpurea, T. sp. Embelia ribes burm, E. tsjeriam-cottom Artemisia annua, A. maritima, Artemisia nilgirica, A. scoparia, A. vulgaris, A. sp. A. asiatica, A. bibersteinii, A. borealis, Achilleaconferta, A. falcata, A. filipendulina, A. lanulosa, A. millefolium, A. nobilis, A. santolina Vitex negundo Cannabis sativa Heracleum pinnatum, H. sosnowskyi mandenova, H. sp., H. grande, H. lanatum, H. candicans, Indigofera dielsiana, I. glandulosa, I. linnaei, I. longeracemosa, I. tinctoria, I. colutea Celasrtus paniculatus Plumbago zeylanica 30 28

34

19 22 22

19

17 49 18 22 16 26

33 21 28

Number of accessions 23 241 25

136 P. G. Gore et al.

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Table 4.10 Global status of ornamental germplasm S. N 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Species Anthuriums Antirrhinum Aster Bougainvillea Calanthe Calendula Chrysanthemum Cosmos Crossandra Dianthus Gerbera Gladiolus Gypsophila Iris Jasmine Lilium Marigold Nymphaea Orchids Peony Petunia Bird of paradise Rose Tube rose Tulips Viola Zinnia Total

GENESYS 26 881 15 9 30 425 330 62 4 451 18 369 143 974 44 215 530 10 190 79 199 4 1052 − 490 422 2 6974

USDA-GRIN − 25 6 2 − 80 8 2 − 82 − − 22 24 1 − 43 − 1 1 22 − 61 − − 6 42 428

EURISCO 697 − 111 3 11 355 296 40 3 400 16 379 103 932 50 102 382 8 183 74 129 5 3785 1 532 250 116 8963

Source: Pedapati et al. (2018)

4.26 Access to Genetic Resources Acquisition of the genetic resources (GR) is the primary concern amongst countries and within the countries, as they are continually needed for use in research programs. It is essential that the GR be accessed and utilized sustainably owing to their interdependence. With the advent of the Convention on Biological Diversity (CBD), the paradigm shift from the ‘common heritage of mankind’ to ‘sovereign rights of nations’ regulated access to GR. On the other hand, International Undertaking on Plant Genetic Resources (IUPGR) was negotiated as an International Treaty on plant genetic resources for food and agriculture (ITPGRFA) with the aim to provide for facilitated access to the agreed crops under the Annex 1 or the multilateral system of the Treaty. Access to PGRFA under the Multilateral System of ITPGRFA is

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Table 4.11 Ornamental species conserved at National Genebank, ICAR-NBPGR, India Species name Ageratum conyzoides

Acc.no 1

Artemisia gmelinii Bachiaria sp.

1 1

Bothriochloa odorata

1

Bupleurum falcatum Canna edulis Caraytia auriculata

1 1 1

Dichrostachys cenescens Ipomoea aquatic

1 1

Knema attenuate

1

Meyna laxiflora

1

Mycorrhiza sp.

1

Nerium oleander

2

Nerium sp.

1

Paris polyphylla

1

Peporomia pellucida Picrorhiza scrophulariiflora Pogostemon heyneanus Potentilla atrosanguinea Premna serratifolia

1 1

Quassia amara

1

Setaria viridis

1

Solanum erianthum

1

Stephania japonica Tridax procumbens

1 1 1

Species name Cardiocrinum giganteum Canna indica Dianthus sinensis

Acc.no 1

Cayratia auriculata Cayratia pedata Cayratia sp. Cayratia trifolia

2

Callistephus chinensis Ardisia solanacea Chrysanthemum morifolium Synedrella nodiflora Arisaema caudatum Arisaema leschenaultii Arisaema propinquum Celosia argentea

Species name Loranthus longiflorus Nelumbo nucifera Calendula arvensis Tagetes erecta

Acc.no 1

Tagetes minuta Tagetes patula Asteracantha longifolia Hygrophila auriculata Hygrophila schulli Mimosa cinerea

186 18 3

1

2

Mimosa himalayana Mimosa indica

1

1

Mimosa invisa

1

1

Mimosa rubicaulis Mollugo pentaphylla Tragia cannabina Oldenlandia corymbosa Pinda concanensis Papaver dubium

1

2 1

3 1 3 12 1 1 1

17

Celosia cristata Coldenia procumbens Centaurea cyanus

5 1

Crossandra infundibuliformis Baliospermum montanum Pentapetes phoenicea Ehretia laevis

2

2

5 2 1

1

Erinocarpus nimmomi Eulophia nuda

1 1

3

Neptunia oleracea

2

Papaver pseudo-orientale Papaver rhoeas Calendula eckerlenii Calendula officinalis Calendula suffruticosa Ludwigia octovalvis

3 1 140

2 2 1

1 1 1 1 1 1 1 1 2 1 1 (continued)

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Table 4.11 (continued) Species name Viburnum corylifolium

Acc.no 3

Species name Chrysanthemum coronarium Cuscuta reflexa Naregamia alata

Acc.no 1

Viburnum mullaha Angelica archangelica var. himalaica Angelica glauca

1 1

1

Hedychium spicatum Lonicera japonica Thunb. Dodonaea viscosa Urginea indica Randia dumetorum Belamcanda chinensis Vernonia anthelmintica Knoxia carymbosa

Anisomeles heyneana

1

Anisomeles indica Aster peduncularis Impatiens balsamina Impatiens sulcata

5 1 1 3

Elephantopus scaber

3

Blepharis madraspatensis Blepharis sindica

1

Bistorta affinis

1

Bryonia cordifolia

1

1

Triumfetta pilosa

2

Launaea sarmentosa Loranthus longiflorus

9

Acc.no 1

1 1

Species name Ludwigia parviflora Ruellia rosea Rivea ornata

25

Rosa macrophylla

3

1

Rosa microphylla

1

26 3 8 1

Rosa sericea Waltheria indica Justicia betonica Illicium verum

5 1 1 2

11

Anacyclus pyrethrum Spilanthes acmella Spilanthes paniculata Trichodesma indicum Tecoma grandiflora

2

1

1

1 1

16 2 2 2

facilitated to all member countries, for the crops mentioned in Annex I of ITPGRFA and also referred to as multilateral system (MLS) and solely for utilization and conservation for research, breeding, and training. This does not include chemical, pharmaceutical, and/or other non-food/feed or industrial uses. The governing body of the Treaty has ratified a Standard Material Transfer Agreement (SMTA) for use among member countries. The salient provision of the SMTA is that the material received under the multilateral system of the treaty shall be freely available to others for use in research, breeding, and training. The recipients cannot claim any IPRs on the material in the form received and if any commercial utilization is done, the benefits would be returned to a trust fund of the Treaty. Every day more than 800 transfers of seeds on average are made worldwide for research, training, and breeding purposes and every transfer travel under an SMTA negotiated by the Member States (can now be concluded online). Four forms of SMTA can be used namely clickwrap, shrink wrap, signed, and easy SMTA. For enhanced utilization of the genetic resources, access to GR is the topmost priority and thus regulated access provides acts, guidelines, and procedures to access the GR as per the extant national legislation and international agreements. Information on GR held by various gene

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banks and conservation sites is available for use by researchers through online platforms and databases. The researcher/user can quickly search for and request for accessions through these databases. The Genesys PGR, GRIN Global, and PGR Portal provide for easy search and information on collection held in conservation sites. Genesys PGR online platform provides information about PGRFA conserved in Genebanks worldwide. It contains information about four million genebank accessions which is about half of the estimated number. The platform is mangled by Global crop Diversity Trust (GCDT) and supported by Consultative Group Consortium donors via Genebank Platform. The online platform provides for quick search and one can request crop samples located in over four hundred institutes around the world through the singleentry point. The information is available accessions with respect to holding institute, biological status, type of germplasm, availability, MLS status, and related images. Also, the information in whether the accessions are conserved as a safety duplicate in Svalbard Global Seed Vault (SGSV) is available. Germplasm Resources Information Network (GRIN Global) is the simple database that gives information of plant, animal, and microbial collection conserved in the seed genebank, field genebank, in vitro genebanks, cryobanks of US National Plant Germplasm System. Data of 51 organizations of Europe is accessible through EURISCO database (http:// eurisco.ecpgr.org). The information on GR is now available through various sets of databases. However, the emergence of a new IPR regime, in relation to GR calls out modalities for benefit-sharing between the private and public sectors to ensure continuity of germplasm exchange and synergy between the two sectors. Well defined procedures are in place with regard to access to GR. Although biological resources/living things and essentially biological processes are not patentable, certain concerns are still debatable as who owns these resources? How to ensure that we do only what we are allowed when these resources and technologies are being exchanged with certain conditions?

4.27 Characterization and Evaluation of PGR Genebanks are reservoirs of germplasm variability containing essential genes that play crucial role in crop breeding programs. Efficient and effective use of genebanks in crop improvement depend upon a thorough understanding of the existing genetic variability, knowledge of the genes presents in individual accessions, knowledge enhanced through the activities of characterization and evaluation. Without knowledge of the traits about the agreements held in genebanks, they become museums of plant accessions, or living herbaria. Due to raising concern of conservation through use, the scope of characterization and evaluation of genetic resources has been widened. Characterization, evaluation and regeneration are important activity of PGR management. The characterization is the description of accessions with a set of descriptors and recording of highly heritable characters which may be used

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Table 4.12 Difference between characterization and evaluation Item Definition

Characterization Assessment of attributes in a given accession those are relatively stable across environments.

Attributes

Often qualitative, mono-genic inheritance, environmentally stable and easy to measure and use for classification. Colour, pubescence and shape of plant parts

Examples

Evaluation Measurements of attributes in a given accession, generally of agronomic significance, those are environmentally sensitive. Often quantitative, multi-genic inheritance, influence by testing for environment and less easy to measure and use for classification. Yield attributes, days to flowering and maturity, nutritional & phytochemical contents, tolerance to abiotic & biotic stresses.

in establishing taxonomic identity. At the same time, the evaluation deals with assessing the potential of an accession, including quality parameters and resistance to various abiotic and biotic stresses. Thus, the activity of characterization and evaluation facilitates the utilization of germplasm in crop improvement programme. Regeneration/maintenance of germplasm without losing genetic integrity is also an essential activity of PGR management. The clonal repositories, field genebanks and herbal gardens are strategies for maintenance of germplasm of perennial or vegetatively propagated species. Table 4.12 represents the difference between characterization and evaluation.

4.28 Principles of Germplasm Characterization, Evaluation and Maintenance The characterization and preliminary evaluation involve recording of highly heritable morphological characters and identifying promising accessions (Gore et al. 2019; Pandey et al. 2019). Germplasm characterization and evaluation cover the whole range of activities starting from the receipt of the new samples by the curator and growing these for seed increase, characterization, preliminary evaluation, and also for further detailed evaluation and documentation. There is a need for its systematic evaluation in order to know its various morphological, physiological and developmental characters including some special features, such as stress tolerance, insect pest and disease resistance. The germplasm accessions are usually evaluated for two consecutive years for documentation and preparation of crop catalogue. For the effective evaluation of germplasm, a close collaboration between curator and breeder is necessary in the context of breeding objective vis-a-vis evaluation programme (Tripathi et al. 2018).

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4.29 Utilization of PGR Genetic conservation aims to maximize the maintenance of genetic diversity, but further, it is explicitly utilitarian; it is that there is an intimate link between plant genetic diversity, conservation, and utilization. As has already been emphasized, genetic conservationists often emphasize the link between conservation and use or exploitation. It can be further argued that the ultimate reason for conserving biodiversity, whether ‘living’ or ‘suspended’, is to make it available for use by humankind, either now or in the future. Genebanks mainly focus on the conservation aspects of PGR and there is an urgent need for active collaboration with all stakeholders to enhance their utilization. Pre-breeding is considered as a prior step of sustainable plant breeding because it leads to value addition in the germplasm. It is the most promising alternative to link genetic resources and breeding programs. Pre-breeding plays a role of a bridge between wild species and improved genotypes. Pre-breeding has been successfully done in several crops like rice, tomato, soybean, cotton, maize, chickpea, pigeonpea, sorghum etc. and many cultivated varieties have been improved for different qualitative and quantitative traits. The basic objective of pre-breeding activities as follows: (i) To use genetic diversity that was not previously accessible due to genetic in-compatabilities. (ii) Enhancement of genetic variability in the germplasm for its further use in regular breeding programs. (iii) To reset the genetic variability of crops by reintroducing genetic diversity left behind. Figure 4.8 represents the flow chart of utilization genetic resources. However, how humans use plant diversity are themselves very diverse; plant genetic resources are not just simply used as trait donors by plant breeders, plants may be used as (i) food, crop species including beverages; (ii) food additives, including processing agents and other additives used in food and beverage preparation; (iii) feed (animal foods), the fodder or forage species eaten by vertebrate and invertebrate animals; (iv) materials, such as wood, fibers, cork, cane, tannins, latex, resins, gums, waxes, oils, etc.; (v) fuels, wood, charcoal, etc.; (vi) poisons, medicines, human and veterinary; (vii) environmental: these will include species that are ornamentals, recreational, hedges, shade plants, windbreaks, soil improvers, plant for regeneration, erosion control, indicator species (e.g. pollution, underground water); (viii) gene donors: plants that contain desirable traits that can be transferred to other species to improve their use.

4.30 Conclusion FAO (2013) estimated that to feed 9.6 billion people, we need to produce 70% more food by 2050. Genetic resources have the great potential to contribute for resolving problems like, hunger, food insecurity, malnutrition and climate change, hence can help in attaining the Sustainable Development Goals (SDGs). Genetic resources include plants, animal, aquatic breeds, and agriculturally important insect and microbes. Considerable development has been made on the way to the collection,

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Fig. 4.8 Utilization of genetic resources

conservation, documentation and use of agrobiodiversity related genetic resources, however much more needs to be done towards their sustainable use of their full potential, exchange of on global level and technology transfer. Efficient use of genetic resources is often limited by policies, investment, infrastructure, technical capacity as well as cross-sectoral coordination and partnerships. Hence, top priority and policy support by world leaders and organizations is necessary for enhanced use of genetic resources. From the past few decades deterioration of genetic resources are taking place at an alarming rate, extinction of genetic resource and associated traditional knowledge is an irretrievable process and hence must receive priority consideration. The loss of a gene is a major loss for our future generations. Hence, it is the need of the hour to conserve these resources for future generations. From 90s, conservation biology has opened up new arenas through the new technologies for long term conservation of genetic resources. The development of efficient methods for germplasm conservation of genetic resources is a high priority at the global level because of the rapid depletion of these valuable resources from their natural habitat. As no single conservation strategy of in situ and ex-situ can conserve the maximum genetic diversity of any species. Application of most appropriate and combination of conservation strategies depend on the type of material, conservation facilities, size of the sample to be stored, and the use of the conserved germplasm. Hence, by concerning the above factors, most appropriate complementary conservation strategies should be developed for effective long-term and safe conservation of genetic resources without being lost.

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Acknowledgements Authors thank Director, ICAR-NBPGR for encouragement.

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Tyagi, R. K., Agrawal, A., Mahalakshmi, C., et al. (2007). Low-cost media for in vitro conservation of turmeric (Curcuma longa L.) and genetic stability assessment using RAPD markers. In Vitro Cellular & Developmental Biology-Plant, 43, 51–58. Tyagi, R. K., Goswami, R., Sanayaima, R., et al. (2009). Micropropagation and slow growth conservation of cardamom (Elettaria cardamomum Maton). In Vitro Cellular & Developmental Biology, 45(6), 721–729. Ungricht, S. (2004). How many plant species are there? And how many threatened with extinction? Endemic species in global biodiversity and conservation assessments. Taxon, 53, 481–484. Vertucci, C. W., Berjak, P., Pammenter, N. W., et al. (1991). Cryopreservation of embryonic axes of an homeohydrous (recalcitrant) seed in relation to calorimetric properties of tissue water. Cryo Letters, 12, 339–350. Wang, Q., Tanne, E., Arav, A., et al. (2000). Cryopreservation of in vitro-grown shoot tips of grapevine by encapsulation-dehydration. Plant Cell, Tissue and Organ Culture, 63, 41–46. Wen, B., Wang, X., Tan, Y., et al. (2013). Differential responses of Mimusops elengi and Manilkara zapota seeds and embryos to cryopreservation. In Vitro Cellular & Developmental Biology. Plant, 49, 717–723. Wesley-Smith, J., Vertucci, C. W., & Berjak, P. (1992). Cryopreservation of desiccation-sensitive axes of Camellia sinensis in relation to dehydration, freezing rate and the thermal properties of tissue water. Journal of Plant Physiology, 140, 596–604. Wetzel, M. M. V. S., Reis, R. B., & Ramos, K. M. (2003). Metodologia para criopreservação de sementes de espécies florestais nativas. Circular Téc Embrapa, 26, 1–5. Wu, Y., Engelmann, F., Zaho, Y., et al. (1999). Cryopreservation of apple shoot tips: Importance of cryopreservation technique. CryoLetters, 20, 121–130. Yamuna, G., Sumathi, V., Geetha, S. P., et al. (2007). Cryopreservation of in vitro grown shoots of ginger (Zingiber officinale Rosc.). CryoLetters, 28(4), 241–252. Yan, Q., Wen, B., & Zhang, N. (2014). Cryopreservation strategies for pomelo seeds from Xishuangbanna, South China. Seed Science and Technology, 42, 202–213. Yin, M., & Hong, S. (2010). A simple cryopreservation protocol of Dioscorea bulbifera L. embryogenic calli by encapsulation-vitrification. Plant Cell, Tissue and Organ Culture, 101, 349–358. Zeven, A. C., & De Wet, J. M. J. (1982). Dictionary of cultivated plants and their regions of diversity: Excluding Most ornamentals, Forest trees and lower plants. CAPD: Wageningen. Zhao, Y., Wu, Y., Engelmann, F., et al. (1999). Cryopreservation of apple shoot tips by encapsulation-dehydration: Effect of preculture, dehydration and freezing procedure on shoot regeneration. CryoLetters, 20, 103–108. Zhao, X., Li, P., Jiang, L., et al. (2016). Effects of preculture on cryopreservation of nodes from Dioscorea opposita Thunb. cv. Tiegun plantlets by encapsulation-vitrification. Medicinal Plant, 7, 22–26.

Chapter 5

Plant Adaptation to Environmental Stress: Drought, Chilling, Heat, and Salinity Khayelihle Ncama, Oladapo Adeyemi Aremu, and Nkanyiso Justice Sithole

Abstract Climatic change is often manifested as changes in the intensity, frequency, and extent of abnormally low or high thresholds of factors such as temperature, precipitation, radiation, and concentrations of atmospheric gases. These changes will eventually extend to a state where it will be difficult to ameliorate the impact of those factors in agricultural crop production. It is ideal for outlining adapting mechanisms of plants as that could be magnified via means of genetic improvement or gradual breeding for lines that can fairly adapt to the changing environmental conditions. This chapter aims to advance the understanding of the ability of plants to adapt to extreme conditions or to react to sudden changes in their environment. The chapter outlines the mechanisms used by plants to sense and signal abiotic stresses, morphological and physiological changes that take place in plants as they adapt to stressful conditions, and the associated alterations in metabolic and biological reactions. Findings demonstrated that the adaptation of plants to stressing environment results from complex biological reactions including changes in morphological characteristics (roots, leaves, reproductive and cellular structures), physiological processes (respiration, transpiration, and photosynthesis) and metabolism (production of metabolites such as proline, abscisic acid, sugars, stress-specific enzymes, and proteins). Plant breeding and genetic modification research fields currently have wide parameters to target for improving plant adaptation to stress conditions. However, the success in the introduction of adaptive commercial crops is likely suppressed by supplemental cultivation techniques that are usually used in optimal production for profits.

K. Ncama () · N. J. Sithole Department of Crop Science, Faculty of Natural and Agricultural Sciences, North-West University, Mmabatho, South Africa e-mail: [email protected] O. A. Aremu Indigenous Knowledge Systems (IKS) Centre, Faculty of Natural and Agricultural Sciences, North-West University, Mmabatho, South Africa Food Security and Safety Niche Area, Faculty of Natural and Agricultural Sciences, North-West University, Mmabatho, South Africa © Springer Nature Switzerland AG 2022 C. M. Galanakis (ed.), Environment and Climate-smart Food Production, https://doi.org/10.1007/978-3-030-71571-7_5

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Keywords Antioxidant · Abiotic stress · Abscisic acid · Climate change · Plant-signaling · Phytochemical · Reactive oxygen species

5.1 Introduction Effects of global warming are gradually reaching adverse stages where measures to ameliorate their impact in agricultural crop production are limited. Climate change has increased the intensity, frequency, and extent of abnormally low or high thresholds in environmental factors such as temperature, precipitation, and light intensity (Farooq et al. 2011; Yamori et al. 2014). Furthermore, increases in atmospheric carbon dioxide (CO2 ) and a decrease in ozone (O3 ) levels have been ubiquitously significant throughout the decade (Lobell and Gourdji 2012). Commercial crops are commonly grown in monitored irrigated systems where they are not exposed to gradual changes in climatic factors. This fact reduces their ability to survive when they get exposed to stressing conditions such as drought, salinity, or heat. Plants are physiologically capable of adapting to gradual environmental changes, but their physiology does not allow sudden adaption to unexpected changes. However, some plants have specific physiological and biochemical changes that allow tolerance for stress caused by the adverse temporal condition. Plants can alter several physiological, metabolic, and genetic reactions, as well as morphological processes to adapt under abiotic conditions. Studies focusing on the mechanisms of plant adaptation show similar changes in physiological and metabolic reactions of stressed plants. There are common factors affecting plants under drought, high temperatures, or soil salinity stresses, which is inadequate water availability. Most plants show considerable ability to alter their photosynthetic characteristics to acclimatize to temperatures when they are exposed to heating conditions during their growth and development (Yaromi et al. 2014). For instance, increased production of reactive oxygen species (ROS) in sorghum improved the crop water retention and osmolytes content during drought stress (Nxele et al. 2017). Further, the activities of superoxide dismutase, catalase, and ascorbate peroxide, ascorbic acid, and total carotenoids were indicated as vital antioxidant substances that improve tolerance of maize (Zea mays) seedlings to drought (Van Staden and Jager 1998). Exposure of perennial grass to high temperature decreased plant biomass, green leaf area, leaf water potential, photosynthetic rate, maximal efficiency of photosystem II (PSII) photochemistry, actual PSII efficiency, the activities of nitrate reductase and glutamine synthetase. Moreover, a significant increase in the ratio of leaf area to leaf weight (SLA), endopeptidase activity, and malondialdehyde content was observed (Xu and Zhou 2006). Those changes were significant on grass that was exposed to water stress before heat stress. The exposure of wheat to water stress also improved its PSII response to heat stress (Lu and Zhang 1999). The improved adaptation can be associated with the water-stressed leaves being able to regulate the photosynthetic electron transport down to match decreased carbon dioxide (CO2 ) assimilation.

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Different kinds of plant species differ in their response to abiotic stresses. For instance, when C4 plants are exposed to shortages of water, they increase carbon dioxide concentration in their leaves to reduce the drought effect by altering stomatal conductance in such a way that leaf transpiration is reduced. This mechanism is unique to C4 plants and results in higher adaptation of C4 plants to water stress than C3 plants. The increased CO2 concentration inhibits photosynthetic enzyme activity and nitrate assimilation, induction of early senescence, and changes in the leaf anatomy and ultrastructure, which adopts the reduction in transpiration water loss (Ghannoum 2008). The adaptation mechanisms of plants to various abiotic stresses have been studied intensively. However, abnormal ranges of atmospheric gases, shading, or excessive light stresses in field crops are very scarce and will not be considered in this chapter. This chapter advances the understanding of the ability of plants to adapt or react to low or high temperatures, low water availability, and salinity conditions in their environment. The chapter outlines mechanisms of sensing and signaling stressing conditions by plants, as well as changes in reactions that are morphological, physiological, metabolic, and genetics in the adaptation process.

5.2 Mechanisms Utilized by Plants to Sense and Signal Abiotic Stress 5.2.1 Osmosensing The movement of water from the soil into the roots of plants mainly depends on the osmotic balance of the two regions. Roots signals are transported through the xylem to the leaves. The signals resulted in reduced water loss through transpiration, and decreased leaf growth is there is gradual drying of the soil. The effects of factors such as abscisic acid (ABA), pH, cytokinins, and malate content have been implicated in root to shoot signaling under drought conditions (Schachtman and Goodger 2008). The foliar parts detect the concentration of these chemicals in the sap absorbed by a stressed plant using cellular osmotic efflux and influx movement of ions through the cell membrane (Rengel 1992). The reduction of available water in the cytoplasm may be the precursor of altered Ca+ and K+ available in the extracellular regions, which results in the closing of stomata and reduction in leaf size. In maize (Zea mays), increased production of malondialdehyde (MDA) was associated with the severity of water stress during deficit water conditions (Ge et al. 2006). The content of MDA in roots was lower than that in leaves. This fact could be associated with the reduction in stress level experienced by roots towards the end of the experiment because of the reduced amount of water taken up by plants, or more water is available in the soil when crops started their senescence. The concentration of MDA transported with the solution from the roots to the foliar parts signals the

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extent of water stress that the roots are exposed. If the concentration is high, it initiates the reactions of foliar parts such as wilting and senescence. Various studies have indicated different metabolites that can be used by plants to signal drought conditions (Table 5.1). The metabolites associated with signaling drought stress have been found in different plant organs from the roots of common bean (Walton et al. 1976), leaves of mulberry (Barathi et al. 2001), and reproductive shoots of false wheatgrass (Xu and Zhou 2005). Most studies have found different metabolites on different plant species, but the metabolites have been demonstrated to be mostly on the leaves.

5.2.2 Thermosensing Plants are immobile and cannot escape temperatures beyond their optimal thresholds. Their escape mechanisms from adverse temperatures are mainly based on the alteration of biological reactions and morphological structures. The process of acclimation or tolerance to very low or high temperatures differs between species and varieties within the same plant species. However, the majority of plants originating from subtropical and tropical regions suffer chilling disorders under low temperatures, while plants from temperate climates are more likely to suffer heat stress under high temperatures (Mckersie and Ya’acov 1994). Both the disorders result in specific changes in biochemical and morphological reactions that support their survival during the period of exposure to the extreme temperatures. Changes in the metabolism of different biochemical compounds have been associated with heat stress (Table 5.2). Various studies have demonstrated the expression or activities of various metabolites on different plants exposed to heat stress. Higher production of metabolites such as protease in creeping bentgrass (Veerasamy et al. 2007), proline in barley (Chu et al. 1974), phenolic compounds and phenylalanine ammonia-lyase in tomatoes (Rivero et al. 2001) have been demonstrated in plants exposed to heat stress. Interestingly, some studies also indicated the involvement of proline in response to heat stress (Chu et al. 1974; Nagesh Babu and Devaraj 2008; Naji and Devaraj 2011). Proline was also highly associated with signaling drought stress. Metabolites associated with sensing chilling stress have been highly investigated in fruits and leaves compared with other stresses that also included assessment of roots. Table 5.3 highlights certain kinds of metabolites that plants produce under chilling stress on fruit or leaves of various plant species including grapefruit (Maul et al. 2008), pepper plants (Mercado et al. 1997), zucchini fruit (Palma et al. 2014) and maize plants (Ying et al. 2000).

Lipid peroxidation Nitrate reductase, glutamine synthetase, and glutamate dehydrogenase, net photosynthetic rate, stomatal conductance, transpiration rate, maximal efficiency of photosystem 2 photochemistry (Fv/Fm), actual quantum yield, and photochemical quenching Endopeptidase activity and malondialdehyde contents

Leaves

Vegetative and reproductive shoots

Leaves and pods Flag leaves Flag leaves

Leaves and pods Roots, leaf blades, and stems

Soybean (Glycine max L. Merr.) Rice (Oryza sativa L.) Rice (Oryza sativa L.)

Soybean (Glycine max L. Merr.) Wheat (Triticum aestivum L.)

Mulberry (Morus alba L. cv. K-2)

Vegetative and reproductive shoots Leaf

Activities of RuBPCO and SPS in the leaf extracts, the chlorophyll content, and PS2 activity in isolated chloroplasts. Hexose (glucose + fructose) GSH, Ascorbic acid, chlorophyll Activities of superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), Superoxide free radical (O- ) and peroxide (H2 O2 ) Sucrose and starch Glucose, fructose, sucrose, and fructan

Phosphatidylcholine and galactolipids ABA and 9-cis-epoxycarotenoid dioxygenase (named VuNCED1) CO2 assimilation rate Nitrogen level

Leaves Whole plant

Leaves Leaves

Metabolite Abscisic acid

Plant part Roots

False wheatgrass (Leymus chinensis)

Cowpea (Vigna unguiculata (L.) Walp.) False wheatgrass (Leymus chinensis (Trin.) Tzvelev) False wheatgrass (Leymus chinensis (Trin.) Tzvelev) False wheatgrass (Leymus chinensis (Trin.) Tzvelev)

Plant Common bean (Phaseolus vulgaris L.); pea (Pisum sativum L.) and sunflower (Helianthus annuus L.) Cotton (Gossypium hirsutum L.) Cowpea (Vigna unguiculata (L.) Walp.)

Table 5.1 Metabolic changes in plants exposed to deficit water conditions

Decreased Increased

Increased Decreased Increased

Decreased

Increased

Decreased

Increased

Decreased Decreased

Decrease Increased

Concentration response Increased

Liu et al. (2004) Kerepesi and Galiba (2000)

Liu et al. (2004) Wang et al. (2010) Wang et al. (2010)

Barathi et al. (2001)

Xu and Zhou (2005)

Xu and Zhou (2005)

Xu and Zhou (2006)

Souza et al. (2004) Xu and Zhou (2006)

Anh et al. (1985) Luchi et al. (2000)

References Walton et al. (1976)

Glutathione reductase (GR) and catalase (CAT)

Whole plant

Whole plant

Developing kernels Shoots

French bean (Phaseolus vulgaris)

Hard red winter wheat (Triticum aestivum L.)

Hypocotyls

Leaves

Sunflower (Helianthus annuus L.)

Tomato (Lycopersicon esculentum L. cv. Tmknvf2) and watermelon (Citrullus lanatus) Tomato (Lycopersicon esculentum L. cv. Tmknvf2) and watermelon (Citrullus lanatus) Tall fescue (Festuca arundinacea L.) and Kentucky bluegrass (Poa pratensis L.)

Leaves

Leaves

Hypocotyls

Sunflower (Helianthus annuus L.)

Shoots

Soluble crude proteins and Chlorophyll content

Leaves

Horse gram (Macrotyloma uniflorum (Lam.) Verdc.) Horse gram (Macrotyloma uniflorum (Lam.) Verdc.)

Protease activity

Leaves

Ascorbate peroxidase (AP), and glutathione reductase (GR) activities

Catalase, Glutathione reductase (GR; EC1.6.4.2) and β-amylase (EC 3.2.1.1). contents Oxidative stress indicators (H2 O2 , TBARS, and proline) and antioxidant enzymes (superoxide dismutase, guaiacol peroxidase and acid phosphates) Nitrogen oxide content as well as S-nitrosoglutathione reductase (GSNOR) expression Tyrosine nitration (NO2 -Tyr) and ferredoxin–NADP reductase (FNR) contents Soluble phenolics and high phenylalanine ammonia-lyase activity Peroxidase and polyphenol oxidase activity

Antioxidant enzyme, guaiacol-specific peroxidase (POX), glutathione, ascorbic acid, and proline Ethylene production

Metabolite Proline production

Plant part Leaves

Plant Barley (Hordeum vulgare; cv.Prior) and radish (Raphanus sativus; cv.White Icicle) Creeping bentgrass (Agrostis stolonifera L. cv. Penncross) Creeping bentgrass (Agrostis stolonifera L. cv. Penncross) French bean (Phaseolus vulgaris)

Table 5.2 Metabolic response of plants exposed to heat stress

Decreased

Decreased

Increased

Increased

Reduced

Increased

Decreased

Increased

Increased

Decreased

Decreased

Increased

Jiang and Huang (2001)

Rivero et al. (2001)

Rivero et al. (2001)

Chaki et al. (2011)

Chaki et al. (2011)

Naji and Devaraj (2011)

Naji and Devaraj (2011)

Nagesh Babu and Devaraj (2008) Nagesh Babu and Devaraj (2008) Hays et al. (2007)

Veerasamy et al. (2007)

Veerasamy et al. (2007)

Concentration Response References Increased Chu et al. (1974)

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Table 5.3 Metabolic response of plants to low temperature stress Plant Plant part Metabolite Blood orange (Citrus Fruit Flavonoid biosynthesis, × sinensis) anthocyanin biosynthesis, Grapefruit (Citrus Fruit Cell wall integrity, paradisi Macf., cv. photosynthesis, ‘Marsh’) respiration, ABAglucosyltransferase, gibberellic acid (GA), and nucleic acid Grapefruit (Citrus Fruit Lipid, sterol, paradisi Macf., cv. carbohydrate, and ACC ‘Marsh’) synthase transcript biosynthesis Maize (Zea mays L.) Leaves Carbon exchange rate, and Chlorophyll fluorescence Pepper (Capsicum Leaves Protein tyrosine annuum L.) nitration (NO2 -Tyr), lipid peroxidation, levels of the soluble non-enzymatic antioxidants ascorbate and glutathione, and the activity of the main NADPH-generating dehydrogenases Pepper plants Leaves Sucrose, chlorophyll (Capsicum annuum and soluble protein L.; hybrid Latino) content Pepper plants Leaves Starch, and total (Capsicum annuum nitrogen L.; hybrid Latino) Zucchini fruit Fruit Glucose, fructose, (Curcubita pepo L. pinitol, and acid morphotype Zucchini invertase activity cv. Sinatra) Zucchini fruit (C. Fruit Glucose, fructose, pepo L. morphotype pinitol, and acid Zucchini cv. Natura) invertase activity

Concentration Response References Increased Crifò et al. (2011)

Decreased

Maul et al. (2008)

Increased

Maul et al. (2008)

Decreased

Ying et al. (2000)

Increased

Airaki et al. (2012)

Decreased

Mercado et al. (1997)

Increased

Mercado et al. (1997)

Decreased

Palma et al. (2014)

Increased

Palma et al. (2014)

5.2.3 Salinity Sensing Most documented studies of plant performance in saline soils reveal that soil salinity is mainly caused by the accumulation of sodium chloride (NaCl) due to overexploitation of freshwater resources and resultant marine intrusion (Bailey-Serres and

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Voesenek 2008). Under saline soils, plants susceptible to salinity become succulent, exhibit reduced growth, cease reproductive development, and suffer continuous organ abscission. In natural conditions leading to salinity, osmotic, and ionic effects happen concurrently and lead to the plant symptoms and plant death if the condition persists. However, under conditions of increasing or accumulation of salts in the soil, the accumulation of saline ions and compatible osmolyte biosynthesis in plants is triggered to counterbalance the osmotic balance between soil and roots (Visser and Voesenek 2005). Various metabolic reactions are affected by plant exposure to salinity, and that alters the concentrations of specific metabolites found in different parts of a plant (Table 5.4). Some plants reduce the production or expression of specific metabolites to improve their tolerance to salinity. Various metabolites have been found, including catalase in horse gram grass (Naji and Devaraj 2011), chlorophyll, and carotenoids pigments in bean plants (Qados. 2011), and soluble crude proteins in dry beans (Ashraf and Bashir 2003). Production of specific metabolites is increased such as the free amino acids in dry bean (Ashraf and Bashir 2003), non-structural carbohydrates in wheat (Guo et al. 2015), and antioxidant enzymes in horse gram (Naji and Devaraj 2011) while production of other metabolites may not be associated with stress because of their independent stress production. Jouve et al. (2004) demonstrated that the production of malondialdehyde and lipid peroxidation is not affected by the exposure of European aspen to salinity stress.

5.2.4 Stress Signaling in Plants Signaling in plants is mainly based on concentrations of solutes found in the osmolyte solution. However, there are many other enzymes, proteins, biochemical compounds, and synthetic pathways involved in the initiation of reactions to stressing factors. Abiotic stress signaling in plants has been generally associated with the reactive oxygen species (ROS) that play essential regulatory roles in various biological reactions. The ability of ROS to monitor various stress reactions is supported by respiratory oxidase homologs (RBOHs), which can regulate different signal transduction pathways under fluctuating environmental conditions (Baxter et al. 2014). The potential of ROS to mediate stress has been associated with the coordination of associated biochemical compounds and hormones such as abscisic acid (ABA; Agarwal and Jha 2010), ethylene (Voesenek and Sasidharan 2013), and salycilic acids (Jayakannan et al. 2015). One of the majorly studied pathways is the Mitogen-activated protein kinase (MAPK). The pathway transfers information from sensors such as roots for water stress, or leaves for heat, to cellular responses in all eukaryotic plants. Various genes encoding MAPK pathway components have been discovered by analyzing model plant genomes, suggesting that MAPK cascades are abundant players of signal transduction (Nakagami et al. 2005). Pathways of non-structural carbohydrates, certain enzymes and proteins have also been proven to play a role in signal transduction.

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Table 5.4 Metabolic changes in plants exposed to saline soils Plant Bean plant (Vicia faba L.)

Dry beans (Phaseoulus vulgaris) and Sesbania aculeata Dry beans (Phaseoulus vulgaris) and Sesbania aculeata European aspen (Populus tremula) European aspen (Populus tremula)

Plant part Shoots

Metabolite chlorophyll ‘a’, chlorophyll ‘b’, total chlorophyll, and carotenoids contents Whole plant Free amino acids, proline and glycine betaine

Concentration response References Decreased Qados (2011)

Increased

Ashraf and Bashir (2003)

Whole plant Soluble crude proteins

Decreased

Ashraf and Bashir (2003)

Stems and leaves Stems and leaves

Neither

Jouve et al. (2004) Jouve et al. (2004)

Horse gram (Macrotyloma uniflorum (Lam.) Verdc.)

Shoots

Horse gram (Macrotyloma uniflorum (Lam.) Verdc.)

