Innovation for Environmentally-friendly Food Production and Food Safety in China (Sustainability Sciences in Asia and Africa) 9819928273, 9789819928279

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Sustainable Agriculture and Food Security

Nicholas Clarke Deliang Peng Jihong Liu Clarke   Editors

Innovation for Environmentallyfriendly Food Production and Food Safety in China

Sustainability Sciences in Asia and Africa

Sustainable Agriculture and Food Security Series Editor Rajeev K. Varshney, Semi-Arid Tropics, International Crops Research Institute, Patancheru, Telangana, India

This book series support the global efforts towards sustainability by providing timely coverage of the progress, opportunities, and challenges of sustainable food production and consumption in Asia and Africa. The series narrates the success stories and research endeavors from the regions of Africa and Asia on issues relating to SDG 2: Zero hunger. It fosters the research in transdisciplinary academic fields spanning across sustainable agriculture systems and practices, post- harvest and food supply chains. It will also focus on breeding programs for resilient crops, efficiency in crop cycle, various factors of food security, as well as improving nutrition and curbing hunger and malnutrition. The focus of the series is to provide a comprehensive publication platform and act as a knowledge engine in the growth of sustainability sciences with a special focus on developing nations. The series will publish mainly edited volumes but some authored volumes. These volumes will have chapters from eminent personalities in their area of research from different parts of the world.

Nicholas Clarke • Deliang Peng • Jihong Liu Clarke Editors

Innovation for Environmentally-friendly Food Production and Food Safety in China

Editors Nicholas Clarke Norwegian Institute of Bioeconomy Research (NIBIO) Ås, Norway

Deliang Peng Institute of Plant Protection Chinese Academy of Agricultural Sciences Beijing, China

Jihong Liu Clarke Norwegian Inst of Bioeconomy Research (NIBIO) Ås, Norway

ISSN 2730-6771 ISSN 2730-678X (electronic) Sustainability Sciences in Asia and Africa ISSN 2730-6798 ISSN 2730-6801 (electronic) Sustainable Agriculture and Food Security ISBN 978-981-99-2827-9 ISBN 978-981-99-2828-6 (eBook) https://doi.org/10.1007/978-981-99-2828-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Foreword

Global food security has become an urgent issue. Almost 10% of the world’s population experienced chronic food insecurity in 2021, and the situation has not improved since then. The causes are complex, ranging from armed conflicts to natural disasters and extreme weather due to climate change. On top of these factors, around the world the COVID-19 pandemic brought shutdowns and lower economic growth. We have been reminded of the importance of agriculture—and of the common need for food security. Climate change and environmental degradation constitute a threat to food production. At the same time, more than a third of global greenhouse gas emissions originate from food systems. However, food production can also be a part of the solution. Carbon sequestration in soil and agronomic methods that reduce the use of nitrogen fertilizer and improve soil quality are some of the ways agriculture can benefit the climate and the environment. In this respect, the project “Sinograin II: Technological innovation to support environmentally friendly food production and food safety under changing climate opportunities and challenges” is both relevant and timely. This book, Innovation for Environmentally Friendly Food Production and Food Safety in China, is one of the concrete deliverables from the Sinograin II project. The book provides a good overview of the results achieved and the insights gained. Our hope is that the lessons learned in Sinograin II can benefit other countries too. The Sinograin II project agreement links to the United Nations 2030 Agenda, and to both Norwegian and Chinese priorities. Farming practices with more targeted use of fertilizer, and improvement of food quality and safety are important for both countries. The Norwegian Institute of Bioeconomy Research (NIBIO) is Norway’s largest agricultural research institute. NIBIO and the Chinese Academy of Agricultural Sciences (CAAS) began their bilateral cooperation almost 20 years ago. NIBIO and the six cooperating institutes in Sinograin II also have a long history of cooperation, based on an agreement from 2014. v

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In addition, the Sinograin II project is rooted in the MoU between the Ministry of Agriculture and Food in Norway and the Ministry of Agriculture in China, as well as an MoU between NIBIO and CAAS, both signed in 2017. Food security and food safety are clearly defined as prioritized thematic areas for Norway-China and NIBIO-CAAS cooperation. Sinograin II is part of a portfolio of projects between Norway and China that focus on the environmental and sustainable development. We are glad to see that these concerns are gaining more and more attention including in China, where broad efforts aim to improve environmental quality. The Norwegian and Chinese authorities have a long history of bilateral environmental collaboration. During the UN Biodiversity Conference (COP 15) in Montreal in December 2022, the Norwegian Ministry of Climate and Environment and the Ministry of Ecology and Environment of China signed an MOU on cooperation on environmental policy and management within broad areas, including climate change, chemicals, nature conservation, biological diversity, and plastic pollution. The focus of Norway’s support for China’s development has changed over time, from poverty reduction to other aspects of sustainable development, as China has had great success in economic development over the last decades. The projects supported by the Royal Norwegian Embassy in Beijing are mainly cooperation between government institutions on both sides. The projects contribute to capacity building, to better mutual understanding, and are also helpful to international negotiations and standard-setting. Sinograin II has fitted well into this overall portfolio. Sinograin II research has revealed new knowledge on efficient use of nitrogen and pesticides, improved safety with reduced environmental footprint, better soil management leading to increased soil health, improvement in extension services, and promotion of information accessibility and flow. Sinograin II has been supported by the Royal Norwegian Embassy in Beijing. I would like to thank and congratulate all the project partners, both in Norway and in China. This book is a great testimony of their important and hard work. Beijing, China February 2023

H. E. Signe Brudeset

Editorial

The global food production systems require expanding their capacity to produce almost twice the current levels to safeguard the food security of the burgeoning population worldwide. More than 800 million suffering from undernourishment worldwide poses a great risk to the attainment of sustainable development goal (SDG) 2 of the UN that targets “End hunger, achieve food security and improved nutrition and promote sustainable agriculture” within the next 7 years. The challenge is further exacerbated by the rising weather extremities and unpredictability in rainfall patterns and pest-pathogen dynamics associated with global climate change that has a profound negative impact on agricultural productivity and farm incomes worldwide. Also, the future targets of food production should be secured in resourceconstrained agricultural settings and with least environment footprint, thus calling for sustainable innovations in agri-farming systems and enhanced participation of women in agriculture. The challenge to reduce hunger is alarming in the case of developing nations, particularly Asia and Africa that house the largest proportion of people suffering from malnutrition and other nutrition-related issues. Furthermore, the agri-food systems in Asia and Africa are severely constrained by the subsistence nature of farming, declining land and other agricultural resources, increasing environmental pollution, soil and biodiversity degradation, and climate change. Therefore, this book series, “Sustainable Agriculture and Food Security,” has been planned to support the global efforts towards sustainability by providing timely coverage of the progress, opportunities, and challenges of sustainable food production and consumption in Asia and Africa. The series narrates the success stories and research endeavors from the regions of Africa and Asia on issues relating to SDG 2: Zero Hunger. It fosters research in transdisciplinary academic fields spanning sustainable agriculture systems and practices, post-harvest and food supply chains. The focus of the series is to provide a comprehensive publication platform and act as a knowledge engine in the growth of sustainability sciences with a special focus on developing nations. As we stand at the precipice of a rapidly changing world, it is increasingly apparent that the challenges of the twenty-first century demand innovative and vii

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sustainable solutions. The global population continues to grow, exerting immense pressure on our planet's finite or even declining resources, while the threat of climate change looms large. In response to these challenges, the urgent need for environmentally friendly food production and food safety has emerged as a paramount concern for governments, researchers, and consumers alike. Within this context, China, as the world's most populous country and one of its largest economies, has a crucial role to play in the global effort to ensure food security and minimize the environmental footprint of food production. Recognizing its influence and potential for driving positive change, China has committed itself to actively exploring and implementing novel strategies to address these urgent matters and cultivate a more sustainable and resilient food system for its people and the global community. One of the key areas of focus in the quest for sustainable food production is the adoption of practices that both benefit the burgeoning population and preserve the environment. Carbon sequestration in soil and agronomic methods that reduce the use of nitrogen fertilizer and improve soil quality are prime examples of how agriculture can contribute positively to these objectives. By harnessing such practices, China is demonstrating its commitment to creating a more sustainable food system that mitigates the impacts of climate change and supports environmental health. From precision agriculture, and soil management to alternative protein sources and integrated pest management, China is at the forefront of embracing groundbreaking innovations to tackle the challenges of food security, environmental sustainability, and climate change. In view of the above, the present book, Innovation for Environmentally Friendly Food Production and Food Safety in China edited by Jihong Liu Clarke, Deliang Peng, and Nicholas Clarke, brings together a diverse range of expertise from researchers, policymakers, and industry professionals, creating a rich and multifaceted tapestry of perspectives on the subject matter. The book is a concrete deliverable of the Sinograin II project, part of a portfolio of projects between Norway and China that focus on the environment and sustainable development. It showcases groundbreaking technologies and innovative practices that will be the key drivers to revolutionizing food systems, reducing greenhouse gas emissions, and minimizing environmental degradation. I firmly believe this book is a great resource for students, researchers, policymakers, and other stakeholders engaged in the quest for sustainable change within China's vast and dynamic food system. The book will serve as a catalyst for further dialogue, research, and collaboration, as we collectively work towards the shared goal of a more sustainable, equitable, and resilient global food system. I wish to extend my sincere thanks and gratitude to the Springer staff, particularly Aakanksha Tyagi, Senior Editor (Books), Life Sciences, and Naren Aggarwal, Editorial Director, Medicine, Biomedical and Life Sciences Books Asia, for their constant support for the accomplishment of this compendium. The cooperation received from my senior colleagues such as David Morrisson, Peter Davies, Daniel Murphy, and my laboratory colleagues—Vanika Garg, Anu Chitikineni, and Abhishek Bohra from Murdoch University (Australia)—is also gratefully acknowledged. I would like to thank my family members—Monika Varshney, Prakhar

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Varshney, and Preksha Varshney—for their love and support in discharging my duties as Series Editor.

WA State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Perth, WA, Australia

Rajeev K. Varshney

Contents

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Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jihong Liu Clarke, Deliang Peng, and Nicholas Clarke

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Developing Precision Nitrogen Management Strategies for Different Crops and Scales of Farming Systems in North China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Krzysztof Kusnierek, Yuxin Miao, Junjun Lu, Xinbing Wang, Hainie Zha, Rui Dong, and Jing Zhang

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Food Safety and the Importance of Comprehensive Analytical Methods for Pesticides and Other Contaminants . . . . . . . . . . . . . . . Marianne Stenrød, Kathinka Lang, Marit Almvik, Roger Holten, Agnethe Christiansen, Xingang Liu, and Qiu Jing Artificial Intelligence and Hyperspectral Modeling for Soil Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jiangsan Zhao and Shuming Wan Biochar-Based Technology in Food Production, Climate Change Mitigation, and Sustainable Agricultural Soil Management: Post Terra Preta Era . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoyu Liu, Cheng Liu, Genxing Pan, and Nicholas Clarke

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Diversity and Ecological Functions of Soil Microbial Community in Black Soil in Northeast China . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Xueli Chen, Nicholas Clarke, Shuming Wan, and Baoku Zhou

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Lignosulphonates as Soil Amendments in Agriculture . . . . . . . . . . . 127 Nicholas Clarke, Xueli Chen, Xiaoyu Liu, and Shuming Wan

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Ecological Functions of Arbuscular Mycorrhizal Fungi in Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Lingbo Meng, Shumin Li, and Yufei Meng xi

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The Agro-Extension Service Evolution in China and Norway: Different Pathways to Tackle Evolving Challenges . . . . . . . . . . . . . 181 Xiande Li, Zhilu Sun, Giovanna Ottaviani Aalmo, Fangfang Cao, Divina Gracia P. Rodriguez, Chen Qian, Yongxun Zhang, and Knut Øistad

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Climate-Smart Agriculture in China: Current Status and Future Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 Xiaobo Qin and Xue Han

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China–Africa Joint Force on Integrated Pest and Disease Management (IPM) for Food Security: Fall Armyworm as a Showcase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Jingfei Guo, Ivan Rwomushana, and Zhenying Wang

Editors and Contributors

About the Editors Nicholas Clarke has a PhD in analytical chemistry applied to environmental issues, from the Royal Institute of Technology in Stockholm. He works with sustainability of agricultural soils in China as co-leader of Work Package 4 in the project “Sinograin II: Technological Innovation to support environmentally-friendly food production and food safety under a changing climate—opportunities and challenges for Norway-China cooperation.” He was Chairman of the Expert Panel on Deposition of the European forest monitoring program ICP Forests from 2006 to 2010 and led Working Group 1 in COST Action FP0903. He was a co-editor of the book “Climate Change, Air Pollution and Global Challenges: Understanding and Perspectives from Forest Research” as well as being guest editor for special issues of the journals Biomass and Bioenergy and Energy, Sustainability, and Society. He has 117 publications on ResearchGate (peer-reviewed papers, book chapters, reports, etc.) Deliang Peng is a Councilor of the International Federation of Nematology Society and the President of the Chinese Society of Plant Nematology. He is the leading scientist on cyst forming nematodes in China and has done nematode research for more than 30 years since 1984. He has researched diagnosis, identification, biology, resistance, biocontrol, biofumigation, chemical fumigation, nematocides, and integrated management of plant nematodes including root-knot nematodes, cyst nematodes, and root rot nematodes on wheat, soybean, sugarbeet, potato, vegetables, rice, peanut, and sweet potato. He got his PhD degree from China Agricultural University in 2001, he visited and studied at the Istituto di Nematologia Agraria, Bari, Italy, as exchange scholar from 1993.8. to 1994.2, studied at the International Institute of Parasitology, CAB international, UK, as Darwin Fellow (1994.5.–1995.5) and visited ILVO five times from 2000 to 2005. He developed the quick molecular

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diagnosis and LAMP detections of Meloidogyne incognita, M. enterobii, M. hapla, Heterodera glycines, H. avenae, H. filipjevi, H. elechista, and got 16 patents, developed integrated nematology managements of potato root-knot nematodes, vegetable root-knot nematodes, sweet nematodes, and cereal cyst nematodes, and published more than 100 papers. He is co-leader of the project “Sinograin II: Technological Innovation to support environmentally-friendly food production and food safety under a changing climate—opportunities and challenges for Norway-China cooperation.” Jihong Liu Clarke is coordinator for China relations at NIBIO—the Norwegian Institute of Bioeconomy Research. She is a biotechnologist and heads a research group on plant genetic engineering consisting of senior scientists, postdocs, PhD students, MSc students, and technicians. She has led many past and ongoing projects, either as project leader or work package leader, funded by among others the EU, EEA, ERA-NET, and Research Council of Norway. She is a member of the editorial board of three journals (Plant Biotechnology Journal, GM Crops and Food, Acta Agriculturae Scandinavica, Section B) and was guest editor for a Plant Molecular Biology Special Issue on Plant Biotechnology for Bio-economy in 2013 and a Special Issue on Chloroplast Evolution and Biotechnology in 2011. She is leader of the project “Sinograin II: Technological Innovation to support environmentallyfriendly food production and food safety under a changing climate—opportunities and challenges for Norway-China cooperation.” She has over 200 publications, of which 87 are in peer-reviewed journals.

Contributors Giovanna Ottaviani Aalmo Norwegian Institute of Bioeconomy Research, Ås, Norway Marit Almvik Division of Biotechnology and Plant Health, Norwegian Institute of Bioeconomy Research (NIBIO), Ås, Norway Fangfang Cao Institute of Agricultural Economics and Development of Chinese Academy of Agricultural Sciences, Haidian, Beijing, China Xueli Chen Heilongjiang Academy of Black Soil Conservation and Utilization, Harbin, China Heilongjiang Joint Laboratory of Soil Microbial Ecology, Heilongjiang Academy of Black Soil Conservation and Utilization, Harbin, China Agnethe Christiansen Division of Biotechnology and Plant Health, Norwegian Institute of Bioeconomy Research (NIBIO), Ås, Norway Jihong Liu Clarke Norwegian Institute of Bioeconomy Research, Ås, Norway Nicholas Clarke Norwegian Institute of Bioeconomy Research, Ås, Norway

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Rui Dong Institute of Tobacco Research, Chinese Academy of Agricultural Sciences, Qingdao, China Jingfei Guo State Key Laboratory for Biology of Plant Diseases and Insect Pests, MOA-CABI Joint Laboratory for Bio-Safety, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China Xue Han Institute of Environment and Sustainable Development in Agriculture, Center for Carbon Neutrality in Agriculture and Rural Region, Chinese Academy of Agricultural Sciences, Beijing, China Roger Holten Division of Biotechnology and Plant Health, Norwegian Institute of Bioeconomy Research (NIBIO), Ås, Norway Qiu Jing Institute of Quality Standards and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing, China Krzysztof Kusnierek Center for Precision Agriculture, Department of Agricultural Technology, Norwegian Institute of Bioeconomy Research (NIBIO), Kapp, Norway Kathinka Lang Division of Biotechnology and Plant Health, Norwegian Institute of Bioeconomy Research (NIBIO), Ås, Norway Shumin Li Resource and Environmental College, Northeast Agricultural University, Harbin, Heilongjiang, China Cheng Liu Institute of Resource, Ecosystem and Environment of Agriculture, and Department of Soil Science, Nanjing Agricultural University, Nanjing, Jiangsu, China Xiaoyu Liu Institute of Resource, Ecosystem and Environment of Agriculture, and Department of Soil Science, Nanjing Agricultural University, Nanjing, Jiangsu, China Xingang Liu Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China Xiande Li Institute of Agricultural Economics and Development of Chinese Academy of Agricultural Sciences, Haidian, Beijing, China Junjun Lu Lhasa Plateau Ecosystem Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, China Lingbo Meng School of Geography and Tourism, Harbin University, Harbin, Heilongjiang, China Yufei Meng School of Environment, Harbin Institute of Technology, Harbin, Heilongjiang, China

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Yuxin Miao Precision Agriculture Center, Department of Soil, Water and Climate, University of Minnesota, St. Paul, MN, USA Knut Øistad Norwegian Institute of Bioeconomy Research, Ås, Norway Genxing Pan Institute of Resource, Ecosystem and Environment of Agriculture, and Department of Soil Science, Nanjing Agricultural University, Nanjing, Jiangsu, China Deliang Peng Chinese Academy of Agricultural Sciences, Beijing, China Chen Qian Institute of Agricultural Economics and Development of Chinese Academy of Agricultural Sciences, Haidian, Beijing, China Xiaobo Qin Institute of Environment and Sustainable Development in Agriculture, Center for Carbon Neutrality in Agriculture and Rural Region, Chinese Academy of Agricultural Sciences, Beijing, China Divina Gracia P. Rodriguez Norwegian Institute of Bioeconomy Research, Ås, Norway Ivan Rwomushana CAB International (CABI), Nairobi, Kenya Marianne Stenrød Division of Biotechnology and Plant Health, Norwegian Institute of Bioeconomy Research (NIBIO), Ås, Norway Zhilu Sun Institute of Agricultural Economics and Development of Chinese Academy of Agricultural Sciences, Haidian, Beijing, China Xinbing Wang Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China Zhenying Wang State Key Laboratory for Biology of Plant Diseases and Insect Pests, MOA-CABI Joint Laboratory for Bio-Safety, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China Shuming Wan Heilongjiang Academy of Black Soil Conservation and Utilization, Harbin, China Hainie Zha International Center for Agro-Informatics and Sustainable Development (ICASD), College of Resources and Environmental Sciences, China Agricultural University, Beijing, China Jing Zhang Eagle Ltd., Anqing, Anhui, China Yongxun Zhang Institute of Agricultural Economics and Development of Chinese Academy of Agricultural Sciences, Haidian, Beijing, China Jiangsan Zhao Norwegian Institute of Bioeconomy Research, Ås, Norway Baoku Zhou Heilongjiang Academy of Black Soil Conservation and Utilization, Harbin, China

Chapter 1

Introduction Jihong Liu Clarke, Deliang Peng, and Nicholas Clarke

Abstract With the impact of the COVID-19 pandemic globally and the energy as well as environmental crises we are facing, achievement of the UN sustainable development goals (SDGs), including SDG2, zero hunger, by 2030, has become very challenging. Sustainable food production and supply is a daunting task requiring the international community to work together to improve agricultural productivity with minimum climate and environmental footprint. Through the support of the Norwegian government’s Ministry of Foreign Affairs to the Sinograin I and Sinograin II projects, Norwegian and Chinese partners have established successful collaboration on food security and sustainable agricultural development. The important results achieved and the experience obtained are shared in this book describing the technologies in-depth and the lessons learnt in detail. Readers are provided with insight into the decade-long fruitful collaboration on agriculture between Norway and China, the similarities and differences in Chinese and Norwegian agriculture, the outcomes of technology implementation in selected regions in China, the benefits of good extension services to farmers in Norway and China, as well as future directions for further collaboration and development of agricultural technologies. This book aims to provide valuable information to all stakeholder groups from policy-makers, to the agro-technology industry, to farmers. Keywords Sustainable development goals · China · Agriculture · Sustainable food production · International cooperation The world population has reached 8 billion, and the demand for food production and supply has become higher than ever. According to the State of Food Security and Nutrition in the World 2021 report (https://www.un.org/en/global-issues/food), there J. L. Clarke · N. Clarke (✉) Norwegian Institute of Bioeconomy Research, Ås, Norway e-mail: [email protected] D. Peng Chinese Academy of Agricultural Sciences, Beijing, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Clarke et al. (eds.), Innovation for Environmentally-friendly Food Production and Food Safety in China, Sustainable Agriculture and Food Security, https://doi.org/10.1007/978-981-99-2828-6_1

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are challenges to achieve the UN’s Sustainable Development Goal 2, Zero Hunger, by 2030 and, despite some progress, most indicators are not on track to meet global nutrition targets due to, among other things, the health and socio-economic impacts of the COVID-19 pandemic. Moreover, the climate crisis, the declining quantity and quality of arable land, the collapse in biodiversity, the energy crisis, and the geopolitical situation globally have added new challenges to reaching the SDGs by 2030. Technology-based modern agriculture with high productivity but less environmental footprint is essential and urgently needed and can only be achieved with joint efforts of the international community. China feeds about 18% of the world population (https://zhidao.baidu.com/question/561809814375678452.html) with only 7% of global arable land (https://www.51dongshi.com/eedfsrbvvhd. html), demonstrating the importance of agriculture for China and the necessity of technology-based modern agriculture with high productivity and less negative climate and environmental footprint. Due to long-term focus on high yield alone, over-application of fertilizers and ineffective management, a large area of Chinese agricultural land is currently in critical condition with severe environmental pollution in run-off in soil and surface water. Agricultural practices need to be improved to maintain sustainable food production with minimum environmental footprint. The Chinese government is well aware of this challenge. For example, the 13th 5-year plan in China (2016–2020) stated the need and urgency of reducing (and at least zero growth in) application of fertilizers, pesticides, and herbicides. Moreover, the growth of population and decline of arable land make the traditional mode of agricultural production in China difficult to sustain. Therefore, organic fertilizers and innovative farming technologies such as precision farming have been encouraged or taken into consideration in China, together with an effective understanding and management of soil health. International partnership and successful cooperation with global partners that China has established over several decades have shown significant results. Norway has been one of the international partners of China with broad collaboration on food security and agricultural research. Only approximately 3% of Norway’s area is arable land for its 5 million people, and it has a very short growing season. Thus, Norwegian agriculture has been developed with decades of effort to achieve technology-based agriculture with high productivity, a good extension service, and relatively low environmental footprint. Through a decade-long successful collaboration on food security and agro-technology implementation, the Chinese and Norwegian partners have established a successful collaboration by sharing agrotechnologies, experiences of extension service, and communication tools and have implemented several key agro-technologies in selected regions in China. This decade-long fruitful collaboration on agriculture and food security was supported by the Norwegian Ministry of Foreign Affairs through the Sinograin I and Sinograin II projects which have generated important results that can be learnt about through the 11 chapters in this book. New technologies for precision nitrogen management have been implemented in selected regions of northern China, representing different crops and scales of the

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farming system (Chap. 2). Both Chaps. 2 and 10 highlight the climate smart agriculture technologies and APP-based tools for precision agriculture and nitrogen management in Norway and China. The APP tools have provided assistance for Chinese farmers who can learn about their crop growth, soil health, what fertilizer is needed and whether pesticide spray is necessary. Farmers can provide their feedback and comments for the optimalization of the APP tools. Effective analytical methodologies are essential for determination of pollutants and pesticide residues, and thus for monitoring and management of food safety. In Chap. 3, Norwegian and Chinese co-authors describe their advanced analytical methods and the experience acquired. Soil is a key component of agricultural ecosystems and essential for crop production. In Chaps. 4, 5, 6, 7, and 8, in-depth understanding of soil health and its effective management is well presented. Modern technologies such as machine learning and artificial intelligence are exploited in addition to biological solutions to maintain and improve soil health using biochar, lignosulphonate, and arbuscular mycorrhizal fungi. Without strong support from local and national extension services, neither smart technology nor APP tools can generate the anticipated benefit to farmers. In Chap. 9, Chinese and Norwegian experts share their experiences with agricultural extension service to provide farmers with maximal support and ensure technologies and smart tools are applied according to the guidelines. The UN SDGs must be reached through international joint efforts as no nation can achieve the goals alone. Successful international cooperation is the foundation for reaching SDG2 with zero hunger by 2030, and the Sino-Norwegian collaboration on sustainable agriculture with climate smart solutions through Sinograin I and Sinograin II has set an excellent example of how this can be achieved. Other examples include Chinese collaboration with African countries. Chapter 11 presents an example of the importance of south–south cooperation to promote sustainable agriculture, with the successful showcase of the China–Africa joint force to combat fall armyworm using integrated pest and disease management. The Sinograin I and II projects are good examples of successful collaboration between countries with very different geographical, social, and cultural conditions and show what can be achieved with mutual understanding and respect. This book aims to provide valuable information to all stakeholder groups including policymakers, the agro-technology industry, the general public, and farmers. We hope too that this book will serve as an inspiration for others, so that the SDGs can indeed be achieved by 2030.

Chapter 2

Developing Precision Nitrogen Management Strategies for Different Crops and Scales of Farming Systems in North China Krzysztof Kusnierek, Yuxin Miao, Junjun Lu, Xinbing Wang, Hainie Zha, Rui Dong, and Jing Zhang

Abstract This chapter describes the work performed within the Sinograin II project on implementation of new precision nitrogen management technologies in three regions of North China. Each of the analyzed regions represents a different crop and scale of a farming system: large-scale rice farming system in Heilongjiang province, medium-scale maize farming system in Jilin province, and small-scale wheat farming system in the North China Plain. A village was selected in each region to represent the agricultural practices and current nutrient and crop management K. Kusnierek (✉) Center for Precision Agriculture, Department of Agricultural Technology, Norwegian Institute of Bioeconomy Research (NIBIO), Kapp, Norway e-mail: [email protected] Y. Miao (✉) Precision Agriculture Center, Department of Soil, Water and Climate, University of Minnesota, St. Paul, MN, USA e-mail: [email protected] J. Lu Lhasa Plateau Ecosystem Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, China X. Wang Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China H. Zha School of Computer and Informatics, Anqing Normal University, Anqing, Anhui, China Anhui Eagle Information Technology Co Ltd, Anqing, Anhui, China R. Dong Institute of Tobacco Research, Chinese Academy of Agricultural Sciences, Qingdao, China J. Zhang Anhui Eagle Information Technology Co Ltd, Anqing, Anhui, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Clarke et al. (eds.), Innovation for Environmentally-friendly Food Production and Food Safety in China, Sustainable Agriculture and Food Security, https://doi.org/10.1007/978-981-99-2828-6_2

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strategies of the tested region. Moreover, the initial regional optimum crop management, the current agricultural extension, as well as the precision nitrogen technologies implemented in the respective regions are described. During the course of the project, a number of novel tools and strategies for precision nitrogen management were developed for the respective regions and published in scientific papers. This chapter reviews and discusses the selected findings and indicates directions of the upcoming research. Keywords Nitrogen · Fertilization · Rice · Maize · Wheat · Remote sensing · NDVI · UAV · Machine learning · Online service

2.1

Introduction

With the world’s largest population, China is facing one of the greatest challenges of this century to continue to increase annual cereal production and ensure food security with shrinking cropland and limited resources, while maintaining or improving soil fertility, and protecting the environment. China is now the world’s largest producer, consumer, and importer of chemical fertilizers, accounting for over 30% of the world’s nitrogen (N) fertilizer consumption (Zhang et al. 2013). However, mismanagement of N fertilizer is common in China (Miao et al. 2011; Norse and Ju 2015) and recovery efficiency (RE) of N has been declining steadily from 37% in 1960 to 29% in 2007 (Conant et al. 2013). On the other hand, per capita N footprint in China has increased 68% from 19 kg N yr-1 in 1980 to 32 kg N yr-1 in 2008 (Gu et al. 2013). Nitrogen fertilizer related greenhouse gas (GHG) emissions account for about 7% of total GHG emissions in China and have exceeded soil carbon gain related to N fertilizer use by 700% (Zhang et al. 2013). It has also contributed to the widespread surface and groundwater pollution (Norse and Ju 2015). China has fully realized this important issue and adopted a new “Zero Growth” policy to restrict the further increase in chemical fertilizers after 2020. Chinese scientists have developed regional optimum crop management (ROCM) practices and guidelines to increase crop yield and nutrient use efficiencies compared with current farmers’ practices (Chen et al. 2014). In addition to using the current extension systems to promote the application of the ROCM technologies, China Agricultural University has developed the Science and Technology Backyard (STB) program, with graduate students living in villages to work together with farmers to do on-farm research, demonstration, training and consulting services, to help smallholder farmers adopt such crop management technologies (Zhang et al. 2016). However, these ROCM practices don’t consider field-to-field and within-field variabilities, as well as year-to-year weather variability impacts on crop management (Cao et al. 2012, 2017). Precision agriculture considers both spatial and temporal variability in crop production and aims to optimize the key factors in both space and time for improved yield, quality, safety, resource use efficiency and profitability with reduced environmental pollution (Gebbers and Adamchuk 2010; Zhao et al. 2013). Precision agriculture has huge potential to further improve crop management

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through the ROCM practices to support China’s food security, safety, and sustainable development. Sinograin I WP1 developed a framework of the digital system for providing site-specific N recommendations for the farmers through an on-line service. We have identified tremendous variability of farm sizes and growth conditions. The major objective of WP1 in Sinograin II was to build on the progress of Sinograin I WP1 and further develop precision N management (PNM) strategies, technologies, and service providing systems for different (large, medium, and small) scales of farming systems in northern China at the village level to support the “Zero Increase” policy and “Green Growth” of Chinese agriculture.

2.2

Various N Management Strategies in Different Scales of Farming Systems

Three different crops and scales of farming systems in North China were selected in this project: rice, summer maize, and winter wheat. Each crop was grown in a respective region: Heilongjiang, Lishu, and the North China Plain (Shandong) (Fig. 2.1a). Various agro-ecological and structural conditions in the three selected regions had had different implications for the agricultural productivity, farmer practices, and the implementation of the PNM strategies (Fig. 2.1b). In the following sub-sections, the three selected regions with the demonstration villages, the regional optimum crop management, and the current agricultural extension systems will be introduced and the PNM technologies that had been tested and/or implemented in these regions before the onset of the project will be shown.

2.2.1

N Management in Large-Scale Rice-Growing System of Heilongjiang Province

The study area of the Heilongjiang Province in Northeast China is located in Jiansanjiang on the Sanjiang Plain (47.2°N, 132.8°E), which is an alluvial plain of three rivers, Heilongjiang, Songhua, and Wusuli Rivers, and covers about 108,900 km2. The region is bordered by Siberia in the north and east. The Sanjiang Plain was dominated by marshes and wetlands, and it was converted to agricultural production in the 1960s. The main soil type of the study site is Albic soil (classified as Mollic Planosols in the FAO-UNESCO system). Organic matter (OM) content, pH, total N, Olsen-phosphorus, and available potassium contents measured in the topsoil (0–20 cm) before rice production were 40.5 g kg-1, 6.58 g kg-1, 1.59 g kg-1, 46 mg kg-1, and 192 mg kg-1, respectively. The region is characterized by a cooltemperate sub-humid continental monsoon climate with a warm summer and cold winter. The temperature ranges from -41 °C in the winter to 38 °C in the summer, with the mean annual air temperature of about 1.9 °C. The annual average

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Fig. 2.1 Different (a) scales and (b) structures of farming systems in the selected locations of North China

precipitation is 550–600 mm, with 70% occurring from July to September. The region receives about 2300–2600 h of direct irradiation annually, and the frost-free period is only about 120–140 days. Jiansanjiang has 740,000 ha arable land and rice is grown on 86% of the total area. With only 200,000 population, each farmer’s household manages on average about 25 ha of agricultural land, which makes it the largest scale of farming in China. Field operation including soil cultivation, transplanting rice seedlings, spraying and grain harvesting is mainly mechanized. Rice yield ranges for a typical farmers’ practice in Qixing Farm is 6–10 t ha-1, with the majority falling in the range of 7–9 t ha-1. Nitrogen partial factor productivity (PFP—the ratio of grain yield to the amount of applied N, kg kg-1) was mainly in the range of 60–100 kg kg-1. Ninety-five % of the rice production in this area is

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exported to other regions in China, making Heilongjiang increasingly important for China’s food security. China Agricultural University established Jiansanjiang Experimental Station in this region in 2011, which also features a Science and Technology Backyard, providing rice management support to farmers. With a Rice Office and a Modern Agriculture Development Center, the rice farmers of Qixing Farm receive help with soil sampling and nutrient management. Based on the agricultural research performed in the region, a regional optimum crop management (ROCM) practice was established targeting the yield-limiting factors to optimize rice management for this region. The ROCM aimed at improving farmers’ practice by: suitable rice transplanting at warm weather, reducing planting seed rate by around 20%, increasing density by 20% and seeding less per hole by 30%, performing alternate wetting and moderate drying irrigation, optimizing N with about 100–120 kg N ha-1 applied as three splits (40% at planting, 30% at tillering, and 30% at stem elongation stage), applying a total of 45 kg P2O5 ha-1 as basal fertilizer and the total K2O rates were 75 kg ha-1 applied in two equal splits, before transplanting and stem elongation stage. The development of fast, cost-effective, and reliable methods for in-season sitespecific diagnosis of rice N status is crucial for the success of wider applications of PNM strategies to improve N use efficiency (NUE) and reduction of negative environmental impacts. Different remote- sensing techniques were applied in this region. At a leaf-level, SPAD and leaf color cards were commonly used by farmers for N diagnosis. At farmers’ rice plots, active crop canopy sensors (GreenSeeker and Crop Circle) have been used successfully for non-destructive diagnosis of crop N status and PNM (Yao et al. 2012). These sensors include light sources, and their operation is not limited by environmental light conditions. At a village scale, it is time-consuming and challenging to operate the hand-held active crop sensors across large paddy fields. Satellite remote sensing is potentially more efficient for monitoring crop growth status across large areas, although its implementation is also challenging (Huang et al. 2015). These studies showed the potential of implementing PNM strategies in the Heilongjiang province, which could substantially increase NUE but does not have a significant impact on yield when compared with farmer’s practice.

2.2.2

N Management in Mid-scale Maize Growing System of Jilin Province

The study area of the Jilin Province in Northeast China is located in Balimiao Village (124.39°E, 43.33°N) belonging to Lishu Town, Lishu County. It is in the hinterland of the Songliao Plain and in the golden corn belt of Northeast China. This village has a subhumid continental monsoon climate, with the annual average temperature of 6.6 °C, annual accumulated temperature (>10 °C) of 3056 °C, annual average rainfall being at 556 mm, annual sunshine hours being at 2656 h, and annual average

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frost-free period being at 142 days (Fig. 2.1). The predominant soil type in the region is black soil (loamy clay) equivalent to typic Haploboroll according to the United States Department of Agriculture (USDA) Soil Taxonomy. The typical soil properties are: soil organic matter of 25.5 g kg-1, total nitrogen of 1.20 g kg-1, available phosphate of 35.0 mg kg-1, exchangeable potassium of 210 mg kg-1, and pH of 5.5. Scaled services and cooperatives are the main way for small farmers of this region to advance crop production, but the development of cooperatives was greatly limited by the abilities of their leaders. How to train a group of high-level farmer leaders quickly and develop scale service was an urgent problem to be solved. Lishu County of Jilin Province educated scientific and technological leaders through a 10-year high-yield competition, and the scientific and technological leaders established cooperatives to provide small-scale farmers with land and crop management and other large-scale services. The results showed that the competition was an effective way to select and train excellent farmers. The competition activities, the establishment of cooperatives, and the provision of large-scale services strengthened the larger and more progressive farmers, but this could also be a driver of the productivity and income of smaller farmers. Affected by the variation of crop management capabilities of the local farmers, the differences in cultivars and soil types, as well as limitation of the water resource for irrigation, etc., most farmers were at the middle and low yield levels (6–12 t ha-1). At present, the agricultural market in Lishu County is mixed, with a large selection of maize varieties, including a lot of miscellaneous and non-mainstream varieties (varieties with 1% adoption rate by farmers). The planting density is often at 5–7 plants m-2, which is far less than the recommended planting density for Lishu County. Compared with the local recommended N application range of 160–240 kg ha-1, the N application rates of farmers were higher, with 41% of farmers applying more than 250 kg ha-1. The traditional single-rate fertilization at sowing leads to the loss of nutrients through the ground or volatilization into the atmosphere, causing environmental pollution. The typical size of a farmer’s field in the region is about 0.5–1.0 ha. The average yield gap of spring maize is 6.0 t ha-1, and the average yield was 65.4% of the simulated yield potential. The average N efficiency gap was 20.2 kg kg-1, and 70.1% of the efficiency potential was realized (Cui et al. 2014). The ROCM practice recommends the N rate to be 180–200 kg N ha-1 with split applications before planting and at around 6–8 leaf stage. Deep soil tillage and improved planting is also suggested. Selecting a high yield variety with resistance to high density and disease is recommended. Planting density in the range of 6.5–7.0 plants m-2 is considered optimal in the region. This region also features the STB involving agricultural scientists and graduate students living in villages among farmers, advancing participatory innovation and technology transfer, and garnering public and private support. They identified multifaceted yield-limiting factors involving agronomic, infrastructural, and socioeconomic conditions. When these limitations and farmers’ concerns were addressed, the farmers adopted recommended management practices, thereby improving production outcomes. PNM is in the preliminary stage, with research and promotion started in this region

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in 2014. The models of real-time N status estimation and crop yield potential prediction for different soil types were established using agronomic data and vegetation indices obtained with active canopy sensors (GreenSeeker and Crop Circle ACS 430). Preliminary crop N fertilizer recommendation algorithms based on active canopy sensors were developed. Compared with regional optimum N management, PNM based on remote sensing technology showed the potential of increasing NUE in spring maize production under on-farm conditions by 37%.

2.2.3

N Management in Small-Scale Wheat Growing System of North China Plain

The study area of the North China Plain (NCP) was located in Nanxia village (37°43′ N and 117°13′E) in Laoling county, Shandong province. The climate in the village is warm temperate semi-humid continental monsoon. The region receives about 2500 h of direct irradiation annually and the annual mean temperature is 12.4 °C, with the maximum and minimum being 13.6 °C and 11.2 °C, respectively. The annual mean precipitation is 527 mm and the mean frost-free period is 198 days. Winter wheat is generally planted in early October and harvested in early June the following year. For typical farmer’s practice in the region, most farmers use their own wheat seed reserves, perform soil cultivation together with neighbors to cover many fields, use rotary tillage at 12–15 cm depth, plant 300 kg seed ha-1 with 17 cm row spacing, and apply 280 kg N ha-1 in total, with 108 kg N ha-1 and 172 kg N ha-1 applied as basal fertilizer before planting and as topdressing at stem elongation, respectively. They generally apply 140 kg P2O5 ha-1 and 38 kg K2O ha-1 in total as basal fertilizer, irrigate in early spring at the time of growth recovery and after top-dressing and apply insecticide once at seedling stage, with no use of fungicide throughout the growth period. Due to the large population but limited land resources, agricultural production in the NCP at the reference spatial scale of 20 ha would be managed by about 100 farmers. In Nanxia Village, a typical village in Shandong province, there are about 53 ha of farmland for winter wheat managed by 440 farmers. This means the average arable land per capita is only about 0.12 ha. Due to insufficient income from farming, most young people perform other types of work in the city centers, and farming is mainly performed by elders and women without advanced farming skills. Due to the small scale of farming enterprise, the mechanized production in the region is only at 40%. Economic benefits derived from improved management practices generally do not translate into economic incentives for them to adopt new technologies. The yield variation is also large. Based on a household survey performed in the region, the yield of winter wheat ranged from 5.4 t ha-1 to 11.0 t ha-1, with a mean of 8.5 t ha-1. The N PFP ranged from 17 kg kg-1 to 46 kg kg-1, with a mean of 29 kg kg-1 (Chen et al. 2018). A number of farmer practices lead to yield loss in winter wheat, including the use of unsuitable varieties for this region, inappropriate sowing dates, shallow tillage and poor sowing quality, high N application before planting, and poor water

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management. In order to achieve high yield and high NUE, new management practices were applied, including planting suitable varieties, optimizing sowing date, improving sowing quality, optimizing water management and N management to apply 60% of total N use in shooting stage. According to a survey of 150 farmers in the study area, the technology adoption rate was encouraging with suitable varieties (97.8%), optimizing sowing date (66.7%), water management (66.3%), N management (38.4%), and sowing quality (30.8%) (Zhang et al. 2016). Challenges of introducing modern agricultural strategies in this region included lack of knowledge, risk-aversion tendency, and lack of quality regulation for the agricultural material market. Another aspect was a loss of agricultural labor. Part-time farmers who hold additional jobs in nearby cities found it difficult to perform important farm work such as sowing, fertilizing, and harvesting in time. Moreover, smallholder farmers in the region do not have access to sufficient agricultural machinery and agricultural services. The ROCM in the region recommends the following practices, including using high yield and high efficiency varieties, organizing neighboring farmers for coordinated planting, irrigation, fertilization, plant protection and harvesting using modern machinery, deep tillage sowing at 27 cm row spacing with 165 kg seed ha-1, applying 219 kg N ha-1 (with 81 kg N ha-1 and 138 kg N ha-1 applied before planting and at stem elongation, respectively), applying 27 kg P2O5 ha-1 in total as basal fertilizer, and 112 kg K2O ha-1 in total applied in two equal splits, irrigating at four key stages (before seeding, before winter, stem elongation stage, and flowering stage), and applying fungicide and insecticide twice during the crop growth period. The ROCM was supported by the national “high yield high efficiency” (DH) umbrella project, funded via several grants by government agencies (Zhang et al. 2016). With the development of new sensing technologies, PNM aims to optimize N fertilizer inputs by considering the spatial and temporal variability. It has been regarded as a promising strategy to improve NUE and protect the environment. Active canopy sensors (Crop Circle and RapidSCAN) have been tested as tools for N management in the region. Zhou et al. (2017) used Crop Circle active sensor-based in-season N management algorithm in winter wheat cropping systems and found PNM strategy significantly increased PFP by 29% compared with farmers’ practices but did not have any significant improvement over the ROCM system. Fixed-wing Unmanned Aerial Vehicle (UAV) with multispectral cameras is a tool with a large potential in this fragmented region.

2.3

Overview of Precision N Management Strategies Developed in the Sinograin II Project

In the course of the Sinograin II project, the precision N management working group conducted numerous scientific investigations based on the cooperation in the previous and current phases of the project, which resulted in the development of PNM

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tools and strategies as published in scientific journals. In the following sub-sections, these studies will be reviewed according to the regions or the scale of the farming systems.

2.3.1

Precision N Management in Large-Scale Rice Farming System of Heilongjiang Province

As described above, the level of research on PNM in the Heilongjiang test village was already high before the onset of the project. Therefore, the scientific work was focused on continuing the research performed earlier and moving it a step forward. That included developing improved tools for estimating agronomical parameters of the rice crop using spectral data collected from remote-sensing platforms. It is essential for China’s food security and sustainable agricultural development that N management in rice is optimized. In order to gather spectral reflectance data, a crucial data source for PNM, fixed-wing UAV-based remote sensing, a low-cost, simple-to-use technology, can be deployed. Typically, vegetation indices are derived from spectral reflectance data and used as predictors, but their relationships with crop parameters are known to be nonlinear. Nonlinear machine learning techniques that may increase the precision of estimating rice crop parameters were investigated (Zha et al. 2020). This study concluded that the estimation of rice N status including aboveground biomass, plant N uptake, and N nutrition index (NNI) at stem elongation and heading stages in Northeast China using UAV remote sensing could be significantly improved by random forest regression (RFR). In another study, the use of RapidSCAN, a portable active canopy sensor with three spectral bands: red, red-edge, and near infrared, was investigated as a tool for sensor-based PNM strategy for high-yielding rice in Northeast China (Lu et al. 2020). Using normalized difference vegetation index (NDVI) at the stem elongation stage and normalized difference red edge (NDRE) at the heading stage, respectively, the results showed that the sensor performed well for estimating rice yield potential and yield response to additional N application. In contrast to previously developed strategies, recommending N rates only at the stem elongation stage, a new RapidSCAN sensor-based PNM strategy, making N recommendations at both the stem elongation and heading growth stages was developed. In comparison to farmers’ N management practice, this new PNM approach could reduce N fertilizers by 24% and increase NUE by 29–35% without materially affecting rice grain yield or economic returns. The new PNM strategy increased grain yield by 4%, NUE by 3–8%, and economic returns by $148 per ha when compared to regional optimal N management (Fig. 2.2). This study concluded that the developed PNM strategy was suitable for guiding in-season N management in high-yield rice management systems. In a following study, this strategy utilizing the proximal sensing tool was extended to develop an integrated precision rice management (PRM) system for improving grain yield and quality, NUE, and lodging resistance (Lu et al. 2022a).

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Fig. 2.2 The outline and results of a portable active canopy sensor-based precision nitrogen management strategy for high-yielding rice in Northeast China (from Lu et al. 2020)

The previous study showed that PNM using active crop sensors had the potential to significantly increase NUE, but generally had little impact on crop yield. The new study aimed at developing a PRM system incorporating many management practices and targeting yield increase in comparison to farmer practice, the ROCM system advised by the extension service, and a chlorophyll meter-based PRM system. The results showed that the integrated PRM system based on canopy sensors significantly increased rice grain yield (by 9.4–13.5%) over farmer practice, while enhancing NUE, grain quality, and lodging resistance. In the cool weather year, the developed system reduced the N rate applied in one of the tested rice cultivars by 12% and improved NUE without causing yield loss. This was in comparison to the already optimized regional optimum rice management system. The canopy sensorbased system tested in another rice cultivar, suggested applying an 8% higher N rate than the regional optimum rice management in the warm weather year, which increased the number of rice panicles per unit area and ultimately resulted in an increase of grain yield by over 10%, while improving NUE. Integrating multiple crop management practices with canopy sensor-based N management systems for yield increase is an interesting research direction, and it should be further tested in a

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wider range of on-farm conditions. It is also important to scale it up for field applications using UAV or satellite remote-sensing technologies. Other scientific reports published in the framework of the Sinograin II project on rice production in Heilongjiang province included: in-season calibration of crop growth model (CERES-Rice) with proximal canopy reflectance data for rice yield prediction (Zha et al. 2021) and improving active canopy sensor-based in-season rice nitrogen status diagnosis and recommendation using multi-source data fusion with machine learning (Lu et al. 2022b).

2.3.2

Precision N Management in Mid-Scale Maize Farming System of Jilin Province

The mid-scale maize farming system was indicated in our investigations as a region with the highest potential of implementing PNM due to low current implementation level and large upscaling potential. The challenge in this region is due to relatively small scale of a single farming enterprise mitigated by joint field operation within farmer cooperatives. Taking into account its high potential, more resources were allocated to this region testing several N management strategies including various PNM tools like proximal remote sensing and crop growth models. In-season N status diagnosis and recommendation methods must be reliable and effective for PNM to be successful. It has been discovered that crop management techniques, weather patterns, and soil characteristics all have an impact on how accurate these methods are. To improve N management during the growing season, it is critical to effectively incorporate all of these variables. The ability of machinelearning techniques to process various data types and model both linear and non-linear relationships makes them promising in improving the prediction models. In one of the studies conducted in Jilin province of Northeast China, soil, weather, and management data were combined with active canopy sensor data using random forest regression instead of Lasso linear regression to improve in-season predictions of maize NNI and grain yield (Wang et al. 2021a). Also, a new in-season side-dress N fertilizer recommendation strategy at eighth to ninth leaf growth stage (V8–V9) was developed in that study using the machine-learning model. In comparison to using only active canopy sensor data (R2 = 0.66 and 0.62–63) based on the test dataset, the results showed that maize NNI and grain yield were better predicted by combining soil, weather, and management information with active canopy sensor data using a non-linear model (R2 = 0.86 and 0.79). The grain yield prediction model was used to simulate maize grain yield responses to a series of side-dress N rates at the V8–V9 stage and to develop an innovative in-season side-dress N recommendation strategy. These response curves were then used to calculate the optimal sidedress N rates for a given site and year. The results of the scenario analysis showed that this RFR model-based in-season N recommendation strategy could suggest sidedress N rates with root mean square errors (RMSE) of 17 kg ha-1 and relative errors

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Fig. 2.3 Flow chart of the procedure to determine economic optimal N rate for basal N application at planting stage and side-dress N application at eight-leaf (V8) stage using crop growth model and weather data fusion (from Wang et al. 2021b)

(RE) of 14–15% that were comparable to those based on measured agronomic optimum N rates (AONR) or economic optimum N rates (EONR). In comparison to an approach based solely on crop sensor data, it was concluded that using RFR to combine soil, weather, and management information with crop sensor data could significantly improve both in-season maize NNI and grain yield prediction, as well as N management. The unpredictability of future weather at the time of decision-making is another challenge in PNM. To run yield simulations, crop growth models need a full season’s weather data, which can be forecasted or replaced by historical data. Another study performed in Jilin province on maize focused on (1) using weather data fusion to develop a model-based in-season N recommendation strategy and (2) assessing the effectiveness of the strategy in comparison to farmers’ N rates and regional optimal N rates (Wang et al. 2021b). The calibrated CERES-Maize model was used to forecast grain yield and plant N uptake at two N decision dates, planting stage and V8 stage, by combining current and historical weather data (Fig. 2.3). This method resulted in a model that accurately predicted grain yield and plant N uptake (R2 = 0.85–0.89). The in-season EONR was then established in accordance with simulations of marginal returns in response to N rates at the planting and V8 stages based on predicted grain yields. Approximately 83% of predicted EONR values were within 20% of actual values, and when compared to farmers’ N rate and regional optimal N rates, the model had the potential to increase marginal return by up to $183 ha-1 and $83 ha-1, as well as to increase NUE by up to 71% and 38%, respectively. The CERES-Maize model proved to be a useful tool for simulating yield responses to N under various planting densities, soil types, and climatic conditions. The study concluded that comparing the model-based in-season N recommendation strategy with weather data fusion to current farmer practices and regional optimal management practices can increase maize NUE.

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In addition to active crop reflectance, proximal leaf, and canopy fluorescence sensors were also investigated (Dong et al. 2021a, b, 2022). The twofold objective of the study of Dong et al. (2021a) included assessing the potential of using Dualex four sensor indices measured on three differently positioned leaves to estimate NNI across different growth stages and determining if the incorporation of environmental and management information can significantly improve the in-season N status prediction and diagnosis. The outcomes of the study showed that at various growth stages, the two Dualex indices (Chl and NBI) had strong relationships with NNI, and both stage-specific and cross-stage models could estimate NNI based on their values obtained from variously positioned leaves. However, with Kappa values all below 0.40, the N diagnostic accuracies based on the estimated NNI by the Dualex indices were unsatisfactory. However, the prediction of NNI (R2 = 0.81–0.85) and the accuracy of maize N status diagnosis (areal agreement = 0.79 and Kappa = 0.52–0.55) were significantly improved when weather and management variables were combined with Dualex sensor measurements in multiple linear regression analysis. This study indicated that further investigation is needed to develop strategies combining more environmental and crop management variables with fluorescence data to further improve in-season N status diagnosis and N management in Jilin province. Other selected scientific reports published in the framework of the Sinograin II project on maize production in Jilin province included: developing active canopy sensor-based PNM strategies (Wang et al. 2019), assessing variability of EONR in response to soil and weather conditions (Wang et al. 2020), and investigating the use of proximal fluorescence sensing for in-season maize nitrogen status diagnosis at an early growth stage (Dong et al. 2021b) and across growth stages (Dong et al. 2022).

2.3.3

Precision N Management in Small-Scale Wheat Farming System of North China Plain

About 80% of China’s farming land is made up of smallholder farms, which must produce food that is both economically viable and environmentally sustainable. The small-scale wheat farming in NCP is very interesting to PNM due to the many challenges occurring in implementation of the management systems. The size of a single field is very small, and the time of sowing and crop varieties used are very different among the farmers, which is problematic for deployment of remote-sensing platforms for estimation of crop properties. The low level of net returns renders the implementation of precision technologies and fertilization strategies difficult in this region, limiting the applicability of the scientific investigations. In one of the studies performed in NCP in the framework of the Sinograin II project, the fixed-wing UAV-based remote sensing was demonstrated to be a promising technology for in-season diagnosis of winter wheat N status in smallholder farmer fields at village scale (Chen et al. 2019). Utilizing vegetation indices,

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Fig. 2.4 The N diagnosis maps created using UAV remote-sensing data for the smallholder fields of Nanxia village, Shandong province of the North China Plain in 2018 (from Chen et al. 2019)

two indirect and two direct approaches were evaluated for estimating NNI. The estimates were used to produce N diagnosis maps, indicating areas with N surplus, N deficiency, or optimum N delivery (Fig. 2.4). Three frequently used normalized difference vegetation indices were compared to the best performing vegetation indices from 59 tested indices in order to facilitate practical applications. The most practical and reliable method (R2 = 0.53–0.56) involved using vegetation indices to determine the N sufficiency index (NSI) and then estimate NNI non-destructively. Directly diagnosing N status using NSI thresholds had a 57–59% diagnostic accuracy rate, which was quite consistent. This approach worked well and was least impacted by the selection of vegetation indices across fields, varieties, and years. The considerable variability in local soil conditions and crop management practices influenced the overall accuracy of N diagnosis. Therefore, in order to develop practical UAV remote-sensing-based in-season N recommendation methods, more studies are needed to optimize the reported strategy and cover the high variability in this region. Another study compared the use of crop yield proxies derived from historical satellite images with soil information derived from remote-sensing data, and the integration of these two data sources, in order to define management zones for a

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single village (Cammarano et al. 2020). The tested village covered approximately 177 ha and comprised 540 single farmer fields. Dividing the village into three or four zones was decided to be the most practical arrangement, because it is simpler to manage many fields in a few zones rather than on an individual basis when low mechanization is the rule. The Green Normalized Difference Vegetation Index (GNDVI) was calculated using Landsat satellite data, and management zones were considered a reasonable predictor (up to 45%) of measured variation in soil N and organic carbon. This approach proved to be functional with a minimal data set, but a more holistic decision support system should be developed to integrate management zones and agronomic recommendations to improve crop management. In a different study, the agro-ecological variability of the smallholder farming systems in two regions in China including NCP and its influence on estimation of plant N status was tackled (Li et al. 2022). With an objective to use RFR models together with multi-source data to improve the estimation of winter wheat N status across two agro-ecological zones, the study encompassed 15 site-year plot and farmers’ field experiments involving different N rates and 19 cultivars. Based on such a comprehensive dataset, the results showed that using machine learning models integrating climatic and management factors with vegetation index (R2 = 0.72–0.86) outperformed the models based only on vegetation indices (R2 = 0.36–0.68) and that they performed well across agro-ecological zones. Using Pearson correlation coefficient-based variable selection strategy, 6–7 key variables were selected. The contributions of climatic and management factors to N status estimation varied with respect to the location of a zone and N status indicators. Climate factors, particularly those related to water, were more crucial to N status estimation in higher latitude regions. The performance of the RFR models for NNI estimation has been significantly enhanced by the addition of climatic factors. While management variables were more important for the estimation of N status in lower latitude regions, climatic factors were crucial for the estimation of aboveground biomass. This portrays the differences between the regions and necessity of validating PNM tools in all agro-ecological regions.

