Propositions 1. Adaptive recovery from risk is more important than immunity to impact. (this thesis) 2. Farming systems adopting resilience-enhancing strategies must navigate multiple stressors across time scales for sustainability. (this thesis) 3. In the digital age, data generation is as important as research ideation in science. 4. External PhDs are uniquely positioned to apply science for impact. 5. Culinary elitism hinders community cohesion. 6. Activism that targets heritage alienates allies. Propositions belonging to the thesis, entitled Feeding the future: risks, resilience, and adaptation pathways in farming systems Shalika Vyas Wageningen, 31 May 2024 Feeding the future: Risks, resilience, and adaptation pathways in farming systems Shalika Vyas Thesis Committee Promotors Prof. Dr M.P.M. Meuwissen Personal Chair, Business Economics Group Wageningen University & Research Prof. Dr Martin Kropff Professor of Crop and Weed Ecology Wageningen University & Research Co-promotors Dr Tobias Dalhaus Assistant Professor, Business Economics Group Wageningen University & Research Prof. Dr Julian Ramirez Villegas Special Professor, Plant Production Systems Group Wageningen University & Research Other members Prof. Dr A.R.P.J. Dewulf, Wageningen University & Research Prof. Dr R. Finger, ETH Zürich, Switzerland Dr A. Mukherji, CGIAR, Nairobi, Kenya Dr H. Biemans, Wageningen University & Research This research was conducted under the auspices of the Graduate School Wageningen School of Social Sciences. Feeding the future: Risks, resilience, and adaptation pathways in farming systems Shalika Vyas Thesis submitted in the fulfilment of the requirements for the degree of doctor at Wageningen University, by the authority of the Rector Magnificus, Prof. Dr C. Kroeze, in the presence of the Thesis Committee appointed by the Academic Board to be defended in public on Friday 31 May 2024 at 11 a.m. in the Omnia Auditorium. Shalika Vyas Feeding the future: Risks, resilience, and adaptation pathways in farming systems, 225 pages. PhD thesis, Wageningen University, Wageningen, the Netherlands (2024) With references, with summary in English DOI: https://doi.org/10.18174/657201 Table of Contents Chapter 1 General introduction ................................................................................................ 11 1.1 Background .................................................................................................................... 12 1.2 Problem statement .......................................................................................................... 14 1.3 Research gap .................................................................................................................. 15 1.4 Research objectives ........................................................................................................ 16 1.5 Multi-dimensional and multi-disciplinary focus of the thesis ........................................ 17 1.6 Theoretical framework ................................................................................................... 19 1.7 Thesis outline ................................................................................................................. 21 1.8 References ...................................................................................................................... 24 Chapter 2 The gap between intent and climate action in agriculture ....................................... 29 2.1 Introduction .................................................................................................................... 31 2.2 Data and methods ........................................................................................................... 34 2.3 Results ............................................................................................................................ 34 2.4 Discussion and conclusion ............................................................................................. 37 2.5 References ...................................................................................................................... 39 Supplementary information .................................................................................................. 42 Chapter 3 Mapping global research on agricultural insurance ................................................ 45 3.1 Introduction .................................................................................................................... 47 3.2 Data and methods ........................................................................................................... 48 3.3 Results ............................................................................................................................ 55 3.4 Discussion and conclusion ............................................................................................. 73 3.5 References ...................................................................................................................... 77 Chapter 4 Limited impact of heat extremes in Indian wheat and soybean under climate-smart agriculture ................................................................................................................................ 87 4.1 Introduction .................................................................................................................... 