Shoots

Saltwort (Salsola Shoots dendroides Pall, S. richteri (Moq.) Karel ex Litw. and S. orientalis S.C. Gmel.) Wheat (Triticum Roots, leaf aestivum L.) blades, and stems Wheat (Triticum Leaves and aestivum) roots Wheat (Triticum Leaves and aestivum) roots

Malondialdehyde and lipid peroxidation Proline, spermine, sucrose, mannitol, and raffinose levels Catalase, Glutathione reductase (GR; EC1.6.4.2) and β-amylase (EC 3.2.1.1). contents Oxidative stress indicators (H2 O2 , TBARS, and proline) and antioxidant enzymes (superoxide dismutase, guaiacol peroxidase and acid phosphates) Na+ , proline

Glucose, fructose, sucrose, and fructan

Increased

Decreased

Naji and Devaraj (2011)

Increased

Naji and Devaraj (2011)

Increased

HeidariSharifabad and MirzaieNodoushan (2006)

Increased

Kerepesi and Galiba (2000)

Na, Cl, fumaric acid and Increased malic acid K, Ca, Mg, Fe, Cu, Zn, Decreased glucose, glucose-6-P, fructose-6-P, 3-phosphoglyceric acid, and Phosphoenolpyruvate

Guo et al. (2015) Guo et al. (2015)

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The metabolic reaction and signaling ability of sucrose non-fermenting-1-related protein kinases (SnRKs), SnRK1-activating protein kinases (SnAKs), calciumdependent protein kinases (CDPKs) and ABA response element-binding proteins (AREBPs) have been thoroughly reviewed by Hey et al. (2009). Hey et al. (2009) discovered that the link between sugars and amino acids is evident, with nitrate reductase being a target for regulating both SnRK1 and GCN-2 employing different mechanisms. The stress signaling in plants is, therefore, accomplished as a cascade of different pathways combined to transport a signal from one plant part to the other through signal proteins activated by specific enzymes.

5.3 Morphological Changes of Plants Adapting to Abiotic Stress Some plant species possess favorable morphological reactions and mechanisms to stress environmental conditions. Most mechanisms rely on monitoring the evapotranspiration of the plant by modifying plant organs involved in moisture transpiration. The modified plant structure differs depending on a plant species and variety. However, it has been shown that reproductive structures are less likely to adapt to the stressing environment. Ghooshchi et al. (2008) discovered that water deficit before silking, silking, and filling growth stage of maize decrease yield by 12.5, 42.0, and 22.5%, respectively. Leaves and roots are the most common structures altered by higher plants to reduce their exposure or suffering from extreme conditions (Paulsen 1994). The main aim of modified leaves and roots is to reduce or increase transpiration depending on the osmotic balance between the atmospheric and endophytic humidity. Adaptive plants are likely to reduce their transpiration rate under low temperatures (Downes 1969), inadequate moisture available for plant uptake from the soil (Oren and Pataki 2001; Razzaghi et al. 2011), or exposure to accumulating salinity in the soil (Plaut et al. 2000; Razzaghi et al. 2011).

5.3.1 Leaf Alterations Most plants under stressed conditions are characterized by smaller leaves that can be rolled or thick leaves covered with a waxy cuticle. Reducing the exposed leaf area reduces the amount of moisture plant losses via transpiration, which eventually reduces the amount of water the plant requires the soil (Smith 1978). Reduced water uptake does not only reduce water requirements by plants in areas with drought stress but also reduces the number of salt ions absorbed by plants in saline conditions. Some plants can roll their leaves during exposure to high temperatures and release them at cooler times of the day to recover the level of their transpiration reaction (Kadioglu and Terzi 2007). The efficiency of this mechanism is based on

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plants’ ability to photosynthesize in the absence of light, which ensures that their photosynthesis is not affected during the period of avoiding stress by rolling leaves. Leaf rolling induced by the loss of turgor of bulli-form cells is also a common mechanism of reducing water loss via transpiration (Monneveux and Belhassen 1996). Plants with the mechanism of rolling leaves usually have the brighter side of the leaf underneath, which, if exposed during the leaf-rolling reaction, reflects light and avoids the exposure of the upper part of the leaf to high temperatures. Usually, plants increase their transpiration rates for cooling benefits during exposure to high temperatures (Van Foreest et al. 2009). Plants with the temporal leaf rolling mechanism reduce their stomatal opening (moisture loss) and improve the efficiency of water use during the exposure to high temporal temperatures or drought conditions. Rolling of leaves by common plants under waterlogged conditions is usually resulting from the senescence of leaves (Srivastava et al. 2007). Since some plants cannot increase their transpiration rate to reduce the level of water available for plant uptake in the soil, they abscise some leaves to reduce the surface exposed to the atmosphere. This mechanism reduces the amount of water absorbed by the plant, which maintains acceptable concentrations of cellular sap and other solutions found inside different plant organs. Most plants adapted to drought conditions are characterized by leaves covered with a waxy material on their surfaces (De Micco and Aronne 2012). The wax reflects a portion of light intercepted by leaves and reduces their heat absorption. The amount of wax on leaves of plants of the same variety depends on the extent of the heat to which the plant has been exposed (Kim et al. 2007). The high amount of wax reduces the number of open stomata and ensures the reduction of water loss from the leaf surface. Some plants adaptive to saline stress conditions also possess the trait of waxy leaf surfaces. Water available for plant uptake in saline soils is constrained by the high concentrations of salts that are found between soil particles. Therefore, the ability of plants with waxy leaves to adapt to saline soils is based on their reduced water requirements. Most plants are adapted to desert conditions through succulent leaves (Landrum 2002). The thickness of the leaves enables them to absorb high amounts of water and retain it for an extended duration. Although this mechanism is observed in plants that are already adapted in dry areas, it is a mechanism that is likely used by common plants that can grow in semi-desert areas. Plants in semi-desert areas can slightly increase their leaf thickness during the wet season in order to absorb more water. The thickness of the leaves then gradually decreases to improve adaptation as the dry season progresses. Another change that can be observed on leaves of most aloe species and adaptive plants is the high development of white powdery substance during the dry season. The closing or opening of stomata has been widely reported as a significant mechanism that plants use when exposed to temporal water stressing conditions (Urban et al. 2017). Many metabolic reactions are resulting in the production of biochemical substances and the absorption of elements that are responsible for stomatal opening and closing. Potassium absorption has been indicated as one of the significant metabolic reactions to abiotic stressing conditions in plants. Potassium is

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known for regulating stomatal conductivity. The ability of leaf mesophyll to retain potassium was demonstrated to correlate with salinity tolerance in wheat and barley (Wu et al. 2013). A high correlation was observed between drought, salinity, and polyamine accumulation in Conocarpus lancifolius (Al-Kandari et al. 2009). This correlation suggested that the polyamine is among the molecules responsible for stomatal conductivity besides potassium and calcium ions. Shedding of leaves in most plants is a sign of senescence (Munné-Bosch and Alegre 2004). However, some species shade their leaves to reduce the area or parts of the plants with high sensitivity to stresses (Munné-Bosch and Alegre 2004). Shedding old leaves reduces the amount of water and nutrients a plant needs. It further reduces the amount of photosynthesis and transpiration reactions the stressed plant can perform, which is accompanied by less demand for photosynthesis assimilates. The need for photosynthesis is usually reduced under stressed conditions because the rate of growth and reproductive processes of a stressed plant is also reduced. This fact reduces the amount of assimilates, such as carbohydrates required by the sink and increase the source: sink ratio.

5.3.2 Root Alterations Plants striving on deserts and semi-desert regions are characterized by thick roots that absorb a high amount of water during the wet season and keep the water for use by the plant during the dry season (Caldwell et al. 1998). Common plants such as wheat (Blum and Sullivan 1997), soybean (Hoogenboom et al. 1987) and peas (Benjamin and Nielsen 2006) can also alter their anatomy to imitate the desert plants. Root enlargement in girth, as well as length as a result of water shortages either because of drought, heat, or soil salinity, is a mechanism plants use to absorb moisture at regions without possibility to reach with the average root size. The increase in the distribution of roots is accompanied by new fibrous roots that are responsible for water absorption in an extended region. Some plant species, such as maize, have been found to reduce the size but significantly increase the number of fibrous roots under water deficit conditions (Nejad et al. 2010). Most morphological changes observed in plants under drought stress are similar to those that are observed from plants in heat, chilling, and salinity stresses (Table 5.5).

5.3.3 Drought Avoidance Plants that cannot alter their anatomy to adapt to drought are usually characterized by thin leaves that continually utilize water without any water-saving mechanism. Such plants are referred to as water spenders. Those can only alter parts other than leaves. Most water spenders would increase root depth and root to shoot ratio while some species can produce phytochemicals that enhance mycorrhizae development

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Table 5.5 Effects of abiotic stress on morphological appearance of different plants Stress Drought

Drought

Drought

Drought

Drought

Observation Reduced growing degree-days (GDD) revealed that dry matter accumulation (DMA), crop growth rate (CGR) and relative growth rate (RGR) Reduction in fresh and dry weights of plant organs, leaf number, total leaf area and leaf relative water content; and increase in specific leaf weight (SLW) and leaf abscission Reduced berry diameter and high skin to pulp ratio Decline in grain number per ear, 1000-grain weight, and ear number per plant Decline in moisture content, and low degree of stomatal closure

Heat

Reduced number of pollen grains per flower and decreased viability

Heat

Kernel abortion and suppression of grain maturation

Heat

Reduced percentage pollen germination, pollen tube length, pod length, seed number per pod, and the seed–ovule ratio, and increase in pollen wall (intine) thickness and percentage pollen germination Yield decline, increased vegetative growth, and reduced pod number,

Heat

Low temperature Low temperature

Wilting damage on reproductive organs, but not on the leaves Disorganization of the internal lamella of chloroplasts, grana unstacking, as well as a general swelling of plastids and mitochondria, and loss of cell compartmentalization

Plant Faba beans (Vicia faba L.)

References GhassemiGolezani et al. (2009).

Almond (Prunus dulcis)

Fathi et al. (2017)

Table grapes (Vitis vinifera L.) Maize (Zea mays)

AcevedoOpazo et al. (2010) Greaves and Wang (2017)

Mediterranean Aegilops (Aegilops biuncialis) Tomato (Lycopersicon esculentum Mill.) Winter wheat (Triticum aestivum L.) Pea (Pisum sativum L.)

Molnár et al. (2004)

Chickpea (Cicer arietinum L.) Faba bean (Vicia faba L.) Cherimoya fruit (Annona cherimola Mill.)

Jumrani and Bhatia (2014)

Pressman et al. (2002)

Hays et al. (2007) Jiang et al. (2015)

Zhou et al. (2018) Gutierrez et al. (1992)

(continued)

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Table 5.5 (continued) Stress Low temperature

Observation Reduction in shoot length, leaf number and shoot dry weight

Low temperature

Visible losses in chlorophyll pigment, unstacking of chloroplast grana, and membrane instability

Low temperature

Enhancement of transcripts involved in the defence mechanisms against oxidative damage, osmoregulating processes, lipid desaturation as well as many ESTs implicated in the primary and secondary metabolisms Decrease in plant height, number of leaves and leaf area Increase in shoot dry and fresh mass

Salinity Salinity Salinity

Reduction in nodule fresh and dry mass of roots and shoots, shoot length and leaf area; nodule number,

Salinity

Reduced growth of seedling leaves and roots

Salinity

No effect in root and shoot growth

Salinity

Decrease in fresh and dry weights of shoots and roots,

Plant Pepper plants (Capsicum annuum L.; hybrid Latino) Soybeans (Glycine max L. Merr. cv Ransom) Blood orange (Citrus × sinensis)

References Mercado et al. (1997)

Bean plant (Vicia faba L.) Bean plant (Vicia faba L.) Dry beans (Phaseoulus vulgaris) and Sesbania aculeata Wheat (Triticum aestivum) Saltwart (Salsola dendroides Pall., S. richteri (Moq.) Karel ex Litw. and S. orientalis S.C. Gmel.) Borage (Borago officinalis L.)

Qados (2011)

Musser et al. (1984)

Crifò et al. (2011)

Qados (2011) Ashraf and Bashir (2003)

Guo et al. (2015) HeidariSharifabad and MirzaieNodoushan (2006)

Zahed Chakovari et al. (2016)

when exposed to reduced moisture conditions (Benjamin and Nielsen 2006; Nadeem et al. 2014). Plants adapted to drought stress conditions are commonly characterized by an ability to store water and are thus referred to as “water-savers.” Watersavers are characterized by altering anatomical characteristics such as closing their stomata, rolling their leaves, and altering the shapes of their epidermis structures. However, some hardy plants such as trees are unable to show foliar symptoms of drought until they have reached a stage at which they suffer dehydration

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of some parts. Leaf greenness, leaf size, shoots growth, shoot dry weight, and stomatal density was not useful markers for drought resistance in almond seedlings (Yadollahi et al. 2011). Therefore, hardy plants such as the almond trees are likely to develop tolerance into the drought condition because of their delayed ability to alter morphological characteristics to perform the stress avoidance or escape mechanism.

5.3.4 Drought Tolerance Tolerance of plants to abiotic stresses results from various morphological, biochemical, and anatomical changes. The adjustment in concentrations of osmotic chemicals, as well as elevated production of protective solutes and desiccationtolerant enzymes, are the most common features common plants adjust to tolerate stressful conditions. The adaptation of physiological reactions results from the altered biochemistry of the stressed plants (Ashraf 2010). The morphological features such as leaf size or root size changes as a result of physiological processes under the limited amount of water, chilling, or heat exposure or soil salinity.

5.3.5 Drought Escape Plants avoid or develop tolerance to abiotic stress when they are in their vegetative stages as a result of altering their morphology and biochemical reactions. However, stress sometimes occurs during the reproductive stages of the plants. The plant stressed on its reproductive stages can only rely on the escape mechanisms, including early maturity, developmental plasticity, and assimilate remobilization (Bijanzadeh and Emam 2012; Shavrukov et al. 2017). Early maturity reduces the development of additional vegetative parts, which reduces the amount of assimilates required to conduct the fundamental processes of development. Reduction in vegetative development changes the source to sink relationships. Photosynthesizing leaves are likely to channel assimilates to reproductive parts such as buds, flowers, or fruits if the stressed plant has reached the stage at which it has developed these parts.

5.4 Physiological Changes of Plants Adapting to Abiotic Stress Soil salinity and high temperatures result in reduced moisture availability for plant uptake in the soil. The reduced water availability results in protoplast dehydration in most common crops. The physiological reactions resulting from activities of

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amino acids such as proline, dimethylesulfonipropionate, and polyhydric alcohols (mannitols, and osmotins) have been found to contribute to the adaptation of plants to different abiotic stress conditions (Osmond et al. 1987). Alterations of physiological reactions result from the alteration of cellular concentrations when drought or heat stressing condition is experienced (Mundree et al. 2002).

5.4.1 Acclimation of Plants to Low Temperatures Subtropical and tropical plants are susceptible to physiological disorders such as chilling injury when they are exposed to low temperatures for an extended period. However, some plants can tolerate low temperatures better than others. The ability of tropical plants to maintain their biological processes at shallow temperatures has been widely associated with various physiological reactions. Nevertheless, all those physiological changes can be associated with reducing the movement of liquid material into and out of cellular structures via osmosis. Liquid material expands during freezing (Akyurt et al. 2002). If the concentration of cellular liquids is kept low at freezing temperatures, it could result in the disruption of cellular compartments. That is why the reduction of cellular water content is vital under shallow temperatures. Studies have proven no significant changes in the contents of photosynthetic enzymes in maize (Naidu et al. 2003) and sugarcane (Saccharum officinarum; Du et al. 1999) exposed to temperatures cooler than their optimal growth temperature. However, an increase in photosynthetic enzymes can ensure that there is an increased chloroplast material, which buffers the freezing of intracellular moisture. This mechanism is supported by the expression of cold stable isozymes, which increases the cytoplasmic concentrations. The improvement of membrane fluidity is also a means of increasing cellular strength and reducing freezing potential, which results in adaption to low temperatures (Murata and Los 1997). The mechanism reduces the amount of liquid found in extracellular spaces as well as the influx movement of liquids into the cytoplasm.

5.4.2 Acclimation of Plants to High Temperatures Acclimation of plants to high temperatures is the most complicated and critical acclimation compared to other abiotic stresses. Plants exposed to adverse temperatures are prone to deleterious reactions such as water shortages, wilting, and burning at the same time. Features such as the opening of stomata or increasing transpiration for pulling cooling water has been intensively explored and demonstrated as the significant processes leading to plants survival from high temperatures (Chaves et al. 2016). However, various changes in processes and morphology characteristics also take place for plants to adapt or tolerate heat. The stability of thylakoid reactions

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is amongst significant reactions to sudden heat stress. Stabilization of thylakoid membranes in isoprene-emitting plants reduces the formation of reactive oxygen species (Velikova et al. 2012). Moreover, this reaction maintains the photosynthetic potential of the stressed plant uncompromised by the exposure to heat. However, it has a limited extent to which it can be efficient as it may not withhold in conditions where the moisture availability is drastically reduced (Mohanty et al. 2012). The elevation of temperature a heat-stressed plant is exposed to increases electron transport capacity (Liu et al. 2014). This resultant reaction is increased by various factors including expression of heat-stable rubisco activase, expression of heat shock protein (HSP), and chaperone, which act as media for transportation of charged ions that are necessary for reactions such as alteration of cytoplasmic concentrations. Some plants also rely on a decrease in respiration (Almeselmani et al. 2012). Respiration is a biochemical process in which cells of a plant acquires energy by combining oxygen and glucose, resulting in the release of carbon dioxide, water, and ATP (Amthor 2012). Decreasing the process of respiration can be associated with reduced metabolite (sucrose) requirement, which reduces the rate of photosynthesis as well as the need for utilization of the water taken up from the soil. As a result, the majority of the absorbed water can be used by a stressed plant for cooling effects. Plants experiencing heat stress are also characterized by loss of cell turgor and slower growth of the entire plant organs. These reactions are caused by membrane disruption as the high temperatures increase the fluidity of the fatty acids and proteins found in the cell membrane. Some plants produce heat shock proteins that disintegrate membrane proteins if a plant is exposed to extremely high temperatures (Parsell et al. 1994). Excessive temperatures are usually associated with excessive light. Photosystem II can be damaged by overfeeding of light into the complex of proteins responsible for the light reaction of photosynthesis, which results in membrane dysfunctions and changes in a potential gradient across bio-membranes. This response is similar to the physiological softening that is observed when plant tissues are suffering from chilling injury (Parkin et al. 1989). Lipids associated with membranes are also heated sensitive and may be converted to unsaturated fatty acid strands under high temperatures (Gargano et al. 1995). The desaturation of membrane lipids results in loss of cellular strength, which eventually results in wilting of the entire plant organ as a symptom of heat stress.

5.5 Changes in Metabolic Reactions Associated with the Adaptation of Plants to Abiotic Stresses The phytochemistry plays a significant role in a plant’s potential to perform specific physiological processes. Besides enzymes and proteins being precursors of most physiological processes, the concentration of some phytochemicals is known to enhance mechanisms that increase the adaptability of plants to stressing environments. Parida et al. (2007) identified proline, sugars, and polyphenol

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compounds as primary compatible solutes used to maintain osmotic balance, protect cellular macromolecules, detoxify the cells, and scavenge free radicals in cotton (Gossypium hirsutum) under water-stressed condition. It is, therefore, important that the metabolism of different biochemical compounds associated with the adaptation of plants to stress conditions is identified during the exploration of plant adaptation. Various chemicals and ions, including sugars, ABA, sodium, calcium, and potassium, have been linked with the adaptation of plants to different abiotic stresses. Moreover, alterations in the metabolism of various phytochemicals have been discovered and associated with abiotic stresses as well.

5.5.1 Changes in Carbohydrates Metabolism by Plants Under Abiotic Stress Sucrose is essential in plants because it plays a role in growth, development, and signaling (Wind et al. 2010). Sensor proteins detect the sugar status of plant cells through signal transduction cascades that commonly involve mitogen-activated protein kinases, protein phosphatases, Ca2+, and calmodulins, which results in appropriate gene expression in most plant species (Gupta and Kaur 2005). Various genes are either expressed or repressed based on the cellular concentrations of soluble sugars. Genetic analyses have revealed extended interactions between sugar and plant hormone signaling and hexokinase (HXK) as a conserved glucose sensor. The transcription and translation of signals to various proteins and enzyme activities has been associated with HXK-dependent and HXK-independent pathways that utilize different molecular mechanisms to signal changes in sugar concentrations (Rolland et al. 2006). Abiotic stresses in plants result in significant alterations in sugar status. Previous studies have found an increase in concentrations of glucose, fructose, and sucrose in wheat exposed to drought and saline stress (Kerepsi and Galiba 2000). The concentration of soluble sugars has an effect on down- or upregulating expression of various genes. The sucrose transporter has been found to act as a sucrose sensor, which regulates the process of phloem loading (Gupta and Kaur 2005). The sensor also acts as a signal molecule and affects the activity of a proton-sucrose symporter in various plants.

5.5.2 Metabolism and Role of ABA in Adaptation to Abiotic Stress There are many important regulatory roles of abscisic acid (ABA) in plants. However, one instinct role is that of enhancing expressions of essential enzymes that regulate pathways and reactions to different stress conditions. Exposure of plants to conditions such as floods, drought, excessive radiation, and low or high-temperature

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results in an increased level of ABA. The concentration of ABA regulates essential reactions and their rates, as well as biosynthesis and catabolism of various enzymes (Seki et al. 2007). The ABA-dependent reaction of Arabidopsis was associated with its positive regulator known as AtAIRP1, a C3 H2 C3 -type ring-E3 ubiquitin ligase (Ryu et al. 2010). The ability of ABA to enhance the expression of responsive genes has also been proven as the central mechanism for increased reaction to stress. Studies of responsive promoters have discovered several cis- and trans-acting regulatory elements and genes involved in biochemical pathways regulated by the ABA level (Swamy and Smith 1999). The activity of ABA in Tall Fescue grass (Festuca arundinacea) under drought stress was demonstrated to magnify the 23and 27-kDa dehydrin polypeptides presence after a 10 days drought stress of ABAtreated plants (Jiang and Huang 2002). The authors observed that the ABA increased the expression of cytosolic-heat shock protein (HSC 70).

5.5.3 Ca2+ and Na+ in Sensing and Signaling Calcium ion (Ca2+ ) is a predominant intracellular secondary messenger responsible for various signaling pathways based on the cytosolic concentrations that regulate a vast array of signals and responses (Lecourieux et al. 2006). Sodium-ion (Na+ ) uptake into the cytosol is immediate, which results in physiological effects on the extracellular and intracellular regions. The sodium ion competes with Ca2+ for binding sites in the plasma membrane, which inhibits the influx and enhances the efflux of Ca2+ and depletes the stores of Ca2+ inside cell membranes (Rengel 1992). The reduction in cellular Ca2+ homeostasis is the primary response to salt stress that is associated with root cells. High salinity around the roots immediately reduces the amount of Ca2+ that is transferred to the leaf cells, with Ca2+ concentration being reduced while the Na+ concentration is increased in the cytosols of leaf cells. The displacement of Ca2+ by Na+ in the roots extracellular and cytosol sites is the signal that is transported to leaves together with limited water supply as the signal of high soil salinity (Rengel 1992). The Ca2+ signaling is responsible for various pathways, including Ca2+ binding proteins that signal plant defense responses (Lecourieux et al. 2006). Genetic and hormonal signals can be associated with secondary responses that are caused by the Na+ related disturbance of the root cell’s Ca2+ homeostasis. Physiologically, the stress-induced cytosolic Ca2+ alterations vary in frequency, which creates different shapes of Ca2+ signatures depending on the type and the magnitude of stress experienced. Relevant variations in the activity of Ca2+ -ATPase pumps and Ca2+ /H+ exchangers are satisfactory indicators of abiotic stress magnitude and also determine the shape of cytosolic Ca2+ signatures (Bose et al. 2011).

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5.5.4 Protein Responses in Plants Under Abiotic Stress Production of essential proteins that improve reactions to stressing conditions is one of the major mechanisms guiding the rate of adaptation of plants. The production of specific proteins responsible for adaptations can be associated with the process of DNA transcription and translations that lead to the expression of specific genes. In barley (Hordeum vulgare), expression of various genes during adaptation to water stress discovered (Guo et al. 2009). Those included genes encoding heatshock protein (HSP17.8) and dehydrin (DHN3). These genes were not expressed in barley that was not stressed and therefore marked as drought-responsive genes in barley. Moreover, certain enzymes were also discovered and associated with playing significant roles in the adaptation process. Those included elevated production of l-pyrroline-5-carboxylate synthetase (P5CS), protein phosphatase 2C-like protein (PP2C), and several chaperones that were expressed (Guo et al. 2009). Seven known annotated genes hypothesized to enhance drought tolerance through signaling pathways like calcium-dependent protein kinase (CDPK), membrane steroid binding protein (MSBP), anti-senescence (G2 pea dark accumulated protein, GDA2), and detoxification (glutathione S-transferase, GST) (Guo et al. 2009). A “dehydrin-like” protein was also identified by Samarah et al. (2006) in soybean plants that exposed to drought stress. An increase in the expression of 20- and 29-kDa polypeptides and 35-kDa polypeptide observed in Tall Fescue grass grown under drought stress (Jiang and Huang 2002). The effect of drought on groups of proteins has also been investigated. By decreasing water potentials, total soluble protein content first increased, and then decreased in the roots and leaves of two maize varieties (Mohammadkhani and Heidari 2008). Drought stress-induced expression of both dehydrin proteins and soluble proteins in three Tall Fescue grass cultivars (Pan 2011). Therefore, there are various genetic and protein alterations that are associated with different abiotic stresses in plants. These are usually plant-specific and change from one species to another.

5.5.5 Na+ Homeostasis in Plants The salinity of soil and water commonly reduces the production of crops and global biomass. Research in the adaptation of halophyte plants over the latest decades has identified genes associated with salt acclimation and linked these to physiological and morphological mechanisms and processes. Saline environments predominated by sodium chloride are ubiquitous. Current understanding of critical transport determinants that facilitate intra- and intercellular Na+ homeostasis of plants are associated with the presence of sodium and chloride ions in the plant sap (Hasegawa 2013). The homeostasis of different metabolites is based on activities of specific genes. A gene called Arabidopsis thaliana high-affinity potassium transporter 1

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(AtHKT1) was discovered as the facilitator of Na+ homeostasis and K+ nutrition in the Arabidopsis thaliana plant (Rus et al. 2004). Studies in AtHKT1 activities demonstrated that the primary site of the gene is in the plant shoots. The AtHKT1 gene expression results in accumulation of Na+ into shoots by a mechanism in which Na+ is loaded in the shoot phloem to facilitate its movement from shoot to the roots (Mäser et al. 2002; Berthomieu et al. 2003). There are specific enzymes that are associated with the accumulation of Na+ into the cellular environment of plants. Enzymes regulating the pumps in the plasma membrane and the tonoplast, as well as the H+ pyrophosphatases (AVP1) enzyme, were designated to proton osmotic gradient that magnifies Na+ efflux to the apoplast and influx into vacuoles (Hasegawa 2013). Various studies discovered certain enzymes involved in the movement of ions within and between cellular environments. The plasma membrane Na+ /H+ antiporter SOS1 indicated to be responsible for apoplastic efflux, and NHX type Na+ /H+ antiporters for vacuoles and endosomal compartmentalization. Calcium and sodium ions are known to compete for the transport system in most plants. This fact happens due to a mechanism in which halophyte plants regulate intracellular Na+ homeostasis in order to minimize the cytotoxic effects of the ions and osmotic adjustments (Munns and Tester 2008). Findings have indicated that the calcium found in the extracellular region (Ca2+ ext ) competes with Na+ and reduces Na+ influx into the cytoplasm. The mechanism entails decreasing the Na+ transport through high-affinity K+ uptake systems that are presumed to be cation channels through the SOS signal pathway, the SOS1 plasma membrane Na+ /H+ antiporter (Hasegawa 2013). Moreover, it is known that cellular transport systems function integrative to facilitate tissue and organs Na+ homeostasis. The HKT1 Na+ transporters dominate the process and regulate Na+ loading into the root xylem, limiting flux to and accumulation in the shoot (Munns and Tester 2008). Acidification of apoplast (pH ~5.5) results from this pump, which accounts for 1.5–2.0 pH units compared with the cytosol (pH ~7.2). The pump is also associated with an inside negative potential of approximately −120 to −150 mV through the plasma membrane (Palmgren et al. 2011).

5.5.6 Potassium (K+ ) Homeostasis in Plants Potassium or potassium ion (K+ ) is the principal inorganic constituent found in plant cells, dominantly in the cytosol. Functions of K+ at the cell level vary widely but include electrical neutralization of anionic charges, protein synthesis, long and short-term control of membrane polarization, and regulation of the osmotic potential (Chérel et al. 2014). In the regulation of osmotic potential, the K+ regulates the whole-plant response with osmotic driven functions such as phloem transport, stomatal aperture, or wilting cellular movements (Ragel et al. 2019). Therefore, besides K+ being involved in the processes of saving water, the growth

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and development of plants require that large amounts of K+ are taken up from the soil and translocated to various plant parts. Potassium assists in osmoregulation, cation: anion balance, and energy transfer, among many other processes. It takes part in enzyme activation protein synthesis and carbohydrate metabolism. The translocation of mineral compounds such as nitrates (NO3− ), phosphates (PO4 3− ), calcium (Ca2+ ), magnesium (Mg2+ ), and amino acid uptake is reduced in cases where K+ supply is inadequate (Schwartzkopf 2018).

5.5.7 Protein Alteration During Adaptation of Plants to Abiotic Stresses The expression of genes that regulate the process of development is highly associated with DNA methylation. The DNA post-translational modifications that happen in nucleosome histone are the central site of the adaptation reactions that occur during adaptation to stress (Chinnusamy and Zhu 2009). Alterations in the photosynthetic pigments, proteins, and osmotic components of cotton under drought stress have been extensively investigated. These alterations have been extensively discussed in previous sections of this chapter. Various changes have been reported and can always be associated with anabolism or catabolism of specific proteins. For instance, the content of total soluble proteins decreased in drought-stressed cotton and increased when the plants recovered from the stress (Parida et al. 2007). In the study, an increase in the content of proline and total free amino acids was observed. Amino acids are building blocks of proteins (Jalkanen et al. 2004). Therefore, the soluble proteins catalyzed into amino acid compounds during the drought stress.

5.6 Cell Detoxification by Halophyte Plants Phyto-extraction of saline soils is a standard procedure to reduce the concentration of salt ions found in the soil. It is by this process that researchers of halophyte plants gained a high interest in the importance of understanding the ability of plants to absorb a high amount of salt ions without suffering from toxicity. Unlike drought, chilling, and heat stresses where plants can only rely on atmospheric intervention for escape. Halophyte plants are characterized by their ability to detoxify or reduce the level of salt ions that they absorb from the saline soil. Various mechanisms can be utilized by plants to immobilize salt ions in the cytoplasmic solutions. Cellular sodium compartmentalization, organ integration, and carbon acquisition and allocation are among the ordinary mechanisms that have been studied in depth in the field of cellular detoxification (Cheeseman 1988). Sodium compartmentalization in conventional crops such as cowpea (Webb and Newcomb 1987), maize (Pfeffer et al. 1992; Fortmeier and Schubert 1995), soybean (Durand and Lacan 1994), and Arabidopsis (Aharon et al. 2003) has been reported.

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Studies focusing on sodium compartmentalization of citrus tree species revealed that Na+ transporter genes, including SOS1, HKT1, and NHX1, were the fundamental components associated with the varieties that can retrieve sodium from xylem and sequestrate it into the root vacuoles (Martínez-Alcántara et al. 2015). Some plants can perform the compartmentalization mechanisms in different organs to allow yield productivity under high salinity soils. Zhang and Blumwald (2001) demonstrated that a transgenic tomato genotype overexpressing a vacuolar Na+ /H+ antiport was able to grow, flower and produce fruit in the presence of 200 mM sodium chloride, which is a saline condition. In the study, the leaves stored high sodium concentrations while the tomato fruit had low sodium content that did not compromise the fruit’s organoleptic quality. Although this mechanism can be valued in the cultivation of fruit crops, it could be challenging in leafy crops. Some plants can display adverse osmotic and ionic effects to root and shoot growth, but the extent of sensitivity has been shown to differ from one part to the other. In wheat, salt stress reduced fresh shoot weight by 77%, plant height by 25%, and flag leaf length by 36% (Abebe et al. 2003). This result demonstrates that different types of cells have different sensitivity to salinity, which could be explored for finding the responsible cellular parameters that vary among these tissues and be taken for breeding programs.

5.7 Conclusion Adaptation of plants to abiotic stresses is controlled by complex mechanisms, including changes in their morphological characteristics, and physiological or metabolic reactions. The mechanisms and the extent of their application differ among plants, but most plants utilize similar metabolites and morphology to mitigate stressing condition(s). A wide variety of crops have been studied previously, and their specific mechanisms to adapt have been identified. However, there is an inadequate amount of recent studies exploring breeding for specific mechanisms. Most studies assessed the mechanisms that are already discovered in various plants rather than improving the mechanisms in plants that possess them. It was noted that the majority of studies were carried out on a few plants of high commercial importance, which could be associated with difficulties in monitoring commercial plants that are usually grown in supplemented conditions.

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Chapter 6

Innovations in Plant Variety Testing with Entomological and Statistical Interventions Luke Chinaru Nwosu and Ugochinyere Ihuoma Nwosu

Abstract Criteria and methods for plant variety testing must be efficient, holistic, and innovative to enable the approval of new varieties that perform well. Apart from providing innovations in plant variety testing, this chapter provided more insights on crop-specific characters that support resource efficiency and resilience to challenging environments. The technical issues addressed and new features (entomology and statistics)- are expected to mitigate climate change effects, enhance performance, and significantly improve existing criteria and methods. For high performance of new plant varieties, technical proficiency of concerned experts, the robustness of techniques, methodological sophistication and capacity, statistical inputs, and adherence to guidelines must significantly improve. More affordable and easy-touse tools are required to spend less time and resources. For efficient testing of new plant varieties, particular importance has to be accorded to model species, D.U.S., distinctness problems in certain species, and needs of breeders. By adopting the novel tactics suggested in this work, it is believed that data quality and interpretation will improve to eliminate or reduce redundancy of analyses and make more value of existing processes. Optimized and harmonized protocols will culminate in quicker and robust decisions on D.U.S., V.C.U. and V.C.U.S. It has been advised that the required improvements should focus on (i) finding new tools for DUS and performance testing (ii) identifying potential synergies between D.U.S. and V.C.U. and utilization during performance testing. (iii) maintaining better coordination and sharing of data and databases between partners. (iv) improving protocols and (v) strengthening networks and communication tools towards stakeholders and policy makers. The work offered insights on crop characteristics, and crop management practices that can be manipulated to produce plant varieties of high performance and high market value.

L. C. Nwosu () Department of Crop and Soil Science, University of Port Harcourt, Port Harcourt, Rivers State, Nigeria e-mail: [email protected] U. I. Nwosu Department of Statistics, Federal University of Technology, Owerri, Owerri, Imo State, Nigeria © Springer Nature Switzerland AG 2022 C. M. Galanakis (ed.), Environment and Climate-smart Food Production, https://doi.org/10.1007/978-3-030-71571-7_6

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Keywords Criteria and methods · Innovative · Climate change · Varieties · Model · D.U.S. · V.C.U.S

6.1 Introduction 6.1.1 Background As we talk about plant variety testing, a fundamental question arises. What is plant variety, and how does it differ from a cultivar? Incidentally, most people (including scientists) use the two interchangeably. It will be nice to first address the confusion in using “plant variety” and “cultivar”. It is not essential to highlight all aspects of plant variety and cultivar misnomer; however, we shall identify the technical difference between the two terms. Unquestionably, a cultivar is a cultivated plant. According to Carneiro et al. (2015), cultivars are identical plants with distinct morphological, physiological, chemical and cytological characteristics preserved during seed multiplication by sexual or vegetative techniques. The author made two notable assertions. First, cultivars can emanate from cloning and secondly, varieties and cultivars have the same meaning; however, the term variety is restricted to open-pollinated plants as found in maize. From a more holistic perspective, a plant variety is developed, characterized, and designated a particular name through human interventions and science-driven creative process. Authoritatively, plant variety (i.e., the term under discourse) is a legal term consistent with the International Union for the Protection of New Varieties of Plants (UPOV) Convention. In other words, recognizing a cultivated plant (a cultivar) as a variety furnishes the breeder with some legal protection. Ultimately, the varietal selection process is driven by biological evaluations (in the relevant target environments) and knowledge of genes and genomes. From time to time, breeders strive to improve on the existing plant varieties. The new varieties developed will adapt to climatic conditions of the environment, improve resistance to pests and diseases, improve yield, enhance the proximate/nutritional contents and improve palatability and cultural acceptability. When these improvements come with the release of new varieties (as claimed by breeders), there is a need for concerned bodies to subject the new plant varieties to test using criteria and methods already in place. Given the changing climate, the emergence of new pests and diseases, shift in agricultural practices, industrial and commercial demands, there is a need for continuous review of criteria and test procedures to realize maximum yield potentials. Innovating the existing criteria and methods are expected to achieve meaningful impacts. Briefly, such innovations are expected to culminate in the introduction of new plant varieties that have stable, high, or higher yields with increased adaptation to biotic and abiotic conditions, significantly to reduce the impacts of climate change. Incontrovertibly, this will significantly assist in introducing varietal traits that respond to new environmental challenges and needs in the conventional and organic sectors. This has to take into account

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the economic returns of farmers. This chapter will provide more insights on cropspecific characters that support resource efficiency and resilience to challenging environments. There is an increased need to consider a wide range of parameters during plant varietal testing. Making the test criteria holistic to address entomological and statistical deficiencies of the existing methods is another prominent feature the present contribution hopes to tackle. With the emergence of new insect pest species attacking plant varieties (previously not attacked) and climate change effects, there is a need to use statistical models that can forecast with precision the performance of new varieties after field trials. Adequate information on new experimental designs, methods and tools to enhance performance testing of new varieties is indispensable in ensuring sustainability. Also, there is a need to extend the test to postharvest situations. These shall include testing for postharvest resistance to insect pests and proximate/nutrient contents of the new plant varieties after storage to ensure the quality of the variety is not affected by storage. This suggestion is necessary because plant varieties are usually stored for use during off-seasons. The use of extended criteria and methods in variety testing will make the value of the performances of the new plant varieties under conditions that are associated with sustainable and more variable farming practices. This means that the chapter will address issues that shall ultimately enhance the effectiveness of various testing and availability of reliable information to stakeholders on varietal performance under series of production conditions and ecosystem stressors (Bruins 2019). For emphasis, new plant varieties are expected to adapt to climate change in order to ensure food security and protect natural resources. Therefore, in the planning, statistical modeling and database management should be considered sacrosanct. The holistic approach (comprising modeling, software management, use of advance molecular tools, and entomological interventions) will provide concerned bodies with more tactical and robust testing criteria and tools to forecast the performance of new plant varieties in different pedo-climatic and agronomic conditions. After the chapter, it is hoped that we would have successfully improved on existing information with recommendations on plant variety testing for the benefits of farmers. In the long run, enhanced test criteria and methods will ultimately promote the marketing and the use of more adaptable and sustainable plant varieties by growers operating in the right environment.