2.4

Designing the Service Providing System

In the previous Sinograin project phase, our working group developed a framework of the digital system for providing site-specific N recommendations for the farmers through an on-line service. Using available tools for presentation of spatial content in cooperation with a company specialized in information technology, our working group took part in development of a system providing information for crop management as an online service. This Service Providing System was developed in two versions, as an online browser-based system to be used by the local governments providing extension services to the farmers and as a WeChat APP, available directly to the farmers in the test region. The application has been first developed for the Jiansanjiang site in Heilongjiang province, as this region, among the tested regions,

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Fig. 2.5 Snapshots from the prototype service providing system implemented in the Heilongjiang test site showing NDVI maps deployed within (a) a We-Chat APP and (b) a browser-based online tool, as well as (c) a map of farmers adopting the service tested in the region

is the most advanced region agronomically, and the relatively large scale of agricultural operation allows for rapid implementation among the users. While the browserbased big-screen system gives a user an overview over a region allowing for implementing agricultural policies and support to the farmers (Fig. 2.5b), the WeChat APP is a simple means of providing individual farmers with agronomic information personalized to their fields (Fig. 2.5a). WeChat is a very popular mobile communicator in China, also used by farmers. Deploying the apps through WeChat has therefore a chance of success, as the users do not need to download the app; they just need to open and directly use it. With satellite image used as the base map, the farmers may draw boundaries of their field, and the vector layer is superimposed onto the background. By clicking on the plot, the users can get the basic information of the field, available from the database including major nutrient contents of the soil. In addition, users can obtain N-P-K fertilization plans according to the target yield output of the field, which can be converted into the amount of compound fertilizer to be used. Users can also access products derived from the satellite spectral reflectance data. The system has a link to the Google Earth engine and the latest satellite data such as Sentinel-2 and Landsat-8, which are stored in time series and can be retrieved by a user’s query. From the reflectance data, we have calculated several commonly used vegetation indices. Users can also calculate a custom vegetation index or agronomic parameters using the available raw data. Another available product is weather data from the nearest meteorological station. In the future, a number of sensors may be deployed in the field to collect microclimate data. Increasing spatial resolution of the weather

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data will allow for supplying users with more accurate information on their crop growth conditions. We have implemented a beta version of precision rice management into the system. At given times, the service will inform farmers about the recommended agricultural work, including fertilization, plant protection, and irrigation. In addition, investigating NDVI field maps derived from the satellite data, leaf area map, or a map of NNI for a given field at different growth stages is very educational to the farmers. The system has been deployed in the region’s Chuangye Farm. Several farmers agreed to be test users of the service providing systems, and their usage may be monitored and utilized in further development of the system (Fig. 2.5c).

2.5

On-Farm Demonstration Trials in Jilin Province

The on-farm demonstration trial was conducted in Balimiao Village, Lishu County, Jilin Province, in cooperation with the Lishu Experimental Station of China Agricultural University and Luwei Agricultural Machinery Farmers Professional Cooperative. Two crop management strategies (farmers’ routine management and the high-yield, high-efficiency precision management) were set up in a 1.5-ha farmer demonstration field, with three replicates for each strategy. The farmers’ routine management (FM) was carried out in accordance with the crop management practices of local farmers (Planting density: 60,000 plants ha-1, N rate: 220 kg ha-1, P2O5 rate: 100 kg ha-1, K2O rate: 100 kg ha-1). All N, P, and K fertilizers were applied as basal fertilizers before planting. The high yield, high efficiency precision management (PM) was carried out according to the high yield and high efficiency technical regulations of spring maize in Northeast China, meanwhile remote- sensing technology was used to recommend side-dress N fertilizer at 8–9 leaf stage (Planting density: 70,000 plants ha-1, basal N rate: 60 kg ha-1, P2O5 rate: 100 kg ha-1, K2O rate: 100 kg ha-1). All P and K fertilizers were applied as basal fertilizers before planting. The recommended algorithm of nitrogen fertilizer application adopts the method proposed by Wang et al. (2021a), where a series of nitrogen application amounts ranging from 0 kg N ha-1 to 300 kg N ha-1 were set, with 20 kg N ha-1 as the gradient. Additional supporting information included: crop management, soil characteristics, weather data, and crop reflectance data (NDVI and RVI collected by GreenSeeker during the 8–9 leaf stage of maize development). Then, the random forest regression model was established to simulate the grain yield potential of maize under different nitrogen application rates in the current season, and the optimal nitrogen application rate was determined according to the response curve of corn grain yield to nitrogen application rates. Base fertilizer and side-dressing fertilizer were ditched mechanically and applied in strips. During the maize harvest stage, three points were randomly selected in each plot for yield measurement, and the

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Fig. 2.6 The results of the on-farm demonstration trials in Jilin in 2021 and 2022, including: grain yield, partial nitrogen fertilizer productivity (PFPn), and marginal return. FM Farmer’s routine management, PM Precision management

measured yield area was 66.7 m2. The maize ears were weighed, threshed, and the grain water content was determined. The results were recalculated to the standard grain yield containing 14% grain water content. Then, NUE and economic returns were calculated. According to 2 years of demonstration trial results, the PM could increase corn grain yield by 7.2% and 9.3% (Fig. 2.6a), marginal return by 10.5% and 13.7% (Fig. 2.6b), and nitrogen use efficiency by 31.5% and 27.9% (Fig. 2.6c) over FM in 2021 and 2022, respectively.

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Discussion and Future Perspectives

Along with the agricultural intensification, information technology and the continuous development of mechanization, the application of remote-sensing technology for crop N status diagnosis and N fertilizer recommendation has become an important research direction. Structure of agricultural production is considerably different in different regions and scales of farming in China. The reality makes implementation of novel PNM strategies developed by research challenging as a function of scale of farming systems. In the regions with large-scale farming enterprises, like the Jiansanjiang region in Heilongjiang for large scale rice farming, where a considerable level of mechanization and agronomic expertise is already present, the implementation of precision technologies is more likely than in the regions with smallholder farms, where farmers obtain small production margins and are prioritizing other sources of income. The farmers, operating large-scale farming systems of Heilongjiang, are keen to learn about and test the new technologies for sustainable N management. Proximal active canopy sensors have been well established by research as rapid tools for estimating crop properties and establishing N fertilizer recommendation strategies, but their application may be more suitable for smallscale farmers in North China. However, the practitioners in medium- and large-scale agriculture in Jilin and Heilongjiang provinces, where the working efficiency of the handheld canopy sensor may be low, require other, more scalable remote-sensing tools such as UAVs and satellites. These tools also come with their challenges. It is hard to obtain satellite image data at the growth stages needed for guiding in-season topdressing N recommendations because of complete overcast weather conditions, common in the rice production areas. The UAV-based remote sensing was considered a very promising tool for PNM, but its deployment and data processing require special training, which could be supported by local government or private enterprises. Another opportunity for larger scale farming systems in China is to apply active canopy sensor systems installed on large farm machinery and equipped with the corresponding variable rate N fertilizer application system. This solution has successfully been implemented in Western agriculture and may greatly improve the work efficiency of the Chinese agricultural production. Other data sources should be fused with crop reflectance data to improve modeling accuracy. Under various climate conditions and diversity of rice varieties grown in the cold region, remote-sensing-based N status diagnosis and management models should be innovative and include weather and soil data. Using machine-learning methods to develop new fertilization algorithms may improve the estimation accuracy and performance of PNM. Fitting N nutrition to crop N requirements typically improves N uptake efficiency but rarely improves yield over a flat fertilization in a given farmer’s field. An important future perspective should include integrating other crop management practices, i.e., use of an appropriate cultivar for an agroecologic zone, sowing density, irrigation strategy, etc., to nitrogen management in order to obtain higher yields. This topic was already investigated in the framework of

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the Sinograin II project but should be followed up to include more crop species and regions. All in all, new agricultural technologies will be more likely adopted by farmers if their implementation leads to an increased yield that can be turned into extra profit, which could pay for the implementation cost. Otherwise the implementation of the technology should be covered by direct subventions from the government or projects. The introduction of precision nitrogen technologies in the smallholder systems of the NCP is very challenging in many aspects. First of all, the agricultural land is very fragmented. The farmers are generally elderly people with low education, which will definitely limit the adoption of the new technologies. The extension system in the region is generally very weak, with no extension personnel at the village level. Due to the relatively low level of wealth among the farmers and limited economic incentives from the government, modern facilities and machineries are not available. The modern agricultural technology is generally complex and time and labor consuming, rendering it uninteresting to the potential users. The active canopy sensors, such as Crop Circle 430 and RapidSCAN, are not affected by sunlight and measuring height. Being relatively cheap and easy to operate, they could be implemented in NCP. However, considering the efficiency of diagnosis and wide applications, UAV-based remote sensing may overcome the limitations of ground-based sensing and was suitable for village-scale crop monitoring and N management in NCP. There are several challenges for using N diagnosis and topdressing N recommendation at village scale. For example, how to distinguish nutrient deficiency from water deficiency and how to consider the influence of soil and meteorological variation on N status diagnosis. More studies are needed to further develop and evaluate fixed-wing UAV remote-sensing-based PNM strategies in small farming systems. The software-based service for precision management was lacking in the investigated agricultural regions. Its development enabled farmers to conveniently use precision agriculture tools and strategies developed by the agricultural scientists. Our working group contributed to the design of the system, function implementation, and promotion of the system in the large-scale rice farming system of Heilongjiang. In the next steps, the system will incorporate the strategies published in the upcoming scientific papers and will possibly get implemented in the middle-scale maize farming systems of Lishu. Acknowledgements The work was funded through the project “CHN17/0019 Sinograin II: Technological innovation to support environmentally-friendly food production and food safety under a changing climate-opportunities and challenges for Norway-China cooperation” (funded by the Norwegian Ministry of Foreign Affairs through the Royal Norwegian Embassy in Beijing).

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Chapter 3

Food Safety and the Importance of Comprehensive Analytical Methods for Pesticides and Other Contaminants Marianne Stenrød, Kathinka Lang, Marit Almvik, Roger Holten, Agnethe Christiansen, Xingang Liu, and Qiu Jing

Abstract To ensure compliance with food safety regulations, monitoring programs and reliable analytical methods to detect relevant chemical pollutants in food and the environment are key instruments. Pesticides are an important part of pest management in agriculture to sustain and increase crop yields and control post-harvest decay, while pesticide residues in food may pose a risk to human health. Thus, the levels of pesticide residues in food must be controlled and should align with Maximum Residue Levels regulations to ensure food safety. Food safety monitoring programs and analytical methods for pesticide residues and metabolites are well developed. Future developments to ensure food safety must include the increased awareness and improved regulatory framework to meet the challenges with natural toxins, emerging contaminants, novel biopesticides, and antimicrobial resistance in food and the environment. The reality of a complex mixture of pollutants, natural toxins, and their metabolites potentially occurring in food and the environment implies the necessity to consider combined effects of chemicals in risk assessment. Here, we present challenges, monitoring efforts, and future perspectives for chemical food safety focused on the importance of current developments in high-resolution mass spectrometry (HRMS) technologies to meet the needs in food safety and environmental monitoring.

M. Stenrød (✉) · K. Lang · M. Almvik · R. Holten · A. Christiansen Division of Biotechnology and Plant Health, Norwegian Institute of Bioeconomy Research (NIBIO), ÅS, Norway e-mail: [email protected] X. Liu Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China Q. Jing Institute of Quality Standards and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Clarke et al. (eds.), Innovation for Environmentally-friendly Food Production and Food Safety in China, Sustainable Agriculture and Food Security, https://doi.org/10.1007/978-981-99-2828-6_3

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Keywords Food safety · Environmental pollution · Monitoring · Quantitative analytical methods · Non-target screening · Pesticides · Natural toxins · Antimicrobial resistance · China · Norway · Europe

3.1

Introduction

Food safety is an important part of food security and refers to the nutritional value and the absence of any harmful substances in a food commodity (Savary et al. 2017). Ensuring food safety requires a regulatory framework aimed to minimize the environmental impact and food safety risks that may arise from agricultural production practices as well as comprehensive systems for monitoring and regulatory compliance check. Food production that complies with food safety and environmental regulatory requirements is a challenging and complex task under ever-changing conditions. Climate change for instance is expected to change weather patterns and increase temperatures, which can compromise food safety by exacerbating foodborne disease and increasing contamination by mycotoxins (Duchenne-Moutien and Neetoo 2021). In addition, anti-microbial resistance is a concerning issue which is also affected by climate change (Duchenne-Moutien and Neetoo 2021). Healthy plants are important for food security and food safety with plants providing up to 80% of food (FAO 2021), making threats to plant health a threat to food security and food safety. Throughout the 10,000-year-old history of agriculture and farming crops, mankind has faced the risk of crops being destroyed by fungi or insects. The need to protect these crops led the early farmers to look for remedies around them that could reduce these damages. The Sumerians 4500 years ago found that sulphur compounds could control insects and mites, and 3200 years ago the Chinese used mercury and arsenical compounds to control lice. Modern pesticide history dates back to the 1940s when the need to increase food production led to the development of synthetic crop protection products (Tudi et al. 2021). Since then, pesticides have included a range of chemical classes, the number of substances has increased, and these chemicals have been widely adopted in agriculture and used across the globe to protect crops against damage from pests like insects, fungi, and weeds. Pesticides are an important part of pest management in agriculture and help to increase crop yields and control post-harvest decay (Guo et al. 2020). Without them, the loss of fruits, vegetables, and cereals could amount to 78%, 54%, and 32%, respectively (referred to in Tudi et al. 2021), making agricultural production dependent on pesticide use and at the same time leading to environmental problems and food safety challenges. Regular spraying of pesticides can cause a continuous and diffuse contamination of ecosystems, resulting in toxicity to non-target species and persistence in the environment. In a review on the fate of pesticides in the environment, Gavrilescu (2005) states that 4 million tons of pesticide-active ingredients are applied to crops annually around the world to control different pests. This number has been confirmed by Zhang (2018) who also stated that global pesticide use increased steadily until 2007 when numbers seemed to stabilize or slightly decrease. Of applied

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pesticides, less than 1% reach their target due to loss during spraying, run-off, and photodegradation (Bernardes et al. 2015), leaving a large amount in the environment. So, despite the regulatory efforts to prevent release or non-target effects of pesticides in the environment and effects on human health (e.g. Directive 2009/128/ EC and 2013/39/EU, Regulation (EC) 1107/2009; Regulation EC 396/2005; Food Safety Law of China 2009; Chinese Pesticide Regulations 1997, 2001, 2017; Law of the People’s Republic of China on Quality and Safety of Agricultural Products 2006, 2014), pesticides are detected in soil, water, and air samples (Balmer et al. 2019; De Souza et al. 2020; Larsbo et al. 2016; Lefrancq et al. 2017; Silva et al. 2019; Tudi et al. 2021), ultimately affecting food commodities and human health. New analytical technology providing high resolution and accurate mass determination of molecules in complex mixtures is now available for the discovery and targeted and non-targeted screening of pesticides, transformation products/metabolites, and other emerging contaminants (Dzuman et al. 2015; Llorca et al. 2016; Pico and Barcelo 2015; Storck et al. 2016). A comprehensive analytical approach with such rapid and broad-scope screening methods is increasingly important and essential to ensure minimal pesticide residue levels in food and the environment (Guo et al. 2020). To ensure compliance with regulations, monitoring programs and reliable non-target screening methods are key instruments (Flynn et al. 2019; Guo et al. 2020; Wu and Chen 2018). Pesticide use and human health issues have been shown to correlate, and this has been extensively reviewed by, e.g. Rani et al. (2021) and Kalyabina et al. (2021) with pesticide residues in food posing a risk to human health by playing a role in the development of cancer, diabetes, and various other conditions. Thus, the levels of pesticide residues in food must be controlled and should align with Maximum Residue Levels (MRL) regulations to ensure food safety. The MRL describes the maximum allowed pesticide residue concentration in a food product that is still considered safe for human consumption (FAO 2020a). When a crop protection product is used in accordance with the instruction on the product label, there should be no pesticide residues above the MRL in the produce. MRLs are part of the Codex Alimentarius, a collection of international standards, practices, and guidelines regarding food safety and fairness in international trade (FAO/WHO 2018). The Codex MRLs are however not mandatory, and countries or regions still need their own legislation on pesticide residues, and not all are in alignment with the Codex MRLs. Ensuring compliance with regulations is also an important aspect of trade. A major cause of conflict between the EU and China (Beestermöller et al. 2018; Qian et al. 2020; Zolin et al. 2018) is food safety issues. Scientists from the Norwegian Institute of Bioeconomy Research (NIBIO) and the Chinese Academy of Agricultural Sciences (CAAS) have through collaboration in the Sinograin II project explored the possibilities of using innovative technologies in order to improve food safety and sustainability in Chinese agriculture. In the following, we present challenges and monitoring efforts to secure chemical food safety with a specific focus on residues of pesticides and metabolites in food and the environment in China and Norway. The importance of current developments in highresolution mass spectrometry (HRMS) technologies to meet the needs in food safety

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and environmental monitoring is discussed with specific reference to the risks posed both by pesticide use and the presence of natural toxins due to pest management challenges. Future perspectives presented include the importance and relevance of HRMS methods for food safety monitoring of emerging contaminants and to assess combined toxicity risks. There is also a need for comparatively broad screening methods for (micro)biological parameters to assess the occurrence and risks connected to persistence of biopesticides and occurrence of antimicrobial resistance in food and the environment.

3.2 3.2.1

Pesticide Use and Environmental Challenges Agricultural Use of Pesticides

China achieved remarkable increases in food production, feeding around 20% of the world population with less than 10% of the arable land (Lal 2018). This depends on the intensive use of chemical inputs like fertilizers and pesticides, which in turn cause environmental pollution and food safety issues, showing the challenge of balancing increased food production while minimizing environmental problems and food safety challenges. In 2020, 2.7 million tons of pesticide active ingredients were used worldwide, resulting in a global application rate of 1.8 kg/ha (FAO 2022). However, there is a great disparity in the amounts applied in different countries, with Africa using on average 0.46 kg/ha compared to the Americas where 2.83 kg/ha are applied (FAO 2022). China holds the world’s largest pesticide production with 1.73 million tons produced in 2008 and has seen a steady increase in pesticide use from 1991, reaching 1.5 million tons in 2005 (Zhang et al. 2011). According to Zhang (2018), the total use of pesticides has declined since 2015 when Chinese authorities launched a zero growth action in the use of pesticides. China has implemented policies aimed to reduce pesticide use and the occurrence of residues in food with the Pollution-Free Food Action Plan and the Action Plan for Zero Growth in Pesticide Use by 2020 (Liu et al. 2020). However, a use of approx. 1.8 million tons annually has been reported for the years 2013–2017 in China (Rani et al. 2021). Within the EU, statistics on pesticide use are lacking, but sales numbers show that in 2016 a total of about 400,000 tons active substance were sold within the EU. The sales numbers have been more or less constant in the period between 2011 and 2016 (EEA 2019). In about the same period (2012–2016), sales numbers in Norway averaged 775 tons active substance per year (NFSA 2015, 2018). Sales numbers and the use of pesticides vary from year to year due to variation in the pest situation, but looking at the last 5 years, the annual average sales numbers in Norway amounted to 703 tons per year for the period 2017–2021, a significant reduction from the prior 5-year period (NFSA 2022). This is in line with the adoption of the EU regulations for sustainable use of pesticides (EC 2009) in Norway from 2015, which focus on the use of integrated pest management practices with minimum use of chemical pesticides in crop protection. Overall, although the sales and use of

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pesticides vary between the years, it appears that global pesticide sales and use are declining. The European Green Deal is part of the European Commission’s priorities for 2019–2024 and includes a Farm-to-Fork Strategy for a fair, healthy, and environmentally-friendly food system. This policy initiative aims to reduce pesticide use, in general, and specifically to reduce the use of hazardous pesticides by 50% by 2030 (EC 2020).

3.2.2

Development of Pesticide Resistance

The first reported case of resistance of a pest to a pesticide was documented in 1914 for an insecticide (Melander 1914). Since then, cases of pesticide resistance have been rising, leading to the formation of cross-industry resistance action committees for insecticides (IRAC), herbicides (HRAC), and fungicides (FRAC) in the 1980s (Sparks et al. 2021). As of today, over 500 cases of herbicide-resistant weeds, involving 267 species with resistance to 21 out of 31 known sites of action are reported (Heap 2022). About 600 insect species have been reported being resistant to over 325 insecticides and for fungicides a total of 273 cases of resistance involving more than 100 active ingredients (Beckie et al. 2021). In addition, pests can develop resistance to multiple different modes of action. The development of resistance was faster for insecticides and fungicides compared to herbicides (FAO 2014). Resistance as defined by the FAO is a “genetically-based characteristic that allows an organism to survive exposure to a pesticide that would normally have killed it” (FAO 2014). Through genetic mutation and inheritance, resistance genes spread naturally in a population with increased selection pressure due to the repeated use of pesticides. Resistant individuals survive and reproduce while individuals susceptible to the pesticide get eliminated. With continuing use of the pesticide treatment, the susceptible population will decrease to a point where the pesticide provides no acceptable control (FAO 2014). Several agronomic practices for pest control have been identified as facilitating resistance development. These are the continued and frequent use of a single pesticide or closely related pesticides, using application rates above or below what is recommended, poor coverage of the treated area, frequent treatment of organisms with large populations and short generation times, and the lack of incorporation of non-chemical control practices (FAO 2014). It is also important to follow good agricultural practices that help to prevent the spread of resistance, such as proper cleaning of farm equipment and crop rotation (FAO 2014). Another factor is the type of pesticide. Generally, pesticides with a single target site that are applied frequently to large pest populations with short generation cycles are more at risk of resistance development than pesticides with several target sites with less frequent use. Broad spectrum pesticides are also more likely to cause resistance. They are effective against a wide range of pests and therefore used more frequently making resistance development more likely (FAO 2014).

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As a response to increasing resistance to various pesticides, especially of insects to insecticides, integrated pest management (IPM) was proposed (Stern et al. 1959). Today, IPM is a fundamental part of resistance management. It entails alternative pest management techniques, reduced pesticide use and generally using pesticides only when absolutely necessary, thus minimizing the selection pressure for resistance (FAO 2014). When pesticides are used, avoidance of repeated use of pesticide with the same mode of action is important (Sparks et al. 2021). This is especially challenging in weed management where only a few sites of action remain available (Lamichhane et al. 2016). With the loss of active ingredients due to resistance, the available choices for pesticide resistance management decline, leaving the remaining pesticides at a higher risk for development of resistance (Busi et al. 2013; Hillocks 2012). Fewer pesticides are available not only because of developed resistance but also due to a decline in new agrochemicals entering the market (Phillips 2020) and others being phased out. Further challenges include the effects of climate change on the distribution of pests, resistant and non-resistant species, as well as pesticide interaction with climate change (Duchenne-Moutien and Neetoo 2021). For example, rising CO2 concentrations in combination with elevated temperatures and direct sunlight might increase the volatilisation of pesticides, resulting in lower concentrations (Delcour et al. 2015) and therefore less exposure for pests. As a result, pesticide resistance management might become more challenging under climate change.

3.2.3

Persistence and Transport of Pesticides in the Environment

Understanding the behaviour of pesticides in the environment is important for reducing food contamination. Pesticide contamination of food can be due to both the direct spraying of pesticides on crop plants as well as the pesticide behaviour in the environment. The transport from plants and soil to water and other parts of the ecosystem as well as plant uptake of residual pesticides from the growth medium are processes that might lead to food contamination (Tudi et al. 2021). After the spraying of pesticides on weeds, or crop plants infested with pathogens or insect pests, the pesticides will often move away from the target plants, resulting in environmental pollution. Such chemical residues can impact human health either via the environmental contamination directly or through subsequent food or drinking water contamination. Monitoring and research studies show that applied pesticides can be transported away from the area of application. Transport can occur through several pathways (Fig. 3.1) including vaporization into the atmosphere, surface runoff to nearby ponds or ditches, or leaching downwards through the soil. In the latter case, pesticides can reach drainage pipes that transport the pesticides towards surface water, or they can reach the groundwater (Riise et al. 2004; Sandin et al. 2018; Stone et al. 2014). A range of factors influence the fate and behaviour of pesticides in the environment (Fig. 3.2) including the pesticide properties, climate and soil factors, cropping

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Fig. 3.1 Different transport pathways of pesticides in the environment (figure adapted from L. Torstensson, www.slu.se/ckb, 2022)

Fig. 3.2 Factors influencing the fate and behaviour of pesticides in the environment and the resulting exposure concentrations in different environmental compartments. (Figure adapted from O.M. Eklo, lecture notes PJH300, Norwegian University of Life Sciences)

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practices, and the processes governing the transformation of the pesticides in the environment. Based on their structure and functional groups, pesticides may have a range of intrinsic chemical-physical properties that are closely connected and depend on either each other or other factors present in the environment in which the substances are applied, e.g. soil and water pH, soil composition, and climatic factors. Substances with high vapour pressure have a higher risk of vaporization from either soil or plant surfaces into the atmosphere and later deposition in more vulnerable areas (Balmer et al. 2019; Guida et al. 2018). Water solubility determines to a high degree whether the substances mix in soil water and follow the water flow through the soil or are retained in other phases or matrixes in the soil. Biotic degradation processes are where soil microorganisms (e.g. fungi, bacteria) utilize the pesticides as a source of carbon, either directly or indirectly (co-metabolic). Some molecules, e.g. highly halogenated (i.e. containing one or more bromine, chlorine, fluorine, iodine) molecules, have a structure that makes them less accessible for microbial attack, resulting in very high degradation halflives (DT50-values). An example is DDT which has reported half-lives of over 6000 days (Lewis et al. 2016). Compounds with DT50 values of more than 120 days in soil or freshwater sediments are defined as persistent within the EU legislative framework (EC 2011). Abiotic degradation includes, e.g. hydrolysis and photolysis. Hydrolysis is a process where the substance reacts directly with water leading to a decomposition of the compound. Photolysis involves the decomposition of the pesticide by reactions with photons. Both these chemical processes often depend on pH and temperature. Pesticides can sorb to soil particles such as organic material or clay minerals depending on their net charge in soil. Pesticides that sorb strongly to soil particles are less prone to transport in the water phase in soil but can rather be transported bound to soil particles (Holten et al. 2019; Tang et al. 2012). The pesticide dissociation constant (pKa) is important in describing the behaviour of ionizable pesticides for which sorption is strongest near their pKa, and sorption increases as pH decreases (Stougaard et al. 1990). Sorption may become irreversible over time, and this is termed aged sorption. Substances that undergo aged sorption often tend to be less available for microbial degradation and become more persistent with time (Barriuso et al. 2008; Beulke et al. 2015). Soil composition, i.e. the content of minerals like sand, clay, or silt and organic carbon, can have great impact on the fate and behaviour of pesticides in soil. The proportion of clay, silt, and sand affects the surface charge and surface area for pesticide sorption. Soils with high clay content have a greater ability to adsorb pesticides, but they are also more susceptible to runoff. Sandy soils on the contrary provide fewer sites for adsorption and leach more readily. The way soil particles aggregate is also important and can affect water movement quite dramatically. Soil consists of a mixture of soil particles which forms aggregates, and in between these aggregates pores of different sizes are formed. The amount of these pores, or the pore volume and the pore size, depend largely on the texture of the soil. Clay soils can have a pore volume of 50%, but these pores are often very small. Water is transported quite slowly under these conditions and may be adsorbed to soil

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particles. Sandy soils often have much smaller pore volumes, but the pores are bigger, and water can be transported more easily. Large cracks or macropores caused by heaving, roots, or soil animals can cause rapid pesticide movement in the soil, even in fine textured soils like clay, and especially in partially frozen soil. This can transport pesticides to deeper layers where less degradation occurs. Eventually, pesticides can be transported to groundwater or drainpipes leading water to surface water (Holten et al. 2018; Kjær et al. 2011). Soil pH and the content of organic matter can also affect pesticide transport as mentioned above. Regarding field topography, a general rule is that a field with a steep slope will give larger surface runoff than a flatter field. Furthermore, the longer the slope, the more soil will potentially be eroded and lost from the field. Depressions in flat fields may act as hot spots with regard to leaching of both nutrients and pesticides (Doppler et al. 2012; Tang et al. 2012).

3.2.4

Impacts of Climate and Climate Change

The amount of rainfall and the temperature may play a significant role in the fate and behaviour of pesticides. Heavy rainfall immediately after pesticide application can be expected to cause losses both due to runoff and leaching. This has been observed in studies where surface water has been sampled from streams or groundwater near agricultural fields that have suffered heavy rains shortly after pesticide application (Chen et al. 2019; Lefrancq et al. 2017). Heavy rain or rapid snowmelt on frozen ground in winter/spring can also lead to runoff and erosion (Commelin et al. 2022; Ulrich et al. 2018; Willkommen et al. 2019). Soil moisture conditions at the time of pesticide application can also be very important since pesticides to a large extent are transported within the water phase. Infiltration of water into soil depends on the soil’s content of water, and when the soil is very dry, water can infiltrate faster than if it is saturated (Chen et al. 2019; Willkommen et al. 2021). Degradation of pesticides is highly influenced by temperatures. At temperatures below 0 °C, very little microbial degradation occurs in soil, but degradation rates increase with increasing temperature (Farha et al. 2016). The soil microbial activity, and hence biological degradation of pesticides, is affected by moisture content and soil temperature. It is also well established that temperature affects sorption processes, and higher temperatures usually result in desorption of sorbed pesticides (Rani and Sud 2014; Stenrød et al. 2008). If temperatures fall well below 0 °C for a longer period, soil water will freeze. When soil freezes in autumn bigger pores are often air filled, being able to facilitate fast transport of larger amounts of water and pesticides into the soil. If temperatures continue to stay below 0 °C, bigger pores can also become blocked by ice and the soil becomes more or less impermeable (Holten et al. 2018). Soil moisture content is also important in areas with soil frost during winter as the water content governs the amount of ice that forms in the soil in autumn. If the soil is saturated upon freezing, it may take a long time to thaw the soil in spring. This is because it takes a lot of energy to thaw the ice throughout the soil

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profile. The degree of water infiltration into the soil is hence very important. Ponds or lakes can form in depressions in fields during snowmelt in spring. Water can then infiltrate very fast when the soil thaws (Holten et al. 2018). Climate change models predict that the weather in temperate areas will become warmer and wetter and that the frequency of freezing-thawing cycles will increase in cold areas. All this will naturally affect transport processes in soil. The same models also predict higher temperatures and more heavy rainfall (Hanssen-Bauer et al. 2017). Higher temperatures may cause faster degradation of pesticides, while increased rainfall and/or more intense rainfall may cause higher risk of surface runoff (Delcour et al. 2015). The transport of mobile pesticides in soil is to a large degree influenced by the time gap between pesticide application and extreme weather events with transport of maximum concentrations/amounts often coinciding with heavy precipitation shortly after spraying (Blenkinsop et al. 2008; Nolan et al. 2008). Furthermore, in a changing climate, pesticide use itself is expected to be affected. In northern Europe, one can already see that the thermal growing season (defined as the period when the mean temperature exceeds 5 °C) has been extended, with approximately 12 days in the south-east of Norway compared to the period before 1990 (Aalto et al. 2022). In an analysis of data from the long-term Norwegian Agriculture Environmental Monitoring Program (JOVA), Wenng et al. (2020) showed that on average the thermal growing season had increased with 0.66 days per year in the period 1994–2017, summing up to approximately 16 days for seven different catchments. Looking at the data for the Nordic countries as a whole, the thermal growing season starts about 15 days earlier and lasts about 23 days longer in 2019 compared to 1950 (Aalto et al. 2022). A longer thermal growing season may result in earlier sowing dates in spring, the introduction of new crops and/or crops adapted to the longer growing season and higher crop yields, but may also result in increased pesticide use (Wiréhn 2018). In addition, increased temperatures and changes in precipitation patterns are major factors with an impact on insect pest, pathogen, and weed infestations. With changed climate, pests might have a longer window for survival and even spread to new areas, resulting in new challenges for farmers in these areas (Olesen et al. 2011; Patterson et al. 1999). This may result in higher intensity of pesticide use in the form of higher amounts, higher application frequencies, and different types of applied products. Increased exposure in the environment with potentially detrimental effects to both human health and the environment may be the result (Delcour et al. 2015; Grace et al. 2019; Halsch et al. 2021; Kattwinkel et al. 2011; Steffens et al. 2015; Zhang et al. 2018). With pesticide residues remaining in the environment and food products, sufficiently comprehensive analytical detection methods are crucial to assess the exposure to pesticide residues and to secure food safety and the environment.

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Meeting the Challenges of Detecting Pesticides and their Degradation Products in Food and the Environment Application of Low Resolution Mass Spectrometry (LRMS) in Pesticide Residue Detection

To meet plant protection products regulations, food and environmental testing laboratories rely on analytical systems capable of targeted detection and quantification of pesticides at low concentrations. Triple quadrupole mass spectrometry instruments such as LC-MS/MS (LC: liquid chromatography) and GC-MS/MS (GC: gas chromatography) are especially suited for this and are designed to give highly sensitive analyses of pre-selected groups of compounds in the food samples (Stachniuk and Fornal 2016). The instruments are optimized for detecting selected pesticides in food and environmental samples that the laboratories would expect to potentially find in these products. LC-MS/MS has been demonstrated to provide a wider scope of pesticides and better sensitivity than GC-MS (Alder et al. 2006; Carmona et al. 2013). The national standard of China recommends the use of liquid chromatography-mass spectrometry (LC-MS) for multi-residue detection due to its proven superior performance in terms of sensitivity, selectivity, and capacity of analysis of a broad scope of compounds (Galani et al. 2019; Huang et al. 2019). As an integral component of the food safety risk analysis framework, monitoring and surveillance have developed rapidly in China after the promulgation of the Food Safety Law of the People’s Republic of China in 2009. There are developed standards for determination of pesticides in different food matrices by use of LC-MS methods including, amongst others, the national food safety standard “Determination of 448 pesticides and related chemicals residues in tea by liquid chromatography-mass spectrometry” (GB 23200.13-2016), the national food safety standard “Determination of 512 pesticides and related chemicals residues in fruit and vegetable juice and wine by liquid chromatography-mass spectrometry” (GB 23200.14-2016) and the national food safety standard “Determination of 440 pesticides and related chemicals residues in edible fungi by liquid chromatography-mass spectrometry” (GB23200.12-2016). In Norway, the Norwegian Institute for Bioeconomy Research (NIBIO) holds a national reference laboratory (NRL) for the analysis of residues of pesticides in food of plant and animal origin and is part of the community of European NRLs for which the European Reference Laboratories (EURLs) develop and validate methods. Such a collaborative network with joint workshops and trainings ensures the use of stateof-art and standardized methods for pesticide residue monitoring across the EU/EEA. The 1300 food samples NIBIO annually analyses on commission for the Norwegian Food Safety Authority are analysed with two targeted multi-methods using LC-MS/MS (NIBIO method no. M86) and GC-MS/MS (NIBIO method no. M93) technology, respectively. A selection of samples also undergoes analysis with a set of single-residue methods targeting more analytically challenging pesticides that cannot be measured properly by the multi-methods. The multi-methods

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and most of the single residue methods are accredited (NS-EN ISO/IEC 17025: 2017). The two multi-methods cover in total 379 pesticides and selected pesticide metabolites. The targeted analysis methods are in line with the requirements of current EU/EEA regulations and are annually updated to fulfil the mandatory scope of pesticides and metabolites (EC 2022a). The standardization of analytical methods as well as the extent of the monitoring is far more developed within the field of food safety as compared to the environment. However, there are long-standing regulatory and policy initiatives to maintain good environmental quality and health both in Europe and China that would benefit from a larger degree of standardization to enable better comparisons and a broad scope of monitoring parameters to be able to make a more holistic assessment of the exposure situation. Key amongst these is the development of environmental quality standards for surface water and requirements for exposure assessment and restoration to good status of contaminated water bodies, as well as monitoring initiatives for agricultural soils at regional, national, and larger scales.

3.3.2

Application of High-Resolution Mass Spectrometry (HRMS) in Pesticide Screening

The triple quadrupole mass spectrometry methods described in the above section perform mass measurement at a low mass resolution (LRMS) and are not designed to detect all components in a sample. Hence, they are limited in their ability to detect non-targeted, unknown compounds. This ability is however a prominent and increasing need considering today’s widespread use of pesticides in food production, the fact that pesticides occur in mixtures in food and environmental compartments rather than as single compounds, and the degradation these compounds undergo in the environment increasing the complexity of the mixture by introducing metabolites. Current development in hybrid high-resolution mass spectrometry (HRMS; e.g. quadrupole orbital trap mass analysers [Q-Orbitrap MS] and quadrupole–timeof-flight [Q-TOF] tandem mass analysers) meets this need in food safety and environmental monitoring (Hollender et al. 2019) with recently developed suspect screening methods for up to 850 pesticides (Wang et al. 2019). These novel comprehensive screening approaches include both non-targeted methods aimed to identify unknown compounds and suspect screening approaches aimed to identify known unknowns by use of comprehensive mass spectrometric reference data. High resolution mass spectrometry (HRMS) can simultaneously carry out accurate mass determination of the molecular ion of the compounds and collect rich fragment ion information after collision fragmentation. Based on the relevant mass spectrometry database (MS data) and fragmentation spectra library (MS/MS data), the unknown substances can be tentatively identified. The compound identity must be verified by comparison to a reference standard of the suspected compound. Finally, a quantitative determination of the compound can be obtained using

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reference standard calibration curves, which is very important to enabling the use of results from qualitative screening methods in risk assessments that require quantitative exposure data (McCord et al. 2022). LC-HRMS and GC-HRMS screening methods vastly expand the pesticide scope of the laboratories and reduce the occurrence of false negative detections (Guo et al. 2020). This is particularly important for rooting out new or misused pesticides, while also allowing retrospective analysis. Retrospective analyses allow the identification of substances that were not part of the analytical scope of previously analysed samples (Hernandez et al. 2012). HRMS screening methods may not only address pesticide residues but can be specifically useful for screening and detection of many types of contaminants in a sample, including emerging pesticide contaminants, co-formulants, mycotoxins, plant toxins, and pesticide metabolites that are not in the scope of the targeted methods (e.g. Dzuman et al. 2015). To meet food safety monitoring needs in China, the Institute of Quality Standards and Testing Technology for Agro-Products (IQSTAP) developed a screening method for 642 pesticide residues and metabolites in rice, wheat, and wheat straw, which is applicable also for fruits and vegetables. The screening method integrated a simple QuChERS method for sample preparation and HRMS of Q-exactive orbitrap mass spectrometry with a high resolution. The analytical protocol was drafted and applied to analysis of real samples from different provinces in China through the Sinograin II project (cf. Acknowledgements). At present, high-resolution mass spectrometry technology is widely used in the field of proteomics research, and it is relatively less used in the analysis of small molecular chemical pollutants, so its good application prospect is worthy of attention. Due to the complexity of the matrix of food samples, false positive results often appear in the multi-component residue analysis by low resolution mass spectrometry (LRMS), resulting in misjudgement of the results. The development of high-resolution mass spectrometry combined with low sample injection volumes has made up for this defect to a great extent and is a very useful tool for the rapid accurate mass identification of pesticide residues. At the same time, the high-resolution mass spectrometry technology has also played a certain role in promoting the development of less laborious sample pre-treatment technology. A simplified dispersive solid-phase extraction method is sufficient to maximize compound collection and reduce sample clean-up time. The main obstacles faced by high-resolution mass spectrometry are primarily that high-resolution instrumentation is far more expensive compared to low-resolution GC-MS and LC-MS, and second, that quantitative sensitivity might be less than triple quadrupole mass spectrometry, at least for certain compounds. In Norway, NIBIO has established a screening method which includes a pesticide scope of 850 pesticides and metabolites to improve the food safety monitoring and mapping of pesticide residues in soil. For this, NIBIO utilizes LC-HRMS (Thermo QExactive, NIBIO method nos. M121 (qualitative screening) and M119 (quantitative screening)). NIBIO’s screening method has proven to report the correct pesticide results in the annual proficiency tests (i.e. inter-laboratory comparison) in onion, eggplant, and tomato arranged by the European reference laboratory for pesticides in fruit and vegetables in the period 2020–2022. In most cases, the reported pesticide

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concentration from the LC-HRMS screening method is within ± 20% of the concentration reported by the targeted methods (LC- and GC-MS/MS). These tests also showed the advantage of the screening method through the detection of several pesticides not included in our targeted methods. The limit of quantification is 10 μg/ kg or better for most compounds in food and soil. Under current EU Commission regulations, there is an option for the member states –- and Norway –- to apply qualitative screening methods on 15% of the samples to be analysed in the joint EU multiannual food safety control programme (EC 2022a). This is a recognition of the advantage of high-resolution mass spectrometry as a tool for the broad scope detection of pesticides. However, the same regulation also says that ‘Where the results of qualitative screening are positive, Member States shall use a usual target method to quantify the findings’, implying that a screening method cannot be both qualitative and quantitative. It would certainly be more efficient to perform both screening and quantification of the pesticides on one and the same instrument. Typically, the qualitative screening would be performed in full scan MS-data independent acquisition mode with matches to a suspect pesticide database, whereas the quantitative/confirmatory analysis would be performed in full scan MS-datadependent acquisition mode with a targeted pesticide inclusion list, both on the same high-resolution instrument (e.g. Wong et al. 2021). The definition – or understanding – of what a screening method can provide is further restricted by the current requirements for the validation of pesticide screening methods (Pihlström et al. 2022) where retention time and screening detection level have to be determined for each pesticide. Hence, the laboratory must have reference standards of all pesticides in a validated screening method, even though the measured accurate mass of the molecular ion and the MS/MS spectral similarity matches in our experience are often sufficient for a tentative identification by a screening method. Up to now, pesticide screening methods have been regulated more or less in the same way as targeted triple-quadrupole methods, meaning that the full potential of highresolution screening methods has not been fully exploited. This is going to change with the ever-increasing availability of instrument-specific MS/MS spectra libraries for pesticides and their metabolites and the on-going development from suspect screening to the fully non-targeted screening of any unwanted contaminant in the sample extract.

3.4 3.4.1

Food Safety Monitoring Chemical Food Safety and Pesticide Residue Monitoring in the EU and Norway

The EU food safety policy and action is concentrated in the areas of (1) food hygiene (i.e. food businesses, from farms to restaurants, dealing with both domestic and imported produce, which must comply with EU food law), (2) animal health

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(i.e. sanitary controls and measures for pets, farmed animals and wildlife are in place to monitor and manage diseases, and trace the movement of all farm animals), (3) plant health (i.e. detection and eradication of pests at an early stage is a priority to prevent spreading and ensuring healthy seed), and (4) contaminants and residues (i.e. monitoring is in place to keep contaminants away from food and animal feed, and maximum acceptable limits are set and apply to domestic and imported food and feed products). Monitoring programmes for undesirable/harmful substances in food are an important measure to ensure availability of safe food to the consumers. Chemical food safety monitoring and the monitoring for pesticide residues, which is the most developed part of the chemical food safety monitoring programmes within the EU/EEA, are the main focus here. The European Union (EU) monitoring programme on pesticide residues in food consists of two parts, the EU-coordinated multiannual control programme (MACP) and the national control programmes (MANCP) for the member states, including Norway and Iceland (EFSA 2022). In the MACP, the most consumed products in the EU are sampled in a 3-year cycle according to Regulation (EU) 2022/741 where combinations of pesticides (about 200 pesticides) and a minimum number of samples for 12 food products to be analysed by all EU member states is specified. This allows for a direct comparison of the sampled products and their development over the years. It is crucial to have good and up-to-date knowledge of changes in consumption patterns and products with a high risk of the occurrence of substances harmful to health, limit values, and a health risk assessment to uncover real health risks to the greatest extent possible. In 2015, EFSA published an evaluation of the procedure for selecting the products to be included in the EU coordinated programme (EFSA 2015), where representativeness and uncertainty both for findings exceeding the MRL and the associated estimate for consumer exposure were assessed. Today’s sampling considers the requirements that were revealed here and must be revised every 3 years to capture changes in consumption patterns and use of pesticides. However, there are differences between the countries that participate in the monitoring as the sampling in the EU-coordinated programme is planned nationally and it also includes a nationally controlled monitoring. In the national programme (MANCP), member states can focus more on countryspecific diets as well as products that are expected to exceed the legal limits and pose a risk to consumer safety (Article 30 of Regulation (EC) No 396/2005). Legal limits or maximum residues levels (MRLs) are the highest level of a pesticide residue that is legally tolerated in or on food or feed when pesticides are applied according to Good Agricultural Practices (GAP) (EU 2022). MRLs are harmonized in the EU and established in Regulation (EC) No. 396/2005 and are defined for over 1300 pesticides covering 378 food products/food groups (EFSA 2022). If a pesticide is not covered in the regulation, a default MRL of 0.01 mg/kg is applied. Member states are obliged to share the results from the national monitoring programme with the EU, and an annual report covering the MACP and the national programmes is published by the European Food Safety Authority (EFSA 2022).

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Norway joined the EU-coordinated programme in 1997 but has a long history of monitoring residues of pesticides in food commodities dating back to 1977 (NFSA and NIBIO 2021). The Norwegian Food Safety Authority (NFSA) is responsible for the monitoring of pesticide residues in food in Norway to ensure compliance with the MRLs. All chemical analyses are carried out on contract by NIBIO (Norwegian Institute of Bioeconomy Research) which also plays a main part in the planning and follow-up of sampling, reporting on a national level and to EFSA, as well as giving knowledge support to NFSA. The methods used are accredited according to ISO/IEC 17025: 2017 and great effort is spent on method development and laboratory improvements. NIBIO is appointed as National Reference Laboratory (NRL) for analysis of pesticide residues in food by the NFSA. Each year, approximately 1300 samples are analysed for pesticide residues and of these about 10% are organically produced. The samples are relatively equally divided between domestic food products, samples produced in the EU/EEA and samples imported from third countries. The EU-coordinated programme covers at least 12 samples of each of 12 commodities, including two commodities of animal origin. All other samples are part of the national control programme and are of plant origin. In 2021, the total sampling covered 99 different commodities of fruit, vegetables, cereals, rice, baby food and other food products from 60 different countries. Samples for the monitoring programme are taken by inspectors from the NFSA and are sampled according to the EU directive on methods for sampling for the official control of pesticide residues in and on products of plant and animal origin (EC 2002). In general, samples are taken at the wholesale level, warehouses, packing companies and at retailers. All actors within the food systems are responsible for adhering to the regulatory requirements set for food and food safety. This part of the food safety control is performed by the actors themselves and depends on well-developed self-monitoring/ self-inspection systems at all levels of the food systems, including the primary producer, retailer, wholesaler, importer , etc. However, the public surveillance monitoring and control is a very important and consistent part of the system to ensure safe food with low residues of pesticides on the European and Norwegian markets.

3.4.2

Routine Monitoring Programmes of Agro-Product Quality and Safety in China

A legislation and monitoring system for food safety in China has developed especially through the last two decades (Liu et al. 2019; Wu and Chen 2018), enabling food import/export in line with the requirements under the WTO agreement and with a food safety monitoring that ensures that marketed products comply with current national (Chinese) and/or international (OECD/EU) maximum (residue) limits. The ‘Law of the People’s Republic of China on Quality and Safety of Agricultural

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Products’ clearly requires the state to establish a monitoring system for the quality and safety of agricultural products, and the establishment of China’s agro-products risk monitoring system has played an important role in improving the quality and safety of agricultural products and protecting consumer health.

3.4.2.1

Development Process

The routine monitoring of the quality and safety of agricultural products in China has developed through four stages, including ‘from scratch’, ‘development and expansion’, ‘improvement’, and ‘reform and innovation’ (Gao and Yao 2021). A description of the characteristics of these development stages is given below. 1. ‘From scratch’ stage (2001–2005): After entering the twenty-first century, the agricultural production capacity has been greatly improved. However, the food safety and environmental problems caused by illegal and/or excessive use of agrochemicals such as pesticides and veterinary drugs have become increasingly prominent. Acute poisoning incidents caused by pesticides occur frequently, and the quality and safety of agricultural products have caused widespread concern. Therefore, it is urgent to carry out risk monitoring of agricultural products to ensure consumer safety. In 2001, the former Ministry of Agriculture in China selected five cities including Beijing, Tianjin, Shanghai, Shenzhen and Shouguang City, Shandong Province, to perform monitoring for the detection of pesticide residues in vegetables and fruits. The monitoring included 57 monitoring sites, with analysis of 12 pesticides in 241 vegetables. The sampling with analysis was performed three times throughout the year, and the overall qualification (i.e. detected residue levels below regulatory limits) rate was 63.9%. Monitoring of clenpenterol residues in pig liver and urine was carried out in the four pilot cities Beijing, Tianjin, Shanghai, and Shenzhen. This monitoring included analyses of 498 pig liver and urine samples that were sampled at three time points through the year and showed an overall qualification rate of 66.5%. After 2 years of pilot monitoring, the former Ministry of Agriculture expanded the scope in 2003. The designated cities for monitoring of pesticide residues in vegetables were expanded to a total of 37 cities including all municipalities directly under the central government, provincial capitals, and cities separately listed in the national plan. Four pyrethroid pesticides, including cypermethrin, cyhalothrin, alpha-cypermethrin, and lambda-cyhalothrin, were added to the monitoring parameters in addition to the basis of organic phosphorus and carbamate pesticides, to give a total of 13 monitoring parameters. These parameters were monitored for a total of 18,021 vegetable samples selected at five time points throughout the year, yielding a qualification rate of 82.2%. The number of designated monitoring cities for animal by-products was, correspondingly, expanded to 16 cities, and five sulfonamides were added to the monitoring parameters including sulfamonomethoxine, sulfamethazine, sulfamethoxazole, sulfadimoxine, and sulfaquinoxaline. Monitoring of these six parameters in a

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total of 8178 animal by-products sampled at five time points throughout the year resulted in a qualification rate of 97.3%. In 2003, the overall qualification rate of vegetables and animal by-products was 86.9%. In 2004, in order to further improve the routine monitoring system for the quality and safety of agroproducts, the former Ministry of Agriculture undertook the monitoring of chloramphenicol in aquatic products in Beijing, Tianjin, Shanghai, Shenzhen, and Guangzhou, with the analysis of 750 samples throughout the year. 2. ‘Development and expansion’ stage (2006–2009): ‘Law of the People’s Republic of China on Quality and Safety of Agricultural Products’, which was promulgated and implemented in 2006, authorized the original agricultural department to establish a national monitoring system for the quality and safety of agro-products. This law clearly required the agricultural administrative departments of the people’s governments at or above the county level to formulate and organize the implementation of this monitoring plan. Since then, routine monitoring has entered the stage of development and expansion. During 2007 and 2008, the monitoring of vegetables and animal products achieved full coverage in provincial capitals. The monitoring of aquatic products covered most provincial capitals except the northeast and northwest. In terms of food and feed commodities included in the monitoring, the monitoring of animal by-products increased to include chicken. Therefore, animal by-products were expanded into animal products. The monitoring of aquatic products was expanded to freshwater fish and invertebrates such as prawns, grass carp and carp, and seawater fish, such as large yellow croaker and flounder. In terms of monitoring parameters, pyrethroid insecticides and some fungicides were added to the monitoring of vegetables. Fluoroquinolones were added to the parameters for monitoring of animal products, and malachite green and nitrofurans were added to the parameters for aquatic products. In 2007 and 2008, the monitoring included 36,500 and 38,400 batches, respectively, of vegetables, animal products, and aquatic products, with overall qualification rates of 96.2% and 96.9%, respectively. The monitoring scope was further expanded in 2009, now with monitoring of vegetables, fruits, and tea in a total of 89 cities, and with analysis for 58 pesticide residues. For animal products, the monitoring included 88 cities and analysis for 18 veterinary drug residues, while aquatic products were monitored in 84 cities and analysed for 18 veterinary drug residues. In total, six categories of agroproducts (32,000 batches of samples) including vegetables, edible fungi, fruits, tea, animal products, and aquatic products were monitored this year, and the overall qualification rate was 96.3%. 3. ‘Improvement stage’ (2010–2019): With the rapid improvement of detection capability in most cities in China, the variety of detection products, the range of detection parameters, and the detection sensitivity have been greatly improved. Routine monitoring of agro-product quality and safety entered the stage of improvement; the sampling frequency for the monitoring programme was adjusted from three times in 2009 to four times in 2010, and the area, commodities, and parameters included were further increased. The number of monitoring samples for three major categories of agro-products stabilized at about 40,000

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batches, while the monitoring parameters increased steadily from 2011 to 2019. After four rounds of adjustment and improvement in 2010, 2014, 2018, and 2019, more than 110 different commodities of agro-products and 130 monitoring parameters were included in the routine monitoring in 2019. ‘Management Regulations on Food Safety Risk Monitoring’, which were passed at the beginning of 2010, enforce the systematic and continuous collection of monitoring data and related information on foodborne diseases, food contamination and harmful factors in food, and the carrying out of comprehensive analysis and timely notification. On 13 October 2011, the National Center for Food Safety Risk Assessment was formally established. As a national technical institution in charge of food safety risk assessment, the centre undertakes technical support work such as national food safety risk assessment, monitoring, early warning, communication, and development of food safety standards. The establishment of this centre filled a long existing need for a specialized technical institution for food safety risk assessment. It plays an important role in enhancing food safety research capacity, improving food safety level, protecting public health, and strengthening international cooperation and exchange. 4. ‘Reform and innovation’ stage (2020–present): To further improve the representativeness of routine monitoring results, the Ministry of Agriculture and Rural Affairs officially launched a random confirmation working system of prefecturelevel cities in each quarter in 2020. The monitoring of prefecture-level cities in each quarter is randomly generated by a computer system and most prefecturelevel cities in the country are included in the monitoring. At the same time, a risk monitoring sampling APP system was officially launched and implemented to achieve a paperless and information-based operation of sampling and to enhance the scientific nature of the sampling work. This provided strong technical support to fast sampling, fast testing, and fast reporting of monitoring. In 2020, during the COVID-19 pandemic, routine monitoring was still carried out and 34,000 batches of samples from 304 large and medium-sized cities nationwide were included in the monitoring. The overall qualification rate for monitored commodities was 97.8%, which was 33.2% higher than that in 2001. The qualification rates of vegetables, animal products, and aquatic products were 97.6%, 98.8%, and 95.9%, respectively. In 2021, the monitoring scope of China covered more than 150 large and medium-sized cities in 31 provinces, about 110 different kinds of agroproducts and 130 different agricultural and veterinary drug residues and illegal additives.

3.4.2.2

Status

At present, there are two main types of safety monitoring projects for agro-products in China. The first is risk monitoring which mainly involves monitoring and analyses of potentially harmful substances affecting the quality and safety of agro-products. The second category is supervised spot checks. Supervised spot checks include

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sampling and testing activities for law enforcement purposes, enabling the regulatory bodies to react and implement measures when residues are detected above regulatory limits. Compared with risk monitoring, supervised spot checks are irregular, random, and according to strict enforcement requirements of the procedure. In accordance with the requirements of the State Council’s institutional reform, food safety management in China is divided into two parts. The first part includes food safety supervision before entering the wholesale and retail markets or production and processing enterprises. This is called the quality supervision of agro-products and is the responsibility of the agricultural and rural departments. The second part refers to agro-products being managed as foodstuffs after entering the market, enterprises, and catering and is the responsibility of market supervision departments. The import and export of food is regulated by the General Administration of Customs and the customs administrative departments. ‘Administrative Measures for Monitoring the Quality and Safety of Agricultural Products’, revised in 2022, makes a clear distinction between risk monitoring and supervision spot checks. The agricultural administrative departments at or above the county level are responsible for implementing these measures. Nearly 20 years of efforts to improve the quality and safety of agro-products on the Chinese market have made an impact, and there has been an improvement from 2001 to 2021. The year of 2004 was a turning point, before which the quality and safety problems of agro-products were relatively prominent. With awareness and attention from the government of China and continuous and strong promotion of the quality and safety monitoring of agro-products, the level of agro-product indicators is improving year by year, and has stabilized at a higher level. The routine monitoring of national agro-product quality and safety has become an important and objective indicator for authoritative evaluation of China’s agricultural product quality and safety level. The monitoring results are reported to the CPC Central Committee and the State Council every quarter, which has been highly valued and affirmed by leaders. The monitoring efforts and results have provided strong support for the country to make decisions on the supervision of agricultural product quality and safety. However, while routine monitoring has effectively promoted the quality and safety level of agro-products in China, the quality and safety work regarding agroproducts still faces many difficulties and challenges. First, the attention of relevant departments needs to be increased. The quality monitoring of agro-products is very important, but in many areas, especially in rural grassroots areas, the awareness and concept of monitoring the quality and safety of agro-products are seriously insufficient. The lax and inadequate supervision of agricultural products has led to many agricultural products directly flowing into the market without supervision (Zhang 2017). Second, the monitoring capacity and level of agricultural product quality and safety are still not sufficient. Due to the lack of funds, monitoring equipment and advanced technology, the scale and scope of agricultural product quality and safety monitoring are restricted. At the same time, due to the large variety of agro-products, the complex detection technology, and the general lack of professional ability of some technicians, it is difficult to obtain an authoritative monitoring result for the

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quality and safety of agro-products. Third, a sufficiently sound and coordinated monitoring system for the quality and safety of agro-products is lacking. At present, the quality and safety monitoring of agro-products entering the market is monitored by the market supervision department, while the monitoring of pesticide residues, veterinary drug residues, feed and additives in agro-products is monitored by the agricultural and rural departments. Thus, the quality and safety monitoring of agroproducts is decentralized, overlapping, and inefficient. At the same time, due to the lack of funds and equipment, the quantity and results of agricultural product quality and safety monitoring at the grass-roots level in China are not satisfactory, which affects the fairness and scientific nature of the monitoring results. Therefore, it is very necessary for the market supervision department and other relevant departments to jointly develop an improved quality and safety monitoring system for agroproducts (Tang 2021).