89 4.2 Data and methods ........................................................................................................... 91 4.3 Results ............................................................................................................................ 97 4.4 Discussion and conclusion ........................................................................................... 102 4.5 References .................................................................................................................... 106 Supplementary information ................................................................................................ 113 Chapter 5 How do production systems recover from production shocks? A global recovery analysis ................................................................................................................................... 143 5.1 Introduction .................................................................................................................. 145 5.2 Data and methods ......................................................................................................... 146 5.3 Results .......................................................................................................................... 152 5.4 Discussion and conclusion ........................................................................................... 162 5.5 References .................................................................................................................... 167 Supplementary information ................................................................................................ 174 Chapter 6 General discussion................................................................................................. 185 6.1 Introduction .................................................................................................................. 186 6.2 Synthesis....................................................................................................................... 187 6.3 Limitations and opportunities for future research ........................................................ 195 6.4 Scientific contribution .................................................................................................. 199 6.5 Policy and business recommendations ......................................................................... 201 6.6 Main conclusions.......................................................................................................... 204 6.7 References .................................................................................................................... 206 English summary ................................................................................................................... 215 About the author .................................................................................................................... 220 Acknowledgements ................................................................................................................ 223 List of abbreviations and acronyms CCAFS Climate Change, Agriculture and Food Security CHIRPS Climate Hazards Group InfraRed Precipitation with Station data CIMMYT International Maize and Wheat Improvement Center CMIP Coupled Model Intercomparison Project CSA Climate-Smart Agriculture CSV Climate-Smart Village ENSO El Niño–Southern Oscillations FAO Food and Agriculture Organization of the United Nations IMD India Meteorological Department IPCC Intergovernmental Panel on Climate Change LMICs Low- and Middle-Income Countries NAPs National Action Plans NAMAs Nationally Appropriate Mitigation Actions NDCs Nationally Determined Contributions ND-GAIN Notre Dame-Global Adaptation Index SDGs Sustainable Development Goals SSPs Shared Socioeconomic Pathways UNFCCC United Nations Framework Convention on Climate Change List of figures Figure 1.1 Theoretical framework used in this thesis and positioning of research chapters therein based on their core focus. ............................................................................................ 20 Figure 2.1 Framework for analyzing climate action for adaptation and mitigation in agriculture. ............................................................................................................................... 33 Figure 2.2 Illustration of framework with a scatterplot of intent, need, scope and readiness of different countries for adaptation in agriculture.. .................................................................... 35 Figure 2.3 Global assessment based on intent, need, scope and readiness in adaptation for agriculture. ............................................................................................................................... 36 Figure 3.1 Schematic flowchart of the key steps of the systematic review. Grey blocks represent the methods used. ..................................................................................................... 49 Figure 3.2 Summary of agricultural insurance research by agricultural product insured, research theme, income group, insurance product type, and the hazard covered. ................... 