6.1.2 The Release of New Plant Varieties: What Readers Should Know For inevitable reasons, the procedures for the release of new plant varieties vary widely in the world. According to Allard (1960), the decision to release a new variety depends on several essential factors that must not (in the future) downplay the decision to release. The agricultural experiment station constitutes a review

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board with powers to assess proposals to release a new plant variety. The breeder or agency (whose proposal is under consideration) must tender proof that the new variety has a place in the agriculture of a specific environment or area. Most of the time, the required evidence comprises observations on the response of the new plant variety to different environmental conditions and data (with precision and accuracy) from comparative trials in which the breeder has compared the performance of the new variety with standard varieties over years at some locations. In other words, the comparison must be through replicated experiments (true replicates, not pseudo replicates). Response to pests and diseases, quality, suitability for mechanical handling and other factors of farm practice are data parameters of new plant variety which must be probed through replicated field experiments. Notably, data from regional tests are useful to ascertain the range of adaptation of the new plant variety in areas outside the experimental station. Before production on a commercial scale, the new varieties which private companies produce (for their use or for the use of the growers that patronize them) are subjected to thorough tests. The new variety is named if the test gives the expected result. The proposing breeder suggests a name for the new variety to make identification easy. The majority of the experiment stations prefer one-word names. After successful nomenclature, the plant breeder makes a limited increase of the new variety and passes it to the foundation seed department or agency to produce foundation seed. At this point, provided other stations have had the opportunity to test the variety in regional co-operative trials and now know the potentials and limitations, they receive a portion of either breeder or foundation seed if they also wish to release the new variety. The distribution of foundation seed to seed growers for increase and classification as certified or registered seed follows depending on the agency (providing the certification) and the merits of a particular case. After successful registration, the certified seeds are distributed and this is the final step in the release of a variety. This entails the distribution of registered seed to certified seed growers and the use of the registered seed to produce certified seed, which will be available without restriction. In order to maintain the productivity of every variety for cultivation, it is vital to carry out precise and controlled production of the seed. In other words, the maintenance of pure seeds and breeding stock must be considered necessary. Otherwise, the varietal yield may drop abruptly and diminish following out-crossing and little mutations. Efforts of farmers or certified seed growers; notwithstanding, it is possible that a new plant variety can become objectionably contaminated with off-types. Therefore, the seed certification agency and the plant breeder are expected to work harmoniously to maintain varietal purity and quality through periodic seed purification. It has been advised that parental genotypes must be maintained without changes for the hybrid to remain genotypically the same from year to year. To evaluate the consistency of production levels, there must be an accurate description of the new plant variety, and description must be linked with testing of the variety over some years and then entering in seed register. The breeder, too, (irrespective of affiliations) must maintain breeding stock in his germplasm, namely, gene bank, seed store, and herbarium. He must test his stock regularly to provide elite plants

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for propagation. Notably, the large-scale production of high-quality seeds must be under independent control and without interferences or influence of the breeder. Furthermore, for seed production to remain effective, it must be supported by relevant State laws, and this will ultimately stimulate plant breeding.

6.2 Crop Characteristics for Maintenance of Stable Yield 6.2.1 Overview Crop characteristics are parameter values that define a plant variety. One does not have to visit the planet moon to know that crop characteristics are directly or indirectly related to yield. In basic agriculture, yield (also known as agricultural productivity or agricultural output) is defined as the amount of crop cultivated per unit area of land. According to van Vliet and Giller (2017), crop characteristics that define potential yields include phenological and physiological traits. The emphasis on phenological and physiological traits will guide discussions on the capacity of new varieties to maintain yield under more variable conditions and more sustainable crop management practices about the use of fertilizer, water, or plant protection products. Hall et al. (2016) also agreed that the response of a variety and its potential yield under optimum conditions is genetically determined. Crop characteristics vary between varieties, and therefore, in testing new plant varieties, the characteristics specific for the variety should be considered. Apart from that, it is important to consider characteristics required for improving crop yields such as carbon, oxygen, hydrogen, and nitrogen. Increased crop yields suggest by logical inference, that fewer hands are needed on-farm for commercial productions, too. Capturing and linking phenological and physiological traits to potential yields in simulation studies/statistical modeling during varietal testing is advocated. Plant phenological traits should emphasize on the time and performance of plant-bud development, leaf development, seed weight/development, shoot/branch development, inflorescence emergence, flowering, pod number, reproductive node number, fruit development, fruit maturity, and senescence and the start of dormancy (Igbal et al. 2017). Phenological phases (during plant development) exhibit great interannual variability and large spatial differences. Individual characteristics (example genes and age) and environmental characteristics (such as weather and climate conditions in the micro and macro scale, soil conditions, water supply, pests, and diseases competition) influence plant varieties and yield performance (Koch et al. 2009). Meanwhile, the seasonal cycle of plants is influenced largely by temperature, photoperiod, and precipitation. To determine the impact of climate change on ecosystems, phenology has recently changed from traditional data collection to highly essential integrative parameters. In other words, long phenological records would be necessary during plant varietal testing because they constitute the basis of several climate change investigations. Phenology is a vital instrument to communicate general climate

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characteristics and climate change effects on the public. In agriculture, phenological data are provided as input for crop models and for the timing of management activities. Management systems are needed to control and deal with data from the traditional observations and, in some cases, Normalized Difference Vegetation Index from satellites (Koch et al. 2009). Increasing varietal yield and incorporating higher nutritional value to ensure greater food availability has become essential. Therefore, during plant varietal testing, important yield traits should be taken seriously. Other factors that should be considered are earliness for flowering and cycle, lodgingresistance, and possession of exemplary architecture. Apart from encouraging crop rotations and management practices of the area, earliness trait in a variety is necessary for adequate planning of sowing and harvest times. In some plant species, earliness has been characterized by early flowering and early cycle. Yield traits of grain varieties, number of pods per plant, number of grains per plant, number of grains per pod, and mass of 100 grains have been used to predict yield potentials. In recent years, field observations have revealed an increase in grain yield by increasing some traits such as the number of pods per plant, number of grains per pod, and mass of 100 grains (Ribeiro et al. 2018). In current breeding programs, the development of plant varieties with higher yields is a major focus. If complemented with the first and upright plant architecture, these varieties will represent marketing advantages to new varieties. This is because correlations have been found between these traits and yield. So, in-plant variety testing, it is recommended that these factors should be considered very important too. Furthermore, methodologies are available for estimating phenological parameters of new plant varieties using data from field trials. Crop phenology models play key roles in forecasting yields under climate change. Variety-specific model parameters are vital and should be utilized when precision and accuracy are the targets of prediction. To estimate plant phenological parameters objectively, Fukui et al. (2015) suggested the combination of a stochastic parameter estimation method (genetic algorithm) with the use of a database of several years of records emanating from variety trials conducted at an experimental station. The proposed methodology can be used to achieve quantitative evaluations of environmental responses of some varieties without depending on extensive and rigorous experiments. This is part of statistical interventions. Earlier, it was mentioned that crop characteristics that define potential yields of varieties include physiological traits. Nitrogen and phosphorus are recognized as essential elements in crop production with notable physiological roles (Sinclair and Vadez 2002). Almost all the biochemical compounds in plants that support development and growth contain nitrogen and phosphorus. Notably, a quantitative relationship exists between crop yield and accumulation of nitrogen and phosphorus by plants. This means that deficiencies in either nitrogen or phosphorus will culminate in the lost ability for plant growth. With advances in crop science, there are few available options to significantly reduce the requirement for either element in crop growth and yield formation (Sinclair and Vadez 2002). As a

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result, yields cannot be increased without the increased acquisition of nitrogen and phosphorus by plant varieties. Under a low nutrient environment, little opportunity is available to increase nitrogen recovery, whereas many options exist for increasing phosphorus accumulation by crops. The substantial limitation on yields of a crop variety with inadequate nitrogen implies that without external supplies of nitrogen for the cropping system, biological fixation of nitrogen must be enhanced to increase nitrogen input. Research is needed to enhance symbiotic nitrogen fixation in crop varieties for better yield performance. Improved management of phosphorus will allow plant varieties to achieve high yields. Indeed, genetic manipulation is an interesting technique, and new plant varieties with surprisingly outstanding yields are possible under little use of fertilizer. This reduces the running cost that will otherwise be incurred through high-volume fertilizer application in high-demanding plant varieties. With the following information, the point to make is simple. Physiological traits for crop yield improvement in low nitrogen and phosphorus environments should be incorporated during breeding and checked during testing. Widening test criteria to monitor desirable traits during plant variety testing is not a bad idea. Having provided an overview of crop characteristics with the capacity of new varieties to maintain yield under more variable conditions and more sustainable crop management practices, the possible options that are not only workable but also innovative are discussed.

6.2.2 Introgression of Physiological Traits to Achieve Drought Adaption in New Varieties: Manipulation of Crop Characteristics to Sustainably Improve Yield Increasing human-population, industrialization, and climate change (resulting in erratic monsoon rains) are expected to significantly limit fresh water availability for agriculture both in irrigated and rain-fed ecosystems (Sreeman et al. 2018), and this stands to pose massive liability on varietal performance. Research is needed to develop strategies to save water in irrigated conditions and to breed superior plant varieties with improved water productivity to sustain yield under rain-fed conditions. Traits associated with maintenance of positive tissue turgor and maintenance of increased carbon assimilation are regarded as most relevant to improve crop growth rates under water limiting conditions and to enhance water productivity. The advent of water-saving agronomic practices notwithstanding, a genetic enhancement strategy of introgressing distinct physiological, morphological, and cellular mechanisms on a single elite genetic background is vital for achieving a comprehensive improvement in drought adaptation in crop plants. With introgression as a technique, food security is assured.

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6.2.3 Desirable Crop Characteristics at the Expense of Yield and Resistance to Insect Pests: Pros and Cons It is worthwhile to note that not all desirable crop characteristics ultimately favor yield. Some crop characteristics favor growers and markets in serving as teasers while at the same time favor insect pests because they equally attract insects. Anew evidence has emerged to support this assertion (Ikodie 2018). Figure 6.1 shows six varieties of cowpea and common bean seeds that differed in beauty and differed in their attraction to Callosobruchus maculatus insect. There is evidence that some growers do not mind having these crop qualities in their varieties because they create better markets. The decision to have some crop characters incorporated into new plant varieties in order to promote the marketing and use of the variety should not be made in a hurry. This is because sustainable varieties appear to be economically more beneficial in the long run. Consideration of traits that are adaptable to climate variability for the realization of climate-smart food production is crucial in a challenging environment. Therefore, in decision-making, considerations should capture both the market and sustainability. Van Vliet and Giller (2017) provided some practical illustrations using three cocoa varieties, namely, Criollo, Trinitario and Forastero. The Criollo variety has been commended for its fine flavor; nevertheless, it lacks vigor, produces low yield and is highly susceptible to pests and pathogens. Forastero cocoa variety manifests greater vigor and can withstand the extremes of climate better than Criollos. Trinitario cocoa variety is believed to have originated as a cross between Criollo and Forastero cocoa varieties, and many of Trinitario cocoa attributes are intermediate between Criollo and Forastero

1 Farin wake

2 99K-573-2-1

4 Waken challa red kidney

Banjaur

5 Kwaklik lilian beans

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6 Waken challa red black kidney

Fig. 6.1 Cowpea (seeds 1, 2 and 3) and common bean (seeds 4, 5 and 6) varieties with differences in beauty and attraction to Callosobruchus maculatus insect pest in storage. (Source: Ikodie 2018)

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varieties. Consequent to their preferred crop characteristics culminating in high productivity, the Forastero cocoa variety currently accounts for approximately 80% of global production. Criollo variety has a nice flavor but produces poor yield and is highly vulnerable to infestations and diseases. In the global production of cocoa, yield, and good resistance to pests and pathogens were not sacrificed for flavor. It has been mentioned earlier that crop characteristics vary between varieties, and so, decisions should be guided by characteristics that support good yield. Amount of leaf chlorophyll, canopy size and structure, radiation tolerance, and harvest index are some examples of important characteristics that differ between varieties (van Vliet and Giller 2017). Susceptibility to insect pests, palatability, nutrient composition, morphological and physical characteristics differ among varieties (Okpile 2012; Nwosu et al. 2015a, b; Nwosu 2016). It has been shown that palatability, nutrient composition, physical and morphological characteristics of varieties (Figs. 6.2 and 6.3 and Tables 6.1 and 6.2) are related to susceptibility to insect pest infestation (Okpile 2012; Astuti et al. 2013; Ikodie 2018). Some of these characters enhance visual appeal, impact good taste and nutrition and increase marketability, but unwittingly render crop varieties susceptible to insect pests attack. In Nwosu (2016), some elite maize varieties had high protein content and good mineral nutrition but are unfortunately susceptible to maize weevil pest, Sitophilus zeamais Motschulsky. Insects are lovers of protein, starch and minerals. They require protein, starch and minerals to grow and lay eggs (Ichiro et al. 2009; Nwosu 2016). The clash in human and insect food requirements has posed a major problem to breeders and resulted in the haphazard satisfaction of growers and consumers. Attempt to improve nutrient contents of new plant varieties ultimately attracts the insects whose feeding and developmental activities usually lead to damage of the variety. So, who should the breeder satisfy? Ordinarily, efforts to satisfy human users will also promote insect pest infestation since both groups share a similar nutritional interest. In consideration of the pros and cons, it is easily discernible to recommend the selection of traits that support good yield while retaining nutrients at a level necessary for human users. It is also possible to identify the threshold difference between the nutritional requirements of human beings and the levels at which insects are attracted to cause damage. Manipulations of insect-loving qualities during resistance breeding programs in order to deter them from infestations are advocated. This will require in-depth knowledge of the bases of resistance and susceptibility to insect pests. When a phenological or physiological trait is considered indispensable because it determines the market but not yield, good yield can as well be achieved through proper farm practices and management, namely adequate fertilizer application, regular weeding, stable water supply and application of plant protection products. To achieve aggressive marketing, the role of teaser traits cannot be overemphasized because they are responsible for magical sales and amazing profit. However, a new plant variety has its superior characteristics spelled out and so will be tested based on those characteristics and in combination with criteria already in place. To ensure sustainability, existing criteria should be critically reviewed with the possibility to innovate to realize the maximum yield potentials.

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Palatability of ten brands of rice

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y = 0.043x + 2.491 R² = 0.046

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Mean (±SEM) number of emerged F1 progenies of Sitophilus oryzae (n = 4)

Fig. 6.2 Association between rice brand palatability and number of F1 progenies of Sitophilus oryzae. (Source: Okpile 2012)

6.2.4 Altering Crop Characteristics Through Bio-fortification to Develop New Plant Varieties: Can It Enhance Consumer Acceptance? Bio-fortification is an innovative approach that is in line with current global realities. It is a strategy that enhances the nutrient content of staple food varieties through agricultural means (conventional breeding, genetic engineering, mutagenesis), and agronomic approaches (Hotz 2013). The primary objective of bio-fortification is to address micronutrient deficiencies in developing country populations. Therefore, we have been inspired to encourage the use of this technique in developing new plant varieties with the capacity to maintain yield under more variable and sustainable conditions. According to Hotz (2013), the acceptance of new varieties by growers depends mostly on many crop characters such as agronomic performance and consumer-preferred traits such as color, size, shape, texture, odor, and cooking qualities. It has been inferred that increased mineral content does not change the visual or sensory qualities of varieties. Therefore, it is easy to predict that consumer adoption will be significantly influenced by agronomic performance and consumerpreferred characters in the bio-fortified crop. The tricky part is the possibility of consumer acceptance of the color change (for instance) from white to yellow, orange, or any other. Many research methods have been used to evaluate consumer

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0.2

0.25

0.3

0.35

0.4

0.45

Weight of frass generated by Sitophilus oryzae in ten rice brands

Fig. 6.3 Association between rice brand palatability and weight of frass generated by Sitophilus oryzae. (Source: Okpile 2012)

acceptance of provitamin A-bio-fortified orange sweet potato and corn in many Sub-Saharan African countries (as a case study). Findings from these studies suggest that the orange-colored varieties are equally acceptable or preferred to standard white crops. The adoption of the orange-colored varieties will increase when information on their nutritional qualities are also supplied. Nevertheless, these studies are short-termed, and methods are drawn from economics-research employ some hypothetical conditions. Therefore, longer-term investigations are required, but available information suggests that these orange-colored food varieties cannot be rejected entirely- without reservation. These findings agree with the successful release of orange sweet potato for household production and rural Mozambique (Hotz (2013). From the analysis and with the market as part of the primary focus, it is practicable to alter crop characteristics through biofortification technique to enhance consumer acceptance of new varieties. When such biofortified new varieties appear for the test, long-term sustainability studies, and acceptance in different populations are strongly suggested. This is because altering crop characteristics of a vegetable crop to achieve high leaf area can result in higher insect pest attacks because of insects like broad surfaces for anchorage, feeding, and oviposition (Nwosu et al. 2015b). Depending on merit and the traits in question, the theoretical consequences for biomass and yield of altered crop characteristics can be assessed using a simulation model (Elings et al. 1997).

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Table 6.1 The matrix correlation coefficient (r) between physico-chemical characteristics of milled rice varieties and eggs number, F1 progeny emerged, median development time of Ryzopertha dominica and the susceptibility index Physicochemical charateristics Protein Fat Ash Carbohydrate Phenolic Hardness

Number of eggs 0.741 0.483 0.915* −0.285 −0.962* −0.838*

F1 progeny emerged 0.682 0.509 0.896* −0.345 −0.892* −0.833*

Median developmental time −0.646 −0.560 −0.849* 0.357 0.990* 0.936*

Susceptibility index 0.728 0.393 0.963* −0.180 −0.898* −0.946*

*Significant at α < 0.05 Source: Astuti et al. (2013)

6.2.5 Yield Is Quantitative Trait Most Important in Many Crops: Other Crop Characteristics and Environmental Factors That Influence Yield Yield is a quantitative trait that is the most important in many crops and considered during the breeding program. Any manipulation to incorporate a particular trait (such as resistance to a specific pest species or any other) must also consider yield status for high market performance. Focusing on a new trait in developing new plant varieties with little consideration to yield may lead to low adoption by farmers who do not see anything desirable except yield. The incorporating of crop traits that will have good yield in the background is one recommendation we would like breeders to take seriously. This usually creates an overwhelming market for the newly released plant variety. Numerous crop characteristics and environmental factors may influence yield (Hall et al. 2016), but literature is scarce on essential crop characteristics that also affect yields, such as canopy expansion and light interception, dry matter production, and partitioning. For instance, light use efficiency and harvest index have been found correlating with grain production under favorable growing conditions during the reproductive phase of oilseed flax (D’Antuono and Rossini 1995; Hall et al. 2016). In crop water requirements, index of climatic demand on evaporation and how specific crop characteristics are influenced are vital considerations (Pereira and Alves 2005). Crop-soil surface resistance, crop height and the fraction of incident light or radiation reflected by the crop-soil surface are examples of characteristics that can be integrated to achieve distinguishing effect in comparison to a reference variety. Crop-soil surface resistance is related to leaf area, degree of stomata control, a fraction of ground covered by vegetation, leaf age and condition, and soil surface wetness). Crop height affects roughness and aerodynamic resistance whereas the fraction of incident light or radiation reflected by the crop-soil surface is influenced by the fraction of ground covered by vegetation and soil surface wetness. The

b Common

varieties bean varieties Source: Ikodie (2018)

a Cowpea

Cowpea/common bean varieties Farin wakea Banjaura 99K-573-2-1a Waken challa red- kidneyb Kwaklik lilian beanb Waken challa-red-black kidneyb

Color Cream-white Brown White Maroon Mottled white-black Red-black

Shape Kidney Kidney Oval Oblong Oval Oblong

Texture Rough Rough Slightly-rough Smooth Smooth Smooth

Eye size Large Large Large Small Medium Small

Eye color White White White White White White

Susceptibility/resistant status Moderately resistant Moderately resistant Highly susceptible No damage No damage No damage

Table 6.2 Morphological characteristics of some varieties of cowpea and common bean tested for susceptibility to Callosobruchus maculatus Fabricius infestation in storage

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time-averaged influence of the specific crop characteristics is useful for planning, irrigation system design, and typical irrigation management or for extensive scale water balance studies. In potential production situations where water and nutrients are not limiting, crop growth rate and productivity are principally determined by light, temperature, and crop characteristics. Light interception by a crop canopy is determined by the leaf area index of the species and the light absorption characteristics of the leaves. These are however, related to leaf thickness and leaf angle distribution. The leaf angle distribution determines the amount of radiation absorbed per unit of leaf area. In plants, horizontally-oriented leaves capture light with higher efficiency than vertically-oriented leaves. In mixed canopies, apart from leaf area index and light absorption characteristics, plant height is an essential factor in the distribution of light over the competing species. If a leaf is positioned above another leaf, it will absorb a more considerable amount of radiation. A strong correlation between plant height and competitive ability has been demonstrated for many crop species. In rice, Oryza sativa L., for instance, it has been demonstrated that taller varieties are more competitive. Still, these taller varieties have a lower yield potential due to lodging and a reduced harvest index (Bastiaans and Kropff 2017). Light cannot be stored in the system of the plant. If the canopy does not intercept it, it will get to the soil surface and incidentally lost for crop growth. Larger plants receive a disproportionate share of the light-resource. Plants that grow fast in early growth stages maintain a decisive advantage and build up a larger share in the canopy. Consequently, competitiveness is not only related to light absorption characteristics but also light use efficiency. New plant varieties with the capacity to adapt to fluctuations in light supply are necessary.

6.2.6 Crop Characteristics, Crop Management Practices, and Eventual Yield Among the production constraints met by growers in different parts of the world are nutrient, water, and pesticide limitation. Herein, the need for efficient crop management practices, especially concerning the use of fertilizer, water, and plant protection products is emphasized. This has become a necessity given the influence of climate change. In other words, nutrient requirements of newly-developed plant varieties, their yield response to fertilizer and pesticide application in relation to management (example, weed management), climatic, and soil conditions should be tested. Data from such tests are critical in the overall recommendation of a new variety. In some plant species (example, cocoa), harvesting and little nutrient losses (triggered by leaching, for example) culminates in nutrient exports and cause gradual soil nutrient depletion (van Vliet and Giller 2017). As an innovation, every newly-developed variety should have exact nutrient requirements. Leaf and soil test interpretations are required to identify additional nutrient needs. Recommended

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nutrient application rates are necessary. Field trials have shown that fertilizer application more than doubled plant yield; however, in other cases, the response was minimal. Differences in response between varieties, fields, and regions are yet to be completely understood and explained, and guiding further research. Dear readers, how familiar are you with the phenomenon of “crop ratios” in sustainable agriculture? According to Sadras et al. (2009), crops characteristic of subsistence agriculture, including Zea mays (corn), Phaseolus vulgaris (bean), and Manihot esculenta (cassava) frequently show yield ratios about current management over 2. What this means is that high ratio combinations can be used to promote organic agriculture as the solution to developing-countries’ agriculture. If breeders can develop varieties with yields doubled without fertilizer, indeed, there will not much need to buy fertilizers. However, there are two sides to crop ratio application during breeding and fertilizer application as a management practice. If we may ask, reader, which option do you consider more rewarding? Do the time and resources required for achieving substantial crop ratio effects justify the output? Or is it better to apply just small doses of fertilizer and pesticides to have the same substantial productivity? Which of the two options provide a long-term solution to hunger? To cut short the endless imagination, let us consider the opinion of Sadras et al. (2009). According to these authors, increasing the yield of maize crops (for example) from 0.5 to 1.2 t ha−1 (ratio >2) is hardly a long-term remedy to hunger when the same gains could be achieved on all fields with little of fertilizer and even realize outstanding yields of 5–10 t ha−1 with larger fertilizer applications. Given this debate, what appears more logical is to develop new plant varieties with the capacity to maintain high yield under more variable conditions and under crop management practices such as fertilizer application (to remediate soil and prevent impoverishment), moisture augmentation (through irrigation), pesticides applications (to mitigate pest attack) and weed control (to reduce competition for light and nutrients). Much effort should be made to understand varietal interactions with agroecology and management for sustainable productivity. Fundamental knowledge is thus required for farm level recommendations to have a strong scientific base. Therefore, multifactorial crop characteristic–fertilizer response trials will be essential to address some of the fundamental knowledge gaps. Improvement in experimental designs suggested here and useful simulations are necessary to achieve sustainability (Stockle and Debaeke 1997). The development of robust crop models to focus on multiple plant aspects (as variables) such as evaporation at different growth stages, the morphology of leaves (to capture water behavior) are expected from crop physiologists and the model should have a high degree of details for the entire model to be well-balanced. Indeed, essential information on soil-water balance to estimate (with higher precision) crop irrigation requirements are required (Landsberg and Sands 2011) in an era that emphasizes precision agriculture (Zakka et al. 2019). Data on soil-water balance can be provided by non soil scientists using available databases and pedotransfer functions in terms of soil texture, bulk density and organic matter content to water availability (Pachepsky and Rawls 2003). The use of robust and tactical models that emphasize important crop characteristics and the adoption of viable crop management practices will favor yield outcomes of new plant varieties.

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6.3 Criteria and Methods Employed in Plant Variety Testing Some criteria and methods are already in place to test the performance of new plant varieties. Three fundamental criteria/methods are used during testing. They are Distinctness, Uniformity, and Stability (D.U.S.), Value for Cultivation, Use and Sustainability (V.C.U.S.), and Value for Cultivation and Use (V.C.U.). Test at this capacity is handled by approved national bodies and agencies. Their fundamental duty is to carry out field testing of new varieties and seed lots for all cultivated plant species. In other words, all crops submitted for registration in the Catalogue are subject to these necessary tests, and to qualify for registration, a newly-developed plant variety must satisfy the criteria above. To propose a new plant variety for these tests, some documents are usually submitted to the Seed Control Department, namely, application, the famous description of the variety, technical form of plant variety description, authorization in case the applicant is not the breeder and certification of executed payment for registration of the application. On receipt of application, an agreement is signed between the applicant and the State Plant Protection Service (SPPS). The agreement prescribes the rights and obligations of parties, deadlines, and quantities of seed deliveries, settlement procedures, and other obligations about performing the test. Part of the Agreement is agreement protocol, drawn on an annual basis. It contains the number of varieties to be tested, the number and location of testing places, and the payment procedure. Point-topoint discourses are required to fully understand these criteria and methods and appreciate innovations that will be suggested. Further reading, check https://www. geves.fr/about-us/variety-study-department/dus-vcus-testing/.

6.3.1 Distinctness, Uniformity and Stability (D.U.S.) Test It is a well-known fact that D.U.S. tests are conducted to ensure that a new plant variety is distinct from existing varieties, characteristics are uniform and variety is stable with consistent phenotypic characters from one generation to the next. Tests are conducted following harmonized international procedures, based on plant material provided by the applicant. To identify the variety, a description is required. A variety of description/plant material produced can later provide the basis for seed certification and plant variety protection applications. This is because the D.U.S. test is also carried out to establish a basis for registering new varieties in the catalog of plant varieties and protecting a new plant variety with the required plant variety right. The D.U.S. test generates a description of the plant variety specifying its relevant characteristics. Morphological characteristics (including color) and phenological attributes such as flowering and ripening phases are mostly used for crops. Some plant species are also tested for disease resistance. To examine the distinctness of a new variety compared to similar existing varieties, concerned bodies such as GEVES (the French Variety and Seed Study and Control Group) keep an archive

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of well-known reference varieties. They also maintain large databases of existing similar varieties for comparing performance using high-capacity sensitive software. This is a necessary practice that should be sustained to have the best results. Much as existing good criteria are sustained, innovations are necessary to address current realities and market demands. According to Bruins (2019), “the next challenge is to find criteria and methods to test and make the value of the performances of new plant varieties under conditions that are associated with sustainable and more variable farming practices”. Thus, the incorporation of plant traits that respond well to new challenges and demands in conventional and organic sectors, while considering the economic return of farmers is required. To test or identify new traits, the robustness of existing criteria/methods should be enhanced to increase the efficiency of variety testing by assessing performance under different production conditions and biotic and abiotic stresses. Ekvad (2019) has recently stressed that varieties must perform well. To achieve this, the author revealed that there is a need to adapt plant variety testing to new characteristics (example traits relevant to sustainability and resilience), new agricultural practices (such as heterogeneity thresholds for alternative materials), and new requirements (such as resistance to pests and abiotic stresses). New requirements are important to reduce agricultural treatments/inputs. Bruins (2019) supported the argument by complementing that agriculture is increasingly being expected to reduce overreliance on external inputs, reduce ecological impact, and cope with more variable climatic conditions. This is feasible given advances in molecular techniques, genotyping tools, and models. Weitz (2019) advised that the D.U.S. test requires financial motivation for optimal performance. Large capacity, sophisticated and efficient tools will undoubtedly increase the quality/reliability of results. There will be a need to make inferences based on extensive data bases and these can only be possible if funding is received. Therefore, financial motivations are as necessary as suggested by Weitz (2019), and this will help alleviate the burden/cost of trials. The reechoing need to develop varieties to maintain yield under climate change uncertainties is most concerning. Climate change is a new global phenomenon that can introduce disorderliness in a functional system, and its effects on crops have been adequately forecasted. We thereby suggest the following meaningful innovation lines, when executed, will favor plant variety testing. It is no longer news that climate change will lead to the emergence of new pests and diseases and increase infestations and pathogenic attack, a shift in rainfall patterns, flooding and higher environmental temperatures among others (Bebber 2015; Eigenbrode and Macfadyen 2017; Wu et al. 2019; Lehmann et al. 2020; Ileke et al. 2020). Considering that climate change fluctuations and problems are inevitable, breeders should develop new varieties that are resistant to drought, pests, and pathogens, and varieties that can equally thrive in flooded sites or at least cope with flooding and elevated and low temperatures. Releasing new plant varieties with traits to cope with climate variability is good progress in plant breeding. In late July 2020, the first author cultivated (in Imo State, Nigeria farm) twenty different early-maturing maize varieties which his Ph.D. student obtained from IITA, Ibadan, Nigeria. Suddenly, there was an unusual drought throughout August in the place. Without irrigation, all the twenty maize varieties had high yield

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when harvested. This is good because it was likely that apart from the early-maturity trait, the varieties have the drought-resistant trait. These are part of the innovations required in breeding that a plant variety will have multiple desirable new traits to help to cope with uncertainties associated with climate change. Furthermore, the development of new approaches to testing genetics × environment × management interactions is suggested. Supposing the maize varieties cultivated by the first author (as indicated above) had early-maturity trait but not drought-resistant trait. Irrigation (as a management practice) would have been necessary to save the maize plants. Thus, D.U.S. test should be innovative to a versatile design. Furthermore, Collonnier (2019) suggested the applications of bioinformatics to genomic data analysis. According to the author, this will lead to the development of new sets of molecular markers to improve the management of reference collections for the D.U.S. test by minimizing the number of reference varieties used in the field when a candidate plant variety is tested. Therefore, bioinformatics is an economical and convenient approach because by reducing labor, the cost is also reduced. We gathered that genome-wide association studies are required to identify new markers linkable to several phenotypic traits to facilitate the assessment of certain D.U.S. characteristics. Unquestionably, the use of modern high processing phenotyping tools will also speed up observations and provide extensive and accurate phenotypic data. Biostatistical techniques are needed to improve the assessment of uniformity especially for cross-pollinated species. To improve and expand existing criteria and methods, ideas ahead of current thinking should be the focus of plant variety testing. The idea to use available molecular, phenotypic and agronomic data obtained in a given context to forecast with precision and accuracy a variety’s response to a range of pedo-climatic environments is worthwhile. It has been predicted that this approach will help to identify meta-environments for varietal performance testing. As concerns D.U.S., statistical models will contribute immensely to assessing the resilience of certain D.U.S. characteristics to environmental impact (Weitz 2019). We like this, and from all available information, statistical interventions are indispensable to plant variety testing. Another suggested innovation we found thrilling is the idea from INVITE (Innovations in Plant Variety Testing, Europe) reported by Collonnier (2019), which centers on finding new synergies between D.U.S. and V.C.U. According to the report, finding synergies between D.U.S. and V.C.U. to improve plant variety testing will maximize synergies between them through related activities based on phenotyping, genotyping, modeling, and database management. Where characteristics are suitable for D.U.S. and V.C.U., then the possibility to describe the characteristics comparably should be sought. Maturity in soya bean is a D.U.S. characteristic, as well as a V.C.U. characteristic. The proposition aims to find ways to name and describe the characters in the same way for D.U.S. and V.C.U. purposes. It has been assured that this will not shift the existing balance of importance between D.U.S. and V.C.U. or redefine the purpose of why plant varieties are tested under the two schemes. The target is to improve each of them separately and possibly find new synergies. We like this too, and we are happy that through this book, the International Community will have access to the information in their most logical and organized manner.

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Information we want concerned bodies to take seriously is the statistical implication of the use of sample selection (during D.U.S. test) to test for distinctness, uniformity, and stability. Uniformity assessment requires large collections; assess “whole” not “samples”.

6.3.2 Value for Cultivation, Use, and Sustainability (VCUS) The purpose of VCUS tests is to assess the suitability of a new plant variety for cultivation in agro-climatic conditions and the use made of harvested crops and products produced from the variety in question. A new plant variety must have added value in comparison to similar existing varieties. How can one be sure that a test plant variety added value? The answer is simple. This is easily determined by comparing the new variety to a set of existing (similar) reference varieties. The emphasis on “similar” is mandatory because one cannot compare a new cowpea variety with reference bean varieties. The closeness between cowpea and bean does not justify such unrelated comparison not to compare a new variety of potato with reference varieties of yam. End-users can make an informed decision based on the information acquired over two test-cycles. During the field test, further considerations should check the ability of the new variety to adapt well, to the target environment. This means that the test variety should show the capacity to adapt to the climatic conditions of the environment in which it is to be cultivated. Direct use of the new variety and its products must be beneficial and acceptable by the people. For instance, if a new variety of pawpaw has ample fruit and nutritious but tastes sour it may not be acceptable by the people. Thus, “value addition” should not compromise any one trait (crowd-puller trait) that determines the market. This has to be checked during testing, whether a new trait has compromised the effect of a crowd-puller trait.

6.3.3 Value for Cultivation and Use (VCU) The difference between V.C.U. and V.C.U.S. (already discussed) is sustainability. It is fresh in memory that V.C.U. is the value for cultivation and use, whereas V.C.U.S. stands for value for cultivation, use, and sustainability. Therefore from the first principle, there is not much difference in the priorities of the two schemes. The objective of V.C.U. test is to establish the differences between the test variety and the reference variety in terms of productivity, biological characteristics, quality of produce, chemical and technological characteristics, resistance to pests and diseases, growing suitability under agro-climatic conditions, and other characteristics of high commercial interests.

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6.4 Statistical Interventions: Necessity in Plant Variety Testing 6.4.1 The Benefits of Simulation Models Simulation models are increasingly utilized for the assessment of crop productivity and impact on the environment that may emanate from given combinations of weather, soil, crop characteristics, and water and fertilizer management. Proper simulation of crop requirements is essential in terms of the amount required and distribution throughout the growing season. For instance, many approaches have been proposed to simulate crop nitrogen requirements. The four representatives able to model wheat nitrogen requirements correspond to the ones included in the following crop growth models: AFRCWHEAT2, Daisy, EPIC, and CropSyst (Stockle and Debaeke 1997). In these approaches, crop nitrogen requirements are expressed in terms of characteristic plant nitrogen concentration curves, which represent the expected concentration for a given crop nitrogen status throughout the cropping season. Similarly, options for breeding for higher maize yields in the tropical region of the world have been quantitatively studied with a crop growth simulation model tested against field data of five genotypes in four different environments. The simulation results showed that at high production levels, grain filling of maize is sink-limited. Increasing the number of kernels per m2 through larger primary ears, prolificacy, or higher plant densities, will culminate in increased grain yields (Elings et al. 1997). Theoretically, it is concluded that larger primary ears give greater grain yields at all plant growth rates and that increased prolificacy results in higher grain yields only on the condition that plant growth rate exceeds a threshold. In nitrogenlimited conditions, the selection of genotypes that extract more significant nitrogen from soils and for delayed leaf senescence have demonstrated great promise for increasing yields. The role of simulation models in providing important scientific information that leads to more outstanding yield production in plant varieties cannot be overemphasized.

6.4.2 Modeling Performance of Plant Varieties: A Necessary Approach for Sustainable Yield Performance and Higher Economic Benefits The availability of statistical models for inevitable reasons has made forecasting in agriculture an uncomplicated task. Modeling is an innovative approach utilizable in diverse areas (applications not limited to statistics and agriculture). In agriculture, models have been used to predict yield outcomes of various plant species, predict climate variability, and possible impacts on plants. According to Shi et al. (2013) previous studies have identified climate contributions to crop yields based on

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statistical models at different spatial scales (Table 6.3). In this chapter, we present modeling as a tool necessary for forecasting the performance of test plant varieties. New statistical models are periodically developed by experts in the skill, tested, and compared for efficiency and possible applications. The search for high-performance models to predict the yield of crops using important variables and sustainable criteria has led to the identification of some highly adaptable and innovative models which can be used to forecast the future performance of plant varieties under test. This will help validate the field observations of the test varieties and provide further

Table 6.3 Previous studies of identifying climate contributions to crop yields based on statistical models at different spatial scales Research scales Global scale

Study areas Globe

Study periods 1961–2002

National scale

Globe

1980–2008

National scale

Sub-Saharan Africa

1961–2006

Provincial scale

China

1951–2002

Provincial scale

America

1982–1998

Country scale

Wisconsin

1976–2008

Country scale

America

1950–2005

Country scale

Hefei, Changsha, Zhengzhou, Tianshui, Zhengzhou and Harbin stations Hailun station in Northeast China Nanyang, Zhengzhou and Luancheng stations in North China Africa China

1981–2000

Country scale Site scale

Site scale Different scales comparison

Source: Shi et al. (2013)

Crop types Rice, wheat, maize, soybean, barley and sorghum Rice, wheat, maize and soybean Maize, sorghum, millet, groundnut and cassava Rice, wheat, maize and soybean Maize and soybean Maize and soybean Maize, soybean and cotton Rice, wheat and maize

Reported by Shi et al. (2013)

1987–2004

Soybean

Shi et al. (2013)

1981–2005

Wheat and maize

Shi et al. (2013)

1999–2007 1981–2005

Maize Rice

Shi et al. (2013) Shi et al. (2013)

Shi et al. (2013)

Shi et al. (2013)

Shi et al. (2013)

Shi et al. (2013) Shi et al. (2013) Shi et al. (2013) Shi et al. (2013)

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guidance for policy and measurement. Here we shall consider existing models and recommend high-performance models suitable for varietal testing as part of the innovation. Broadly, statistical models can be classified into three, namely the timeseries model, cross-section models, and panel models (Lobell and Burke 2010). Brief information on their suitability and applications are as follows.