3.5 3.5.1

Food Safety Challenges from Natural Toxins Mycotoxins

Mycotoxins are naturally occurring toxins produced by certain moulds (fungi) and can be found in food. Available estimates of the number of mycotoxins existing range from about 300 to 300,000 (Lee and Ryu 2017). Plants provide up to 80% of food (FAO 2021) and a reduction in yield, harvest losses, or loss due to contamination can have long-reaching consequences. Estimates show that foodborne illnesses are a serious problem which has been reported to affect 600 million people yearly (Havelaar et al. 2015). Contamination of food crops with mycotoxins is a major concern due to their toxicity to humans and animals and their occurrence in a variety of crops. Mycotoxins are secondary metabolites produced by specific fungi, mainly Aspergillus spp., Fusarium spp., and Penicillium spp., that can contaminate a wide variety of agricultural produce such as cereals, fruits, and nuts after infection by these fungi (Bennett and Klich 2003). Mycotoxins are toxic to humans and animals and are amongst major causes of foodborne deaths worldwide (Havelaar et al. 2015; Williams et al. 2004). Cereals, which provide around 60% of global energy needs for human consumption (Tilman et al. 2011), are most affected, with maize, rice, and wheat alone providing 24%, 17%, and 16% respectively. Contamination is not visible, and mycotoxins are especially problematic since removal by food processing can be challenging (Bullerman and Bianchini 2007). Estimates on what percentage of cereals is contaminated with mycotoxins vary, but it might be as high as 60–80% of grain samples (Eskola et al. 2020). There are many different mycotoxins with aflatoxins (aflatoxin B1; AFB1), fumonisins, trichothecenes type A (e.g. T-2 toxin; T-2) and type B (e.g. deoxynivalenol; DON), ochratoxins (OTA), patulin, and zearalenone (ZEN) belonging to the mycotoxins with highest importance worldwide (Bennett and Klich 2003; Wild and Gong 2010). The prevalence and type of

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mycotoxin-producing species largely depends on the commodity, growing conditions from year to year, and the geographical region. A study comparing the mycotoxin occurrence in feed found that there is a distinct pattern for raw feed, with maize showing a high prevalence of fumonisins and often containing DON, ZEN, and AFB1 (Gruber-Dorninger et al. 2019). Rice contained most frequently ZEN, AFB1, and DON and wheat and barley mainly contained DON with T-2 and ZEN additionally (Gruber-Dorninger et al. 2019). This pattern matched previously described associations with fungi and crop plants and climatic conditions under which different fungal species thrive. Aflatoxins were more prevalent in high concentrations in samples from Sub-Saharan Africa, Southeast Asia, and South Asia (Gruber-Dorninger et al. 2019). Aflatoxin-producing fungi thrive in tropical and subtropical conditions with high temperatures and high moisture levels and in general periods where drought stress occurs (Williams et al. 2004). The highest fumonisin concentrations were found in South America, Central America, Sub-Saharan Africa, and within Europe in southern Europe where hot temperatures and low precipitation favour fumonisin contamination (Gruber-Dorninger et al. 2019). In temperate climates, mycotoxins from Fusarium species are considered the most important source of contamination in grains (Bernhoft et al. 2022). Deoxynivalenol and zearalenone occurred in higher concentrations in North America, Central Europe, and East Asia where precipitation and mild temperatures during flowering and maturing favour their development (Gruber-Dorninger et al. 2019). It is also known that different fungal species thrive in similar climatic conditions, leading to co-occurrence of different mycotoxins (Streit et al. 2012; Sun et al. 2011). Fungi can infect agricultural products pre- and post-harvest, making agricultural practices important tools for reducing fungal infections and mycotoxin development. Methods are mainly preventative and include good agricultural practices and sufficient drying of harvested crops (Bennett and Klich 2003). Agricultural practices that can help reduce mycotoxins are crop rotations that avoid having the same crop in a field in two consecutive years, ploughing before sowing a new crop to destroy residues from the previous crop which can be hosts for fungi, reducing plant stress by fertilizing appropriately, using varieties that are resistant to fungi and insect pests, planning planting and harvesting appropriately and avoiding tight plant spacing by maintaining recommended row and inter-plant spacing (CODEX 2012). Measures during harvest, storage, and transport are just as important and include minimizing damage to the grain, making sure the grain is dried and stored under dry and ventilated conditions, and testing the grain for mycotoxins (CODEX 2012). The use of pesticides can also help with preventing or treating plant disease and pests, but other challenges might arise like resistance to pesticides (Barzman et al. 2015), as well as residues of pesticides in the environment and food commodities (Damalas and Eleftherohorinos 2011). The effectiveness of pesticides against mycotoxins might also vary from year to year (Karron et al. 2020). With climate change, some agricultural practices that can reduce or prevent fungal infections like using pesticides or ploughing might be in conflict with other efforts like practices aiming to preserve soil structure or reduce the use of pesticides (Montanarella and Panagos 2021). Additionally, sub-optimal harvest times due to rain late in the season

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(Marroquin-Cardona et al. 2014) could further increase the risk of contamination under climate change. Climate change is expected to modify fungal growth patterns and mycotoxin production since temperature and precipitation are the main factors influencing fungal infections (Marroquin-Cardona et al. 2014). Additionally, insect attacks, drought, and stress in general increase the ability of fungi to infect a plant and produce mycotoxins (Miraglia et al. 2009). The distribution of pests is also expected to change with climate change, with species moving from central areas towards the poles (Bebber et al. 2013), potentially making plants more susceptible to fungal attacks due to damage, highlighting the importance of plant health and good agricultural practices as a way to reduce contamination with mycotoxins. Another factor influencing prevalence and exposure of a population are social and economic factors. Mycotoxin exposure is often highest in communities that produce and consume their own food (Wild and Gong 2010). In addition, in conditions where food handling and storage is poor, regulations to protect consumers are lacking and malnutrition is prevalent, the exposure to mycotoxins can be higher (Bennett and Klich 2003; Williams et al. 2004). Maximum levels (MLs) are a tool to reduce the exposure of a population to mycotoxins. CODEX Alimentarius has established MLs for different mycotoxins in 1995 and since updated them with the latest amendment in 2019 (CODEX 2019). MLs for aflatoxins have been established in different nuts, figs, milks, cereals (wheat, maize, and barley), flour and cereal-based foods for infants and young children, while MLs for deoxynivalenol were established in the same commodities excluding nuts and figs. Ochratoxin MLs were established in wheat, barley, and rye and for patulin in apple juice. Many countries have their own regulations on MLs for mycotoxin. A review by Anukul et al. (2013) summarizes regulations on mycotoxins from Asia, and ML regulations for the EU can be found in Regulation (EC) No 1881/ 2006 (EC 2006). No Codex MLs are set for rice, and contamination of rice is less often reported than for other cereals (Tanaka et al. 2007), but high levels of mycotoxin contamination in rice have been documented (Ferre 2016; Fredlund et al. 2009; Khodaei et al. 2020). The co-occurrence of mycotoxins (i.e. simultaneous contamination of foods and feeds by numerous mycotoxins) appears to be widespread, but little is known about the toxicological effects of co-occurrence (FAO 2020b; Gruber-Dorninger et al. 2019). Another area where better methods and research are needed is masked mycotoxins. Masked mycotoxins are mycotoxin derivates that have a changed structure due to changes that occurred in the plant, for example binding to proteins or other alterations, making their detection difficult (Berthiller et al. 2013).

3.5.2

Plant Toxins

Plants biosynthesize a range of secondary metabolites to protect them against herbivores and insects. Some of these metabolites can be toxic to humans too. The

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most toxic metabolites are produced by certain weeds but weed plant parts can contaminate food plants during harvesting. Toxins known as pyrrolizidine alkaloids are estimated to be produced by about 6000 plant species worldwide, representing 3% of all flowering plants (Smith and Culvenor 1981). To date, approximately 600 different pyrrolizidine alkaloid structures are known (EFSA 2011b). Toxins known as tropane alkaloids have been reported to occur in plants of seven Angiosperm (flowering plant) families and to date more than 200 structures are known (EFSA 2013). At least 60 different cyanogenic glycosides have been identified in plants (Seigler 1991). When the plant cells are physically destroyed, e.g. by chewing or grinding, the cyanogenic glycosides in the vacuoles come into contact with the glycosidase enzymes and are degraded with the release of toxic hydrocyanic acid (HCN) (EFSA 2019). The Papaver somniferum plant contains pharmacologically and toxicologically relevant active opium alkaloids in the latex (milky sap). The poppy seeds may be contaminated with latex and opium alkaloids during cropping (EFSA 2011a). Erucic acid is present at high concentrations mainly in the seeds of some species of the plant family Brassicaceae, e.g. rapeseed (Brassica napus L.), turnip rape (Brassica rapa L.), Indian or oriental mustard (Brassica juncea [L.] Czern.), Ethiopian or Abyssinian mustard (Brassica carinata A. Braun.), and black mustard (Brassica nigra [L.] Koch) (Velasco and Fernandez-Martinez 2009). Currently, the EU Commission has set maximum levels for the plant toxins pyrrolizidine alkaloids, tropane alkaloids, opium alkaloids, hydrocyanic acid, and erucic acid in certain foodstuffs (EC 2006). Upcoming relevant plant toxins for which maximum levels are under discussion are glucosinolates, quinolizidine alkaloids, and cannabinoids.

3.5.3

Analytical Methods for Food Safety Analysis and Monitoring of Natural Toxins

Food safety monitoring programmes and analytical methods for pesticide residues and metabolites are very well developed. In comparison, the regulatory side as well as the instrumentation and analytical methods employed in the food safety control for natural toxins are at a less advanced stage. Only a very small amount of the potentially occurring natural toxins are included in current regulations and monitoring programmes. This is to a large degree due to lack of information/data on the toxicity and health risks associated with these substances, which is a prerequisite for setting relevant maximum levels and initiating monitoring efforts. In the EU, the European Food Safety Authority (EFSA) publishes opinions on the risk of different mycotoxins based on data submitted to them. Data on occurrence is collected through food and feed testing companies and member states who are obliged to submit data on the occurrence of mycotoxins yearly (Eskola et al. 2018). In China, monitoring plans to detect some typical mycotoxins in grain including rice, maize,

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and wheat are performed each year. These monitoring data are expected to be used in risk assessment and proposing their MRLs in food by the National Center of Food Safety Risk Assessment (CFSA). The national standard GB2761 (National Standard for Food Safety—Limits of Mycotoxins in Foods) is to be periodically updated according to new risk assessment results. An efficient method for the simultaneous determination of 16 mycotoxins in various cereal grains was developed at CAAS using high performance liquid chromatography-tandem mass spectrometry after clean-up with a multi-functional filter (MFF) based on a quick, easy, cheap, effective, rugged, and safe method (QuEChERS) (Li et al. 2020; Zhang et al. 2019). The targets included aflatoxin B1, aflatoxin B2, aflatoxin G1, aflatoxin G2, T-2 toxin, HT-2 toxin, ochratoxin A, ochratoxin B, sterigmatocysin, zearalenone, deoxynivalenol, 3-acetyl deoxynivalenol, 15-acetyl deoxynivalenol, fumonisin B1, and fumonisin B2. 2.0 g of cereal sample is extracted with 5 mL water and 10 mL of 1% formic acid in acetonitrile and followed by salting-out with 4 g MgSO4, 1 g NaCl, 1 g Na3Cit
2H2O and 0.5 g Na2Cit
1.5H2O. The extracted solution is filtered directly through an MFF containing 150 mg MgSO4 and 25 mg PSA before LC-MS/MS analysis. The recoveries of the 16 mycotoxins at the three spiked concentration levels are 61–113% with the relative standard deviations (RSDs) less than 20%. The limits of detection (LODs) for targets in cereal grains are below 20 μg/kg, and are all below the MRLs for mycotoxins of the EC or FDA. This method is more effective and faster in cleanup of mycotoxins from cereal grains compared to conventional methods based on traditional QuEChERS and solid phase extraction. At NIBIO, a sensitive method for the detection of 12 mycotoxins in cereals has been developed using high resolution mass spectrometry after clean-up of extracts with a pass-through sorbent, as previously described in Brodal et al. (2020). The targets include T-2, HT-2, deoxynivalenol (DON), nivalenol, the sum of 3- and 15-acetyl-deoxynivalenol, beauvericin, zearalenone, and the enniatins A, A1, B, and B1. The sample preparation is done according to the procedure published by Klötzel et al. (2006) except that only 5 g aliquot of each sample is extracted with 20 mL mixture of acetonitrile and water (80:20 v/v). The mycotoxin analysis is carried out using HRMS LC-Q-Exactive Orbitrap, using electrospray polarity switching in order to detect all the ionized mycotoxins in one run. The toxins are separated on a Thermo Accucore aQ (100 × 2.1 mm i.d., 2.6 μm) column. The mobile phase is water and methanol, both in 5 mM ammonium acetate. The injection volume is 1 μL. To achieve high sensitivity with LOQs (limits of quantification) at 1–10 μg/kg, the mycotoxins are monitored using targeted-SIM followed by data-dependent MS/MS (t-SIM-ddMS2). In the negative ionization mode, mycotoxins are detected as acetate-adducts [M + CH3COO]- and in the positive mode the mycotoxins are detected as ammonium adducts [M + NH4]+ or hydrogen adducts [M + H]+. The identification criteria are (1) retention time (RT) of analyte matched to reference standard, (2) precursor ion accurate m/z mass within 5 ppm accuracy, and (3) the presence of at least one targeted product ion with accurate mass within 5 ppm accuracy and produced by fragmentation of the precursor ion. An in-house library of product ion spectra (MS2) for the mycotoxins aids in the identification. The

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recovery of HT-2 and T-2 has been confirmed to 100% using a certified oat reference material. Recovery of the other toxins as determined from spiked control samples prepared with each batch of samples is 60–100%, but 45% for beauvericin. Our method reported the correct levels of HT-2, T-2, DON and ZEA (z-scores lower than 0.35) in oat meal in a proficiency test arranged by the European reference laboratory (Elbers et al. 2019). The target compounds and extent of the monitoring of mycotoxins and other natural toxins is in development but is per today not sufficient to elucidate or reveal the actual challenges with natural toxins in food and feed produce (Yang et al. 2020). However, the needs and challenges with regard to analytical methods for monitoring purposes are similar for mycotoxins and plant toxins as for pesticides, even though their patterns of occurrence do not align due to their different paths for introduction into the production system. At present, quantification of the transformation products of mycotoxins in complex food matrixes and implementation of regulations for emerging mycotoxins are highly desired (Iqbal 2021).

3.6 3.6.1

Future Perspectives for Food Safety and Environmental Monitoring Improved Food Safety Monitoring in China

There is a continued need to improve the national agro-product quality and safety monitoring and ensure the safe consumption of agro-products in China. According to the ‘“Opinions of the CPC Central Committee and The State Council on Deepening Reform and Strengthening Food Safety Work’” requirements, routine monitoring should in good time discover problems and hidden dangers. In order to further improve the role of monitoring in securing food safety, the Ministry of Agriculture and Rural Affairs has organized the study and formulation of a national monitoring plan for the quality and safety of agro-products, definition of the key points of risk monitoring, exploration of the use of screening and confirmation technology, expanded coverage of monitoring parameters, and improved efficiency of problem discovery. Such a national agricultural product quality and safety monitoring plan must be formulated and implemented, and sampling and testing at the national, provincial, municipal, and county levels should be arranged as a whole. The key points of risk monitoring at all levels need to be clarified to achieve the relevant emphasis and complementarity to improve the utilization efficiency of the monitoring results. The use of web-based solutions in risk monitoring of agro-products could realize an integrated implementation of monitoring work deployment, sampling, data reporting, analysis and disposal of unqualified samples. In addition, data analysis and results sharing should be further strengthened to improve the utilization efficiency of monitoring results (JiaLiang and Xuanyun 2021).

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Monitoring coverage should be expanded with regard to selection of geographical areas, commodities, and parts of the food chain included in the sampling programme. Although China has continuously strengthened the monitoring of agricultural product quality and safety risks, there is still a big gap compared with the overall demand for agricultural product quality and safety supervision. Furthermore, multiple analysis technologies such as screening, confirmation, and quick detection should be utilized in the monitoring programme to improve/increase the parameters covered at the different steps of inspection or supervision. The possibilities presented by high-throughput screening and quantitative detection should be used in the coming risk monitoring plan as this could greatly improve the coverage of monitoring parameters. So far, the use of rapid detection technologies has been limited by the detection scope and other reasons. With the further development of immune chromatography and artificial intelligence image recognition, the rapid detection technology is expected to make a breakthrough for the detection of commonly used pesticides (Samsidar et al. 2018). With the integration of screening, confirmation, quick inspection, and other technologies, the efficiency of risk monitoring is expected to be greatly improved.

3.6.2

Emerging Contaminants and Combined Effects: The Necessity of HRMS Screening Tools

Food is one of the major sources of human exposure to undesired chemicals that might pose a threat to human and environmental health due to toxicity and/or magnitude of exposure. Through the food supply chain from primary production to consumption, produce may encounter contaminants during many different handling processes. Despite a great regulatory and monitoring effort that aims to secure a high level of food safety, there are still potential food safety challenges posed by the exposure to chemicals that have not yet been detected, studied, or regulated. Such emerging contaminants include a variety of substances with both new chemicals, well-known chemicals not included in monitoring programmes and compounds for which there is a lack of fate and toxicological data (Sauvé and Desrosiers 2014). This challenge further implies the value and necessity of untargeted screening methods, as pointed out above, to allow for an overall, or at least broader, view of all contaminants in a sample, including emerging contaminants and metabolites that are not necessarily in the scope of the targeted methods employed in monitoring designed from known chemical food and environmental safety challenges. Furthermore, although numerous analytical methods have been developed for pesticides and mycotoxins, the occurrence of rapid transformation of pesticides/mycotoxins during the food process brings difficult challenges for their detection. The transformation products of pesticides or mycotoxins, whose structures are different from their parents, usually cannot be detected by the conventional target analytical method. Un-targeted screening technique based on high resolution mass spectrometry has

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been increasingly attractive for analysis of unknown transformation products of pesticides/mycotoxins in foods (Iqbal 2021; Li et al. 2022). Results from current chemical food safety monitoring programmes, perhaps specifically relevant for pesticide residues, show that chemical contaminants (e.g. pesticides, pollutants, natural toxins) occur more often than not in mixtures rather than as single compounds (Ma et al. 2019). This reality implies the necessity to consider combined effects of chemicals in a food matrix or in an environmental sample in risk assessment, as analysing each substance independently may lead to underestimation of the real risks. Furthermore, for both pesticides and other chemical contaminants, the formation of degradation products and metabolites in the environment or during food processing should also be evaluated to assure that risk is not increased in comparison to the parent compounds. Characterization of the emerging hazards connected to chemical environmental and food contaminants has been aided by technological advances in analytical chemistry alongside development in toxicological assessment models (EFSA 2021) but more needs to be done.

3.6.3

Biopesticides and Low-Risk Pesticides: Need for Regulatory Efforts and Further Analytical Developments

A recent evaluation of the EU directive for sustainable use of pesticides is expected to lead to the replacement of said directive in 2023 with a regulation including more stringent requirements to improve the implementation of IPM practices and reduce the pesticide use across Europe (EC 2022b). Alongside the preventative and non-chemical tools employed in integrated pest management, there is a need for novel approaches such as drone or precision technologies reducing the amount of pesticides, as well as low risk and biological alternatives to the traditional chemical pesticides to achieve the policy goals of pesticide reduction. For countries like China with a large population of small-scale farmers, studies indicate that this group are more likely to adopt the use of biopesticides (i.e. pesticides derived from natural materials such as animals, plants, bacteria, and certain minerals) and low-risk pesticides (i.e. chemical or microbiological substances that meet specific regulatory low-risk criteria) rather than more expensive technological tools that might be viable for large-scale farmers to reduce the use of pesticides (Liu et al. 2022). In general, the European approval process for plant protection products might limit the market access to low-risk alternatives to the traditional chemical pesticides, and there are fewer active substances defined as biopesticides registered in the EU compared with the United States, India, Brazil, and China (Balog et al. 2017). All the while, the current policy actions aim for integrated pest management where such alternatives are imperative tools. There are recent and on-going research efforts (i.e. EU project RATION: Risk AssessmenT InnOvatioN for low-risk pesticides) aimed to assess how the regulatory modelling and assessment tools could or should

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be altered to allow for an improved future process, focusing on (1) plant extracts, (2) semiochemicals like pheromones and allelochemicals, and (3) microbial pesticides. A recent comparative analysis of the approval process for microbial plant protection products in northern Europe (i.e. Scandinavia) (Kvakkestad et al. 2020) showed large differences between countries and how a strict safety judgement might unnecessarily restrict the farmers’ access to a viable alternative to the traditional chemical pesticides. Chinese studies also point to the importance of viable low risk and biological alternatives to improve food and environmental safety. China recognizes microbials, botanicals, biochemical pesticides, agricultural antibiotics, and natural enemies as biopesticides (Yuan et al. 2018). China is a leading country in the research and development of biopesticides (Ahmed et al. 2022) but awareness raising amongst farmers is needed for a broad adoption of biopesticides in current agricultural practices. Furthermore, awareness about food safety issues from the use of traditional chemical pesticides, as evidenced by the results from well developed and implemented chemical food safety monitoring programmes, is shown to motivate farmers to adopt novel and more sustainable pest management practices (Liu et al. 2022). The necessary shift towards use of biopesticides and low-risk pesticides will require increased efforts in developing relevant and sufficient analytical tools to assess their persistence and spread in the environment and as residues in food and feed. A large proportion of the groups of active substances recognized as biopesticides and low-risk pesticides are biological materials and microorganisms and the analytical methods employed will differ from the methods presented here for chemical food safety monitoring. However, with the expected development of a broad selection of such substances approved for use in agriculture there will also be a need for broad analytical methods to enable an efficient monitoring approach for these substances.

3.6.4

Antimicrobial Resistance (AMR): Exploring the Connections with Pesticide Residues Using High-Throughput Screening Tools

Antimicrobial resistance (AMR) is identified as an urgent health challenge for this decade. Arising and disseminating between human, animal, and environmental reservoirs, AMR can only be successfully addressed in a One Health perspective. The One Health definition developed by the OHHLEP (WHO 2021) states: One Health is an integrated, unifying approach that aims to sustainably balance and optimize the health of people, animals and ecosystems. It recognizes the health of humans, domestic and wild animals, plants, and the wider environment (including ecosystems) are closely linked and inter-dependent. The approach mobilizes multiple sectors, disciplines and communities at varying levels of society to work together to foster well-being and tackle threats to health and ecosystems, while addressing the

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collective need for clean water, energy and air, safe and nutritious food, taking action on climate change, and contributing to sustainable development. A strategic framework has been developed for collaboration between the World Health Organization (WHO), the Food and Agriculture Organization of the United Nations (FAO), the World Organization for Animal Health (WOAH), and the United Nations Environment Programme (UNEP) (WHO et al. 2022) for a One Health response to AMR at a global, regional, and country level. The emergence of resistance towards commonly used antimicrobials (e.g. antibiotics, fungicides, bactericides) is critical, as it may cause treatment failure and increased morbidity and mortality (Dadgostar 2019). Recently, focus on AMR in the environment and in foods of non-animal origin has increased. Open field (arable) production of vegetables involves exposure to a range of environmental factors and contamination of these products can occur via irrigation water, organic fertilizers, etc. Manure and faecal pollution may carry AMR bacteria and AMR genes and partly explain the abundance of these genes in the environment (Nielsen et al. 2020; NORM/NORM-VET 2022). Hence, vegetables which are consumed raw without prior heat treatment may represent a source for ingestion of AMR bacteria. The use of pesticides and biocides has been suggested as a potential driver of the development of AMR in the environment, through both co- and cross-resistance (reviewed in Miller et al. 2022). Studies also indicate that combined exposure to pesticides and antibiotics, as may be the case in manured (Fang et al. 2018) and wastewater irrigated soils (Shafiani and Malik 2003), might accelerate the development of antibiotic resistance. Despite the rapid development of new technologies enabling reduced pesticide use through precision spraying, broadcast and foliar application are still important techniques that result in frequent and regular pesticide loads on large agricultural areas. Climate change, increasingly more intensive cropping practices, and the development of pesticide resistance, as described above, are all challenges that are accompanied by increased, new, or altered pest challenges. These challenges are accompanied by the need for continued use of chemical pesticides as an important tool for pest management in agriculture. There is, however, a lack of scientific data to elucidate the possible links between pesticides, AMR development, and AMR bacteria and their genes in fresh produce, and the environment on a broader scale that can be assessed in relation to production practices and natural/climate conditions to identify high-risk situations. Such scientific data are vital for the assessment of the need and possibility for implementation of specific measures and monitoring to ensure food safety, environmental, and broader One Health concerns. There is a similar need for high-throughput methods for simultaneous screening for a wide array of antibiotic resistance genes (e.g. Røken et al. 2022) in different kinds of clinical, food, and environmental samples, to assess the occurrence of antimicrobial resistance throughout the ecosystem, similar to what has been described above for chemical contaminants in food and the environment. On-going long-term monitoring programmes for pesticide residues in both imported and domestic food and in the environment (Bechmann et al. 2021; NFSA and NIBIO 2022) should be coupled to studies of AMR to further explore the potential co-occurrence of these contaminants and undesirable substances and allow for a

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better understanding of the interactions and connections between these aspects in food and the environment.

3.6.5

Concluding Remarks

Food safety is currently one of the focus topics all over the world. The use of pesticides in agricultural production has greatly increased the yield of crops. At the same time, it has also inevitably caused environmental pollution. The problem of pesticide residues in agricultural products has become one of the major food safety issues of concern to all countries. With climate change, new challenges will appear, both with regard to environmental and human exposure to pesticides. The impacts climate change may have on the use, fate, and behaviour of pesticides is uncertain and a topic that needs more attention in the years to come, e.g. with the help of computer models. In the import and export trade and in ensuring the food safety of domestic consumers, the rapid screening and detection of multi-residue pesticides is particularly important. Because of the advantages of high-resolution mass spectrometry (HRMS), the combination of liquid chromatography (LC) with HRMS has been more and more used in food safety analysis, but mainly by using analytical methods aimed to detect specific known compounds. For unknown, potentially harmful, compounds in food, non-targeted screening methods are essential and an important complement to targeted methods. Moreover, monitoring programmes and regulations depend on good screening methods to uncover potential health problems, but also to further develop monitoring programmes and get more comprehensive legislation to protect the consumer and the environment. Acknowledgements This work is part of the project CHN-2152, CHN-17/0019 SINOGRAIN II, funded by the Royal Norwegian Embassy, Beijing/The Ministry of Foreign Affairs, Norway. Hilde Helgesen, NIBIO, is acknowledged for her valuable comments to the manuscript.

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Chapter 4

Artificial Intelligence and Hyperspectral Modeling for Soil Management Jiangsan Zhao and Shuming Wan

Abstract Soil management is important for sustainable agriculture, playing a vital role in food production and maintaining ecological functions in the agroecosystem. Effective soil management depends on highly accurate soil property estimation. Machine learning (ML) is an effective tool for data mining, selection of key soil properties, modeling the non-linear relationship between different soil properties. Through coupling with spectral imaging, ML algorithms have been extensively used to estimate physical, chemical, and biological properties quickly and accurately for more effective soil management. Most of the soil properties are estimated by either near infrared (NIR), Vis-NIR, or mid-infrared (MIR) in combination with different ML algorithms. Spectroscopy is widely used in estimation of chemical properties of soil samples. Spectral imaging from both UAV and satellite platforms should be taken to improve the spatial resolution of different soil properties. Spectral image super-resolution should be taken to generate spectral images in high spatial, spectral, and temporal resolutions; more advanced algorithms, especially deep learning (DL) should be taken for soil properties’ estimation based on the generated ‘super’ images. Using hyperspectral modeling, soil water content, soil organic matter, total N, total K, total P, clay and sand were found to be successfully predicted. Generally, MIR produced better predictions than Vis-NIR, but Vis-NIR outperformed MIR for a number of properties. An advantage of Vis-NIR is instrument portability although a new range of MIR portable devices is becoming available. In-field predictions for water, total organic C, extractable phosphorus, and total N appear similar to laboratory methods, but there are issues regarding, for example, sample heterogeneity, moisture content, and surface roughness. More precise and detailed soil property estimation will facilitate future soil management.

J. Zhao (✉) Norwegian Institute of Bioeconomy Research, Ås, Norway e-mail: [email protected] S. Wan Heilongjiang Academy of Black Soil Conservation and Utilization, Harbin, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Clarke et al. (eds.), Innovation for Environmentally-friendly Food Production and Food Safety in China, Sustainable Agriculture and Food Security, https://doi.org/10.1007/978-981-99-2828-6_4

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Keywords Machine learning · Artificial intelligence · Hyperspectral modelling · Soil management · NIR · MIR

4.1

Introduction

Soil is largely a non-renewable resource, and it provides many functions including water filtration, plant growth, and human nutrition (Amundson et al. 2015). With the growth in population and demand for agriproducts, pressure on the environment due to the expansion of agricultural land is increasing. Improper soil management results in challenges in food security and soil degradation (Liu et al. 2006). The massive use of fertilizers and other agrochemicals in agriculture has made the situation worse than ever. Effective monitoring of soil quality has become a critical need for effective soil management and ensuring economic efficiency while minimizing the impact on environment. Precision soil management is emerging as a mainstream practice driven not only by environmental concerns but also by efficiency and profitability considerations. Optimization of soil management begins with data collection from sensors or laboratory measurements. The physical, chemical, and biological properties of soil all play critical roles in crop growth and development, and they can largely affect different soil management practices. For example, nutrient/fertilizer application is one of the largest expenses incurred in crop production not only in terms of direct capital but also in terms of its ultimate impact on the environment. Hence, precise monitoring of nutrients in soil enables better application efficiencies and enhanced sustainability in agriculture (Ng et al. 2020). In order to provide the basis for more effective soil management, all different soil properties have to be known in advance. However, the conventional laboratory methods require the use of chemical reagents, which are often not eco-friendly, require a whole range of sophisticated laboratory equipment, and the protocols are time consuming and expensive. In this review, we first review different properties of soil, then these different machine- learning (ML) methods; third, different soil properties that can be measured through coupling ML and spectroscopy; then how machine learning can help make decisions in soil management based on easily measurable soil properties. Finally, we list the current limitations of machine learning in soil management and future directions that we can potentially work on to advance this field. Soil analysis using traditional laboratory technology (e.g., wet chemistry) can be prohibitively expensive and often time consuming, prompting the use of pedotransfer functions as a substitute (Soriano-Disla et al. 2014). As a consequence, these expensive soil analyses are often restricted to a few samples or to samples that are bulked from throughout an area to provide representative composites. Such data will have little or no information on the spatial variability of soil. Development of rapid and more cost-effective methodologies is required to meet this demand and surrogate methods based on spectroscopic data in the visible (Vis), near-infrared (NIR), and mid-infrared (MIR) frequency regions (or combinations) have good potential (Reeves et al. 2006; Rossel et al. 2011).

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There is an increasing interest in using diffusive reflectance infrared spectroscopy for the rapid and cost-effective prediction of soil physical, chemical, and biological properties. Spectral reflectance methods are nondestructive, suitable for neat (undiluted) soils, and provide spectra that are highly characteristic of the soil type and composition, thus allowing for the analysis of many soil properties (Rossel et al. 2006; Reeves et al. 1999). Furthermore, prediction methods based on diffuse reflectance spectroscopy require minimal or no sample preparation, avoid the use of environmentally harmful extractants in the laboratory, and are easily adaptable for proximal sensing, especially with Vis-NIR (Du and Zhou 2009), further expanding the uses of reflectance spectroscopy in recent years.

4.2

Soil Properties

Different measurable soil properties can be divided into three major categories, chemical, physical, and biological ones. Changes in soil physical property affect all the ecosystem services that soil could provide and other soil chemical and biological properties (Johnson and Hoyt 1999). Soil functions are regulated by different properties, which are the prerequisite to support soil management decisions. Nitrogenous fertilizer has been applied without supplementary phosphorus and potassium fertilizers; the resulting nutrient imbalances make soil fail to function well in agriculture. Proper soil management practices can be carried out to ensure sustainable crop production only when the soil quality is known in advance.

4.2.1

Soil Physical Properties

Soil texture is the composition of sand, silt, and clay particles in soils, which is essential to know for the estimation of the potentials and limitations of land use and management. Soil texture has been treated as an important property for the analysis of structure development, carbon sequestration, nutrient retention, and water infiltration and storage (Bronick and Lal 2005). Specifically, increased soil compaction (i.e., bulk density, penetration resistance) affects seedling emergence, root growth, crop production, and porosity, while water infiltration can directly obstruct seedling establishment and enhance weed growth, and ultimately reduce crop yield (Sithole et al. 2016). Mean weight diameter (MWD) is the most widely used index in relating soil aggregate size. Water-stable aggregate (WSA) is a common indicator of soil structural strength (Bronick and Lal 2005). Poor aggregate structure and stability have been reported to decrease water infiltration, enhance erodibility (Omondi et al. 2016), and reduce soil carbon and nutrient protection in the macro- and micro-aggregates (Six et al. 2000). Soil hydraulic properties (i.e., water infiltration, saturated hydraulic conductivity) are a measure of the ability of the soil to capture, retain, and release water (Bormann and Klaassen 2008). Available

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water capacity (AWC) is an indicator of soil water accessibility to the plants, and high AWC is correlated with sufficient soil water retention (Blanco-Canqui and Ruis 2018). Soil water content is critical not only to supply the water needs of the crop but also to dissolve nutrients and make them available to the plant. Excess water in the soil, however, depletes oxygen (O2) and builds up carbon dioxide (CO2) levels. While O2 is needed by roots to grow and take up nutrients, high CO2 levels are toxic.

4.2.2

Soil Chemical Properties

Soil organic matter (SOM) is a building block for the soil structure, acts as a large carbon sink in the biosphere, and plays an important role in the CO2 balance. It is important both as a driver of, and a response variable to, climate change, capable of acting as a source and sink of carbon (Smith et al. 2008). Soil organic carbon (SOC) is a useful indicator of soil fertility and an essential parameter in controlling the dynamics of various agrochemicals in soil. The decreasing SOC content in agricultural soils is generally considered a major threat to the sustainability of soil cultivation. Its role is essential in many productive and non-productive soil functions as it controls the dynamics of various agrochemical processes in the soil. These can also affect the soil carbon stocks, especially in the topsoil layer. The mineral elements that are required in relatively larger quantities for crop growth are categorized as primary macronutrients and include nitrogen (N), phosphorous (P), and potassium (K). The deficiencies of any of these may lead to significant yield losses, and they are therefore commonly added as fertilizers to meet the plant’s needs. Calcium (Ca), magnesium (Mg), and sulfur (S) are classified as secondary macronutrients as they are needed in relatively lesser amounts and are often present in sufficient quantities in soil. Micronutrients are easily accessible for plants even though plants only need trace amounts; they can also be toxic when the concentrations become too high. Chemical elements exist in solution as cations or anions. In soil, a high cation exchange capacity (CEC) is desirable as it means there is high retention of cations on the soil particle surfaces. Soil pH is a measure of the acidity or alkalinity of soil. Soil pH regulates soils’ nutrient storage and supply processes, which has cascading effects on productivity control in terrestrial ecosystems (Slessarev et al. 2016). If surface soil pH is too high or too low, the efficacy of chemical reactions may be affected. Additionally, soil pH can strongly affect soil microbial diversity (López-Fando and Pardo 2012), soil enzyme activities (Miao et al. 2019), nutrient availability (Fernández and Hoeft 2009), and ultimately crop productivity (Li et al. 2019). Soil salinization is a global problem which is especially severe in irrigated land. Soil salinization, measured by electrical conductivity (EC), has severely increased the rate of soil degradation, arable land loss and ecological deterioration, resulting in soils with poor structure, low fertility, low microbial activity, and other attributes that inhibit crop growth (da Rocha Neto et al. 2017).

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Soil Biological Properties

Soil biological properties mainly include the amount and diversity of animals and soil microorganisms, as well as enzyme activities. In agroecosystems, the soil microbiome can largely reflect changes in soil properties due to land-use and agricultural management practices (Hartmann et al. 2015), nitrogen fertilization regime (Zhalnina et al. 2021), and tillage practices (Degrune et al. 2017). Soil biological properties are also interconnected with other soil physical and chemical properties, e.g. aeration, soil organic matter, or pH, which affect the activity of many microorganisms in soils; these in turn perform important activities in carbon and nutrient cycling and ultimately affect plant growth. Microbes also play significant roles in maintaining the balance of biogeochemical cycles through bio-transformations. Soil microbial activity decreases soil organic matter due to mineralization. Soil enzymes play key biochemical functions throughout the decomposition of organic matter in the soil system, not only catalyzing microbial life processes in soil and stabilizing soil structure, decomposing organic waste, forming organic matter and cycling nutrients but also maintaining soil ecological physicochemical properties and soil health (Kotroczó et al. 2014). The recovery of soil microbial activity is vital to maintaining the ecosystem sustainability. Soil microorganisms are also active participants in processes that underlie soil health, such as soil aggregate formation (Lehmann et al. 2017) and moisture retention and erosion control (Zheng et al. 2018). Soil microbial biomass is also an important indicator of soil quality to maintain soil fertility and crop productivity (Kaschuk et al. 2010). Soil microbial biomass can be used constantly as an indicator for soil health status (Schloter et al. 2003). The greater the microbial biomass in the soil, the greater is the capacity of the soil to provide nutrients to plants through mineralization of organic nutrients. Soil microbial biomass C, N, and P all reflect the soil total C, N, and P availability (Smith and Paul 1990).

4.3

Machine-Learning Algorithms

Machine learning (ML) is a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data or to perform other kinds of decision making under uncertainty (Murphy 2018). With big data technologies and high-performance computing capabilities, machine learning has opened up new opportunities for data-driven science in soil. Efficient soil management using data-driven machine-learning algorithms has significantly lowered the adverse impacts on the environment (Diaz-Gonzalez et al. 2022). In soil science, ML is mostly based on the mapping of existing datasets to new data sets processed by sensors. Deep learning (DL) has also been accepted to build more exceptional models for more effective soil management (Srivastava et al. 2021).

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There are four main categories of ML, supervised, unsupervised, semisupervised, and reinforcement learning; however, only the first two are more widely used in soil science. Supervised ML (SML) models are built based on data with labels, while unsupervised models (USML) search for internal data patterns without relying on label information. Intuitively, semi-supervised ML is to maximize the data as only a small portion of data is labeled (Murphy 2018). In contrast, reinforcement is to maximize the reward through trial and error during model training and is mostly deployed in robotics only (Woergoetter and Porr 2008). Supervised ML can be further classified into classification and regression based on tasks. Different SML algorithms include partial least squares (PLS), decision tree (DT), Gaussian process (GP), support vector machine (SVM), random forest (RF), naïve bayes (NB), artificial neural networks (ANN), and K nearest neighbor (KNN), all of which can manage both classification and regression well. The unsupervised ML (USML) algorithms mainly contain k-means, autoencoder, principal component analysis (PCA), and Gaussian mixture (GM).

4.3.1

Supervised Machine Learning

A PLS tries to build the relations between predictors and labels in the latent space (Abdi and Williams 2013). A DT makes a series of decisions based on continuous and/or categorical input variables to predict an outcome; the tree stops splitting when additional splits don’t further decrease node impurity or reach specified minimum number of observations in terminal nodes or the minimum error (Loh 2011). A GP is a collection of random variables such that the joint distribution of every finite subset of these random variables is a multivariate Gaussian (Neal 1998). A SVM seeks to create a hyperplane between or among different classes through transforming original feature (Boser et al. 1992). A RF was first proposed by Breiman in 2001 and aggregates the outcome of hundreds of decision trees to make a decision (Biau and Scornet 2016). A NB is a probabilistic algorithm based upon assumption of variables’ independence; the probability associated with each possible class is calculated based on prior possibility and likelihood function (Lewis 1998). An ANN mimics the neurons’ complex signaling pathways to analyze complex interactions between a group of measurable variables in order to predict an outcome (Olden and Jackson 2002). The KNN predicts a data point based on its similarity to neighbors; “K” refers to the number of nearest neighbor points to include during the majority voting process (Kramer 2013).

4.3.2

Unsupervised Machine Learning

USMLs are a group of models that are completely data driven. PCA is one of the most common USML methods used for noise reduction, visualization, and

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interpretation improvement through keeping only a few top ranked PCs covering the majority variance of the original features (Abdi and Williams 2010). An autoencoder is a type of artificial neural network which uses data itself as label to create latent variables for dimension reduction (Lopez Pinaya et al. 2019). The k-means clustering is the simplest algorithm that partitions data into predefined number of clusters so that the within-cluster variation is minimized (Lloyd 1982). GM is a probabilistic model that assumes all the data points are generated from a mix of Gaussian distributions and can be used to classify data into different categories based on the probability distribution (Maugis et al. 2009).

4.3.3

Feature Selection in Machine Learning

High-dimensional data poses difficulties for effective data analysis and decision making. Feature extraction methods such as PCA and autoencoder have been discussed above in unsupervised learning, while feature selection is discussed in this section. Feature selection has become indispensable for more effective ML in practice, and it has been proven effective in both theory and practice in processing high-dimensional data and in enhancing learning efficiency (Cai et al. 2018; Liu and Motoda 2012). Feature selection is to select a subset from the original dataset in order to remove noisy redundant and irrelevant features (Zhao et al. 2010). Effective feature selection methods can improve learning accuracy, reduce learning time, and simplify learning results. Feature selection methods can be broadly classified into filter, wrapper, and embedded methods. Correlation, Euclidean distance, consistency, dependence, and information measure are the common criteria to keep more informative features. Forward increase, backward deletion, random, and hybrid models are the common strategies that can be used for dimension reduction (Liu and Motoda 2012).

4.3.4

Deep Learning

Deep learning (DL) is a subcategory of ML which consists of many models, such as Deep Belief Networks (DBN) (Hinton 2009), Deep Neural Networks (DNN) (Yosinski et al. 2014), Convolutional Neural Networks (CNN) (O’Shea and Nash 2015), Recurrent Neural Networks (RNN) (Medsker and Jain 2001), and Generative Adversary Networks (GAN) (Goodfellow et al. 2020). The advantage of deep learning lies in the removal of feature preparation and feature reduction during the training process, which is especially true in image analysis (LeCun et al. 2015). Deep learning generally performs better in both classification and regression compared to ML algorithms because more complex levels of features are extracted to represent the input data hierarchically (LeCun et al. 2015). A certain level of feature selection can reduce cost model training; however, the weight of irrelevant features will

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automatically be assigned as values that are close to zero during the training process, which makes feature selection less relevant for deep learning models.

4.4

Machine Learning in Soil Management

Major applications of ML in soil management lie in spectral imaging for quick and effective soil properties estimation, estimation of unknown or latent soil parameters based on easily measurable ones. The ML estimated parameters help decision making in soil management, e.g., regarding irrigation, pH adjustment, tillage, fertilization to improve soil quality, agricultural production, and ecological services.

4.4.1

The Combination of Spectroscopy and Machine Learning in Soil Properties Measurements

The conventional soil laboratory measurements are often not eco-friendly, time consuming, and expensive. The use of infrared spectroscopy has become an essential option providing efficient, robust, and cheap methods for soil characterization. Spectroscopic techniques are considered as physical methods of characterization that can be defined as the study of the interaction of electromagnetic waves in the ultraviolet, visible, and infrared wavelengths with the material under consideration. Both near infrared reflectance (NIR), visible and near infrared reflectance (Vis-NIR), and mid-infrared reflectance (MIR) spectroscopy have been used for physical properties estimation coupling with different ML algorithms. Non-linear models have generally had better performance in soil properties prediction. Spectral preprocessing techniques also help reduce noise and improve model performances in different soil properties prediction.

4.4.2

Soil Physical Properties Estimation

Soil clay content was predicted with excellent performance using PLS to analyze Vis-NIR spectra (Adeline et al. 2017). The ability of Vis-NIR spectroscopy to estimate the fractions of sand, and clay using PLS regression was not interfered with by water content (Tang et al. 2020). Soil samples of different particle sizes were classified by ML models accurately using NIR spectral reflectance (Sun et al. 2014). The clay and silt contents of soil samples were predicted with excellent accuracies by RF using portable MIR spectroscopy (Martínez-España et al. 2019). The contents of clay, silt, and sand were predicted by ML models with high performance regardless of soil water content using Vis-NIR spectra (Xu et al. 2018a). Spectra of soil samples

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from different depths together helped build more robust models in predicting soil texture differences using Vis-NIR spectroscopy (Coblinski et al. 2020). ANN showed the highest prediction accuracy in predicting soil texture differences across Denmark compared to several ML models (Katuwal et al. 2020). SVM was confirmed to be superior compared with PLS in predicting soil sand and clay content using MIR spectral data (Deiss et al. 2020). CNN was able to predict soil clay and sand content from raw spectral data at much higher accuracy compared to PLS (Padarian et al. 2019).

4.4.3

Soil Chemical Properties Estimation

Most soil chemical properties were successfully predicted using different ML models based on NIR, Vis-NIR, and MIR spectroscopy (Clingensmith et al. 2019; Rosero-Vlasova et al. 2019).

4.4.3.1

Linear SML

Applications of NIR and Vis-NIR spectral techniques with the PLS algorithm were found to be efficient for estimating the SOC (Ba et al. 2020; Amin et al. 2020). Three soil properties, namely pH, free iron oxide, and CaCO3 were accurately predicted through applying a PLS algorithm on Vis-NIR spectra (Adeline et al. 2017). Vis-NIR spectroscopy was used to estimate SOC, total nitrogen (TN), and pH through PLS regression (Sithole et al. 2018). SOC and TN content were determined accurately by analyzing Vis-NIR reflectance using PLS (Liu et al. 2014). Good predictions of TN, SOC, S, P, and pH were obtained using PLS to analyze in-field NIR measurements (Cozzolino et al. 2013). PLS was used to predicted pH, cation exchange capacity, SOC, Ca, Mg, TN, total phosphorous (TP), iron (Fe), copper (Cu), K, and sodium (Na) in Canadian soils based on spectra collected by MIR spectrometer in the field (Metzger et al. 2020). PLS helped pH and EC characterization of saline-alkali soils using VIS-NIR (Bai et al. 2018).

4.4.3.2

Non-linear SML

The SVM model showed better prediction accuracy in estimating Fe in different forms compared to PLS (Xu et al. 2018b). The support vector machine (LS-SVM) model predicted soil salt content more accurately than the partial least squares regression (PLSR) model in the field based on hyperspectral images (Wu et al. 2018). The combination of a non-linear SVM regression and the fused data of both Vis-NIR and portable X-ray fluorescence (PXRF) spectrometry sensors was able to predict soil pH more accurately (Wan et al. 2019). ANN showed excellent performance in predicting SOC using Vis-NIR spectral data (Katuwal et al. 2020). SVM

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regression had the best performance compared to PCA, PLS, and ANN in quick and precise prediction of SOC, TN, TP, and total K using Vis-NIR spectral data (Xu et al. 2018a). SVM regression was used to accurately predict soil TN based on NIR spectral reflectance (Adeline et al. 2017). The CEC and exchangeable sodium were predicted with excellent accuracies through combining RF and portable MIR spectroscopy (Martínez-España et al. 2019). SVM was confirmed to be superior compared with PLS in predicting pH, sand, clay, and SOC using MIR spectral data (Deiss et al. 2020). PLS had the best performance to predict soil EC through combining original reflectance, PCA score, and first-order derivatives of narrow band hyperspectral images (da Rocha Neto et al. 2017). Compared to PLS, RF was more stable and accurate in characterizing the spatial distribution of surface soil salinity (Zhu et al. 2022). Random forest helped UAV-borne hyperspectral imagery is a useful tool for field-scale soil salinity monitoring and mapping (Hu et al. 2019). DL was able to estimate SOC, CEC, pH, and TN at much higher accuracy compared to PLS (Padarian et al. 2019).

4.4.3.3

Data Preprocessing and Data Fusion Improve ML Models’ Prediction Performances

Different preprocessing strategies include Savitzkye-Golay smoothing, first derivative, log (1/R), mean centering, standard normal variate, and multiplicative scatter correction (Gholizadeh et al. 2015). The coupling of Vis-NIR spectra with SVM and using SG derivative preprocessing method led to an improvement of the prediction accuracy of SOC (Seidel et al. 2019.; Peng et al. 2014). A study comparing different preprocessing strategies and ML models in estimating soil TN concluded that the combination of SG preprocessing and PLS had the best performance in terms of prediction accuracy regardless of the SMC of samples (Liu et al. 2014). A study used fused spectra of laser-induced breakdown spectroscopy (LIBS) and attenuated total reflectance Fourier-transform mid-infrared spectroscopy (FTIRATR) in combination with ANN confirming the possibility to estimate SOC fast and non-destructively (Xu et al. 2019). One study showed improved accuracy of prediction for pH, SOC, TN, and P contents when the data of reflectance spectroscopy (DRF), attenuated total reflectance spectroscopy (ATR) and Fourier transform infrared photoacoustic spectroscopy (PAS) in the MIR range were combined (Sila et al. 2016). The fused data of multispectral and thermal images successfully predicted in situ soil moisture content under moderate canopy coverage using a random forest algorithm (Cheng et al. 2022).

4.4.4

Soil Biological Properties Estimation

MIR is more appropriate for soil biological properties estimation. The estimated geometric mean soil quality index was estimated accurately by the combination of

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PLS and NIR even though individual soil enzyme activities were not (Comino et al. 2018). Results coupled with PLS revealed that MIR could predict biological properties such as soil microbial biomass, soil enzymes, soil respiration, and Q10 (Nath et al. 2021). Those phospholipid fatty acid (PLFA)-derived soil microbial properties were estimated by PLS models using combined data for Vis-NIR and MIR with moderate accuracies; MIR generally gave better performance compared with Vis-NIR (Hutengs et al. 2021).

4.4.5

Soil Property Estimation Based on Known Properties

Soil pH and CaCO3 were predicted with high accuracy based on Moderate Resolution Imaging Spectroradiometer (MODIS) products, climatic variables, and normalized difference vegetation index (NIDVI) using ML models (Lu et al. 2023). RF was able to predict SOC with moderately high accuracy based on environmental variables and other soil chemical measurements (John et al. 2020). Microbiome data could more accurately predict other biological properties than either physical or chemical properties (Wilhelm et al. 2022). ANN was used to predict soil aggregate stability based on soil texture, SOC, pH, and water-stable aggregates with high accuracy (Rivera and Bonilla 2020).

4.4.6

Improved Soil Quality Through Different Soil Management Practices

Soil management can greatly influence different soil properties, and proper management practices are beneficial for better soil quality and productivity (Robinson et al. 1994). Soil compaction can lead to soil erosion, nutrient depletion, and pollution (Hartemink 2008). Solutions such as cross-tillage, next tillage, and deeper tillage should be done to deal with surface, within surface and below topsoil compaction problems, respectively (Batey 2009). The application of farm-yard manure can also improve soil physical conditions (Hati et al. 2006). Soil moisture content (SMC) is a critical indicator of the surface-water cycle (Seneviratne et al. 2010). Accurate SMC estimation can provide a reliable reference for precision irrigation management (Chen et al. 2020). Soil water thresholds which indicate water availability for plant consumption can change depending on the plant type, soil and climate. Irrigation should be applied well before SWC starts approaching the permanent wilting point (PWP) at which no water is available for plants to take up (Datta et al. 2017). The balance of different macronutrients is important to maintain healthy plant growth. N, P, and K are the nutrients most massively added to farmland, and their efficient application will create huge benefits both economically and

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environmentally. Besides the direct application of chemical fertilizers, biological fertilizers can also be applied to improve the soil nutrients’ availability (Miransari 2013). N fixation can significantly decrease the amounts of chemicals needed for fertilization both under dry and humid conditions. The triple super phosphate compound is commonly used to ensure high P solubility. Plant growth promoting rhizobacteria (PCPR) have also been applied to increase P availability through producing phosphatases, organic acids, and protons in the soil (Houser and Richardson 2010). The optimal pH for most macro- and micronutrient absorption is neutral; soil pH can be lowered by adding elemental S, aluminum sulfate, or iron sulfate and increased by adding limestone (Miransari and Smith 2007). Soil organisms and their activities are closely related to land management practices and climate. Conservation and reduced tillage increased microbial carbon (MBC) and microbial activities. The addition of compost or farm-yard manure (FYM) significantly increased soil MBC in comparison to chemical fertilizer (Mohammadi et al. 2011).

4.5 4.5.1

Hyperspectral Modeling and Prediction of Soil Properties Spectroscopic Modeling

Regarding the calibration approach, various algorithms have been applied in the field of soil spectroscopy. Partial least squares regression (PLSR) is a relatively simple, linear modeling approach which enables response variables to be estimated using a large number of highly correlated predictors (Greenberg et al. 2022). Other multivariate approaches which incorporate variable selection (e.g., a genetic algorithm coupled with PLSR) or account for nonlinear responses (e.g., support vector machine regression with a radial kernel) have outperformed PLSR in some cases (Rossel and Behrens 2010; Ludwig et al. 2018, 2019). In general, complex machinelearning algorithms appear to provide the greatest advantage over simpler approaches (e.g., PLSR) when applied to large, diverse datasets (Padarian et al. 2020). However, Clingensmith et al. (2019) found that the performance gains achieved by implementation of more sophisticated algorithms may be less than those achieved by replacing random division of the dataset into calibration and validation sets with strategic subsetting based on lab data (systematic sampling), spectral data (Kennard–Stone algorithm), or both. For the latter subsetting methods, principal component analysis (PCA) is a useful tool due to the high dimensionality and collinearity of spectral data. By describing the major sources of variance in the spectra along orthogonal axes in lower dimensional space, PCA enables improved understanding of the distribution of sample units (Greenberg et al. 2022). In the case of significant correlations between spectral principal components (PCs) and key soil properties (Rossel and Behrens 2010),

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spectral PCA is a powerful a priori tool to select a representative sample of soils for model calibration or to judge the suitability of applying an existing calibration in a new context. For example, to improve the accuracy of field vis-NIR models, Mouazen et al. (2006b) used spectral PCAs to identify clusters of soils with similar texture or moisture, and then created calibration models for each cluster.

4.5.2

Prediction of Soil Moisture Content

Soil moisture content (SMC) is an important parameter in crop growth and produces a significant variation in soil spectral reflectance. As SMC varies significantly with time and space, it is desirable to acquire SMC in real-time for timely adjustment of seeding strategies (such as seeding depth, seeding density, etc.) in precision agriculture. Visible and near-infrared (vis-NIR) spectroscopy is a popular method among many ways to achieve real-time SMC measurement. The rationale for such a technique is that soil moisture will affect the scattering and absorption of light on soil particles that affect the reflection intensity (Liu et al. 2022). In general, increasing SMC would decrease the reflected light intensity on the soil surface. Reports showed that the absorption peaks of SMC are at around 1400 nm and 1900 nm (Soriano-Disla et al. 2014; Stenberg et al. 2010). However, now lower cost spectrometers at limited wavelengths can also perform as well as full NIR spectrometers (Tang et al. 2020). Thus, these limited vis-NIR sensors can be cost-effective for SMC monitoring (Kweon and Maxton 2013). Soil type greatly influences the intensity of reflected light, which directly affects the SMC prediction using vis-NIR spectra. Some studies (Han et al. 2010) reported that the response of vis-NIR soil spectra to different SMC levels varies between soil types as they were affected by texture and color of soil samples. Thus, a generalized prediction model for predicting SMC from vis-NIR spectra is challenging to achieve. Few have studied the possibility of establishing a generalized prediction model by reducing the influencing factors, e.g., soil texture (ST) and soil organic matter (SOM) (Liu et al. 2020). Liu et al. (2022) found that vis-NIR spectroscopy at 400–980 nm can successfully predict different SMCs of seven soils in the North China Plain. To achieve a generalized prediction of SMC of different soils, they used the EPO-PLSR model. Their results showed that with direct PLSR modeling it is difficult to achieve a unified SMC prediction of various soil types, whereas EPO-PLSR can achieve a generalized SMC predictive model for different types of soil.

4.5.3

Prediction of Soil Organic Matter

Soil organic matter (SOM) is the primary source of soil nutrients and an important indicator for measuring soil fertility (Zeraatpisheh et al. 2020). It plays a vital role in

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the growth and development of crops and the improvement in soil quality (AldanaJague et al. 2016). Therefore, estimating SOM content quickly and accurately is crucial to improving soil and increasing crop yield. Many researchers have studied the relationship between the SOM and soil spectral reflectance based on visible and near-infrared (Vis-NIR) spectroscopy to establish a relevant predictive model to achieve the rapid determination of SOM content (Reeves 2010). Studies have shown that Vis-NIR spectroscopy can not only accurately predict the SOM content but also effectively overcome the limitations and deficiencies of traditional monitoring methods (Xie et al. 2022). Bai et al. (2022) found that in the range of 400–715 nm, SOM and spectra showed a positive correlation. Above 715 nm, there was a negative correlation between SOM and Vis-NIR spectral reflectance. Among these wavelengths, the correlation between SOM and spectrum reached a highly significant level in the range of 1235–2400 nm. The highest correlation between SOM and Vis-NIR spectra was reached at 2060 nm. As observed by Yang et al. (2021), based on spectral characteristic bands selected by a successive projection algorithm (SPA) in the range of 500–550 nm, 506 nm, 532 nm, and 540 nm were reserved for SOM prediction. Wang and Pan (2016) found that soil moisture undoubtedly affects the relationship between spectral reflectance and SOM. When using the calibration model derived from air-dried samples to predict the SOM of moist samples, a substantial decrease occurred in the accuracy for all SMC groups compared with that of air-dried samples.