56 Figure 3.3 Panel of maps showing the geographical distribution of agricultural insurance research literature by agricultural product insured. ................................................................. 64 Figure 3.4 Panel of maps showing the geographical distribution of agricultural insurance research literature by research theme. ...................................................................................... 66 Figure 3.5 Panel of maps showing the geographical distribution of agricultural insurance research literature by insurance product type. ......................................................................... 68 Figure 3.6 Panel of maps showing the geographical distribution of agricultural insurance research literature by hazards covered. .................................................................................... 70 Figure 3.7 Research intensity of papers on agricultural insurance with four risk indicators. . 72 Figure 4.1 Hourly temperature effects on soybean yields from Climate-Smart Agriculture (CSA) farms based on ECMWF Reanalysis version 5 (ERA5) and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) weather data. ................................................. 97 Figure 4.2 Hourly temperature effects on wheat yields from Climate-Smart Agriculture (CSA) farms based on ECMWF Reanalysis version 5 (ERA5) and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) weather data. ................................................. 99 Figure 4.3 Hourly temperature effects on soybean and wheat sub-sample yields from Climate-Smart Agriculture (CSA) farms based on ECMWF Reanalysis version 5 (ERA5) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) weather data.. 100 Figure 4.4 Hourly temperature effects on district yields of: a) soybean and b) wheat crop on ECMWF Reanalysis version 5 (ERA5) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) weather data.. ........................................................................... 101 Figure 5.1 Map showing exposure to national production shocks in A) Maize and B) Milk production systems................................................................................................................. 153 Figure 5.2 Map showing average shock size observed in the study time-period in A) Maize and B) Milk production systems at national level.. ............................................................... 155 Figure 5.3 Map showing average recovery time (in years) from national production shocks in A) Maize and B) Milk production systems.. .......................................................................... 157 Figure 5.4 Global functions of recovery likelihoods for maize (A) and dairy milk (B) production, shown as reverse Kaplan-Meier curves, n is the number of shocks observed.. . 158 Figure 5.5 Sub-regional reverse Kaplan-Meier functions showing recovery likelihoods for maize (orange) and milk (blue) production. .......................................................................... 160 Figure 6.1 The global priority areas identified in this thesis.. .............................................. 193 List of tables Table 1.1 Multiple dimensions and disciplines in this thesis. ................................................. 18 Table 1.2 Data, time-period, and methods of the research chapters. ...................................... 23 Table 3.1 Classification and number of papers in the review by research themes and sub- themes. ..................................................................................................................................... 57 Table 4.1 Summary statistics of farm yield data ..................................................................... 92 Table 4.2 Summary statistics of district yield data. ................................................................ 92 Table 4.3 Summary statistics for hourly temperatures for soybean and wheat farms. ........... 93 Table 4.4 Summary statistics for seasonal rainfall for soybean and wheat farms. ................. 94 Table 4.5 Weather data sources............................................................................................... 94 Table 5. 1 Key concepts used in this analysis and their description. .................................... 146 Chapter 1 General introduction 1.1 Background Food systems are facing many challenges since the dawn of the Anthropocene. The number of extreme weather events is rising across many regions of the world. For instance, 2023 continued to be the hottest year on record1, with extremes such as five consecutive droughts in the Horn of Africa followed by floods, extreme heat in Southern Europe, wildfires in Northern America, among many others. Climate change is projected to increase the likelihood and intensity of many such extreme weather events (Fischer et al., 2021). The projected impacts from climate change across all sectors of food systems (Cheung et al., 2021; Jägermeyr et al., 2021; Thornton et al., 2021) have the potential to detrimentally affect food production in many regions (Glotter & Elliott, 2016; Rahimi et al., 2021). For instance, humid heatwaves are estimated to significantly decrease labour productivity in major breadbaskets of South and Southeast Asia (Freychet et al., 2022; Horton et al., 2021; Wang et al., 2022). At the same time, the world is witnessing a rapid depletion of natural resources. Almost half the species on earth are undergoing population decline2 (Ceballos et al., 2015; Pimm et al., 2014) and six of the nine crucial planetary boundaries have already been breached (Richardson et al., 2023). Accelerated groundwater depletion is a concern in many food basket regions of the world (Jasechko & Perrone, 2021; Mukherji, 2022a, 2022b), while nitrogen and pesticide pollution (Bijay-Singh & Craswell, 2021; Kanter et al., 2019) remain a global policy challenge. All these factors also contribute to soil health decline (Amundson et al., 2015; Kaiser, 2004; Mueller et al., 2012; Wuepper et al., 2019) and plateauing of crop yields in many parts of the world (Grassini et al., 2013). It is also an era of rapid urbanization and socio-economic changes, causing significant changes in human food consumption (Ivanovich et al., 2023). Geo-political tensions are also projected to escalate with resource depletion and extreme weather events (Brück & D’Errico, 2019). Climate change is often seen as a “risk amplifier” (Hsiang et al., 2013; Jagermeyr et al., 2020; Mach et al., 2019), exacerbating already existing risks, inequalities, and poverty traps. 1 European Centre for Medium-Range Weather Forecasts (ECMWF) media article, 2024. 2 The Guardian media article, 2022. 12 | Chapter 1 These risks are likely to affect all areas of food systems. Consequently, pathways to optimize food processing, reduce food wastage, shift food consumption patterns, and guarantee food safety are important. At the heart of food systems lies food production, interventions in food production can have a cascading effect on the entire food system chain. Building resilient agriculture to climate variability and climate change is an important research-policy agenda to minimize the impacts on food production, stabilize farm income and employment, and ensure food security across the world. The agricultural sector is the economic and social mainstay of more than 570 million farmers. The most vulnerable of these farmers live in Sub- Saharan Africa, South Asia, and Southeast Asia (Lowder et al., 2016). Accordingly, climate change adaptation needs are the greatest in these regions (Niles & Salerno, 2018; Rosenzweig et al., 2014; Warren, 2014). Thus, reducing the vulnerability of agricultural production systems and strengthening adaptive capacities to climate change are top priorities to safeguard and improve the livelihood standards of millions of people. Scientists and policy makers are giving increasing attention to the need for more resilient agriculture by breeding efforts to improve productivity under extreme weather events (Langridge et al., 2021), promoting crop diversity and preserving genetic diversity through seed banks (Galluzzi et al., 2016), scaling out farm risk management policies, energy transitions in food production like solar irrigation (Yang et al., 2023), and promotion of climate-smart agriculture (CSA) (Lipper et al., 2014)—among many other technologies and practices. Despite these efforts, scaling these technologies and practices is a monumental task. The complex intertwining of adaptive capacities of local communities, institutional and governance inefficiencies, readiness to scale and lack of enabling conditions including service delivery mechanisms—all affect the adoption and scaling of these climate resilient technologies (Acevedo et al., 2020). Further, reductionism and oversimplification of the way these technologies interact and operate in local agro-ecological and socio-economic conditions can also lead to maladaptive and unsustainable pathways. For example, irrigation can make crops more resilient to weather and climate stresses. However, excessive water withdrawals have led to significant groundwater depletion—22% of global food production and 2.3 billion people rely on unsustainable use of water resources (Rosa et al., 2020). To add to these challenges, the world faces the daunting task of feeding more than 9 billion people by 2050 in the face of mounting climate risks, and on the other hand, reducing emissions— General introduction | 13 the agricultural sector (including land use change) contributes to over one-third of global emissions (Menegat et al., 2022). 1.2 Problem statement To counteract the challenges discussed above, de-risking food production systems is one of the most important policy and research agenda across many nations. Insights on the state of resilience of farming systems, identification of blind spots and priority action areas is crucial for the development of any de-risking strategy. Increasing the resilience of farming systems in the face of climatic and environmental changes—including incremental and transformative changes in the way food is produced, is a crucial step forward. Fostering enabling environments to innovate, adopt and scale agricultural technologies for addressing risk3, involves multiple actors and mechanisms. It is known that many regions in the world already face high degree of food production risks (Cottrell et al., 2019). This coupled with barriers to adequately adapt to these risks, in the form of policy, finance and societal constraints, often leads to low resilience4 of farming systems to these risks (Meuwissen et al., 2020). Herein lies the need of a systematic analysis of how farming systems are exposed to and respond to different risks. Risks can also emerge from the unpredictability and uncertainty of outcomes, due to complex interactions between various factors that influence food production— including climate variability, biological threats, and market dynamics, among others (Hardaker et al., 2004). The risk reducing pathways include a range of mechanisms to manage, adapt and change to risks as a response, which ultimately contribute to farming systems resilience. Together, these three umbrella concepts of risk, resilience, and adaptation5 are important for scientific advancement and cornerstone of debate on the future of farming systems (Intergovernmental Panel on Climate Change (IPCC), 2023). 