6.4.2.1

Time-Series Models

These are models based primarily on time-series data from a single point or region. Models that are time-series-based are believed to have the advantage of capturing the behavior specific to the given area, and such models are not vulnerable to errors emanating from omitted variables, for example fertilizer input and soil quality, which varies spatially (Shi et al. 2013). A time series model of the form in Eq. (6.1) is usually fitted on time-series data in each area. log (Yt ) = β0 + β1 Tt + β2 Pt + · · · + εt

(6.1)

Where Yt , Tt , Pt , . . . are crop yield, average temperature in the growing season, total precipitation and other climatic factors that may be of interest in the growing season of year t, β 0 , β 1 , β 2 , . . . represents the parameters of the model to be fit, and εt is the error term. Michel and Makowski (2013) reviewed and compared the performance of some specific time series models in predicting the yield of wheat at national and regional scales. These models are: (i) The linear model: This model assumes that there is a constant rate of yield increase over time. It is defined as follows: Yt = a + b × Tt + et

(6.2)

Where Yt is yield in year t, Tt represents the year index and is equal to 1 for the first year of the time series, a and b is the linear trend parameters and et represents the residual error which is gotten from the difference between Yt and the linear trend. (ii) The quadratic model: This model does not assume a constant rate of yield increase over time as seen in the linear model. It is defined as follows: Yt = a + b × Tt + c × Tt2 + et

(6.3)

where a, b and c are the parameters of the model, estimated by fitting the mode to the data.

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(iii) Cubic model: This model is more flexible than the quadratic model but has an additional parameter (d). It is defined as Yt = a + b × Tt + c × Tt2 + d × Tt3 + et

(6.4)

(iv) The linear plus plateau model: In this model, it is assumed that yield increases at a constant rate R before a date Tmax and then assumed to attain a plateau Ymax . Therefore, if Tt ≥ Tmax , Yt ≥ Ymax + et

(6.5)

This model is also characterized by a marked stagnation (Brisson et al. 2010). (v) First Holt-winters model: Unlike the linear model, the Holt-winters model does not assume that the linear trend parameter values a and b are constant over time; instead it assumes that they may vary over time. This yield prediction can be calculated using Eq. (6.6). Yˆt+k = at + bt × k

(6.6)

Where Yˆt+k is the k year ahead yield prediction, at and bt are the parameters of the linear trend assumed not constant over time, instead, their values are updated once there is a new yield value Yt as follows: at = λ0 × Yt + (1 − λ0 ) × Yˆt

(6.7)

bt = λ1 × (at − at−1 ) + (1 − λ1 ) × bt−1

(6.8)

And

The method is initialized with a2 = Y2 and b2 = Y2 – Y1 . It recognizes only two parameters (λ0 and λ1 ) which are estimated from the data. However, it does not accommodate diverse trends associated with varying increases or decreases in the rate of yield increase over time. (vi) Second Holt-winters model: This model is also called “simple exponential smoothing” (Brockwell and Davis 2002). Unlike the first Holt-winters model, this version did not include a linear trend (that is, bt = 0). It is defined as Yt+k = at with at = λ0 × Yt + (1 − λ0 ) Yˆt

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there is only one parameter λ0 that must be estimated from data. Holt-winters models are used to forecast future yield but are not used to estimate past yield trends. (vii) Dynamic linear model: This model has two types, namely polynomial dynamic linear and random walk dynamic linear model. The polynomial dynamic linear model yield predictions can be calculated using Eq. (6.6), but the values of at and bt are not derived from Eqs. (6.7) and (6.8). However, they are derived from the Kalman smoother algorithm. at and bt are estimated by E (at /Y1 . . . , YM ) and E(bt /Y1 . . . , YM ). Where Y1 . . . , YM are the M yield data of the series. The conditional variance can also be calculated using the Kalman smoother algorithm. The expected values and variances are calculated analytically using two equations; an observational equation relating yield data to at and a system equation describing the changes in at and bt from year to year. The observational equation is given by Yt = at + εt

(6.9)

  Where at is the yield level at time t and εt ∼ N 0, σε2 . The system equation is given by Zt = GZ t−1 + ηt−1

(6.10)

With Zt =

at bt



,G =

 σ2 0

11 a and = , ηt−1 ∼ N 0, 0 σb2 01

The state variables of this model are the time-varying level and slope explaining the yield dynamic. The two unobserved state variables are assumed to vary with varying years according to a stochastic process defined by the system equation. The local growth rate is represented by the slope bt , the yearly yield increase. Equation (6.9) shows the relationship between yield data Yt, t = 1,2, . . . ,N, and yield level, while Eq. (6.10) shows the relationship between the values of the two-state variables at time t and the values at time t−1. The model has three unknown parameters: σε2 , σa2 , and σb2 . σε2 represents the variability of yield around the trend. σa2 , and 2 σb represent the variability of the level and slope of the yield trend and also define their change over time. The random walk dynamic linear model is a simplified version of the polynomial dynamic linear model with bt = 0, and σb2 = 0. The comparison of these time series models by Michel and Makowski (2013) reveals that the dynamic linear models performed better than the others, projecting their

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usefulness in reconstructing past yield trends retrospectively and in the analysis of uncertainties. Whereas, the polynomial dynamic linear model has an attractive, practical advantage in that it can be used to estimate both yield levels and yearly increase or decrease rates. The result of the yield increase or decrease rate is essential in a geographical area of interest because it gives relevant information about trend changes. This dynamically estimated yield increase, decrease, or stagnation, yearly is also useful in studies on food security (Ye et al. 2013).

6.4.2.2

Cross-Section Models

These are models built based on variations in space, for example, average yield, mean temperature, total precipitation and other climate factors of interest. The model is defined in Eq. (6.11).   log Yi,avg = β0 + β1 Ti,avg + β2 Pi,avg + β3 T 2 i,avg + β4 P 2 i,avg + · · · + εi (6.11) Where, Yi, avg , Ti, avg , Pi, avg , T2 i, avg , P2 i, avg , . . . represents average yield, average temperature, total precipitation and other climate factors of interest in the region or point i, T2 i, avg , P2 i, avg , . . . represents climate factors squares in region or point i, β 0 , β 1 , β 2 . . . are the model parameters and εi is the error term.

6.4.2.3

Panel Models

It can integrate yields and climate variables of all regions or stations under consideration to build a model of the form in Eq. (6.12)   log Yi,t = β0 + β1 Ti,t + β2 Pi,t + β3 T 2 i,t + β4 P 2 i,t + · · · + εi,t

(6.12)

Where Yi,t , Ti,t , Pi,t , T2 i, t , P2 i, t , . . . represent crop yield, temperature, precipitation and other climate factors of interest in the region or point i, and their squares. β 0 , β 1 , β 2 . . . are the model parameters and εi is the error term. Furthermore, statistical emulators of crop yields based on global gridded crop model simulations are possible (Blanc 2016). Here, the ensemble of simulations is used to build a panel of annual crop yields from different crop models and corresponding monthly weather variables as the case may be. With statistical emulators, the statistical relationship between yields, temperature, precipitation and carbon iv oxide can easily be obtained. In- and out-of-sample validations show that the statistical emulators can closely replicate crop yields projected by crop models and perform well out-of-sample. As an innovation, statistical emulators provide a reliable and accessible alternative to global gridded crop yield models. The tools present a computationally efficient method to account for uncertainty in climate change

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impact assessments. Advances in modeling have resulted in developing stepwise multiple linear regression techniques as a model for yield forecasting and machine learning-based model forecasting/ high-resolution statistical models (Garde 2012). The applications of these high-performance models will help to ensure better evaluations and approve plant varieties that perform well.

6.5 Postharvest Varietal Crop Characteristics and Entomology Test Criteria The need to make test criteria holistic in plant variety testing by incorporating entomological evaluations during varietal testing was mentioned above. Extending plant variety testing to postharvest situations is a worthwhile suggestion towards sustainability. With the emergence of new insect pest species infesting and damaging plant varieties previously not infested and reinforcement of existing species, adequate information on new experimental designs, methods, and tools to enhance performance testing of new varieties has become necessary. These include testing for postharvest resistance to insect pests, moisture, and nutrients. Where a specific varietal trait (say palatability) is sustained at the expense of resistance to an insect pest species, the use of plant protection products as crop management practice should be adopted. Discussions will cover plant protection products test to ensure that the quality of the variety is not affected by storage.

6.5.1 Protocols for Testing Resistance to Insect Pests Entomological protocols that can be used to test the resistance status of new varieties of stored products such as maize, bean, cowpea, melon, rice, etc, are briefly discussed here, pointing out innovations where applicable. A newly-released plant variety may not be tested for resistance against all potential insect pests. Still we propose that tests should be made compulsory for the significant postharvest insect pests whose attacks sabotage the expected impact of crop management practices such as fertilizer and insecticides applications. Nwosu (2018) provided insights and guides relevant to resistant testing of new plant varieties. (i) Culturing: The test insect pest should be cultured (usually in the laboratory) within the test station. Sound knowledge of the biology of the pest species in question is a necessary prerequisite. For optimum development of the insect, the diet that permits prolific development and emergence and adequate ventilation, must be considered necessary. During culture, it is vital to maintain reasonable sanitary measures and use new and sterilized culture containers to prevent the effect of contamination on

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insect development and their multiplication. Insects of similar age should be used for the test (Nwosu 2019; Nwosu et al. 2020). (ii) Inclusion of a check variety during the test: In testing a new variety for resistance to an insect pest, it is necessary to include a known resistant or susceptible variety as a check. A check variety is a standard/model variety for comparing the performance of a new plant variety. Resistance is a relative phenomenon, and the rating is based on the performance of the test varieties. Therefore, the inclusion of a check variety during testing is necessary to determine the performance of a plant new variety and know whether the new variety is superior, has comparable strength, or inferior. (iii) Old and New formulas: According to Nwosu (2018), the susceptibility test is a required method for assessing the resistance of plant varieties to insect pest infestations. The author accepted that many experts in crop science and storage entomology had employed this technique in making inference on the resistance status of genetically-improved and newly-released plant varieties to insect pest infestations (Abebe et al. 2009; Adedire et al. 2011; Mwololo et al. 2012). We have advised that new plant varieties should also be tested for resistance to major insect pests to provide information on resistance in addition to existing parameters usually sought during plant variety testing by concerned bodies/ agencies. The plant variety resistance test being proposed should not be haphazard. Still, it should run for three insect generations to obtain data upon which valid and reliable inference can be drawn in favor of sustainability. Dobie (1974) interestingly developed a mathematical formula for testing varietal resistance (at F1 generation) by determining an index called susceptibility index. Since its invention, many scientists have been applying the Dobie formula for determining resistance/susceptibility. Nwosu et al. (2015a) observed that many scientists used Dobie’s (1974) formula beyond the F1 generation of insect pests. The authors found it impracticable/unsuitable for application beyond F1 generation and, based on necessity, developed new test formulas feasible for application beyond F1 generation of insect pest. The formulas were published by Cambridge in 2015 and reviewed and published by Oxford in 2018 (Nwosu et al. 2015a; Nwosu 2018). At present, the formulas are being used to screen newlyreleased plant varieties for resistance to insect pests. As an innovation, we suggest that concerned bodies/agencies should adopt and incorporate these new insectresistance testing formulas during variety testing. For inevitable reasons (climate change and other new environmental issues), there is a need to widen test criteria of “value for cultivation, use, and sustainability (V.C.U.S.)”, “distinctness, uniformity, and stability (D.U.S.)” and “value for cultivation and use (V.C.U.)” through the incorporation of other parameters. Dobie’s 1974 formula for testing varietal susceptibility at F1 generation: 100 × Loge (total number of F1 progeny emerged) Median development time

208 Table 6.4 Scale for rating the susceptibility index of twenty maize varieties tested for resistance to infestation and damage by the maize weevil, Sitophilus zeamais Motschulsky

L. C. Nwosu and U. I. Nwosu Susceptibility index 0.0–3.00 4.00–7.00 8.00–11.00* > 11**

Susceptibility status Resistant Moderately resistant Susceptible Highly susceptible

*The range in Dobie’s classification is 8.00–10, **The range in Dobie’s classification is ≥ 11 Source: Nwosu et al. (2015a)

The median development period is the time (in days) from the middle of the oviposition period to the emergence of 50% of the F1 generation. Nwosu et al. (2015a) formula for testing varietal resistance at F2 generation: 1 2

Natural log F 2 or Ln F 2×100  [T erminal date of F 1 assay−time (in days) of f irst emergence of F 1] + T ime f or 50%of F 2 progenies to emerge

Nwosu et al. (2015a, b) formula for testing varietal resistance at F3 generation: 1 2

Natural log F 3 or Ln F 3×100  [T erminal date of F 2 assay−time (in days) of f irst emergence of F 2] + T ime f or 50%of F 3progenies to emerge

(iv) Old and New scales: As a complement to the mathematical formulas indicated above, the susceptibility index value must be rated on a standard scale to establish the resistance status of the new plant variety. Maize as a crop has been primarily tested using these formulas. In Southern and Eastern Nigeria and other Universities in different countries globally, including Brazil, the formulas are currently being applied to cowpea, bean, and rice crops. We recommend a standard rating scale. Table 6.4 presents the rating scale, which is a modified version of Dobie’s scale used to rate resistance of twenty maize varieties newly-released by the International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria and Institute of Agricultural Research and Training (IAR&T), Ibadan, Nigeria. Table 6.5 presents the twenty maize varieties released by IITA and IAR&T, Nigeria. (v) Statistical correlations: Principles and applications to the basis of resistance: From entomological experience, during testing of plant varieties for resistance to an insect pest, statistical correlations play a key role in identifying the basis of resistance in a test variety. For the scientific community, we must clarify that the use of statistical correlations usually complements specific analysis, and the holistic approach often produces a result with high validity and reliability. The need for specific analysis manifested in the correlations between plant morphological and anatomical characters and stem borer susceptibility (Table 6.6). According to Pathak (1969) each of the plant morphological and anatomical characters contributed to

6 Innovations in Plant Variety Testing with Entomological and Statistical Interventions Table 6.5 Twenty maize varieties released by the International Institute of Tropical Agriculture Ibadan, Nigeria and Institute of Agricultural Research and Training, Ibadan, Nigeria

Serial number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

209

Maize varieties DTSYN-11-W TZBRCOMP.2C1F1 IWDC3SYN-W TZBRELD3C5 PVASYN-6F2 2000SYNEE-WSTR 2008DTMA-YSTR PVASYN-3F2 WHITEDTSTRSYN BR9943DMRSR ILE-1-OB IFEMAIZEHYBRID-1 IFEMAIZEHYBRID-2 IFEMAIZEHYBRID-5 IFEMAIZEHYBRID-6 ARTCOMPOSITE-A-Y ARTCOMPOSITE-B-Y ART/98/SW1-OB ART/98/SW4-OB ART/98/SW5-OB

Serial numbers 1 to 10: Released by IITA, Ibadan, Nigeria Serial numbers 11 to 20: Released by IAR&T, Ibadan, Nigeria

borer resistance but none by itself appears to be the real cause of resistance. However, the correlation technique is simple to apply by seeking a relationship between resistance parameters and varietal characteristics using a correlation assay. In principle, the measure of the strength of the linear relationship between two variables X and Y is called the correlation coefficient (Nwachukwu 2005). According to this author, the population correlation coefficient is denoted by p, and r, the sample correlation coefficient mainly estimates it. The correlation coefficient, r, takes values between −1 and +1. If r = −1, there is perfect inverse/indirect linear correlation/ relationship between the two variables under test. When r = +1, there is a perfect direct linear relationship between the two variables. If r is close to zero or equals zero, then there is little or no linear relationship between X and Y. In this context, Y stands for resistance status of a test plant variety while X stands for the basis for the observed resistance degree. The coefficient of determination expressed in percentage (r2 × 100) helps the analyst to find the percentage of the total variability in Y that is explained by the regression of Y on X (Nwachukwu 2005). In other words, it helps to find the extent to which a given chance in the value of X results in a corresponding change in the value of Y. One of the most frequently used measures of linear correlation between two variables X and Y in

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Table 6.6 Correlations between plant morphological and anatomical characters and stem borer susceptibility (In all cases, correlations were significant) Plant characters Height of culm Number of elongated internodes Length of the 3rd elongated internode Length of the flag leaf Width of the flag leaf External diameter of culm at half of its length External diameter of culm at ¼ to the base Internal diameter of culm at half of its culm Internal diameter of culm at ¼ to the base Number of tillers per plant Number of borers per plant versus percentages of infested tillers Percentage of stem area occupied by vascular bundle sheaths

Correlation coefficient 0.796** 0.632** 0.715** 0.798** 0.836** 0.672** 0.785** 0.671** 0.790** −0.756** 0.863** −0.756**

**Significant Source: Pathak (1969)

entomological evaluations is the pearson product-moment correlation coefficient. Mathematically, it is given as:   XY − X Y r=         X2 Y2 n X2 − n Y2 − n



6.5.2 Moisture Test According to Lale (2002), the moisture content of agricultural products for storage over a long time is crucial to the safety of the material in postharvest storage. Therefore, adequate knowledge of the moisture behavior of a plant variety is of paramount importance. Our thinking is that it is necessary to check moisture dynamics in new plant varieties since moisture is one of the most critical factors that affect the survival of a plant variety in storage. Plant varieties must be stored for important agronomic, economic, and food security reasons, and so, postharvest considerations are equally essential and should not be neglected. Hence, measuring the moisture content of new varieties and how it changes during postharvest storage is of utmost essence. Standard methods (Official Analytical Methods) are available to test moisture content and behavior.

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6.5.3 Nutrient Test Testing of new plant varieties should capture yield proximate and nutritional analyses for further recommendation. This is supported by Jimoh and Abdullahi (2017). This suggestion is necessary, and protocols are available for achieving it. The International Official Methods of Analysis suffices.

6.5.4 Protocols for Testing Plant Protection Products Against Insect Pest Attack 6.5.4.1

General Comments/Precautions

To determine the efficacy of natural products, standard methods should be used to have unbiased results. Assay procedure may differ among insect families, orders, classes, and species; however, the key points to note are the following: (i) The bioassay should be carried out in a well-ventilated environment because suffocation can seriously affect the performance of the test insect species. (ii) New/sterilized containers should be used for the experiment to prevent the effect of contamination on insect performance. (iii) The right experimental design should be used. It is necessary to consult a Biometrician to authenticate design. (iv) The essential parameters that should be evaluated (but not limited to) are contact toxicity, outright mortality, LD50 determination, oviposition, progeny emergence, percent damage, weight loss, perforation index and seed viability (before and after pest control process). (v) In the assessment of reproductive parameters such as oviposition and progeny emergence, sex determination is required to ensure that both male and female insects are present. (vi) The bioassay should be monitored and protected against intruders. (vii) The terminal date of the assay should be respected (i.e. not compromised, except situations when impacting influence of the environment is felt). (viii) Insect pest repellency trials are necessary. In repellency trials using plant protection products, the standard method described by Laudani et al. (1955) is widely used. (ix) Antifeedant test should be conducted. It is crucial to confirm whether plant protection products show antifeedant activity. Extract of the plant protection product should be applied to wafer discs, filter paper, or paper packing material. The number of holes produced by boring insect pests should be counted per unit time. An exposure period of 7 days suffices typically. (x) The plant protection products should be tested for fumigant toxicity to know whether the vapor has an effect on the biological parameters of the potential insect pests of the new plant variety. To achieve this, an extract of the plant

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protection product should be applied to filter paper, left to dry, and then suspended in a glass fumigation chamber. Then, an insect pest is introduced into the chamber, and mortality at a specified dose is recorded within a given period of time.

6.5.5 Parameters Assessed During Testing of Plant Protection Products The parameters usually assessed during plant protection products testing have already been listed above. They include contact toxicity, mortality, LD50 determination, oviposition, progeny emergence, percent damage, weight loss, and seed viability after the pest control process. In contact toxicity testing, apart from direct toxicity to the adult stage of the pest, toxicity to eggs, the effect on hatchability, and the emergence of first filial adults are also evaluated (Golob et al. 1999). In some contact toxicity experiments, indirect measurements such as the number of damaged commodities and loss in weight of the stored product due to pest feeding are made. Whichever is the case, the natural product (either extract or powder) should be thoroughly mixed with the commodity (to enhance coating) at a defined concentration. Contact toxicity testing, in a holistic form, requires sexing, but when mortality is the only focus, sexing is not compulsory. When solvents are used, they have to be allowed to evaporate. After coating with powder, Nwaubani and Fasoranti (2008) advised that about 2 h should be allowed before the introduction of the storage pest. Mortality of adult storage pests should be observed within 3– 24 h of treatment (high-sensitivity test) or could be delayed to 7 or 15 days after exposure to the natural product. Nwosu (2018) reported that any natural product that cannot give good control of a storage insect pest within 24 h might not be comparable to traditional pesticides. This is because some storage insect pests can lay a large number of eggs within 24 h. In oviposition assay, it may be precautionary to allow the pair(s) of the storage pest to mate for 2 h (in the absence of the stored product, for example, seeds) before using them to infest the commodity (Sulehrie et al. 2003). It is also necessary to use newly emerged adults as parents for the next generation. In adult fecundity testing, newly emerged females of the same age cultured on different hosts can be crossed with newly emerged males reared on the control. This technique standardizes the effect of males and ensures that only female pests contributed to any difference observed in adult fecundity. Note that mortality data can be corrected using Abbott (1925) formula. Abbott (1925) is given as: Pr = (Po × Pc ) / (100–Pc ) . Where; Pr = Corrected mortality (%) Po = Observed mortality (%)

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Pc = Control mortality (%) Lethal concentration (LD50 ) can be determined in a bioassay or recently with LeOra software, Petaluma, CA (Suthisut et al. 2011). However, in contact toxicity assay, all dead and live adults should be sieved out immediately after mortality count to ensure that the emerging adults are a direct consequence of the number of eggs oviposited. The storage insect pest is confirmed dead when it fails to respond to probing with a sharp pin at the sensitivity part of the body like the abdomen. Percent mortality should be calculated using the standard formula: Number of dead insect × 100 T otal number of insects The eggs laid on the commodity after a specified time (usually 24-hourly up to n hours) in a no-choice protocol should be counted and the average number calculated per treatment of the natural product. Eggs of some storage pests such as the maize weevil, Sitophilus zeamais Motschulsky are not visible to the naked eyes and require staining and observation under a stereoscope. Under the same experimental conditions for a given period of time, emerged-adults from different treatments should be counted and recorded. Note that the period allowed before checking for the emergence and terminal dates of assay vary according to the type of storage pest. All intricacies should be duly observed. In progeny emergence assay, either daily emergence or accumulated emergence is applicable. According to Trematerra et al. (1996), interpretation of daily emergence data is more complicated and less reliable compared with accumulated emergence data. When data are limited to daily assessment, errors are more likely to occur because of the influence of the times of each assessment on the data. In the statistical analysis of data on accumulated emergence, the sigmoid three-parameter model (y = a/1 + exp (−[x−b/c]) is known to be best adapted to accumulated emergence (Lopes et al. 2016). The assessment of damage is mostly based on holes and tunneling. Damage index can further be defined in terms of the type of damage peculiar to a pest. So it is not all about holes and tunneling. Weight loss can easily be determined by finding the difference between the initial and final weight of the stored product. The difference is usually expressed as percent (to have percent weight loss). These days, digital weighing machines are used to measure the weight of a commodity. Check the ones in your laboratory, and be sure they work well before use. Percent weight loss can also be obtained using the method of FAO (1985) as follows. Percentage weight loss = [UaN (U + D)] × 100/UaN Where; Ua = Average weight of undamaged seeds or any other commodity. N = Total number of seeds in the sample. U = Weight of undamaged fraction in the sample. D = Weight of damaged fraction in the sample.

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Before and after the pest control process, seed viability is usually determined, either in the laboratory or in the field, because viability is directly related to seed damage. Irrespective of the experimental site, some necessary conditions should be satisfied to eliminate all possible bias from the experiment. It is advisable to determine pre infestation and post infestation seed viability at the same time to maintain the same experimental conditions. The experimental seeds should be placed on a moistened filter paper (usually Whatman filter paper) inside a container (petri dish, plastic, glass, etc.) with a source of aeration because oxygen is one of the conditions necessary for germination. Distilled water is mostly used as a source of moisture. Moisture should be augmented when necessary. Before planting, seeds should be treated with a suitable agrochemical (such as Apron plus) to prevent fungal growth and ensure that all viable seeds germinate. For enhanced results, seeds should be spaced appropriately and not overcrowded to reduce competition for space, sunlight, and nutrients. A maximum of 10 seeds spaced out in a dish of 12 cm diameter does not suggest overcrowding. Data can be collected for a period as defined by the interest of the experimenter, but most authors terminate after 7 days. Statistical comparison is generally made between infested and un-infested samples at the end of the assay.

6.5.6 Common Weakness in the Screening of Plant Products for Insecticidal Activities Nwosu (2018) has recently addressed common weaknesses in the screening of plant materials for insecticidal activities. It has been advised that during plant product testing, concerned agencies should maintain technically-sound protocols to obtain valid and reliable results. During testing, the inclusion of untreated control and a reference/standard insecticide is mandatory for higher scientific value. An untreated control is biologically necessary to confirm the occurrence of adequate pest infestation and to confirm that there is no natural decline in number, during trials (FAO 2006). Whereas, the inclusion of a reference insecticide is to ensure that the observed insecticidal performance of a plant product is inferred in comparison with a standard insecticide whose efficacy is already proven. Another common mistake in efficacy testing of plant products for insecticidal activities is the nonuse of a scoring system when more than one parameter is being investigated. How will the plant species with the highest efficacy be identified among test plants given that about three parameters such as oviposition suppression, mortality, and damage need to be measured? A scoring system that incorporates all the parameters should be developed and used during testing. For insect pests, entomologists are available to handle that. The inability to characterize for active ingredients responsible for insecticidal activity in the plant product and subjecting the effective plant product to toxicological test in order to reveal its safety to man and the environment is another concern (Nwosu et al. 2017). All this information is necessary during trials to identify plant protection products required in the field and postharvest management of new plant varieties.

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6.6 Conclusion Criteria and methods for plant variety testing should be innovative to enable the approval of new varieties that perform well. Plant variety testing is an exercise that both the regulator and breeders should consider essential for certain that varieties that enter the market perform well. For high-performance, technical know-how of experts working in the concerned agencies, robustness, technological sensitivity, sophistication and capacity, statistical inputs, and adherence must significantly improve. However, costs for D.U.S. and V.C.U.S. tests are continually increasing. To cope with monetary challenges emanating from expansion and use of advanced tools, financial motivations are paramount, and this can come from industries, member States, non-governmental organizations, and philanthropists. As a way forward, more affordable and easy-to-use tools are recommended to spend less time and resources. It is in the interest of growers and all countries that variety testing is performed in efficient manners. For efficient testing of new plant varieties, particular importance has to be accorded to model species, D.U.S., distinctness problems in certain species, and needs of breeders. By using novel statistical models and tools, it is believed that data quality and interpretation will improve to eliminate or reduce redundancy of analyses and to make more value of existing processes. Optimized and harmonized protocols will culminate in quicker and robust decisions on D.U.S., V.C.U., and V.C.U.S. It has been advised that the required improvements should focus on (i) finding new tools for D.U.S. and performance testing (phenotyping and genotyping tools and model speciation). (ii) identifying potential synergies between D.U.S. and V.C.U. and their utilization during performance testing. (iii) maintaining better coordination and sharing of data and databases between partners. (iv) improving protocols and (v) strengthening networks and communication tools towards stakeholders and policy makers.

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Chapter 7

Global Resource Flows in the Food System Wayne Martindale and Kate Lucas

Abstract The capability to project resource flows in food supply chains of great importance if the UN Sustainable Development Goals are to be realised and embedded into the global food system. The research reported in this Chapter explores how current digital techniques build on established capabilities to provide important examples of resource flows in the global food system. The use of geographic information and Digital Twins are tested where the importance of using robust metrics and their data content are defined. This approach also describes how metrics of connectiveness between resources are being used to identify critical controlling points in resource flows where sustainable outcomes are possible. The Chapter will define the global resource flows of agriculture and food production in the worldwide system that are now the focus of new circular economies and carbon-zero programs. The terms carbon neutral, carbon zero, and climate-neutral refer to the same outcome. The approach is to review long term data and study existing applications that assess how nutrients flow through food supply chains to their eventual consumption as the food and beverage products that make up our diets. The analysis presented in this Chapter will show how technologies and models can be used to identify systemic interventions by companies and operators in the global marketplace. They will demonstrate the delivery of environmentally climate-smart food production that increases carbon responsibility; regional food solutions that enable access and affordability; and, supply chains The original version of this chapter was revised: Affiliation for the Author “Wayne Martindale” has been updated. The correction to this chapter is available at https://doi.org/10.1007/978-3-03071571-7_13 W. Martindale () Food Insights and Sustainability, National Centre for Food Manufacturing, University of Lincoln, Holbeach, UK e-mail: [email protected] K. Lucas Kent Community Health NHS Trust Eureka Park, Upper Pemberton, Kennington, Ashford, Kent, UK e-mail: [email protected]

© Springer Nature Switzerland AG 2022, corrected publication 2022 C. M. Galanakis (ed.), Environment and Climate-smart Food Production, https://doi.org/10.1007/978-3-030-71571-7_7

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that provide consumers with product assurance and nutrition for healthy lifestyles. A specific focus is the ‘middle’ part of food supply chains, these are the food and beverage manufacturing functions where product development decisions will become a reality. The requirement to build-in sustainability and circularity into these operations is of extreme importance in meeting the UN Sustainable Development Goals, providing a circular economy, and reaching more carbon-neutral outcomes. This Chapter develops geographic information, supply chain models and Digital Twins to demonstrate the potential of using them to define and measure resource flows in the global food system. Keywords Sustainability · Food manufacturing · Digital twin · Quality systems

7.1 The Resource Flow Starting Point A starting point for resource flow is made by a straightforward enough observation from Daniel Hillel, who opens his book ‘Out of the Earth, Civilization and the Life of the Soil,’ with the line ‘all terrestrial life ultimately depends on soil and water’ (Hillel 1992). This study opened-up many of the ideas that have been packaged in established histories of soil and water resource flow from ancient societies that have, time and time again, shown us the limits of soil fertility and water quality. While these all seemed to demonstrate the apparent limitations of natural resources, it is the opening statement of Hillel’s that is so woefully overlooked and Vaclav Smil, another author who has a skill of developing the worldviews that remain latent unless we appreciate the value of systemic thinking. Smil takes the soil and water process snapshot of Hillel and applies a systemic view so that processes are dynamic with interlinked resource and energy flows. Smil has described a critical event that changed everything, the production of industrial ammonia, which Smil calls a detonator for step-changing agriculture and food production (Smil 1999). Smil applies a dynamic system view to the global nitrogen cycle where ammonia production is a platform for industrial growth and shows us that 24% of the nitrogen in the protein consumed by global citizens is derived from this industrial fixation of nitrogen. It is fixed into the ammonia molecule, whose production is the feedstock for manufacturing plant nutrients used as fertilizers in agriculture (Smil 2002). The work of these two authors has been transformative, and their systemic view of resource and energy flows were tested before the food and beverage industries understood the importance of meeting the UN Sustainable Development Goals, carbon-neutral targets, and environmental standards (Casini et al. 2019). The system thinking approach to resource flows was developed because they make sense with respect to productivity to many of us who work with soils and the energy balance of agriculture that depends on the efficient capture of solar energy. The importance of productivity and the energy balance in food systems are developed in further detail later because they are the platform on which food resource flows are built. They have supported the dramatic impact of Norman Borlaug’s

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first Green Revolution, which focussed on improvements to genetic resources to remove a billion global citizens from hunger (Conway and Barbier 2013). Without an understanding of systemic resource flow for these improvements, many of the benefits would not have been measured, and many of the methods for assessing sustainable outcomes would not have been developed. These scientific leaps are what are now called ‘moon-shots’ in the crop and livestock improvement programs, and the genetic resources should not be overlooked when we are concerned with balancing resources, carbon-neutral outcomes, and healthy lifestyles. The use of carbon-neutral as a term indicates the requirement to offset GHG emissions in the food system, so that release and fixation of them in biomass, soil, and water is balanced and neutral (Martindale 2010). The language associated with this emitting source and fixing sink system varies, and the terms carbon neutral, carbon zero, and climate-neutral refer to the same process and outcome. The agronomy and genetics moon-shots of the twentieth century have developed our current understanding of sustainability where carbon neutral outcomes are used to define environmental goals that commerce, policy, and consumer arenas seek (Donati et al. 2016).

7.1.1 The Platform and Elementary Parts of the Food System Are the Essential Plant Nutrients Moving from a soil and water processes viewpoint to a system-wide view will need to first consider the necessary plant nutrients, the building blocks of the food system. They are required for crop metabolism, and it is observed that rigid boundaries here, such as those defined by the optimal function, will limit system thinking. This is because processes in agriculture, food, and consumption are variable, and they are open or leaky with respect to resource flows, which have control points that can change in response to these processes. An appreciation of these limits is vital for forecasting resource changes, and the models we use where the resource flows that concern us start with the soil, water, atmosphere, and essential nutrients (Martindale and Leegood 1997). An assessment of how they are utilized is vital in the understanding of resource flow, and the atmospheric and energy components of these flows are discussed later. Still, we are concerned with these agronomic nutrients here. The essential plant nutrients do not include those nutrients that may not be essential in agronomy but influence quality, such as sodium, which can impact the palatability of feeds and foods. There are many examples of nutrients that affect the quality of ingredients from agriculture, and their influence in nutrition and taste has been characterized for many crops and livestock products in particular micronutrients such as zinc, selenium, Vitamin A, and unsaturated oils for example (Bouis et al. 2019). The essential plant nutrients also do not include elements that provide functional integrity; for instance, silicon is critical in providing structural support in plants, and this is of importance when considering the harvesting of plants in protected or hydroponic production where silicon may be limiting (Song et al. 2016).

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The mass of essential plant nutrient in crops correspond to the optimal metabolic function and their concentration can be determined by the analysis of tissues, saps and remote sensing of chlorophyll and light harvesting pigment activities as indicators of crop health (Ye et al. 2020). There are critical levels of plant nutrients below which there will be a decrease in yield, and this principle is used in agriculture when plant tissue analysis determines a correction of essential values for these nutrients using a fertilizer recommendation that reduces the probability of yield losses. This approach is used successfully in agriculture when remote sensing of disease or nutrient deficiencies can be verified by analytical measurements of crops providing the opportunity to treat pre-symptomatic nutrient deficiencies or infections (Machovina et al. 2017). The response curve of crop yield in relation to nutrient supply is hyperbolic; that is, it has an initial linear phase at a low concentration of a variable such as a nitrate supply where biomass accumulates rapidly and linearly, followed by a curvilinear step at higher concentrations where biomass accumulation is reduced. This type of response can be viewed in terms of limiting factors such as nitrate availability and control theory, where several factors are involved in restricting production. Limiting factors can be overcome by providing essential nutrients as fertilizers, water in irrigation systems, crop protection measures to reduce disease, and so on. The mass of nutrients required per hectare of land is relatively stable across crop production, and the essential plant nutrients are classified by this mass, which is needed for fertilizer recommendations to provide optimal crop quality and yield (Martindale 2015). The classification of the plant nutrients are described as the major nutrients, the secondary nutrients, and the micro-nutrients or trace elements; they are described now using this classification. The macronutrients are nitrogen, phosphorus, and potassium, whose mobility in the soil water system is very different, with nitrate ions being mobile, potassium ions less mobile, and phosphate ions least mobile in soil and water. They are required by crops and forages in quantities of 40–400 kg of nutrient per hectare, and their mobility changes in different soil and water processes dependent on mineralogy and pH, but in general, nitrate ions can be lost through water flow and potassium and phosphate ions through the particulate loss associated with soil erosion. Phosphate and potassium can accumulate in soil delivering long-term fertility over many years because of their reduced mobility, known as the slow-release nutrients. The mobility of all nutrients in the soil and water environment will impact on their availability and it is also determined by the pH of soils. The minor nutrients of sulfur, calcium, and magnesium are required by crops and forages in quantities of 10–40 kg per hectare. The final group is the micronutrients needed in amounts of 0.01–5 kg per hectare and include boron, manganese, copper, zinc, molybdenum, and chlorine. Micronutrients can be toxic at relatively low concentrations so that they may be controlled by legislation. Still, the availability of micronutrients is dependent on pH, with a general condition of a pH below 7.0 provides increased bioavailability in soils. Soils that are older than 10,000 years or very light soils are likely to have more trace element deficiencies than younger soils or those with more significant amounts of clay or organic matter.

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7.1.2 The Geopolitical and Food Security Implications of Resource Flows The term mineral plant nutrient is used to describe nutrient products such as ammonia or urea that are industrially fixed, and phosphate or potassium salts that are mined, quarried and refined. Organic plant nutrients are derived from a biological fixing or mineralization process, such as the fixation of nitrogen by legume crops or the production of manures by animals. We are concerned with the mass flow of these resources and the different environmental impacts associated with the trade-offs from their use. The relationship between plant nutrient supply, fertilizer manufacture, and economic development is well characterized by the manufacture of agricultural mineral fertilizers being called a detonator or initiator of the modern food system by Vaclav Smil. Plant nutrient supply and their use are crucial interventions by industry and governments for alleviating poverty and hunger, so plant nutrient supply and soil management programs are an integral part of global food security actions. A typical fertilizer recommendation embodies this delivery, and it is simply represented as the ratio of nitrogen to phosphorus pentoxide to potassium oxide (N: P2 O5 : K2 O). Oxides are provided for P and K because they are the legal declarations for fertilizers, demonstrating that chemists used to rarely work with legislators since the oxides are not used as fertilizers (Xiang et al. 2020). The global production of N: P2 O5 :K2 O in 2018 was 117:47:44 million tonnes of fertilizer. The analysis of the year-on-year trends is essential, but in the context of this Chapter, one of the most important things to observe is the production of macronutrients continues to increase. This is despite issues of peakproduction being forecast, most notably in the mid-1990s when it seemed nitrogen fertilizer manufacture had hit a peak and a production crisis was forecast. This never happened because of the restructuring of the European industry following European reunification. A dip in Eastern European production caused the projections to point to a crisis, but this led to increased productivity, reconstruction, and little turmoil. This was also a time where globalization was gaining pace, and the emergence of economies in Brazil, China, India, and Russia meant the demand for plant nutrients was increased. These events are important because they demonstrate how the supply of fertilizer, plant nutrients, feed, and food is often a politically motivated resource due to economic importance. In our lifetime, the trading of wheat between the USA and USSR was a political masterpiece of intrigue and espionage (Domike 1982; Brada 1983). The capacity for the then USSR to produce wheat was measured by the digital and remote sensing capabilities of the 1970s for forecasting food supplies, and it took the outcomes to the heart of global power-brokering (Benjamin 1994).