4.5.4

Prediction of Soil Nitrogen

Nitrogen is one of the primary nutrients critical for the survival of all living organisms, and its cycle is significantly affected by human activities (e.g., agricultural practice) in the local and global ecosystems (Liu et al. 2014). During the plant growth process, plants obtain available nitrogen through decomposition of organic nitrogen and subsequent nitrogen mineralization (ammonification and nitrification) by microbes. Thus, it is of great importance to obtain soil nutrient content such as soil nitrogen quickly and accurately for precision fertilization and agricultural production (Xiao and He 2019). The visible and near infrared (Vis-NIR) spectroscopy technique mainly measures overtones and combination bands of fundamental vibrations of O-H, N-H, and C-H bonds in the mid-infrared region (Rossel et al. 2006). Numerous analyses of soil N have been conducted during the past decades using this technique, for example to predict the soil N mineralization rates (Fystro 2002; Mutuo et al. 2006) and to classify conventional and conservation agricultural practices (Yang et al. 2012). Soil N content is often highly correlated with that of C (Chang and Laird 2002; Martin et al. 2002). Although the N-specific absorbing features are present in the soil spectra, their absorbance is not as strong as the absorbance of C bonds as the mass of C in soil is generally an order of magnitude greater than that of N. Thus, it was

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explained that N is best predicted by its correlation with C if a large C-to-N correlation exists (Martin et al. 2002). Soil nitrogen was detected with the multiple linear regression (MLR) method at the spectral bands of 1702 nm, 1870 nm, and 2052 nm using NIR spectroscopy (Dalal and Henry 1986), and the correlation coefficients between measured and predicted values of soil nitrogen reached 0.931 (Bao et al. 2007). Moreover, it was found that the sensitive bands of soil total nitrogen (TN) were different for different soil types, and the characteristic bands were affected not only by soil type but also by sampling depth (Xiao and He 2019). Yang et al. (2021) found that the sensitive bands of TN were densely distributed in the ranges of 400–440 nm and 480–540 nm. Reducing large spectral datasets to parsimonious representations is of value for more efficient storage, computation, and transmission, as well as easing analysis (Rossel and Lark 2009). In addition, with fewer variables, it is possible to use a simpler and cheaper spectrophotometer (Yang et al. 2012).

4.5.5

Prediction of Soil Phosphorous

Phosphorous (P) is a nutrient required in relatively large amounts by plants for root and seed development. P is a difficult element as variable amounts can be fixed in the soil. There are many methods to determine P, but each method addresses different P fractions. Shortages of P in the field are corrected by adding phosphate fertilizer (inorganic P) in an even distribution over the field. In contrast, applying variable P levels within a field is needed in precision agriculture since the available P is not equally distributed over the field. For the last 40 years, NIR has been used to rapidly and accurately determine the composition of agricultural materials. Recently, there have been reports on the use of Vis-NIR for measuring soil properties such as organic matter, moisture, and total nitrogen. However, there are few successful experiments on P determination by NIR spectroscopy. Maleki et al. (2006) developed an empirical relationship between soil available P and Vis-NIR spectral characteristics of fresh soil samples. Although, there is no proof available in the literature of direct absorption by P in the Vis-NIR region, differences in absorption due to different P levels can be distinguished for the average spectrum as each spectrum covers a large number of wavelengths. The higher the P concentration of the soil samples, the lower is the reflectance of the soil samples over the entire wavelength range from 401 nm to 1663 nm. This result agrees with that found by Bogrekci and Lee (2005a) who concluded that higher absorbance is associated with high P concentration. However, as P is not spectrally active in the Vis-NIR range, difference in shape and reflection levels due to available P are still under investigation. Mouazen et al. (2006a) demonstrated that P is better predicted in wet samples as compared to dried samples. They performed the test with two spectrophotometers of different wavelength range. This is in contrast with the prediction of all other elements where the best results were found on dried soil samples.

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Maleki et al. (2006) coupled a spectrophotometer in the Vis and NIR range of 305–1710 nm with partial least squares (PLS) cross-validation techniques to correlate soil reflectance with soil P on a carefully prepared calibration dataset of fresh soil samples. They found that in the Vis range, the higher peaks for available P for the fresh soil were observed at wavelengths of 421 nm, 441 nm, 448 nm, 454 nm, and 494 nm, whereas in the NIR range, most high peaks were observed at wavelengths of 1003 nm, 1009 nm, 1103 nm, 1128 nm, 1372 nm, 1378 nm, 1474 nm, 1492 nm, and 1629 nm. Bogrekci and Lee (2005b) found that the peak at 1122 nm is associated with magnesium phosphate hydrate, while the peaks at 1439 nm and 1374 nm are associated with calcium phosphate.

4.5.6

Prediction of Soil Potassium

Potassium (K) is a crucial element for plant nutrition and its availability and spatial distribution in agricultural soils are influenced by many agro-environmental factors. It is generally recognized that soil K occurs in soil in four forms: water-soluble, exchangeable, non-exchangeable, and structural (Blanchet et al. 2017). Among these different forms, dynamic equilibrium reactions control the release and/or fixation of K according to soil biogeochemical properties and processes (Zorb et al. 2014). According to the classification of Rossel et al. (2006), soil N and SOC are primary properties and possess direct spectral responses in the NIR region, whereas soil K is a secondary property that does not possess a direct spectral response for NIR predictions. It is predictable because of correlation with certain primary properties. However, because of the complexity of overtones and their combinations in the NIR region (Mouazen et al. 2010), it is difficult to assign absorption features from the full spectrum to specific functional groups for N and SOC. It is also difficult to explain what fractions of K are correlated with NIR spectra (Jia et al. 2014). LS-SVM models and PLS models were established by Shao and He (2011) for the prediction of K based on NIRS and MIR spectroscopy, and it was observed that models within the MIR region (4000–600 cm-1) were best for K prediction. One possible explanation for this is the indirect relationship of P and K with C–H–O–N bonds. Malley et al. (2002) found the same result when testing manure-amended soils. Instead of measuring full spectral variables, Jia et al. (2014) found analyses of effective variables that have significant impacts on the prediction accuracy may provide an easier explanation of correlation between the spectra and K. They used the Monte Carlo-uninformative variable elimination (MC-UVE) method to select effective wavelength variables and found the selection bands for K were mainly focused on the three distinct absorption peaks around 1400 nm, 1950 nm, and 2250 nm. As stated by Post and Noble (1993), the absorption peak near 2400 nm may represent illite or mixtures of illite and smectite in soil. The peak around 2150 nm can have resulted from the absorption of Al-OH (Rossel and Behrens 2010). Because both K and Al are major components of illite, this peak also

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indicated the presence of some illite in soil. Generally, explaining the correlation between spectrally inactive properties and NIR spectroscopy is still difficult; improving their prediction accuracy should be further investigated.

4.6

Conclusion

ML works in an interdisciplinary way and estimates soil properties rapidly and costeffectively to facilitate soil management through coupling with spectroscopy. With the wide deployment of cloud computing, soil property estimation can be managed more effectively and in real-time to optimize soil management. ML helps farmers to make better decisions in choosing more appropriate soil management practices. The spectroscopic methods for soil nutrient analysis offer fast and efficient measurements for single to simultaneous multiple ion detection. However, the accuracy is dependent on other factors such as soil particle size, organic matter, moisture content, environmental temperature, and surface roughness, making the sample preparation procedure as well as the data analysis method of critical importance. More robust preprocessing methods or ML or DL methods should be developed to increase the model’s generality. In order to successfully implement precision site-specific management, high spatial resolution soil property maps are essential. Soil sample collection and preparation are current bottlenecks for more efficient application of different sensing technologies in soil nutrient monitoring. Spectral imaging using either UAVs or satellites covering larger areas for soil properties estimation is rapid, inexpensive, and eco-friendly and has become an important alternative to conventional chemical analytical methods. However, detailed composition of the soil is not readily measurable through remote-sensing methods. Satellite images are also limited by the shallow penetration depth, low in both spatial and temporal resolutions regardless of the ability for long-term and large-scale measurements. UAV images feature high spatial resolution; however, they cannot cover large areas, for example, continental scale. With the help of spectral imaging super-resolution, images that are high in both spatial and spectral resolution can be obtained to estimate soil properties. Further advances in the sensor technologies and development of new ML models in combination with various preprocessing tools and DL will promote more effective soil management. Soil chemical properties are the most widely studied, while soil physical and biological properties that are also vital for soil health are largely lagging behind, so future efforts should be put into their estimation with the help of DL and spectral imaging. SMLs have been extremely important and extensively used for soil property estimation compared to other ML algorithms. An extensive range of soil properties can be predicted using reflectance spectroscopy. Considering the accuracy of hyperspectral modeling, the predictions of soil water content, clay, sand, organic C, SOM, total N, total K, and total P can be

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considered reliable. Although successful predictions were obtained for other soil properties, more research is still needed to confirm prediction capabilities for aggregation; total concentrations of Ca, Mg, and S; different N forms (N fractions, mineral, and organic); micronutrients and other elements; mineralogical composition; and soil contaminants. References reporting calibrations using portable devices account for only a small percentage of the total number of references available, so comparisons are still difficult. The most suitable spectral regions for the prediction of soil properties were MIR, NIR, and Vis-NIR, with much of the literature focused on these three spectral regions. The extension of spectral ranges to the combinations NIR-MIR, UV-Vis-NIR, and Vis-NIR-MIR generally resulted in worse or no prediction improvement compared to single spectral ranges. Acknowledgements The work was funded through the project “CHN17/0019 Sinograin II: Technological innovation to support environmentally-friendly food production and food safety under a changing climate-opportunities and challenges for Norway-China cooperation” (funded by the Norwegian Ministry of Foreign Affairs through the Royal Norwegian Embassy in Beijing).

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Chapter 5

Biochar-Based Technology in Food Production, Climate Change Mitigation, and Sustainable Agricultural Soil Management: Post Terra Preta Era Xiaoyu Liu, Cheng Liu, Genxing Pan, and Nicholas Clarke

Abstract Biochar, derived from organic waste via pyrolysis, is proposed as a soil amendment in the early twenty-first century. In this chapter, we summarize the great potential of pure biochar application in food production, soil fertility improvement, plant disease suppression, climate change mitigation, and heavy metal contamination control, based on field experiments globally. However, large-scale pure biochar implementation is restricted by high cost in terms of high price and application rate. The difficulty of biochar application using machines further reduces the farmers’ willingness to use biochar. Based on the experience of biochar usage in China, we propose a framework for large-scale implementation of industrialized biochar. Biochar can be developed into three products including liquid fertilizer, biocharbased organic fertilizer, and inorganic fertilizer. The soluble components in biochar after water extraction or in the wood vinegar during biochar production can be used to develop liquid fertilizer and used in fruit and vegetable growing. For fertile soils, biochar-based inorganic fertilizer is recommended for use instead of pure biochar. For degraded soils, biochar-based organic fertilizer is recommended to improve soil structure and provide nutrients for crops. Pure biochar is recommended to apply to heavy metal contaminated soil to decrease their uptake by crops. Keywords Biochar · Biochar-based fertilizer · Soil amendment · Crop yield · Soil fertility · Carbon sequestration · Greenhouse gas emission mitigation · Large-scale biochar implementation

X. Liu (✉) · C. Liu · G. Pan Institute of Resource, Ecosystem and Environment of Agriculture, and Department of Soil Science, Nanjing Agricultural University, Nanjing, Jiangsu, China e-mail: [email protected] N. Clarke Norwegian Institute of Bioeconomy Research, Ås, Norway © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Clarke et al. (eds.), Innovation for Environmentally-friendly Food Production and Food Safety in China, Sustainable Agriculture and Food Security, https://doi.org/10.1007/978-981-99-2828-6_5

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Copying Terra Preta to the World

Converting organic waste from agriculture into biochar and amending it into agricultural soils has received great attention in the last 15 years. Biochar production and soil amendment are regarded as a promising way of organic waste management and soil productivity improvement globally. This is inspired by the historical and scientific re-discovery of Terra Preta in the Amazon in South America. In the Amazon, the soils are originally very poor due to intensive weathering under the warm and humid climate (Glaser et al. 2002). They are very acid and have low cation exchange capacity to retain nutrients. In addition, soil organic matter content is extremely low due to the rapid decomposition of organic matter. All these issues challenge the sustainable management of agriculture in most tropical regions. However, the very fertile soil named as “Terra Preta” coexists with the poor soil. The total area covered by Terra Preta is estimated to be more than 50,000 ha in Central Amazonia. The American geologist, James Orton, mentioned the dark fertile soil for the first time in his book in 1870. From then on, many scientists have been dedicated to studying the black fertile soil. In the late nineteenth century, Friedrich Katzer reported that the soil originated from anthropogenic activities. Further research discovered that the soils contain residues of charcoal, artifacts, animal bones, and human excretions (Glaser et al. 2001). It is probable that the soil was made by the native Amerindian population intentionally. According to Glaser and Birk (2012), Terra Preta was formed by repeated input of nutrients in the form of garbage and human excrements together with charred residues from cooking fires. One ingredient makes Terra Preta distinct from the surrounding infertile soil: Terra Preta contains a large amount of black carbon. The charcoal content is about 70 times more than surrounding soils. It is composed of poly-condensed aromatic rings and is resistant against chemical and microbial decomposition. This not only resulted in higher soil porous structure but also higher retention of water and dissolved nutrients. Therefore, Terra Preta provides an excellent model for the sustainable management of agriculture in the humid tropics. It can also be copied in other regions around the world. In the first 10 years of this century, many articles were published. All of these papers pointed out a way of “charring the organic waste” into biochar and adding it into soils from the point of view of sequestering carbon in soil to mitigate climate change. Lehmann (2007) calculated that pyrolysis of forest residues could avoid 25% of carbon release and sequester about 10% of the annual U.S. fossil fuel emissions. Conversion of biomass carbon to biochar leads to sequestration of about 50% of the initial carbon compared to the low amounts retained after burning (3%) and biological decomposition (600 °C) had higher increase for soil aggregation. The improvement of soil structure favors soil water retention. In biochar amended soils, available water content was usually higher than the soil without biochar addition. Water-holding capacity increased by 10–28% across studies following biochar amendment (Edeh et al. 2020; Ibrahimi and Alghamdi 2022; Omondi et al. 2016; Wu et al. 2022). In addition to soil physical properties, biochar also alters the nutrients’ status and their availability and biogeochemical cycling processes (Sun et al. 2022). This is resulting from the direct addition effect and the indirect effect on soil microbial activity. There are large abundances of nutrients in biochar feedstocks, such as animal manure and crop residues. While converting into biochar, most of the nutrients can be kept in the biochar, such as phosphorus, potassium, calcium, magnesium, and silicon. When adding to soils, these nutrients can be taken up by plants. Biederman and Harpole (2013) summarized the existing studies and found that biochar amendment increased the concentrations of P, K N, and organic C significantly. Biochar could raise soil pH by as much as 1.2 units in strong acidic soil (Chan et al. 2007). A lot of studies have shown that biochar addition alters nutrients’ status and their cycling in soil, which leads to an increase or reduction in plant nutrients’ availability. Nitrogen and phosphorous are two of the most frequently observed nutrients. Gao et al. (2019) and Tesfaye et al. (2021) found that biochar addition increases soil available P content by 45–65% on average of the existing individual studies and plant P uptake increases by 55% on average. Biochar produced from crop, agricultural, or animal residues showed the highest effect, as well as biochars produced at temperatures below 450 °C (Glaser and Lehr 2019). For soil, the highest effect was observed in strong acidic and fine-textured soil or in soils with initial low P availability. Biochar amendment in agricultural soils decreases the concentration of inorganic N. The concentration of NO3- or NH4+ in biochar amended soil decreased by 10–12% (Gao et al. 2019; Nguyen et al. 2017). The reduction of soil inorganic N content is probably resulting from N immobilization following a large amount of carbon input with biochar and the higher N use

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efficiency under biochar amendment. Many studies have shown that biochar addition increases N fertilizer use efficiency although it decreased the N concentration in soils (Liu et al. 2018). Biochar is regarded as slower to capture N and then it is released slowly and taken up by plants. The positive effect for NO3- and NH4+ reduction is that biochar could decrease N loss from nitrate leaching and N2O emission during the processes of nitrification and denitrification (Borchard et al. 2019). Nevertheless, the higher pH of biochar may pose a great challenge as it might promote NH3 volatilization from agricultural soils (Liu et al. 2018), which is one of the main N losses of N fertilizer use. However, Sha et al. (2019) found that biochar generally had no effect on nitrogen volatilization on average in existing studies. It only occurs in acidic or coarse-textured soils or when biochar was applied with ammonium fertilizer (diammonium phosphate, ammonium chloride, and ammonium nitrate). In some cases when wood biochar was applied or N fertilizer application rate was less than 200 kg ha-1, biochar even decreased ammonia volatilization. In addition to N and P, many other nutrients are applied to soil with biochar. Their availability and cycling will definitely be altered, but this still needs further study. For example, Major et al. (2010) reported a long-lasting yield increase effect of one-time biochar application which they attributed to the increased availability of soil calcium and magnesium. In rice paddies, Liu et al. (2014) found biochar significantly increased the plant-available Si pool, and rice shoot Si uptake increased by up to 58%.

5.2.4

Carbon Sequestration and Greenhouse Gas Emission Mitigation

Biochar is regarded as a soil amendment also because of its great potential in soil carbon sequestration from the start. It is apparent that biochar added to soil can increase soil organic carbon storage as it is biochemically resistant to decomposition. Nonetheless, a great number of studies have been conducted to investigate the stability of biochar carbon in soil. These studies showed that biochar could remain in soil for hundreds to thousands of years. Once applied to soil, it hardly decomposes. The carbon sequestration potential of biochar has been questioned by several earlier studies due to the priming effect (Bruun and Luxhoi 2008; Wardle et al. 2008). Wardle et al. (2008) reported that fire-derived biochar soil amendment would cause a great loss of native soil organic matter. However, following studies demonstrated that biochar addition has a minor effect on soil respiration despite a growthlinked co-metabolic microbial decomposition (Kuzyakov et al. 2009; Liu et al. 2016a, b). Field experiments showed that soil organic carbon (SOC) stocks increased by 13.0 Mg ha-1 on average, which corresponded to a 29% organic carbon increase (Gross et al. 2021). Greenhouse and laboratory studies showed an absolute SOC increase of 6.1 g kg-1, corresponding to a relative SOC increase of 75% following biochar addition. The soil organic carbon increase rate increases with

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biochar application rate. In addition to the organic carbon pool, the pools of inorganic carbon, dissolved carbon, soil microbial biomass carbon, and humus carbon also increased significantly following biochar addition (Chagas et al. 2022). A lot of studies have been conducted to investigate the effect of biochar amendment on methane (CH4) emission. The response of CH4 emission from agricultural soil varied across studies. Zhang et al. (2020) found that biochar addition could increase CH4 emission by 15% on average from individual case studies, while Ji et al. (2018) reported that biochar could decrease soil CH4 emission by 12%. However, most meta-analyses showed that biochar has no effect on CH4 emission (Atilano-Camino et al. 2022; Cong et al. 2018; He et al. 2017; Jeffery et al. 2016; Shakoor et al. 2021). Therefore, it is still very difficult to predict the effect on soil CH4 emission following biochar amendment. The large variation among these studies may be attributed to (1) the inclusion of datasets on CH4 in upland soils, and (2) inclusion of datasets from incubation and pot studies. For upland soils, CH4 uptake was relatively small compared to N2O emission, and it can be neglected. Incubation studies can be used to reveal the mechanism of CH4 production and emission, whereas they are not suitable for use as a way of estimating CH4 emission in the field following biochar addition. Methane emission is a biogenic process that strongly depends on rice plant growth. For pot, especially incubation studies, there is no rice plant involvement. We recommend that more effort should be paid to field measurement of CH4 from rice paddies. When considering these two exceptions, biochar amendment would increase (He et al. 2017) or have no effect (Ji et al. 2018; Zhang et al. 2020) on CH4 emission. However, in the long-term, biochar had no effect on methane emission. Differently from methane, nearly all the meta-analyses (Kaur et al. 2023) showed that biochar addition could reduce nitrous oxide (N2O) emission from agricultural soils. On average, Kaur et al. (2023) showed that biochar application decreased soil N2O emissions by 39% with a decrease rate of 27% under field conditions, which was significantly lower than the values from laboratory incubation experiments. Many factors regulate the response of N2O emission to biochar addition, including biochar feedstocks, application rate, initial soil acidity, texture, and crop types. Generally, biochar produced from animal manure had weak effect on N2O reduction compared to other feedstocks, and for most of the case studies, manure biochar amendment had no effect on N2O emission (Borchard et al. 2019; Cayuela et al. 2014; Zhang et al. 2020). This is probably due to the higher N content and availability in manure biochar. Increasing the biochar application rate often leads to a higher reduction magnitude of N2O. Applying biochar to soil grown with vegetables often causes a great reduction in N2O emission due to the higher N fertilizer application rate in cultivation. The reduction effect was greater in sandy soil than in clay soil, in alkaline soil than in acid soil (Cayuela et al. 2014; Zhang et al. 2020). For a dry cropland system, it is easy to understand that biochar could reduce greenhouse gas emission from upland soil by mitigating N2O emission. However, in a paddy rice ecosystem, biochar may promote methane emission, which will offset the mitigation effect of N2O reduction. Therefore, it is necessary to calculate the global warming effect of both methane and N2O in the paddy rice ecosystem. Zhang

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et al. (2020) found biochar decreased the global warming potential by 23% and the yield-scale global warming potential decreased by 41% with a yield increase of 21%. Liu et al. (2019) showed that after biochar amendment, the yield-scale global warming potential decreased by 27% in a rice paddy ecosystem.

5.2.5

Heavy Metal Contamination Control

Due to its porous structure and high surface area, biochar is believed to be able to adsorb heavy metals from water and thus decrease their availability in soils. In 2010, for the first time, the effect of biochar application on the availability of multiple heavy metals was investigated via a pot experiment (Namgay et al. 2010). The authors found that biochar application decreased the concentrations of As, Cd, and Cu in maize shoot. One year later, Cui et al. (2011) reported that biochar addition to a heavy metal contaminated paddy field could reduce Cd uptake by rice in a 2-year field experiment. From then on, a large number of papers (more than 100 peerreviewed articles according to Peng et al. (2018)) have been published on the possibility of using biochar to remediate heavy metal contaminated soils. To draw a general conclusion on whether biochar can be an effective agent to tackle the problem of metal polluted soils, meta-analysis has been conducted in recent years on the basis of the large number of research papers that have been released. By the end of 2022, there were 12 meta-analyses since the first one by Peng et al. (2018). Despite the differences, these studies showed that biochar amendment in heavy metal contaminated soils could reduce the accumulation of most toxic elements in plant shoots, especially in the edible parts (Albert et al. 2021; Chen et al. 2018; Hu et al. 2020; Peng et al. 2018; Rehman et al. 2021; Tian et al. 2021; Zhang et al. 2022b). The elements tested include Cd, Pb, Zn, Ni, Mn, Cr, Co, Cu, As, Sb, and V. Biochar is able to complex metal ions on its surfaces and therefore decrease their availability in soils (Arabi et al. 2021; Atilano-Camino et al. 2022; El-Naggar et al. 2022; Yuan et al. 2021). One exception is that biochar addition might increase the availability of As in soil (Arabi et al. 2021; Peng et al. 2018; Tian et al. 2021; Zhang et al. 2022b), but the mechanism remains unclear and needs further study. More attention should therefore be paid to biochar usage in As-contaminated soils. It should be noted that the immobilization effect of biochar on heavy metals will change with biochar aging, which depends on soil pH, texture, aging time, and biochar pyrolysis temperature (Yuan et al. 2021). With biochar aging, the availability of heavy metals decreased in acid, coarse, and medium-textured soil, whereas in alkaline and fine-textured soil, biochar increased their availability. The bioavailability of heavy metals kept decreasing in soils amended with higher temperature (> 500 °C) biochar but increased with low temperature biochar. In terms of different metal species, only Zn availability kept decreasing after aging, while Cd, Cu, and Pb availability increased with biochar aging. Therefore, biochar usage for heavy metal remediation should consider the biochar aging effect and a long-lasting effect of biochar is needed. One option may be activation of the biochar via pre- or

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post-pyrolysis treatment, which significantly improves the adsorption capacity for removal efficiency of heavy metals (Pathy et al. 2023). Pre-pyrolysis activation with metal salts/oxides was the most effective method of enhancing biochar’s potential for adsorption and removal of a wide range of heavy metals.

5.3

Limitations for Large-Scale Pure Biochar Implementation

Although it has been well documented both in science and technology that biochar amendment can increase crop yield, promote carbon sequestration, and reduce greenhouse gas emissions, it remains unclear whether farmers are willing to pay for biochar usage. The main issue is the high price of biochar and the large dose required. Dickinson et al. (2015) investigated the economic value of biochar as an agricultural technology for long-term improvement of arable farming. The cost and benefit of using biochar technology to enhance cereals agriculture were evaluated in two generalized geo-economic agricultural scenarios: north-western Europe (NWE) and sub-Saharan Africa (SSA). They found biochar application for cereals agriculture in NWE was never able to reach a positive net present value (NPV) in any economic simulation regardless of how long the investment time period is extended into the future. In SSA, a positive NPV was obtained if 7 years of yield enhancement can be achieved. In the northern part of China, our analysis showed that the NPV for biochar application was negative, which suggested that biochar was not cost effective for the farmers. The carbon gain benefit from biochar use had little influence on the NPV. The farmers can get additional money using biochar only when: (1) biochar price is cut by 30% ($90 t-1); (2) yield increase is maintained for at least 5 years (10% yield increase, biochar amendment rate at 20 t ha-1); and (3) biochar application rate is reduced to 1 t ha-1 with similar yield increase as that obtained with a biochar application rate of 20 t ha-1. However, biochar price in China now has doubled since 2016 ($600 t-1). Biochar cost represents the largest portion of variation in the NPV for biochar application. To understand how a sample of Chinese farmers in the study areas view biochar, two questionnaire surveys were conducted in two villages in the southeastern part of China. Most of the farmers think biochar is a very good product for farming, but the high biochar price might influence their willingness to buy and use biochar. They prefer to buy and use biochar-based inorganic fertilizers (see below) compared to pure biochar, because the biochar-based inorganic fertilizer is not so expensive, and it is easy to apply using the machines that farmers currently own. However, the farmers could make money using biochar as a soil amendment, but this only occurs in regions with adequate and cheap labor and biochar production using a flame curtain soil pit kiln for smallholder farmers (Pandit et al. 2018). Therefore, it is impossible to promote large-scale biochar implementation based on the first precondition for profit-making. It remains

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unclear for how long the yield benefits from biochar usage can persist. The second and third preconditions need further study.

5.4

Biochar-Based Fertilizers

Even though biochar enhances crop productivity, the economic feasibility of high biochar cost and the difficulties during application restrict large-scale implementation of biochar worldwide. Making biochar into biochar-based inorganic fertilizers provides one possible way of solving this problem. Biochar-based inorganic fertilizer, also named biochar compound fertilizer in the literature (Qian et al. 2014), is the mixture of biochar and chemical fertilizers. It is usually manufactured by granulating the mixture of biochar, nutrients, and binders into small pellets. Its production process is very similar to that of compound fertilizer. The idea of making biocharbased fertilizer is to take advantage of the nano-structure of biochar and its capacity to hold nutrients and the hormone-like substances in biochar, and also its promotion effect on plant roots (Joseph et al. 2013; Liu et al. 2021a). Applying biochar-based inorganic fertilizer instead of pure biochar of course reduces the application rate of biochar and saves the cost to the farmers. Qian et al. (2014) demonstrated that applying biochar-based inorganic fertilizers in the same amount as chemical fertilizer increased rice grain yield by 10.5–31.4% over conventional NPK fertilizer in a paddy field in the eastern part of China. As many as 40 articles had been published by July 24 2021. Melo et al. (2022) summarized these papers with meta-analysis and found that biochar-based inorganic fertilizer increased crop productivity by 10% compared with the chemical fertilized control with a mean application rate of 0.9 t ha-1. This means the crop productivity increase is slightly lower but comparable to that reported when pure biochar is used at around 20 t ha-1. The response of crop productivity did not vary among the sub-groups of biochar type, tested soil properties, crop types or experimental conditions, which implies that any kind of biochar can be used to produce high-efficiency biochar-based inorganic fertilizers, and they work for any soils with no limitations for crop types and climate conditions. In 2016–2017, we conducted a cross-site field experiment in the northern part of China in order to evaluate the effect of one biochar-based inorganic fertilizer formulation on crop yields. There were 146 experimental sites, and 15 crops were evaluated including maize, rice, soybean, and wheat. The results showed that biochar-based fertilizer increased crop yield in a range of 0.5–33.3% across sites. Compared to common compound fertilizer, maize yield increased by 1.6–24.3% with a median of 5.3% following biochar-based inorganic fertilizer addition. Rice yield increased by 0.5–22.5% with a median of 9.8%. Soybean yield increased by 1.6–33.3% with a median of 6.8%. Wheat yield increased by 5.1–14.7% with a median of 10.8%. Sim et al. (2021) outlined the methods that can be used to produce biochar-based fertilizers, which include the impregnation technique, the mixed granulation technique, and the co-pyrolysis technique. In the impregnation method, the nutrients are

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added into the biochar matrix by immersing biochar into the liquid fertilizer, followed by drying the nutrient-enriched biochar. The co-pyrolysis technique involves mixing the feedstock with nutrient-rich materials, such as nutrient-rich minerals, soluble mineral fertilizers, or waste from the fertilizer industry, prior to the pyrolysis process. Ndoung et al. (2021) term this technique the pre-treatment method. However, great caution should be paid when using this way of making biochar-based fertilizer, as some of the additives could corrode the pyrolyzers. The mixed granulation technique is the most popular and reliable way of making biocharbased inorganic fertilizer. Biochar-based inorganic fertilizer employing this method has been industrialized in China. Production of biochar and biochar fertilizers have been authorized by the Chinese central government, and the Ministry of Agriculture of China authorized biochar-based fertilizer in 2016 (NY/T 3041–2016). According to these criteria, biochar-based fertilizer must contain at least 6% of biochar-carbon and over 20% of the major nutrients N, P2O5, and K2O.

5.5

Model for Large-Scale Biochar Implementation

Here, we propose a framework for large-scale biochar implementation (Fig. 5.2). This framework is only for industrialized biochar production. The organic waste from crop straw, wood residues, sewage sludge, and food waste can be pyrolyzed to produce biochar. Animal manure is not suitable for biochar making, because extra energy is usually needed to dry the manure prior to pyrolysis, which makes manure biochar more expensive than other feedstocks. In addition, the pyrolysis process would cause a significant loss of plant available nutrients, especially for nitrogen, which is one of the most abundant nutrients in animal manure. Therefore, we recommend composting animal manure with biochar to produce biochar-based

Fig. 5.2 A framework for large-scale biochar implementation

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organic fertilizer instead of making it into biochar. This not only saves some energy but also retains the nutrients in manure to the greatest extent. For biochar application, we have a couple of suggestions. First, applying pure biochar into heavy metal contaminated soils to decrease their availability. The biochar can be derived from wood residues due to its higher surface area. All biochars from different sources can be used to produce biochar-based organic or inorganic fertilizer. The biochar-based organic fertilizer is recommended to be applied in degraded soils with poor structure and low fertility. After amending degraded soils, the biochar is resistant to decomposition and could help to rebuild a great soil structure. The organic part mainly from animal manure acts as a slow nutrient release pool for plant uptake. The soluble components in biochar after water extraction or in the wood vinegar during biochar production can be used to develop liquid fertilizer and used in fruit and vegetable growing. The bio-gas generated during biochar production can be used for biochar-based inorganic fertilizer making as an energy source. The biochar-based inorganic fertilizer is recommended for use as a substitute for chemical fertilizers in fertile soils to maintain soil fertility. Acknowledgements The work was funded through the project “CHN17/0019 Sinograin II: Technological innovation to support environmentally-friendly food production and food safety under a changing climate-opportunities and challenges for Norway-China cooperation” (funded by the Norwegian Ministry of Foreign Affairs through the Royal Norwegian Embassy in Beijing).

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Chapter 6

Diversity and Ecological Functions of Soil Microbial Community in Black Soil in Northeast China Xueli Chen, Nicholas Clarke, Shuming Wan, and Baoku Zhou

Abstract Black soil is a major agricultural soil in China. Based on published research papers and related research, this chapter reviews the composition, diversity, and ecological functions of farmland soil microbial communities in black soil areas of China. The aim is to summarize the main groups of soil microorganisms in black soil farmland, the ecological processes they participate in, their responses to environmental factors, and the main environmental indicators, and then put forward the importance of isolation and cultivation of indigenous functional microbial strains, so as to provide a basis for the protection and sustainable use of black soil resources. Keywords Black soil · Soil microorganisms · Fertilization · Cropping systems · Reclamation · Tillage

6.1

Introduction

Black soil areas in China are mainly distributed in Heilongjiang Province, Jilin Province, Liaoning Province, and Inner Mongolia; the main crops include maize, rice, and soybean (Han and Yang 2009). Black soil is famous for its soft soil and high content of organic matter. A black soil profile is shown in Fig. 6.1. It is classified as a mollisol, according to the USDA soil classification, and as a phaeozem or chernozem, according to the WRB soil classification. It is an important foundation for agricultural development in northeast China and was protected by the Law of the People’s Republic of China on the Protection of Black Land by the Chinese government in 2022 (Government of the People’s Republic of China 2022). Scientific fertilization, a reasonable rotation system and tillage measures are important

X. Chen (✉) · S. Wan · B. Zhou Heilongjiang Academy of Black Soil Conservation and Utilization, Harbin, China N. Clarke Norwegian Institute of Bioeconomy Research, Ås, Norway © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Clarke et al. (eds.), Innovation for Environmentally-friendly Food Production and Food Safety in China, Sustainable Agriculture and Food Security, https://doi.org/10.1007/978-981-99-2828-6_6

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Fig. 6.1 Profile of black soil in Qinggang county

practices to ensure grain production capacity and soil quality protection in the black soil area. The quantity and diversity of microorganisms are high in black soil. The characteristics of the soil microbial community structure and composition play an important role in the transformation of soil organic matter and nutrient cycling (Bell et al. 2005).

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Proteobacteria, Verrucomicrobia, acid bacteria, and Actinomycetes are the main microorganism groups in black soil. Their activity and community composition are affected and restricted by land use, contents and forms of soil nutrients, soil structure, soil pollution, soil animals, and other environmental conditions. At the same time, soil microbial communities drive soil nutrient cycling and organic matter transformation through their own rapid reproduction and metabolism (Copley 2000).

6.2

6.2.1

Effects of Fertilization on Composition and Ecological Function of Arable Soil Microbial Community in Black Soil Soil Microbial Community

Organic matter content and fertilization are important factors leading to differences in microbial community structure, but fertilization may overshadow the impact of organic matter content on the microbial community (Chen et al. 2021). Black soil is carbon and phosphorus limited under long-term non-fertilization, and the application of chemical fertilizer aggravates the carbon limitation of microorganisms (Cui et al. 2020). The application of organic fertilizer is conducive to the fixation of soil carbon in the black soil profile (Abrar et al. 2020) and significantly increases the content of Olsen P in black soil (Zhan et al. 2015). Therefore, the substitution of organic nitrogen for chemical fertilizer nitrogen can relieve the carbon limitation and significantly reduce the phosphorus limitation of soil microorganisms (Cui et al. 2020). Organic fertilizer application benefits the diversity and community stability of the bacterial community in black soil, while long-term application of chemical fertilizer has stimulated the growth of acidophilic bacteria, fungi, and beneficial bacteria in soil (Fig. 6.2, Ding et al. 2016a; Wang et al. 2018; Xu et al. 2020). Fungi are more sensitive to chemical fertilizer (Wang et al. 2018). In particular, the application of nitrogen fertilizer significantly increases the abundance of the black soil fungal community but significantly reduces its diversity and richness (Wu et al. 2018). The key genera of soil fungi in the black soil area are Trichoderma, Coeloma, and Penicillium (Fig. 6.3, Hu et al. 2017). A large number of studies have shown that long-term single application of chemical fertilizer significantly reduces the soil pH (Qin et al. 2015; Zhou et al. 2015, 2017; Wei et al. 2008). The application of chemical fertilizer generally also leads to the decrease of bacterial community diversity (Hartmann et al. 2015; Zhou et al. 2015, 2017; Wei et al. 2008) but the increase of fungal community richness (Zhou et al. 2015), especially for pathogenic fungi (Hu et al. 2017). Based on 64 long-term field trials around the world, Geisseler and Scow (2014) showed that the microbial biomass carbon increased by an average of 15% when applying nitrogen, phosphorus, and potassium chemical fertilizer compared with the unfertilized control. Some studies also confirmed that some bacterial communities

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Fig. 6.2 Relative abundance of soil microbes under long-term organic and inorganic fertilization (phylum). CK = without fertilizer (control), M = manure, NPK = inorganic fertilizer, MNPK = inorganic fertilizer with manure (Ding et al. 2016b, used by permission)

are favored by chemical fertilizers. For example, the community abundance of Actinobacteria and Chloroflexi increased significantly after the application of chemical fertilizers (Chu et al. 2007; Hartmann et al. 2015). The diversity of bacteria and archaea in the soil was significantly improved by the combined application of organic and inorganic fertilizers (Fig. 6.4, Ding et al. 2016b, 2017a), and these could also effectively improve the fungal community structure, reducing the abundance and increasing the diversity of fungi (Hu et al. 2017; Ding et al. 2017b). Long-term fertilization trials in black soil have shown that the chemical fertilizer and the combination of organic and inorganic fertilizer shared similar indicator species, mainly including Bacillus megaterium, Dyella marensis, and Herbaspirillum sp., while the organic fertilizer treatment mainly included Bacillus megaterium and Bacillus acidiceler (Gao et al. 2021b).

6.2.2

Soil Microbial Function

The effect of fertilization on soil microbial community structure and functional diversity (Biolog) in black soil farmland is higher than that of crop species and cropping system (Yan et al. 2019; Ding et al. 2016a; Gao et al. 2017). Compared

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Fig. 6.3 Relative abundance of the dominant fungal phyla (a) and genera (b) under different fertilization regimes in three locations in the black soil area (Hu et al. 2017, used by permission). NoF, CF, M, and CFM represent non-fertilization (control), chemical fertilization, manure fertilization, and chemical fertilization plus manure at each location, respectively. SB, MB, and NB indicate the three sampling locations in the southern, middle, and northern parts of the black soil region in northeast China, respectively. Bar represents the standard errors of three replicates and asterisk indicates a significant difference (ANOVA, p < 0.05)

with no fertilization, fertilizer input both of chemical fertilizer only, organic fertilizer only and organic–inorganic combination significantly improved the carbon source metabolic capacity and activity of soil microorganisms (Zhang et al. 2012; Liu et al. 2015a). Long-term application of nitrogen fertilizer significantly increases the abundance of the nirS gene and reduces the community diversity of denitrifying bacteria encoded by it. At the same time, it simplifies the network structure of nirS denitrifying bacteria, resulting in the reduction of the stability of their network structure and increasing their susceptibility to external environmental disturbance,

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while their abundance and community structure have no significant correlation with the application of phosphorus and potassium fertilizer (Hu et al. 2020b). Long-term fertilization stimulated the growth of nitrifying bacteria in black soil. The abundance of the nitrifying bacterium Nitrosospira increased by 3.61 times after long-term fertilization. The dominant groups of nitrifying Archaea and Nitrospirae were Nitrososphaera and Nitrospira, respectively. Nitrogen fertilizer significantly changed the community composition of nitrogen-fixing bacteria in black soil,

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while phosphorus and potassium fertilizer had no obvious effect on it (Hu et al. 2019). In terms of the effect of fertilization on soil enzyme activity in black soil, the combined application of organic and inorganic fertilizer can effectively improve the activities of soil catalase, sucrase, and urease. The application of organic fertilizer (15 t/hm2) can alleviate the reduction of soil enzyme activity caused by single application of chemical fertilizer, but the change of soil enzyme activity is not obvious when applying organic fertilizer in a high amount (Yu et al. 2018; Wei et al. 2014). Soil total nitrogen content is the main factor affecting soil enzyme activity in black soil (Jing et al. 2015).

6.3

Effects of Cropping Systems on Soil Microbial Community Composition and Ecological Function in Black Soil of Arable Land

The main crops in the black soil area are maize, rice, and soybean. This section summarizes the effects of soybean continuous cropping, maize continuous cropping, typical rotation systems, and tillage systems such as changing drought to water sufficiency and water sufficiency to drought on the soil microbial community and ecological function.

6.3.1

Soil Microbial Community

Continuous cropping of soybean is a common cropping system in the black soil area. Soybean continuous cropping makes the composition of soil bacterial and fungal community significantly different from a rotation system of maize-soybean. The diversity index of the soil bacterial community in continuous cropping for more than 5 years (5 years, 7 years, 11 years, 13 years, and 17 years) was significantly higher than that of short-term continuous cropping (2–3 years) (Liu et al. 2019; Chen et al. 2018). The diversity of the fungal community in soil under continuous cropping soybean did not change significantly (Chen et al. 2018), but continuous cropping soybean weakens the ecological network of the fungal community, which is more conducive to the reproduction and growth of pathogenic fungi (Zhou et al. 2016; Hu et al. 2020a). The study found that the soil nutrient contents, as well as the richness and diversity index of the bacterial community, were significantly improved in longterm continuous cropping (13 years), and the relative abundance of Bacteroidea and Firmicutes increased in long-term continuous cropping and short-term continuous cropping, respectively (Liu et al. 2019). In addition, soil pH was shown to be the dominant factor for the change of bacterial community structure (Hu et al. 2020a) and was also the main environmental factor affecting the geographical distribution

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pattern of the microbial community in the black soil area (Liu et al. 2014, 2015b, 2016, 2018). The amount of fertilization in maize planting is higher than that for legumes, so the nutrient contents of soil under maize continuous cropping were significantly higher than those of rotation soil (Gao et al. 2021a). The study showed that the diversity of the soil bacterial community was significantly reduced by continuous cropping of maize compared with the rotation system, and Streptomycetaceae and Acetobacteraceae were sensitive in responding to soil nutrient changes. Crop types also have a profound impact on soil microbial ecological functions in black soil. In the crop rotation system in black soil, the composition, diversity, and ecological function of the soil microbial community showed obvious variation in different crop seasons. Results from a long-term fertilization experiment in black soil showed that during rotation of maize, soybean, and wheat, the diversity and abundance of bacterial communities in the maize season were significantly higher than in the soybean season, and the composition of dominant communities also showed differences between the maize and soybean seasons. For example, Acidobacteria (24.47–27.90%) had the highest bacterial abundance in the maize season, while Proteobacteria (27.78–34.40%) had the highest abundance in the soybean season. The abundance of Bacteroidetes and Actinobacteria was significantly different between the two crops (Ding et al. 2016a). Jia et al. (2020) found that the diversity of soil microorganisms under maize was higher than that of soil under soybean, rice, and potato; the diversity of soil microorganisms in the rice field was the lowest, and there was no significant difference between the diversity of soil microorganisms under potato and soybean.

6.3.2

Soil Microbial Community Functions

The composition and function of soil microbial groups were significantly affected by continuous cropping of different crops. Yan et al. (2019) showed that the total number and functional diversity (Biolog) of microorganisms in soybean continuous cropping soil were higher than those in soil of gramineous crops (wheat and maize). Moreover, the relative abundance of the nitrogenase nifH gene in maize continuous cropping soil was significantly higher than that of soil of soybean–maize rotation, while the relative abundance of the nitrogenase nifB gene was significantly lower than that of soybean–maize rotation soil (Gao et al. 2021b).

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Impact of Reclamation on Black Soil Microbial Community Composition and Functions

The physical and chemical properties of soil, such as moisture, heat, and nutrient status, are changed by long-term tillage measures (Han and Li 2018), and this has a significant impact on the diversity and composition of the soil microbial community. Based on long-term experiments on bare land, farmland, and grassland on black soil, the study showed that the aboveground cover increased the content of organic matter, nutrients, and microbial abundance in black soil. The three land uses did not change the community composition of the dominant soil bacteria (Wang et al. 2009; Hu et al. 2021) but had a significant impact on the composition of the community structure of low-abundance microorganisms. It is considered that the genus Granulicella could be used as an indicator to evaluate the change in the black soil environment. The main environmental driving factors include cation exchange capacity, total nitrogen, organic matter, available nitrogen, available phosphorus, and available potassium (Hu et al. 2021). After reclamation (farmland), the carbon metabolic capacity of the microbial community was reduced, but its function had not changed (Yu et al. 2013), and the soil microbial biomass and soil enzyme activity showed a downward trend (Zhang et al. 2012). Land use modes significantly affect the carbon source metabolic diversity of soil microbial communities, e.g. sugars, amino acids, and carboxylic acids, especially sugars (Jia et al. 2020). Amino acids, carboxylic acids, and polymers are the most commonly used carbon sources for microbial metabolism before reclamation of black soil (Yan et al. 2019), and amino acids, sugars, and polymers are the most important carbon sources for microbial metabolism of black soil (Jia et al. 2020; Yu et al. 2013).

6.5 6.5.1

Effects of Tillage Measures on Soil Microbial Community Composition and Functions in Black Soil Effect of Subsoiling on Soil Microbial Community Structure and Function in Black Soil

Changes in moisture and temperature conditions will significantly change the potential interaction between microorganisms in black soil, and the response of core microorganisms is more sensitive. Subsoiling optimized the soil moisture and temperature environment, maintained efficient and stable soil enzyme activity, and affected the bacterial community composition (Zhang et al. 2018). Research shows that subsoiling could improve the relative abundance of proteobacteria and Gemmatimonadetes and decrease the relative abundance of Actinomycetes and Nitrospirae (Zhang et al. 2018; Yang et al. 2020). Compared with tillage, subsoiling can increase the relative abundance of Lysobacter, Arenimonas, and Sphingomonas,

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which is conducive to improving ecological functions such as biocontrol, denitrification, and metabolism of aromatic compounds, decreasing the relative abundance of Streptomyces, Nocardioides, and Lentzea, and also benefitting degradation of soil organic matter, protection of microbial resources, iron metabolism, and antagonism to fungi. Soil with poor iron metabolism is not suitable for continuous subsoiling. The relative abundance of medium- and slow-growing rhizobia increased in the early years and decreased with the increase of subsoiling years. Reasonable subsoiling is conducive to improving soil nitrogen fixation and promoting soil fertility (Yang et al. 2020).

6.5.2

Impact of Conservation Tillage on Soil Microbial Community Composition and Functions in Black Soil

Conservation tillage is a new farming technology compared with traditional farming. In 2002, the Ministry of Agriculture of China defined it as an advanced agricultural farming technology that implements no tillage or less tillage, covers the surface with crop straw, reduces wind and water erosion, and improves soil fertility and drought resistance. Zhang et al. (2010) considered that conservation tillage significantly increased the contents of total carbon, total nitrogen, water-soluble organic carbon, alkaline hydrolysis, soil organic carbon, and microbial biomass carbon (MBC) (P < 0.05) in the surface layer (0–5 cm) of black soil compared with traditional tillage (Jia et al. 2015; Sun et al. 2015), which provided rich resources for microbial metabolism in this soil layer, especially conducive to the accumulation of active fungal biomass. The accumulation of MBC in topsoil under no tillage and ridge cultivation was obvious (Sun et al. 2015). No tillage with straw return significantly increased the proportion of fungi and bacteria in the soil microbial community (Zhang et al. 2012), while the soil microbial biomass and microbial diversity under ridge tillage were significantly higher than for no tillage soil (Liu et al. 2020). In recent years, crop straw return to the field (straw mulch, straw mixed return, straw deeply buried return, etc.) has been a common farming measure, which is conducive to the turnover and accumulation of soil organic carbon. Zhang et al. (2020) studied the effects of no tillage + straw mulch on soil microbial communities and soil enzyme activities and found that under no tillage, the contents of organic carbon, total nitrogen and water-soluble carbon, the microbial biomass of bacteria, fungi, and actinomycetes, and the activity of β-glucosidase, cellobiase, and β-Nacetylglucosaminidase were significantly higher than under traditional tillage. No tillage without straw return stimulated the biological functions of soil. Straw return could significantly increase the ratio of fungi to bacteria in the sub-surface layer (20–40 cm) of soil, which is more conducive to maintaining the stability of the ecosystem (Cong et al. 2020). Compared with maize straw mulch and deep tillage, the soil nutrient contents of maize straw rotation tillage were highest, and the soil bacterial community structure

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was mainly affected by the contents of available potassium, total nitrogen, and organic matter. The relative abundances of Streptomyceteae and Burkholderiaceae under straw rotary tillage were significantly higher than under straw deep ploughing and mulching and were significantly positively correlated with soil organic matter and total nitrogen content (Gao et al. 2021b). In the subsoiling straw return treatment, the species abundance of Gemmatimonadetes was significantly higher than in the rotation tillage straw no-return treatment (Fu et al. 2019). Yu et al. (2015) found that in the long-term continuous cropping soil of maize, the deep burial of straw could increase the soil bacteria, actinomycetes and major physiological microbiota, reduce the number of soil fungi, and improve the soil urease and invertase activities.

6.6

Discussion and Conclusions

The community structure and ecological functional properties of soil microorganisms in black soil farmland have both particularity and universality. Studies have shown that the addition of organic matter and the improvement of the soil environment are conducive to the increase of the proportion of beneficial microbial groups and their ecological functions in black soil farmland. However, the composition of the soil microbial community in the region has obvious geographical distribution characteristics, and its driving factors are soil pH and organic matter content, which are consistent with other soil types. Therefore, soil microorganisms play an important role in indicating the health and sustainability of farmland ecosystems. With the continuous updating of research methods, the understanding of the role and importance of soil microorganisms in the ecosystem has become clearer. However, there is still a long way to go from understanding and description to application. Isolation and purification of functional microbial strains in soil, targeted and purposeful establishment of functional synthetic bacteria groups, returning to farmland soil in the form of bio-organic fertilizer, bacterial fertilizer, etc., and construction of transformation efficient and disease-resistant soil through niche theory will be important research directions in the near future. Of these, isolation of target strains is core and a prerequisite, and also a way to better understand and reveal the important role of microorganisms in the ecosystem. Therefore, the seemingly basic work such as isolation and fermentation of indigenous functional microbial strains needs to be paid enough attention. With increasing knowledge of the role of microorganisms and of the factors that affect them, agriculture can be carried out in a way that optimizes the services obtained from beneficial microorganisms and minimizes the effect of harmful microorganisms, to the great benefit of all people. Acknowledgements The work was funded through the project “CHN17/0019 Sinograin II: Technological innovation to support environmentally-friendly food production and food safety under a changing climate-opportunities and challenges for Norway–China cooperation” (funded by the Norwegian Ministry of Foreign Affairs through the Royal Norwegian Embassy in Beijing).

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Chapter 7

Lignosulphonates as Soil Amendments in Agriculture Nicholas Clarke, Xueli Chen, Xiaoyu Liu, and Shuming Wan

Abstract Lignosulphonates are water-soluble polymeric by-products from wood pulp production using sulphite pulping and can be used as soil amendments in agriculture, amongst other uses. Here, we review effects of lignosulphonates as biostimulants and in enhancing the action of fertilizers. In soils, they affect the nitrogen and phosphorous cycles, as well as acting as transporters of micronutrients. The action of tree-associated fungi can be improved, and plant growth and yield can be increased. The beneficial effects of lignosulphonates in agriculture mean that there is likely to be a market for commercial specialty lignosulphonate products. Keywords Lignosulphonate · Soil amendment · Biostimulant · Nitrogen · Phosphorus · Micronutrients · Crop yield · Plant health

7.1

Introduction

Lignosulphonates are water-soluble polydisperse anionic polyectrolyte polymers that are produced as by-products from wood pulp production using sulphite pulping. Unlike other technical lignin products, lignosulphonates have a large number of ionizable functional groups, primarily sulphonate groups but also carboxylic acid groups. These groups make lignosulphonates water soluble. Hydrophobic groups, N. Clarke (✉) Norwegian Institute of Bioeconomy Research, Ås, Norway e-mail: [email protected] X. Chen Heilongjiang Joint Laboratory of Soil Microbial Ecology, Heilongjiang Academy of Black Soil Conservation and Utilization, Harbin, China X. Liu Institute of Resource, Ecosystem and Environment of Agriculture, and Department of Soil Science, Nanjing Agricultural University, Nanjing, Jiangsu, China S. Wan Heilongjiang Academy of Black Soil Conservation and Utilization, Harbin, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Clarke et al. (eds.), Innovation for Environmentally-friendly Food Production and Food Safety in China, Sustainable Agriculture and Food Security, https://doi.org/10.1007/978-981-99-2828-6_7

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Fig. 7.1 Schematic structure of a sodium salt of lignosulphonate (based on Adler (1957))

including most importantly aromatic and residual aliphatic units but also some oxygen-containing groups (Ruwoldt 2020), are found in the skeletal lignin structure. The balance of hydrophilic and hydrophobic groups determines the behaviour of lignosulphonates (Ruwoldt 2020). Most delignification in sulphite pulping involves acidic cleavage of ether bonds (Eq. 7.1, in which R can be a wide variety of groups found in lignin). Electrophilic carbocations (R+) are produced, and these can react with bisulphite ions (HSO3-) to form lignosulphonates (R-SO3H) (Eq. 7.2). R - O - R’ þ Hþ → Rþ þ R’ OH

ð7:1Þ

Rþ þ HSO3 - → R - SO3 H

ð7:2Þ

The schematic structure of a sodium salt of lignosulphonate is shown in Fig. 7.1. Production of lignosulphonates has been reviewed by Aro and Fatehi (2017). The primary site for ether cleavage is the α-carbon (carbon atom attached to the aromatic ring) of the propyl (linear three carbon) side chain. Sulphonation occurs on the side chains rather than on the aromatic ring. Lignosulphonates arise from three phenylpropanoid monomers, mainly coniferyl, sinapyl, and p-coumaryl alcohol (Low et al. 2019). They contain numerous functional groups, such as carboxylate, phenolic hydroxyl, cathechol, methoxyl, and sulphonate, as well as various combinations of these. Many of these functional groups and also electron lone pairs can bind metals (Wurzer et al. 2021). Lignosulphonates are recovered from spent pulping liquids (red or brown liquor) from sulphite pulping, using for example ultrafiltration. The weight average molecular weight of lignosulphonates has been found to be in the range 4600–398,000 g/mol (Fredheim et al. 2002) and varies depending on for example the type of wood (hardwoods 5700–12,000 g/ mol vs. softwoods 36,000–61,000 g/mol, Braaten et al. 2003). Variation in lignosulphonates could cause variation also in the effects found. Lignosulphonates are anionic, and the countering cation has importance for their properties: often, calcium is used, but sodium, potassium, and ammonium are alternatives. Calcium lignosulphonate was stabilized by the divalent Ca2+ counterion, showing a greater conformational stability than ammonium lignosulphonate with its monovalent NH4+ counterion (Savy et al. 2018). Calcium and ammonium lignosulphonates differ in hydroxyl, sulphonate, and phenolic content (Savy et al. 2018), and also in their effects on plants. Plant bioassays showed that ammonium

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lignosulphonate enhanced the elongation of the root system, whereas calcium lignosulphonate significantly increased both total and shoot plant weights (Savy et al. 2018). P retention was lower when ammonium lignosulphonate was used rather than calcium lignosulphonate (Xie et al. 1991). Although much less lignosulphonate is produced than Kraft lignin, lignosulphonate is the technical lignin derivative most used for industrial applications (Wurzer et al. 2021). Largely because of their amphiphilic nature, lignosulphonates have a very wide range of uses (Ruwoldt 2020), largely but not only in low-value applications. The largest use is as plasticizers in concrete production, while other uses include as dispersants, binders, feedstocks, and antioxidants. Amongst other uses, vanillin (artificial vanilla) can be produced from oxidation of lignosulphonates. In addition, lignosulphonates can be used as a soil amendment (or enhancement) in agriculture (Wurzer et al. 2021). They are not in themselves fertilizers (although they do appear to be biostimulants); however, lignosulphonates can enhance the action of fertilizers. Fertilization effects are therefore likely to appear only if lignosulphonate is added in addition to fertilizers. In these cases, addition of lignosulphonate may make it possible to apply lower quantities of fertilizer and still obtain the same yield, which will have clear environmental benefits by reducing the risk for eutrophication, as well as reducing economic costs for fertilizers. In this chapter, we will give an overview of the effects of lignosulphonate application on soil chemistry and biology, yield, plant growth and disease control, and plant metabolites, with a focus on the mechanisms involved.