3 Risk is defined as potential to have adverse consequences for human and ecological systems, from a given action, event, or decision. Risk also includes uncertainty and unpredictability of these consequences. Risk can be of different types including biological risks (livestock epidemics) and climate risks (extreme weather events and projected climate change impacts), among others. Source: IPCC glossary, Hardaker et al., 2004. 4 Resilience is defined as capacity of a system (social, economic, and ecological) to cope with a hazardous event, respond to and reorganize in ways to maintain their essential functions, identity, and structure. Source: IPCC glossary. 5 Adaptation is defined as the process of adjustment to actual or expected risks (like climate change), to minimize harm and capitalize on opportunities, wherever possible. Source: IPCC glossary. 14 | Chapter 1 1.3 Research gap Various studies have focused on risks, resilience, and adaptation in food systems. However, I find critical gaps in literature which I classify as a) lack of a global overview on how farming systems respond to risks, b) fragmented evidence on this response, in the form of different coping and adaptation mechanisms across diverse scales, and c) lack of focus on the interconnectedness of these components and a general mono-disciplinary and reductionist approach to risk and resilience in farming systems. Diverse farming systems have their own unique characteristics and capacities to manage and respond to risks. A global perspective is crucial to understand these dynamics to address food security concerns and draw policy lessons at a larger scale. Individual global studies in the past have a segmented focus on risks (usually a specific type of risk), the projected impacts from these risks and investigate a specific dimension of resilience. For example, some global studies have focused on the impact of extreme weather events like drought and heat stress, on crop production (Lesk et al., 2016), while global assessments of the livestock sector have focused on risk exposure to heat stress (Thornton et al., 2021). Similarly, various crop modelling studies at the global scale have focused on the future impacts of climate change on major crops (Hasegawa et al., 2021, 2022; Iizumi et al., 2017). Review of existing literature on resilience in food systems indicates a lack of integrative global and broad scale analysis (Béné et al., 2016). To this effect, comprehensive global assessments focusing on diverse elements of risk, resilience enhancing strategies like adaptation, and their interconnections, help in identifying blind spots from previous research, enabling us to identify synergies across different regions based on contextual similarities and differences—shaping future research agendas, and identifying hotspots for priority action. Evidence on adaptation across farming systems (especially in the context of climate change) is an important research agenda (Nalau & Verrall, 2021). However, key research gaps are documented in terms of fragmented evidence across geographies and sectors, and lack of clear risk reduction and resilience building pathways identified in the literature (Berrang-Ford et al., 2021). The research on farming systems adaptation is limited to certain geographies and based on biophysical modeling studies, in the absence of farm data in low- and middle- income countries (henceforth LMICs). The global stocktake and review of adaptation in many cases, for instance, agricultural insurance, is scattered across regions, agricultural General introduction | 15 sectors, and insurance product types (de Leeuw et al., 2014; Sarr et al., 2012). Another example is the lack of evidence on risk reducing effects of climate-smart agriculture (henceforth CSA), mostly restricted by data availability, especially in the LMICs. To summarize, the existing literature across the dimensions of risk and farming system resilience is primarily anchored in a unidimensional view, isolating important elements, and rarely exploring their interconnections. A unidimensional analysis often overlooks the complexities and multifaceted ways in which farming systems operate, including the sociological, political, economic, and environmental domains. This thesis addresses these critical research gaps, identifies different elements across risks and resilience, highlights their interconnections, and provides entry points for future policy and research action. 