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7.1.3 The Value of Data in Reporting Food System Resource Flows The geopolitical aspects of nutrient use are essential in demonstrating the importance of data ownership regarding international resources associated with food supplies, and they provide indicators of future responsibility. A stable supply of fertilizer was known to result in improved wheat production in the USSR. The capacity for nations to manufacture fertilizer was under the attention of trade negotiations and satellite remote sensing (Kussul et al. 2017; García-Berná et al. 2020). The wheat negotiations between the USA and then USSR were essential markers in power-brokering and the in-depth knowledge of international wheat production and food supply chains obtained from the remote sensing technology of the 1970s, namely the Landsat Satellites that are still used today (Xu et al. 2020). These food and political relationships should not be viewed lightly because they characterize many of the pressures and fears we see today in arenas where natural resources can steer geopolitics. In the case of removing wheat trade restrictions between the USA and USSR, which occurred in 1981, it has been highlighted as one of the critical parts of eventual Glasnost and Perestroika. The fertilizer production figures used here represent a global average, and there are drastic differences between nations and regions of the world regarding the amount of mineral fertilizer used and the ratio at which they are applied. These variations in utilization and accessibility will be more incisively determined with the improved sensing and data management technologies that are now involved in our food system beyond leveraging power in trade deals. Another demonstration of resource sovereignty is access to rare earth metals used in the manufacture of electrical consumer goods and the engineering of turbines and motors used in the energy generation industries (Bown 2019; Schmid 2019). The control of rare earth metal exports from specific global regions has proved to limit the manufacturing of electrical goods and energy supply, providing a new globalized worldview on the criticality of resources in digital and data industries (Jaroni et al. 2019). Researchers have presented global rare earth metal supply as a periodic table of critical elements for industry, and this is useful because it maps the risks associated with sourcing them (Graedel et al. 2015). These risks are relevant in the agri-food industry, which also depends on renewable energy supply to meet sustainable goals, digital hardware to enhance data flow, and electrical motors to manufacture food and beverage products. The food industry now has access to applications that can provide enhanced traceability of food and non-food material resource flows in supply chains, and the transparency of the information associated with these is crucial for reporting sustainability and carbonneutral outcomes (Martindale et al. 2018a; Keogh et al. 2020). This has been driven by integrating digital technologies in the food and beverage industries and the mobilization of web-based data with cloud computing applications. It is the need for traceability and responsibility that has become important to consumers who do not want products such as those containing rare earth metals to be tainted by poor

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ethical standrards or deficient labor regulations, for example. Recent disruptions have shown these non-ingredient resource flows have reduced the resilience of food and beverage manufacture when shortages of them can restrict manufacturing capacity such as with rare earth metals used in electric motors or switches, gases such as carbon dioxide used to carbonate drinks, or even skills that provide specific products such as the supply chain management of frozen foods. These have all limited food or beverage supply in recent years. They are not directly related to agricultural production, so they can be overlooked by organizations who do not practice material and resource criticality actions.

7.2 In the Beginning, the Scientific and Systemic Approaches to Resource Flow Are Demonstrated by the First Agri-Food Trials We must understand how the modern approaches to such surveillance of resource criticality have developed. Otherwise, we will have a viewpoint that suggests we have always had access to digital tools such as remote sensing, on-line open databases, and global positioning systems. These have been accessible to agri-food industries in the last 30 years, but the foundation for system thinking is evident in the first scientific trials for agricultural production. Resource flow in the farming operations was well characterized by the establishment of practices 170 years ago in the long-term agricultural experiments founded on the Rothamsted Estate in the UK by Sir John Bennet Lawes and Sir Joseph Henry Gilbert (Leigh and Johnston 1994). These were the world’s first scientific agricultural trials, and they were initially set up by Lawes and Gilbert to demonstrate the crop yield benefits of applying fertilizers manufactured by Lawes. It was this that provided the detonating commercial link because the world’s first manufactured superphosphate fertilizers were manufactured in Lawes factories close to the Thames at Deptford near the city of London. The value of superphosphate needed to be demonstrated in the Rothamsted trials and these two commercially astute scientists were looking for an innovative application. Lawes had an estate to finance and had explored the production of colomel mixtures of mercury chloride and sulfur; surprisingly, these were used in medicinal applications for intestinal complaints. Gilbert had worked in the dye industry, a real melting pot of much chemical innovation at the time, leading to colors, antibiotics, and countless other outcomes that built our modern chemical industries (Garfield 2002). They also recognized the power of demonstration and what they may not have appreciated entirely at the time was that they were laying the foundations for scientific and sustainable agriculture. This would prove valuable to not only demonstrate how soil fertility could be maintained using fertilizers and manures but also how sustainable agricultural production could be shown in the twenty-first century.

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The scientific evidence from the Rothamsted field trials demonstrated how nutrients could be lost from soils concerning leaching of nitrate and the loss of calcium by cation exchange. The complex interaction of soil with phosphatic fertilizers was shown to result in phosphate being released slowly over many years, and it did take Lawes and Gilbert longer to define even though it was their commercial goal. Lawes made his fortune from superphosphates; phosphate nutrient flows in agriculture were represented in the twentieth century. It took several decades to accurately define how soil and water act on nutrient flow; this was longer than a lifetime and why we need long term experiments. They demonstrate soils are dynamically linked to water and atmospheric resource flows, and we must remember that nutrient utilization by crops was a mostly unknown process before them. Lawes and Gilbert disputed established scientific thinking when it was thought sufficient nutrients were obtained limitlessly from the soil or atmosphere. The disruption of experiments led by Lawes and Gilbert suggested there was a requirement to balance flows of nutrients, and this clearly ruffled the reputations of scientific giants of the time such as Justus von Liebig, who was establishing the emergent study of biochemistry. Nutrients were not only supplied by the air and soil, but they also needed to be maintained, and the Rothamsted trials provided proofs that have supported modern agronomic thinking with respect to nutrient flows, increasing soil organic matter, and maintenance of soil pH (Bull et al. 2000). The practice of using field trials to support agricultural industries and research laid the foundations of modern sustainable agriculture, and these principles were transferred globally from Rothamsted. The development of the Morrow Plots in Illinois in the USA, the Sanbourne Field in Missouri in the USA, and Palace Leas grassland experiment near Newcastle in the UK are all derivations of them demonstrating nutrient flows in agriculture (Shiel 1986; Jordan et al. 1995; Rasmussen et al. 1998; Soman et al. 2017). These experiments laid the foundation of sustainable agricultural systems in the 1850s around 30 km north of London in the United Kingdom, and they take us to where we started in this Chapter, with Daniel Hillel’s opening line of ‘all terrestrial life ultimately depends on soil and water.’ What Lawes and Gilbert began in the 1850s has enabled a dynamic link between soil and water to food systems and human nutrition. Global programs of improved national food security have focussed on maintaining soil fertility. This was revolutionized again in 1926 when a Professor at Ohio State University, Edgar N Transeau, reported the energetics of photosynthetic dry matter production from a systematic and thermodynamic viewpoint, some 70 years after Lawes and Gilbert had established their trials (Transeau 1926; Colinvaux 1979). It took this length of time for the consideration of energy and atmospheric resource flow to be formalized because there were no modern reporting methods to support international journals and collegiate practice with data resource flows. The Laws of Thermodynamics provide a demonstration of the limits to increasing the efficiency of accumulating biomass because there will be a point where the marginal benefit from improving efficiency result in too low a benefit to be investable in. When the marginal return reaches these limits, the only place to look for improvements is to consider genetics or system resource flows, and

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this was opportune because Transeau’s study was carried out in the corn belt state of Ohio. Maize was the crop considered. It is this geographic aspect of innovation that is often so important; where Lawes had a growing demand for fertilizer in London, Transeau had the corn marketplace on his doorstep. Transeau determined with an accuracy that still stands today that the photosystems of plants utilize around 5% of the incident light, which is now known to be the red and blue wavelengths of the visible spectrum. We also now know that the photo-transfer of energy results in around 12 g of carbon being fixed every second for a hectare of wheat and 15 g for every hectare of maize, providing up to 15 tonnes of dry matter (straw, roots, and grain) per hectare each year with some exceptional growers reporting 20–30 tonnes of dry matter per hectare (Lawlor 1995). The determination of energy balance for the production of agricultural biomass has improved since 1926 with the development of systems that measure the photosystem efficiency and the fixation of carbon of crops and forage more incisively (Habyarimana et al. 2019). The accumulation of dry matter was the indicator for changes in resource flows, and they were the focus of Transeau’s study, which remain important even though we can now use different hands of productivity to pre-empt yield losses in crops such as the fluorescence of chlorophyll in leaves (Murchie and Lawson 2013).

7.2.1 The Transformative Step of Balancing Energy and Nutrients in Agri-Food Systems The energy balance of agriculture can now be developed with our understanding of the essential plant nutrient resource flows so that we achieve a systemic view of the food system where energy transfer or flow is needed to assimilate carbon dioxide and nitrogen into metabolizable forms. It revolutionizes the work and insight of Lawes, Gilbert, and Transeau relating processes that accumulate biomass to those of the whole food system where the relationship with solar and fossil fuel energy can be integrated with atmospheric carbon dioxide and nitrogen resource flows. Without this type of assessment, we could easily assume that atmospheric nitrogen fixed into ammonia and carbon dioxide fixed into sugars was free of any energetic cost. This is not the case, and the second law of thermodynamics will always limit these processes because energy can be transferred or converted at the expense of entropy increasing stifling any notion of getting something for nothing (Rifkin and Howard 1989). This has led us to the modern view of resource flows in food systems where the carbon footprinting and Life Cycle Assessment (LCA) methodologies have been developed to determine the impact of greenhouse gas emissions associated with crop production, food manufacture, and the distribution of food and beverage products to consumers (Martindale 2015). The systems approach is now mainstream in the food industry because there are an added value and commercial benefits associated with the communication of resource efficiency outcomes. These include the ability to reduce food losses, food wastes, and greenhouse gas emissions, and they are

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associated with supply chain and product responsibility, which has an increasing amount of commercial value for consumer products. LCA methodologies have enabled food manufacturers to understand the impact of the amount of greenhouse gas emissions associated with the production of agricultural produce and manufactured food and beverage products. These are reported as Global Warming Potentials (GWPs) or carbon footprints, which are the mass of greenhouse emissions embodied in products, processes, or services. The GHGs include carbon dioxide, methane, nitrogen oxides, and fluoro-chloro-carbons, and the GWP measures their ability to increase the global surface temperature as a function of their capacity to absorb and radiate solar energy. The GWP is proportional to the lifetime of the gases in the atmosphere where carbon dioxide has a relatively low half-life in the atmosphere because it can be readily metabolized by photosynthetic and microbial activity, methane and nitrogen oxides have increased half-lives, and chlorofluorocarbons have half-lives that extend to hundreds of years. This impacts their ability to force global warming, and nitrous oxides have a GWP that is 150 times that of carbon dioxide. With methane having a GWP of 50 times that of carbon dioxide because of this half-life relationship. Even this is not without controversy because it has been demonstrated the metabolizing of methane from biological systems such as livestock production differs from those released from fossil fuel sources. After all, methane from biological systems is more likely to be recycled (Lynch et al. 2020). This is an essential consideration for measurement methodologies that seek to define boundaries within structures because outcomes can be flawed by agriculture, which involves open and leaky resource flows with regard to the diffusion of atmospheric and water resources that flow into and out of their boundaries (Pizzol et al. 2017). The GWP of European agricultural products have been measured using LCA methods, and they show non-renewable energy (diesel) and nitrogenous fertilizers account for up to 50% of the 20–30 Giga Joules per hectare per year energy input required for crop production (Hülsbergen et al. 2001; Nielsen et al. 2003). Production of crops and forages require nitrogen inputs of 60–140 kg nitrogen per hectare to obtain energy outputs of 80–200 Giga Joules per hectare per year. GHG emissions principally occur through the non-renewable energy used for soil cultivation (plowing, sowing, harvesting) and through organic and mineral nitrogenous fertilizer use. Although the nitrous oxide emissions associated with these nutrients are low mass in the order of 5 kg per hectare, they are vital because they have a GWP that is 298 times greater than carbon dioxide. The recorded agricultural nitrous oxide emissions of 5 kg per hectare are less than 1 kg per tonne of grain for a typical European wheat crop (Brentrup et al. 2004; Küstermann et al. 2010). Nitrous oxide emission deserves specific consideration owing to the dependency of high-yielding agriculture on ammonia manufactured from industrially fixed nitrogen. The efficiency of manufacturing ammonia has almost reached the theoretical minimum energy input of 23 mega Joules per kilogram to synthesize in modern natural gas steam reforming processes, which run 30 mega Joules for a kilogram of ammonia. Furthermore, the requirement for nitrogenous fertilizer is made more glaringly necessary because 23% of all nitrogen

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consumed by humans is derived from industrial fixation (Smil 2002, 2018). The energy efficiency of ammonia synthesis and nitrogenous fertilizer clearly has limits because nitrogenous emissions are identified as a significant impact of a global warming worldview presenting the Anthropocene dilemma (Steffen et al. 2011). LCA helps us to identify the only sustainable option for reducing these emissions is to increase crop yields using the same amount of nitrogenous fertilizer, so the nitrogen use efficiency per unit area decreases nitrogenous emissions per unit of biomass produced. This maximum efficiency approach has been adopted in agricultural production as sustainable intensification. While it works, it does not rid us of the nitrogenous gas or nitrate emissions to the atmosphere or watercourses. It demonstrates the limits of taking the maximum efficiency route and sustainable intensification in that we still very much have the problem, and it only becomes diluted. It leads to a consideration of a systemic approach as the only way of tackling these issues, and with respect to nitrogenous fertilizer, we need to determine if crops and forages utilize all of the nutrients supplied to them in a sustainable way. This is now demonstrated for nitrogen use by wheat crops using a resource balance methodology for wheat grown in the UK. Nitrogen gas can be fixed by symbiotic associations by plants but this is not important for wheat at present and nitrogen is made available to plants as ammonia and nitrate ions, and this is assessed here in. The data required to do this is obtained from agricultural resource flows, nutrient applications, and the release or the mineralization of nutrients. The biomass dry matter yield is as follows, for grain yield of eight tonnes per hectare, ten tonnes per hectare of straw, and 6 tonnes per hectare of root dry matter are produced. This will be equivalent to nitrogen, phosphorus, and potassium content (N, P, K) contents of 160 kg nitrogen, 40 kg of phosphorus, and 400 kg of potassium based on the percentage of N, P, K in plant tissue of 2% nitrogen 0.5% phosphorus and 5% potassium (Martindale 2015). This will align well with residual biomass in the soil and fertilizer recommendations. Where these do not provide the plant tissue requirements, the nutrients must be derived from the soil or atmosphere; otherwise, there is a nutrient deficit. This is the case for the mineralization of phosphorus that is typically held in calcium salts and potassium that is exchanged for other nutrients. Soil mineralization in a single growing season can account for the release to plants of nitrogen at 30 kg per hectare, 3 kg per hectare phosphorus (dependent on soil mineralogy), and 50 kg/ha for potassium (again reliant on soil mineralogy). The water used for C-3 photosynthesis- wheat is a C-3 photosynthesis type plant- is 650–800 g of water per gram of dry matter, and this has been determined by metabolic balance calculations, which means for this wheat demonstration of 24 tonnes per hectare of dry matter 15.6–19.2 thousand tonnes of water per hectare is utilized for growth where one thousand tonnes is one million liters (Pearcy and Ehleringer 1984). We can even demonstrate that the nutrient held in crop canopies aligns with current nutrient recommendations, and this is important because canopies intercept the solar energy required to accumulate dry matter. The optimal leaf canopy-cover or green index for wheat is a 3-m squared area of leaf cover per meter of ground known as the Leaf Area Index (LAI), which is equivalent to 30,000 m squared per hectare of

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leaf surface area (Lawlor 1995). The nitrogen required for C-3 biomass is 200–260 millimoles nitrogen per meter squared, equivalent to 6000–7800 moles of nitrogen per hectare in a wheat canopy, nitrogen has an atomic weight of 14, and this means 84–109 kg per hectare are in the canopy alone. This again aligns with nitrogen fertilizer recommendations where 40–60% of dry wheat matter is harvested as grain.

7.2.2 The Modernization of Food System Insights Is Introduced by Climate Change and Ecosystem Services Understanding resource flows become a requirement of the current food system because of the need to tackle climate change caused by GHG emissions. Balancing these resource flows for agricultural production has identified agriculture as not only a source of GHG emissions but also a means to capture and fix GHG emissions so that they become locked into soils and organic matter. Soil organic matter, for example, was once overlooked as an unchanging component of soils static part of soils until it was noticed that more disturbed or cultivated soils had less of it, and more fertile soils had up to 500 kg of microbial biomass in every hectare. This was in part demonstrated using the Morrow Plots long term experiments in Illinois, USA, and the long-term experiments have typically provided demonstrations of the scale of resource capture possible (Aref and Wander 1997). The scale of the GHG contribution is significant, with global agricultural GHG emissions reaching 8 billion tonnes of carbon, 0.53 billion tonnes of methane, 0.13 billion tonnes of nitrogen oxides. A further 0.58 million tonnes of industrial gases are released with GWPs of 12–15,000, so their GWP impact must be put into context to be meaningful. Emissions derived from agricultural operations were identified in international agreements such as the Kyoto Protocol in 1997, and this aligned with the emergence of ecosystem service methods for assessing impact (Costanza et al. 1997). The ecosystem services represent a range of ecological functions or services that have defined economic outcomes associated with natural capital. An example is land use, which will change or regulate gaseous and soluble emissions from soils (Costanza et al. 2017). One of the most critical processes is the carbon cycle and soil because of the requirement to conserve and manage carbon dioxide emissions. Increases in atmospheric carbon dioxide concentration as a result of industrial activity and energy generation from fossil fuels are directly correlated to increased global warming. These can be ameliorated by land-use options, which can be built into product development if they are quantified as ecosystem services that will fix atmospheric carbon dioxide from the atmosphere (Costanza 2006). These services are beneficial if we consider global environmental change was first described as a smoking gun’ by James E Hanson concerning its threat to our planet and the fact it was because of our actions it is happening. The global cycle is dynamic and has 45,000 Gigatonnes of carbon locked into the marine environment, 750 Gigatonnes

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are locked into the atmosphere, and 2000 Gigatonnes are locked into the plant and soil system. Industrial activity contributes two Gigatonnes to the atmosphere each year, and this is tipping GWP and changing global climate (Houghton and Woodwell 1989). It has been nearly 50 years since the threat of global warming was raised, and the assessment of carbon transfer in the international system has established the value of using ecosystem services to assess the value of programs that can reduce GHG emissions. Carbon resource flows are finely tuned, with soil resources able to lock-in carbon as organic matter and carbonates, which is why they are called carbon sinks. These are dynamic processes in that they lock in carbon but can release carbon dioxide and methane in response to land-use change perturbing a finely tuned carbon cycle with accumulative industrial emissions that eventually disrupt the climate. These tipping points are being tackled by a range of actions such as using minimal soil cultivations in crop management, which reduce the use of the plow where possible and minimize soil disturbance so that soil organic matter increases the fixing of carbon. In a similar way, crop residue management will lock carbon into soil organic matter and reduce oxidizing processes that release carbon from the soil. These carbon conservation processes associated with soil management can fix 10.4 million tonnes of carbon per year in the UK, equivalent to 3–4% of UK national carbon emissions, demonstrating carbon-neutral outcomes will require several different approaches to carbon management, including soil carbon capture. The combination of maintaining current land use using sustainable intensification, the incorporation of crop residues in minimal cultivations to lock carbon into soils, the use of bioenergy in farming enterprises, and responsible manure management that reduces the emission of nitrogen oxides will all have an essential role in climate or carbon-neutral food systems. These practices are translated into environmental costs, and benefits are assessed by tools such as LCA, carbon footprinting, and ecosystem services (Kløverpris et al. 2008).

7.3 Assessing Added Values in Food Systems with Circular Economy and Climate-Neutral Outcomes The circular economy view has been an essential outcome of understanding the interaction between resource cycles and ecosystems, changing the scientific viewpoint of developing the perfect agricultural product, be it a breed of a dairy cow or variety of cereal, in that it no longer focuses on yields and efficiencies alone. This has also extended the linear view of developing a food or beverage product to one of the product life cycles where the utilization and disposal of products are fully considered. The development of LCA methods takes on this approach, it was initially developed for use in the petrochemical industries in the 1970s, and it has now been tested for many agricultural and food products (Nielsen et al. 2003). LCA seeks to define boundaries of activities within which resource

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flows are forensically accounted for, and these are aligned to environmental impacts for a range of polluting activities. What concerns us here is the LCA reporting of greenhouse gas emissions as a carbon footprint or Global Warming Potential (GWP) and the embodied energy used within the LCA boundaries (Clune et al. 2017; Martindale et al. 2019). It has transformed how the value of resources are used in business and society, leading to systemic solutions in the farming, food and beverage, and retailing industries (Stylianou et al. 2016). The LCA method has most impressively used to provide insight for value-added, transparency in processes and customer confidence in biotech products and it has not been yet used across all biotech and specialized ingredient sectors where there is an opportunity to do so (Nielsen et al. 2007; Jegannathan and Nielsen 2013; Martindale et al. 2014). An important driver for this has been the business structures that have become globalized, and the requirement to generate energy with lower climate impact has stimulated the emergence of support for cleaner technologies in energy generation markets that were already well developed. These either seek to use fossil fuels more efficiently or utilize alternative fuel sources, and their impact has become globally crucial in reducing the effects of fossil fuel use and GHG emissions. The growth of crops for fuel, chemical feedstocks, and materials has developed during a period of volatile oil prices, and the projected lifetime of fossil fuel reserves, which have been capped at some 300 years which have intensified action for carbon-neutral outcomes. This is where GHG sinks in the system to balance GHG sources close to zero or neutrality, and it is also called carbon zero or climate-neutral in resource policy arenas (Martindale 2010). What was unheard of before these globalized actions were the combined partnerships in green chemistry, biology, and agriculture that have enabled the development of bioeconomy across the established chemical, material, fiber, and fuel industries. It has provided a means to combat GHG emissions, reduce pollution, and reduce manufacturing risk. The use of crops and biomass for fuel, chemicals, and materials was also not far removed from existing processes in the agrifood industries. Companies have established large scale extraction of plant oils, fermenting biomass into alcohol, and processing sugars, all from biomass. The development of sugar beet and oilseed rape (Canola) in the northern hemisphere due to market requirements and innovative technologies associated with alcohol fermentation are some of our oldest industries. The primary industries have been transformed by the circular economy and the methods used to assess it.

7.3.1 Responsible Management of Resource Flow Data and Evidence The datasets used in LCA are now well developed because of the need for carbonneutral outcomes, and primary data for LCAs is collected from sources. It will be defined for a particular system with specific boundaries. It has stimulated the growth

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of using secondary data from several LCA studies and made carbon footprinting possible for many foods and beverage products. The consideration of boundaries in LCAs is crucial for using secondary data because the limits may only consider agricultural production and not manufacturing, retailing, or consumption. Many LCAs are robust enough to make forecasts to define such supply chain outcomes with greater confidence than ever before, and they do guide policy (Notarnicola et al. 2017). LCAs have facilitated definitions of what type of food production is more suited to specific markets, and an example is provided by the intensification of agriculture. A more cautious ‘sustainable intensification’ of agriculture and food production has been adopted rather than a full commitment to ‘industrialized intensification’ because both have demonstrated a reduction of GHG emissions. This has been demonstrated for global rice production, for example, where 13 Gigatonnes CO2 e per year would be emitted if it did not exist (Burney et al. 2010); the reassessment of methane in livestock systems has also suggested something similar happening (Allen et al. 2018)and these systemic views are often missed in analysis of isolated causal impacts. These are often seen as controversial views of what sustainability should be, and this is because food production capacities are not limitless, but significant yield gaps and waste minimization opportunities exist that can be considered in a sustainable resource flow worldview. The Demonstrator of a Digital Twin method which analyzes a food consumption system is shown later where a more sustainable diet can actually result in more significant food waste because more substantial amounts of perishable food are required for the sustainable diet. The awareness of such trade-off scenarios is essential when considering consumption choice and consumer goods. What has become more transparent in debates across these views of intensification is low yielding agricultural systems and unconnected processing and manufacturing industries are not the answer for 9 billion people and meeting the UN Sustainable Development Goals (SDGs). An authentic limit to our food system is energy supply where all current oil production estimates suggest the collection in 2050 will be maintained at 90–100 million barrels per day, and this will fall short of the projected demand of 120 million barrels per day even when we include extraction from shales and tar-sands, so there is a focus on renewable energy from biomass, solar and wind to fill this gap. Such renewable and energy recovery technologies can transform the food industry. They are now closely linked to the requirement for carbon-neutral food and beverage products because GHG emissions associated with the supply chain activities can essentially be recycled within the economic activities of businesses. This can be achieved by buying-in green utilities that are more carbon-neutral than fossil fuel sources of energy. Such actions have raised how responsible consumption and waste reduction can also bring products to a net carbon zero baseline. Combining procurement of greener utilities and designing or building-in carbon-neutral decisions into product development is a more robust approach to getting carbon zero products into the marketplace. There is no silver bullet solution here for stable carbon or climate-neutral outcomes, and a combination of carbon capture with recycling GHGs from farm to fork will need to be the route to carbon-neutral food and beverage consumption. We cannot get

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something for nothing here; the second law of thermodynamics will not allow this (Rifkin and Howard 1989). The influence of biofuel and biomass is essential because agriculture is in the business of growing biomass, and it is often a co-product of the food manufacturing industries where processing and fermentation are in the portfolio of existing industrial expertise. This was the case for the UK with the development of three large bioethanol refineries utilizing three million tonnes of wheat grain biomass, and the methodology for a Digital Twin Demonstrator analysis is presented later (Martindale 2009). It is clear that conflicts between using biomass for food, feed, or fuel arose, and it was demonstrated these conflicts could be ameliorated using alternative feedstocks or increasing wheat yields, but a thorough understanding of the supply chains solved many of these issues. Mapping where these biomass resources have determined the mass of material available for food, feed, and fuel supply where assessments show flexibility in biomass production if connectivity between food, feed, and fuel producers is managed (Martindale 2009; Martindale et al. 2020a). The processing of co-products and biomass has enabled new systems of resource management where ‘waste’ has never been a word used lightly in the manufacturing sector. There is intense activity to reduce waste across the whole supply chain and divert ‘waste’ streams to valuable co-products. How waste is minimized globally is exceptionally variable, and a waste minimization system for all supply chains will require a system of joint responsibility and fairness (Martindale 2017b). Geographic data is crucial because resources are not equitably distributed, and the biomass, soil, and water resource maps that are now established demonstrate this. An important example is provided by water availability where irrigation of agricultural systems is highest where water scarcity is most intense, and with many temperate zones experiencing Mediterranean and sub-tropical climate, there are emerging challenges for food manufacturers (Gain et al. 2016). A crucial area for optimizing water-use in manufacturing is the maintenance of assurance and safety protocols, which require increased water use intensity for maintaining hygiene. The requirement for high water use efficiency is likely to become more apparent in the food supply chain, and water-saving technologies, crop breeding, efficient cleaning in place, and new assurance protocols will all have a role to play. An example of technology intervention in this space is provided with fresh vegetable processing that now uses modified atmosphere packing in the manufacturing cutting and washing of vegetables to inhibit the activity of Phenyl Alanine Lyase (PAL). This enzyme causes the browning of tissues in the presence of oxygen. Traditionally, sulfites were used as a preservative for inhibiting PAL; this entailed using large volumes of water to dissolve sulfur dioxide and continually wash produce. Continuous washing is used for most hygienic methods, so reduced ware use and maintaining hygiene are very much sought after. It has been demonstrated that flushing with nitrogen gas to remove oxygen decreases the activity of PAL, and it not only removes the allergenic sulfite from operations but also reduces water use in factories. This also demonstrates another common feature of sustainable outcomes where an environmental deliverable is achieved in response to a regulatory

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requirement to remove a particular attribute, which is an allergen (sulfite). How these solutions are communicated environmentally across their supply chains is a new area of required expertise for many manufacturers, and LCA or systems thinking can help to do this. The reporting of resource flow in food supply chains has also highlighted the importance of data flows to assess how connected operators in supply chains are resource inventories are the glue that can hold the whole food system together here. Data resources are critical in determining whether the information is correct or can be trusted, and some designs can help enhance both of these, such as blockchains and Distributed Ledger Technologies, which distribute responsibility for trust (Martindale et al. 2018a). This is demonstrated for carbon footprint data that can provide a measure of supply chain efficiency for processing and manufacturing inputs from farm to fork or farm to taste. The carbon footprint is an appropriate means to report energy use and resource flows for many food and beverage products, and it can be used to communicate impact across supply chain functions, including consumption. This is because there are specific data inputs for each activity associated with a footprint. As an example, a typical 200 g mixed livestock and plant ingredient sandwich will have 220–290 g of GHG emissions associated with growing and processing its ingredients, transport and packaging will contribute 20–50 g GHG emissions, and greenhouse gas emissions such as methane (from livestock production) and nitrous oxide (from organic and mineral nitrogenous fertilizer use) can significantly increase these emissions. Furthermore, they can be reduced by fit-for-purpose agronomic management and efficient supply chain planning, which will be dependent on efficient data management (Martindale et al. 2018b).

7.3.2 Mapping Resource Flows as Food and Beverage Products Move Through Supply Chains Data for supply chain volumes and product choice from farm to shopper across food industry sectors (e.g., meat, dairy, vegetables) are often a limiting part of our understanding of energy inputs and the LCA for food products. This is because LCA does not deal with system boundaries changing very well, and consumer goods need to do this regularly in response to changes in market distribution and demand. Mapping resource flows have been reported for geographically regional food manufacturing and distribution. The primary data has been collected for refrigerated transport, diesel consumption, and food miles using LCA methods. The food distribution and logistical data collected from companies has been converted to GHG emission and social cost using environmental impact and LCA conversion factors derived from reported secondary data. The distribution and LCA data have been represented in a Geographic Information System (GIS) model to provide a hybrid GIS-LCA for supply chain groups. This provides and important step in

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developing Digital Twin methods that can forecast outcomes for most probably activity in food systems. An example of this approach is shown in Fig. 7.1, a Demonstrator of the GIS-LCA approach for the carbon footprint and social impact of distributing products within 70 km of an urban center in the UK (the Leeds area) for 10 meat manufacturing companies based in the region. This type of GISLCA analysis is developed to be extended to agricultural livestock production for regional food networks, health trends, and consumption in populations. The spatial information for UK beef production is also shown in Fig. 7.1; it is unlikely that the relationship between centers of farm production and food manufacturing will always be closely related because of the requirement to deliver products to urban centers. GIS-LCA methods do offer the potential to build scenarios of how agricultural production and food manufacturing can be planned with the delivery points in this case for 10 meat product manufacturing companies and their associated impacts during a typical week of logistical activity. The fuel consumed to transport manufactured products has been calculated using conversion constants for freight described in research presented by Martindale and others (Martindale et al. 2008), the fuel consumed for whole freighting operations (products and vehicles) have been obtained using the UK Department of Transport Freight Best Practice KPI publications, typically 3.6 km per liter of diesel for HGV and LGV vehicles. The conversion factors for the economic cost of food transport for each 1000 km in this study were £ GBP 4.4 for GHG emissions, £ GBP 31.2 for the total social cost that includes accidents, £ GBP 222.6 for congestion, £ GBP 0.8 for transport infrastructure, £ GBP 5.70 for noise and £ GBP 10.10 for air quality using reported conversion factors (Smith et al. 2005). The highest costs of transporting food and beverage products in the UK are social costs that include accidents and congestion, which directs the analysis for conserving transport resources to the thorny issue of more informed planning of transport rather than a shifting of fuel sources to more carbon-neutral options for transportation as a solution to a range of environmental impacts. The conversion factors are obtained in this study utilizing the costs of GHG emissions, accidents, congestion, transport infrastructure, noise, and air quality reported by Defra (and dividing them by the documented food miles by HGV (5.8 billion km) and LGV (4.7 billion kilometers) vehicles to obtain the typical cost per km for a particular impact (Smith et al. 2005). The sum of accidents, congestion, transport infrastructure, noise, and air quality cost is presented as the sum of social cost, and this is sometimes called the accurate cost accounting method, which is relevant to resource management. This is because it provides an essential insight beyond fuel costs for fiscal planning, data management, and carbon footprinting to be integrated for meaningful applications in the food system. The limits in regional agricultural product supply have been traditionally extended by logistical infrastructure, preservation, and packaging, and we can account for these limits in terms of GHG emissions and social costs for sustainable business systems.

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Km

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Fig. 7.1 Logistical operations and product flow for meat manufacturers. This demonstrator shows the how Digital Twins are developed to assess how food companies report carbon emissions to carbon management schemes (e.g., carbon credits). Retailers and customers increasingly require GHG emission information for their products and supply chains because Corporate Social Responsibility (CSR) outcomes are communicated, and mapping footprints can provide a method for doing this shown here. Beef production data here has been obtained from the Agcensus database; it represents a resolution of 2 km2 and demonstrates the distribution destinations for 10 meat manufacturers within 70 Km of Leeds city center; CSR criteria can be mapped for these points. The blue background grid shows the intensity of beef herd production, with the lighter blue through to yellow/red grids showing increase areas of beef cattle production. (Copyright, W Martindale 2015, developed in MapInfo Pro 10.0)

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7.4 How a Farm to Taste Trusted Assessment Have Developed the Digital Twin Methods to Provide Value-Added Resource Flow Insights The farm to fork view of supply chains aligns with these types of GIS-LCA methods. Still, a future goal will be to provide a farm to taste assessment that includes the functions of utilization, consumption, and consumer experience (Martindale 2017a). The farm to taste view is system-wide, and feedback regarding the taste, aroma, nutrition, and dietary impacts- which are the consumer experience- are becoming possible using digital solutions that include systems built to collect retail sales data efficiently. The expertise of foods is often left outside of the analysis boundaries of LCA, but they are some of the most important because they determine purchase and popularity. The GHG emissions associated with them are even identified in the USA by the Environment Protection Agency as potential targets for full assessment as Scope 3 GHG emissions, which are those associated with the value chain. Until digital technologies demonstrated how Distributed Ledger Technology (DLT) and blockchains could trace high volume and high variability financial flows in systems as immutable data, it was thought Scope 3 emissions were far too variable and complex to deal with. They are not, and these types of value chain processes have a role to play in how much product is likely to be used and wasted so that their impact is significant in understanding how to project resource flows in models called Digital Twins (Martindale et al. 2020a). What is most important to observe here is that successful retail businesses well understand the inventory of value chain impacts in the food and beverage industry because the value chain can determine product choice, preference, and sales. An indication of the importance of taste and consumer experience is provided by the global production of herbs and spices reported by FAOStat selected because they are used to formulate preference. Garlic production has reached 28.50 million tonnes, increasing 16% year-on-year since 1990; cinnamon production reached 0.22 million tonnes, increasing 12% year-on-year since 1990; and, vanilla which is more complicated but 0.08 million tonnes were produced globally, increasing 8% yearon-year over this period that saw the emergence and growth of globalization that changed consumption dramatically. Cereal and sugar crops such as wheat and sugar cane generally show a less than 5% year-on-year increase over this period. They tend to have constant production rates in developed and regulated markets that import agri-products such as spices for taste. Removing the farm to taste approaches from any system model will not provide a full-frame of what is happening with the food system (Martindale 2017c). Digitalization has projected these changes more incisive where prior models could only realistically produce a projection of a likely state of change. Digital Twins are tools that utilize large datasets to project that change, and they are dynamic because feedback from the point of sale or consumption will be continuously updated (Martindale et al. 2019). The current goal of many digital companies is to make these feedbacks instantaneous as changes in food production or consumption occur and, in doing so, provide a real-time inventory of resources in supply chains.

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A Demonstrator of a Digital Twin is shown here for the UK biofuel and wheat production system previously described for food manufacturers utilizing wheat and farmers producing premium bread wheat for the food system. The Digital Twin Demonstrator, shown in Fig. 7.2, has projected the impact of biofuel production on bakery supply chains in the UK. A robust and sustainable biofuelfood policy was developed for collaborative supply chains of food manufacturers, farmers, and bioethanol producers (Martindale 2009). Without a Digital Twin approach, collaboration would have had reduced impact because projected targets could not incisively minimize risk and the Digital Twin approach provides the basis for delivering this producer, manufacturer, and processor driven frameworks. The Digital Twin Demonstrator also provided evidence that recycling carbon through agricultural production systems and biofuel supply chains can reduce GHG emissions and improve air quality (by replacing fuel oxygenates in fossil fuels) (Martindale and Trewavas 2008). The initial research provided a spatial analysis of crop production and bioethanol refineries using the Agcensus Database from EDINA in the UK to pilot agricultural land use and cross-referenced these with the UK Defra Agricultural and Horticultural Survey (AHS). Land use requirements for biorefineries were defined within 50 km radii of three national bioethanol refineries of Vivergo, Ensus, and Cargill companies. Using this method, the Digital Twin demonstrator estimated the regional bakery demand for local grain-based on 36 bakeries benchmarking their wheat requirements concerning annual financial revenue. The Digital Twin demonstrated that were contingencies of over 0.5 million tonnes of wheat in this regional system that would support biofuel, feed, and food production, and this was used to guide sustainable food system outcomes.

7.4.1 The Digital Twin Approach and the Promise of Instantaneous Resource Inventory for Food Supply Chains Digital Twins of food supply chains offer an instantaneous and incisive analysis of resource flows. However, they are still dependent on quality data. Otherwise, there is the possibility of Garbage In- Garbage Out scenarios (GIGO), and digital solutions such as blockchains are making this harder to do, but it is still a risk. The potential to falsify or create data will always exist, and there is a requirement to build a culture of trust for any data input; blockchains do this by making data immutable so useful data (trusted) and insufficient data (potential lies) are always on the blockchain (Rejeb et al. 2020). Technological fixes such as blockchains can also make data input accessible, so they complement structures of trust that will ultimately be developed by establishing communities, collectives, and business ecosystems. LCA and carbon footprinting are routinely used to assess the environmental impacts and more comprehensive sustainability reporting of products where there is an increasing need for trust. LCAs are used to report claims such as those associated

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Fig. 7.2 An application of a GIS that extends to LCA and Digital Twin projections for biofuel resource flows. This Demonstrator shows bioethanol production plants in England and their relationship to crop production within specific biorefineries’ specific distances (Fig. 7.2a). The LCA outputs used in this Demonstrator deliver projections for carbon footprint, water use, and competition between food, feed, and fuel supply. The concentric circles are 5 km wide circles with a radius of 50 km from the refineries. The grids show the intensity of wheat production. Red510 ha/2 km2 ; blue- 0 ha/2 km2 and this Demonstrator develops their relationship and connectivity with manufacturers, the black circles show the location of significant bakeries in these regions (Fig. 7.2b) (Martindale et al. 2020a) and crop biomass produced within these regions suitable for fermentation (Fig. 7.2c). (Copyright, W Martindale 2020, developed in MapInfo Pro 10.0)

with carbon-neutral products (Martindale et al. 2019). The use of traceability or transparency software solutions such as blockchains that can trace the LCA or footprint data from source to product to consumers helps overcome these limits and gaps. In many respects, the blockchain approach and the footprinting approach are similar in that the flow of trust and materials must balance; that is, what data or material goes into a system must come out; it must be 100% in and 100% out in terms of mass-flow. If the 100% balance is not achieved, the blockchain will flag

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where imbalances in material flows or trust are in the system, and the difference between this and an LCA is they are immutable and in the blockchain forever. Global events demonstrate time and time again that supply chains are resilient, with food supply and consumer demand being finely tuned but not as flexible or agile as many consumers in Europe have come to expect because of the impact of untrusted data on commercial claims. The indicators of global food supply and price show primary food commodities, including small grains, oils, and dairy products, are volatile. Still, it is trusted real-time data that is often the most limiting in terms of the speed that this system responds. This is despite the impact of digitalization and the new applications of blockchains, which is counterintuitive because technology should improve outcomes. Still, the major limitation remains a collaboration between companies across supply chains. An example is provided by production and trade data, which is typically reported annually, so forecasting in business is generally made using annual assessments of the financial worth and mass volume of resource flow. This is when suppliers themselves own much of the data required to report this on a day-by-day basis, so it is exciting and innovative to consider the very blockchain tools we are beginning to use could start to enable collaboration and benchmarking across supply chains so that they are agile in responding to day-on-day changes that are the reality of modern trading and potential crisis. While many of the currently used blockchains have developed to enhance assurance and reduce the risk of safety failures, the reach of their applications goes much further in that they are secure and gated collective sources of supply chain data.