7.2

Application of Lignosulphonates in Agriculture

Lignosulphonates are often provided in solid form as a brown powder (Fig. 7.2) but are readily soluble in water (Fig. 7.3), so application in agriculture is generally in liquid form, through fertigation, drip, or spray systems. Strategies used in application include complexation with micronutrients, co-pelleting largely with macronutrients, capsule formation for coating of fertilizers or pesticides, action as a biostimulant, and ammonoxidation (Wurzer et al. 2021). As lignosulphonates contain a diversity of functional groups, many of these are available to form complexes with metal ions and either transport them to the soil in the case of micronutrients or remove them in the case of toxic heavy metals (Wurzer et al. 2021). There will be some variation in the complexation ability due to the variable composition of the lignosulphonates. Potassium lignosulphonate has been shown to remove lead and copper from contaminated soils, while organic matter, ammonium N and available P and K increased (Liu et al. 2019). Leachability is lower than is the case for metal chelates with EDTA (ethylenediaminetetraacetic acid), although complex stability is also lower (Wurzer et al. 2021).

130 Fig. 7.2 Lignosulphonate in powder form from Borregaard, AS, Norway

Fig. 7.3 Lignosulphonate dissolved in water for field application, Heilongjiang Academy of Agricultural Sciences, China

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Lignosulphonates function well as binders of pellets, due to their emulsifying, dispersing, and stabilising properties (co-pelleting) (Wurzer et al. 2021). Their addition aims to improve the quality and/or efficiency of the fertilizer. Both inorganic and organic nutrients can be mixed with lignosulphonates and be either pressed into pellets or granulated. Capsule formation allows the slower release of nutrients or pesticides, leading to slower leaching of these and potentially less loss, thus enabling the same result with less fertilizer or pesticide. A coating should ideally be hydrophobic enough to prevent disruption and water-soluble enough to be able to transport fertilizers, and lignosulphonates have suitable properties for environmentally friendly capsules (Wurzer et al. 2021). Lignosulphonates act as biostimulants, affecting plant growth (Docquier et al. 2007; Ertani et al. 2011, 2019; Savy et al. 2018; Wurzer et al. 2021). This is discussed in more detail in the following sections. Ammonoxidation is the process of modification of lignin in the presence of oxygen and ammonia, normally under increased temperature and pressure, resulting in an increased N content: about 10–20 weight %N can be fixed in the lignin depending on the conditions (Wurzer et al. 2021). Thus, N becomes available for an extended period, reducing the need for inorganic N fertilizer. However, ammonoxidation has rarely been applied to lignosulphonates.

7.3

Effects of Lignosulphonates on Soil Chemistry and Biology

Lignosulphonates’ properties, such as the above-mentioned abilities to complex metals and adsorb to surfaces, as well as the presence of biologically and chemically active phenolic groups, indicate that they have the potential to participate in a variety of reactions in soil (Meier et al. 1993). These affect the cycles of major nutrients, including nitrogen and phosphorus, as well as micronutrients. It appears clear that lignosulphonates can affect nitrogen cycling in soil. Amendment of soil with lignosulphonates can either stimulate or inhibit microbial activity depending on concentration and appears to cause fixation of N, possibly through reaction of phenolics with nitrite (Meier et al. 1993). It has been hypothesized that lignosulphonate might increase the proportion of urea-N in the plant-available pool. Due to their phenolic nature, lignosulphonates may inhibit urease activity thereby reducing urea hydrolysis, and inhibit nitrification thus reducing the potential for denitrification and leaching loss (Xie et al. 1993a). In their experiments, low nitrate content after soil incubation with calcium lignosulphonates was attributed to microbial immobilization, nitrification inhibition, and possibly induced denitrification of native nitrate by sugars added with lignosulphonate (Xie et al. 1993a). Inhibition of urea hydrolysis and reduction of soil pH by lignosulphonates could reduce ammonia volatilization: Amending urea with ammonium lignosulphonate induced between

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46% and 85% reduction in ammonia losses compared with unamended urea (Al-Kanani et al. 1994). Adsorption of lignosulphonates to clays could block clay fixation of ammonium (Meier et al. 1993). Ca lignosulphonate reduced nitrate leaching from New Zealand pasture soils in combination with gibberellic acid following urine application; efficacy increased with increasing application rate (Bishop and Jeyakumar 2021). The effect of lignosulphonates is greatest on nutrient-poor soils, especially those with low available P, while in nutrient-rich soils an effect may not be observed at all. Lignosulphonates compete with phosphate for adsorption sites on soil, and thus reduce P retention (Xie et al. 1991, 1993b). A clay soil was incubated with various amounts of calcium lignosulphonate and phosphate for 240 h under moist conditions; additions of lignosulphonate increased P retention, suggesting that Ca added with lignosulphonate precipitated P, probably as hydroxyapatite (Xie et al. 1991). P retention was much lower when ammonium lignosulphonate was used and soluble native Ca removed (Xie et al. 1991), indicating the importance of the counterion. In another study, addition of ammonium lignosulphonate reduced P retention, suggesting competition with phosphate for adsorption sites in the soil (Xie et al. 1993b). Soil organic C increased with increasing ammonium lignosulphonate addition and decreased with diammonium phosphate additions (Xie et al. 1995). Ammonium lignosulphonate alone had little effect on native soil P fractions, while applying it together with diammonium phosphate improved uptake of fertilizer P by maize (Zea mays L.) (Xie et al. 1995). Calcium lignosulphonate application reduced phosphate fixation and led to a higher water extractable phosphate concentration near the surface of simulated acid soil, suggesting that adding lignosulphonate would increase fertilizer P availability in a soil of this type, as well as improving Ca nutrition, which is poor in these soils (Hao et al. 2000). A rhizobox experiment by Almås et al. (2014) using the lignosulphonate product BorreGro produced by Borregaard AS (Sarpsborg, Norway) showed that lignosulphonate has a positive effect on increasing soil pH in soil of low Al- and Fe-oxide contents and that this was related to the concentration of lignosulphonate. Borregro addition increased the availability of dissolved Mn as BorreGro has a high Mn content. Increased concentrations of dissolved organic carbon (DOC) close to the roots after low addition of BorreGro may have been linked to increases in bacterial and fungal biomass and increased excretion of root exudates. An effect of lignosulphonate on phospholipid-derived fatty acid (PLFA) composition was likely indirect, mostly through its effect on root activity. Lignosulphonates are micronutrient carriers (Meier et al. 1993; Wurzer et al. 2021) and can provide for example zinc (Martín-Ortiz et al. 2009; López-Rayo et al. 2012; Cieschi et al. 2016), manganese (López-Rayo et al. 2012), and iron (Carrasco et al. 2012) to plants. In root promotion, lignosulphonate complex formation with Fe, Zn, and Mn prevents precipitation of these micronutrients, instead providing relatively fast release of the soluble micronutrient ions to cells for plant growth. They are therefore a good alternative to synthetic chelates, which, although effective, constitute an environmental problem. Zinc lignosulphonate is a potential Zn source for wheat (Triticum aestivum L.) and maize, at least in a hydroponic system (Martín-

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Ortiz et al. 2009). In the case of iron, strong Fe3+-lignosulphonate complexes appeared to be preferred when they were applied to the leaf, whereas root uptake in hydroponics was aided by lignosulphonates forming weaker complexes with iron (Carrasco et al. 2012).

7.4

Effect of Lignosulphonate on Tree-Associated Fungi

A few studies have considered the effect of lignosulphonates on tree-associated fungi (Niemi et al. 2005; Dumas 2011). Ammonium lignosulphonate was found to stimulate the germination and growth of the saprophytic fungus Phlebiopsis gigantea (Fr.) Jülich. When incubated in 1% and 2.5% solutions, germination rates were higher and germ tubes longer at all temperatures tested when compared to water controls. When ammonium lignosulphonate was used to inoculate P. gigantea into stumps of red pine (Pinus resinosa Sol. Ex Aiton), the spores germinated and hyphae were easily observable after 16 h, while no germination had occurred in the stumps treated with oidia in water (Dumas 2011). Iron lignosulphonate complexation facilitated early-stage interactions between an ectomycorrhizal fungus (Pisolithus tinctorius [Pers.] Coker and Couch) and host Scots pine (Pinus sylvestris L.) plants (Niemi et al. 2005). Iron lignosulphonate enhanced formation of lateral roots induced by P. tinctorius and had a positive effect on the establishment of mycorrhizae on the seedlings. Also, the growth of the fungal mycelium was improved by iron lignosulphonate. Lignosulphonate might therefore be a potential tool to improve the efficiency of fungal inoculations (Niemi et al. 2005). Although these results were for fungi associated with trees, an effect of lignosulphonates for fungi associated with agricultural crops, such as arbuscular mycorrhizal fungi, is a distinct possibility.

7.5

Lignosulphonate, Plant Growth, and Crop Yield

Lignosulphonate humates have been shown to lead to increased root and leaf growth in hydroponic experiments in maize, i.e. in the absence of soil and soil microorganisms (Ertani et al. 2011, 2019). Root growth increased more (+ 51–140%) than leaf growth (+ 5–35%), which was hypothesized as being due to higher nitrogen metabolism stimulation in roots as shown by increased activity of N-assimilation enzymes and high consumption of sugars. Enhanced photosynthesis was also attributed to humates. Ertani et al. (2019) concluded that the lignosulphonates tested in their study functioned as biostimulants and were more efficient than humates, perhaps because of the type of starting material and process used for their production. Specialty lignosulphonates were especially effective, suggesting the importance of commercial development of new lignosulphonate products. In maize, for example,

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glutamine-synthetase, glutamate-synthase enzyme activities, and protein content all increased after application of lignosulphonate humates or leonardite. Their addition also increased chlorophyll content, glucose, fructose, and rubisco enzyme activity, suggesting a positive role for two lignosulphonate humates as well as leonardite in the photosynthetic process (Ertani et al. 2011, 2019). Preliminary results from hydroponic experiments carried out on wheat and lettuce at Ås, Norway, also suggest that, even in the absence of soil and soil microorganisms, addition of lignosulphonates can sometimes, but not always, lead to increased plant weight (Clarke et al. unpublished results). A field experiment in Nanjing, China, showed that lignosulphonate addition improved rice grain quality with a significant decrease in chalky seeds (Liu et al. unpublished results). The effects of ammonium and calcium lignosulphonates on plant growth depend on the applied concentrations, their molecular properties, and conformational stability (Savy et al. 2018). As stated above, calcium lignosulphonate was stabilized by the divalent Ca2+ counterion, showing a greater conformational stability than ammonium lignosulphonate with its monovalent NH4+ counterion (Savy et al. 2018). Calcium and ammonium lignosulphonates differed in hydroxyl, sulphonate, and phenolic content (Savy et al. 2018). Ammonium lignosulphonate enhanced the elongation of the root system in early-stage maize, while calcium lignosulphonate increased total and shoot plant weights significantly (Savy et al. 2018). Sodium lignosulphonate has been shown to be a better shoot growth enhancer in rice (Oryza sativa L.) compared with calcium lignosulphonate, possibly acting through upregulation of photosynthetic activities and reduced accumulation of reactive oxygen species (Kok et al. 2021). Calcium lignosulphonate complexes have a stimulating effect on growth of the orchid Phalaenopsis and on rooting of Sequoiadendron (Docquier et al. 2007). Lignosulphonates improved callus proliferation rate and adventitious root formation as well as modulating physiological responses during plant growth in recalcitrant indica rice (Low et al. 2019). Absorption by the plant cell of Ca2+ complexed with lignosulphonate triggered the calcium-binding protein calmodulin and consequently activated calcium-dependent protein kinases (Low et al. 2019). In shoot promotion, lignosulphonate could either elevate the endogenous auxin level that demotes shoot induction or depress certain cytokinin transport proteins that affect the distribution of cytokinin throughout the cells (Low et al. 2019). Low et al. (2019) also found that the genes OsCAB1R and OsYSA related to chlorophyll biosynthesis, chloroplast development, and photosynthesis were noticeably reduced. Thus, regulating the auxin balance and gene expression could be the mechanism for plant shoot promotion by lignosulphonate.

7.6

Effect of Lignosulphonate on Plant Diseases

Lignosulphonates appear to affect some plant diseases, even in some cases when applied directly to leaves. Weekly foliar application by spraying of aqueous solutions of ammonium lignosulphonate and potassium phosphate reduced the severity

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of bacterial spot disease on tomato and pepper plants, both in the greenhouse and in the field (Abbasi et al. 2002). Although total tomato and pepper yield were not increased significantly after treatment, there was an increase in the marketable fruit yield of lignosulphonate-treated pepper plants in 2001 (Abbasi et al. 2002). Significant reduction of potato scab (50–80%) and verticillium wilt (40–50%) was found after application of a concentrated ammonium lignosulphonate to soil in four Ontario potato fields (Soltani et al. 2002). Ammonium lignosulphonate significantly increased marketable yield at all sites in the year of application. Numbers of soil microorganisms, especially fungi, increased at all sites within weeks of application (Soltani et al. 2002).

7.7

Discussion

Lignosulphonates show great promise as soil amendments, enhancing the effect of fertilisers and thus making it possible to reduce the amounts of fertilisers needed. Effects are greatest on nutrient-poor soils, especially those with low available P, while with nutrient-rich soils an effect may not be observed at all. Both the nitrogen and phosphorus cycles are affected, for example through reducing N volatilization and leaching as well as improving P uptake, and lignosulphonates can also function as carriers of micronutrients such as zinc, iron, and manganese. In addition, they can form complexes with heavy metals, which might reduce the toxic effect of these. Effects of lignosulphonates related to agriculture are summarised in Table 7.1. Lignosulphonates do not only act through the soil or soil microorganisms. Results obtained in hydroponic experiments (Martín-Ortiz et al. 2009; Ertani et al. 2011, 2019; Clarke et al. unpublished results) and in culture media (Docquier et al. 2007; Savy et al. 2018; Low et al. 2019; Kok et al. 2021) show that lignosulphonates can affect plant growth without the presence of soil microorganisms, for example as biostimulants and as carriers for micronutrients. Lignosulphonates appear to have a direct effect on plant diseases, in some cases when applied to leaves and in some when applied to soil (Abbasi et al. 2002; Soltani et al. 2002). However, the mechanisms for their action remain unclear. All lignosulphonates are not the same. Their molecular weight varies greatly (Fredheim et al. 2002) and they contain a diversity of functional groups. Factors such as hardwood vs. softwood may be important (Braaten et al. 2003). This variation opens the way to producing a variety of specialty lignosulphonates especially designed for specific crops or soil types. Specialty lignosulphonates have shown themselves to be especially effective as biostimulants (Ertani et al. 2019), further suggesting the potential for commercial development of new lignosulphonate products. Counterion is also important, as it affects the properties of lignosulphonates (Savy et al. 2018; Kok et al. 2021). As the effect varies with the counterion, the most suitable counterion might be chosen depending on the effect desired. In practice, the counterion is often calcium, although ammonium and sodium are also used.

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Table 7.1 Summary of the effects of lignosulphonates related to agriculture Effect Nitrification inhibition Inhibition of urease activity Microbial N immobilization Reduced ammonia volatilization Reduced nitrate leaching Reduced P retention Improved P uptake Increased bacterial and fungal biomass Micronutrient transport

System Soil Soil

References Meier et al. (1993) and Xie et al. (1993a) Xie et al. (1993a)

Soil

Xie et al. (1993a)

Soil

Al-Kanani et al. (1994)

Soil

Bishop and Jeyakumar (2021)

Soil Soil Soil

Xie et al. (1991, 1993b) and Hao et al. (2000) Xie et al. (1995) Almås et al. (2014)

Soil

Martín-Ortiz et al. (2009); Carrasco et al. (2012); LópezRayo et al. (2012) and Cieschi et al. (2016) Liu et al. (2019)

Heavy metal complexation Improved fungal/tree interaction Increased root growth

Soil

Increased shoot/leaf growth Improved enzyme activities Increased protein content Increased chlorophyll content Callus proliferation Reduced plant diseases

In vitro

In vitro, tree stumps In vitro

Niemi et al. (2005) and Dumas (2011)

In vitro

Docquier et al. (2007); Ertani et al. (2011, 2019); Savy et al. (2018) and Low et al. (2019) Docquier et al. (2007); Ertani et al. (2011, 2019); Savy et al. (2018) and Kok et al. (2021) Ertani et al. (2011, 2019)

In vitro

Ertani et al. (2011, 2019)

In vitro

Ertani et al. (2011, 2019)

In vitro Leaves, soil

Low et al. (2019) Abbasi et al. (2002) and Soltani et al. (2002)

In conclusion, lignosulphonates appear to have great potential in agriculture, both directly as biostimulants and through their effects on soil biology and biochemistry. However, it is necessary to know which lignosulphonates function best under different conditions. There is likely to be a market for further development of commercial specialty lignosulphonate products. Acknowledgements The work was funded through the project “CHN17/0019 Sinograin II: Technological innovation to support environmentally-friendly food production and food safety under a changing climate-opportunities and challenges for Norway–China cooperation” (funded by the Norwegian Ministry of Foreign Affairs through the Royal Norwegian Embassy in Beijing). We thank Anders Jensen, Daniel Gomez, Vebjørn Eikemo, Alejandro Aliaga, and Guro Elise Fredheim (Borregaard AS) for helpful discussions.

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Low L-Y, Abdullah JO, Wee C-Y et al (2019) Effects of lignosulfonates on callus proliferation and shoot induction of recalcitrant indica rice. Sains Malays 48:7–13. https://doi.org/10.17576/jsm2019-4801-02 Martín-Ortiz D, Hernández-Apaolaza L, Gárate A (2009) Efficiency of a zinc lignosulfonate as Zn source for wheat (Triticum aestivum L.) and corn (Zea mays L.) under hydroponic culture conditions. J Agric Food Chem 57:226–231 Meier JN, Fyles JW, MacKenzie AF et al (1993) Effects of lignosulfonate-fertilizer applications on soil respiration and nitrogen dynamics. Can J Soil Sci 73:233–242 Niemi K, Kevers C, Häggman H (2005) Lignosulfonate promotes the interaction between Scots pine and an ectomycorrhizal fungus Pisolithus tinctorius in vitro. Plant Soil 271:243–249 Ruwoldt J (2020) A critical review of the physicochemical properties of lignosulfonates: chemical structure and behavior in aqueous solution, at surfaces and interfaces. Surfaces 3:622–648. https://doi.org/10.3390/surfaces3040042 Savy D, Cozzolino V, Drosos M et al (2018) Replacing calcium with ammonium counterion in lignosulfonates from paper mills affects their molecular properties and bioactivity. Sci Tot Environ 645:411–418 Soltani N, Conn KL, Abbasi PA et al (2002) Reduction of potato scab and verticillium wilt with ammonium lignosulfonate soil amendment in four Ontario potato fields. Can J Plant Pathol 24: 332–339 Wurzer GK, Hettegger H, Bischof RH et al (2021) Agricultural utilization of lignosulfonates. Holzforschung 76(2):155–168. https://doi.org/10.1515/hf-2021-0114 Xie RJ, Fyles JW, MacKenzie AF et al (1991) Lignosulfonate effects on phosphate reactions in a clay soil: causal modeling. Soil Sci Soc Am J 55:711–716 Xie RJ, Meier J, Fyles JW et al (1993a) Effects of calcium lignosulphonates on urea hydrolysis and nitrification in soil. Soil Sci 156:278–285 Xie RJ, O’Halloran IP, Mackenzie AF et al (1993b) Phosphate sorption and desorption as affected by addition sequences of ammonium lignosulphonate and diammonium phosphate in a clay soil. Can J Soil Sci 73:275–285 Xie XH, Mackenzie AF, Xie RJ et al (1995) Effects of ammonium lignosulphonate and diammonium phosphate on soil organic carbon, soil phosphorous fractions and phosphorous uptake by corn. Can J Soil Sci 75:233–238

Chapter 8

Ecological Functions of Arbuscular Mycorrhizal Fungi in Agriculture Lingbo Meng, Shumin Li, and Yufei Meng

Abstract Arbuscular mycorrhizal (AM) fungi can form a mutually beneficial symbiotic system with most crops, which plays an important role in maintaining farmland ecosystem functions and contributes to sustainable development. First, we discuss the diversity of AM fungi in rhizosphere and roots of soybean and maize. Second, the promotion of N, P uptake, and N transfer in intercropping by AM fungi is introduced. Third, we discuss the influence of AM fungi on soil fertility and the soil carbon pool. Finally, the application of AM fungi in agricultural production is introduced. Keywords Arbuscular mycorrhizal fungi · Soil fertility · Nitrogen · Phosphorus · Intercropping · Structural equation model · Carbon sink function

Abbreviations AK AM AN AP G.m. OTUs RDA

Available potassium Arbuscular mycorrhiza Available nitrogen Available phosphorus Glomus mosseae Operational taxonomic units Constrained ordination of redundancy analysis

L. Meng (✉) School of Geography and Tourism, Harbin University, Harbin, Heilongjiang, China S. Li Resource and Environmental College, Northeast Agricultural University, Harbin, Heilongjiang, China e-mail: [email protected] Y. Meng School of Environment, Harbin Institute of Technology, Harbin, Heilongjiang, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Clarke et al. (eds.), Innovation for Environmentally-friendly Food Production and Food Safety in China, Sustainable Agriculture and Food Security, https://doi.org/10.1007/978-981-99-2828-6_8

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Structural equation modeling Total nitrogen Total organic carbon

Introduction

A mycorrhiza is a reciprocal symbiosis formed by soil fungi and terrestrial plant roots. Mycorrhizal fungi formed a symbiotic relationship with ancient terrestrial plants as early as 350–450 million years ago. According to their morphological and anatomical characteristics, mycorrhizae can be divided into seven types: Ectomycorrhiza, Arbuscular mycorrhiza, Ectendomycorrhiza, Orchid mycorrhiza, Arbutoid mycorrhiza, Monotropoid mycorrhiza, and Euphorbia mycorrhiza (Smith and Read 2008). Among these, Ectomycorrhiza and Arbuscular mycorrhiza are the most important and common types. Arbuscular mycorrhiza (AM) fungi are a reciprocal symbiosis formed by a Glomus fungus and the plant root system, named for the formation of typical arbuscule structures within the plant cortex cells (Fig. 8.1). In addition, AM fungi form vesicles, hyphal coils, intraradical hyphae, and intercellular hyphae in the plant root system and produce a large number of extraradical hyphae and spores outside the roots (Fig. 8.2). AM fungi, being the most widely distributed mycorrhizal type in nature, exist in almost all terrestrial habitats and form symbiotic relationships with about 80% or more of terrestrial plants, which include bryophytes, ferns, gymnosperms, and angiosperms (Zhang et al. 2004b).

Fig. 8.1 Electron microscopy of vesiculararbuscular mycorrhizae (Kinden and Brown 1975)

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Fig. 8.2 Structure of an arbuscular mycorrhiza fungus

8.2

Diversity of AM Fungi

AM fungi are an essential component of natural and agricultural ecosystems and have been widely considered as a significant factor in improving host plant nutrient uptake and growth. At present, AM fungi are classified into 1 phylum (Glomeromycota), 1 class (Glomeromycetes), 4 orders (Glomerales, Diversisporales, Archaeosporales, Paraglomerales), 11 families (Acaulosporaceae, Ambisporaceae, Archaeosporaceae, Claroideoglomeraceae, Diversisporaceae, Geosiphonaceae, Gigasporaceae, Glomeraceae, Sacculosporaceae, Pacisporaceae, Paraglomeraceae), and 25 genera (Acaulospora, Ambispora, Archaeospora, Cetrospora, Claroideoglomus, Corymbiglomus, Dentiscutata, Diversispora, Funneliformis, Geosiphon, Gigaspora, Glomus, Intraornatospora, Otospora, Pacispora, Paradentiscutata, Paraglomus, Recocetra, Redeckera, Rhizophagus, Sacculospora, Sclerocystis, Scutellospora, Septoglomus, Tricispora (Schübler et al. 2001). Research results for AM fungal diversity showed that eight species at the genus level were identified in rhizosphere of maize and soybean in a Mollisol of Heilongjiang Province, China. They are Glomus_f_Glomeraceae, Paraglomus, Acaulospora, Diversispora, Archaeospora, Gigaspora, unclassified_f_Diversisporaceae, and unclassified_c_Glomeromycetes. Seven genera were found in the roots of maize and soybean which included Glomus_f_Glomeraceae, Paraglomus, Diversispora, Archaeospora, Gigaspora, unclassified_f_Gigasporaceae, and unclassified_c_Glomeromycetes. The variety and abundance of AM fungi in soil and roots of crops also change in different crops and different growth periods (Zhang et al. 2020). AM fungi can form a huge mycelial network underground; their mycelium is thinner and longer than root hairs and can stretch to areas that root hairs cannot

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Fig. 8.3 Functions of AM fungi in soil ecology

reach, so AM fungi can mobilize soil nutrients and help plants absorb water and mineral elements from the soil. Also, the huge mycelial network of AM fungi connects root systems between plants below ground and has the function of attaching soil particles, which could improve soil fertility (Fig. 8.3). Therefore, mycorrhizal fungi play an important role in maintaining farmland ecosystem functions and promoting sustainable development.

8.3 8.3.1

Facilitation of Crop Nutrient Uptake Facilitation of P Uptake

AM fungi were particularly significant in promoting the uptake of P. Studies have shown that up to 90% of the P obtained by plants originate from AM fungi (Jakobsen et al. 1992). For example, maize inoculated with different mycorrhizal fungi will allocate a portion of carbohydrates to AM fungi after inoculation, compared with no inoculation treatment. Root infection rate and extrinsic mycelium density increased, and uptake of P in maize included both the direct pathway of root absorption and the mycorrhizal pathway through AM fungi. The increase of phosphorus content in maize during inoculation treatment was mainly due to the contribution of the mycorrhizal pathway to total phosphorus content in the plants (Igiehon and Babalola 2017). After AM fungi infect one plant, the extrinsic mycelium of AM fungi can infect another plant in the surrounding soil, thus forming a mycelial bridge between the roots of two plants (plants of the same species or different species). Mycelial bridge formation plays an important role in material and signal transmission between plants (Fig. 8.3). In AM symbionts, a large number of extraneous mycelia extending into the soil expands the absorption range and surface area of roots and shortens the

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Table 8.1 Effects of inoculation on P uptake (mg pot-1) in shoots and roots of soybean with different P sources. For explanation of the symbols, see the text

Shoot P uptake

Root P uptake

Inoculation R-MR+MR-M1+ R-M2+ R+M1+ R+M2+ Mean R-MR+MR-M1+ R-M2+ R+M1+ R+M2+ Mean

P sources P66.64ca 70.58bc 76.55ab 78.37a 81.19a 82.43a 75.96db 15.92d 16.75cd 17.35bc 17.72bc 18.13ab 19.25a 17.52d

P+ 86.32c 91.52c 102.31b 103.99b 111.96ab 120.68a 102.79a 23.60d 24.58cd 25.45bc 25.92b 28.07a 28.37a 26.00a

P1 75.17d 83.49c 92.86b 93.40b 102.98a 104.20a 92.02b 18.98e 20.70c 22.62c 23.28bc 24.04b 26.04a 22.61b

P2 71.27e 82.16d 86.98c 93.88b 96.87b 102.05a 88.87c 18.14d 18.65d 20.34c 22.35b 22.80b 24.11a 21.06c

Mean 74.85e 81.94d 89.68c 92.41c 98.25b 102.34a 19.16f 20.17e 21.44d 22.32c 23.26b 24.44a

a Values in each line of same P source followed by different letters are significantly different at the 0.05 level b Mean values for different P source treatments with the same inoculation in each column followed by different letters are significantly different at the 0.05 level

diffusion distance of P. The mycelial network can absorb water and nutrients instead of host fine roots or root hairs and transfer excess nutrients to the host plants. Thus, mycelia promote the rapid growth and comprehensive resistance of host plants to adverse environmental conditions (Cardoso and Kuyper 2006). AM fungi also promote the absorption of soil organophosphorus by plants. In 2010, a pot experiment was conducted to study the promoting effects of Glomus versiforme (M1) and Glomus mosseae (M2) on soil organic P using different phosphorus sources. Four treatments were used in this experiment: control without P fertilizer (P-), KH2PO4 (P+), sodium phytate (P1), and lecithin (P2). The following inoculation methods were used: without inoculating rhizobium (R-), inoculation with rhizobium (R+), without inoculation with AM fungi (M-), inoculation with Glomus versiforme (M1+), inoculation with Glomus mossseae (M2+), double inoculation with rhizobium and Glomus versiforme (R+M1+), and double inoculation with rhizobium and Glomus mossseae (R+M2+). Different inoculation methods significantly affected the phosphorus uptake of shoots and roots (Table 8.1). On average, phosphorus uptake of shoots and roots of soybean inoculated with rhizobium, Glomus versiforme, Glomus mosseae, rhizobium, and Glomus versiforme, and double inoculation of rhizobium and Glomus mosseae were increased by 1.09, 1.20, 1.24, 1.31, 1.37 times and 1.05, 1.12, 1.17, 1.21, 1.28 times, respectively, compared with the corresponding control treatment. These results indicated that inoculation with AM fungi could promote the utilization of organic P in soybean, and the promotion of phosphorus uptake from sodium

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Table 8.2 Effect of inoculation on soil acid phosphatase activities in the rhizosphere of soybean with different P source (μmol g-1soil h-1). For explanation of the symbols, see the text

Soil acid phosphatase activities in the rhizosphere

Inoculation R-MR+MR-M1+ R-M2+ R+M1+ R+M2+ Mean

P sources P+ Pa 0.75e 0.76f 0.81d 0.79e 1.11c 1.10c 1.16b 1.04d 1.25a 1.22a 1.26a 1.18b 1.06cb 1.01d

P1 1.46e 1.48e 1.70c 1.56d 1.94a 1.87b 1.67b

P2 1.43f 1.47e 1.68d 1.75c 1.87b 1.90a 1.68a

Mean 1.10f 1.14e 1.40c 1.38d 1.57a 1.55b

a Values in each line of same P source followed by different letters are significantly different at the 0.05 level b Mean values for different P source treatments with the same inoculation in each column followed by different letters are significantly different at the 0.05 level

phytate and lecithin by inoculation with Glomus mosseae was greater than that of inoculation with Glomus versiforme. Nitrogen uptake of soybean was greatest in the double inoculation treatment, which led to improvement of mycorrhizal infection rate and increased the secretion of acid phosphatase of soybean roots. Thus, phosphorus acquisition of soybean from organic phosphorus sources such as sodium phytate and lecithin was enhanced (Tong et al. 2009). Previous studies have shown that AM fungi can increase the phosphatase activities of rhizosphere soil, and thus improve the absorption and utilization of phosphorus in soil by plants (Singh 2006). Acid phosphatase plays an important role in phosphorus conversion; its activity can be used as an indicator of phosphorus tolerance of crops and has a significant relationship with the strength of activated organophosphates. Different phosphorus sources had significant effects on acid phosphatase activities in rhizosphere soil of soybean (Table 8.2). Also inoculation significantly affected acid phosphatase activities in rhizosphere soil of soybean. The activities of acid phosphatase in rhizosphere soil of AM fungi inoculated and double inoculated soybean was significantly higher than that of the control. Acid phosphatase activities in rhizosphere soil of soybean increased by 27.27%, 25.45%, 42.73%, and 40.91%, respectively, when inoculated with Glomus versiforme, Glomus mosseae, rhizobia and Glomus versiforme, and rhizobia and Glomus mosseae (Tong et al. 2009). These results indicated that AM fungi inoculation could significantly improve the activities of acid phosphatase in soybean rhizosphere soil, improve the ability of roots to utilize organophosphorus, and thus promote the growth of soybean. In addition, rhizobium and Glomus versiforme had a better ability to utilize organophosphorus sodium phytate, while rhizobium and Glomus mosseae had a better ability to utilize organophosphorus lecithin.

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Facilitation of N Uptake

AM fungi can promote the N uptake of plants, influence the conversion of N in soil organic matter, and reduce the loss of N. The mycelium can extend to the area outside the rhizosphere, connect roots with the surrounding soil microhabitats, and enlarge the volume from which roots absorb nutrients (He et al. 2003). Thus, water and nutrients can be transported by the huge hyphae network to be finally absorbed by plants (Vassilev et al. 2001). The N transfer facilitated by AM fungi in intercropping systems is also verified through 15N isotopic labeling experiments. A pot experiment was conducted at a greenhouse in Northeast Agricultural University in China to test facilitation of N uptake and N transfer between maize and soybean (Meng et al. 2015). Three root separation patterns between soybean and maize were designed (Fig. 8.4) to study N uptake facilitation in an intercropping system (Fig. 8.4). They were as follows: (1) solid barrier, roots were separated by a hard plastic sheet (0.5 mm) and had no root contact or material exchange; (2) mesh barrier, roots were separated by a 30-μm nylon mesh and had no contact but water, nutrients, and hyphae were allowed to exchange and permeate; (3) no barrier, which allowed for complete contact between the roots of soybean and maize. Plastic pots were cut in the middle, separated into two compartments, and then reconstructed for solid barrier and mesh barrier patterns. The experiment involved four treatments: inoculation with Bradyrhizobium japonicum SH212 (SH212), inoculation with Glomus mosseae (G.m.), dual inoculation (both of Bradyrhizobium japonicum SH212 and G. mosseae, SH212 + G.m.), and NI as a control. The total was 12 treatments (3 barriers × 4 inoculations) with four replicates for each treatment. At sowing, 30 g per compartment of AM fungi

Fig. 8.4 Schematic diagram of the root separation in pots (Meng et al. 2015)

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inoculum and 15 mL per compartment of rhizobium (density 8.2 × 108mL-1) were thoroughly mixed with the soil for the inoculated treatments.

8.3.3

N Concentration and Uptake

SH212 + G.m. treatment significantly increased the N concentrations in soybean shoots and roots by 13.72% and 18.47%, respectively, compared with the NI treatment in the no barrier pattern. In addition, a uniform facilitation was found in maize shoots and roots, as the N concentrations increased by 28.34% and 34.94%, respectively. The root separation patterns had little influence on the N concentration of soybean shoots and roots. However, separating maize from soybean plants by a plastic sheet significantly decreased the N concentration of maize shoots (by 5.04–13.25%) compared with no barrier patterns in all inoculated treatments (Table 8.3). The highest N uptake of soybean was found with the SH212 + G.m. treatment in all three root separation patterns (Fig. 8.5a). No significant difference was observed in the N uptake of soybean shoots and roots between different root separation patterns (Table 8.3). For maize, both the root separation pattern and microbial inoculation had significant effects on N uptake (Fig. 8.5b). Dual inoculation increased maize shoot N uptake by 59.93%, 63.00%, and 63.62% and root N uptake by 78.57%, 93.87%, and 96.08% compared with the NI treatment in solid barrier, mesh barrier and no barrier patterns, respectively (Table 8.3). In addition, the N uptake of maize was significantly enhanced by intercropping with soybean, and the N uptake of the no barrier pattern was 12.01% higher than the solid barrier pattern under non-inoculated conditions (Fig. 8.5b).

8.3.4

N Transfer in Soybean/Maize Intercropping Systems

The results of 15N labeling showed that G. mosseae and rhizobium SH212 inoculation alone enhanced the N transfer from soybean to maize in a soybean/maize intercropping system. However, the more significant enhancement was observed in dual inoculation in the mesh barrier and no barrier systems (Table 8.4). The amount of N transferred from soybean to maize (Nt) of SH212 + G.m. was 11.45 and 12.46 mg more than that of NI, and it was also significantly more than SH212 or G.m. alone in the mesh barrier and no barrier patterns. In addition, the transferred N from soybean to intercropped maize accounted for 3.13–6.01% of the N uptake of maize (Table 8.4). However, no significant difference was observed in the percentage of transferred N in maize N uptake (No%) between G.m. and SH212. The N transfer was also increased by intercropping. For example, the amount of N transferred from soybean to maize (Nt) in a no barrier system was 19.63–43.33% more than that of a mesh barrier system (Table 8.4).

No barrier

Mesh barrier

Solid barrier

N uptake 86.88 ± 1.46cA 106.32 ± 1.15bcA 113.57 ± 1.84bA 150.83 ± 5.37aA 84.78 ± 1.76cA 111.53 ± 5.35bA 110.34 ± 6.48bA 151.30 ± 2.84aA 82.48 ± 1.70cA 108.27 ± 2.89bA 106.50 ± 4.08bA 149.45 ± 5.23aA

Root N concentration 18.87 ± 0.80cA 19.74 ± 0.60bcA 21.71 ± 0.80abA 22.78 ± 0.90aA 18.90 ± 0.20cA 20.90 ± 0.40bA 21.57 ± 0.50bA 22.80 ± 0.20aA 18.79 ± 0.30cA 19.85 ± 0.10bcA 20.70 ± 0.70bcA 22.26 ± 0.30aA N uptake 21.06 ± 1.36cA 42.96 ± 1.15bA 48.33 ± 2.91bA 57.59 ± 2.43aA 21.49 ± 5.30cA 46.78 ± 1.15bA 49.48 ± 1.41bA 58.21 ± 1.15aA 21.10 ± 0.85cA 43.86 ± 0.63bA 45.24 ± 0.53bA 55.97 ± 1.59aA

Maize Shoot N concentration 11.78 ± 0.50cB 13.77 ± 0.30bB 15.27 ± 0.10aB 14.73 ± 0.50abB 11.99 ± 0.40bB 14.27 ± 0.50aAB 15.86 ± 0.50aAB 15.33 ± 1.00aAB 13.23 ± 0.20dA 15.24 ± 0.10cA 16.08 ± 0.10bA 16.98 ± 0.20aA N uptake 99.87 ± 4.20 dB 130.88 ± 2.69cB 145.21 ± 1.76bB 158.89 ± 5.62aB 103.02 ± 2.99cB 137.54 ± 5.41bB 153.00 ± 5.68abA 167.87 ± 10.70aAB 115.08 ± 1.65dA 149.67 ± 0.68cA 159.14 ± 1.87bA 188.31 ± 2.26aA

N concentration 14.15 ± 0.20cA 15.39 ± 0.40bA 15.46 ± 0.20bA 17.35 ± 0.10aB 14.22 ± 0.10cA 15.55 ± 0.20bA 15.71 ± 0.20bA 18.85 ± 0.30aAB 14.48 ± 0.50bA 15.89 ± 0.80bA 15.90 ± 0.60bA 19.54 ± 0.80aA

(continued)

N uptake 70.99 ± 1.44cB 92.71 ± 1.92bB 93.52 ± 1.93bB 126.86 ± 1.79aB 73.23 ± 0.91cAB 96.98 ± 0.95bAB 97.68 ± 1.34bAB 141.93 ± 2.54aA 76.36 ± 2.26cA 100.06 ± 4.91bA 100.31 ± 3.79bA 149.61 ± 6.11aA

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SH212 + G. m.

G.m.

SH212

SH212 + G. m. NI

G.m.

SH212

SH212 + G. m. NI

G.m.

SH212

Treatments NI

Soybean Shoot N concentration 19.93 ± 0.50baAb 19.96 ± 0.20bA 21.19 ± 0.40abA 22.63 ± 0.80aA 19.71 ± 0.40bA 20.77 ± 1.00abA 20.29 ± 1.20abA 22.52 ± 0.30aA 19.61 ± 0.30bA 20.22 ± 0.50bA 19.94 ± 0.70bA 22.30 ± 0.50aA

Table 8.3 Shoot and root N concentrations (mg g-1) and N uptake (mg pot-1) of soybean and maize inoculated with AM fungi and rhizobium with three root separation patterns, from Meng et al. (2015). For explanation of the treatments, see the text

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Soybean Shoot N concentration * ns ns

N uptake * ns ns

Root N concentration * ns ns N uptake * ns ns

Maize Shoot N concentration * ns ns N uptake ** ** ns

N concentration * ns ns

N uptake ** ** ns

Root

The data above are expressed as the means ± SD (n = 4) * and ** mean significant at 5% and 1% levels, respectively a Mean values of inoculated treatments with the same root barrier followed by different lower case letters (a, b, c, and d) are significantly different ( p < 0.05) b Mean values of three root barriers with the same inoculation treatment followed by different capital letters (A, B, and C) are significantly different ( p < 0.05), ns indicates no significant difference

Treatments Inoculation Root separation Inoculation × root separation

Table 8.3 (continued)

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Fig. 8.5 N uptake of the whole plant of soybean (a) and maize (b) inoculated with AM fungi and rhizobium and with three root separation patterns in a soybean/maize intercropping system. Bars with different lower-case letters indicate significant differences between different inoculated treatments in the same root separation pattern, and bars with different capital letters indicate significant differences between different root separation patterns in the same inoculated treatment ( p < 0.05). Means ± SD of four replicates. From Meng et al. (2015). For explanation of the treatments, see the text

Table 8.4 N transferred from the 15N labeled soybean to the associated maize with three root separation patterns and inoculation with AM fungi and rhizobium. For explanation of the treatments and symbols, see the text

Treatments NI SH212 G.m. SH212+ G. m.

Nt% Mesh barrier 5.19caBb 6.29bB 6.08bB 8.10aB

No barrier 7.57cA 8.36bA 9.16bA 9.88aA

Nt (mg/pot) Mesh barrier 5.52cB 9.96bB 9.72bB 16.97aB

No barrier 7.84bA 12.72aA 13.90aA 20.30aA

No% Mesh barrier 3.13bB 4.25bB 3.88bB 5.48aB

No barrier 4.10bA 5.09aA 5.36aA 6.01aA

The data above are expressed as the means (n = 4) Mean values of inoculated treatments with the same root barrier followed by different lower case letters are significantly different ( p < 0.05) b Mean values of three root barriers with the same inoculation treatments followed by different capital letters (A and B) are significantly different ( p < 0.05) a

AM fungi are important components in intercropping agrosystems. In this study, N was transferred under non-inoculation conditions in mesh barrier patterns, but the rate and amount of N transferred in SH212 + G.m. inoculations were 1.56 and 3.07 times more than that of the NI group (Table 8.4), which resulted from the improved AM fungi colonization rate of soybean and maize by inoculating with both rhizobium and AMF. The 30-μm nylon net prevented the direct contact of the roots of soybean and maize but allowed hyphae to penetrate and link, and the hyphae enhanced the degree of contact of soybean and maize and the degree of contact of

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roots affected N transfer significantly, in agreement with Zhang et al. (2004a). Many researchers have suggested that there are two pathways for N transfer. One is a direct transfer in which N fixed by legumes is transferred to associated non-N-fixing plants via a mycorrhizal fungal hyphae network (Sierra and Nygren 2006). The N concentration of legumes is generally higher than that of graminaceous plants; therefore, N could transfer to intercropped graminaceous plants along the gradient of concentration via hyphae (Chu et al. 2004). The other pathway is an indirect transfer, in which the residues and root exudates (Jalonen et al. 2009) of legumes release N to the rhizosphere when they decompose, and the mineralized inorganic N can then be taken up by the intercropped graminaceous plants or mycorrhizal hyphae (Tomm et al. 1994). In our experiment, the rate and the amount of N transferred from soybean to maize were improved by microbial inoculations. Hence, no matter which way the N is transferred, the hyphae play an important role in N transfer from soybean to associated maize. AM fungi and rhizobium establish beneficial symbiosis with legumes and enhance the advantage of intercropping, and the nutrient uptake and biomass of intercropped crops were significantly increased. Therefore, co-inoculation with both AM fungi and rhizobium should be considered for the sustainable development of the legume/graminaceous plant intercropping pattern.

8.4

Response of AM Fungal Diversity to N Fertilizer and Cropping Systems

The AM fungal diversity and community structure can be influenced by plant communities. Some studies have shown that crop diversity can increase the soil fungal and bacterial diversity (Guo et al. 2019). The AM fungal diversity and composition might be affected by different plants in intercropping systems because intercropping increased crop diversity in the agricultural ecological system. Our studies showed that the AM fungi colonization rates of intercropped maize were significantly higher than that of monocultured maize in a maize/soybean intercropping system (Meng et al. 2015). Using phospholipid fatty acids profile analysis, Lacombe et al. (2009) found a higher abundance of AM fungi in two different tree-based intercropping sites compared to adjacent conventional monoculture sites. The pressure on AM fungi in different niches in the process of resource allocation could be alleviated due to interspecific interactions in diversity planting patterns, which affect the AM fungal diversity in cropping systems. Plant characteristics, soil nutrient conditions, and mycorrhizal fungal characteristics were significant factors in common mycorrhizal network effects on plant competitive responses. Agricultural practices have been shown to have a significant effect on soil properties and biodiversity. Recent research indicated that in karst ecosystems, the AM fungi abundance was sensitive to nitrogen addition, but the diversity was sensitive to phosphorus addition (Xiao et al. 2019). Moreover, one study showed

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that a long-term experiment with diammonium phosphate fertilizer applications of 30 g m-2 yr-1, 60 g m-2 yr-1, 90 g m-2 yr-1 , and 120 g m-2 yr-1 under field conditions reduced the AM fungal colonization of roots and soil microbial activity (Liu et al. 2012). In a legume/cereal intercropping system, nitrogen fixation function by the legumes is improved due to the large amount of nitrogen absorbed by the cereal crops, and the soil mineral nitrogen was maintained at a relatively low level, which could reduce the nitrogen inhibition of legume crops (Corre-Hellou et al. 2006). These research results indicated that cereal crops can stimulate the nodulation and nitrogen fixation of legumes through competing for the nitrate or ammonium nitrogen in the rhizospheres of legumes. Maize can help to alleviate the effects of nitrogen inhibition on faba bean in intercropping. This kind of change can influence the AM fungal diversity and composition in the rhizospheres and roots. Therefore, the effects of cropping systems and different fertilization treatments on the AM fungal diversity and community structure have been given particular attention (Lian et al. 2018). In our previous study, we found that AM fungal colonization rates in intercropping were higher than in monoculture and AM fungi promoted the growth of crops and N uptake, which owing to AM fungal hyphae contributed to N transfer in maize/soybean intercropping systems (Meng et al. 2015). Most studies have concentrated on the effects of monoculture cultivation on AM fungal diversity. The changes in the soil AM fungal community structure caused by intercropping systems under different nitrogen application rates have been rarely studied. Research on the AM fungal community structure within both the soil and roots could provide a greater understanding of AM fungi–host interactions in agroecosystem management (Tian et al. 2013). With the improvement of highthroughput sequencing technology, a series of statistical analysis indices to estimate microbial species abundance and diversity were used. For example, Shannon and Simpson indices could be used to reflect the diversity of groups, and NMDS analysis could be used to reflect the AMF community structures. Therefore, a field experiment was conducted from 2017 at the Acheng experimental site (45°50′N, 126°39′ E), Northeast Agricultural University experimental station, Heilongjiang Province, P.R. China. The aim of the experiment was to investigate the indigenous AM fungal community structure and diversity in the rhizosphere soil and roots of maize/soybean intercropping under different nitrogen fertilization rates, and the relationship between AMF diversity and community and soil characteristics.

8.4.1

Experimental Design

The research was designed as a two-factor completely randomized block experiment with three replicates. The first factor was the nitrogen application level, which was designated as N0, N1, and N2. The N application rates of maize at the N0, N1, and N2 levels were 0 kg ha-1, 180 kg ha-1, and 240 kg ha-1, and those of soybeans were 0 kg ha-1, 40 kg ha-1, and 80 kg ha-1. The second factor was the cultivation method, which included three cropping systems: (1) monocultured maize,

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(2) monocultured soybeans, and (3) maize intercropped with soybeans. There were three replicates for each treatment, giving a total of 3 × 3 × 3 = 27 plots (Zhang et al. 2020).

8.4.2

AM Fungal Diversity in Rhizosphere Soil and Roots

N fertilization had a significant influence on the AM fungal alpha- (α-) diversity of maize rhizosphere soil indices, including the Shannon (P ≤ 0.05), Simpson (P ≤ 0.05), and Chao indices (P ≤ 0.05), but no significant difference was observed between monoculture and intercropping for the corresponding component crops. With the increase of N fertilization, the Shannon’s index of maize rhizosphere soil in both intercropped and monoculture treatments indicated a decreasing trend (Table 8.5). On average, the Shannon’s index of maize soil at the N0 level was significantly higher than at the N1 and N2 levels (P ≤ 0.05). However, no significant difference was observed between the N1 and N2 levels. Simpson’s index exhibited an opposite trend to Shannon’s index in maize rhizosphere soil. The effects of nitrogen application rates on the maize rhizosphere soil AM fungal α diversity shown by the diversity indices were similar, in that a high nitrogen application rate showed a negative effect on the maize rhizosphere soil AM fungal diversity. Moreover, a lower Shannon’s index was found in the rhizosphere soil of intercropped maize compared to that of monocultured maize at the N1 and N2 levels, but there was no significant difference between them. The N application rates also significantly affected the AM fungal α diversity of maize roots, including the Shannon’s index, Simpson’s index, and Ace and Chao indices (P ≤ 0.05) (Table 8.5). Compared with maize, the Chao index showed that the soybean soil was significantly influenced not only by the cultivation method but also by the N fertilization and the interaction between them. The Shannon’s index of intercropped soybean rhizosphere soil showed a significant increase (P ≤ 0.05) compared to that of monocultured soybeans at the three N levels. Moreover, the AM fungal diversity results obtained from soybean roots presented a significant response to N fertilization and the interaction of nitrogen fertilization with the cultivation method, including the Shannon’s index and Simpson’s index (P ≤ 0.05) (Table 8.6). At the N0 and N2 levels, the Shannon’s index of soybean roots was significantly higher in intercropping than monoculture. On average, with the N applied, the results of Shannon’s index for monocultured and intercropped soybean roots were significantly decreased from 1.44 to 0.64 and 1.89 to 1.19, respectively, at the N0 and N2 levels (P ≤ 0.05). In this study, we observed that maize/soybean intercropping and nitrogen application rates have significant effects on the rhizosphere soil and root AM fungal diversity (Tables 8.5 and 8.6). A similar result was found by Guo et al. (2019) that increasing crop diversity could induce the improvement of the soil microbial diversity. Different responses of the AM fungal α diversity indices to intercropping and

30.81 B

0.860

0.687

0.692 0.937

0.536

0.001

26 b

27 b

26 B

27 B

N2_IS

N2_S

N1_IS

N1_S

N*C

Cultivation method (C)

0.525

0.964

0.084

34.13 b

27.49 b

26 b

28 b

N0_IS

N0_S

N*C

0.169 A

27.73 AB

39 a

Ave 39 A

0.503

0.017

0.178 a

0.159 a

26.38 ab

29.09 ab

Chao 39 a

Cultivation method (C)

2.17 b

N2_IM

2.19 B

0.15 A

41.04 a

Ave 40.36 A

ANOVA

2.20 b

N2_M

0.146 a

0.155 a

Ace 39.68 a

N fertilizer (N)

2.25 b

N1_IM

2.35 B

Ave

0.078 B

0.000

2.46 b

N1_M

0.075 b

0.082 b

Simpson

N fertilizer (N)

2.92 a

N0_IM

2.89 A

Ave

ANOVA

2.87 a

N0_M

Shannon

0.064

0.001

0.323

1.93 a A

1.5 a B

1.93 a A

1.78 a B

2.38 a A

1.49 a B

Shannon

1.71 A

1.85 A

1.93 A

Ave

0.148

0.004

0.126

0.208 a B

0.321 a A

0.185 a B

0.204 a A

0.164 a B

0.313 a A

Simpson

0.265 A

0.195 A

0.239 A

Ave

Ave

Chao

0.057

0.571

0.007

20 b A

18 b B

23 b A

18 b B

34 a A

20 a B

0.002

20.03 B

18.24 B

32.56 A

0.116

0.008

20.78 b

19.28 b

23.33 b

13.16 b

35.07 a

30.04 a

Ace

19 B

20 B

27 A

Ave

Table 8.5 Rhizosphere soil AMF α-diversity indices in maize/soybean intercropping with different nitrogen application rates. Adapted from Zhang et al. (2020). For explanation of the treatments, see the text. Ave = average. Different lower-case letters in the same column indicate significant differences between monoculture and intercropping treatments (P ≤ 0.05). Different capital letters in the same column indicate significant differences between the three nitrogen levels (P ≤ 0.05)

8 Ecological Functions of Arbuscular Mycorrhizal Fungi in Agriculture 153

19.40 B

0.293

0.054

0.556 0.114

1.000

0.001

25 b

17 b

21 B

25 B

N2_IS

N2_S

N1_IS

N1_S

N0_IS

N*C

Cultivation method (C)

0.279

0.227

0.009

27.76 b

11.04 b

22 b

29 b

36 a

N0_S

N*C

0.353 A

26.64 B

36 A

Ave

0.438

0.001

0.299 a

0.408 a

22.81 b

30.47 b

36.00 a

36 a

Chao

Cultivation method (C)

1.68 b

N2_IM

1.45 B

0.230 B

Ave 37.07 A

N fertilizer (N)

1.22 b

N2_M

0.24 b

0.22 b

0.123 c

38.15 a

Ace

0.001

1.88 a

N1_IM

2.02 A

Ave

0.140 C

N fertilizer (N)

2.17 a

N1_M

0.157 c

Simpson

ANOVA

2.50 a

N0_IM

Ave

2.40 A

ANOVA

2.31 a

N0_M

Shannon

0.004

0.876

0.013

1.19 b

0.64 b

0.89 a

1.98 a

1.89 a

1.44 a

Shannon

0.91 B

1.44 A

1.67 A

Ave

0.001

0.425

0.004

0.417

0.739

0.531

0.210

0.254

0.382

Simpson

0.578 A

0.370 B

0.318 B

Ave

0.250

0.558

0.422

27.76

11.04

12.72

23.06

30.15

24.90

Ace

19.52 A

17.89 A

27.53 A

Ave

0.134

0.751

0.048

16 b

13 b

11 ab

23 ab

28 a

23 a

Chao

15 B

17 AB

26 A

Ave

Table 8.6 Root AMF α-diversity indices in maize/soybean intercropping with different nitrogen application rates (Zhang et al. 2020). For explanation of the treatments, see the text. Ave = average. Different lower-case letters in the same column indicate significant differences between monoculture and intercropping treatments (P ≤ 0.05). Different capital letters in the same column indicate significant differences between the three nitrogen levels (P ≤ 0.05)

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nitrogen application may be attributed to the change in soil fertility caused by the interaction between maize and soybeans. When maize is intercropped with soybeans, maize will generally show a strong competitive advantage for soil nutrients compared with soybean, which results in changes of the soil properties (Wang et al. 2016). The belowground root–root interaction between intercropped crops could cause heterogeneous nitrogen distribution in the soil profile and improve N input from symbiotic nitrogen fixation into the cropping system (Chapagain and Riseman 2015). It was shown that when faba bean is intercropped with wheat, the increased N derived from symbiotic N-fixation and nodulation results in the biological fixation of 74 kg N ha-1 more in intercropped faba bean compared to in monoculture faba bean. Due to the change of soil nutrient content in intercropping, the α diversity of maize rhizosphere soil and roots could be changed. The changed AM fungal diversity was mainly caused by the changes in soil nutrients, such as available nitrogen (AN) which was higher in intercropped maize soil. The soil fungal diversity has been reported to be affected by factors such as the soil condition, soil type, nutrients, and plant species (Marschner et al. 2004). Therefore, soil fungal diversity could be affected by intercropping systems and could change as a response to plant diversity and soil fertility changes (Larkin and Honeycutt 2006). Compared to maize, the α diversity of intercropped soybean rhizosphere soil indicated by values such as the Shannon’s index and Chao index was significantly higher (P ≤ 0.05) than that of monocultured soybean soil among the three N supply levels. Both Shannon’s index and Simpson’s index indicated higher AM fungal diversity in intercropped soybean soil. The increased AM fungal diversity in intercropped soybean soil might be caused by a lower AN content in soil. When cereal is intercropped with legumes, legumes generally show a weak competitive advantage, and the roots of intercropped cereal can extend to the leguminous root system region and compete with the legumes for soil nutrients, influencing the fixation of leguminous plants and producing differences of root distribution between the legumes and cereals, leading to changes of the soil nitrogen content (Fan et al. 2006). Moreover, the higher diversity in soybeans of the intercropped treatments might have been caused by root exudates in the intercropping system (Zhou et al. 2011). Additionally, in the maize/soybean intercropping system, due to the large amount of nitrogen absorbed by maize, the soil mineral nitrogen was maintained at a relatively low level, which could reduce the nitrogen inhibition of soybeans (CorreHellou et al. 2006) and promote the growth of AM fungi. A previous study reported that root exudates differed between plant species, which resulted in differences in soil properties, and species-specific shifts in the soil microbial community could be affected by these differences (Singh et al. 2007; Welbaum et al. 2004). Our results showed that the lower soil AN in the intercropped soybean rhizosphere could induce AM fungal growth. Thus, the high diversity in intercropped soybean could be explained by the low nitrogen environment caused by the interspecific root competition. In addition to cropping patterns, different nitrogen inputs also influenced the soil AM fungal diversity. Increased nitrogen application level significantly decreased the maize soil diversity but had no significant effect in soybean soil. Our results were

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similar to those of previous studies, in which fertilization reduced the AM fungal diversity of an alpine meadow ecosystem (Liu et al. 2012) and N addition had a significant negative impact on the soil bacterial α diversity in an old-growth N-rich tropical forest in southern China (Wang et al. 2018). Mycorrhizal colonization under high soil N conditions will often be inhibited compared with the activities in N-starved plants when the N supply is increased, and the soluble carbon distributed to the apoplasts of the plant roots is reduced, leading to inhibition of the AM fungal diversity and communities in the rhizosphere soil and roots (Schwab et al. 1991). This is because the development of arbuscules and other AM fungal structures in plant roots depends on the carbon exchange within the apoplast (Kiers et al. 2011; Parniske 2008). A previous study also observed that nitrogen enrichment altered the mycorrhizal allocation in five mesic to semiarid grasslands in North America (Johnson et al. 2003). Some studies indicated that the competitive abilities for carbohydrate uptake in host plants were different among AM fungal species (Bennett and Bever 2009; Cano and Bago 2005). Therefore, the responses of different AM fungal species to N fertilization would be different. The nitrogen application rates also significantly affected the AM fungal diversity of maize roots, but no significant differences were found between the monoculture and intercropping treatments. This is in agreement with the results of a previous study conducted in semi-natural grasslands, in which the negative correlation between N in the soil and the number of AM fungal sequence groups in roots was found to produce a decrease in the AM fungal diversity in response to fertilizer application (Santos et al. 2006). However, some research has shown that N fertilization at agronomic rates has minimal impacts on the overall AM fungal diversity and colonization of maize roots in a system of long-term nitrogen fertilization and rotation of maize with soybeans (Tian et al. 2013). In our study, the results of the AM fungal α diversity in roots were different from these results, which indicated that interspecific interaction between species could affect not only the soil AM fungal diversity but also the root AM fungal diversity. Most previous studies investigated the soil AM fungal diversity, but we studied the AM fungal diversity in both rhizosphere soil and roots. Our AM fungal α diversity showed different results between rhizosphere soil and roots. In a 10-year continuous crop rotation system, the diversity index showed that the AM fungal communities did not differ significantly between treatments and habitats, but the AM fungal community composition established in maize roots and that of the rhizosphere soil were significantly different (Hontoria et al. 2019).

8.4.3

AM Fungal Community Structures and Abundance in Rhizosphere Soil and Roots

We found eight genera (Fig. 8.6) of AM fungi in rhizosphere of maize and soybean; these were Glomus_f__Glomeraceae, Paraglomus, Acaulospora, Diversispora, Archaeospora, Gigaspora, unclassified_f__Diversisporaceae, and

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Fig. 8.6 Percentages of AMF abundance at the genus level in rhizosphere soil of maize (a), rhizosphere soil of soybeans (b), roots of maize (c), and roots of soybeans (d). From Zhang et al. (2020). For explanation of the treatments, see the text

unclassified_c__Glomeromycetes. Except for Acaulospora, AM fungi in roots were the same as those of rhizosphere soil. Glomus_f_Glomeraceae, unclassified_c_Glomeromycetes, and Paraglomus were the dominant AM fungal genera in maize rhizosphere soil at all three N levels (Fig. 8.6a). Moreover, the relative abundance of Glomus_f_Glomeraceae in intercropped maize soil increased by 21.09%, 4.07%, and 12.45%, respectively, at the N0, N1, and N2 levels compared with those in monocultured maize rhizosphere soil; cultivation method significantly affected the relative abundance of Glomus_f_Glomeraceae in maize rhizosphere soil. Additionally, with the increase of nitrogen fertilization, the relative abundance of Paraglomus significantly decreased in maize rhizosphere soil at all three N levels (P ≤ 0.05). Furthermore, the genera Gigaspora and Diversispora could only be found in maize rhizosphere soil at the N0 level, and they showed high abundance in monoculture treatments. Nitrogen fertilization significantly affected the relative abundance of Gigaspora as well (P ≤ 0.05). Moreover, the interaction between the nitrogen fertilizer and cultivation method also influenced the relative abundance of the Diversispora

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genus (P ≤ 0.05) (Fig. 8.6a). The other genera including Acaulospora and Archaeospora could also be found in maize rhizosphere soil at the N0 level, with relative abundances lower than 1%, and these are not shown in Fig. 8.6a. Compared with the results for maize rhizosphere soil, the genus Acaulospora was not detected in the maize root sample. As the dominant genus of maize roots, the relative abundance of Glomus_f_Glomeraceae was higher than those of other genera. Gigaspora and Diversispora were found in maize root samples at the N0 level and were not found at the N1 and N2 levels (Fig. 8.6c). Among maize root samples, nitrogen fertilization significantly affected Glomus_f_Glomeraceae, Paraglomus, Acaulospora, and Archaeospora (P ≤ 0.05). With the increase of nitrogen fertilization, although the AM fungal α diversity decreased, the relative abundance of Glomus_f_Glomeraceae and unclassified_c_Glomeromycetes increased. In addition, Paraglomus and Acaulospora were observed to be influenced by not only the cultivation method but also the interaction between nitrogen fertilization and the cultivation method (P ≤ 0.05). In maize root sample, unclassified_f_Gigasporaceae was detected, which was not found in maize rhizosphere soil. Because of their low relative abundances (lower than 1%), unclassified_f_Gigasporaceae and Archaeospora are not shown in Fig. 8.6c. In soybean rhizosphere soil samples, nitrogen fertilization had significant impacts on unclassified_c_Glomeromycetes, Gigaspora, and Paraglomus (P ≤ 0.05), except Glomus_f_Glomeraceae (Fig. 8.6b). When N was applied, the relative abundance of Paraglomus was significantly decreased in soybean rhizosphere soil (P ≤ 0.05), while the relative abundances of Glomus_f_Glomeraceae and unclassified_c_Glomeromycetes exhibited increases with the increase of N fertilization. Compared with the results for maize rhizosphere soil, Acaulospora and Archaeospora were not detected in soybean rhizosphere soil (Fig. 8.6b). As the most abundant genus of soybean rhizosphere soil, Glomus_f_Glomeraceae had considerably higher relative abundances in the intercropped treatment than the monoculture treatment at the N0 and N2 levels (P ≤ 0.05). In addition, the relative abundance of Gigaspora was not only affected by nitrogen fertilization but also by the cultivation method and the interaction between them (P ≤ 0.05). At the N0 and N2 levels, the relative abundances of Gigaspora in monocultured soybean rhizosphere soil were significantly higher compared with those in intercropped soybean rhizosphere soil (P ≤ 0.05). Gigaspora was found in 48.88% of monocultured soybean rhizosphere soil and 5.68% of intercropped soybean rhizosphere soil at the N0 level. At the N1 level, the relative abundances of Gigaspora were 38.07% and 2.66%, respectively, in monocultured and intercropped soybean rhizosphere soil. Like in maize soil, Diversispora was only found in soybean rhizosphere soil at the N0 level, with a lower relative abundance of 1.04%. The results for soybean roots were almost similar to those of soybean rhizosphere soil, but one more genus was detected in soybean root samples, which was unclassified_f_Gigasporaceae (Fig. 8.6d). Nitrogen fertilization had high impacts on Glomus_f_Glomeraceae, Gigaspora, unclassified_c_Glomeromycetes, and unclassified_f_Gigasporaceae (P ≤ 0.05). With the increase of nitrogen fertilization, not only did the AM fungal α diversity exhibit a decreasing trend, but the relative

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Fig. 8.7 Nonmetric multidimensional scaling (NMDS) plot of AM fungal communities in rhizosphere soil of maize (a), rhizosphere soil of soybeans (b), roots of maize (c), and roots of soybeans (d) (Zhang et al. 2020). For explanation of the treatments, see the text

abundance of Glomus_f_Glomeraceae also showed a decrease. However, the relative abundance of unclassified_c_Glomeromycetes was increased when N was applied. Moreover, Gigaspora was also significantly affected by the cultivation method, which was similar to the result of soybean rhizosphere soil. The relative abundance of Gigaspora in monocultured soybean roots was significantly higher than in intercropped soybean roots (P ≤ 0.05). The AM fungal community composition was further analyzed by nonmetric multidimensional scaling (NMDS) based on the Bray–Curtis distance. Permanova analysis was used to compare the differences between different groups. The results showed that the AM fungal communities in the rhizosphere soil and roots of the monoculture treatments were clearly separated from those in the intercropping treatments at the three N levels (Fig. 8.7). Intercropped maize rhizosphere soil fungal communities at the N1 level were present in the second quadrant, while those of intercropped soybean treatments were present in the fourth quadrant (Fig. 8.7a). AM fungal communities in plant roots were clearly separated from each other among the different treatments. The results of intercropped and monocultured maize roots differed from those of the intercropped and monoculture soybean roots at the N1

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level (Fig. 8.7c). The results of NMDS indicated that the AM fungal community structures were affected not only by the supplied N levels but also by the different cultivation methods. Due to the root distributions of the crops and differences in the ability to compete for soil N, the AM fungal abundances of soil and roots were significantly different under monoculture and intercropping treatments (Yu et al. 2014). As the dominant genus, the abundance of Glomus_f_Glomeraceae in intercropped maize rhizosphere soil was higher than in monocultured maize soil, which is consistent with the results of a previous study indicating that mulberry/alfalfa intercropping could increase the fungal community of the rhizosphere (Zhang et al. 2019a). Moreover, the other genera exhibited different results in monoculture and intercropping treatments, which indicated that intercropping patterns could influence the AM fungal community abundance in maize rhizosphere soil. Generally, in an intercropping system, the roots of the two crop species overlap, as do their rhizospheres. This overlap can be considered as the phenotypic plasticity (Yu et al. 2014). In intercropping, phenotypic plasticity in root growth and distribution and nutrient uptake ability of the roots can contribute to the competitive ability of crops. Liu et al. (2015) showed that in the maize/wheat intercropping system, the roots of intercropped maize could extend up to 10–80 cm in the soil under neighboring wheat plants with decreasing N application, which leads to enhancement of nutrients in later maize/wheat intercropping. Moreover, the root systems of the two crops may be interconnected by the common mycelial network of AM fungi. In such circumstances, it is difficult to segregate the AM fungal species individually contributed by the two crop species in the intercropping system. Therefore, we measured both the rhizosphere soil and root AM fungal relative abundances, the results showing differences which were affected by intercropping. The relative abundances of genera in soybean rhizosphere were different from for soybean roots. These differences in AM fungal abundance could be attributed to the alteration of the soil fertility caused by niche complementarity and interspecific facilitation through enhanced acquisition of soil resources in the intercropping system, which was consistent with the results of a previous study showing that in agricultural intercropping systems, a potentially important mechanism underlying such facilitation is the ability of some crop species to chemically mobilize otherwise unavailable forms of one or more limiting soil nutrients (Li et al. 2014). Our results showed that AM fungal abundance in soybean roots showed different trends to those in soybean soil. These results suggested that the responses of different AM fungal genera to the alteration of soil fertility were caused by interactions between maize and soybean in intercropping systems, which is in agreement with a previous study showing that AM fungal community composition in maize roots and that of the rhizosphere soil were different (Hontoria et al. 2019). In addition, different AM fungal genera responded differently to nitrogen stress. These results were similar to results indicating that the long-term heavy application of mineral nitrogen under field conditions reduces the AM colonization of roots and soil microbial activity (Liu et al. 2012). However, we found that the dominant genus Glomus_f_Glomeraceae in maize soil and roots increased with the increase of the N

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supply, which is consistent with the results of a previous study indicating that in karst ecosystems, N addition significantly increased the abundance of AM fungi during the growing season (Xiao et al. 2019). Moreover, our results indicated that Glomus_f_Glomeraceae was the most frequent genus in this intercropping system. The AM fungal taxa belonging to Glomus are commonly found in agricultural fields (Alguacil et al. 2014; Hontoria et al. 2019). As the most frequent genus in cultivated fields, Glomus could be better adapted to disturbed environments, because of the greater sporulation and symbiotic relationship with plant roots (Oehl et al. 2003). On the other hand, Diversispora showed very low relative abundance, which is consistent with studies that the family Diversisporaceae does not contribute greatly to agricultural systems (Borriello et al. 2012). Therefore, different AM fungal genera showed different responses to a changed environment, and this could provide further information for use in the screening of functional genes in future studies. Consequently, owing to the different responses to the changed environment and the function of the genera, the AM fungal community could not only be affected by the intercropping or nutrients but also shape the soil fertility in the agricultural system. Studies have revealed that AM fungi can improve the plant growth, soil structure, and uptake of relatively immobile elements (Cardoso and Kuyper 2006; Higo et al. 2018). Our results described in Sect. 8.3 above indicated that, in a maize/ soybean intercropping system, when inoculating AM fungi, growth of maize and soybean were improved and N transfer was affected significantly (Meng et al. 2015). The reason for the change in N transfer was that the mineralized inorganic N can then be taken up by the intercropped graminaceous plants or mycorrhizal hyphae. Thus, our results on changes of AM fungal structure and relative abundance will further reveal the mechanisms of the AM fungal contribution to improved yield and nutrient utilization efficiency in maize/soybean intercropping.

8.4.4

AM Fungal Colonization of Roots

Our results showed that roots of intercropped maize and intercropped soybeans were well colonized by AM fungi, with colonization ranging between 58.89% and 82.22% in maize and between 44.44% and 74.45% in soybeans (Table 8.7). Colonization in intercropped maize and soybeans was higher than in the corresponding monocultures. In addition, the effect of nitrogen fertilization on colonization was observed. In maize, the AM fungal colonization of intercropped plants at the N0 level was higher than at the N2 level. The colonization of soybeans in both monoculture and intercropping treatments showed a similar trend to that of maize. Moreover, the colonization of maize was higher than that of soybeans among the three nitrogen levels. N fertilization and the cultivation method significantly affected the results for AM fungal colonization in both monoculture and intercropping treatments (P ≤ 0.05). With the increase of N fertilization, the colonization of crops in both monoculture and intercropping treatments decreased.

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Table 8.7 Root mycorrhizal colonization of monocultured (mono) maize, intercropped (inter) maize, monocultured (mono) soybean, and intercropped (inter) soybean at different N fertilizer supply levels (Zhang et al. 2020). For explanation of the treatments, see the text. Ave = average Treatments N0 N1 N2 ANOVA: P(significance) N fertilizer (N) Cropping system (C) N×C

Maize(%) Mono 75.56 ± 4.16 b 70.00 ± 2.72 b 58.89 ± 4.16 b

Inter 82.22 ± 1.57 a 73.33 ± 2.72 a 67.78 ± 1.57 a

Soybean(%) Ave Mono 78.89 53.33 A ± 5.44 b 71.67 51.11 B ± 1.57 B 63.33 44.44 C ± 1.57 b

0.000 0.003

0.083 0.041

0.446

0.000

Inter 68.89 ± 4.16 a 74.45 ± 5.66 a 67.78 ± 3.14 A

Ave 61.11 AB 62.78 A 56.11 B

Data in the table are the mean of three replicates with standard error. Different lower case letters in the same column mean significant differences between different cultivation methods (P ≤ 0.05), different capital letters in the same column mean significant differences between three nitrogen levels (P ≤ 0.05). Values under ANOVA are the probabilities

8.4.5

RDA Analysis Between Soil Physicochemical Characteristics and the AM Fungal Community

Constrained ordination of redundancy analysis (RDA) was used to analyze the relationships between AM fungal communities and soil physicochemical characteristics (Fig. 8.8). The RDA analysis results showed that the relationships between AM fungal communities in soil and soil physicochemical characteristics were clearly different. The results indicated that total nitrogen (TN) had a distinct effect on the rhizosphere soil and root AM fungal community structures under the three N levels. Our results showed that the soil AM fungal diversity and community structures in rhizosphere soil and roots were affected by the soil physicochemical characteristics. The results of the RDA indicated that TN and AN had distinct effects on the maize rhizosphere soil and root AM fungal community structures under the three N levels. Soil organic matter significantly affected the soybean root AM fungal community. From the RDA results, the dominant genera affected by environmental factors could be shown. The most abundant genus Glomus_f_Glomeraceae exhibited strong relationships with TN, AN, and the N: P ratio. The length of genera and distance from genera to soil physicochemical characteristics revealed the correlations between the AM fungal community and soil physicochemical characteristics. According to a previous study, one of the advantages of intercropping is that the soil nutrient utilization efficiency, such as for soil nitrogen and phosphorus, could be improved through interspecific facilitation (Li et al. 2011). Some studies have reported that the soil total nitrogen, total carbon, and available potassium

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Fig. 8.8 Effects of soil characteristics on the distribution of AMF communities in rhizosphere soil of maize (a), rhizosphere soil of soybeans (b), roots of maize (c), and roots of soybeans (d) (Zhang et al. 2020). For explanation of the treatments, see the text

concentrations were significantly affected by intercropping, which resulted in changes of the microbial community structure (Hu et al. 2019; Zhang et al. 2019a). In our intercropping system, the soil AN in intercropped maize was higher than in monocultured systems (P ≤ 0.01), which might have been caused by the increased nitrogen input due to plant interactions such as nitrogen fixation in legume crops and differences in the root distributions and root areas of different plants. Moreover, the AM fungi could also promote the nutrient uptake caused by the enhancement of soil nutrient activation and high colonization by AM fungi (Bainard et al. 2014). In a chili pepper/maize intercropping system, root mycorrhizal colonization, soil acid phosphatase activity, and AM fungal abundance in intercropped maize and pepper were higher than in monocultures; the constitution of hyphal networks increased mycorrhizal colonization with both intercrops (Hu et al. 2019). Thus, the AM fungal community was not only affected by intercropping and nitrogen application but also shaped the soil fertility and promoted colonization.

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Contribution to Soil Fertility

AM fungi are essential for enhancing the soil quality, increasing soil microbial diversity, influencing the soil biological environment, and reducing soil erosion, thus resulting in improved soil ecosystem function. They rely on host plants to obtain carbon, meanwhile stimulating the growth and reproduction of crops through enhanced absorption of nutrients (P and N) and water uptake from the soil by the mycelium. These processes can promote the secretion of various organic compounds (e.g., glomalin-related protein) by AM fungi, which contribute to the soil carbon content. Moreover, studies have indicated that AM fungi can promote the production of root exudates (e.g., extracellular polysaccharides and root mucilage), which can stick soil particles together, thereby leading to macro-aggregate formation and soil stability (Rillig and Mummey 2006). A study by Miller and Jastrow (1990) highlighted that AM fungi may also affect the stability of soil structure by facilitating the formation of soil aggregates through their interaction with roots. Zhang et al. (2019b) found that AM fungal colonization significantly increased the content of macro-aggregates (>2 mm) compared to those without colonization in a karst soil system. Generally, the structure and diversity of AM fungal communities are affected by N fertilizer and cultivation systems (Zhang et al. 2020). At the same time, a higher AM fungal diversity has significant effects on soil fertility. A 3-year positioning experiment based on a two-factor experimental design at two N application levels (N0 and N2) and different cropping systems was started in 2017 in a black soil of northeast China. The experimental design can be seen in Zhang et al. (2021). The cropping systems included monoculture maize (M), monoculture soybean (S), and maize/soybean intercropping (intercropping maize (IM)), (intercropping soybean (IS)).

8.5.1

Changes in the AM Fungal Diversity of Different Soil Profiles

For N0, the Shannon indices for IM across soil depths were higher than M by 15%, 8.5%, 1.8%, and 31%, at depths 0–15 cm, 15–30 cm, 30–45 cm, and 45–60 cm, respectively (Fig. 8.9). However, the AM fungal diversity in the IM system was lower than the M system by 9.1%, 35%, and 9.3%, respectively, at 15–30 cm, 30–45 cm, and 45–60 cm soil depths for N2. Moreover, the Shannon indices of M and IM highlighted different trends after the N application. The Shannon index increased in the M system at 0–15 cm and 15–30 cm soil depths, while the Shannon index decreased at 30–45 cm and 45–60 cm soil depths with N application. At the same time, the Shannon index in the IM for N0 was higher than N2. For the maize soil, the Simpson and Shannon indices showed opposite trends. The Shannon index of the soybean soil was affected by the cropping system and N application, as well as by the interaction of these two factors (Fig. 8.10). The

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Shannon index of each layer of IS was significantly higher (P ≤ 0.01) compared with the S system for both N0 and N2. This indicated that the AM fungal diversity in each layer of the IS system was higher than that of the S system, e.g., for N0, the AM fungal diversity in each intercropped layer was increased by 19%, 46%, 74%, and 46% at depths 0–15 cm, 15–30 cm, 30–45 cm, and 45–60 cm, respectively, when compared with the monoculture system. Similarly, the AM fungal diversity in soybean soil was also affected by N application. The application of N significantly improved AM fungal diversity for both S (increased by 13% and 17% at 0–15 cm and 15–30 cm soil depths, respectively) and IS (increased by 25% and 8.7% at 0–15 cm and 45–60 cm soil depths, respectively) systems. However, the AM fungal diversity of soil decreased with increasing N application at soil depths of 30–60 cm and 15–45 cm in the S and IS systems, respectively.

8.5.2

Distribution of AM Fungal Composition Across the Soil Profiles

In the M and IM systems, four genera including Glomus_f_Glomeraceae, Paraglomus, unclassifed_c_Glomeromycetes, and others were observed at each soil depth for both N0 and N2 (Fig. 8.11a). Glomus_f_Glomeraceae was the major

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genus in the M and IM systems, and its abundance was significantly positively correlated with the cropping system. The relative fraction of Glomus_f_Glomeraceae in the M system ranged from 61% to 99%, which is lower than the IM system (87–99%). On the other hand, the relative fraction of Paraglomus in the IM system (0–6.7%) was lower than the M system (0–39%). However, the cropping system showed no significant influence on the relative fraction of unclassifed_c_Glomeromycetes, while N application led to an increase in the relative fraction of unclassifed_c_Glomeromycetes. Compared with the AM fungal communities in the maize soil, unclassifed_f_Gigasporaceae and Gigaspora were observed in the soybean soil at 0–15 cm depth (Fig. 8.11b). Similarly, intercropping led to a decrease in the relative fraction of Gigaspora in the soybean soil at 0–15 cm depth for both N0 and N2. For example, Gigaspora formed 22% and 4.8% of the total AM genera in the S and IS systems, respectively, for N0. Moreover, the relative fraction of Glomus_f_Glomeraceae in the soybean soil after N application was lower than N0, while the relative fractions of Paraglomus and unclassifed_c_ Glomeromycetes after N application were higher than N0. Results from principal coordinates analysis indicated that the AM fungal communities in the monoculture system were separate from those of the intercropping

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Fig. 8.11 Percentages of AMF relative abundance at the genus level at different soil depths (0–15 cm, 15–30 cm, 30–45 cm, 45–60 cm) in (a) maize monocropping and intercropping (M, IM), and (b) soybean monocropping and intercropping (S, IS) systems with different nitrogen application rates (N0 or N2). From Zhang et al. (2022)

system (Fig. 8.12). Similarly, N application resulted in significantly different AM fungal communities in the IM system from those of the IS system. In addition, the AM fungal communities across soil depths were different for N0 and N2. Similarly, there were differences in these communities among intercropping and monoculture systems across soil depths. These results indicate that both N application rates and cropping systems affect the composition of AM fungal communities. Previous studies have shown that soil fungal diversity is closely related to the soil environment and plant species (Marschner et al. 2004; Zhang et al. 2020). Zhang et al. (2020) observed that the AM fungal diversity across soil depths in the maize/ soybean intercropping system was higher in the monocultures for N0. These findings are consistent with those observed in a previous study involving legume/cereal

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intercropping systems (Wang et al. 2016). The roots of cereals in the legume/cereal intercropping system may extend to the root regions of legumes to form an interactive system. The underground root–root interactions between the intercrops can cause changes in the soil characteristics (Wang et al. 2016), which affect the diversity of soil AM fungi (Guo et al. 2020). Moreover, the changes in AM fungal α-diversity indices in the intercropping systems may be due to changes in soil fertility. In the maize/soybean intercropping system, maize generally shows a stronger competitive advantage for the soil nutrients, which leads to changes in the heterogeneous distribution of soil nutrients such as N (Jensen 1996; Zhang et al. 2020). In this study, the soil nutrients may primarily lead to changes in the AM fungal diversity as the available nutrient contents in the intercropping system were found to be higher in the monoculture systems. The changes in α-diversity were observed in the maize/soybean intercropping system across soil depths, probably due to the differences in distribution characteristics of soil nutrients in the intercropping system. Thus, the plant diversity in the intercropping system can lead to changes in soil fertility and nutrient content, thereby affecting the diversity of soil microbes (Larkin and Honeycutt 2006; Xu et al. 2009). This study also indicated that the diversity of AM fungi across soil depths was affected by N application rates. N application significantly reduced the AM fungal diversity in the soils at all depths in the IM system, while improving it at 0–15 cm and 45–60 cm soil depths in the IS system. These results may be due to the different response mechanisms of different plant species to N stress (Zhang et al. 2020). Previous studies have reported that high N in the soil generally inhibits mycorrhizal colonization because AM fungal growth is positively correlated with the amount of soluble carbon in the soil system (Zhou et al. 2011). Generally, the soluble carbon content is negatively correlated with the N levels. The soluble carbon content has been reported to decrease under high N levels, resulting in the suppression of AM fungal communities (Zhou et al. 2011). In the intercropping system, the maize root system is usually dominant. Therefore, a large amount of N was taken up by the maize roots, resulting in a low N level in the soybean soil. This alleviates the inhibitory effect of high N on soybean mycorrhizal fungi and promotes the growth of AMF in the soybean soil (Corre-Hellou et al. 2006). This study found that the diversity and the relative fraction of AM fungal communities were significantly correlated with the cropping patterns. In addition, there were significant differences between different plant species, which may be because root distribution and competition for carbon and nutrients differ between different crops. Moreover, the abundance of AM fungal communities across soil depths was found to differ significantly between the monoculture and intercropping systems. Previous studies have indicated that an intercropping system could influence the relative abundance of AM fungal communities in the topsoil (de Araujo Pereira et al. 2018; Yu et al. 2015). However, in this study, intercropping significantly improved the abundance of AM fungal communities in the deeper soil. This can be attributed to the overlap between the roots of different plants in the intercropping system leading to differences in the composition and the relative abundance of AM fungal communities in the soils at different depths (Zhang et al. 2020). Liu et al. (2015) demonstrated that the root overlap between the

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two crops in a maize/wheat intercropping system occurred at 10–80 cm depth, thereby contributing to the abundance of AM fungal communities in deep soils. Thus, this study further indicated that the intercropping system can influence the composition and relative fractions of AM fungal communities in the topsoil as well as deep soils.

8.5.3

Soil Aggregates Affected by AM Fungi and Soil Nutrients

Based on the changes in the soil aggregates, AM fungal communities, and soil nutrients, structural equation modeling (SEM) was conducted to evaluate the direct and indirect relationships between the AM communities and soil fertility (Fig. 8.13). The findings indicated that the AM fungal communities were positively correlated with macro-aggregates (>5 mm) and soil nutrients. SEM suggested that both the AM fungal communities and TOC promoted the formation of soil macro-aggregates (>5 mm). However, high N application was negatively correlated to the formation of macro-aggregates. Moreover, intercropping enhanced AM fungal diversity and soil nutrients, thus promoting the formation of soil macro-aggregates (>5 mm). As soil aggregates and TOC are directly correlated to soil stability and fertility, these results suggest that the AM communities might indirectly influence soil stability and fertility by regulating soil aggregates and organic carbon. Moreover, N application and cropping system may also indirectly influence the soil stability and fertility via

Fig. 8.13 Structural equation model (SEM) showing the direct and indirect effects of the key factors on the soil stability and quality (a). Standard total effects (direct plus indirect effects) derived from SEM (b). Red and black solid arrows represent the pathways that are significantly positive and negative, respectively, and blue dashed arrows indicate the non-significant pathways. The path coefficients are adjacent to the arrows, * means P ≤ 0.05, ** means P ≤ 0.01, *** means P ≤ 0.001. From Zhang et al. (2022)

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the regulation of the AM community. Therefore, it can be inferred that the interactions between AM fungal communities and soil aggregates may promote soil stability and fertility. AM fungi are essential for soil ecosystem functions and play an important role in soil fertility by forming mutually beneficial symbioses with plants (Bainard et al. 2014). Previous studies have indicated that these symbioses become the bridge for nutrient fluxes. Meanwhile, various compounds (e.g., glomalin) are exuded from AM fungi during symbiosis, playing a pivotal role in soil carbon/nitrogen storage (Piliarová et al. 2019). Moreover, AM fungi were also positively correlated to the stabilization of soil aggregates in wheat/maize and faba bean/maize intercropping systems (Song et al. 2007). The SEM analysis was conducted to investigate the relationship between AM fungi and soil aggregates. The results highlighted a positive effect of AM fungi on the soil macro-aggregates (>5 mm) across soil depths. The stronger AM fungal hyphae entangle and enmesh primary particles or micro-aggregates to form macro-aggregates (Piotrowski et al. 2004; Wang et al. 2010). Previous studies have found that AM fungi facilitate the formation of soil macro-aggregates through the formation of several secondary metabolites (CaesarTonthat and Cochran 2000; Tian et al. 2019). For example, the polysaccharides and phenolic acids secreted by AM fungi can combine with soil clay particles and microaggregates, promoting the formation of macro-aggregates (Tian et al. 2019). AM fungi can increase the glomalin-related protein content, which can affect the formation of soil macro-aggregate fractions and contribute to aggregate stability. Rillig et al. (2015) reported that saprotrophic fungi degrade organic materials (plant leaves or roots) and provide the necessary raw materials for the formation of soil aggregates. Similarly, Cao et al. (2022) indicated that AM fungi could indirectly contribute to the decomposition of organic materials and the abundance of overall extracellular enzymes by stimulating the growth of saprotrophic fungi through symbiosis with these. This may also be a pathway by which soil AM fungi promote the formation of soil aggregates.

8.6

Carbon Sink Function

AM fungi not only use the carbon sources fixed by plant photosynthesis but also act as important carriers to transfer carbon sources from the plant to the soil. Hobbie (2006) analyzed the results of 14 studies under controlled indoor conditions and found that 27–68% of net photosynthetic products from different plants were transported to the subsurface, from which ectomycorrhizal fungi received 1–21%; in contrast, analysis of the results of five field studies showed 14–15% of the net photosynthetic product of the plant to be obtained by the exogenous AM fungus. Although different AM fungi vary considerably in the amount of carbon they obtain from different plants (4–26% of carbon fixed by plant photosynthesis) and emit most of the carbon obtained to the atmosphere through respiration, it should not be overlooked that carbon sources fixed by AM fungi in the form of biomass play an

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important carbon pooling function in natural ecosystems and that fungi generally have a higher microbial carbon use efficiency than bacteria (Hannula and Morriën 2022). Therefore, studying the effect of AM fungal mycelial networks on carbon and nitrogen transfer between plants and mycorrhizae is an important factor in understanding the role of AM fungi in regulating the strength of interspecific interactions and thus the effect of AM fungi on soil organic carbon enhancement. AM fungi are an important bridge between the plant root system and the soil, forming a large mycorrhizal network system underground, connecting different species of plants and different individuals of the same species through the mycelial network to achieve one-way or two-way transfer of C, nutrients such as N and P, and information between plants (Perry et al. 1989; Read 1997; Simard et al. 1997; He et al. 2006). AM fungi cannot photosynthesize, need to obtain carbohydrates from host plants to meet their own growth requirements, and are important carriers of carbon sources from plants to soil (Staddon et al. 2003; Jiang et al. 2017). It is estimated that the host plants of AM fungi exceed 200,000 species (Öpik et al. 2014), which is more abundant compared to ectomycorrhizal fungal hosts. AM fungal biomass can account for up to 20% of microbial biomass (Parihar et al. 2020) and has a rapid turnover, averaging 5–6 days. This results in the production and accumulation of large amounts of mycelial residues (cell walls, etc.), far from the root zone where microbial activity is strong (Parihar et al. 2020), resulting in organic carbon that is not easily decomposed. In addition, glomalin-related soil protein, which is released into the soil following the decay and degradation of AM fungi, can account for 3–12% of total soil organic carbon in different ecosystems (Rillig et al. 2001) and is also an important source of soil organic carbon. However, the contribution of AM fungal biomass to soil organic carbon input, especially in deep soils, is not clear. AM fungi participate in the soil carbon cycle by influencing the size and distribution of soil aggregates and pores (Rillig and Mummey 2006), and diverse cropping patterns contribute to soil carbon storage by improving the stability of soil aggregates through the action of AM fungi. The AM fungus secretes balcomycin, which promotes the formation of stable soil macroaggregates and provides further physical protection for organic carbon in soil aggregates, contributing to the stabilization of soil organic carbon (Six et al. 2004); while the AM fungus promotes the stability of macroaggregates, it can also indirectly influence the formation of microaggregates (Rillig and Mummey 2006). In addition, the extrarooted mycelium of AM fungi plays an important role in the formation of soil aggregates and the sequestration of organic carbon. Mycelia can align and entangle soil particles or provide sites for agglomerates, contributing to their formation and helping to stabilize them by changing their surface polarity. More importantly, mycelia can adhere to soil aggregates and continuously transport plant-derived carbon to the surface of the aggregates, which helps to sequester soil carbon in these aggregates (Rillig and Mummey 2006). Recent studies have also shown that plants in symbiosis with AM fungi increase soil organic carbon sequestration by increasing root carbon inputs, reducing natural soil organic matter decomposition and inter-root excitation effects (Zhou et al. 2020). Long-term localization

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experiments have also found a significant positive correlation between soil carbon pools and AM fungal extra-rooted mycelia. We hypothesize that the increased crop diversity of the intercropping system promotes the growth of AM fungal extrarooted mycelium and the secretion of balcomycin through the action of soybean root secretions, thereby influencing the formation of soil aggregates and the sequestration of organic carbon.

8.7

Application of AM Fungi in Agriculture

In agroecosystems, AM fungi can establish symbiotic systems with most food, vegetable, oilseed, flower, and medicinal crops, except for a few crop species belonging, for example, to the Brassicaceae and Chenopodiaceae. Because of their irreplaceable ecological importance, agricultural applications of AM fungi can reduce fertilizer application and improve fertilizer use efficiency, control disease occurrence and reduce pesticide application, and enhance host plant adaptation to environmental stresses such as drought. The promotion and application of mycorrhizal technology is therefore in line with the needs of sustainable agricultural development. The effect of AM fungal inoculation is influenced by a combination of factors, not only the requirements of the crop and soil but also the quality of the AM fungus, the method of application and the accompanying management practices. All these aspects need to be accounted for in the practice of AM fungal applications.

8.7.1

High-Quality AM Fungal Inoculants

AM fungi can be divided into exogenous and indigenous fungi depending on the source of the AM fungi. Indigenous AM fungi have been screened for both crop and soil environmental effects and have a strong environmental adaptation and survival advantage, while exogenous AM fungi entering the soil may have a strong competitive effect with indigenous AM fungi resulting in inoculum failure. There is a strong correlation between the abundance of indigenous AM fungi and the success of exogenous AM fungal inoculation. Frew (2020) inoculated Hordeum vulgare and Sorghum bicolor with a single exogenous AM fungus, a mixed exogenous AM fungus and an indigenous AM fungal community, and found that inoculation with indigenous AM fungi was more effective in regulating crop biomass allocation and increasing leaf P content. However, the large variability of indigenous AM fungi in soils from different regions and the tedious process of screening and enrichment of indigenous AM fungi have constrained the production and widespread use of indigenous AM inoculants. In modern intensive agricultural production, the excessive application of chemical fertilizers, especially phosphate fertilizers, soil tillage, and monoculture cropping systems have led to a decrease in the abundance and

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diversity of indigenous AM fungi and a decline in their functions, making inoculation with exogenous AM fungi an important means of regulating the structure and function of soil AM fungal communities. Exogenous AM fungal agents usually contain species such as Rhizophagus irregularis and Funneliformis mosseae. These strains are known as the “superfungi” among AM fungi because of their wide adaptability to crops and the long duration of the inoculation effect (Pellegrino et al. 2011). Studies have shown that competition between exogenous AM fungi and indigenous AM fungi can be mitigated by increasing the inoculum dose, changing the inoculation method, and improving field management practices such as tilling before inoculation, thus enhancing the competitive advantage of exogenous AM fungi.

8.7.2

Inoculation Methods for AM Fungi Inoculants

Five types of inoculants have been introduced in South China, namely suspension, solid, pellets, powder, and AM pellets. Among these, suspension and AM pellets inoculants are the most popular. Currently, 90% of commercial AM fungal inoculants are solid fungicides (65% powders and 25% granules), while only 10% are liquid inoculants, and most fungal applications are by soil application or seed treatment. Soil application methods mainly include spreading, furrowing, hole application, and seed mixing. Certain amounts of inoculant, crop seed, and growing substrate are usually mixed into the soil, or the inoculants can be applied in the bottom before sowing. Soil application methods can reduce physical damage to seeds and cotyledons, reduce the impact on seeds of insecticides and inoculants that may be mixed in with inoculant, and increase the chance of root infestation due to exposure of small seeds to inoculants. However, to ensure good inoculation, the soil application method requires a high-quality fungicide and a high application rate. In recent years, AM fungal seed coating treatments have received increasing attention. The main advantages of this technology are that it requires less inoculant, has lower economic costs for later transport and application, and allows precise delivery of the inoculants and related active ingredients using the seed as the target. The main problems faced by this technology are the inability to control the coating thickness well enough to affect seed germination (Acha and Vieira 2020) and the low inoculum and uniformity of single seeds, which affects commercial production and application. Nevertheless, due to the potential advantages of coated seeds, coating may become an important way to commercialize AM fungal inoculants on a large scale in the future. At present, there are already some countries where the production and application of mycorrhizal inoculants have been commercialized, such as the United States, the United Kingdom, France, and Japan. AM fungi agents are available for sale, and the preparations produced by them are of high purity and good quality, mostly through strict control of various factors for greenhouse factory production. For example, AM inoculants produced by the Lausanne Agricultural Research Centre in the United States have been widely used on vegetables, flowers, and fruit trees in countries such as the United Kingdom, France, Denmark, the

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Netherlands, and Japan, while significant economic and ecological benefits have been achieved in pasture production in New Zealand and Australia.

8.7.3

Supporting Farming Systems

A farming system is a general term for the way in which land is used for crop cultivation and the technical measures that go with it and is also known as a cropping system. Cropping systems such as crop allocation, planting sequence (e.g., crop rotation, continuous crop), planting methods (e.g., mixed (inter) crop, set crop, monocrop), and management practices, such as soil tillage, soil fertilization, water management, and pest and weed control can significantly affect the community composition and diversity of AM fungi in agricultural fields. Farming systems that are compatible with AM fungal applications can better exploit the functions of AM fungi in agroecosystems. Long-term continuous cultivation patterns of a single crop species often result in reduced diversity and abundance of indigenous AM fungal communities in the soil. A study by Cofré et al. (2020) comparing soybean monoculture and soybean–maize rotations showed that AM fungal spore abundance was significantly higher in rotated crops than uncultivated grassland, although lower than monoculture, and that AM fungal infestation rates on crops showed similar trends. The effect of crop rotation and intercropping, for example, on AM fungal diversity is mainly due to differences in the dependence and preference of different crops for AM fungi. Higher crop diversity and the legacy effects of its management can have a positive effect on the abundance and diversity of AM fungal communities. Highly mycorrhizal-dependent crops from previously or from intercropping can promote the accumulation of AM fungal propagules in the soil, and the mutually beneficial relationship between AM fungi and crops is prolonged over time, which in turn improves farm productivity and stability (Orio et al. 2016). Using the ecological principle of plant-soil feedback, the abundance, community composition, and ecological functions of AM fungi in the soil can be significantly optimized through a variety of cropping systems such as reasonable crop rotation, mixed (inter)crop, and crop set. Acknowledgements The work was supported by CHN17/0019 Sinograin II: Technological innovation to support environmentally-friendly food production and food safety under a changing climate—opportunities and challenges for Norway–China cooperation (funded by the Norwegian Ministry of Foreign Affairs), and by the National Natural Science Foundation of China (grant no. 32271657). We thank Prof. Nicholas Clarke from Norwegian Institute of Bioeconomy Research for his great help in improving the language of the manuscript. Author Contributions Lingbo Meng: Organizing the writing of this chapter, writing the introduction, Part II (Facilitation of crop nutrient uptake), Part IV (Contribution to soil fertility) and Part VI (Application of arbuscular mycorrhizal fungi in agriculture). Shumin Li: Writing Part I (Diversity of AMF) and Part III (Response of AMF diversity to N fertilizer and cropping systems). Yefei Meng: Writing Part V (Carbon sink function) and editing.

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Chapter 9

The Agro-Extension Service Evolution in China and Norway: Different Pathways to Tackle Evolving Challenges Xiande Li, Zhilu Sun, Giovanna Ottaviani Aalmo, Fangfang Cao, Divina Gracia P. Rodriguez, Chen Qian, Yongxun Zhang, and Knut Øistad

Abstract Agricultural extension services are integral to technology adoption where they play a key role in delivering relevant agricultural information and technologies to farmers. In China, agricultural extension services are provided through experimentation, demonstration, training, and consulting. In Norway, agricultural extension is focused on collecting, developing, and coordinating agricultural knowledge to farmers. This chapter focuses on why agricultural extension is needed, how it is developed, and what services agricultural extension provides to its clients. It discusses experiences from China and Norway where agricultural extension has led to or is necessary for boosting agricultural productivity, increasing food security and safety, and improving the well-being of farmers. Keywords Agricultural extension service · China · Precision agriculture development · Norway · Agricultural Knowledge and Innovation System · Funding sources

9.1

Introduction

In its adoption of the sustainable development goals (SDGs), the UN has stated the importance of efficient utilization of resources to meet the ever-increasing demand for food in both a safe and environmentally responsible manner. This is also reflected by the “Zero-Growth” policy aiming at arresting further increase in nitrogen fertilizer application in China by 2020.

X. Li · Z. Sun · F. Cao · C. Qian · Y. Zhang Institute of Agricultural Economics and Development of Chinese Academy of Agricultural Sciences, Haidian, Beijing, China G. O. Aalmo · D. G. P. Rodriguez · K. Øistad (✉) Norwegian Institute of Bioeconomy Research, Ås, Norway e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Clarke et al. (eds.), Innovation for Environmentally-friendly Food Production and Food Safety in China, Sustainable Agriculture and Food Security, https://doi.org/10.1007/978-981-99-2828-6_9

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International cooperation is vital to tackle climate and environmental problems, among which high-yield-driven food production, together with transport and letting food rot, contribute to more than 8% of global greenhouse gas emissions (Xiao and Shi 2023). Food security and food safety are among the thematic collaboration areas prioritized by the governments of China and Norway, and in order to tackle the new challenges facing agriculture (environment, rural development, etc.), appropriate knowledge has to be produced. The role extension services can play in this process is very significant, through targeted campaigns to increase awareness and knowledge, and the promotion and adoption of precision agriculture tools to allow optimization of use of inputs such as fertilizers and pesticides according to farm- and crop-specific needs. By doing so, suboptimal application of these inputs is avoided, leading to positive gains in terms of productivity and the environmental footprint of agriculture. The successful application of these methodologies implies a responsive extension service that can facilitate adoption and utilization, setting up mechanisms that provide farm-, crop-, and region-specific information to end users. Therefore, access to extension services plays a key aspect in technology adoption. Agricultural extension field staff usually inform the farmers about the existence and benefits of a new technology. They also seek to build and strengthen the capacity of farmers by (1) informing them of the existence and effectiveness of a new technology or practice, (2) providing training on improved agricultural operations, and (3) making agricultural input- and output-related information available and accessible to them in a timely fashion. Extension agents usually target specific farmers who are recognized as peers (farmers with whom a particular farmer interacts, usually called “farmer champions”) exerting a direct or indirect influence on the whole population of farmers in their respective areas (Genius et al. 2010). This chapter focuses on the evolution of the agro-technical extension services in China and Norway, exploring the initial needs for their existence—why they were initiated, their development—how they advanced together with changing needs and technologies and finally their roles—what the agro-technical extension services in China and Norway are able to offer nowadays.

9.2

The Characteristics of Agro-Technical Extension in China

To strengthen agro-technical popularization and facilitate the application of agricultural scientific research and practical techniques to agricultural production, the Chinese government issued The Law of the People’s Republic of China on the Popularization of Agricultural Technology on July 2, 1993 (Government of China 1993), and On Deepening Reform to Strengthen the Construction of Primary Agricultural Technology Extension System on August 28, 2006 (Government of

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The infancy stage (1950-1977)

A four-level network of agricultural technology was established, consisting of local institutes at the county level, related stations at the commune level, brigades as well as production teams.

The restoration stage (1978-1992)

The reforming stage (1993-2001)

A five-level extension system involving the central government, provinces, cities, counties and towns with one model family unit and one technician was set up.

The Law on the Extension of Agricultural Technology of the People’s Republic of China was issued in 1993.

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The enhancing stage (since 2002)

A new system is in the making Governmentled agrotechnical organizations, universities, specialized farmer cooperatives, agricultural enterprises, rural technicians, etc.

Fig. 9.1 Development stages of agro-technical extension in China

China 2006). On August 31, 2012, the 28th Meeting of the Standing Committee of the Eleventh National People’s Congress reviewed and approved On Amending the Law of the People’s Republic of China on the Popularization of Agricultural Technology (Government of China 2012). According to the revised Law of the People’s Republic of China on the Popularization of Agricultural Technology, agricultural technology refers to the scientific research results and practical techniques in the fields of farming, forestry, animal husbandry, and fishing. Agrotechnical extension refers to the popularization of using agricultural technology before, during, and after agricultural production. Extension is carried out through conducting experiments, setting examples, training, guiding, and consulting services. The extension of agricultural technology requires national organizations to work together with research units, schools, specialized farmer cooperatives, agricultural enterprises, public scientific and technological organizations, and technical personnel. The Chinese government encourages cooperatives, enterprises, public institutions, civil society, and other technical staff from different sectors to promote agro-technical popularization.

9.2.1

The Development Stages of Agro-Technical Extension in China

Since the People’s Republic of China was founded in 1949, the establishment of the agro-technical extension system in China has experienced four stages (Fig. 9.1; Chen 2015a; Chen 2015b).

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The Infancy Stage (1950–1977)

This stage featured the restoration of agricultural production and the construction of an agro-technical service unit and system, especially the construction of primary extension workstations in rural areas so as to advance the national extension system. Later, a four-level network of agricultural science and technology was established, consisting of local institutes at the county level, workstations at the commune level, brigades, and production teams.

9.2.1.2

The Agricultural Production Restoration Stage (1978–1992)

At this stage, the four-level network in the restoration of agricultural science and technology gradually broke up to meet the requirements of the Family Contracted Management Land System (Household Responsibility System) and agricultural development in rural areas. A five-level extension system with one model family unit and one technician was set up. The five levels involved the central government, provinces, cities, counties, and towns. At the county and township levels, the system was led by the counties’ agro-technical extension centres, and the agricultural technology was promoted to farmers through the township agro-technical extension stations.

9.2.1.3

The Reforming Stage (1993–2001)

At this stage, rules and regulations regarding agro-technical extension were rolled out. The Law of the PRC on the Extension of Agricultural Technology issued in 1993 clearly defined the meaning, system, institutions, and roles of extension work, which set Chinese agro-technical extension on a track of law-based governance. Financial support for the country’s extension organizations was provided, and the formation and functions of organizations were also clarified in 1996. With these measures, China’s extension system underwent great transformation and has been developing ever since.

9.2.1.4

The Enhancing Stage (since 2002)

With the booming development of agricultural technology and services, the agricultural technology extension system has been developing in China since 2002. A new system is in the making, featuring cooperation among government-led agro-technical organizations, agricultural science and technology organizations, universities, specialized farmer cooperatives, agricultural enterprises, and rural technicians.

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9.2.2

The Current Status of the Agro-Technical Extension System in China

9.2.2.1

Main Features of the Agro-Technical Extension System in China

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Over the past few decades, the agro-technical extension system has become a multipolarized system with the government as its main actor. The government’s agrotechnical extension system includes five levels: national, provinces, cities, counties, and towns. The construction of the agro-technical extension system in China has achieved significant results (Wei 2021).

Organizational Structure of Government’s Agro-Technical Extension System By the end of 2020, a total of 24,800 government agro-technical extension institutions in the planting industry had been set up in the four-level agro-technical extension system of provinces, cities, counties, and towns, accounting for 33% of the total number of national agro-technical extension institutions in agriculture, including 9400 at the county level and 14,000 at the township level (including regional stations), and the number was generally stable. At the same time, through measures such as joint construction of carriers, dispatching temporary posts and mutual dispatch of personnel, the integration of institutions, teams and services of public welfare extension services with the organizations, subjects and services of new operational services provides an organizational guarantee for solving the “last mile” problem of agro-technical extension. 1

Professional and Educational Level of Agricultural Technicians The introduction and training of grassroots specialized talents have been strengthened, and the professional and educational levels of agricultural technicians have been improved through measures such as the introduction of targeted talents, the recruitment of specially hired agricultural technicians, graded and classified training and education to improve academic qualifications. At present, there are 231,400 agricultural technicians in China’s planting agro-technical extension institutions, of which 78.8% have a college degree or above, and 56.4% have intermediate or above professional and technical titles, both of which figures are nearly 10% higher than in 2015.

The “last mile” problem of agro-technical extension refers to the final link of the agro-technical extension system connecting farmers not being perfect, resulting in mismatch between the agricultural scientific and technological achievements and the agro-technical needs of farmers.

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Governance and Evaluation of Agricultural Technicians By allowing agricultural technicians to carry out value-added services and reasonable remuneration, the Chinese government has explored the integration and development of the government’s agro-technical extension institutions with operational service organizations, through which performance evaluation methods have improved and the enthusiasm of agricultural technicians has been enhanced. Beijing takes the transformation of scientific and technological achievements in the seed industry and the distribution of rights and interests as a breakthrough point to increase agricultural technicians’ income. Thirty-six counties in 13 provinces, including Anhui, Zhejiang, and Jiangxi, support agricultural technicians to explore reasonable remuneration for value-added technical services and encourage agricultural technicians to start businesses.

Services and Support Provided by Agro-Technical Extension First, machine services and simplification techniques, which include “Rice+”, corn machine harvesting, corn and soybean rotation technology, multi-functional development and utilization of rapeseed, high oleic peanut whole process quality control, and vegetable green light cultivation and other technologies, as well as fruit trees cultivated without bags, tea machine picking mechanisms, and other simplification models. Second, green and efficient production technology. Through standard formulation, integrated innovation, policy guidance, and typical demonstrations, China has accelerated the demonstration and popularization of green and ecological planting technologies and implemented measures to control the use of plastic sheeting and water for irrigation and reduce the use of chemical fertilizers and pesticides, with remarkable results. In 2020, the average effective utilization rate of chemical fertilizers and pesticides for the three major grain crops (including wheat, paddy rice, and maize) was 40.2% and 40.6%, respectively, an increase of 5% points and 4% points over 2015, respectively. Third, the whole industry chain service. Relying on projects such as green and efficient production, crop rotation including fallow, soybean revitalization plan, super rice demonstration and promotion, sugar cane seed improvement and subsidies for good technological methods, as well as cotton quality improvement and efficiency improvement, new breakthroughs have been made in the integration of green and efficient technologies and the construction of a “whole-industry-chain” social services system.

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Main Categories of the Agro-Technical Extension System in China

For many years, the government’s agro-technical extension system has played an important role in the transfer and extension of agricultural research output. Based on multilateral collaboration, complementary advantages, and equal competition, the Chinese government supports and encourages cooperation on extension work among farmer cooperatives, leading agricultural enterprises, agricultural science and research units, and agricultural colleges to promote a wide variety of agro-technical extension models. So far, various extension models have been established and China’s agro-technical extension system is on a healthy track aiming at offering non-profit and multi-polarized services. China’s current agro-technical extension system can be divided into the following four categories.

Agro-Technical Extension System with the Government as the Lead The government is at the core of China’s agro-technical extension system, a non-profit system consisting of extension organizations at the national, province, city (prefecture), county, and township levels. Such a model featuring governmentguided organizations is the primary model of China’s extension system. Specifically, the five-level governmental extension organizations are the National Agro-technical Extension Service Center (NATESC), the Provincial Agro-technical Extension Center (Headquarters), the Municipal Agro-technical Extension Center (Station), the Agro-technical Extension Center (Station) at the county level, and the Agrotechnical Extension Station at the township level (Agricultural Comprehensive Service Center at the township level).

Agro-Technical Extension System Driven by Government Technological Projects Since the 1980s, to satisfy the needs of agricultural development, governments at different levels have chosen some key agro-technical achievements to be promoted by extension organizations. Advanced technologies and new products have been widely popularized through different strategies and implementation methods, which are fulfilled by cooperation between agricultural science and research units, educational institutes, and extension organizations. Such an extension system driven by the government’s science and research projects is mainly based on the following nine models. First, the 110 technical model, which relies on cities, towns, and villages to build 110 agro-technical service centres. Such service networks involve vertical connectivity among provinces, cities, and towns, as well as horizontal collaboration. They

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provide guidance and service for farmers through the internet, video, and face-toface consultation. Second, the technological modeling zone, which takes an operation model involving enterprises, bases, farmers, and technology. A certain area is used to build model zones in coastal cities and the suburbs of large and medium cities with strong agricultural science and research abilities, technical talent, and welldeveloped economies. Investment in high-tech agricultural achievements is made to speed up transfer and extension. Third, the experts’ courtyard model. Experts are encouraged to form a community of shared interests with relevant organizations by technological and capital investment. They work in the fields and offer advice so that new technologies and products are transferred to farmers. Fourth, the Agricultural Extension Special Task Force (AESTF), a model with technical special agents. The special agents live and work in the villages and offer comprehensive and multi-level agro-technical services. Fifth, the model of sending technology to farmers. Through integrating the efforts of agricultural science and technology units, education and extension organizations, agricultural enterprises and technical service organizations, the technical staffs choose well-educated farmers and assist them in agricultural activities over a period of time. Sixth, a model of popularizing technology in rural areas. Each year, the agricultural sectors will launch a variety of activities to transfer advanced and applicable technology into rural areas based on the principle of practicality and high efficiency. Seventh, the model based on the telephone, television, and computer (information & communication technology—ICT). The government’s agricultural sectors will work with radio and TV departments to provide personalized and agriculturalbased information programs for farmers. Eighth, the model of Farmers’ Field Schools (FFS). Aimed at meeting the technical needs of farmers, this model takes farmers as its centre and takes fields, livestock and poultry farms as classrooms to address problems in farming and breeding through different educational methods like interaction and heuristics. Ninth, the model of technical coordinators. Based on technical training or market surveys, farmers or agricultural workers act as technical coordinators to introduce new technologies and products. They are also responsible for teaching and spreading the technologies to other farmers with practical needs.

Agro-Technical Extension System Led by the Market This extension system mainly includes the following two models. First, enterprises work with farmers. The leading agricultural enterprises work with farmers and form economic communities in which an order-oriented operation model involving companies, farms, and farmers is applied. Companies keep their promises to provide all-round services, including services on products, means of production, and new extension technology.

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Second, technical associations or specialized cooperatives are set up. Technical associations and specialized cooperatives form a new kind of rural economic cooperative, consisting of extension organizations, agricultural production bases, agricultural production operators, and agricultural product processors. They provide technical services for their members before, during, and after agricultural activities.

Agro-Technical Extension System Led by a Third Party A third-party-led agro-technical extension system is made up of related science and technology institutes and agro-technical extension centres at the provincial, district, and county levels. They work together to popularize agricultural technology. This extension system includes the following two representative models. First, a cooperative model between the Heilongjiang Academy of Agricultural Sciences and agricultural technical staff from different towns. Experts from the academy work together with technical staff in different towns. They work at the Courtyard of Experts all year round and launch projects to enrich farmers. They show farmers how to use agricultural technology and guide them in their agricultural activities in the field. Second, a model featuring the “Taihang Mountain Pathway.” The Agricultural University of Hebei sets up high-tech zones and bases integrating teaching, research, and production in different parts of Hebei Province. Technical achievements are transferred through development and consultation and thereby promote the integration of teaching, science and research, and production.

9.2.3

A Case Study of Agro-Technical Extension in Heilongjiang Province

9.2.3.1

Basic Situation of Heilongjiang Province and Jiansanjiang Authority

Heilongjiang Province, located in northeast China, is the country’s northernmost province with the highest latitude. According to the National Bureau of Statistics of China (2020, 2021), the total arable land area of Heilongjiang was 17.20 million ha in 2019, accounting for 13.45% of national arable land, and the arable land per capita was 0.48 ha, significantly higher than the national average (0.09 ha). According to the Heilongjiang Bureau of Statistics (2021), the total population of Heilongjiang in 2020 was 31.71 million, among which the rural population accounted for 35.4%, with a per capita GDP of 42,635 yuan, and a disposable income per capita of 16,168 yuan for rural residents. In 2020, the sowing area for grain crops for the whole province was 14.438 million ha, and grain output reached 75.408 million metric tons. The province is a major grain-producing area in China, with food production ranking first in the country.