7.4.2 Effectors of Sustainable Actions Can Be Projected Using Digital Twins- The Impact of Preservation and Packaging Preservation processes and packaging materials provide effect by extending the shelf life of foods, so they are systemically used across most food and beverage products presented to consumers (Janssen et al. 2017). Preservation is generally achieved by heating, cooling, or chemical treatments, but there is a suite of emergent processes such as high pressure, pulsed-field, and irradiation that can be considered (Swainson 2018). The packaging is a systemic attribute where material barriers enhance the preservation and utilization of food and beverage products. Like preservation remain overlooked by LCAs because it is not systemically used for agricultural products. The frozen food supply chain demonstrates these principles, and most importantly, it has been tested to verify how the use of frozen products and freezing can reduce food waste and improve sustainability outcomes. Packaging materials and temperature treatments require energy inputs associated with GHG emissions, but it is the utilization of products that is just as important here. Freezing of food and beverage products has made significant gains in conserving energy, reducing greenhouse gas emissions, and manufacturing efficiency in part due to

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reforming the use of refrigerant gases, which have incredibly high GWPs. A far more important outcome is the sustainable use of seasonal foods that can be consumed out of season by preserving them, reducing food waste through extending shelf life, and the resulting improvement in portion control in domestic and foodservice sectors. Frozen and chilled preservation provides a systemic example of waste reduction that has been tested with other processes such as canning and dehydration (Martindale and Schiebel 2017). Preservation and packaging processes must also be benchmarked against providing safe and assured products to citizens served with safe food every day. The cold chain enables the storage and delivery of chilled and frozen products. This is most visible to consumers as refrigeration in retail environments, and the operation of cold supply chains is a mainstay of modern convenience lifestyles. The cold food supply chain reduces the risk and variability in production (e.g., seasonality) and manufacturing outputs (e.g., localizing or shortening supply chains) so that postharvest loss and food waste due to variability in price or demand are minimized. Understanding preference and why other food groups, including bread and prepared foods, are ‘thrown and not frozen’ is a challenge for the food system where shelf life can determine the probability that foods are wasted. How preservation and packaging choice relates to the utilization of foods and cooking them is an essential aspect of how sustainable they will be, which is recognized in innovative approaches to New Product Development (NPD). The use of circular economy and supporting methodologies to report carbon neutrality has also led to many food and beverage businesses seeking to align their products and processes with the UN SDGs. While these 17 SDGs have provided a management framework and force of change, they are likely to be as transformative as Agenda 21 some three decades ago. What is very different now is the ability to align and transfer data so that the 17 SDGs can be calibrated with specific business practices. The impact of networking has, of course, been revolutionized by the World Wide Web. Still, it is the integration of networking across supply chains that have enabled incisive capture of resource flow data. It has been some time since Robert Costanza and colleagues have identified a commercial requirement to do this by placing a value on the global ecosystem services in the journal Nature in 1997 (Costanza et al. 2017). Their initial paper followed the significant sustainability hurdle of 1991, the Rio Earth Summit and Agenda 21, where it provided awareness for the need for an economically and environmentally reasoned use of natural resource assessment. At the time, the authors estimated the global natural ecosystem gross economic product was more than 33 trillion US dollars per year, far more than any global, national financial revenues (total GDP’s), at least twofold that of global economic value at the time. The awareness of many in the international community to realize financial wealth could well be related to environmental and ecological wealth was new to us in 1997, and that changed forever. More importantly, the paper showed environmental and economic wealth could be inextricably linked, a realization that was sometimes overdue back then and is now mainstream in resource development and the SDGs.

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The food and beverage system can now provide an instantaneous audit of resource flows and product inventory for whole supply chains that can be aligned to the metrics described in the SDGs. Such practices would report the GHG emissions, food loss, or resource use at any time during a production cycle to provide a transformative assessment of sustainability for Fast Moving Consumer Goods (FMCGs). An initial example of this approach is being tested by assessing the amount of time a food or beverage product is placed in a chilled or frozen environment because consumer demand will influence its time spent in refrigeration, which in turn will impact on carbon footprint. When the market is high chilled foods will spend less time on refrigerated shelves in retail environments meaning the product carbon footprint decreases, so getting the balance between production, logistics and demand just right will optimize this lowering of carbon footprint. This is dependent on as close to instantaneous data flow across supply chain operators as possible for optimal carbon footprint and waste reduction outcomes because operations are no longer working in a flywheel motion that cannot stop and tends to overproduce. Instead, production can respond instantaneously with demand when data flow from the market, ‘downstream’ or consumption functions of the supply chain can be virtually instantaneous. It has enabled the development of more robust Digital Twins that can project outcomes for system-level events across supply chains and populations where the use of data from the National Census to project dietary scenarios has already been tested. An example of these Digital Twin tools is shown in Figs. 7.3 and 7.4, that uses a GIS change agent-based model using nearest neighbor methods and cluster analysis to measure the connectivity of thousands of producers, processors and food businesses. The datasets that develop the Digital Twin are used to build sustainability and consumption space metrics at population or meta-levels. An important application of this type of Digital Twin system will be to define what supply and demand functions across the food system will enable carbon zero or carbon-neutral goals to be reached and guide SDG targets.

7.4.3 The Digital Twin Application in Projecting Supply, Demand, and Consumption in Food Systems The ability to interrogate food system data using metrics and report indices that will guide businesses and consumers is critical in making dietary recommendations that can change the global food system. Many studies have suggested changes in plant and livestock protein balance as a means to obtain a sustainable food system, and what follows them is usually a growing realization that sustainability has a very real boundary of a healthy diet (Berry et al. 2015; Donati et al. 2016; Lukas et al. 2016). Such studies have found the route to a sustainable diet is not clear because it is as much a cultural as a scientific issue where a straightforward substitution of animal proteins for plant proteins may not be possible because of age, lifestyle, or geography. The debate on the GHG emissions associated with diet is vital to

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Fig. 7.3 Mapping consumption using GIS allows us to begin to present the data derived from 100 to 1000’s of supply chains and build a Digital Twin that can project sustainable production and consumption outcomes, including carbon-neutral deliverables. The map generated here by a GIS shows the carbon footprint for the Sheffield region in the with the population analyzed at Lower layer Super Output Area population for the National Census where each area has approximately 1500 citizens. The Livewell diet and typical UK citizen diet as reported by the National Dietary and Nutrition Survey were constructed for 1 week of consumption and projected here. The consumption spaces are 0.5 km2, and red grids account for 30 t GHG per 0.5 km2 through blue grids, which is 20 t GHG per 0.5 km2 . The Demonstrator shown here was benchmarked against national inventories of GHG emissions and retail expenditure so that the calibration of the Digital Twin was rested. This test demonstrated a robust projection of consumption is delivered using these methods. (Copyright, W Martindale 2020, developed in MapInfo Pro 10.0) 

Fig. 7.4 (continued) as Food Business Operators (FBO’s) by the Food Standards Agency for England and Wales to processors, and it is the three closest processors to 830 retail FBO’s. The lowest level data grid shows the intensity of dairy products derived from the number of cows, where the red dots show major dairy producers and the black dots are processors. This data is from Companies House and the Agricultural and Horticultural Survey. Figure 7.4b, shows the same scenario for food business and customer (n = 1636) to packhouse (n = 71) to processor (n = 245) and producers for orchard fruit (215). The Digital Twin supports and enables traceability back to the most probable producer and processor. (Copyright, W Martindale 2020, developed in MapInfo Pro 17.0)

Fig. 7.4 (a) A Digital Twin Demonstrator for the dairy supply chain and (b) Orchard (stone) fruit in England (principally apples for sale and cider production), the Digital Twin Demonstrators shows in Fig. 7.4a, the 3 – node connections of 823 food and beverage retailers to 166 Dairies and 202 dairy producers. The top-level data grids show the connectivity of food business reported

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food and beverage businesses because it can develop net carbon zero-emission, carbon-neutral, or neutral climate products (Brouwer et al. 2020). This will be achieved by embedding ecosystem services into their product development, that is, assessing how GHGs can be recycled in the food or beverage product supply chain. It will be transformational because the food and drink industries can account for over a fifth of national economic revenue when economies have sought out resilience in response to COVID19. The need for more excellent stability in supply chains will only be made possible by testing added values that identify critical resource flows. Nutritional delivery must be the ultimate boundary in the food and beverage system that should be met for every meal equitably delivered to every global citizen. Understanding how this can be achieved has been tested using the Delta Model developed by the Sustainable Nutrition Institute at the Riddet Institute in New Zealand (Smith et al. 2020). This model is transformative in that the diffuse boundaries of consumer goods that cause so much bother for LCA methods becomes clarified by the delivery of recommended nutrients. There are still many gaps in this approach concerning bioavailability, aligning with ecosystem services or planetary boundaries, but the Delta Model has made an essential start in making sense of a variable and leaky global food system by defining healthy boundaries. Scaling LCA and nutritional data to consumption spaces have always been challenging to do because of the risk of introducing far too much variability with the consumption behaviors of populations of millions of consumers. Mapping this type of data for urban regions has been considered impossible because the decision of what to consume is often made at the point of sale surrounded by thousands of other choices and planning such dynamics in a structured projection model is fraught with difficulties. The test of this type of scaling requires extrapolation for data, which many LCA experts get very uncomfortable with. Still, none the less we attempt this here because consumer goods businesses achieve it by using benchmarking, market insights, and national statistical data. Such commercial projections are refined by obtaining granular data of household behaviors using commercial consumer panels and focus groups that support marketing campaigns and product launches. The ability to use these methods have been tested in the sustainability arena to project consumption, food and beverage carbon footprints, and the waste impacts of products in diets. The tests have projected the effect of reducing meat in flexitarian diets, but the approach goes further and relates to the interaction of all consumers with a product; it is not just concerned with specific sets of consumers who wish to switch to a particular diet. If these projections can be built into product concepts, it will result in more resilient product development that is likely to succeed in marketplaces where many new manufactured food and beverage products do not achieve. Such an approach of building projections from Digital Twins at product concept stages is termed meta-NPD because it seeks to scale the impact of New Product Development (NPD) at population scale. The effect of post-farmgate food and beverage consumption will require these meta-projections that sum the results of all supply chain impacts at population scale because they can validate net-zero, carbon-neutral, or climate-neutral GHG emission options.

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This Digital Twin system, shown in Fig. 7.3, has been tested for regional a consumption space of 1.32 million consumers, and secondary data were used to calculate GHG emissions of food categories (Wallén et al. 2004). Where food categories did not align, the closest match was made, and the food categories were supplied fresh, omitting the value of the preservation, packaging, and utilization of foods and beverages. The consumption of a more sustainable diet containing 5% less meat and 10% more fresh fruit and vegetables was assessed as demonstrated by World Wildlife Fund and the UK’s Rowett Institute research (Macdiarmid et al. 2012; Macdiarmid 2013). This diet was benchmarked against the National Dietary and Nutrition Survey diet for the UK, where food choices were extrapolated from the Livewell Diet, and the consumption of both diets for 7 days was mapped across the selected population of the Sheffield City Region in the UK. The GWP data used for food is now well established, and the GIS-LCA hybrid approach in the Digital Twin allows the mapping of based LCA data with demographic data trends (Wallén et al. 2004). Figure 7.3 shows the Demonstrator, and how national census data sets and National Dietary and Nutrition Survey in the UK have been used to do this for the annual consumption of foods. It is essential to note that beverages are not included in these diet projections. The NDNS dietary scheme is projected for a meal plan that follows the Eatwell Plate. It follows the Livewell Diet plan for a reduced GHG emission outcome, increasing or decreasing specific food groups where necessary to align with the Livewell Diet. The consumption footprint for the population investigated results in a GHG emission of 1.08 million tonnes from a population of 1.32 million people. The Digital Twin shows 1.08 million tonnes of GHG produced each year for the National Dietary and Nutrition Survey diet consumption for 1.32 million people in the region analyzed, which is reduced by 5–10% if the Livewell Diet is consumed. If this level of consumption is scaled to 67 million UK consumers, the NDNS diet (the typical Uk diet) has a carbon footprint of 54.85 million tonnes of GHG emissions, which aligns with the UK national GHG inventory for the whole agri-food supply chain, which is 55 million tonnes of GHG. Initial Digital Twin of Consumption (DTC) demonstrator shown does not include beverages, and it does not consider the consumption of food outside of the home, the influence of preservation, or food wastage. Initial tests of the DTC show that diets resulting in lower GHG emissions will result in up to 25% more domestic food waste because they include greater amounts of fruit and vegetables which are at more risk of being wasted in the domestic household. These scenarios need further testing but the trade-offs between GHG emissions and waste production must be defined for food and beverage systems. The inventory provides a robust test for our GIS-LCA scenario building model using the UK GHG budget of 550 million tonnes. This Digital Twin framework has been used to map and calculate expenditure on purchased food from retailing. Again it is robust when scaled to the national population of 67 million UK citizens when it projects a cost of £ GBP 144 billion each year where the total expenditure and revenues from retail, wholesale, and service are £ GBP 163 billion annually. While purchasing gains are variable, the Digital Twin developed would indicate the method is robust and can be developed further. An essential goal of determining the consumption footprint of populations

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is to guide a net-zero GHG emission consumption space by demonstrating how it will be most efficiently achieved. What is apparent from the established practice of LCA in the food and beverage sector is GHG emissions are more incisively managed in the processing, manufacturing, distribution, and retailing functions of supply chains. This is because resources are metered and controlled across these functions, whereas the agri-production and consumption functions are not as finely controlled or metered. In agriculture, the weather confounds attempts to measure and quantify GHG emissions and consumption operates in arenas of consumer choice, which introduce increased variability. The targets for reaching carbon zero without offsetting or carbon crediting will require incisive methods of measuring the processes that result in GHG emissions across the food supply chain. This will require an index that can be used across food and beverage businesses. Figure 7.4a and b, has developed the Digital Twin to include measures of connectivity between operators in food supply chains. These are currently being tested and demonstrated for assessing food safety risks and food defense policies; the Demonstrators shown are for the dairy and fruit sectors. Their application offers much promise in identifying potential risk, resource flow dynamics, and most probable sources of resources in supply chains (Martindale et al. 2020a).

7.5 Building a Sustainability Index for Food and Beverage Manufacturing Building-in sustainability during product development requires guidance. There are established indices of food sustainability that operate at national scales that need to be embedded or re-scaled into business operations for this to happen. Without engaging all food businesses, a sustainable food system will not be possible. Hence, the measurement of sustainability needs to be embedded in the culture of an industry, and a sustainability index scorecard approach can help to achieve this. Testing the index shown here has identified attributes that can be assessed by collecting quantitative and qualitative data by food and beverage manufacturers. The universal use of such a measure would be necessary because the use of consumer goods is interwoven into all of the 17 SDGs and while consumer goods improve the lives of billions of people, an inevitable outcome of their manufacture is increased consumption and utilization of natural resources (Martindale 2017a). For many consumers, this is perceived as a net depletion of resources despite a legacy of sustainable reporting that demonstrates a circular economy is possible. Net-zero and carbon-neutral manufacturing is now possible but reducing consumption when much of the world aspires to increase it remains antagonistic. Food and beverage products provide specific issues where global indices of nutrition and food sustainability have already facilitated practical actions that improve food security. The methodologies to do this are applied to a national scale, and the methods demonstrated here can be used from product concept to consumer to achieve sustainable consumption goals. The idea of assessing the

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sustainability scope of product development ambitions within minutes is required by companies taking directional decisions daily and production operators making operational choices at each production period. This must be achieved expediently, and the ability to guide these decisions for sustainability attributes and consumer experience are essential to every food and beverage business. The current national index measurements have been developed so that their scope can be applied to food categories and be used by food processors and manufacturers for specific food supply chains. The focus is to apply these to decisions made across the food product development and manufacturing arena so that greater sustainability is an outcome. Current indices of food security and sustainability not only identify high-level policy risks, but they also focus on the production of agricultural commodities and do not provide detailed insight into the resilience of the manufacturing, distribution, and retailing functions of supply chains. This means that such static or state indicators remain of use as a snapshot of the food system, but the potential to collect data from the system all the time means there is a need for indicators that can assess food security, nutritional goals, and sustainability at the product development level. The established indices include the Global Food Security Index (GFSI), Global Access to Nutrition Index (ATNI), and Food Sustainability Index (Gustafson et al. 2016; Chaudhary et al. 2018; Haddad 2018; Chen et al. 2019). These have provided the platform in the sustainability index shown here so that the impact of these high-level indices is extended to individual manufacturers and product developers. In the UK, there are over 350,000 food businesses; the goal of the Sustainability Index is to be applied to improve security and sustainability in each of these food businesses when products are at concept, development, or manufacturing stages. The application at such an operational level must be open-sourced and available to food businesses because there are known barriers where current indices for nutrition, security, and sustainability exist, but they are only typically used by large food groups and companies. In an open data food system, access to methods for assessing food products’ sustainability must become available across the food system, and this is often limited by resourcing and skills. Certifications offer an example where the application can be limited because even though they have transformed practices, the potential of 100% certification has only been shown for foods such as bananas (Wilson and Jackson 2016). In others, such as chocolate and coffee, and increased market share is seen compared to non-certificated products. However, they still only account for less than a fifth of total food production in other markets (Grunert et al. 2014). In the case of the Marine Stewardship Council (MSC), 12% of wild fish caught (Lester et al. 2013); the Roundtable on Sustainable Palm Oil (RSPO) 19% of palm oil suppliers and the Roundtable on Sustainable Soybean (RTSS) 1% of the global production, are certificated (Rueda et al. 2017). The goal sounds simple; it is possible to embed responsible values in a process from the start (producer) to finish (consumer) in a specified supply chain and scale this activity so that there is a 100% responsible, ethical, and carbon-neutral shopping basket for a sustainable diet. The approach of relating nationally reported metrics to diets has been tested at a food category scale (e.g. fruits, vegetables, meat and so on) using the Centreplate Model with national food balance datasets, which operate at the scale of diets rather than national indicators and seeks to consider the realistic diversification of diets if

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they are only-plant derived or nationally sourced (Martindale et al. 2020b). It has exposed the importance of such tools in the NPD functions of food manufacturing companies because new protein ingredient categories are required to be available on a national scale. Applying the model has highlighted the ability to assess the value of NPD with respect to nutrition, distribution, and food loss, but the application to product development was identified as an area that could be further developed for sustainability outcomes. A transformative approach here was to consider how food and beverage products are utilized by consumers and use this to feedback data associated with utilization into the product development processes, so that product design essentially builds-in sustainability and weeds-out food waste. The measurement of meals being wasted provides important outcomes for consumption because if food is not fully utilized, any resources used to manufacture it are lost. It is this insight on the utilization of foods by consumers that is the core principle here, and it conveniently connects the sustainability attributes of nutritional improvement and waste reduction which are universally desirable impacts across supply chains (Martindale 2016). The development of the Centreplate model has identified the requirement for a robust and accessible analysis of sustainable product development used by food manufacturers and processors. This approach was developed as a model that used six attributes or functions that defined protein content of foods, distribution of products, the energy embodied in their production and processing, waste associated with their use, and GHG emissions (Martindale 2017a). What was explicitly crucial in these models was the use of meal concepts developed by chefs, and the method of categorization of meal types was derived from chefs and cooking books (one in particular by Jamie Oliver, The Ministry of Food, which arranges meal types in a way that is robust and accessible). The route to meal and categorization for nutrition and sustainability has been explored by others, including the Australian Total Well Being Diet that demonstrated it could be achieved, and it placed nutritional sciences into meal planning (Noakes and Clifton 2005). The study has shown possible applications and routes to simplification, where the GHG emissions of products are related to their nutrient density, risk of food waste, and distribution. The need for robust and accessible analytical tools in the manufacturing sector is vital because if sustainability is built into New Product Development, it will need guidance for every food and beverage product.

7.6 The Requirement for a Balanced Global Diet that Connects 9 Billion Consumers Best practice in the food and beverage industry has been transformed by sustainability. It resonates across industry and consumers as an ideal we should rightly strive to achieve. Much of what we have been aiming for is to reduce the greenhouse gas emissions associated with the production and consumption of foods. Manufacturers are now reporting carbon zero product categories, including whole milk and beef,

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which was unthinkable 10 years ago. Our improved understanding of how resources flow through food systems has made carbon zero a reality.1 Programs that sought to reduce greenhouse gas emissions 10 years ago exposed many gaps in our understanding of food systems. The initial debates tended to demonize food and beverage products with increased carbon footprints – namely livestock products and beef. What these studies do not consider was nutritional delivery and consumer experience, both of which are important because, without them, sustainability will never be delivered. This is because every meal must provide balanced nutrition and a favorable experience. If it does these two things, it is more likely it will not be wasted and result in optimal health. Carbon zero thinking has been transformative in breaking the deadlock between carbon footprint and nutrition with the launch of branded zero-carbon livestock products such as whole milk, beef, and lamb have shown that food producers and manufacturers are confident in claiming it.2 The subsequent re-thinking of carbon footprinting is enlightening because it can be related to achievable and nutritious diets and lifestyles so that responsible consumption is possible. Improvements mustn’t get lost in purely carbon footprinting diets. We are developing models for the UK that identify where critical points and connectivity in the food system control resource flows (Martindale et al. 2020a). New Product development (NPD) is the operational activity we are focusing on because if product developers and technologists build in sustainability at the concept stages, there is an increased possibility that the final product will deliver it (Jagtap and Duong 2019). One of our models- Centreplate- is currently being tested to NPD strategies, improving protein supply, and reducing waste (Martindale et al. 2020b). We are now at a point where food system insights have the potential to bring sustainability and nutritional datasets together because of two technological advances we would consider most notable. The first is the ability to embed digital technologies into resource packaging so that traceability and analysis of supply chain data can be enabled securely for most food companies (Martindale et al. 2018a). The other is the projection of the dietary impact of nutrition on populations where product development has a controlling role (Jagtap and Duong 2019). This changed forever a generation ago in response to the newly sequenced human genome and what followed was a scramble for therapeutics, but the interaction of health and nutrition through our diet was largely overlooked by all of this (King et al. 2017). We now have a greater understanding of how genes and metabolism interact with what we choose to eat. It is essential to keep the food system lens, which the Sustainable Nutrition Initiative’s Delta model does (Smith et al. 2020). Connecting datasets and making sure we speak to each other is becoming increasingly important. This is otherwise known as interoperability in

1 New

Zealand’s first carbon zero milk see, https://www.fonterra.com/nz/en/our-stories/articles/ new-zealands-first-carbonzero-milk.html, accessed 18th December 2020 2 CN30 overview see, https://www.mla.com.au/research-and-development/Environmentsustainability/carbon-neutral-2030-rd/cn30/, accessed 18th December 2020.

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the digital arenas, we can deliver a net-zero sustainable food system, but without interoperability, it will not happen. Our food future depends on all partners in the global network connecting methods and data that will guide sustainable dietary choices.

7.7 Conclusion The requirement to define and measure resource utilisation in food and beverage supply chains has a legacy of very clear commercial motivation to apply methods that improve optimisation and efficiency improvements. There has always been a different perspective in food and drink manufacturing than with other consumer goods because access to them is a human right and product quality has such a large impact on consumer experience. The influence of shelf life, product stability and packaging were exceptionally important among all of the usual drivers of preference that include the dominance of branding. This has traditionally made it incredibly difficult to measure the impact of resource flows in food supply and beverage supply chains beyond the commercial goals of financial optimisation. This worldview changed completely with the development of ecosystem services and green capital assessments by the Green Giants of the business ecosystem (Williams 2015). The importance of balancing production and consumption for sustainable outcomes became a values mission for many companies and this has since become one that reduces waste in all its forms. The methods introduced to assess this new approach provided added values to resource flows where there was a growing realisation that any solutions to optimising resource use in a globalised economy means a system-wide view is required. It has since been demonstrated that added value is obtained through collaboration and cooperation of not only materials or commodities but innovation, finance, people and business resources. These are all well characterised processes, they have been so for over 50 years and are what should be known has landmarks in our development of Digital Twins (May 2019). When theoretical ecologists first used computers to scale-up simple mathematical relationships between growth and resources to population level they began the route to developing the Digital Twins demonstrated in this Chapter. These can guide sustainable outcomes through the chaotic and complex outcomes scaling-up of meta-NPD in food systems. The issue in getting to this system viewpoint was always the methodologies used which the food system had now developed with LCA, and the instruments to deliver them to every food and beverage company, which the system did not have. That was until now, this Chapter has reported and demonstrated the digital tools that can assess and construct guidance from large data sets of resource flow in our global food system. The use of Geographical Information, Digital Twin approaches and assessment methodologies such as LCA have been tested for food and beverage resource flows here. What is changing in our food and beverage industry is the ability to package these tools into digital applications that can be accessed by millions of end-users in a secure way to enhance

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the credibility and trust associated with data. This has developed the blockchain and other technologies that were initially tested in finance sectors where trust was critical in digitized monetary transfers for example. They have become an application that is being used by food and beverage consumer goods companies which is transformative because it enables the transfer of resource flow information that may be relevant to specific stakeholders in a food or beverage supply chain e.g. carbon footprint information. The impact of these technologies is yet to be shown because even though the technologies offer an instantaneous assessment of food system inventory or an instantaneous audit of supply chain operations they are yet to become mainstream applications. The technologies that instantaneously assess how much resource is where in a global system are of high value to commercial operators and the high-level policy goals such as the UN SDGs for example. They offer so much promise and, as ever with any resource flow or supply chain focus event they will depend on collaboration and cooperation, the technology is in place but the frameworks of equitable collective, from smallholder groups through to global groups, are yet to be demonstrated. As ever, demonstration is critical to acceptance where the next few years will determine how such collectives in food and beverage can be made for worldwide resource use that delivers sustainable production and consumption. Funding This work was supported by the UEZ Business Incubation Development Support Food Enterprise Zones Project, Research England; and the Greater Lincolnshire Agri-Food Innovation Platform ERDF based at the National Centre for Food Manufacturing, University of Lincoln, UK.

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Chapter 8

Vertical Farming: Under Climate Change Effect A. Teoman Naskali, Ozgun Pinarer, and A. Cagri Tolga

Abstract The daily life of people is changing due to the increase in climate change effects. In the times of historic Covid-19 pandemic event, by the precautions like stay at home, people tried to obey those cautions. This kind of protections decreased the CO2 gas emissions by 17%, obtaining the world returned to 2006 gas emissions values. The air became more breathable and the nature began to repair itself without the touch of people. As the population grows, feeding this population became a separate problem. People started to destroy forest areas because more agriculture was needed for more nutrition. Of course, this destruction also had a direct impact on climate change. Humanity once again saw that it had to develop with nature, not against nature. This eye-opening process will force people to act quickly on what needs to be done for climate change. Even though the relatively less emission comes from the agriculture, this sector has to transform itself by new technologies due to its strategical position. Vertical farming alternative is a candidate for this conversion process. Many methods in vertical farming are handled in this chapter. Some cultivation methods as hydroponic, aeroponic and aquaponic systems are dealt also. Energy and water consumption, yield, and scalability criteria are examined for the vertical farming under climate effects. In addition, some newly technologies like artificial intelligence applied to vertical farming are presented in this study. To see the benefit of this agricultural method a feasibility and economic analysis had to be made, so it’s done with real data. Interesting results and inferences have been obtained and presented at the end of the study. Keywords Vertical farming · Climate change · Hydroponic agriculture · Micro-based vertical farming system · Machine learning · Artificial intelligence · Indoor farming · Vertical farming industry · Economic feasibility analysis · Cost reduction

A. T. Naskali · O. Pinarer · A. C. Tolga () Computer Engineering at Galatasaray University, Istanbul, Turkey e-mail: [email protected]; [email protected]; [email protected] © Springer Nature Switzerland AG 2022 C. M. Galanakis (ed.), Environment and Climate-smart Food Production, https://doi.org/10.1007/978-3-030-71571-7_8

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8.1 Introduction By 2000s, the world population was around 6 billion and the number of people per km2 was 41. Today the population has increased to 7.8 billion and the number of people per km2 has reached 52. The U.N. predicts world population to reach nine point eight million by 2050 with an increasing demand for food in the developing world and an increased demand for meat in particular. Besides, according to the FAA, we will need a 70% increase in global food production by 2050. World is facing major problems: limited sources, water problem, species loss, climate change. Meanwhile, land allocated to agriculture is decreasing to critical levels. Researchers have started searching alternative solutions to overcome these global problems. Since agriculture is one of the important domains, alternative solutions are extremely crucial for the future of agriculture. In this context, vertical farming is proposed as a concrete solution to these listed challenges. Traditional agriculture is one of humans oldest and most important innovations, a practice over several millennia old. Studies on urbanisation prove that a majority of the world population were involved in farming several decades ago, however recently, it is only 1% (Hodges and McKinney 2018). This immense loss of farmers clearly causes a problem feeding people. The amount of food acquired from agriculture will not be sufficient soon to feed the world population. The growth of the world population unfortunately does not provide high agriculture productivity, in contrast, size of agricultural fields are decreasing and the current fields loose their productivity and sustainability (Hazell 2018) due to land degradation. Besides, increased residential population, increase of non-farm business and industry cause a social problem: urbanization and increased urbanization causes a decrease in agricultural productivity (Iheke and Ihuoma 2016). There exists another threat for current agriculture systems: biofuel. Biofuels are basically liquid transportation fuels that include ethanol and biodiesel. Biodiesel is indeed a vegetable oil product. It is made from agricultural crops and residues. To obtain biofuels, new crops may be grown. Some agriculture fields can be reserved for biofuel production. Today, a profitable market for biofuels exists and is expected to increase by 2050. The global bio materials market is also expected to grow significantly. To fulfill these requirements, current agricultural fields should be increase up to 21%. On the other hand, due to the urbanization, existing farm lands are decreasing. Farmable land is set to be turned into an urban environment, with the global urban footprint expanding by 33%. Thus, an important question is arises: Does arable land to sustain this growth even exist? It is estimated that arable land can be created for food production in 2050 at the expense of consuming forests leaving almost no natural habitats for wildlife (Huang et al. 2019). Due to the reallocation of land for agriculture animals are losing their natural habitats and becoming extinct. This extinction process is accelerating rapidly, the World Wildlife Foundation estimates that 38% of land animals have gone extinct in the last 4 decades primarily because of habitat destruction (Labrum and Gomulkiewicz 2020). Besides, global deforestation is another big problem that

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needs to be overcome. Global deforestation in 2018 was 180,000 km2 . Freshwater animal populations have seen an alarming 81% reduction since 1970. Agriculture is the main contributor to this, primarily draining wetlands to create cropland. 50% of the world’s wetlands have disappeared since 1910. Water consumption for irrigation further depletes local water levels, damaging habitats. Agricultural runoff, such as pesticides, finds its way into local water systems, poisoning wildlife. Runoff fertilizer from the fields is a nutrient and some species can take advantage of this such as algae which over reproduce in blooms, draining the water of oxygen and creating dead zones, this upsets the balance of the ecosystem. More critically, to prevent mass starvation, there does not exist enough capacity in hand. Land requirements to keep the global population sustained points us to our next global challenge. Growing food, sustainability and the productivity of current agricultural system is crucial for every country. 40 years ago, China was a net exporter of food. Yet now it’s the world’s biggest importer of food by far (Huang and Yang 2017). All of the countries in the world, with the exception of the United States, have agreed to the Paris climate accords. The aim is to limit the global average temperature rise to one point five degrees Celsius this century against a baseline of pre-industrial levels. Preventing the temperature rise essentially focuses on reducing emissions of greenhouse gases (Long et al. 2018). All this data leads us to believe that we will run out of land in the near future. It appears that in the very near future forests and natural habitats will be converted to farming land and to cities. This will have a very negative effect on the environment and will accelerate global warming which in turn is expected to cause a rise in sea levels further decreasing the available land. The planet will also become more hostile from a crop growing perspective because of global warming and climate instability. If new technologies and techniques are not invented famine appears to be inevitable. To solve such crucial and highly important global challenges, vertical farming has recently become one of the hottest topics. The remainder of this chapter is organized as follows: vertical farming systems will be deeply investigated in the second section. The third section presents the alternative soil-less cultivation methods for vertical farming. The evaluation criteria are provided in section four. The subsequent fifth section puts forward the new technologies applied in traditional and also vertical farming. The sixth section consists of future, feasibility and economic analysis of vertical farming. Section seven gives example of Japanese vertical farms. Then conclusion and future remarks are given at the last section.

8.2 Vertical Farming A vertical farm is essentially an indoor farming method. It provides an unusual farming environment to grow food rather than being restricted to two dimensions. Plants are put on a grow tracks and they are stacked in levels on top of each other and make use of artificial lighting. Using artificial light and world conditions provides

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the ability to grow any plant independently from time and season. The combination of highly controlled growing conditions and optimal light provide a very predictable amount of output for a given area of land. The major advantage of such systems is to be independent of time and location. When the global challenges are examined, vertical farming is considered as a future style of agriculture that claim to solve one of the most critical aspects i.e. the lack of land. To clarify the ambiguity, in some researches, it is confused with urban agriculture where food is not grown in farms but in cities. In fact, urban agriculture handles the problem of transportation of the food to the cities. Hence, researches on the urban agriculture focus on moving the production fields close to the cities.

8.2.1 Vertical Farming as a Factory The scale of a vertical farming may vary to different dimensions: a small corner in a house to sustain a single family or a whole building as a farming factory to mass produce crops. There are few city skyscraper examples that are built to realize vertical farming. Such environments require a huge investment (Beacham et al. 2019). Figure 8.1 indicates the growth and the investment all around the world on vertical farming. On the other hand, since growing requires water and is used as a transport medium for major nutrients, vertical farming environments the installation of a pumped water system is required. From an economical point of view, every cost is important and should be considered. Besides the deployed system, an energy saving approach can help to improve the cost of growing plants in such environments especially for farming factories that contain hundreds of stacked layers.

Fig. 8.1 Investment and growth of vertical farming market (Vertical farming market 2020)

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Farming in living environments has started as roof farming and commenced as a hobby. However, these rooftop surfaces are limited. Researches prove that for a densely populated city such as New York, the rooftop farming utilized to the maximum capacity may supply only 2% of 2015 New York’s population (Despommier 2013). Besides, even if all the roofs in the world participated in rooftop farming, it can be equivalent to 1% of global land (Grard et al. 2018; Despommier 2011). Although there are several building designs intended for vertical farming, until now there are very few skyscrapers that are built for farming. For instance, a skyscraper vertical farming building is under construction in Linköping, Sweden (See Fig. 8.2). It is estimated that its initial cost has reached 40 million dollars and

Fig. 8.2 The world food building plantagon in Linköping, Sweden (Lauri 2017; BUSTAMANTE 2018) (a) Swedish Vertical Farming Building in Linköping (b) Inside view of the Building

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550 tons of vegetables can be produced in a single year. The main problematic of stacking vertically is providing enough light which is one of the primary necessities of plant growth and avoiding shadowing issues since a layered structure is utilized.

8.2.2 Use Case Japanese Vertical Farms Market researches prove that Japan has the most mature vertical farming industry (Benke and Tomkins 2017; Kalantari et al. 2018). Japan has a possibility to be installed over almost everywhere in any kind of size with any kind of producing crops. Based on the researches, there exists 165 vertical farms in Japan. When economical states of these factories are analyzed, only 25% of them are profitable and 25% of them can not profit and are in danger of bankruptcy. Although economical states of these factories are not so eye-opening, it is expected that the vertical farming industry will increase 1.15 billion dollars in 5 years and reach 13 billion dollars.

8.2.3 Vertical Farming in a Container System Besides plant factories, for less scale alternatives container system is provided in vertical farming system. This system provides 21 m2 area for cultivating, however vertical design ensures more space. By its modularity the system could be expanded, and could be moved to everywhere on the plant, all you need is electricity and some water. Electricity could generated from solar panel even in desert, so this is why this system alternative could be preferred among others. In addition, to gather the advantage of being close to the market this container system also could be utilized. This system could be utilized for cultivating fresh plants for restaurants, and even could be used for farming micro-greens and fodder for feeding the live-stocks. A container system alternative is presented in Fig. 8.3 which is the basic one; furthermore solar panel systems could be added to the system. Despite all cited advantages, the first investment cost of this alternative is very high due to it is hard to get the short payback period.

8.2.4 Micro-based Vertical Farming System As healthy lifestyles became popular among people, they began seeking a better way for their nourishment. Cultivating the greens, tomatoes or strawberries in their own indoors became for them not only a hobby but also a requirement. Microbased vertical farming alternatives like seen in Fig. 8.4 gained popularity among healthy life seekers. The system assures climate controlled cultivation process,

8 Vertical Farming: Under Climate Change Effect Fig. 8.3 Container vertical farming

Fig. 8.4 Micro based vertical farming

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moreover it provides whole year production cycle. By these advantages growing period diminishes and per meter square yield doubles in that case. Due to the relatively low initial investment cost, the system is mostly affordable for everyone. One of the effects of the climate change is drinking water decrease, so this alternative farming system utilization of 95% less water than outdoor agriculture may provide a little bit improvement in this direction. While the unit cost is calculated with the costly electricity consumption and LEDs of the micro-based agriculture system alternative, the system provides profit all year due to crop cycle and growth rate. Only one disadvantage of the system might be; you cannot cultivate whatever you want due to space limit for the system in houses, balconies or gardens.

8.3 Soil-less Cultivation Methods in Vertical farming Vertical farming concept usually implies a hydroponic cultivation system however two other techniques called aeroponic and aquaponic systems also could alternatively be utilized in this context. In addition, basically from the meaning of the word vertical, traditional agriculture methods can be used in vertical farming, yet conventional techniques in cultivation are inefficient in every dimension of the vertical farming concept. Traditional farming techniques are only economically viable at the moment due to the lower price of land and water. When the initial investment costs of vertical farming are taken in to consideration, the inefficiencies and lower crop yields of traditional farming techniques in vertical environments are not economically viable.

8.3.1 Hydroponic Systems Hydroponic agriculture is a method of growing plants in a water based system where a nutrient rich solution is used. Studies show that plants can get the nutrients and minerals they need from the water solution and plants use mainly as a support medium relying on rainwater to transport the needed substances. In this way, hydroponic systems emerge as the systems that provide the nutrients that plants need by means of water, without the need for soil (Resh 2016; Singh et al. 2019). Although the plants do not need soil and absorb all of the nutrients through the water solution, they still do need a medium that can mechanically support the plant also called grow medium. Some of the mediums also act like a sponge and or wick temporarily storing a small part of the nutrient solution or transporting the solution short distances. There are several types of hydroponics systems which differ mainly by the method nutrients and water is delivered to the plants. Drip hydroponics systems, as the name implies are simple systems that drip the nutrient solution on to the plants, the runoff is collected in the reservoir that is kept away from plants. A pump pumps the solution through drip irrigation pipes to the individual plants. The timing

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of the pump can be automated. On Flood and drain systems, the plants are placed on trays or tubs, and the tub is flooded by means of a pump up to the root line of the plants. Then the nutrient solution is slowly drained off by gravity back to the reservoir and cycle is repeated. The frequency and duration of flooding is tuned according to temperature, humidity and the water holding capacity of the grow medium that is used. In wicks hydroponics systems a good wicking grow medium such as vermiculite, rock wool, perlite or coco coir is used. Some more conventional materials such as propylene felt strips, fibrous rope, polyurethane yarn, nylon or cotton rope can be utilized. This system is not suited for larger plants due to the transport capacity of the wicking media. The other disadvantage of this system is that the wick can get a buildup of nutrients that can reach toxic levels around the roots, therefore it requires frequent flushing. In water culture hydroponics systems the plants are directly suspended in to the reservoir tank, usually the plants are setup on a float made of a buoyant material such as Styrofoam so that the roots are submerged in to the reservoir and the leaves grow on top of the float. Stagnation of the nutrient solution is not desired and plants benefit from aeration or oxygenation of the roots, therefore a pump is used to pump air in to the solution under the roots which also mixes the solution. The nutrient film technique system has a slightly inclined horizontal grow tray. The plant is placed in the holes on the reservoir and the roots of the plant are released into the water at the bottom. The reservoir is placed with a slight slope so that the aqueous solution circulates like in fish aquariums. Figure 8.5 shows a

C

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Fill Tube

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Drain Tube Electric Air Pump

B Water Flow Electric Water Pump Water 2-3 liter / minute / 24 hours A. Nutrient tank stores nutrient B. Nutrient water pump circulates nutrient C. Nutrient flows into grow channel

Airstone

220 Volt

∼

D. Nutrient absorbed by plant roots E. Unused nutrient flows back into tank

Fig. 8.5 Mechanism of hydroponic system (Hydroponic system basics 2019)

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basic hydroponic system architecture and nourishing film technique. As shown in architecture, a water pump delivers water and other nutrients from a reservoir system to this room. This water circulation is set to deliver water and other nutrients at the most convenient time to maximize growth (Kumar and Cho 2014; Sayara et al. 2016). Figure 8.5 illustrates the hydroponic farming mechanism. The biggest advantage of water-based systems is the increase in growth rate. Because plants do not need to expend energy to obtain nutrients and do not need to invest energy growing larger root structures, based on an optimal experimental setup, plants can mature 25% faster, besides provide 30% more production than traditional farming (Marques et al. 2019). In order to ensure efficiency in waterbased systems, the ambient conditions and pH level of the aqueous solution should be constantly monitored and intervened quickly when necessary. Also, since hydroponic systems carry out soil-less agriculture, no weed problems, pests or plant diseases that we encounter in traditional agriculture can occur. Apart from algae that may occur in the water tank, a negative impact from the outside is minimized. For this reason, it is not necessary to use any pesticides (Ferguson et al. 2014). However, the biggest problem of hydroponic systems that stands out as an alternative to traditional agriculture is cost. Since the vitamins and minerals that the plant should take from the soil under normal conditions are given through an aqueous solution, the chemical and physical values of the established environment and solution should be constantly monitored. The aqueous solution should be constantly monitored and the necessary settings should be made according to the type of plant and the growing phase. This causes the necessity to establish a special monitoring and emergency response system as well as the system’s own cost. For example, in the event of a pump failure, all plants can die within a few hours as plants need a constant water supply. Therefore, environment monitoring systems are of great importance in these systems (Sambo et al. 2019; Cambra et al. 2018).

8.3.2 Aeroponic Systems Aeroponic systems are also water-based much like hydroponic systems, and are based on the spraying of the aqueous solution to the plant roots at regular intervals. In these systems, the aqueous solution is sprayed into the plant roots in the form of mist or steam with the help of a pump. An ultrasonic fogger can also be utilized to generate a mist in the environment where the roots reside. The roots of plants also require oxygen. In traditional agriculture soil is modified to be more permeable to air for this reason. In nature worms help with the soils aeration problem. The architecture of the aeroponic system is shown in Fig. 8.6. Since it is water based and does not require soil and fertilizer, it is offered as a strong alternative to traditional

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timer plant plant supporting tray atomization nozzles plant holder temperature sensor humidity sensor nutrient deliyery line nutrient spray on root

plant root

light intensity sensor

water nutrient solution pressure pump nutrient suction line electrical control system control system

Fig. 8.6 Mechanism of aeroponic system (Lakhiar et al. 2018)

agriculture such as hydroponic systems. In traditional farming, the plants establish a developed root structure to obtain nutrients from a broader region compared to hydroponics or aeroponics. Also soil is relatively stable with regards to temperature and humidity. Since during their growth of crops optimize themselves to the new environment and since a problem in the water supply can have such a drastic impact on the environment of the plant, the values of the aqueous solution must be constantly monitored and rapid intervention is required when necessary.

8.3.3 Aquaponic Systems Basically, an aquaponic farming system is the combination of aquaculture (way of raising fish) and hydroponics system (AlShrouf et al. 2017). In this sort of farming, fish and plants are grown together in one integrated system. Fish eat their nutrition and excrete ammonia. Bacteria converts ammonia into nutrients that plants can absorb. Aquaponics farming system builds an integrated relationship between the animal and the plant environment. These two species share the same environment and maintain stable farming (Tyson et al. 2011; Palm et al. 2018). Figure 8.7 illustrates the mechanism of aquaponic farming system.

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Fig. 8.7 Mechanism of Aquaponic system (Sánchez 2014)

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8.4 Evaluation Metrics for Vertical Farming To be economically viable in the foreseeable future, plants growing in vertical farms ideally need the following characteristics: high edible mass percentage, low plant height to make better use of the vertical axis and fast growing cycles.

8.4.1 Energy Consumption While there are several factors that are a good measures for determining the current profitability of a crop, there is one fundamental barrier to being able to grow every crop type which is energy. Leafy greens do not require much light to grow as they are made of around 95% water, and they are entirely edible. Mass makes up most of the crop. Compared to rice, which provides the most calories worldwide, supplying 90% of global human calories. It is just 15% water that has a much lower edible mass percentage. Unfortunately, growing rice using artificial lighting would require about 30 times more energy than lettuce. Rice, grown in a vertical farm using current technology, will produce extremely expensive rice and have a significant energy demand. As seen from many researches on this domain, energy is the major constraint for plant factories and the overwhelming factor that dictates what plants can be grown economically. For this reason, energy constraint is the major challenge for vertical farms. In fact, each plant has its own luminous requirements. Crop types can be classified into three broad categories based on their approximate energy requirements: • Group 1: Leafy greens and herbs • Group 2: Vegetables, roots, pulses and ground fruits • Group 3: Staple crops, nuts and tree fruits When we compare these groups, Group 1 is considered as a starting point (base). Group 2 requires 2.5 times more energy per kilo than Group 1 and Group 3 requires 30 times more energy per kilo than Group 1.

8.4.2 Water Consumption Water is another important challenge for vertical farming systems. These 3 groups indicated above are highly water intensive. Pulses in particular have a relatively high demand for water. Growing this category in vertical farms would save a significant 23% of global fresh water consumption. This change alone could have a profound impact. Hydroponic systems employed in vertical farms can save 95% of the water if traditional farming techniques were used. In addition, as the nutrient loaded water

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is a financially valuable resource that is closely monitored and kept in reservoirs it is not wasted or dumped in to the environment causing problems. For these systems, it’s essential to apply enhance energy saving approaches. There are many studies on reducing the energy costs of vertical farms by understanding more about yield and how it relates to energy efficiency.

8.4.3 Nutrient Consumption As vertical farms re-utilize their feeding solutions there is very little waste. One of the important nutrients for plants and all living organisms is phosphorus. It is utilized in cell structures as well as DNA. Currently phosphorus is being mined and it is currently not a renewable resource. In conventional farming only 15%–30% of the phosphorus is absorbed by the plants as they are only capable of absorbing nutrients in 1 mm proximity to the roots. A majority of phosphorus contaminates underground and fresh water supplies. As modern farming techniques have broken the phosphorus cycle, the phosphorus used to create the plants and then feed people and animals is not returned to the soil. Estimates show that phosphorus reserves will be depleted within several decades at current consumption rates. Vertical farming can slow down the depletion rate of this non-renewable resource (Childers et al. 2011).

8.4.4 Yield Yield is one of the most significant indicators of a farm’s operational efficiency. While it’s relatively simple for traditional agriculture, there are numerous ways to measure yield in vertical farms. An outdoor field only has one level, so the land and surface area are the same for traditional agriculture. For a vertical farm, things are different. Doubling a five layer farm to a 10 layer farm, doubles the footprint yield, because it produces twice the amount for the same building footprint. Absolute yield remains the same however, because doubling the layers doubles the surface area. The numerical results of vertical farming appear impressive. As doubling the layers of a vertical farm, doubles the land saved from displacing field production. However, it’s important to remember that doing so would double the energy requirement. If solar panels are used to provide the energy for the farm, then doubling the energy would double the solar footprint. This should be factored in when comparing footprint yields. For most crop types, the area of additional solar panels is very small when we compared with the vast land. Plant factories can have an arbitrarily high footprint yield, because they can grow as with as many levels as are economically viable. Future vertical farms will likely have a footprint yield hundreds of times greater

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than the best outdoor farms, such farming environment with high footprint will allow them to leverage a significant economy of scale. While footprint yield is a valuable measure for farming, it’s not necessarily the best indicator of the operational efficiency for a vertical farm. After all, skyscraper farms would boast very impressive footprint yields. Yet would not be profitable. As stated before, the biggest challenge for plant factories is energy. If doubling the surface area doubles the energy requirement, then it makes more sense to use a measure that is independent of the number of levels in a farm. Due to the high investment cost, studies focused on how plant factories may increase the yield at a given level. Plant factories that grow produce faster, get more harvest per year and increase yield as a result. They can also grow plants close together in the horizontal dimension. This greater density increases absolute yield. Increasing the edible mass per plant also increases the yield that is achieved by growing a higher percent edible mass plants or by growing larger plants. Increases in absolute yield for given energy input leads to a higher energy efficiency as such, improvements in absolute yield vital for plant factories. Energy efficiency can be thought of as the most critical metric in vertical farms. The higher the kilos of edible mass per watt hour, the higher for energy efficiency. This metric can be considered the fundamental determinant of what crops can be grown in a plant factory. Not only is it essential for the profitability of current farms, improving this number has a direct result on how much they positively affect the global challenges like climate change.

8.4.5 Scalability of Vertical Farming Technology in this area is improving quickly. But what can plant factories do to reduce their energy overhead? The key is to maximize the kilos of edible mass per watts of light and also reduce the dollar per watt of electricity. So what changes can be made to improve this. Vertical farms can increase the yield of any given plant beyond what is seen in hydroponic greenhouses. And it’s not just because of their additional growing layers. They have much greater temperature, atmospheric and light control than greenhouses. This allows for superior growing conditions and waste elimination. Plants only absorb certain wavelengths of light. Using LED grow lights allows plant factories to use specific light recipes tailored to each plant, enhancing the energy efficiency. While a vertical farms, atmosphere, nutrient and light control already far surpass current growing methods. There are many opportunities to increase it further. Plant growth is complex and affected by many parameters. There is still a considerable amount of work to be undertaken to understand the optimal conditions for plants. Outdoor plants use changes in sunlight to determine when to grow and flower. Normally this is dictated by the environment but LED’s can emit different recipes of light at different growth phases of the plant. These light recipes can alter many of their characteristics. They can be tailored to increase the flowering portion, reduce the root growing phase and

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even control how the plant tastes. This allows plant factories to increase the edible mass percentage significantly. Energy a plant uses building none-edible structure is waste energy. This is inconsequential for sun grown plants but is critical to vertical farms. Field grown lettuce has about 40% edible mass when considering root systems and inedible outer leaves, while vertical farms have managed to achieve 92% edible mass. But that’s not the only advantage of light recipes. Since they can be used to trigger growing cycles, they can accelerate plant growth considerably. While field grown lettuce can be harvested twice per year, vertical farms can harvest up to 12 times per year. Even rice can be harvested about four times more often than when grown in a paddy field. While the edible mass percentage for lettuce is approaching its limit, that’s not necessarily true for other plants. Despite being a new industry, yield improvements are happening quickly.

8.5 Enabled Technologies in Vertical Farming To provide sustainability, vertical farms require a real time monitoring system with deployed IoT based wireless sensors and actuators: temperature, humidity, nutrient levels, luminosity, ventilation level etc. Because plant factories control the environment so effectively, it’s considerably easier to actively run experiments and interpret the data. Maximizing yield by the fine tuning of variables such as CO2 and humidity levels. Not only that, but due to having considerably more harvests per year, they have a lot more opportunities to experiment, collect data and learn. This allows for a learning rate that is a number of magnitudes higher than other growing methods. Hence, sensor data analysis and data engineering domains are also involved in vertical farming researches. Machine learning has also involved in proposed approaches. Despite this, it’s still at an early stage. A number of plants are poorly suited to vertical farms, due to low edible mass percentage, being ill suited to hydroponics, or being a tall crop. Since, current commercial outdoor crops have no need to consider these parameters they breed plant varieties that thrive outdoors and are often incompatible with vertical farms. Plant factories have different priorities and require different seed types as a result. There are many dwarf varieties of existing crops, that could be utilized. If they can match existing crop quality with a seed optimized for short height, hydroponics and high edible mass percentage, then the energy requirement for replacing existing crops could shrink significantly. Additionally, seeds can be bred for faster harvest cycles, not a requirement for most current crops. Many current crops sacrifice breeding for peak yield so as to breed for viral resistances. This isn’t necessary in vertical farms because of their sealed conditions. Unlike greenhouses, they don’t need to vent and are run like a clean room environment. These yield improvements alone can significantly reduce the energy gap for future crop types, but it’s not the only improvement available. This area has a huge potential for improvement, especially for plant factories that utilize genetically engineered seeds. Gene editing techniques are getting much

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cheaper and easier to implement. This has a lot of potential for both indoor and outdoor farming in the future. In the last few years LED lights have improved considerably. Special units are being developed specifically for indoor growing and their efficiency is anticipated to improve by 50% in the next decade. Efficient LED’s run colder, not only does this save electricity but allows them to be placed closer to the plant without risking heat damage. This allows plant factories to fit more levels into a fixed building height, increasing footprint yield. Closer positioning increases light penetration into the canopy allowing plants to be grown closer together and increasing absolute yield. It also reduces light bleed and increases light absorption efficiency, reducing energy requirements. Greater use of reflective bay materials, deeper penetrating green wavelength light and mid level bay lighting can further reduce the total energy requirements. It’s not just efficiency though. LED’s are increasingly capable of delivering a broader spectrum of light, allowing for greater control and yields. The cost of the units are also falling quickly, while the unit lifespan continues to improve. This will reduce the depreciation costs for future vertical farms, and is essential for improving their cost competitiveness.

8.5.1 Renewable Energy Technologies If there is one technology that could transform the potential of vertical farming, it’s renewable energy. It has the potential to solve the environmental and economic outlook simultaneously. Solar for instance is projected to half in cost over the next decade. This will bring its cost below traditional production methods in many areas of the world. Reducing the cost of electricity will be the final step, enabling vertical farms to grow a broader range of products. The future of energy production is looking very promising and is of critical importance to many global challenges, not just those related to vertical farming. If vertical farming can realize the extent of these improvements, their future energy demand can be dramatically lower and will be able to supply much cheaper produce than we see today. If renewable energy sources prove not to be as cost effective and environmentally beneficial as predicted, and nuclear energy proves to be a much cleaner alternative, then vertical farms have the option of shifting their lighting periods to the night. This will help with load balancing the power stations where at night power stations are seeing lower loads and are generally more inefficient.

8.5.2 Machine Learning and Artificial Intelligence Vertical farming shares a lot of aspects with modern farming techniques such as hydroponics, aeroponics, aquaponics, and smart farming. Farming technology and techniques have taken millennia to mature to their current state and most would argue that we still have a lot to learn. In today’s ever growing population and

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financial stresses on farmers, time is not a luxury that can be afforded. Plants have evolved over millions of years and are well adapted to their environments. Improving farm productivity is one of the main aims of farming applications and systems. Using machine learning (ML) techniques and improved artificial intelligence techniques may provide significant solutions to increase productivity and crop harvest. Although monitoring many aspects of the agricultural process is important to give an immediate response to problems or detect nutrient-deficient areas there is too much data to be analyzed. With machine learning and artificial intelligence, it is becoming possible to detect problems, infections or nutrient deficiencies with much greater speed and accuracy. As more data on the environment and plant growth is gathered, machine learning algorithms may be used in the modeling of plant growth, and feedback loops can be utilized to monitor and provide for the plants. This is especially important in vertical farming applications where the plants are grown in indoor environments and their entire interaction from feeding to lighting to CO2 levels to humidity to pollination are controlled by the farmer. For example, plants can tolerate only a certain concentration of nutrition in their feeding solution, and for optimal growth they need a certain nutrient uptake. The amount of uptake is closely related to the amount the plant can excrete from its stomatas which depends on the environmental temperature and the environmental humidity. Therefore, the nutrient concentrations must be strictly regulated according to temperature and humidity. What concentration and composition of nutrients at what stage of growth is another area that is being investigated. There has been a lot of progress made on determining the optimal conditions for plant growth but a unified system that also creates a model of the plant and determines the optimal parameters that have not been created as of yet. Machine learning can also be utilized to determine and predict the optimal harvesting times of crops. In vertical farms, the plants reside in very dense populations all sharing common resources passing through all of the plants or plant groups. This can amplify any problems such as bacterial, viral, and fungal problems. In vertical farms, it is vital to track the plants and immediately react to any problems. In such machine learning applications, support vector machine (SVM) and neural network (NN) are two well-known approaches. SVM approach uses a discrimination function to obtain classes. In these approaches, an unsupervised clustering algorithm such as k-mean is mostly preferred ML algorithm (Behmann et al. 2015). Moreover, artificial neural network (ANN) approaches can also be applicable on farming data. In literature, these various machine learning and deep learning models are applied in the farming domain for crop and yield management, disease, weed detection, species recognition, soil, and water management. Table 8.1 summarizes the existing machine learning applications in the farming domain. For the time being, all of these studies can be merged with decision making systems. Meanwhile, machine learning applications in farming have started to cover data analysis and big data studies since acquired data from the real time monitoring system is required to process and store.

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Table 8.1 Usage of machine learning techniques in smart farming Study Jhuria et al. (2013), Moshou et al. (2014), Bhange et al. (2015), Chung et al. (2016), Ebrahimi et al. (2017), Kim et al. (2018) Geipel et al. (2014) Coopersmith et al. (2014), Morellos et al. (2016), Nahvi et al. (2016) Lottes et al. (2017) Sa et al. (2017) Grinblat et al. (2016) Maione et al. (2016) Pantazi et al. (2016, 2017) Moshou et al. (2014)

Methodology Prediction model, neural Network, SVM classifier and image processing

Application Disease detection

Image processing and regression Multivariate regression models

Managing corn grain yield

Machine learning techniques CNN-based dense semantic classification CNN Classification Hyperspectral imaging LSSVM

Distribution of weeds and classification of crops Crop health

Soil management

Plant identification Geographic origin prediction Weed detection Plant stress

8.6 The Future of Vertical Farming Researches on vertical farming identified that energy consumption as the primary barrier to plant factories having a big impact on the world and presented how vital improving absolute yield is to improving their energy efficiency. Besides, several methods are proposed to optimize and to boost yield, lower their energy consumption, and take advantage of cheaper, cleaner electricity. Current LED’s achieve about an efficiency of approximately 200 lumens/watt. The theoretical maximum lumens/watt is 670 which indicates that there is room for improvement. One interesting research performed at MIT university achieved over unity efficiency in LED’s meaning more luminous power output than electrical power input was achieved, it was observed that the LED was capable of absorbing thermal energy and converting it to lumens. The LED’s were powered with 30 picowatts and produced 70 picowatts of light. Although promising the setup works in the picowatt ranges but has the potential to completely transform the vertical farming industry (Santhanam et al. 2012).

8.6.1 Vertical Farming Industry There are several research directions in this domain. One of them is to realize the proposed improvements. The question to be asked is when, where and how far these

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improvement can be achieved. Based on the currently employed technologies, it is hard to define a time interval. Although in theory, it is possible to grow everything everywhere, in practice, as seen from the previous sections, there are many factors that affect the yield and management. The economic viability of the industry will vary by crop and location at any given time. When we recall the groups indicated in Sect. 8.4.1, crops in Group 1 are already well underway in Japan for instance. However, it does not signify that these crops although they are basic, can grow in everywhere. There are specific environmental, physical and chemical conditions to obtain for every type of plant. With some research, by looking at what percent of vegetables a country produces as a ratio of it’s total vegetable consumption, it is possible to estimate where vertical farming industry may make the most economic sense. Commonly, countries that import high percentages of their vegetables, probably prefer, since producing it locally is difficult and expensive. Thus, it results in high market prices. Besides, the local cost of electricity should be considered, as it’s one of the fundamental costs for plant factories. High electricity prices would make food produced in vertical farms prohibitively expensive. From the crop types, crops of Group 1 is relatively small scale and energy requirements this carbon cost is low. To recall, Group 2 would require approximately 2.5 times the energy of Group 1. Based on the current economical status, Group 2 crops are more or less viable, although profits on non premium products will be very slim. Especially in Asia, vertical farming is very common. The emergence of Group 2 crops such as cucumbers, tomatoes, and even strawberries being produced commercially in these regions. Based on the current trend, if Group 2 continues its course and becomes ubiquitous worldwide, we will start to see significant global freshwater savings in the next 10 years. Thus, the global benefits of vertical farms become a lot more tangible. However, the energy problem remains important. Energy problem may become bigger if Group 2 farms reach a global scale. Besides, carbon cost may become prohibitive if renewable sources aren’t used in these systems. Compared to the Group 1-2 Group 3 has the greatest global impact but it’s also the hardest to achieve for this technology. A significant part of this group would be staple crops such as rice and wheat. Current production of these crops already benefit from a massive economy of scale and have small profit margins. Staple crops store well, thus neutralizing the freshness value that vertical farms provide. Additionally, staple crops are generally tall, which hurts the growing density advantage of plant factories. However, the biggest barrier of all is the 30 times greater energy requirement compared to leafy greens.

8.6.2 Feasibility Analysis: Ways to Reduce Costs Plant density and large energy costs are much bigger problems. To make Group 3 profitable, the energy cost per kilo must be greatly reduced. Wheat is the staple food

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in Turkey and the Mesopotamian region. Currently edible radiation use efficiency (ERUE) of wheat has been measured as 0.33 g/mol of light (Li et al. 2019). In order to grow 1 kg of wheat, 3030 moles of light are needed. Current CREE brand LED’s have been reported to have 2.2 μmol ∗ s−1 W−1 . This equates to 7.92 mol/kWh. From these values we can compute that 382.5 kWh of energy is required to produce 1 kg of wheat. With today’s electricity prices that equates to 275.5 Turkish Liras or 40.3 USD. These numbers are clearly not feasible. For the production of staple foods to be feasible the price has do drop by an order of magnitude. Currently wheat retails for 2 Turkish Liras/kg. In order to lower these prices several things can be done. First of all, grains can be stored for longer periods of time and they their freshness usually does not equate in to their prices. Therefore relocation of production to a location where cheap energy is available has the potential to lower prices. Iran for example has 1/9 the electricity price of Turkey. As growing wheat in closed environments makes no financial sense, it has not been done and not much expertise or data is available on it. Although there are some studies performed for NASA, optimization of light cycles, nutrients, light intensities and various other conditions have not been performed. Optimizing the conditions for wheat growth could substantially decrease the energy requirements to grow them. Furthermore, growing and harvesting times can also be accelerated again reducing the energy requirements. For example, for same amount of consumed energy, 33% yield increase signifies 25% energy saving (Banerjee and Adenaeuer 2014; Kozai et al. 2019). Dwarf wheat varieties can be utilized to reduce energy requirements. In addition, crops can be genetically engineered to further enhance yield and shorten the harvesting time. As vertical farms are closed systems, environmental changes are much easier to perform. Increasing the CO2 concentration in the growing environment will further provide gains. The advances in LED technology will again reduce the amount of energy needed to produce the crops. The price of renewable energy sources is decreasing constantly. A dedicated solar or wind farm could provide the necessary energy for the vertical farm. Furthermore, as the energy required is luminous energy, solar panels that absorb the non-photosynthetic region of light and reflect the rest can be utilized, reflecting or transporting the light through fiber-optic cables or light tunnels to the vertical farm. With these changes the energy consumption of vertical farms can decrease to almost competitive prices. In the perfect conditions indicated below, near 2030s, our world has a chance to have a highly resilient food production system. That scales easily to produce more food and not be vulnerable to climate, flooding, and pest damage. A system that will make food cheaper than it’s ever been before. It’s not the only change required to eliminate world hunger, but it will help significantly. Reducing agriculture’s global fresh water consumption by 91%, will have an even greater effect, both in terms of global water security and environmental impact. Hence, it may be possible to halt and reverse desertification, and drastically reduce most of agriculture’s large greenhouse gas footprint. We could further help reduce atmospheric CO2 by reforesting the 15% of the global land we are able to displace. And this will finally reverse the trend of mass wildlife depopulation we see today. For the global

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issues, the news coming all around the world is promising but more changes and technological improvements will need to happen to solve them entirely. Grown products are consumed by people but also are used for biofuel and livestock feed (respectively 9% and 36%). Meat takes a huge amount of water to produce, as the crops grown for the animals require a lot of water. Producing one kilo of beef requires seven kilos of crops to produce. This makes meat production extremely water inefficient. Meat production is also extremely land inefficient. 70% of all agricultural land is considered grazing land. Currently, foods such as soy and hay are used to supplement livestock feed. Meat production contributes a considerable amount of the global challenges but it’s largely due to its food import. Sprouted barley fodder is considered as an revolution in animal feeding. It can supplement a significant percentage of livestocks’ feed and can improve the health of the animals. It can be grown extremely cheaply in plant factories, with minimal labor, water, and electricity costs. In fact, the energy costs are so low, that it can be grown profitably right now, all across the world. While data for this market is limited, it appears to be growing quickly. If this opportunity is realized by businesses globally, it is highly possible to see an impact on the global challenges sooner than expected.

8.6.3 Economic Comparison of Agricultural Systems Feasibility analysis requires economic analysis at first. Then, to perform a decision analysis additional criteria could be found. As discussed before vertical farming system is a powerful alternative for traditional farming systems in case of climate change conditions. Besides it may repair the effects of climate change conditions due to less usage of scarce resources. For this recovery process vertical farming system has to be used mostly. So, it is necessary to make an economic analysis for all kinds of agricultural systems in yield performance to decide which one will be preferred by a majority in the next decades. All prices are accounted in Turkish Lira. Rates and other considerations are derived from the literature. Here we compare the prices of food production on what is available in the market during seasonal harvesting times. In completely controlled environments with artificial lighting there are no more seasons regarding the production of the plant or fruit. For example strawberries that are a summer fruit can be produced year round in fully closed vertical farms and they can be sold in the winter for a higher price. In vertical farms that utilize ambient light without significant light supplementation, i.e. those that resemble greenhouses rather than factories, the seasonality of the produce remains relevant. However, changes in nutrient composition and temperature can shift the cycle of the plant by a few weeks landing the harvest time in to a much more profitable market. In our calculations we do not take these considerations in to account. Here, we compared the conventional outdoor, greenhouse, and micro-based indoor vertical farming alternatives per 1 meter square. All the costs are reduced

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Table 8.2 Capital expenditures (CAPEX) for alternative farming systems CAPEX Items LED 10 W Air pump Controller ESP8266 Container Container top PU

Outdoor Unit cost ( ) Quantity 58.31 0 25.71 0 28.78 0 68.20 0 40.92 0 Subtotal

Greenhouse Vertical farming Costs ( ) Quantity Costs ( ) Quantity Costs ( ) – 0 – 8 466.49 – 4 102.85 4 102.85 – 0 – 8 230.24 – 4 272.80 4 272.80 – 4 163.68 4 163.68 0.00 Subtotal 539.33 Subtotal 1236.06

Table 8.3 Operational expenditures (OPEX) for alternative farming systems OPEX-Monthly Items Sprout Electricity-LED Electricity-The Others Solution (in L.) Water m3

Outdoor Unit cost ( ) Quantity 0.25 18 0.71 0 0.71 0 31.98 4

Greenhouse Vertical farming Costs ( ) Quantity Costs ( ) Quantity Costs ( ) 1.93 25 4.17 30 10.71 – 0 – 0.16 13.64 – 0.04 3.27 0.11 9.24

0 – 7.20 3.09 Subtotal 5.01

1.00 21.32 0.50 1.33 Subtotal 30.09

1.20 54.82 0.6 3.43 Subtotal 91.84

to a 1 m2 space to be able to compare the apples to apples. First investment costs are presented as capital expenditure (CAPEX) for both alternatives in Table 8.2. 5 years investment period would be sufficient for this analysis, and in addition due to less costs, no interest rate is taken into account. Financing through own equities are considered, and of course if one thinks to finance this investment via credit all the calculations could indeed change. In Table 8.3 monthly operating expenditures (OPEX) for all alternatives are introduced. As in CAPEX, here all costs are calculated in a monthly basis. Water costs 4.00 for greenhouse and vertical farming systems, however in an outdoor area it costs 1.00. Sprout, solution for plants, and water costs are computed according to growth cycles. Costs of electricity for LEDs, and for the other electronics/sensors are calculated in monthly based. As said before, investment period is 60 months, so we transformed this CAPEX into monthly based by dividing it to 60 months. And then we supplemented it to the OPEX to find the unit cost as presented in Table 8.4. In Table 8.4, the major differences between the farming methods are presented and the crucial impact of vertical farming is underlined. In May 31, 2020 the market clearing price of a lettuce is 4.75. Though this study is just for to show the differences among farming alternatives we just took the market price, in practice the seasonal average price and whole year average price should be taken.

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Table 8.4 Benefits of vertical factory farms versus other methods (Urban crop solutions 2020) Growth cycle (days) Water consumption per crop (L.) Number of crops per m2 Waste rate Unit Cost ( ) Crop cycles (days) Pesticides/herbicides Location Post harvest handling Market Clearing Price ( ) Yield / per m2 /year ( )

Outdoor 70 35 lt 18 20 % 0.65 140 Often Open field High 4.75 118.08

Greenhouse 45 15 lt 25 10 % 2.34 225 Less often Open field Medium 4.75 270.58

Vertical farming 21 1.5 lt 30 5% 2.62 365 None Anywhere Low 4.75 1053.32

To compute the footprint yield the net revenue value has been accomplished and we took account the crop cycles to calculate the whole profit, and we considered also waste rates to reach the exact data. At last, as can be seen from the Table 8.4 we achieved that the vertical indoor micro-based framing alternative provides the best profit. Of course short maturing process, low waste rate and possibility of whole year planting assured this result. Pesticides/herbicides non usage ensured more healthy benefits.

8.7 Conclusion It is expected that by 2050 world population will exceed 10 billion people with 68% of us living in urban city centers. This will generate various global challenges such as providing enough food for everyone in a sustainable, efficient and costeffective way. Due to the climate change, restrictions of seasonal weather patterns force researchers to find alternative solutions for agriculture. In this context, vertical farming has became one of the hottest topics. With vertical farming, studies aim to enhance yields for the future of food production. Vertical farms are modular and can be adjusted to fit in almost any building, for instance in an old laser tag area in New Jersey, USA. When the plant layers are stacked and with the special growing methods can lead to 75 times more products per square foot than a traditional farm. Controlled indoor environments disrupt the normal life cycle of pests. Thus, it does not need pesticides, herbicides, or fungicides. In these environments, energy-efficient LED lights are used to help crops grow indoors. A nutrient mist is sprayed on the plant’s roots, using 95% less water than traditional agriculture. As said before, for market-clearing price seasonality effect should be calculated to access exact data for the future studies. Renewable energy or smart farming cost computations could be performed as well.

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Although much development is still needed for vertical farming technologies to mature, vertical farming has the potential to solve the world food problem and become a major tool for fighting climate change.

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Palm, H. W., Knaus, U., Appelbaum, S., Goddek, S., Strauch, S. M., Vermeulen, T., Jijakli, M. H., & Kotzen, B. (2018). Aquaculture International, 26(3), 813. Resh, H. M. (2016). Hydroponic Food Production: A Definitive Guidebook for the Advanced Home Gardener and the Commercial Hydroponic Grower. New York: CRC Press. Sambo, P., Nicoletto, C., Giro, A., Pii, Y., Valentinuzzi, F., Mimmo, T., Lugli, P., Orzes, G., Mazzetto, F., Astolfi, S., et al. (2019). Frontiers in Plant Science, 10. Sánchez, H. J. A. (2014). Aquaponics and Its Potential Aquaculture Wastewater Treatment and Human Urine Treatment. Ph.D. thesis. Santhanam, P., Gray Jr, D. J., & Ram, R. J. (2012). Physical Review Letters, 108(9), 097403. Sayara, T., Amarneh, B., Saleh, T., Aslan, K., Abuhanish, R., & Jawabreh, A. (2016). International Journal of Plant Science and Ecology, 2(3), 23. Singh, R., Upadhyay, S. K., Aggarwal, D., Sharma, I., & Prasad, N. (2019). Bio Science Research Bulletin, 35(2), 59. Tyson, R. V., Treadwell, D. D., & Simonne, E. H. (2011). HortTechnology, 21(1), 6. Urban Crop Solutions. Technical report (2020). Accessed: 2020-05-05. Vertical Farming Market to Grow at Significant Rate of Around 25% in Coming Years Forecasts Acumen Research and Consulting. Technical report (2020). Accessed: 2020-05-05.

Chapter 9

Challenges and Opportunities of Digital Technology in Soil Quality and Land Management Research Vincent de Paul Obade, Charles Gaya, and Paul Thomas Obade

Abstract Soil being a unique life elixir determines ecosystem health and success of sustainable practices. However, despite available suite of indicators, sustainable practices are constrained by unreliable yet conflicting information attributed to model abstraction, scale mismatch and non replicability. Besides, stale scientific evidence precludes its relevance to management or policy. It remains a non-trivial task to determine a universal soil quality metric applicable in diverse ecosystems because of the difficulty in modeling soil multifunctionality and complexity across scale and time. The advent of digital technology which incorporate high performance computing systems with cellular communication devices, coupled with agile earth observation systems, creates new prospects for monitoring, demistifying and unravelling the specific drivers of soil quality dynamics. It is increasingly feasible to integrate multiple (i.e., qualitative and quantitative) datasets to inform site specific application of inputs thereby reducing costs, and simultaneously communicating scientific knowledge in real-time to stakeholders. However, fully operationalizing such initiatives remains elusive due to anticipated uncertainity in complex modeling systems. Here, the value addition presented by digital technology in soil and land management research are explicated. The “whole to part” mapping template to guide sustainable land management is revisited, and scientific techniques discussed include multi-criteria analysis intended to: (i) screen-out sensitive variables to any complex-problem, (ii) hierarchically rank significant variables, (iii) investigate the interrelationships of the facets, and (iv) synthesize information at various scales to explore viable solutions.