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Jiansanjiang Authority, located in northeast Heilongjiang Province, has 15 large and medium-sized state-owned farms and was restructured to the Jiansanjiang Branch of the Beidahuang Group Co. Ltd. in 2018, comprising 220,000 people. It belongs to the Agricultural Reclamation Bureau of Heilongjiang Province, the latter being an institution directly affiliated to Heilongjiang Province and the Ministry of Agriculture and Rural Affairs of China and having been restructured to the Beidahuang Group Co. Ltd. in 2018. Its total land area is 1.24 million ha, including arable land of 0.73 million ha. Jiansanjiang is flat in terrain, fertile in soil, and rich in resources. It is famous for its rich green high-quality rice, known as the “best rice in the East” and “the capital of green rice in China.” In 2018, Jiansanjiang achieved a GDP of 20.96 billion yuan, the total output of grain was 6.68 million metric tons, and the per capita net income of family farm workers was 28,065 yuan.

9.3 9.3.1

The Characteristics of Agro-Technical Extension in Norway The Agricultural Knowledge and Innovation System in Norway

Agricultural advisory (extension) services assist farmers in a broad range of issues, such as technical, financial, business management, ethical (animal welfare), and regulatory issues, which are often interconnected and thus require complementary or joint efforts between several advisors. The agricultural advisory system is part of the broader Agricultural Knowledge and Innovation System (AKIS) (Standing Committee on Agricultural Research 2013), as shown in Fig. 9.2 (OECD 2021), which forms a broad governance framework for advisory services in relation to other innovation support arrangements such as research, education, and innovation funding (Hermans et al. 2015; Knierim et al. 2015). The framework of Norwegian agricultural policy includes negotiations between the government and the national agricultural organizations, the Norwegian Farmers Union (Norges Bondelag), and the Smallholders Union (Norsk bonde—og småbrukarlag). These negotiations set important parameters for the agricultural policy in Norway, including direct support schemes and a welfare program for farmers. As shown in Fig. 9.2, the Agricultural Agreement (Jordbruksavtalen) is also an important funding source for research, innovations, and agricultural extension. This includes the Agricultural Agreement Research Fund and Innovation Norway (Innovasjon Norge). The Agricultural Agreement Research Fund and Foundation for Research Levy on Agricultural Products (FFL) are managed by the Norwegian Agriculture Agency (Landbruksdirektoratet) and are geared toward applied research involving stakeholders from the farming sector and beyond. These activities are closely linked to extension activities needed for development of the Norwegian agricultural sector.

249 MNOK

Research Council of Norway (RCN)

4 509 MNOK (all R&D areas)

Agric: NIBIO, VI & Ruralis Nofima (75 MNOK from FFL) Other: NIFU, SINTEF…

Research institutes:

Fund for Research Fees of Agri Products (FFL)

(4)

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Research Fee on Agr. Products

Forest Owners' Re. Fund

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4 MNOK

Industry, including SMEs

Innovation Norway

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Ministry of Trade, Industry and Fisheries 844 MNOK

Forestry Development Fund

Deduction on wood trade

Agricultural Agreement

Fig. 9.2 The agricultural knowledge and innovation system (AKIS) in Norway. OECD (2021)

(secondary and tertiary: NMBU,…)

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Agri. Agre. Research Funds (JA)

(3)

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178 MNOK (Base funding)

Ministry of Agriculture and Food

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Funding (approx. MNOK/year in 2019, if available) Authority

Source of funds and authority

Education System:

Open

66 MNOK(1)

Ministry of Education and Research

EU Commission Horizon 2020

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Sector of performance

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Innovation Norway manages a substantial part of the funds from the Agricultural Agreement for innovation purposes. For 2022, the amount was 112 million NOK for the program for technological development in the Norwegian agricultural sector and 879 million NOK for the investment and development program (Norwegian Ministry of Agriculture and Food 2022a). The major share of the funds is investment support for farmers and companies and not directly for extension programs, but extension from a market actor or the government is often attached to the investment, and it is clearly an important part of the agricultural knowledge and innovation system. After its establishment in 2004, Innovation Norway has to some extent moved from traditional program management toward a broader portfolio management. In its budget proposal for 2023, the Norwegian government proposed to establish a new separate entity embedded in Innovation Norway. This entity, labeled Bionova, has two main objectives: (1) Climate mitigation actions and increased carbon storage in soil at farm level and (2) increased value added through better resource efficiency and circular bioeconomy (Norwegian Ministry of Agriculture and Food 2022b). It is expected that technological advances and precision agriculture will be emphasized in the Bionova portfolio. The Norwegian parliament endorsed the proposal from the Norwegian government in late 2022. The establishment of Bionova adds another element and layer in the AKIS in Norway. Innovation Norway also has regional offices, focusing on serving the regional stakeholders and processing industry. According to the OECD (2021), public investment in AKIS in Norway is only 3% of total support to agriculture, compared to 5.8% in the European Union and 4.2% across OECD countries. However, the support reaches 4.2% of value added, well above other OECD countries. A particularly strong characteristic of the Norwegian AKIS is its sectoral approach. This has been the tradition since its inauguration but poses some challenges for the future. The OECD (2021) advises Norway to strengthen the independence and cross-sectoral approach of its agri-food research and innovation system. The Norwegian Agricultural Knowledge and Innovation System has shifted away from a governmental-driven strategy with farming and public goods in focus into a commercialized business with farmers in focus (Klerkx et al. 2017). From the late 1980s, the Norwegian agricultural sector, like in many other countries, shifted to more market-oriented production systems with increased focus on competitiveness. As a result, the number of farms declined due to competition; however, the remaining farms became larger and more specialized. Specialization in production then requires the need for specific skills, information, and technologies. Farmers are also highly heterogeneous (e.g., differing in terms of resources, crop and livestock systems, market access, etc.) Hence, agricultural advisory services have to provide the necessary flexibility to make their services more demand-driven, context-specific, and based upon multiple knowledge sources (i.e., a pluralistic extension service). In addition, this transformation in AKIS governance over the last 30 years has led to decreased state interventions in agricultural advisory services in Norway at the institutional level. This was the consequence of political choices and also a result of

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the production specialization at all levels and the increased skills and education of the farmers, requiring a higher quality of the advisory service provided. This led to privatization of the agricultural advisory services, which is an alternative to the reliance on public funding for extension services. Several other governments maintain agricultural extension units in their ministries of agriculture (e.g., Australia, Canada, China, and Japan). Additionally, the employees at municipality level were and are generalists, and their tasks are related to the provision of advice on regulations and governmental support.

9.3.2

The Role of the Norwegian Agriculture Agency, County Governor, and Municipalities in Agricultural Extension

The Norwegian Agriculture Agency, County Governor (Statsforvalteren), and municipalities conduct a mix of duties related to administration, communicating policy priorities, monitoring implementation, and providing extension. The Norwegian Agriculture Agency is a merger of several previous entities under the Ministry of Agriculture and Food. The Agriculture Agency has been operational as an administrative body under the ministry from 2000. The bulk of the work of the Agriculture Agency is management of subsidy schemes, particularly those included in the Agricultural Agreement, but also other schemes relevant for the agricultural sector. County Governors’ offices monitor grants and development of the agricultural sector in the respective county. The governors’ office maintains close contact and supervises municipalities in their implementation of the agricultural policy. Some economic support is directly distributed from the county governor’s office, particularly environmental means. County and municipal administrations are important actors in the implementation of Norwegian agricultural policies. Historically, the link between the central government, led by the Ministry of Agriculture, was directly through the county governor’s office to the municipal administrations. During the 1990s, agricultural administration in the municipalities was included in the normal municipal administration under supervision of the local government. The direct link from the ministry to the municipalities was broken. The municipalities still govern the implementation of the policies based on legislation and overall priorities, with local government being responsible for the day-to-day activities. The funding of agricultural activities in the municipal administration has changed from direct funding from the Ministry of Agriculture and Food to the general funding for the activities of the municipal administration. The change has gradually been expanded during the last decades, placing more of the decisionmaking at municipal level, closer to the farmers and other stakeholders involved in the sector. The ambition to place the decision-making closer to the target audience impacted by these changes was an important political reason for the change.

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One of the impacts of the change was more emphasis on the administrative and legal aspects of the implementation of the agricultural policy. Agricultural extension became gradually reduced in the municipal administrations. This opened a market for more involvement of the private sector in extension services. The municipal target audience for agriculture is farmers and others associated with agriculture and forestry, other entrepreneurs, and new entrepreneurs. The municipal administration provides extensive guidance in connection with legal issues, according to the Land Act and the Planning and Building Act.

9.3.3

The Agricultural Extension Service in Norway

9.3.3.1

Main Providers of Advisory Services in Norway

In a competitive time such as the one we live in, in order to improve the economy of their business, farmers must take advantage of the advisors available, and using their services is becoming more and more common. In the Norwegian setting, advisors can be grouped into the following (Klerkx et al. 2017): (1) Advisors in the input supply industry, often in cooperatives such as Felleskjøpet Agri (https://www. felleskjopet.no/) (fertilizer, machines, and equipment) but also many machinery suppliers and others. This service is provided by organizations that sell to farmers and in some cases buy from farmers. (2) Advisors in the food industry, often in cooperatives such as TINE (http://www.tine.no) (dairy) and Nortura (http://www. nortura.no/) (meat). This service is provided by organizations that buy products from farmers. TINE has organized its advisory service in a specific department, TINE Advisory Service. In the meat sector in particular, several competitors provide advice for farmers to various degrees. (3) Advisors in independent organizations such as the cooperative Norwegian agricultural extension service (Norsk landbruksrådgiving, NLR) (https://www.nlr.no/) and also independent consultants. (4) Advisors in relation to services such as accounting, banking, insurance, breeding, ICT, farmer unions (in specific cases), and so forth. These services are provided or sold in addition to other services.

9.3.3.2

Norwegian Agricultural Extension Service

Organization and Services Provided The Norwegian agricultural extension service Norsk Landbruksrådgiving (NLR) provides advisory services to its members, based on scientific results from field trials implemented by different actors in Norway (including the Norwegian Institute of Bioeconomy Research NIBIO). Close to 600 field experiments are carried out per year, mainly on the members’ farms—many in cooperation with NIBIO within the framework of different projects. NLR conducts the trials on NIBIO’s request on the

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NLR members’ farms, to get results from all over the country and under different conditions. NLR comprises 10 local extension groups, 24,000 members (farmers), 330 extension officers, and a central unit with 22 people (including ten national coordinators). Each extension group is independent, owned and controlled by its members. NLR is led by a national steering committee. The groups have additionally no affiliation or interest with any private and commercial companies. Each extension group has its own extension agents, enabling easy access to advice and counselling. Membership ensures access to local expertise in all fields related to applied agronomy. Anyone running a farm or horticultural business can become a member of the local extension group. Extension officers are specialized mainly in plant production and hold a bachelor’s or a master’s degree. As mentioned above, NLR offers the following services: crop production advisory services, based on the conditions and needs of each individual farm; fertilizer management plans and soil sampling; improvement of product quality and farm economy; improved utilization of farm resources; advisory service in organic/environmentally sound agriculture; management and ecology of the cultural landscape; farm visits, professional seminars, study tours, courses, and demonstrations, etc.; crop-growing manuals, research reports, newsletters for members; constructional engineering, building technology, and planning; machines and tools; and health, safety, and environment after the merger with “Landbrukets HMS-tjeneste” in the early 2000s. Additionally, NLR disseminates knowledge through farm visits, professional seminars, courses, study tours, demonstrations, crop-growing manuals, research reports, weekly extension bulletins for members, and web-sites (www. nlr.no).

9.3.3.3

Funding

The organization receives about 60% of its funds from users, 25% from external R&D projects and commercial services, and the remaining 15% from state funds (currently around 108 million NOK/yr. (Norwegian Ministry of Agriculture and Food 2022c)).

9.3.3.4

TINE

Organization and Services Provided TINE Advisory aims to increase competitiveness of the TINE producers and therefore makes becoming a TINE member more attractive by offering consulting services and developing and operating digital tools. TINE analyses the milk to ensure high quality and handles the milk from the farm tank to the entry point. TINE Advisory has a total of 350 employees. TINE Advisory is headed by the Head

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of Department and Systems Manager, with 39 employees, and by the Advisory Manager and head of 11 district managers with their advisors all over the country. TINE offers consulting services in the fields of feeding, funding, operations management, milk quality, health, animal welfare, and technical innovations. TINE’s consultants have broad and specialized expertise in dairy production and in addition take care of parts of the member work in the regions. The service is tailored to the farmers’ needs, to help strengthen their business. TINE Consulting offers interdisciplinary expertise in combination with advisors in the field, good systematic customer care with own developed advisory methodology, and the best analyses and advice tools based on a unique and comprehensive data material. The service is provided via TINE Key Advice® where any TINE member will be assigned a key advisor who is well-acquainted with the member’s farm and who conducts the annual membership visit. The key advisor helps uncover and concretize members’ wishes and needs and find solutions to how these can be addressed. He/she also connects members with other advisors in TINE with the right expertise and follows up on the delivery and result of measures carried out. TINE Advisory service is also responsible for developing good management systems for the milk producers and increasing the expertise of TINE’s advisors and producers. They develop and maintain the digital tools that both consultants and producers use, and also have responsibility for the functionality and dissemination of news related to the relevant thematic areas, livestock control, and TINE as a whole on the website “members.tine.no”, via newsletter and Facebook.

Relevant Thematic Areas Business and Management Consultancy The TINE advisor is a long-term partner helping to clarify potential and find the right goals. Planning and follow-up of production are key tasks. The TINE advisor has an interdisciplinary and holistic approach and helps with economic calculations showing the impact of different choices on the production. He/she contributes to important decision-making processes. Economy With a broad financial expertise, the economy advisors contribute with long-term follow-up and financial management. The consultant can through account assessments propose measures to improve financial performance. He/she can also assess the profitability of a change in operational setup or development by preparing an operating plan. The economy advisor assists in planning and executing generational change and gives advice on questions about pension/retirement. The consultant will also assist in dialogue with the accountant, bank, or insurance company, if necessary.

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Feeding The feeding advisor helps assessing feeding, makes suggestions, and gives recommendations for improvements. He/she additionally gives advice on how to solve nutritional issues focusing on balanced feed, optimizing economy, sustainability, animal health, and product quality. The feeding advisor has a good knowledge of current management systems and the latest robotic solutions and helps to ensure efficient utilization of the current system. Health TINE veterinarians offer advice to members who want to work purposefully, systematically, and regularly with preventive health measures. The vet will provide suggestions for improvements and help with establishing good routines. Breeding The TINE breeding advisors contribute with the assessment of breeding development. They will draw up breeding plans based on specific goals and livestock availability. Building Planning The TINE building consultant plans and draws both new buildings and changes to existing ones. The building advisor can help with advice and planning of operating buildings with a strong focus on logistics and animal flow. The drawings are prepared in 3D with complete solutions adapted to the farm’s planned production, with cost estimates.

Funding TINE’s advisory service is funded by membership fees and through direct payment of the services provided.

9.3.3.5

Other Advisory Sources

Other sources of advice in agriculture are present in Norway even though they are not organized as an extension service.

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Felleskjøpet Farmers can receive advice with regard to machines, fertilizers, feed and different agricultural crops at Felleskjøpet, a cooperative owned by 44,000 farmers. Cooperation as a form of ownership is important for Norwegian agriculture. This gives the farmer the necessary security and ensures good deliveries of both raw materials, products, and services. Through the cooperative, members are certain that the company is developing for the farmer’s best in the short and long term. Felleskjøpet invests continuously in the development of its own company in everything from member work, optimal infrastructure for handling feed and raw materials, to future investments for the benefit of all Norwegian agriculture. Felleskjøpet Agri has a turnover of 15.5 billion NOK and 3641 employees. Felleskjøpet is the most important supplier of technology and operating equipment to Norwegian agriculture and has around 100 stores aimed at farmers and consumers in Norway. Felleskjøpet owns Granngården in Sweden, which also has 100 stores aimed at the same target group. The business also includes property management, bread and baked goods, as well as equipment for parks, plants, and pets. Felleskjøpet is the market regulator for the grain sector in Norway.

Nortura For meat and egg production, advice can be sought within Nortura (https://medlem. nortura.no), a cooperative of 18,300 egg and meat producers from farms across the country. Each of these farmers helps in managing Nortura through their involvement in the local teams and the election of representatives to Nortura’s corporate board. Nortura provides support in maintaining the farmers’ price levels and aims to give the farmer the best possible economy, providing the best possible market balance at the lowest possible cost for the producers. Members can receive advice in the best production and operating economy; quality production, research, and breeding; back payment/Member Capital Accounts; livestock; and operating credit guarantees.

9.4

Comparison of Agro-Technical Extension Systems in China and Norway

9.4.1

Differences Between Agro-Technical Extension Systems in China and Norway

9.4.1.1

Structure

In China, the agro-technical extension system has become a multi-polarized system with the government as its main actor. The government’s agro-technical extension system includes five levels: national, provinces, cities, counties, and towns. In

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Table 9.1 Comparison of agricultural extension systems in China and Norway Aspect Structure

China The agro-technical extension system has become a multi-polarized system with the government as its main actor

Funding sources

The government often plays a main role in financing in the agricultural extension service

Major participants and their responsibilities

Government, public research institutes, and colleges and universities: Basic research, applied research, experiments and development, and extension and application of results

Enterprises: Applied research, experiments and development, and extension and application of results

Intermediary organizations: Extension and application of results Agribusinesses: Extension and application of results

Norway The agricultural advisory system is part of the broader Agricultural Knowledge and Innovation System (AKIS) The Norwegian agricultural extension service and the forestry extension institute are partially financed by government grants Norwegian agricultural extension service: Agronomy, plant production and plant protection, and expanding to agricultural buildings, mechanical engineering, hydromechanics, greenhouses, etc. Forestry extension institute: Provide continuous education and training in the forestry sector and related fields, as well as to raise public awareness of the importance of forestry The co-operatives: Provide advice from input supply perspective Norwegian forest owners’ federation: Offer forestry and land management guidance

Norway, the agricultural advisory system is part of the broader Agricultural Knowledge and Innovation System (AKIS), which forms a broad governance framework for advisory services in relation to other innovation support arrangements such as research, education, and innovation funding (Table 9.1).

9.4.1.2

Funding Sources

In China, there are two sources of public grants in the agricultural R&D and extension service. The first source is grants from government departments, which have different focuses at different administrative levels. At the national level, in 2021, the total financial expenditure for agricultural R&D was 1118.04 million yuan, and the total financial expenditure for agricultural science and technology transformation and agricultural extension services in 2021 was 29.26 million yuan (Ministry of Agriculture and Rural Affairs of China 2022). The second source is non-government funds coming from various donors, which is more likely to finance basic and non-profit agricultural research. In Norway, the Norwegian Agricultural

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Extension Service and the Forestry Extension Institute are mainly financed by private institutions and partially financed by government grants, such as the Agricultural Agreement (OECD 2021).

9.4.1.3

Major Participants and their Responsibilities

In China, different responsibilities are defined for the actors in the agricultural extension service system, including public agricultural R&D institutions at national, provincial, and municipality level, experimental stations and enterprises’ technology research and development centres (OECD 2018). The national agricultural R&D institutions perform research on key technologies, new and high technologies with strategic significance, basic and applied agricultural research, as well as general basic scientific and technological work. Regional agricultural research centres are responsible for developing key technologies that have a broad application for regional industrial development. These centres also engage in applied basic and frontier research where China boasts strengths and specialties. They are also involved in integrating and transferring key technologies. In Norway, the Norwegian Agricultural Extension Service (NLR) ensures comprehensive, independent and knowledge-based advisory services, and professional links between agricultural research and producers. The Forestry Extension Institute is organized as a partnership between forestry organizations and scientific institutions with 20–25 staff, half of them being professional foresters and extension specialists. The co-operatives, such as TINE and Nortura, provide extension services to their members in their respective sector. The Norwegian agricultural extension system is dominated by commercial private service agencies that provide paid advisory services, while the relevant government public administrations provide agricultural information advisory services.

9.4.2

Examples for China from the Norwegian Agricultural Extension System

9.4.2.1

“Commercialized and government-funded” Operating Model

The Norwegian agricultural extension system adopts a “commercialized and government-funded” model, in which private enterprises and cooperatives are the main providers of paid technical guidance and advisory services, agricultural producers are paid members of private enterprises or cooperatives, and the government finances private enterprises and cooperatives through projects. The model has the following advantages: first, the model can provide “one-to-one” professional technical guidance and advisory services to agricultural producers; second, the model can enable agricultural extension practitioners to obtain reasonable remuneration and maintain work enthusiasm, so that the agricultural extension system can continue to

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operate well; and third, the model can make limited funds play a greater role in promoting the R&D and extension of new agricultural technologies.

9.4.2.2

Digital Extension Tools

The private enterprises and cooperatives in Norway use digital tools such as instant messaging, social media, e-reports, thematic reports, online courses, and other digital tools to provide “one-to-one” agricultural technical guidance and advisory services to agricultural producers. The digital tools not only improve the accuracy and efficiency of agricultural extension services but also provide a realistic basis for subsequent improvement and innovation of existing agricultural technology.

9.4.2.3

Environmentally Friendly Extension Content

Norwegian communities attach great importance to the protection of the ecological environment. The private enterprises and cooperatives in Norway provide organic and environmentally friendly technical guidance and advisory services to agricultural producers and encourage the latter to adopt environmentally friendly technologies and equipment.

9.5

Discussion and Conclusions

This report has discussed the characteristics of agro-technical extension systems in China and Norway. The Chinese agro-technical extension system has passed through four stages: infancy stage (1950–1977), restoration stage (1978–1992), reforming stage (1993–2001), and enhancing stage (since 2002). The present agro-technical extension system in China is led by the government and other participants, such as farmers’ cooperatives, agricultural enterprises, and agricultural institutes and universities. The system can be government-led, government-projectsdriven, market-oriented, or third-party-led. Precision agriculture in China is still in the pilot and demonstration phase, and the Chinese government has implemented a series of policies to support its development, including funding precision agriculture R&D projects, promoting extension of precision agriculture technology, and exploring subsidies for precision agriculture technology. The Norwegian agricultural advisory system is part of the broader Agricultural Knowledge and Innovation System (AKIS), which forms a broad governance framework for advisory services in relation to other innovation support arrangements such as research, education, and innovation funding. The Norwegian Agriculture Agency, County Governors, and municipalities conduct a mix of tasks related to administration, communicating policy priorities, monitoring implementation and

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providing extension and advisory services, covering all aspects of agriculture and forestry. The agro-technical extension systems in China and Norway differ in terms of structure, funding sources, and participants and their responsibilities. The Norwegian agro-technical extension system can provide China with examples in terms of a commercialized and government-funded operating model, digital extension tools, and an environmentally friendly extension content. Acknowledgements The work was funded through the project “CHN17/0019 Sinograin II: Technological innovation to support environmentally-friendly food production and food safety under a changing climate-opportunities and challenges for Norway–China cooperation” (funded by the Norwegian Ministry of Foreign Affairs through the Royal Norwegian Embassy in Beijing).

References Chen S (2015a) Experience and effect of the reform and construction of agricultural technology, extension system in China. National Agro-Tech Extension and Service Center Chen X (2015b) Research on the construction of diversified agricultural technology extension service systems. Science Press, Beijing Genius M, Koundouri M, Nauges C et al (2010) Information transmission in irrigation technology adoption and diffusion: social learning, extension services and spatial effects. Am J Agric Econ 96(1):328–344 Government of China (1993) Law of the people’s republic of China on the popularization of agricultural technology. http://news.xinhuanet.com/legal/2012-08/31/c_112921575.htm, Accessed 2 Jul 1993 Government of China (2006) Opinions of the state council on deepening the reform of and strengthening basic agricultural technology extension system construction., http://www.gov. cn/zwgk/2006-09/05/content_378438.htm Government of China (2012) Law of the people’s republic of China on the popularization of agricultural technology. http://www.moa.gov.cn/zwllm/zcfg/flfg/201209/t20120903_2920262. htm, Accessed 31 Aug 2012 Heilongjiang Bureau of Statistics (2021) Heilongjiang statistical yearbook 2021. http://tjj.hlj.gov. cn/tjjnianjian/2021/zk/indexch.htm Hermans F, Klerkx L, Roep D (2015) Structural conditions for collaboration and learning in innovation networks: using an innovation system performance lens to analyse agricultural knowledge systems. J Agric Edu Exten 21(1):35–54 Klerkx L, Stræte EP, Kvam GT et al (2017) Achieving best-fit configurations through advisory subsystems in AKIS: case studies of advisory service provisioning for diverse types of farmers in Norway. J Agric Edu Exten 23(3):213–229 Knierim A, Boenning K, Caggiano M et al (2015) The AKIS concept and its relevance in selected EU member states. Outlook Agric 44(1):29–36 Ministry of Agriculture and Rural Affairs of China (2022) 2021 Departmental final accounts of the ministry of agriculture and rural affairs. http://www.moa.gov.cn/gk/cwgk_1/ysjs/202207/ P020220802612889608000.pdf National Bureau of Statistics of China (2020) China statistical yearbook 2020. http://www.stats. gov.cn/tjsj/ndsj/2020/indexeh.htm National Bureau of Statistics of China (2021) China statistical yearbook 2021. http://www.stats. gov.cn/tjsj/ndsj/2021/indexeh.htm

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Norwegian Ministry of Agriculture and Food (2022a) Statsbudsjettet 2022 – letter of allocation to innovation Norway Norwegian Ministry of Agriculture and Food (2022b) Prop. 1S (2022–2023). National budget 2023 Norwegian Ministry of Agriculture and Food (2022c) Agricultural agreement 2022–2023 OECD (2018) Innovation, agricultural productivity and sustainability in China. In: OECD agriculture and food policy reviews. OECD Publishing, Paris. https://doi.org/10.1787/ 9789264085299-en OECD (2021) Agricultural innovation systems in Norway. In policies for the future of farming and food in Norway. In: OECD agriculture and food policy reviews. OECD Publishing, Paris. https://doi.org/10.1787/20b14991-en Standing Committee on Agricultural Research (2013) Agricultural knowledge and innovation systems towards 2020–an orientation paper on linking innovation and research. Publications Office of the European Union, Brussels. https://data.europa.eu/doi/10.2777/3418 Wei Q (2021) Review of the “13th Five-Year Plan” and the prospect of the “14th Five-Year Plan” for China’s agricultural technology extension. China Agricultural Technology Extension 37(2): 10–14 Xiao D, Shi W (2023) Modeling the adaptation of agricultural production to climate change. MDPI 13:414

Chapter 10

Climate-Smart Agriculture in China: Current Status and Future Perspectives Xiaobo Qin and Xue Han

Abstract China’s agriculture and rural areas have actively responded to climate change and made great progress in coordinated GHG mitigation, pollution reduction, green expansion, and economic growth. At present, China has achieved basic selfsufficiency in cereals and absolute grain supply security. In recent years, China’s grain output and agricultural carbon emissions have begun to decouple, and the carbon footprint of major agricultural products shows a downward trend. This chapter lists key carbon sequestration and climate change adaptation technologies in agriculture and rural areas. A technological model for rice field straw biochar sequestration is proposed for improving soil quality and carbon sequestration. Climate change risk mapping is integrated into rural development and benefits in climate change risk management, ecological agriculture value enhancement, and low-carbon community construction have been achieved. However, the agricultural system transformation still faces some challenges, including: (1) agricultural production technology needs to be improved, (2) infrastructure disaster response capacity is weak, and (3) transformation of agricultural science and technology is slow. Future perspectives highlight three aspects to improve climate resilience: agricultural disaster warning system construction, and modern agricultural technology application in farming practice and the breeding industry, from food production to food supply. Keywords Climate-smart agriculture · China · Carbon sequestration · Biochar · Climate change adaptation · Risk management

X. Qin (✉) · X. Han Institute of Environment and Sustainable Development in Agriculture, Center for Carbon Neutrality in Agriculture and Rural Region, Chinese Academy of Agricultural Sciences, Beijing, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Clarke et al. (eds.), Innovation for Environmentally-friendly Food Production and Food Safety in China, Sustainable Agriculture and Food Security, https://doi.org/10.1007/978-981-99-2828-6_10

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Introduction

Climate-smart agriculture (CSA) is a new concept of agricultural system transformation under climate change, aiming to achieve three goals: adapting to climate change, increasing agricultural production, and reducing greenhouse gas emissions (Lipper et al. 2014; Faling et al. 2018; Cheng et al. 2020). A lot of practical work and research have been carried out in many countries. As a major agricultural country, China is also a pioneer in climate-smart agriculture and has carried out a series of practical activities to address climate change.

10.2

Research on Climate-Smart Agriculture in China

Ensuring human food supply and mitigating the impact of climate change are challenging issues that the global community must tackle today. In April 2016, the successful signing of the Paris Agreement indicated full global recognition of the adverse effects of climate change on human beings and the environment. Climate change, characterized by rising atmospheric temperature and frequent extreme climate events, has brought great instability to agricultural production, and these impacts are expected to increase over time (Wang et al. 2018; Mume 2021). At the same time, the greenhouse gas emissions caused by agricultural production and agricultural land use changes account for about 30% of the total global anthropogenic greenhouse gas emissions, and the greenhouse gas emissions from animal husbandry account for about 18% of the global anthropogenic greenhouse gas emissions. Moreover, traditional agricultural models also lead to the degradation of biodiversity and ecosystem functions, as well as the pollution of soil and water bodies. Compared with traditional agriculture, climate-smart agriculture (CSA) strives to adjust and enhance resilience to climate change, reduce greenhouse gas emissions, continuously improve agricultural productivity, and increase farmers’ income, using innovative concepts and technologies to optimize and transform agricultural systems. The triple challenges of food production, climate change, and greenhouse gas emissions and the realization of a three-way win–win–win situation of higher crop yields, stronger climate change resilience, and lower carbon emissions in the agricultural sector have attracted widespread attention (Zhou et al. 2022). The Food and Agriculture Organization of the United Nations emphasizes that the development of climate-smart agriculture can not only increase agricultural output but also make agriculture more adaptable to climate change, reduce greenhouse gas emissions, and improve the ability of crops to capture and sequester carbon in the atmosphere. Broadly speaking, agricultural practices that contribute to the sustained increase in agricultural production and income, the ability to adapt to climate change, and the mitigation of greenhouse gas emissions can be considered realistic climate-smart agriculture. The development concept of climate-smart agriculture is generally in

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line with the strategic needs of China’s ecological civilization construction and agricultural transformation and development and is of great significance to ensuring national food security, mitigating climate change, promoting resource conservation and environmentally-friendly agricultural development. At present, agricultural production problems represented by excessive carbon and greenhouse gas emissions have received great attention from various ministries in China. In recent years, a series of technical research activities, project demonstrations and promotions, and countermeasures have been carried out on agricultural carbon sequestration and emission reduction, coping with the impact of climate change and sustainable improvement of agricultural production efficiency, and have achieved preliminary results.

10.3

Climate-Smart Agricultural Technology

In recent years, in order to improve the adaptability of agriculture to climate change, China has carried out a series of research and promotion activities on agricultural technology. In the planting industry, in terms of fertilizer reduction, nitrogen fertilizer operation optimization technology, planting system optimization technology, sustained and controlled release new fertilizer technology, soil improvement technology, and other measures have achieved remarkable results in major grain producing areas. In terms of pesticide reduction and precise application, non-chemical control or low-pollution chemical control methods such as sustained-release pesticides have replaced chemical pesticides as new directions for research and development. Regarding improvement of irrigation methods, measures such as alternate irrigation, intermittent irrigation, field drying, and immediate irrigation have been applied. With respect to nitrogen fixation in cultivated land, some areas have demonstrated and applied technologies such as straw returning to the field, conservation tillage, increased application of organic fertilizers, and soil fertility improvement.

10.3.1

Climate Change Mitigation

10.3.1.1

Technologies for Greenhouse Gas Reduction and Carbon Sequestration

Technologies for greenhouse gas reduction and carbon sequestration are summarized in Table 10.1.

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Table 10.1 Technologies for GHG reduction and carbon sequestration Technology Nitrogen dosage reduction

Category GHG reduction

Nitrogen deep incorporation

GHG reduction

Optimization of water and fertilizer management in paddy fields

GHG reduction

High-efficiency fertilizer and inhibitor

GHG reduction

Decomposed organic fertilizer

GHG reduction and soil carbon sequestration

Replacement of chemical fertilizers by organic ones

GHG reduction and soil carbon sequestration GHG reduction and soil carbon sequestration

Screening & breeding of high-yield and low-emission varieties Biodiversity

GHG reduction and soil carbon sequestration

Tillage time optimization

GHG reduction

Biochar

GHG reduction and soil carbon sequestration

Conservation tillage

GHG reduction and soil carbon sequestration

Applicability For wheat, corn, rice, vegetables, and other crops, according to scientific fertilization recommendations, reduce the amount of nitrogen fertilizer reasonably and improve the nitrogen utilization rate Suitable for field crops. With the application of machinery, fertilizers are applied in depth, and emissions are reduced and controlled Suitable for paddy field crops. From submerged irrigation to mid-term sun-dried fields plus intermittent irrigation or moist irrigation Suitable for field crops. Add urease inhibitor, nitrification inhibitor, urease + nitrification inhibitor, and apply sustained and controlled release fertilizer Biogas residue, biogas slurry, and composted organic fertilizer instead of fresh organic fertilizer are applied in the paddy field Suitable for field crops. Replacing chemical fertilizers with organic fertilizers can increase soil carbon storage Suitable for paddy field crops. Breed or optimize rice varieties with high starch content and large root systems to reduce methane emissions Suitable for paddy field crops. Changing single rice cultivation into symbiotic systems such as “rice-duck” and “rice-crayfish” can effectively improve nutrient utilization and crop yield and suppress greenhouse gas emissions Suitable for paddy field crops. Implementing spring tillage in advance of the winter fallow period, combined with the application of residual stubble, can effectively reduce the net warming effect of double-cropping paddy fields Suitable for field crops. The high temperature (300–700 °C) thermal pyrolysis carbonizes farmland straw into biochar, which can effectively reduce greenhouse gas emissions when applied to farmland Suitable for dryland crops. Conservation tillage (a combination of less, no-tillage and straw return), with a combination of (continued)

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Table 10.1 (continued) Technology

Water saving irrigation

Category

GHG reduction and soil carbon sequestration

Applicability different tillage and soil disturbance measures, can ensure that more than 30% of the stubble enters the soil, enhancing soil carbon pools while reducing greenhouse gas emissions Suitable for field crops. Improve water use efficiency and reduce greenhouse gas emissions by saving surface and underground water pumping energy, water transportation energy consumption, and irrigation facilities energy consumption

Nayak et al. (2015), Qin et al. (2015a, 2016, 2019, 2020a, b), Song et al. (2018), Wang et al. (2016), Zhang et al. (2019b)

10.3.2

Case Study: Greenhouse Gas Reduction and Carbon Sequestration Technology of Biochar Application

10.3.2.1

Introduction

Efficient biochar resource utilization technology in the rice field is based on solving the bottleneck problem of farmland waste resource utilization, and at the same time aiming at the two strategic needs of national food security and greenhouse gas emission reduction. Wastes such as rice straws can be carbonized by anaerobic thermal pyrolysis at high temperature (400–600 °C) to form biochar, which has the characteristics of large specific surface area, strong adsorption, high carbon content (>60%), and great stability. After returning to the field for utilization, the ecological effect of reducing soil bulk density and enhancing soil fertility can be achieved. The production process of biochar is simple and efficient, and every 3 kg of straw can produce 1 kg of biochar. In the early stage of farmland cultivation, in conjunction with other field management processes, the biochar is evenly applied to the field at one time, and this can achieve long-term effects for 5–8 years. Combining the “three controls” (sowing control, fertilizer control, and water control) technology of rice production can minimize greenhouse gas emissions, improve the rice yield, increase the effect of straw biochar, and promote the green and sustainable development of rice. The research team carried out extensive long-term field experiments and field demonstrations in Huizhou and Zhaoqing, Guangdong. The amount returned to the field could simultaneously reduce greenhouse gas emissions in paddy fields by 29% and increase rice yield by 4.6%, providing good environmental, social, and economic benefits. In 2008, the General Office of the State Council issued the “Opinions on Accelerating the Comprehensive Utilization of Crop Straw.” After that, the central government and various government ministries have promoted the utilization of straw resources to varying degrees. In 2019, the General Office of the Ministry of

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Agriculture and Rural Affairs issued a notice on the comprehensive utilization of straw, which fully demonstrates the determination of the Chinese government to strengthen the resource utilization of farmland waste. In September 2020, General Secretary Xi Jinping promised the world that China will gradually achieve carbon neutrality by 2060, which not only puts pressure on the low-carbon development of various domestic industries but also provides us with an opportunity to concentrate on breaking through technological bottlenecks. In October 2020, the Ministry of Agriculture and Rural Affairs, together with the Ministry of Ecology and Environment and four other ministries, jointly issued a document, pointing out that it will improve the quality of straw carbonization equipment and the added value of carbonbased products, and promote the overall improvement of the technical level of the biochar industry. China produces nearly 1 billion tons of crop straw waste every year, and the efficient utilization of it as a resource is a major strategic need for the Chinese government to respond to climate change policies and measures. It is also a major measure to save resources and maintain food security, but at the same time it is a huge challenge. With this background, it is particularly urgent to simultaneously address the scientific issues of increasing food production and reducing environmental costs such as greenhouse gases. In this regard, the climate change, emission reduction, and carbon sequestration innovation team of the Institute of Agricultural Environment and Sustainable Development, Chinese Academy of Agricultural Sciences, based on the main production areas of double-cropping rice, has explored the technological model of double-cropping rice field straw resource utilization through long-term field experiments and field demonstrations.

Technical Flow Chart The application of this technical model includes three main stages, i.e., pre-production, mid-production, and post-production application of biochar in paddy field, and the flow chart of the model is briefly introduced in Fig. 10.1 and its field deployment in Fig. 10.2. The first is the pre-production stage. After hightemperature semi-anaerobic thermal decomposition, the organic farmland wastes such as straw are converted into biochar. About a week before the paddy field cultivation, an appropriate amount of biochar is applied to the field at one time and is evenly mixed with other fertilizers. It is distributed in the soil. Second, in the mid-production stage, combined with the “three control” technology for rice, the field management is optimized for the production of low-carbon and high-yield rice. Finally, in the post-production stage, after the rice is harvested, organic wastes such as straw are collected. The high-temperature thermal decomposition process is used to generate new biochar for return to the field. The above three stages form a complete process for efficient resource utilization of biochar in the rice field.

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Fig. 10.1 Schematic diagram of the resource utilization of biochar in the paddy field

Technical System and Supporting Measures Core Technical Points (1) Sources of biochar materials include but are not limited to rice straw. All agricultural organic wastes can be sources of biochar, such as residual stubble of other field crops and vegetables, animal manure, and wood processing wastes. (2) It is appropriate to apply biochar to the field at one time as the base fertilizer, and there is no need to reapply for several years (5–8 years in general). (3) Biochar is processed and prepared in the pre-production stage and can be applied to the field during the growing season. The effect will be assessed after harvesting. Supporting Technology To guarantee high yield and high quality of rice, this technical model should be supported by the “three control” technology for rice production, which mainly consists of: (1) Control of fertilization: determine the lowest and highest amount of fertilizer according to the aboveground biomass and yield potential in rice fields, to ensure the amount of fertilizer required for food security. Meanwhile, limit the excessive fertilizer nutrient loss. Amounts of nitrogen, phosphorus, and potassium fertilizer

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Biochar weighing

Field application

Experiment in growing season

Micro-tiller

Post-harvest straw collection

Field demonstration Fig. 10.2 Field deployment

applied per season are: 190–210 kg N/ha, 100–120 kg P2O5/ha, and 140–160 kg K2O/ha, respectively. (2) Control of irrigation water: during the growing season of rice, the irrigation model of intermittent irrigation + mid-term field drying should be adopted. (3) Control of seedlings: rational close planting, with sufficient basic seedlings; for early season rice, plant 300,000 roots/ha, for hybrid rice there are 2–3 grains of seedlings per root; for late season rice (mid-season rice), plant 180,000–225,000 roots/ha, and 1–2 grains of seedlings per root; for doubleseason late rice, plant 270,000 roots/ha, and 2 grains of seedlings per root. Policies and Measures (1) It is essential to carry out in-depth household surveys and get a clear understanding of the real demands of people, to promote the new technology and formulate practical technical measures. According to the result of the survey, adverse effects of excessive use of nitrogen fertilizer are widely acknowledged among farmers, such as

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potential great harm to the environment, soil hardening, and slight increase in yield after excessive application of nitrogen fertilizer; thus, the acceptance for straw returning is quite high. However, farmers’ understanding of organic waste conversion and returning (such as biochar) is still very limited; therefore, the government and agricultural technical departments are advised to strengthen its promotion and expand the information channel for farmers to promote and implement more good technologies. (2) Policies should be adapted to local conditions and appropriate financial support should be provided under the conditions that the farmers’ income should be increased, the farmland and environment should be protected, chemical input in rice production should be reduced, the quality of rice should be improved, and unit plot income should be enhanced. (3) Gather highly qualified research teams to tackle the issues together, simultaneously carry out long-term experiments and demonstration projects, and continuously adjust the details of technical measures while exploring the long-term benefits of the technology, to make it better adapted to the rapid development of modern agriculture. Promotion of Technical Demonstration The technology has been promoted and applied in Shapu Town, Dinghu District, Zhaoqing City, Guangdong Province, from 2014 to 2015. The area involved reached 400 ha, covering nearly 20 natural villages. The technology has changed the traditional usage of straw waste, effectively resolved the air pollution problem caused by straw burning, improved the local problem of soil hardening caused by long-term overuse of fertilizer, enhanced the production capacity of soil, promoted the clean production and development model of local agriculture, and increased the rice yields for 2 consecutive years. The average rice yield per hectare increased by 10% for 2 years and the benefit from the yield increase was over 100 yuan. During the demonstration and promotion period of 2014–2015, the input of fertilizer decreased by 675 yuan/ha, the yield increased by 638 kg/ha, income increased by 3525 yuan/ha, and the overall income of farmers in the demonstration area increased by 3.36 million yuan. To a certain extent, the promotion of new technology has therefore produced favorable ecological and social benefits. Technical Assessment and Evaluation From 2011 to 2015, the technical model was carried out in Huizhou and Zhaoqing in Guangdong Province, in the form of long-term plot experiments. In 2012, during the new varieties in agriculture exhibition of Guangdong Province, Academician Yuan Longping inspected the experiment and provided guidance on technical particulars (top in Fig. 10.3). The paper titled “Impact of biochar addition on carbon emissions intensity in double cropping rice field in South China” (Qin et al. 2015b) was written based on the technical model and was awarded the Second Excellent Paper (2009–2015) of the China Association of Agricultural Science Societies (in May 2016), as well as being listed in the 2019 Frontrunner 5000—Top Articles in

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Huizhou Demonstration Area

Academician Yuan Longping inspected and provided guidance in person

Huizhou media report

Proof of promotion and application

Excellent Paper Award by Chinese Society of Agricultural Engineering

High-level think tank seminar at G20 summit Fig. 10.3 Demonstration and appraisal of the technical model

Outstanding S&T Journals of China (middle in Fig. 10.3), which has been cited 36 times and was widely acknowledged among scholars. On May 17, 2012, media such as Southern Daily, Huizhou TV station, Huizhou Radio Station, and Huizhou Daily interviewed us and covered our experiment at Huizhou Institute of Agricultural Science (middle in Fig. 10.3). In November 2019, a high-level think tank of a G20 summit in Japan convened a seminar on “Climate-Smart Agriculture,” and the technical model was listed in the typical climate-smart agriculture technical model list (bottom image in Fig. 10.3). As reported in the special reports from the seminar, broad discussions were conducted on this technical model among delegates (government officials, experts, and

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scholars) and unanimous praise was given. The model was planned to be gradually demonstrated and promoted within the G20 in 2020. Areas Suitable for Promotion This technical model is suitable to be promoted and applied in typical major double cropping rice producing areas in China, which have humid climate and are rainy, with moderate or good soil fertility, and a basic technical capacity above average in China.

10.3.3

Climate Change Adaptation

10.3.3.1

List of Technologies for Use Toward Climate Resilience

Farmland Basic Construction (Water Conservation, Infrastructure, Etc.) Technology Through the construction of water conservation engineering technical measures, ecological engineering technical measures, agricultural product storage, processing and circulation facilities, etc., the natural conditions that are not conducive to the development of agricultural production will be changed, and the high yield and high efficiency of agriculture will be served. Strengthen soil and water conservation and comprehensive management of the ecological environment and enhance the material basis and adaptive capacity of the agricultural system to cope with climate change. Promote land transfer and appropriate scale management, promote the development of rural cooperative economic organizations, strengthen the agricultural social service system, and improve the level of agricultural industrialization. Speed up the process of agricultural mechanization and modernization. Establish a response mechanism to adapt to climate change at the management level.

Breeding Technology for Crop Resistance (Drought Resistance, Waterlogging Resistance, High Temperature Resistance, Disease and Insect Resistance, Etc.) Based on knowledge from modern life sciences, combined with understanding of the impact of drought, flood, high temperature, low temperature, and cold damage on crops under climate change conditions, advanced scientific, and technological means are used to transform and utilize biological components according to pre-designed technology to adapt to climate change and its impacts. Technologies mainly include genetic breeding technology (cross breeding, mutation breeding, transgenic breeding, molecular markers, etc.), cell engineering technology, and tissue culture technology. From the level of genes and cells, mining and adjusting the ability of crops to adapt to climate change is a key technology for crops to cope.

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Crop Strain Tillage Cultivation Techniques In response to the impact of climate change, the technology and principles of crop production are used to enhance the adaptability to climate change by adjusting crop groups or individuals, including technical measures in cultivation, farming, fertilization, irrigation, and drainage. According to the growth and development processes of crops, these can be divided into strain sowing techniques (drought-resistant sowing, waterlogging-proof sowing, timely sowing, etc.), strained tillage techniques (covering drought-resistance techniques, tillage moisture conservation techniques, flood-resistant tillage techniques, etc.), strain cultivation techniques (soil structural improvement technology, water-saving irrigation technology such as pipe irrigation, sprinkler irrigation, drip irrigation, water transfer technology with fertilizer, fertilizer maintenance and leaching prevention technology, etc.), post-disaster remedial technology, etc. For both the individual and group levels of crops, adjusting human farming methods to enhance the ability of crops to adapt to climate change is basic at this stage.

Agricultural Planting Structure Adjustment Technology In view of the impact of climate change, the variety, layout, configuration, and maturity of crop planting in a region or production unit are adjusted to adapt to climate change, including the temporal and spatial distribution of crop planting, species, proportion, arrangement in a region or field, the number of plantings in 1 year, and the order. There are many successful cases in different regions of China, such as winter wheat-summer maize interplanting in North China, double cropping rice in the middle and lower reaches of the Yangtze River, rice and maize expansion in Northeast China, northward migration of winter wheat, northward migration of tropical and subtropical crops, southern winter agricultural development, and “two nights” technology in North China. At the regional level, adjusting human agricultural production so as to effectively use the favorable possibilities brought by climate change while avoiding its adverse effects is an important adaptation measure.

Crop Pest Control Technology Under the influence of climate change, crop pathogenic microorganisms, harmful insects, harmful plants and other harmful organisms show significant changes in their development period, harm period, population growth, reproduction generation, geographical scope, and harm degree. Comprehensive prevention and control through physical, chemical, biological, and other technical means can effectively control the outbreak of crop pests and diseases under climate change. At present, increasing the resistance of host plants to pests and diseases through biotechnology or traditional breeding technology, on the basis of monitoring and early warning, using pesticides, fungicides, and herbicides to control crop diseases, pests, and

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weeds can not only alleviate the impact of climate change but also ensure global food security due to ensuring the sustainability of crop production systems. These will still be the most important and basic measures to be taken in the future.

Agricultural Climate Change Adaptation Insurance This provides insurance for agricultural producers to protect them from economic losses caused by natural disasters, accidents, diseases and insect pests, and other damage caused by climate change in agriculture. According to the stage in the agricultural process, it can be divided into crop insurance and harvest crop insurance. Crop insurance covers food crops, such as rice and wheat, and economic crops, such as cotton and tobacco. It covers losses to the value of harvests or production costs incurred by various crops during their growth due to natural disasters or accidents. Crop insurance in the harvest period takes the value of primary agricultural products after harvest of grain crops or cash crops as the insured object, that is, a short-term insurance for crops in primary processing stages such as drying, threshing, and baking. Agricultural climate change adaptation insurance technology is an effective market means to avoid the adverse effects caused by climate change.

10.3.3.2

Case Studies

Comprehensive Development Project of Liugou Village, Hanzhong County, Shanxi Climate Risks Liugou Village is located in the Qinba Mountains, on the edge of the climate boundary between the subtropical and temperate zones. In addition to the effect of the mountains, the microclimate in this area is characterized by rainy weather. On July 9, 2012, a once-in-a-century flash flood disaster in Nanzheng County caused 280 households and 865 people in Liugou Village, Mujiaba Town, Nanzheng County to be affected, 5 households and 15 rooms collapsed, and 11 households and 27 rooms were seriously damaged. Further damage occurred to 111 households and 254 rooms. Six roads of 21.2 km, 1 bridge, 850 m of river embankments and 850 m of ditches were destroyed. 35 ha of paddy fields were damaged, of which 18 ha had no grain harvest. 67 ha of dry land were damaged, of which 14 ha had no harvest. 36 ha of tea gardens were damaged, of which 10 ha had no harvest. 10 ha of tobacco were damaged, of which 4 ha failed to produce their harvest of flue-cured tobacco. The villagers in Liugou Village were less likely to go out to work, mainly planting a small amount of tea and raising some cattle, with an annual per capita income of 2400 yuan.

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Adaptable Goal Strengthen disaster prevention, reduce disaster damage, improve disaster preparedness measures and management capabilities, and realize a “resilient community” that has resilience in the face of various natural disasters and hazards. Adaptation Countermeasures Disaster Prevention, Mitigation, and Construction of a Resilient Community Emergency rescue: Measures such as research, understanding of needs, establishment of disaster relief standards, and open and transparent distribution procedures were taken to solve the practical difficulties of villagers in a timely manner and meet their basic needs in life. After the government provided emergency rescue with temporary shelter/housing and drinking water, continued provision of the basic needs of life such as hygiene and health. Post-disaster recovery: Villagers were assisted to build safe drinking water facilities, bridges and roads damaged by heavy rain were repaired, the disaster resistance of related facilities was enhanced, villagers were helped to return to their pre-disaster living conditions, and the community’s ability to resist natural disasters was improved. Disaster reduction and prevention: Through vulnerability analysis with the villagers, community disaster reduction and prevention shelters were established, escape route maps, signs, and evacuation plans were formulated, and relevant materials and equipment were provided. In addition, various publicity, training, and drills were carried out to enhance the disaster reduction and prevention capacity of the community/villagers. In addition, training on low-carbon village construction including water and environment, sanitation, and garbage classification was carried out to promote the construction of environmentally friendly communities and prevent various disasters that may occur in the future. Disaster management: By carrying out community-based disaster management training, a disaster early warning and broadcasting system covering eight areas of the village has been established, and a community disaster reduction and prevention shelter (combined with the function of the activity center) has also been established. According to the actual population distribution, nature, housing, traffic, and other conditions in various places, an escape route map, signs and evacuation plan have been formulated, nine disaster shelters and nine liaison leaders have been identified, 23 key villagers have participated, and relevant materials and equipment have been provided. At the same time, Liugou Village has also formulated a disaster prevention action plan, organized structure and division of labor, established a three-level disaster response mechanism, and formed effective disaster prediction, response and management measures to guide villagers before, during and after the disaster, in order to take the right precautions for disaster preparedness, prevention, and mitigation (Oxfam Hong Kong 2019).

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Post-disaster Livelihood Restoration and Ecological Agriculture Development In order to establish the concept of green development and environmental protection in the production and life of the masses, and promote the ecological development of Liugou Village, the Shaanxi Volunteer Mother Association for Environmental Protection started a series of transitions to ecological and organic production methods, and gradually cultivated a green and environmentally friendly production and management concept. Activities were: Improve the quality of tea trees: Under the guidance and advice of the county technicians, the improvement of tea trees has been carried out, including the low-level modification of old trees and the construction of new tea gardens. Through the combination of technical training and financial support, the villagers have been driven to restore a total of 8 ha of water-destroyed tea gardens and more than 26 ha of newly planted tea gardens, expanding the area of the village’s tea gardens to 109 ha. In 2018, the village’s tea income increased by more than 900,000 yuan compared with 2013. Improve the tea garden environment: The establishment of a 20 ha ecological demonstration tea garden was promoted, and ecological field management and technical training conducted, including reducing the use of pesticides and fertilizers in production, using farmyard manure, reusing waste resources through organic compost, and improving soil. Physical treatment technology was used to control pests and diseases, including the use of yellow sticky boards, insect repellent lights, etc., to build an ecological tea garden. In the relatively concentrated and contiguous six tea gardens in the village, 36 sets of solar insecticidal lamps and 18,000 hanging insect traps were installed to carry out physical control of pests and diseases. The number of pesticide sprayings in the tea gardens has been reduced from six to seven times per year to 2–3 times per year. Also, the use of pesticides was reduced by 60%, and highly toxic pesticides were no longer used. More than 100 farmers were assisted in composting more than 660 m3 of organic fertilizers, applying 42 tons of commercial organic fertilizers, and reducing the amount of base fertilizer and chemical fertilizer used in tea gardens by 60%. Traditional farming upgrades: Traditional cattle and chicken raising was supported with a small amount of funds, ensuring the basic living needs of the villagers and a stable income. At the same time, livestock farmers’ basic information was recorded, eight breeding supervisors were selected, and a traceability mechanism for agricultural products was established. Creation of ecological products: Moujiaba Town, Nanzheng County, with a total area of 68 km2 and a total population of 14,232 is one of the main tea-producing areas in southern Shaanxi. In Liugou Village, 125 tea farmers were helped to restore the original old tea tree “Eagle Tea,” founding the “Shenshan Yunwu” Eagle Tea brand, and the “Wotan” Liugou Village ecological product brand, in order to promote the connection between the Liugou Village ecological tea and the ecological products of cultivation and breeding. By the end of 2020, there were more than 5000 eagle tea trees in the village, and more than 500 trees (of different sizes) could be picked. Eagle tea is usually picked once a year. In 2020, the annual output of dry

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tea was about 100 kg at 80 yuan/kg, and the income increased by about 32,000 yuan. It is estimated that by 2025, all eagle tea trees can be effectively picked, the whole village can produce about 1000 kg of eagle tea, and the annual income can be increased by 320,000 yuan. The yield of the eagle tea tree increases with age and can basically remain stable for about 15 years. Establishment of a mode of integration of three agricultural industries: Further improvement of the management measures for ecological tea gardens, promotion of the transformation of small, standardized tea processing plants, purchase and establishment of cooperative tea color sorting workshops, and promotion of the clean production of the tea industry. Through the renovation of the damaged factory building, a 50-m2 tea tourism and farming reception service center has been established, equipped with hand-made tea, tea tables, water dispensers, and guide amplifiers. To seek donations from caring people, four farmhouses were built with 12 standard beds, one public kitchen and dining facilities for 40 people; in addition to the green family accommodation beds, groups of 50 people can come to the village for tea tourism, farming, and nature education activities at any one time. Mutual assistance and cooperation services: The Nanzheng County Shenliu Planting and Breeding Farmers Professional Cooperative consisting of 106 members (including 45 impoverished households) was established and carried out joint purchase of agricultural materials, product exhibition and sales, technical training and guidance, trademark registration, packaging design and production, online store sales, urban-rural mutual assistance, and other services and activities. A total of 32 sessions of green planting and breeding training and guidance have been provided to villagers, and a three-level technical support system composed of experts, agronomists, and soil experts has been established to help villagers deal with technical problems encountered in a timely manner. The official micro-store of the cooperative was opened, and from July 2017 to the end of 2020, the cumulative sales of Huatan ecological green tea exceeded 100,000 yuan. Eco-Homeland and “Low-Carbon Community” Construction The construction of the ecological homeland in Liugou Village (Shaanxi Volunteer Mothers Association for Environmental Protection 2015) was carried out closely around environmental improvement, ecological protection, and humanistic ecological construction. Future needs for sustainable development: Cultivate environmental awareness: The Shaanxi Volunteer Mothers Association for Environmental Protection actively carried out eight training sessions on garbage classification and environmental protection, focusing on explaining the origin, composition, how to classify, and how to dispose of garbage, and gave demonstrations to the villagers on the spot, especially emphasizing that kitchen waste is used for composting. A total of 300 villagers participated in the training. Build a waste management system: Promote rural households to carry out garbage classification, establish village-level management systems including basic facilities, classification knowledge and treatment methods for garbage classification and recycling, including dry and wet garbage classification. Leftover rice, vegetable

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leaves, and other wet garbage are dumped into septic tanks or used as compost and recyclables are collected and sold to villages for waste collection. Batteries, pesticide bottles, and other toxic waste should be stored separately. Increase awareness of community engagement: By organizing and carrying out community public service activities, such as collective birthday parties for the elderly, children’s public welfare and environmental protection activities, women’s festival activities, organizing the whole village to carry out public area cleaning, grouping garbage pickup activities, etc., mobilize the enthusiasm and initiative of the villagers, paying attention to community public affairs, and building a sense of community ownership and responsibility. Cultivate positive energy in the community: Actively organize and commend families and villagers who were encouraged to participate in garbage classification. For example, the “Liugou Village Green Family Appraisal” activity was organized, and 40 households with sanitation selected. In addition, the town government and the village committee announced the commendation at the New Year’s visit to Liugou Village. The efforts of Liugou Village allowed them to win the title of “2019 Ecological Demonstration Village of Nanzheng County, Hanzhong Municipality.” Capacity Elevation and Organization Building In the implementation of various project tasks, summarize and explore the principles of establishing screening standards, implementation mechanisms, and supporting farmers. (a) Standard and easy-to-operate disaster relief principles, methods, and procedures. (b) Establish rescue standards: classify the affected people according to different disaster situations and determine the standards and procedures for material distribution, material procurement requirements, etc. (c) Project management mechanism, including planning, monitoring, and evaluation, and acceptance mechanism (Town, County Women’s Federation, Chinese People’s Political Consultative Conference, Transportation Bureau, Water Resources Bureau, Mujiaba Town Government, Liugou Village Committee). (d) A mechanism for multi-party participation, including the participation of local NGOs in Shaanxi, the participation of local governments and Oxfam. (e) Management mechanisms for village affairs, such as water management mechanism, labor investment mechanism, paid garbage collection management mechanism, etc. (f) Tertiary technical support to serve the village, including agronomists, soil specialists, and specialist connections and consultative support.

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Adaptable Benefit Life Security Through the implementation of the project, the villagers’ sense of life security during natural disasters has been steadily improved, and the recognition of the project’s facilities is very high: (1) Broadcasting early warning mechanism and emergency shelter square, accounting for 12%; (2) drainage ditches, accounting for 11%; (3) repair of destroyed roads and road barriers, accounting for 9%; (4) water diversion to households, road hardening, and bridges, accounting for 8%; (5) reservoir, accounting for 7%; (6) tea gardens covered with grass, accounting for 6%; and (7) widening of the emergency management network and roads for three-level disasters, accounting for 5%. The recognition of natural disasters is clearer. Villagers believe that the common natural hazards in Liugou Village in the past 10 years are rainstorm and flood (38%), frost (17%), drought (spring drought), and abnormal climate (14%). Livelihood Stability After the emergency relief, Liugou Village’s work gradually turned to promoting industrial development and supporting family livelihood development. This work mainly included transforming the original tea gardens toward ecological development, supporting income-increasing projects suitable for farming households, as well as quality improvement and brand development required for industrial development, and gradually extending to promoting the development of the tea industry through cooperatives, forming a tea production and processing system. It is a full value chain tea industry that integrates tea planting, processing, and tea garden tourism, providing an integrated service with three industries. Environmental Livability The construction of various infrastructures, including the hardening of roads and the construction of bridges, provides convenience for the villagers to travel and transport agricultural products and meets the daily production and living needs of the villagers. The villagers said, “When the road is open, the bridge is open, and the people’s heart is open.” Liugou Village is one of the earliest villages in promoting garbage classification, and it is ahead of the entire township. In the process of promoting garbage classification, the dissemination of knowledge and the actions carried out have raised the consciousness and initiative of the villagers, improved the sanitary environment of the village, and cultivated good customs among the people. The construction of the ecological tea garden in Liugou Village has now become a beautiful sight in the village. Many families in the village have also established micro-landscapes in front of, and behind, their houses. The beautiful environment fills people’s life with beauty.

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Social Funding During the course of the project, we have won various types of project support from government funds, social organizations, and scientific research units. These include providing relief materials, distributing subsidies for tea seedlings, and distributing scissors for growing tea trees to poor households. This technical promotion has improved the source quality of tea brands in Liugou Village and promoted the increase of income of tea farmers. The tea color sorting workshop has promoted the development of the village’s tea industry, increasing the income of households, purchase of garbage trucks, and completion of the “last mile” transportation and processing work has changed the appearance of the village. Construction of cultural squares and the provision of sports equipment have brought villagers a place for communication and entertainment. Funds for road hardening support have not only facilitated the transportation of villagers but also promoted the development of the industry. Funding Information This case, led by Oxfam Hong Kong, took 10 years from 2012 to 2022. Through the development of six projects, the overall capacity for climate change emergency relief and ecological industry development in Liugou Village has been improved. In addition to Oxfam funding, Liugou village has also applied for funding from local governments and also from the farmers.

Actions for Communities in the Pearl River Delta to Address Climate Change: The Case of Shiban Community in Foshan, Guangdong Climate Risk The runoff of the Pearl River Basin has increased, but droughts and floods have occurred frequently, and the salt tide has intensified. The phenological period has changed, the rice growth period is shortened, and the yield fluctuation increased, the multiple cropping index increased, and the impact of diseases and insect pests is aggravated. The sea level continues to rise: the degree and probability of storm surge disasters are increasing; coastal cities are frequently flooded, river estuaries are increasingly silted up, and riverbeds are raised, which seriously affects the normal operation of waterways and ports; coastal erosion is intensified; and mangroves and coral reef ecosystems are degraded. Urbanization leads to further enhancement of the heat island effect. The Pearl River Delta urban agglomeration not only has significantly more precipitation than the surrounding areas but is also more likely to experience severe thunderstorms, aggravated urban waterlogging, and threatened urban security. At the same time, urbanization aggravates the haze phenomenon in the atmosphere.

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The threat of high temperatures and heat waves is becoming more and more serious, which will not only have a significant impact on the planting and breeding industries but also have a huge impact on the health and livelihood of urban residents. Rainstorms show the characteristics of increasing frequency, increasing intensity, and the tendency of outbreaks to be concentrated. Since Foshan is significantly affected by typhoons, rainstorms often occur together with these. Therefore, the changing characteristics of rainstorms in Shunde District of Foshan are related to the development of typhoons that landed in Guangdong Province in recent decades. Trends are highly correlated. Two climate scenarios, RCP4.5 and RCP8.5, are selected to predict the climate change (Zhang et al. 2019a) in Shunde District, Foshan, during the period from 2020 to 2050. It is predicted that in the next 30 years, Shunde District will face an intensified trend of high temperature and heat wave events, while low temperature and cold damage incidents will be reduced to a certain extent, which may cause greater losses to the local planting industry and aquaculture and have a huge impact on the health and livelihood of the residents. Rainstorms generally show a trend of decreasing frequency and intensity, which is not conducive to local planting and aquaculture. The development of aquaculture has a certain positive trend. Adaptation Goal Assessment of the climate vulnerability and risk of Shiban community, through literature research, questionnaires, field research, data analysis, and other methods. Carry out risk analysis of extreme climate events in the study area, revealing the characteristics of changes in the risks for key climate disasters in the area. Screen the vulnerability factors of the study area under the influence of climate change, carrying out vulnerability analysis to reveal the key vulnerabilities of Shiban community in Shunde District, Foshan, Guangdong, under the influence of climate change. Finally, identify the key climate disaster risks for Shiban community. Analyze the climaterelated disasters faced by Shiban community agriculture and the Shiban community climate risk. Assess the agro-climatic risks of Shiban community and make adaptation recommendations. Adaptation Countermeasures Cooperate with the Big Dipper community building project (Big Dipper Social Work Service Center 2019), carry out “funding +” cooperation, create resource links, and conduct multi-stakeholder capacity building. Establish community “Being a Farmer” public welfare vegetable orchard demonstrations, develop community renewable energy applications, build rooftop edible gardens, and improve community facilities for waste classification and disposal, while promoting community climate education through community centers and publicity boards. Carry out a baseline survey of the community environment, scan the needs of banana farmers, aquaculture farmers, and flower and tree farmers in the community

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in the questionnaire survey, and at the same time adapt aquaculture to climate change and introduce ecological aquaculture technology. Emphasize network construction and conduct technical exchanges with climate and meteorological experts, agricultural experts, breeding technology companies, and solar technology companies. For example, Roger Street, a researcher from Oxford University who has more than 40 years of practical and research experience in the field of climate adaptation, was invited to introduce basic climate change facts and concepts, the basic concepts of risk-based adaptation planning and its application, as well as domestic and foreign community cases and concepts. Prepare the partners of the network to jointly promote the community to better cope with climate change in the future. Provide technical support, climate education, etc. to farmers engaged in the original agricultural ecosystem to help them adjust and move toward more scientific environmental and economic effects. Adaptation Benefits Economic Benefits Reduce the loss for communities under future climate change trends through conversion between agricultural crops and agricultural and fishery ratios and increase residents’ income by taking advantage of the favorable trend for aquaculture. The training of climate and meteorological instructors is combined with the Pearl River Watcher Project to promote the employment of local residents. Ecological Benefits Improve the storage capacity of the area as part of the “sponge city” construction (Foshan Housing and Urban–Rural Development Bureau 2019) through roof gardens, etc. Build a collaborative network for early warning and disaster relief of extreme climate disasters to improve the overall ability to deal with climate change. Enhance the role of agricultural infrastructure in climate change adaptation. Social Benefits Promote the strengthening of the role of neighborhoods within the community, improve villagers’ awareness and ability for self-management and self-service, and enhance the community’s mutual help and self-healing ability in the face of crisis. Enhance residents’ understanding of climate issues and members’ understanding of regional adaptation work and lay a foundation for understanding and cooperation for follow-up work. Match the transformation and upgrading of the cultural tourism industry with the adaptation goals of the Shiban community and effectively improve the transformation of the local long-term planning and development goals.

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Funding Information Qianhe Community Foundation, with the help of 14 foundations participating in environmental protection funding, jointly launched the “China Environmental Funders Alliance” for efficient sponsorship.

10.4 10.4.1

Demonstration of Climate-Smart Agricultural Practices Plant Industry

From October 31 to November 5, 2010, the Food and Agriculture Organization of the United Nations officially proposed the concept of climate-smart agriculture at the “Global Agriculture, Food Security and Climate Change” conference held in The Hague, Netherlands. In December 2010, Heilongjiang Provincial Baoshan Farm invested more than 7 million yuan to build a local suitable climate-smart agricultural technology project, becoming a pioneer in China’s climate-smart agricultural experiment and demonstration. In 2014, the “Climate-Smart Production of Major Food Crops,” jointly implemented by the Ministry of Agriculture and the World Bank and funded by the Global Environment Facility, was launched in Beijing. In 2015, the project was officially implemented in Yexian County, Henan Province, and Huaiyuan County, Anhui Province. The project implementation period was 5 years. In this project, Huaiyuan County adopted a rice-wheat planting model, and Yexian County adopted a corn-wheat production model. For the three crop production systems of wheat, rice, and corn, the integration and demonstration of key technologies for reducing carbon emissions and increasing carbon in crop production, and innovation of supporting policies, were carried out. The goals were to improve the utilization efficiency of fertilizers, pesticides, irrigation water, and other inputs and agricultural machinery operation efficiency and to strive to create a crop production and policy support system that could save energy, reduce emissions, and increase soil carbon. In 2016, the Global Environment Facility Small Grants Program supported the start of implementation of the “Climate-smart Agriculture Demonstration Project in Xundian County, Yunnan Province” and the “Northwest Oasis Climate-smart Agricultural Ecologically Efficient Threedimensional Cultivation Demonstration Project” in Jinchang City, Gansu Province. The climate-smart agriculture demonstration project in Xundian County was carried out in Yuzega Village and Lugu Village, involving 26.7 ha of rice. Tian is a demonstration base, focusing on the promotion of precise quantitative rice cultivation technology and the establishment of farmers’ mutual aid groups. Through certain financial support, technical training, and guidance in scientific planting, it helps farmers to achieve low input and high output, with carbon emission reduction and environmental protection; the “Northwest Oasis Climate-smart Agricultural Ecologically Efficient Three-dimensional Cultivation Demonstration

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Project” is carried out in Gucheng Village, and the project focuses on land degradation and sustainable forest management.

10.4.2

Animal Husbandry

In terms of animal husbandry, the World Bank will support the implementation of climate-smart grassland ecological construction projects in Qilian County of Qinghai Province, Minle County of Gansu Province, Shandan County and Shandan Horse Farm of the China Animal Husbandry Company; the government and the Nature Conservancy (TNC) signed a cooperation agreement on jointly exploring climatesmart agriculture projects, hoping to establish a climate-smart agriculture and animal husbandry demonstration area suitable for arid and semi-arid regions in Inner Mongolia Autonomous Region.

10.5 10.5.1

Challenges and Problems in the Practice of Climate-Smart Agriculture in China Traditional Farming Methods Are Not Conducive to the Promotion of Climate-Smart Agricultural Projects

At this stage, agricultural production still mostly follows traditional methods. In the field of planting, in a given area, crops are regarded as objects with uniform production conditions and unified agronomic measures such as arable land, sowing, irrigation, fertilization, and chemical use are used for management, which exerts a huge pressure on the bearing capacity of the natural ecological environment. Animal husbandry pays more attention to short-term returns and pays less attention to the destructive effects of feed production and processing, animal manure treatment, and other links to the ecological environment. If the traditional farming methods of agriculture are not fundamentally transformed, it will be detrimental to the vigorous promotion of climate-smart agriculture projects in China.

10.5.2

Lagging Agricultural Infrastructure Hinders the Development of Climate-Smart Agriculture

Under the strong promotion of the national measures to support and benefit farmers, new progress has been made in the construction of agricultural infrastructure. However, due to factors such as a weak foundation and large debts, the basic

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construction of rural meteorology, farmland water conservancy, and agricultural informatization have not been fundamentally improved. The overall ability of agriculture to resist risks is weak and the level of disaster prevention and mitigation is still not high. To develop climate-smart agriculture, there are still many obstacles to be faced (Hu et al. 2018; Das and Ansari 2021). Due to technical, equipment, and other reasons, most meteorological departments in China have poor rural meteorological forecasting capabilities and weak disaster prevention and mitigation capabilities for sudden disastrous weather; agricultural meteorological services are still not perfect, and there is a lack of comprehensive weather forecasting required for agricultural production. With regard to the innovation of meteorological monitoring, there is still a lack of cooperation in meteorological forecasting, monitoring, and evaluation in various places. In some areas, the farmland water conservancy facilities still use the infrastructure built in the 1960s, and the level of irrigation facilities and the ability to discharge floods and waterlogging are far from meeting the needs of agricultural development. In addition, the construction of agricultural informatization is slow, and rural information islands still exist, which is far from the requirements for the development of climate-smart agriculture.

10.5.3

The Slow Progress of Agricultural Technology Delays the Transformation of Climate-Smart Agriculture

The progress of agricultural technology is affected by many factors such as input, talents, and mechanisms, resulting in a mismatch between the speed of technological progress and the development of climate-smart agriculture. In recent years, the state’s “three rural” investment has been continuously increased, but it is an indisputable fact that the investment intensity for agricultural science and technology is still not high. The total shortage of agricultural science and technology talents and the unreasonable structure still coexist, and the phenomena of low educational background, solidified and outdated knowledge, and age and gender structure of promotion personnel in the field of agricultural technology promotion are still quite common. At the same time, the mechanism for talent training, recruitment, and utilization is inflexible and does not operate smoothly. Due to the long-term urban– rural dual structure, China’s agriculture is still not attractive to talented people. The relevant systems and mechanisms to ensure that agricultural talents can come in, be used, and retained are still not perfect. Problems such as agricultural input, talents, and mechanisms make scientific and technological research and development, promotion and comprehensive application unable to meet the actual needs of agricultural development, and delay the transformation and upgrading of climate-smart agriculture in China.

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Future Perspectives Improve Farming Practices to Adapt to the Impacts of Climate Change

In order to ensure national food security and enhance the adaptability of agriculture to the impact of climate change, the current farming methods should be improved and the agricultural planting system should be adjusted (Hellin and Fisher 2019). The first priority is to select and promote the application of high-quality seeds with drought resistance, insect resistance, and high temperature resistance. For example, as the climate warms and temperatures increase, early-maturing maize varieties can be gradually replaced by late-maturing varieties, and winter wheat transitional, semiwinter, or weak winter varieties gradually replace strong winter varieties. Crossbreeding contributes to the stabilization and improvement of total crop yield. Second, adjustment of the agricultural planting system and crop layout. For example, in the arid regions of Northwest China, the abundant light and heat conditions are used to vigorously develop characteristic industries such as cotton and fruit on the basis of ensuring food security, implementing comprehensive management of agricultural water use, controlling the scale of agricultural land reclamation, and returning farmland to forests and grasslands in some areas. In Northeast China, there are favorable conditions of increasing temperatures for appropriate development of rice cultivation, early sowing time, and promotion of the use of crop varieties with long growth cycles. In southern China, considering changes in temperature and precipitation, the scale of tropical and subtropical crops can be further expanded. Third is to carry out protective agricultural practices, such as crop rotation, no-tillage, and less tillage according to local agricultural conditions, and develop measures, such as straw return to the field and other conservation agriculture. Fourth, intercropping with other crops and legumes; among these, fine varieties of legumes with a good soil nitrogen fixation effect should be chosen. At the same time, fertilizer application should be transformed from extensive to precision to improve the utilization rate of nitrogen fertilizer, as well as promoting the use of compound fertilizers, water-soluble fertilizers, and sustained-release fertilizers.

10.6.2

Improve the Adaptability of the Breeding Industry to Climate Change

In order to enhance the adaptability and resilience of the aquaculture industry to the impact of climate change, factors such as feed processing, habitat change, waste disposal, and reproductive capacity should be comprehensively considered. The main measures include: First, use crop residues as raw material sources as far as possible in feed processing, improve feed processing fineness, and promote the application of rumen microorganism control drugs. While effectively reducing

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CH4 emissions in feed rumen metabolism, prevent microbial activities from leading to the formation of more harmful substances. Second, change the free-range mode to the centralized captive mode to reduce the amount of feed and nutrient loss. Genetic research, especially the breeding of new breeds of superior livestock, reduces the threat of livestock disease and increases livestock productivity.

10.6.3

Further Establish and Improve the Early Warning and Prevention Mechanism for Agricultural Disasters

Climate change is uncertain, and the frequent occurrence of extreme events increases the production and operation risks of the agricultural system. To this end, an early warning and prevention mechanism for agricultural climate disasters should be further established and improved. Establish an early warning system for extreme weather, focusing on meteorological information on natural disasters such as drought, floods, freezing, and crop diseases and insect pests, and improve the early warning and prevention and control capabilities for drought and flood disasters, diseases and insect pests (Hu et al. 2018). Strengthen the internet construction in rural areas, provide farmers with agro-meteorological information services through social networks, avoid agricultural risks, and improve farmers’ professional skills. Improve soil testing services in various places, strengthen the construction of the agricultural prevention and mitigation system, promote climate index insurance, improve the agricultural insurance system and post-disaster emergency remediation mechanism, strive to minimize the losses caused by agricultural climate disasters, and improve farmers’ ability to resist risks. Acknowledgements The work was supported by the project “CHN17/0019 Sinograin II: Technological innovation to support environmentally-friendly food production and food safety under a changing climate-opportunities and challenges for Norway–China cooperation” (funded by the Norwegian Ministry of Foreign Affairs through the Royal Norwegian Embassy in Beijing).

References Big Dipper Social Work Service Center (2019). http://www.bdxsw.org.cn/a/qitadiqu/ Cheng K, Zheng J, Pan G (2020) Characterization and quantitative assessment of climate-smart agriculture. J Nanjing Agr Univ 43:1–9 Das U, Ansari MA (2021) The nexus of climate change, sustainable agriculture and farm livelihood: contextualizing climate smart agriculture. Clim Res 84:23–40 Faling M, Biesbroek R, Karlsson-Vinkhuyzen S (2018) The strategizing of policy entrepreneurs towards the global alliance for climate-smart agriculture. Global Pol 9:408–419 Foshan Housing and Urban-Rural Development Bureau, 2019. http://fszj.foshan.gov.cn/ Hellin J, Fisher E (2019) Climate-smart agriculture and non-agricultural livelihood transformation. Climate 7:48

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Hu WL, Ren R, Wang HL et al (2018) Practices, problems and policies of climate-smart agriculture in China. Hubei Agric Sci 57:141–145 Oxfam Hong Kong (2019). https://www.oxfam.org.cn/index.php?c=article&a=type&tid=620 Lipper L, Thornton P, Campbell BM et al (2014) Climate-smart agriculture for food security. Nat Clim Chang 4:1068–1072 Mume ID (2021) Assessment, monitoring and evaluation of climate-smart agriculture: a review. Inter J Food Sci Agric 5:510–518 Nayak D, Saetnan E, Cheng K et al (2015) Management opportunities to mitigate greenhouse gas emissions from Chinese agriculture. Agric Ecosyst Environ 209:108–124 Qin XB, Li YE, Wang H et al (2015a) Effect of rice cultivars on yield-scaled methane emissions in a double rice field in South China. J Integr Environ Sci 12:47–66 Qin XB, Li YE, Wang H et al (2015b) Impact of biochar amendment on carbon emissions intensity in double rice field in South China. Trans Chin Soc Agric Engin 31:226–234 Qin XB, Li YE, Wang H et al (2016) Long-term effect of biochar application on yield-scaled greenhouse gas emissions in a rice paddy cropping system: a four-year case study in South China. Sci Total Environ 569-570:1390–1401 Qin XB, Li Y, Goldberg S et al (2019) Assessment of indirect N2O emission factors from agricultural river networks based on long-term study at high temporal resolution. Environ Sci Technol 53:10781–10791 Qin XB, Li YE, Wan YF et al (2020a) Multiple stable isotopic signatures corroborate the predominance of acetoclastic methanogenesis during CH4 formation in agricultural river networks. Agric Ecosyst Environ 296:106930 Qin XB, Li YE, Wan YF et al (2020b) Diffusive flux of CH4 and N2O from agricultural river networks: regression tree and importance analysis. Sci Total Environ 717:1–10 Shaanxi Volunteer Mothers Association for Environmental Protection (2015). http://www. sxmmhb.org.cn/newshow?id=210 Song X, Liu M, Ju X et al (2018) Nitrous oxide emissions increase exponentially when optimum nitrogen fertilizer rates are exceeded in the North China plain. Environ Sci Technol 52:12504– 12513 Wang B, Li YE, Wan Y et al (2016) Modifying nitrogen fertilizer practices can reduce greenhouse gas emissions from a Chinese double rice cropping system. Agric Ecosyst Environ 215:100–109 Wang Y, Guan D, Wang Q et al (2018) The practical exploration of climate-smart agriculture in China. Chin J Agric Resour Reg Plan 39:43–50 Zhang Y, Fu L, Meng C et al (2019a) Projected changes in extreme precipitation events over China in the 21st century using PRECIS. Clim Res 79:91–107. https://doi.org/10.3354/cr01576 Zhang H, Zhou G, Wang Y et al (2019b) Thinning and species mixing in Chinese fir monocultures improve carbon sequestration in subtropical China. Eur J For Res 138:433–443 Zhou ZM, Wang N, Deng YP et al (2022) The development status and prospect analysis of climatesmart agriculture in my country. Mod Agric 2022:155–157

Chapter 11

China–Africa Joint Force on Integrated Pest and Disease Management (IPM) for Food Security: Fall Armyworm as a Showcase Jingfei Guo, Ivan Rwomushana, and Zhenying Wang

Abstract The fall armyworm (FAW), Spodoptera frugiperda, is a major and destructive crop insect pest of maize worldwide. This highly invasive pest species originates from the Americas and has recently spread across more than 100 countries. This pest was first reported in China in January 2019 from Yunnan Province and has become the most damaging lepidopteran pest in maize. In the past 3 years, Chinese entomologists have made significant progress in understanding the migration routes and dispersal of FAW in China, as well as its invasion biology, prevention, and control. In this review, we summarize the main progress in understanding and managing FAW in China while learning from the experience in Africa, where it invaded in early 2016, and propose research to develop integrated pest management strategies for this pest in the future, which will contribute to the sustainable management of S. frugiperda. Keywords Spodoptera frugiperda · China · Africa · Research progress · Sustainable management

11.1

Introduction

Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae), commonly known as fall armyworm (FAW), is a highly polyphagous pest native to tropical and subtropical America (Sparks 1979; Montezano et al. 2018). FAW invaded and was officially confirmed in China in January 2019. This pest then spread through 27 provinces in

J. Guo · Z. Wang (✉) State Key Laboratory for Biology of Plant Diseases and Insect Pests, MOA-CABI Joint Laboratory for Bio-Safety, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China I. Rwomushana CAB International (CABI), Nairobi, Kenya © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Clarke et al. (eds.), Innovation for Environmentally-friendly Food Production and Food Safety in China, Sustainable Agriculture and Food Security, https://doi.org/10.1007/978-981-99-2828-6_11

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2020, damaging 1.278 million ha of crops, according to data from the “National Information Platform for the Prevention and Control of the Fall Armyworm” in China. Since its invasion, the Chinese Central Government has attached great importance to this major invasive pest and given priority to medium- and longterm measures to manage it. The relevant ministries acted quickly and cooperated closely with each other. The Ministry of Agriculture and Rural Affairs (MARA) convened and then built a national expert group to guide the emergency control measures, and the Ministry of Science and Technology (MOST) launched the special project named “Research and Integration Demonstration of Key Technologies for Prevention and Control of the Fall Armyworm” (MOST 2019). The Ministry of Finance (MOF) allocated a special fund to local governments to prepare emergency supplies for coping with the pest in time, and the local governments mobilized the agricultural extension staff to guide farmers in identifying, monitoring, and controlling the pest. The relevant research institutes and universities started promptly to carry out research regarding the migration, invasion biology, and management measures of FAW. By April 2022, FAW had become one of the most intensively studied pest species, and over 1300 papers have been published on it in China. The experience from Africa, where this pest invaded in 2016, which has led to a coordination mechanism that has resulted in several organizations designing and deploying medium- and long-term management interventions, could provide some lessons for China.

11.2 11.2.1

Migration Routes and Dispersal of FAW Migration Routes in China

FAW was discovered in West Africa (Nigeria and Ghana) in January 2016, and then quickly spread to most countries of sub-Saharan Africa (Goergen et al. 2016; Prasanna et al. 2018). By May 2018, FAW was reported in various districts of Karnataka state in India, which was the first report of the pest invasion in Asia (Sharanabasappa et al. 2018). Since then, FAW outbreaks have been reported in many southeastern Asian countries, such as Myanmar, Bangladesh, and Thailand by late 2018 (Guo et al. 2018). FAW may have invaded China through two main migratory routes: (1) the western route, the origin of which is the westerly winterbreeding region (Myanmar/Yunnan, China), mainly in Guizhou and Sichuan provinces through windborne transport and (2) the eastern route, the origin of which is the easterly winter-breeding region (Indochina Peninsula/Guangxi and Guangdong, China), mainly in east-central China before arriving in the main maize regions (the Huang-Huai-Hai and northeast regions) (Li et al. 2020). FAW was first reported in mid-November 2018 in Bangladesh, and mid-December 2018 in Thailand and Myanmar, which neighbors Yunnan Province, suggesting that FAW likely invaded Yunnan from Myanmar or indirectly from Bangladesh by multiple re-emigrations, as the pest can migrate over 500 km before

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oviposition (Prasanna et al. 2018). In order to monitor possible invasions of this pest into China, Chinese scientists launched a monitoring project using a searchlight trap to counter FAW in Pu’er City, in the border area between Yunnan Province of China and Myanmar. The first FAW moth was captured on December 11, 2018, and it was confirmed using DNA methods that the early invading FAW population was the “corn-strain” (Jing et al. 2019). FAW was then discovered in counties of west and south Yunnan by local plant protection stations in January 2019. Guangxi Province was reported as the second province invaded by FAW, supporting the theory that FAW first invaded China by the western route rather than the eastern one (NATESC 2019). This supports the invasion pathway of FAW from west to east in Asia (from Africa to Asia) during its first invasion in other Asian countries. Consequently, FAW rapidly colonized and spread to the Yangtze River, Yangtze-Huaihe River valley, and Huang-Huai-Hai summer corn region (Guo et al. 2019), and it is predicted that FAW will migrate up to the northern spring maize region. According to spatiotemporal characteristics of the East Asian monsoon, it is speculated that FAW mainly migrates into China by the western route in March, April, and May, and by both routes in June, July, and August (Wu et al. 2019). However, the direction and pace of FAW migration from the Indochina Peninsula might be more complex, because of the influence of many uncertain factors, such as numbers and distribution of FAW populations in source areas, as well as meteorological conditions (Sun et al. 2021).

11.2.2

Migration Routes and Dispersal in Africa

FAW was first reported in West Africa in 2016 (Goergen et al. 2016; Cock et al. 2017), and by 2018, this invasive pest had spread to most countries in sub-Saharan Africa and some of the Indian Ocean islands (Rwomushana et al. 2018; CABI 2022; Nagoshi et al. 2022). The rapid spread of FAW in Africa can be attributed to the strong flight capacity of the insect, though it is possible that it was already more widespread than realized when first detected. The rapid spread to the Indian Ocean islands is harder to explain by natural flight and the potential pathways of spread could be as contaminants of traded commodities and as stowaways on or in aircraft (Fig. 11.1) (Cock et al. 2017). Recent evidence shows that the diversity of fall armyworm that invaded Africa is greater than previously thought, including a haplotype that has not yet been observed in the western hemisphere (Nagoshi et al. 2018). The FAW population that invaded Africa originated in North America, in Florida and the Antilles (Nagoshi et al. 2017). In South Africa for instance, the corn and rice strains are both present (Jacobs et al. 2018), while in Uganda, FAW populations were found to consist of two sympatric sister species (Otim et al. 2018). Most of the areas in Africa where FAW has invaded appear to support year-round populations. However, some parts of southern Africa may be too cold for populations to persist and immigrant populations when the temperature increases could play a role (Early et al. 2018).

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Fig. 11.1 Likely invasion routes of fall armyworm into Africa and other parts of the world through the trade pathway (left) or passenger air travel (right) (adapted from Early et al. 2018)

11.2.3 Spread and Distribution in China Since its invasion into China, FAW successfully propagates 9–12 generations each year and breeds continuously in the tropical zone (Hainan, southern parts of Yunnan, Guangdong and Taiwan) with the minimum temperature higher than 15 °C in winter (Chen et al. 2020a; Yang et al. 2020). In the subtropical zone, which can be subdivided into southern subtropical, central subtropical and northern subtropical regions (mainly from the southern Qinling Mountains–Huaihe River to north of the tropical climate area, and a vast area in the eastern part of the Hengduan Mountains) with daily lowest winter temperature higher than 10 °C, FAW can survive bitter winters (Yang et al. 2021). The tropical and southern subtropical zones in China are the reservoir pool, where FAW can breed year-round. The central subtropical and northern subtropical zones are transitional areas for the migratory FAW. Because FAW cannot survive in harsh northern winters, they fly northward and return to the south in the autumn. In the temperate zone (mainly in the northern Qinling Mountains–Huaihe River, southern Helan Mountain and Yinshan Mountain, and eastern and southern parts of the Daxing’anling Mountains), FAW propagates three to five generations a year, while there are only one to two generations in the high altitudes of Gansu, Inner Mongolia, and other regions in northeastern China (Chen et al. 2020b). In 2021, FAWs were present in 1426 counties of 27 provinces, covering the tropical, subtropical, and temperate climate zones of China (Fig. 11.2) (Zhou et al. 2021b).

11.2.4 Spread and Distribution in Africa The current known and modeled potential distribution suggests that FAW can establish itself in almost all countries in eastern and central Africa and a large part of western Africa under the current climate. Since the first reports of this pest in West

China–Africa Joint Force on Integrated Pest and Disease Management (IPM). . .

Fig. 11.2 Map of China showing the migration pattern of fall armyworm in China. AH Anhui, BJ Beijing, CQ Chongqing, FJ Fujian, GD Guangdong, GS Gansu, GX Guangxi, GZ Guizhou, HA Henan, HB Hubei, HE Hebei, HI Hainan, HK Hongkong, HLJ Heilongjiang, HN Hunan, JL Jilin, JS Jiangsu, JX Jiangxi, LN Liaoning, MO Macao, NMG Inner Mongolia, NX Ningxia, SC Sichuan, SD Shandong, SH Shanghai, SN Shaanxi, SX Shanxi, TJ Tianjin, TW Taiwan, XJ Xinjiang, XZ Xizang, YN Yunnan, ZJ Zhejiang (Source: Zhou et al. 2021b. The figure was authorized by Journal of Integrative Agriculture)

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and Central Africa (Goergen et al. 2016), FAW spread rapidly to reach most sub-Saharan countries by 2018 (Rwomushana et al. 2018). The potential distribution is still expanding, with some Mediterranean fringes of North Africa having pockets of a suitable environment (Early et al. 2018; Timilsena et al. 2022). Future projections suggest that the FAW invasive range will retract from both northern and southern regions towards the equator. A large area in eastern and central Africa is projected to have an optimal climate for FAW persistence. These areas will serve as FAW “hotspots” from where it may migrate to the north and south during favorable seasons and then pose an economic threat.

11.3

Invasion Biology of FAW

The global pest status of FAW can be attributed to several biological attributes that allow for quick invasion and rapid population colonization: lack of diapause, high polyphagy, high reproductive rate, short generation time, formidable ability to migrate long distances, strong adaptability to the environment, and increased resistance to insecticides.

11.3.1

High Level of Polyphagy and Host Preference

As a highly polyphagous pest, FAW larvae can feed on leaves, stems, and reproductive parts of more than 350 different host plant species, including maize, rice, sorghum, millet, sugarcane, vegetable crops, and cotton (Montezano et al. 2018). FAW consists of two strains: the “corn-strain” which feeds primarily on maize, cotton, and sorghum, and the “rice-strain” which feeds on rice and various pastures (Dumas et al. 2015). Maize is thought to be a highly suitable host crop for invasive FAW populations (Wu et al. 2019; Guo et al. 2020b), and all the individuals that invaded China belong to the “corn-strain,” feeding primarily on maize and sorghum in summer. Subsequently, FAW was found on wheat, barley, and gramineous weeds in some areas (Jiang et al. 2019). Some laboratory studies have shown F1 offspring of FAW successfully completed their life cycle on sugarcane, rice, potato, tobacco, wheat, barley, oat and oil-bearing crops (oilseed rape, soybean, and sunflower) (Wu et al. 2019, 2021; Guo et al. 2020a; Tang et al. 2020; He et al. 2021), and FAW damage was also observed in wheat, sugarcane, potato, and other major crops (Xu et al. 2019; Tai et al. 2019; Zhao et al. 2019a). Therefore, FAW may pose a potential threat to these and other economically important crops in China.

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Lack of Diapause and Strong Cold Hardiness

Because FAW does not have the ability to diapause, the number of FAW generations occurring in an endemic area depends on environmental conditions, such as temperatures and host plants (Prasanna et al. 2018). In China, FAW occurred all year round in the six provinces Yunnan, Guangdong, Guangxi, Sichuan, Hainan, and Fujian in southern China in 2019 and did not diapause in winter (Wang and Lu 2020; Qi et al. 2020); however, it was found that the mortality of FAW was 100% when the average temperature was below 10 °C for 8–10 days in central China (Anhui Province) (Xie et al. 2020). The cold hardiness of FAW directly determines its geographic distribution through adaptation to winter temperatures in different regions. Based on the supercooling points (SCPs) of the developmental stages (adults: -15.05 °C; pupae: -13.25 °C; prepupae: -10.50 °C; larvae: -9.03 °C) and the 99% lethal times (LT99) (LT99 was the highest for fourth instar larvae, with 99% of larvae dying after 18.59 d at 2 °C, and the lowest for eggs with LT99 of 5.33 d at 2 °C) at low temperatures and the climatic regionalization of China, it was estimated that FAW may survive in some southern areas of the subtropical zone in China during the winter season (Zhang et al. 2021).

11.3.3

Strong Migratory Ability

The rapid dispersal or invasion of FAW poses a great threat to global food security. The flight capacity of adult FAW undergoing long-distance migration is considered to be the major factor in its rapid wide-scale spread (Early et al. 2018). It was reported that adult moths can migrate over 100 km per night, making them able to find a broad range of habitats or suitable host plants (Tendeng et al. 2019). In China, FAW spread rapidly and can invade almost all maize areas within 1 year (Jiang et al. 2019). The flight performance under laboratory conditions showed that 3-day-old adults have the strongest flight capacity with the longest flight distance up to 62.98 km during 24-h consecutive tethered flight (Ge et al. 2019). When adults were tethered on a flight mill at 10 h per night for five consecutive nights, they showed formidable flight capacity with 163.58 km as the longest flight distance and 46.73 h as the longest flight time (Ge et al. 2021a). These tethered FAW females have shown a significantly shorter pre-oviposition period and enhanced oviposition synchronization than ones that have never flown. Dissection of the reproductive organs from FAW females also revealed that the flight activity of tethered moths promotes reproductive processes and the mating percentage of the flighted adults is higher than that of the control groups (Ge et al. 2021b).

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Prevention and Control in China and Africa Monitoring and Identification

Population monitoring is important for timely responses to the population dynamics and effective management of agricultural insect pests. In China, entomological radar and vertically pointed searchlights have been used to monitor migratory invasive insects. For migratory FAW, a searchlight trap was established in March 2018 in western Yunnan Province, China, where it has served as the “first station” for many pests that have migrated from Myanmar to China; a number of suspected FAW moths were attracted and then captured. On the day with the first captured FAW moth (11th December 2018), only one male and one female moth were caught. In the early invasion period (from 11th to 18th December 2018), six males and four females were caught. In the subsequent 3 months, the number of moths caught was much higher, with a higher proportion of females than males (Sun et al. 2021). The moths were identified by morphological criteria and then further verified by sequence analysis of segments of the mitochondrial COI gene and the sex-linked Tpi gene (Guo et al. 2019; Jing et al. 2019). BLAST analyses of the obtained sequences in the NCBI GenBank Database indicated that samples were identified as FAW, which was supported by 99–100% similarity in sequence data and coverage. Based on polymorphism characteristics in the Tpi gene, the specimens were confirmed as a new “corn-strain” (Jing et al. 2019). Searchlights with high-intensity light-columns attract FAW migrants above the crop canopy, while ground lamps and sex pheromone traps can be used to monitor adults under the canopy (Wu 2020). Based on the trapping effect of light for FAW moths in eight provinces of China, the searchlight is suitable to monitor the regional population dynamics of FAW, with moth peaks appearing during August to October, while the ground lamps are more suitable for use when the FAW population is large, with moth peaks appearing during late September and October (Jiang et al. 2020). Sex pheromones have been used to monitor FAW for over 40 years, and several countries have published sex pheromone components of FAW, while the practical effect varies with geographical ranges and strains. In China, there are a dozen types of commercial sex attractants from different companies used to monitor the occurrence dynamics of FAW adults. Field tests showed that most sex attractants showed high and stable trapping performance within the first 30–40 days (Che et al. 2020; Liang 2020).

11.4.2

Emergency Use of Chemical Pesticides

Chemical pesticides are effective measures to control outbreaks of pests in agricultural production. However, there is actually no available pesticide legally registered for use against FAW after its invasion into China. In view of this awkward situation,

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the Ministry of Agriculture and Rural Affairs of China recommended eight insecticides (chlorantraniliprole, flubendiamide, indoxacarb, lufenuron, tetrachloroandiamide, chlorfenapyr, spinetoram, and emamectin benzoate) for emergency prevention and control of FAW (http://www.moa.gov.cn/ nybgb/2020/202003/202004/t20200416_6341680.htm). Subsequently, scientists tested and screened several effective common commercial chemical pesticides under laboratory conditions. For example, laboratory bioassays showed that fenpropathrin 20% EC, tolfenpyrad 15% SC, deltamethrin 25 g/L EC, and dinotefuran 20% SC had high toxicity to the eggs of FAW, with the corrected hatching inhibition rate of more than 80% 5 days after treatment. Emamectin benzoate 1% EC, emamectin benzoate 5% ME, acephate 75% SP, spinetoram 6% SC, and fenpropathrin 20% EC had strong toxicity to the second instar larvae of FAW, with the corrected mortality rate of over 90% 24 h after treatment (Zhao et al. 2019b). The toxic effects of five different types of common chemical insecticides on the third instar larvae of FAW showed that the corrected mortality rate of 5.7% emamectin benzoate WG, 15% indoxacarb SC and 20% chlorantraniliprole SC reached 100% after 96 h, and the corrected mortality rates of 10% lufenuron SC and 2.5% lambda-cyhalothrin EC were 86.76% and 72.73%, respectively, after 96 h (Chen et al. 2020b). Ovicidal activity results of 14 common insecticides against FAW showed that fenoxycarb methomyl and pyriproxyfen had the strongest activity, completely inhibiting the hatching of eggs (Wang et al. 2019). Spinetoram had better effects than chlorantraniliprole on two to four instar larvae of FAW (Gao et al. 2020). These results provide references for making chemical control plans for FAW management in the field. Based on the field efficacy of chemical pesticides for control of FAW, emamectin benzoate, chlorantraniliprole, cyantraniliprole, methoxyfenozide, cyfluthrin, spinetoram-ethoxyfenozide, and indoxacarb-methoxyfenozide were the most effective, with the control efficacy over 88% 3 days after application (Yan et al. 2019). Similarly to China, insecticides have been the most commonly used method for the control of FAW in Africa. Ampligo, emamectin benzoate, and lambdacyhalothrin are the most commonly recommended and have been used against this pest with varying success in different countries, although others such as alphacypermethrin, profenofos, deltamethrin permethrin, malathion, and chorpyrifos are also used (Togola et al. 2018; Sisay et al. 2019a; Tambo et al. 2020; Osae et al. 2022; Niassy et al. 2021). However, insecticide usage has mainly been based on a calendar schedule rather than on damage levels, which poses a risk of building insecticide resistance among some FAW populations.

11.4.3

Biopesticides

Chemical insecticides are used as the current main management strategy to control FAW in the recently invaded areas in Africa and Asia (Guo et al. 2020b). However, with the wide use of chemical pesticides for control of FAW, it is foreseeable that

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resistance and adverse effects on the agricultural ecosystem will emerge in the near future. In order to delay the development of insecticide resistance and reduce the use of chemical pesticides, biopesticides, including antibiotic, microbial, and botanical extracts, have been assessed and developed. Zhao et al. (2019b) tested the efficacy of nine commonly used biopesticides and proved that the antibiotic biopesticides spinosad 25 g/L ME, spinosad 10% WG and abamectin 1.5% UL caused a high mortality of 82.67%, 76.00%, and 54.67%, respectively, to the second instar larvae. Li et al. (2019) reported the descending order of the lethal concentration to FAW neonate as Vip3A > Cry1Ab > Cry1F > Cry2Ab > Cry1Ac, with LC50 values of 50.3 ng/cm, 161.3 ng/cm, 207.8 ng/cm, 603.7 ng/cm, and over 800 ng/cm, respectively. Liu et al. (2019) showed that three virulent Bt strains (KN50, KN11 and KNR8, Wuhan Kernel Bio-tech Co. Ltd., China) possess very high efficacy against neonates of FAW, with LC50 values of 0.07 μg/g, 0.23 μg/g and 0.43 μg/g. Field trials proved that the strain KN50 at 32,000 IU/mg had a control efficacy of 72.6% and 86.6% for 0.3 g/m2 and 0.6 g/m2, respectively, against larval populations of mixed instars at 7 days post treatment (Liu et al. 2019). Therefore, the spinosad, abamectin, B. bassiana, and Bt were recommended for the field population of FAW. It was reported that Chinese domestic Bt-Cry1Ab and Bt-(Cry1Ab + Vip3Aa) maize have high control efficacy against the FAW (Zhang and Wu 2019). Virus-based biopesticides have been identified as having considerable potential for sustainable FAW control (Barrera et al. 2011). In China, the toxicity of several Nucleopolyhedrovirus (NPV), i.e., Helicoverpa armigera NPV (HaNPV), Spodoptera exigua NPV (SeNPV), Spodoptera litura NPV (SlNPV), Mamestra brassicae NPV (MbNPV), Autographa californica NPV (AcNPV) to FAW was evaluated in laboratory and field conditions. Only Autographa californica NPV was found to delay the hatching of FAW eggs using the immersion method, while the mortalities of newly hatched eggs from HaNPV and SeNPV were 92.32% and 82.49%, respectively. Spraying with SeNPV in a maize field showed control of FAW at 86.03% 10 days post treatment. Therefore, SeNPV was recommended as one of the biopesticides for control of FAW in the field (Zhang et al. 2020). The newly isolated Nucleopolyhedrovirus with wide host range isolated from Ostrinia furnacalis in Hubei Province of China was found to have a median lethal concentration (LC50) against third instar FAW larvae of 4.42 PIBs/mL, which was three times of that of MbNPV (Lei et al. 2019). Up until May 2021, five biopesticide products belonging to Bt, B. bassiana and M. anisopliae were successfully registered and had been widely used to control FAW in the field. The advent of FAW in Africa has seen an unprecedented interest in the potential of biopesticides as a lower risk option for this pest. The biopesticides being used or tested can be categorized as entomopathogenic fungi, entomopathogenic nematodes, baculoviruses, and neem-based products. Isolates of entomopathogenic fungi such as ICIPE 7 and ICIPE 78 have shown up to 90% mortality of FAW (Akutse et al. 2019), and additional screening has identified other potent isolates that could be developed into products (Akutse et al. 2020). Studies on M. anisopliae, B. bassiana, and Isaria isolates have also shown high infectivity on FAW eggs and neonates (Akutse et al. 2019). Baculoviruses are another promising biopesticide for FAW

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management in Africa, and some strains of the S. frugiperda multiple nucleopolyhedrovirus (SfMNPV) are now known to be present on the continent (Wennmann et al. 2021). Other commercial baculovirus products such as Fawligen (AgBiTech) have obtained regulatory approval in Kenya, while Littovir/ RavageX® (Andermatt) is registered in Cameroon (Guo et al. 2020a). There is also the opportunity for using botanically based insecticides for the management of FAW in Africa. In Zambia, aqueous extracts from Melia azedarach, Allium sativa, and Azadirachta indica were effective against FAW (Siazemo and Simfukwe 2020). Similar results using neem-based products have been obtained from Ghana (Babendreier et al. 2020). While there is not yet a commercial entomopathogenic nematode product for FAW, studies are ongoing to formulate a product from native nematode species. Native EPN from Rwanda was found to be as effective as commercial EPNs from the native range of FAW (Mexico) at causing mortality on FAW. The Rwandan Steinernema carpocapsae strain RW14-G-R3a-2 caused rapid 100% mortality of second- and third-instar and close to 75% mortality of sixth-instar FAW caterpillars (Fallet et al. 2022). Lastly, although not widely used, mating disruption has become a novel technology for FAW management in Africa. Regulatory approval has been obtained in Kenya for Pherogen, a product that is applied using aerial dispensers. Although biopesticides appear to have a good effect for the sustainable management of FAW in Africa, farmer perception of their effectiveness remains a concern for their wide-scale use (Constantine et al. 2020).

11.4.4

Natural Enemies

A great diversity of natural enemies (parasites, predators) of FAW has been reported in the Americas (Molina-Ochoa et al. 2003), Africa (Birhanu et al. 2018), and Asia (Shylesha et al. 2018). In China, there is an abundance of natural enemy species, and an investigation of native species in the field and their effectiveness based on functional response of predators or parasitism rate of parasitoids on FAW under laboratory conditions have been conducted. At present, 11 predators, including Propylaea japonica, Harmonia axyridis, Coccinella septempunctata, Chlaenius bioculatus, Hippodamia variegata, Eocanthecona furcellate, Picromerus lewisi, Arma chinensis, Orus sauteri, Chrysopa pallens, and Eurellia pallipes, and 17 parasitoids, including Telenomus remus, Trichogramma pretiosum, T. chilonis, T. dendrolimi, Exorista japonica, Microplitis pallidipes, Tetrastichus howardi, Chelonus formosanus, C. munakatae, Cotesia glomerata, Diadegma semiclausum, Microplitis similis, Meteorus pulchricornis, Exorista japonica, Microplitis similis, Diadegma semiclausum, and Euplectrus laphygmae were found to have the potential to control immature stages of FAW (Gao et al. 2020; Huang et al. 2020; Liang et al. 2020; Tang et al. 2020; Tian et al. 2020). However, for most of these natural enemies, larger scale field testing is still lacking to verify their effectiveness for FAW control.

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For the case of Africa, over 30 indigenous parasitoid species have been reported with the most important ones being Telenomus remus, Cotesia icipe, Chelonus bifoveolatus, C. curvimaculatus, Charops ater, and Coccygidium luteum (Sisay et al. 2019b; Agboyi et al. 2020; Koffi et al. 2020; Amadou et al. 2018; Kenis et al. 2019; Durocher-Granger et al. 2021; Abang et al. 2021; Otim et al. 2021). Telenomus remus is the most studied with 64% parasitism reported in Niger (Laminou et al. 2020), and similar studies have been done in Kenya (Mohamed et al. 2021a) and in Benin and Ghana, where 14.5–25.9% parasitism was reported (Agboyi et al. 2020, 2021). A lot of work has also gone into the use of C. icipe which caused more than 60% parasitism under lab conditions (Mohamed et al. 2021b) and 45% parasitism under field conditions in Ethiopia (Sisay et al. 2019b). Consequently, augmentative releases of T. remus, T. chilonis, and C. icipe have been conducted in Kenya with significant increases in parasitism by the three species reported (Mohamed et al. 2021b). Not much work has been done with predators, although Koffi et al. (2020) found Pheidole megacephala, Haematochares obscuripennis, and Peprius nodulipes feeding on FAW in maize fields. One of the innovative methods employed in Africa that is not yet used widely in China is the push-pull technology. Climate smart push-pull in Africa has been shown to reduce FAW damage in a number of countries, by up to 87% and increased yields 2.7-fold (Midega et al. 2018; Hailu et al. 2018; Niassy et al. 2021). Other methods such as intercropping have also been shown to reduce FAW damage in maize (Midega et al. 2018; Hailu et al. 2018). Some locally adaptable methods have also been tried in Africa for FAW management, such as use of soil, charcoal, ash, detergents, and plant extracts such as chilli, neem, Tephrosia, Tithonia, Lantana, and garlic, with varying results (Babendreier et al. 2020). Ultimately for the African context, the sustainable use of the management methods described above will need to include FAW host plant resistance. Since 2017, the CIMMYT (International Maize and Wheat Improvement Center) maize breeding program in Kenya has evaluated thousands of maize breeding lines for resistance. Some promising tropical maize inbred lines that have been screened so far have exhibited either resistance reactions or moderate resistance in leaf and ear damage induced by FAW, as well as variations in other agronomic traits (Kasoma et al. 2020, 2021, 2022). There have also been some differences observed in acceptance and preference when FAW larvae are given a choice between certain cultivars (Morales et al. 2021).

11.5 11.5.1

China’s Special Control Strategy Against FAW “Two-step” Strategy

Based on the greater understanding of the bioecology of FAW, the lessons from Africa where the pest has become naturalized and the experience from the control of other major agricultural pests such as cotton bollworm Helicoverpa armigera, Wu

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(2020) proposed the “two-step” strategy for FAW control in China. First, in recent years, the integrated pest management (IPM) strategy, including chemical, physical, biological, and agro-ecological control, should be implemented in order to solve the problem of emergency management measures and reduce crop yield losses caused by FAW in the short term. Second, using the innovation and application of modern agricultural information technology and new biotechnology, an IPM strategy based on accurate monitoring and early warning, highly efficient interception of moth migration and planting of Bt maize will be developed and deployed in 3–5 years, to achieve the goal of green, low-cost and sustainable control and to meet the strategic needs of China’s high-quality development of agricultural production and the construction of a social–ecological civilization.

11.5.2

Large-Scale and Regional Control Strategy

Based on the occurrence and distribution of FAW in different regions, MARA divided China into three FAW-infested areas, i.e., the annual breeding area in Southwest and South China, the transitional migration area in Jiangnan and Jianghuai, and the Huang-Huai-Hai region and North China (Yang et al. 2019). In the annual breeding area, the control strategy is to suppress the local pest population and reduce the number of migratory individuals. Based on the characteristics of diverse vegetation and abundant natural enemy reservoirs in the southern region, ecological regulation measures should be fully utilized. In the transitional migration area, the control strategy is to reduce the number of migratory individuals. Unified control on a large scale should be carried out in the invasion and dispersal areas in late spring and early summer to improve the control efficiency. In the key preventive area, the strategy is to strengthen the monitoring and forecasting and pay close attention to the occurrence and damage of FAW from maize seedling stage to silking stage. The emergency use of chemical pesticides should be prepared during the critical period. Based on the results of FAW monitoring, physical control measures, including light and sex pheromone traps, should be carried out on the adult FAW in the concentrated landing area, and insecticides could be used to control the FAW larvae before the third instar in areas of severe pest occurrence.

11.6

Conclusions and Outlook

Based on the invasion biology and migratory pattern, FAW has a tremendous annual damage potential in China and will continue to spread throughout the world. Therefore, there is a growing need for international collaboration in sharing as much information as possible to facilitate management of this globally important invasive species. Asian countries, such as Myanmar and all other countries in Southeast Asia are year-round reproductive regions for FAW, which provide

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continuous streams of FAW moths migrating into China. It is believed that FAW primarily enters China through its land border with Southeast Asian countries; however, trajectory analyses suggest that FAW adults potentially also migrate into China across the South China Sea (Zhou et al. 2021a). Therefore, multi-country collaboration among Asian countries on research of FAW migration routes is needed, as this is helpful for pest forecasting and risk assessment. At present, China is stepping up its efforts to construct national-scale entomological radar networks (Wu 2020), which contributes to monitoring and forecasting migrating populations of FAW and guiding regional prevention work. Since the invasion of FAW in December 2018, China has established a national coordination mechanism to prevent FAW damage in the shortest possible time. Scientists quickly carried out a series of research projects, including on the biology, ecology, and control measures and successfully controlled the invasive pest within 1 year. However, the heavy reliance on use of insecticides for FAW control will pollute the environment and lead to insect resistance. The commonly used chemical agents for the control of FAW included organophosphorus, carbamate, and pyrethroid, and field resistance monitoring results showed that the resistance ratio of these three types of pesticides had reached moderate and high levels in recent years (Wang et al. 2019). Therefore, local governments and agricultural extension departments should provide guidance to adopt flexible management practices in farmlands according to severity of local occurrence. For example, officially recommended chemicals with high efficiency and low risk of resistance could be alternatively sprayed in fields when FAW density is high (MARA 2020). In addition, microbial biopesticides should receive priority to combat FAW, and natural enemies in agricultural ecosystems should be protected to enhance their role in ecological control. Use of Bt maize is another successful method to control FAW, and if Bt maize is planted in China, a “Natal Source IRM” strategy will be suggested to delay the development of resistance (He and Wang 2020). Additionally, the FAW male self-limiting strain OX5382G can exhibit complete female mortality in closed populations. Population models simulating the release of the transgene OX5382G males together with Bt crops and non-Bt “refuge” crops showed that OX5382G releases could suppress FAW populations and delay the spread of resistance to insecticidal proteins (Reavey et al. 2022). In view of potential species of natural enemies against FAW, the mass rearing method must be developed as soon as possible to achieve commercial application. With the advances in information technology, pest monitoring and control will be developed, contributing to alleviate the intensive labor and chemical applications. For example, an intelligent application that relies on an image recognition system has been launched for farmers to identify the allied insect species during field inspections. For FAW, by simply taking a photograph and uploading the image to the website http://migrationinsect.cn, users can identify the FAW in real-time (Wu 2020). In addition, the new biotechniques, such as RNA interference (RNAi), CRISPR/Cas9 and nanopesticides, will also greatly improve pest management. In Lepidoptera, RNAi technology has been used to knock down the immune-related genes, causing increased susceptibility to insect pathogens (Baradaran et al. 2019).

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For FAW, a SfABCC2 knockout strain was generated using the CRISPR/Cas9 system to provide further functional evidence of the role of this gene in susceptibility and resistance to Cry1F (Jin et al. 2021), helping to develop new strategies for FAW control. It is also recommended that the development of IPM measures for FAW be adaptable to climate change utilizing ecological niche modeling. Acknowledgements This research was supported National key research and development program (2021YFD1400700) and China Agriculture Research System of MOF and MARA. We thank the project “CHN17/0019 Sinograin II: Technological innovation to support environmentallyfriendly food production and food safety under a changing climate-opportunities and challenges for Norway–China cooperation” (funded by the Norwegian Ministry of Foreign Affairs through the Royal Norwegian Embassy in Beijing) for support in writing this chapter. Declaration of Competing Interest The authors declare that there are no conflicts of interest.

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