V. de Paul Obade () BioResource and Agricultural Engineering Department, Cal Poly San Luis Obispo, San Luis Obispo, CA, USA e-mail: [email protected] C. Gaya Department of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology, Juja, Kenya P. T. Obade Department of Environmental Science, Kenyatta University, Nairobi, Kenya © Springer Nature Switzerland AG 2022 C. M. Galanakis (ed.), Environment and Climate-smart Food Production, https://doi.org/10.1007/978-3-030-71571-7_9

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Keywords Diagnostics · Land and soil quality · Digital technology · Resource management

9.1 Introduction Land not only remains a vital engine for economic development but is also integral towards addressing twenty-first century challenges such as food, water, energy, security, or lessening ramifications of abrupt climate change (de Paul Obade 2019; Kamilaris et al. 2017; Lal 2019). Climate change generally attributed to anthropogenic actions, entails permanent shifts over say 30 years on weather variables (e.g., temperature, humidity, precipitation wind, or atmospheric pressure) from the average, a situation that triggers extreme events, such as drought, floods, storms, and strong winds. Alternately, climate variability refers to the short-term fluctuations over time scales ranging from months to decades, falling between the extremes of daily weather and the long-term trends (Lal 2004, 2009c; Lal et al. 2012). Climate change exacerbates the frequency and magnitudes of adverse repercussions that trigger migration and conflict. Approximately 12% of global population are malnourished. It remains a daunting task to strategically increase food production by the estimated 75% for the approximately 11 billion people expected on earth by 2050 (Lal 2013, 2018, 2019). Apart from global resource depletion, dwindling arable land and changing lifestyles driven by the spiraling population and affluence, other synergistic effects related to resource overexploitation include land degradation and erosion, deteriorating infrastructure, pollution, biodiversity decline, outbreak of epidemics and pandemics (Lal 2019). However, it is not exactly known whether COVID-19 disease could be directly transmitted or spread through contact with soil, or by water intake. Besides, no study has been conducted to link the soil quality, diet, or even organic agriculture in developing countries (e.g., Africa) with the prevalence and fatalities of COVID-19, although data (https://www.worldometers.info/coronavirus/) shows that developing countries have been least affected by this pandemic (i.e., fewer fatalities). Soils which are critical to ecosystem health are occasionally under threat of being degraded by a complex array of stressors varying in intensity, space and time, with anticipated deadly consequences if unchecked (Lal 2009b, c, 2013; Lal et al. 2012). Thus, scientific techniques are required that generate early warning information to support environmental regulation and prioritization of counter measures against the driving forces causing degradation. Digital or data driven technologies holds sway not only in synthesizing complex variables, but also in lessening the nuance or subjectivity of existing monitoring efforts and disseminating timely information to support proactive management (Boubin et al. 2019; Herrick et al. 2017; Kamilaris et al. 2017; U.N. 2019; Weersink et al. 2018a). Oftentimes subjective opinions based on expert judgement take root when assessing soil or land quality, thereby enhancing the risk of feigned diagnoses and incorrect conclusions. Technological advancements, create pathways to designing

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Food, water, air, soil security and climate smart management goals by: Scientific guided judicious land and waste management Carbon sequestration (4 per mille - COP21) Explore promise of digital technology e.g., GNSS, GIS, digital cameras, UAVs, bigdata and telecommunications, “internet of things” and autonomous systems

Ecosystem Services Biomass production Biodiversity conservation Erosion control Pest and disease control Water quality and supply Climate regulation

Understanding Soil Functions Element cycling Biomass production Regulate micro climate Pollution buffer and water purification Biodiversity conservation Source of raw materials Cultural heritage

scientific information to determine magnitude of response & guide Policy

Threats indiscriminate land management and resource depletion soil erosion and compaction organic matter reduction pollution through excessive inputs biodiversity loss salinization flooding and landslides poverty, hunger, famine, diseases, civil strife

Fig. 9.1 A schematic illustration of driving forces and pertinent information guiding sustainable intensification and climate smart agriculture

objective yet synthesized metrics that integrate detailed with-in field variability maps and ancillary data (e.g., population demographics, incomes, health, soil moisture, water, energy, labor, fertilizers, and pesticide requirements). Figure 9.1 schematic depicts the interconnection between soil systems, technology and climate smart sustainable agricultural practices. Against this backdrop, the question then are: (i) what are the knowledge gaps and challenges posed by digital technology?, and (ii) what are the new technological innovations required for sustainable intensification? Transformations in the field of spectroscopy, variable rate technologies (VRTs), global navigation satellite systems (GNSS) and data manipulation e.g., network analysis, structural equation models, clustering, principal component analyses, machine learning, opens new prospects for innovations on integrating multidisciplinary technologies to support climate smart agricultural systems. In-situ field data is not only multivariate but occasionally varies in acquisition time, quality and quantity, thus may exhibit significant uncertainties thereby providing unreliable information. Although ignored or generalized in most models, soil organic Carbon

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(SOC), a proxy of soil quality should constitute baseline data. This is because soil quality determines soil functions such as supporting biodiversity, biomass production, climate regulation, elemental cycling, water purification and circulation. The magnitude and rate of SOC depletion are exacerbated through soil erosion, leading to repercussions that include decline in soil structure, depletion of plant nutrients, change in soil temperature and moisture regimes which catalyze mineralization. Alternately, soil degradation can be minimized through conversion to restorative land uses, e.g., by adding crop residues or enhancing the SOC pool. Restoring the SOC pool increases soil aggregation, elemental cycling, soil biodiversity and reduces erosion (Lal 2009a, 2013). On the other hand, land evaluation evaluates land performance based on the correlation between agricultural production vis-àvis soil quality, water availability, climate, topography, agro-ecological aptitude, socio-economic and environmental aspects (Fig. 9.2) (Arshad and Martin 2002; Bünemann et al. 2018; Nguyen et al. 2015; Rosa 2005), to identify the optimal land utility based on inherent land use options (Bünemann et al. 2018). Developing climate-resilient agriculture requires prudent SOC management, and scientific data that can be transformed into practical/usable knowledge and roadmaps or action plans (Lal 2014). Indiscriminate land management that hamper agro-ecosystem health are stimulated by: (1) technology without wisdom, (2) humanity without conscience, and (3) education without relevance (Lal 2009b, c). Because of the strong interconnection between soil and land quality, this entry begins by outlining emerging management issues, followed by techniques for untangling individual drivers of land degradation, and exploration of connectivity with future scenarios, and identifies gaps or knowledge requirements to generate credible information from digital agricultural technology. Further, knowledge gaps are identified to support innovations in digital agricultural technology.

9.2 Emerging Issues Figure 9.2 depicts the interconnection between soil and land quality determination. The site specificity of soil quality dynamics is perhaps the most significant challenge when designing robust models for predicting future climatic change effects. The soil is a complex medium that is approximately 50% solid, with 25% liquid and gaseous phases, respectively. The solid consists of mineral and organic matter, whereas liquid contains solutes (e.g., water) which facilitate nutrient circulation, and the gaseous or air compartment with O2 , CH4 , CO2 , N2 O etc. The soil texture categorizes the mineral proportion of the soil (in %) as either sand, silt, clay or mixture. The particle sizes chronologically from largest are of the order sand > silt > clay. Thus, clay soils exhibit a larger surface area compared to silt or sand. Texture determines soil functions, but does not significantly vary with management or climate (Askari and Holden 2014; Bonfante and Bouma 2015). In contrast, SOC is not only critical for adaptation strategies which adjust natural or human systems to minimize the adverse impacts of abrupt climate change, but also for mitigation

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Land Management Stakeholders: Policy Makers, Engineers, Managers, Environmentalist, Scientist, Educators, Farmers Land Evaluation Soil Quality

Qualitative (e.g., visually) Soil color (i.e., the darker the soil the higher the quality and vice versa) Soil tilth (clods for good soils disintegrate easily on tillage) Compaction( no hard pan for good quality agricultural soils) Water infiltration and drainage (good quality soils are well drained, have no slaking, surface sealing and have low erodibility) Land use and management

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Integrates Water andSoil Quality Hydrology Parent material Climate Topography Vegetation Socio-economics

Quantitative (field, laboratory, analytical) Soil physical, chemical, biological and ecological attributes (e.g., SOC, texture, bulk density (ρb), waterholding capacity, pH, aggregate stability, electrical conductivity (EC), earthworm count, soil depth Soil erosion Pollution level Soil tensile strength/stability for civil works/construction Soil Functions (e.g. agricultural productivity –yields, water purification, biodiversity conservation, elemental recycling, climate regulation) PedoTransfer Functions (PTFs) derived from linear or multivariate regression etc.

Hybrid Tools and Methods: USDA soil quality test kit and interpretive guide Cornell Soil Health Testing Digital Technology (internet of things plus cloud computing, spectroscopy, multi scale models e.g., SWAT etc.) Global Navigational Satellite Systems (GNSS) and GRACE for soil moisture mapping Unmanned Aircraft Systems (UAS)

Fig. 9.2 Overview of data and methodological flowchart showing interconnection between soil quality and land evaluation. SWAT represents the Soil and Water Assessment tool (de Paul Obade 2019; de Paul Obade and Lal 2016a, b; Kamilaris et al. 2017; Kamilaris and Prenafeta-Boldú 2018)

strategies which refer to measures taken to reduce the GHG emission through sequestering C. In essence, well formulated climate adaptive strategies contribute to poverty reduction and environmental quality (Doran and Parkin 1994; Doran and Zeiss 2000). Developing climate resilient agriculture requires prudent management of blue and green water. Blue water is precipitation and includes fresh surface or ground water, whereas green water is the soil water, which plants intake and transpire (de Paul Obade et al. 2013a, 2014; Lal 2015a, c). In saturated conditions water flows in response to gravity through large soil pores. However in unsaturated soils, the water within the soil matrix moves in any direction depending on the energy potential and moisture gradient (i.e., wet to dry soil). Plants absorb nutrients from soil by osmosis through the semi permeable root membrane. “Gray” is a byproduct contaminated waste water from domestic use, urban or industrial discharge; whereas virtual water

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encompasses water contained in commodities (e.g., agricultural or industrial) that is traded across international boundaries (DIA 2012; Lal 2015c). Soil moisture content is gravimetrically determined through oven drying a soil fraction at 105 ◦ C (Topp and Ferre 2002). Alternately, water retention is determined by a combination of a tension table (Blanco-Canqui and Lal 2007; Clement 1996), and the pressure plate extractors (Blanco-Canqui and Lal 2007; Klute 1986; Klute and Dirksen 1986), with available water capacity (AWC) computed as the proportional difference in volumetric water at Field Capacity (FC) (− 33 kPa), and at permanent wilting point (PWP) (− 1500 kPa) (Dane and Hopmans 2002; Jemai et al. 2013). Another critical soil attribute is the soil bulk density (ρb ). ρb =

Mass of dry soil g/cm3 T otal soil V olume

(9.1)

The ρb is assayed using core method without roots and stones, and is inversely proportional to porosity. This implies that soils with a high bulk density are less porous therefore contain less air spaces and water. Soil structure is a critical soil attribute that can significantly be transformed by vehicular traffic density and load, crop rotation, organic matter and residue management. Although the terms soil quality and soil health are generally assumed to be interchangeable, soil quality refers to the capability of the soil to produce biomass and support living things without jeopardizing environmental quality, whereas, soil health encompasses the living nature of soil considering its potential to sustain healthy plant growth while simultaneously maintaining soil functions (Doran and Parkin 1994). Soil quality evaluation remains a subjective science because no universal soil quality metric exists. This quagmire is hypothetically attributed to the spatial temporal complexity of soil attributes, soil multi functionality, vague baselines, conflicting historical land use information, data artifacts and fuzzy soil quality definition.

9.2.1 Soil Quality Dilemma Although defining quality soils is challenging because this depends on the purpose for which the soil is used, there is no doubt that degraded soils limit productivity not only because of nutrient deficiency but also through pollution, erosion and biodiversity decay. Soil quality dynamics controls the biotic and abiotic attributes including the food web. Nutrient deficiency impedes microbial activity (bioturbation), and air circulation. Excess soil water is also problematic, because this enhances flooding and landslides, causes soil elemental imbalance, pathogen epidemic and related feedback effects. Besides other ramifications include nitrate denitrification through which greenhouse gases (e.g., CO2 , N2 and CH4 ) are released into the atmosphere. Excessive Phosphorus (P) and Nitrogen (N) from agricultural runoff stimulates

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algae blooms in water reservoirs and cause eutrophication and hypoxia as witnessed periodically in the gulf of Mexico, or the Great Lakes in the boundaries of USA and Canada (de Paul Obade and Moore 2018). Hypoxia impacts on the tourism economy because the water is non swimmable and aquatic life are killed (Toni et al. 2019; Venegas-Li et al. 2019). Sedimentation in water reservoirs reduces sun light and visibility thereby impacting on aquatic metabolism and photosynthesis. Alternately, poor pesticides use enhances pollution risks and lowers crop yields. Thus, soil quality indicators can improve the understanding of ecosystem processes and bridge the gap between information available for scientific and regulatory authorities (Zahm et al. 2008). It is, therefore, imperative to screen out credible soil quality indicators before irreversible environmental consequences occur. To do so requires the technological development and strategies that support sustainable agricultural intensification. It is important to adhere to manual guidelines concerning precise rate and timing of pesticides or fertilizers applications. As a precaution, pesticides should not be applied if the weather forecast heavy precipitation or strong wind currents. Alternately, judicious crop residue management requires at least 30% residue left in soil surface to support soil functions. Crop residues or plant litter control surface energy balance, sequester carbon minimize soil erosion and replenish soil nutrients accounting for 40, 10 and 80% of Nitrogen, Phosphorus, and Potassium of recycled natural fertilizer in U.S.A (Blanco-Canqui and Lal 2009; Blanco-Canqui et al. 2006; Doran et al. 1984). Excessive harvesting of surface crop residues driven by the insatiable demand for bio-energy products could have negative repercussions on micro-climate and soil quality (Blanco-Canqui et al. 2006; Doran et al. 1984; Fargione et al. 2008). Thus, inexpensive yet automated techniques for monitoring surface residue cover are needed, to provide timely information on soil quality dynamics (de Paul Obade and Gaya 2020; Fargione et al. 2008). This is because the commonly used roadside surveys and “walk in the field” methods for assaying surface residue cover such as the line transect, are non-comprehensive, are tedious, expensive, time consuming, and error prone (de Paul Obade and Gaya 2020). Other strategies for enhancing soil quality, include: (i) managing waste through reusing, recycling, avoiding maladaptation, and (ii) terracing, no-till or minimal till with manure incorporated into soil, cover cropping, crop rotation, and tile drains to drain excess water.

9.2.2 Current Approaches for Soil Quality Assessment Soil quality is routinely gauged; (i) by expert judgement or human visual interpretation whereby darker heavier and sticky soils are assumed to be of high quality, (ii) by a soil test kit, or (iii) by score-based models such as the Soil Management Assessment Framework (SMAF) (Bone et al. 2014; Karlen and Stott 1994; NRCS 2012). The soil test kit tests and interprets isolated soil attributes, whereas visual assessment adeptness depends on ambient light, and the subjective SMAF ‘scoring

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functions’ are based on perceived graphical relationships determined by consensus or literature review (Karlen et al. 2008; Wienhold et al. 2004). Recently, onfarm derived indices to assay sustainable intensification have generated mixed interpretations (Coyle et al. 2016; O’Sullivan et al. 2015; Schulte et al. 2014; Wienhold et al. 2004). Soil properties have been modeled from environmental predictors or covariates, abbreviated as scorpan factors, comprising (1) s: soil, other or previously measured attributes of the soil at a point; (2) c: climate, climatic properties of the environment at a point; (3) o: organisms, including land cover and natural vegetation; (4) r: topography, including terrain attributes and classes; (5) p: parent material, including lithology; (6) a: age, the time factor; (7) n: space, spatial or geographic position (Grinand et al. 2008; Lacoste et al. 2014; McBratney et al. 2003). Detailed descriptive statistics include measurements of sample size, mean, standard deviation, coefficient of variation, minimum and maximum values, median, the median absolute deviation, and so forth (Batjes 2011, a; Zornoza et al. 2007). Repeatable sampling design are needed to comprehensively determine the soil property characteristics over the entire study area (Grinand et al. 2008; Louis et al. 2014). Examples of sampling designs include simple random sampling, stratified random sampling, systematic random sampling. Simple random sampling is considered a reference method because of its simplicity, and the fact that data is randomly selected irrespective of location. The major caveat in simple random sampling is that some parameters may be missed or data gaps may occur. Alternately, stratified methods are deliberately divided into separate grids, groups or strata to precisely analyze variables of interest (Louis et al. 2014). Stratification, irrespective of whether applied as stratified random sampling or systematic sampling, usually involves the definition of strata, and the statistician needs to decide on stratum size, orientation and starting point for the grid overlay. Typically, the starting point is randomly selected, and the effect of the starting position on the computed sample variance assumed to be miniscule. This is expected to suffice, unless there are strong directionalities or anisotropic effect, as may occur in valley topography (Wang et al. 2012). Parametric and non-parametric methods are applicable in deciphering complex soil information. Parametric methods can be applied in multivariate regression to sequentially screen out significant predictors, for instance using 3 different approaches: (i) forward selection which starts by selecting predictor variables that best fit the equation, and then adds predictors one at a time based on the weight and statistical significance, until there is no other significant predictor, (ii) backward elimination which begins with the full model but successively deletes nonsignificant predictor variables in each iteration, and (iii) the stepwise method which deletes one non-significant predictor variable at each iteration stage, the reverse of the forward method. Parametric statistics require the following assumptions to hold true: (i) independence of observation, (ii) linearity, (iii) additivity of effects, (iv) homoscedasticity, and (v) normally distributed errors (Chong and Jun 2005; Mehmood et al. 2011, 2012). Because these assumptions are rarely satisfied, nonparametric techniques such as principal component analyses (PCA), partial least

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squares (PLS) regression, decision trees suffice, because these are parsimonious and do not require data transformations. PCA which is an interpretive multivariate technique, simplifies data, eliminating multi-collinearity, and ranks the variables, according to weight. On the other hand, the PLS referred to as “soft modeling” evaluates covariance, and minimizes errors by synchronizing information in both the predictors and response. One way to assess accuracy in PLS is through cross validation (Mehmood et al. 2011, 2012), which entails: (i) removal of one observation from the dataset, (ii) estimating this value using remaining observations, (iii) computing the error based on difference between the observed and predicted variable, and (iv) repeating this process for remaining observations (de Paul Obade and Lal 2013, 2016a). Decision trees are data mining, machine learning, and rule-induction algorithms that classify data by inferring interconnection between a dependent variable and a set of predictors. They consist of nodes and leaves, with each node representing an if-then statement, and the leaves act as terminal nodes, where a decision is made according to the information presented by the class variable (Breiman et al. 1984). A tree based model assesses all predictors so as to establish the optimal rules for splitting a node, where the within node homogeneity is maximized. These splits are dependent on the specific tree-splitting algorithm utilized. The classification tree provides a categorical outcome, whereas the regression tree provides a continuous one (Breiman 2001). Decision tree modeling are propitious because: (i) it analyzes non-parametric data; (ii) it is insensitive to missing data, inclusion of irrelevant predictors, or presence of outliers; (iii) it effectively analyzes numerical, ordinal, binary, and categorical classes; and (iv) it is well suited for synthesizing complex hierarchical relationships between predictors and response variables (Heung et al. 2014). Decision tree models trees have been applied in soil property and predictive mapping (McBratney et al. 2003, 2014; Mulder et al. 2011). Decision tree models are categorized as classification and regression trees. Classification trees are ruleinduction algorithms that group data to unravel their predictive structure and can handle quantitative or categorical predictor variables or both; for example soil type, topography or descriptive terrain attributes (Heung et al. 2014). A classification tree model for soil mapping utilizes a hierarchical subdivision of soil property predictor variables to represent soil types (Oliver and Webster 2014). Classification tree analysis are pertinent for soil-landscape modelling applications, because they are non-parametric, thus no apriori transformations are required (McBratney et al. 2003). Thus classification tree avoids variable transformation caused, for instance, by bi-modal or skewed histograms, which are frequent in soil class signatures; and are non-sensitive to missing data, perform automatic variable subset selection, are insensitive to the inclusion of a large number of irrelevant variables, and can handle quantitative and qualitative data, making it feasible to fuse data of various formats (Davis 1987; Grinand et al. 2008). A metric is only useful if it can be unequivocally interpreted and optimal standard reference values are available. Because soils are “living” multifunctional entities constituting a solid, liquid and gaseous phases (Fig. 9.3), simply selecting an

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Physical bulk density (ρb) and porosity water retention (AWC), and hydraulic conductivity (saturated) soil moisture regime soil temperature structural stability and aggregation (AWD) erodibility soil tilth texture

Chemical pH Electrical Conductivity (EC) Soil organic Carbon (C) Total Nitrogen (TN) Cation Exchange Capacity (CEC) and Electrical Conductivity (EC)

Technology for Soil Quality Index (SQI) Reconnaissance and planning (i.e., analyses, cost implications) Experimental design, sampling strategies and validation Scale (horizontal, vertical, time) Field Data (vector and raster), topo-maps, satellite , ground, aerial, space platforms) Linear and non-linear integrated modeling

Biological microbial activity and biomass biodiversity roots organisms (earthworms, microbes, nematodes)

Ecological Net Primary productivity (grain yield) Root growth Harvest Index Nutrient Cycling

Fig. 9.3 Attributes integrated into a Soil Quality Index (SQI). (Modified from (de Paul Obade and Lal 2016a; Krüger et al. 2018))

individual soil property (e.g., moisture or ρb ) to infer soil quality is insufficient (Bünemann et al. 2018; de Paul Obade and Lal 2016a, b; Ohlson 2014). Besides, metrics are interpreted relatively due to uncertainties attributed to vague baselines (e.g., soils under native vegetation (NV) are usually assumed to be of high quality yet they may not necessarily be fertile) (Bünemann et al. 2018; Vollmer et al. 2016; Vollmer et al. 2018). Notwithstanding, existing metrics provide conflicting information attributed to oversimplification of complex phenomena especially upon upscaling or downscaling (e.g., reliance on surface point measurements for soil quality determination yet the whole profile is more comprehensive and holistic) due to cost and logistic constraints (Andrews et al. 2002; Askari and Holden 2014; Chaer et al. 2009; de la Paz Jimenez et al. 2002; Paz-Ferreiro and Fu 2016; Vasu et al. 2016). The Soil Quality Index (SQI) is a quantifiable numerical index synthesizing multivariate qualitative (e.g., management) and quantitative (primary e.g., SOC, ρb ) environmental data to facilitate identification and understanding of key variables driving soil processes and functions (de Paul Obade and Lal 2016b). SQIs may be computed using either mathematical or statistical models (e.g., multivariate regression models). A feasible strategy for assaying the efficacy of soil quality metrics is to: (i) define the problem or management goal (s) of the SQI (e.g., determine factors influencing crop yield), (ii) define the baseline (i.e., reference soil, which are

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controversially assumed to be soils under natural vegetation (NV) land use), (iii) develop a robust experimental sampling design, and measure identical soil attributes at similar landscape position but different global positioning system (GPS) locations at different stages of plant development, (iv) the computed SQI should integrate quantitative and qualitative attributes into a single objective value (de Paul Obade and Lal 2016b). Soil can be sampled in both the horizontal and vertical dimension at varied scales over time, to directly measure, for instance SOC stocks. Because measurement techniques have different assumptions, challenges arise when comparing models generated from these different techniques. In retrospect, soil quality models are not error free, because of soil spatial heterogeneity, land use history, vague benchmarks, making it difficult to replicate for monitoring purposes (Malone et al. 2011). Because it is impracticable to generate detailed point specific soil maps for the entire earth’s surface, generalization is employed. Practically, sampling protocols entail acquiring data at different depths or layers, for instance, 0 to 10 cm, 10 to 20 cm, 20 to 40 cm and 40 to 60 cm, and measuring soil properties (e.g., SOC, bulk density or ρb , and texture) to estimate SOC stock per layer (Kladivko et al. 2014; Lal 2006). These methods are time-consuming, expensive, and can require complex data handling and preparation procedures, which can be error prone (Jobbágy and Jackson 2000; Lorenz and Lal 2005). Besides soils being heterogeneous and the fact that plants uptake different nutrients simultaneously at variable environmental gradients suggest derived models may provide conflicting information (de Paul Obade and Lal 2016a; Ohlson 2014). To enhance model fidelity requires calibration to assess the “fit” between measured quantities/variables estimated by a new method with the actual/true value of the same variables measured in the field. Although this issue is addressed in detail in other literature (Breiman 1996; Davis 1987; McBratney et al. 2011; Mota et al. 2014; Zornoza et al. 2007), the correlation coefficient (r) is among the common metrics for evaluating model fit in regression models under the null hypothesis that independent and dependent variables are not linearly related. As a precaution, “r” only measures the strength of a relation between two variables, not the agreement between them. For instance, a perfect agreement occurs only if all the points lie close to the line of equality between two variables, yet, a perfect correlation refers to points that lie along any straight line, as observed in multivariate analyses. To reiterate, SQI should quantify the soil quality for similar soils under identical land management, distinguish different soils under similar management, and categorize different soils under different management at diverse landscapes (de Paul Obade and Lal 2016b). In essence, assuming identical soil types are grouped in “classes”, this SQI should be capable of delineating different classes, yet simultaneously decreasing variability within the same class, to discern the impact of land use or management on soil quality (de Paul Obade and Lal 2016a, b). Generally a weighting criteria screens out a minimum dataset consisting of key soil quality attributes (e.g., from Fig. 9.3), information that can also be useful for prioritizing remedial management strategies. The SQI should objectively rate soil quality as per the specific land use and on a % scale, ranging from zero to 100 with for instance, 100% denoting high/excellent quality and 0% poor soils. Conceptually, the SQI metric should be correlated to specific soil functions e.g., crop yield trends. Equation

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9.2 exemplifies environmental variables combined to represent (≡) Aggregated Soil Index (ASI): ASI ≡ {soil attributes (physical, chemical, biological, ecological) , weather (e.g., precipitation, temperature) + land use and management (e.g., fertilizer application, tillage) + soil functions (biodiversity conservation, climate regulation (C sequestration potential) , pollution control, water purification, nutrient cycling, biomass production) + others (e.g., assumed baseline)} (9.2) ASI represents Aggregated Soil Index. Equation 9.2 is a prototype soil quality model that is subsequently synthesized and transformed into SQI (%) (Eq. 9.3). For monitoring purposes or to conduct relative comparison of soil quality which is the gist of SQIs, the units cancel out so long as “apples are compared with apples”, that is, same input variables (e.g., physical, chemical and biologic properties) are modelled per site.  

SQI = ASI MAXASI − MI NASI × 100

(9.3)

SQI: Soil Quality Index computed from derived regression model. MAXASI: Maximum Aggregate Index representing good soil quality water. MINASI: Minimum Aggregate Index representing poor soil quality (i.e., degraded). The explanatory power of models and the prediction accuracy of observed data, can be assayed using the coefficient of determination (R2), mean error (ME), or the root mean square error (RMSE) where a high R2; or smallest RMSE or ME denote high accuracy (Davis 1987). The Pearson correlation coefficient “r”, values range from −1 to 1, with a positive “r” value indicating a positive association, whereas 0 denotes no association between variables. Figure 9.4 is an example of SQI vis-à-vis depth and management for a study conducted in different sites within Ohio, USA.

9.3 State of the Art Digital Technology in Agriculture and Emerging Gaps 9.3.1 Case Study on Synthesized Soil Quality Index (SQI) In a recent study conducted to synthesize qualitative and quantitative data into a single value SQI (de Paul Obade and Lal 2016a), data was acquired from the

Corn & SQI Soybean & SQI

R2 0.74 0.89

Corn grain yield (Mg/ha)

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297 CT Miami (CrA) NT Miami (CrA )

8.5

NTcc Miami (CrA) NT Preble (CtA)

4.5

CT Auglaize (Pw)

NT Auglaize (Pw)

0.5

CT Seneca (GWA)

CrA (Crosby silt loam) kbA (Kibbie fine sandy loam) GWA (Glynwood silt loam) CtA (Crosby Celina silt loams) Pw (Pewamo silty clay loam)

Fig. 9.4 Comparison of Soil Quality Index (SQI) and agricultural yield for data acquired in 2012 on 5 different sites under different land management within Ohio, USA. The management practices are denoted as NV for natural vegetation, CT for conventional till, and NT for no-till. Soil layer information is bracketed after specific management for instance NT (0 to 10) implies NT management 0 to 10 cm. The grain yield are in Mg/ha for (a) Soybean (Glycine max (L.) Merr.), and (b) Corn (Zea mays L.). The error bars represent the standard error from the mean, significant at p < 0.05 (de Paul Obade and Lal 2014, 2016a)

following field sites located in Ohio, USA: Miami (40◦ 10 12 N, 84 ◦ 07 41.7 W), Seneca site 1 (41◦ 00 25 N, 83 ◦ 16 21 W), Seneca site 2 (41◦ 12 43 N, 82 ◦ 54 39 W), Preble (39◦ 46 09 N, 84 ◦ 36 52 W and 39◦ 41 45 N, 84 ◦ 40 36 W), and Auglaize (40◦ 27 34.5 N, 84 ◦ 26 14.8 W). The soil types at the sampled points were: CrA (Crosby silt loam), kbA(Kibbie fine sandy loam), GWA (Glynwood silt loam), CtA (Crosby Celina silt loams), and Pw (Pewamo silty clay loam), respectively. The management practiced in the field were the no-till (NT) (> 30% surface residue) with or without manure (M) and cover crops (cc), natural vegetation (NV), and conventional till (CT). In the CT managed fields the surface residue cover was below 30%. Data was processed according to the USDA-NIFA project guidelines (project web site: sustainablecorn.org) (Kladivko et al. 2014). The soil physical and chemical attributes were determined for all the sampled soil depths. These included pH, electrical conductivity (EC), soil organic carbon (SOC), soil bulk density (ρb ), and available water capacity (AWC), field capacity (FC), permanent wilting point (PWP), nitrate, nitrite and Carbon/Nitrogen (C/N) ratio. The soil moisture content was assayed gravimetrically (Topp and Ferre 2002; Topp and Ferré 2002). The water retention was determined by the tension table, and the

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pressure plate extractors, and the AWC computed using the difference in volumetric water content at FC (−33 kPa), and that at permanent wilting point (−1500 kPa) (Dane and Hopmans 2002). The pulverized, air dried and sieved (through a 250 μm sieve) loose soil samples were chemically analyzed to determine the pH, EC, C/N, and SOC. This SOC content was determined by the dry combustion method at 900 ◦ C using a Vario Max C:N analyzer (Nelson and Sommers 1982, 1996). Alternately, the pH and EC were measured using a a hand-held portable probe1 (Lal 1996; Peech 1965). For pH measurement, a 1: 1 soil/water suspension were put in a tube and mixed after 3 h; whereas EC used the same suspension but was measured after 24 h. The soil pH and EC are critical for soil quality assessment because they determine salinity and nutrient availability (Arnold et al. 2005; Bastida et al. 2008). The soil pH being approximately 7 suggested non significance of inorganic carbonates (Brown et al. 2006; De Vos et al. 2005), thus SOC was considered equivalent to total C. The SOC stocks (Mg ha−1 ) were computed by multiplying the SOC concentration by the specific gravity ρb /ρw , whereby ρw is the density of water. The nitrate/nitrite concentrations were determined using the Ion chromatograph (Zhang et al. 2013). The yield data was determined from crops harvested on the exact spot in which the soils were extracted, with yield measurements done as follows: (i) for corn (Zea mays L.), the ears from a field dimension of 2 rows and 2 m long were hand harvested and weighed; whereas the weight of the soybeans were measured from 1 m2 field dimension, (ii) the corn, and soybean (Glycine max (L.) Merr.) were air dried, shelled, and the dry weight of the cobs, kennels, beans, and stalks measured. The water content was determined by oven drying subsamples of kernel, cob and beans at 60 ◦ C for 96 h, and the grain yields computed after adjusting the respective weights to 15% moisture for corn, and 14% for soybean. The Harvest Index (HI) was computed as the ratio of the harvested grains, or beans to the total above ground vegetative biomass. Partial least squares regression (PLSR) in PROC PLS in SAS 9.2 (SAS Institute Inc., Cary, NC, USA), was used to compute the SQI under the null hypothesis that site specific soil physical and chemical attributes (i.e., AWC, FC, PWP, soil ρb , EC, pH, nitrate, nitrite and SOC concentration, C/N ratio) are not significantly impacted by management or correlated (de Paul Obade and Lal 2014, 2016a). From Fig. 9.4 it can be deduced that NV managed surface soil layers (0 to 10 cm) were of a higher quality, but CT managed soils had high quality at subsurface layers (20 to 60 cm). This hypothetically suggest that soil biota activity was relatively higher for surface NV managed soils. On the contrary, CT managed subsurface layers may have stimulated biota activity following residue incorporation into the soil. The R2 between SQI and corn yield was 74%, whereas soybean had 89%. This suggests that soybean crop was either: (i) ideally managed or impacted less by management, or (ii) the soil conditions ideally supported enzyme activity and nutrient uptake. Further, NT managed soils had higher agronomic yields than the

1 Thermo

Scientific, Orion Star Series, Made in Singapore

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CT soils (Fig. 9.4). Hypothetically, low crop yields are caused by: (i) low crop stand and insufficient contact between the seed and soil, (ii) stunted seedling due to suboptimal soil temperatures, (iii) compaction, (iv) prevalence of weeds, pests or pathogens (de Paul Obade and Lal 2016a; Lal 2013, 2014, 2015b). Because model reliability depends on data utilized to build and calibrate them, future models should include soil biota attributes (e.g., respiration, earthworm density, microbial biomass etc.) which being sensitive to environmental gradients play a central role in soil functioning (Collins et al. 2011; DeForest et al. 2012; Krüger et al. 2018).

9.3.2 Case Study: Reflectance of Soil with Varied Moisture and Residue Cover A randomized complete block field experiment with 36 replications, was conducted in early (10th to 19th) November of the year 2009, and 2010, in field plots of dimension 2 m by 2 m at Aurora (44◦ 32 07 North, 97◦ 22 08 West), and Lennox (43◦ 14 34 North, 96◦ 14 0.9 West) sites within South Dakota, USA. These field plots (Aurora had 72, and Lennox 16) were chisel plowed in spring, and corn was seeded during the first week of May in 2009 and 2010. The soils at Aurora site are fine-silty, mixed, frigid udic haploborolls, while those at Lennox site were fine-silty, mixed, mesic udic haplustolls. Following physiological maturity in October, grain and stover yields were measured. In all plots corn residue was chopped after harvesting, and weighed, and the % of remaining surface residue estimated as demonstrated in Plate 9.1, using the line transect method (Wollenhaupt and Pingry 1993). Spectral reflectance measurements were made with a Cropscan handheld multispectral radiometer (Cropscan Inc., Rochester, Minnesota) (de Paul Obade and Gaya 2020). Three spectral measurements were collected per plot with the radiometer set 2 m above ground at nadir to cover a ground spatial resolution of 1 m2 . The following band widths, 440–530 (blue), 520–600 (green), 630–690 (red), 760–900 (near infra red, NIR), 1550–1750 (mid infra red, MIR), for wide (w) bands, and 506–514, 563–573, 605–615, 654–666, 704–716, 755–765, 804–816, 834–846, 867–876, 900–910, 1043–1057 nanometer (nm) for narrow (n) wavelength bands were used to compute indices in Table 9.1 (Obade et al. 2011). Theoretically, the normalized difference vegetation index (NDVIw ) highly correlates with photosynthesis of healthy plants whereby the leaf pigments containing chlorophyll, which absorb electromagnetic energy located in the red band, whereas in the near infrared (NIR) region the leaf mesophyll scatters electromagnetic energy. The red band reflectance decreases during plant growth. Alternately, the BNDVIw and GNDVIw are modifications of this basic concept, though, the blue energy is absorbed and green reflected. On the other hand, the normalized difference water index (NDWIw ) attributes spectral reflectance differences to absorption of electromagnetic energy by water in plant leaves (Gao 1996).

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Table 9.1 Spectral Index (band combination) Index Normalized difference vegetation index (NDVIw ) Green normalized difference vegetation Indexw (GNDVIw ) Normalized difference water index (NDWIw ) Blue normalized difference vegetation index (BNDVIw )

Equation NDVIw = (R830 −R660 )/(R830 + R660 )

Reference (Rouse et al. 1974)

GNDVIw = (R830 −R560 )/(R830 + R560 )

(Daughtry et al. 2000; Gitelson and Merzlyak 1996) (Gao 1996)

NDWIw = (R830 −R1650 /(R830 + R1650 ) BNDVIw = (R830 −R485 )/(R830 + R485 )

(Hancock and Dougherty 2007)

“w” subscript refers to “wide band”

Four liters of water were sprinkled uniformly to wet the soil in half of the field plots and spectral measurements taken for both the dry and wet soil. Next, surface soil samples were collected in plastic bags for gravimetric soil moisture determination, in which, about 10 grams of soil subsample was oven dried at 105 ◦ C in a Quincy lab inc. 40 GC oven for 2 days, and then weighed again (Topp and Ferre 2002). The soil moisture content in the wet soil was approximately 30%, whereas dry soil was 15%. The data was statistically processed as a mixed model (using Proc Mixed) in Statistical Analysis System (SAS Institute, North Carolina) software. Hypothetically, field reflectance of surface residue cover should be invariable with soil moisture gradients. Table 9.2 read generally from bottom to the top, shows that there were significant differences in reflectance measurements across all bands and indices in Lennox and Aurora sites, with Lennox generally having a lower reflectance when only bare soil is considered, and the reflectance difference between the wet and dry soils miniscule. These reflectance differences may be attributable to soil surface roughness, or carbon content (Daughtry 2001). A detailed view shows that the reflectance significantly varied with surface residue cover for blue, green, red, NIR spectral bands as well as NDVIw , BNDVIw and NDWIw indices, except for MIR band and GNDVIw . Interestingly, the surface reflectance did not significantly vary along a moisture gradient except for GNDVIw and BNDVIw, which is contradictory for NDWIw which was designed specifically to detect moisture differences (Gao 1996). On consideration of residue and wetness interaction effects, the reflectance significantly covaried with surface residue cover along varied moisture gradients, except for the indices. Overall, the data shows no significant difference when the interaction of selected factors that is site, moisture content and residue cover distribution are analyzed simultaneously. However, this does not hold when only residue cover and moisture interactions are considered independently. This creates complications in mapping surface residue cover, because the results can vary depending on the model inputs, thus, necessitates an approach that initially screens-out significant variables,

Aurora Aurora Aurora Aurora Aurora Aurora Lennox Lennox Lennox Lennox Lennox Lennox

Dry Wet Dry Wet Dry Wet

Dry Wet Dry Wet Dry Wet Dry Wet Dry Wet Dry Wet

% residue cover 0 0 50 50 100 100 0 0 50 50 100 100 p value Lsd 0 0 50 50 100 100 p value Lsd

0 0 0.22 0.21 0.44 0.41 res.*wetn.

residue weight (kg m−2 ) 0 0 0.22 0.21 0.44 0.41 0 0 0.22 0.21 0.44 0.41 site*res.*wetn. Blue 6.28 3.71 6.59 7.42 7.57 7.80 6.12 4.00 7.81 8.78 9.48 9.52 0.9 NS 6.20 3.86 7.20 8.10 8.52 8.66 0.003 0.86

Green 8.54 5.18 9.54 10.79 11.08 11.41 7.92 5.22 11.06 12.73 13.82 13.90 0.8 NS 8.23 5.20 10.30 11.76 12.45 12.66 0.009 1.2

Red 12.1 7.5 13.9 16.0 16.4 17.1 10.5 7.2 16.0 18.8 20.5 20.4 0.7 NS 11.3 7.3 15.0 17.4 18.5 18.7 0.007 1.86

NIR 19.7 13.1 22.3 25.9 26.4 27.7 16.8 11.4 25.2 29.0 32.1 31.5 0.8 NS 18.2 12.2 23.7 27.5 29.3 29.6 0.002 2.8

MIR 45.9 28.1 34.9 35.7 34.1 36.3 28.1 22.1 30.3 33.0 39.7 38.2 0.3 NS 37.0 25.1 32.6 34.3 36.9 37.3 0.02 5.3

NDVIw 0.237 0.273 0.231 0.237 0.232 0.238 0.229 0.228 0.222 0.215 0.222 0.214 0.2 NS 0.234 0.251 0.226 0.225 0.226 0.225 0.1 NS

GNDVIw 0.394 0.433 0.400 0.413 0.407 0.416 0.356 0.369 0.388 0.390 0.398 0.388 0.6 NS 0.375 0.401 0.394 0.402 0.403 0.402 0.1 NS

BNDVIw 0.515 0.559 0.543 0.555 0.553 0.561 0.463 0.477 0.524 0.535 0.544 0.536 0.1 NS 0.489 0.518 0.534 0.545 0.548 0.548 0.2 NS

(continued)

NDWIw −0.388 −0.360 −0.220 −0.155 −0.124 −0.131 −0.253 −0.321 −0.094 −0.064 −0.107 −0.097 0.2 NS −0.321 −0.341 −0.157 −0.109 −0.115 −0.114 0.2 NS

Table 9.2 The influence of residue cover, and soil wetness on reflectance in the blue, green, red, NIR, MIR, NDVIw , GNDVIw , BNDVIw , and NDWIw . The mean, p and lsd values are provided for each spectral band, and indices

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% residue cover Green 10.33 9.87 0.43 6.72 11.03 12.55