1.4 Research objectives The objective of this thesis is to understand risks faced by farming systems, the processes in response to these risks (like adaptation), and resilience of the farming systems as an outcome of these processes. To achieve this, the following specific research objectives are addressed: I. Assess the alignment of global climate action policies with projected risks, readiness to scale adaptation based on economic, governance and social capacities of nations, and biophysical scope for adaptation. (Chapter 2) II. Map global research on agricultural insurance across agricultural sectors, geographies, insurance product types, and research themes. In addition, analyze the geographical alignment of research intensity on agricultural insurance with historical and projected risk hotspots. (Chapter 3) III. Identify the impacts of heat extremes on crop production under climate-smart agriculture. (Chapter 4) IV. Assess how farming systems recover from production shocks. (Chapter 5) In the following sections, I discuss in detail the multi-dimensional and multi-disciplinary focus of this thesis (Section 1.5), followed by discussion on the theoretical framework of this thesis (Section 1.6) and the thesis outline in Section 1.7. The following chapters (2 to 5) focus on individual research objectives. Finally in Chapter 6, I discuss the implications from this research and its societal impact. 16 | Chapter 1 1.5 Multi-dimensional and multi-disciplinary focus of the thesis In this thesis, I look at risk, resilience and adaptation in agriculture while taking into consideration multiple dimensions and disciplines (Table 1.1). First, I address different types of risk, i.e., different chapters focus on different types of risks, including long-term climate change (Chapter 2 and 3), biological risks like livestock epidemics (Chapter 3), extreme weather events (Chapter 3 and 4), and production shocks due to multiple climatic, geo- political and economic fluctuations (Chapter 5). This thesis also looks at multiple sectors and farming systems—maize (Chapter 2, 3, 5), rice (Chapter 2, 3), wheat (Chapter 2, 3, 4), soybean (Chapter 3, 4), other crops (Chapter 3), livestock sector (including dairy milk production) (Chapter 3, 5), and fisheries (Chapter 3). Focusing on the entire spectrum of these risks across multiple sectors is important to draw geographical and sectoral lessons, each requiring their own set of risk management strategies. Different spatial scales are also compared—Chapter 2, 3 and 5 are global in scope, whereas Chapter 4 focuses on climate-smart agriculture in India. While I discuss the advantages of having a global scope in the previous section, I also emphasize the importance of having a focused case study approach. In many contexts, due to data limitations (in this case, a lack of a global evidence on climate-smart agriculture), and the highly context-specific nature of some climate adaptations, it is important to also understand the local factors, the social and economic capacities of the communities, and the institutional and governance mechanisms to scale-out adaptations. I therefore explore a balanced approach—and combine insights from global scale to identify hotspots and a more dedicated local study to understand the contexts in which farm adaptation occurs—I discuss this further in Chapter 6. Another dimension used in this thesis is the timescale (Table 1.1). My research on climate policy and agricultural insurance (Chapter 2 and 3) assesses the future risks faced by farming systems. Chapter 3 also looks at risks from a hindsight dimension, focusing on observed events, along with my other Chapters (4 and 5). Prioritizing different time horizons is important to understand past successes and failures, identify current challenges and anticipate future risks (Rippke et al., 2016). In addition to this, I derive learnings from multiple disciplines (right-hand column of Table 1.1) including policy analysis (Chapter 2), risk management (Chapter 3), agronomy and climate econometrics (Chapter 4) and agroecology and risk management (Chapter 5) to draw original insights and a diverse perspective on risks and resilience in farming systems. It is General introduction | 17 important to note that this thesis is not designed to address the entire combination across these diverse elements, but rather provide entry points to frame these issues from multiple points of view. Table 1.1 Multiple dimensions and disciplines in this thesis. Chapters Dimensions Disciplines Risk Sector Geographical scope Temporal scope Chapter 2 (Climate policy) Long-term climate change Major crops (maize, rice, wheat) Global Foresight Policy analysis Chapter 3 (Agricultural insurance) Long-term climate change, extreme weather events (droughts, floods, heat stress) and biological risk (livestock epidemics) Agriculture (multiple crops, livestock, and fisheries) Global Both Risk management Chapter 4 (Climate- smart agriculture) Heat stress Crops (wheat, soybean) Sub-national (India) Hindsight Agronomy and climate econometrics Chapter 5 (Recovery) Production shocks covering all risk types Crops and livestock (maize, dairy milk) Global Hindsight Agroecology and risk management Chapter 6 All the above 18 | Chapter 1 1.6 Theoretical framework The central theme of this thesis is based on the risks faced by farming systems, the processes in response to these risks (like adaptation), and resilience of the farming systems as an outcome of these processes. I briefly introduced these concepts of risk, resilience, and adaptation above in Section 1.1 and 1.2. In this section, I further discuss these concepts in detail and position this thesis based on the framework I developed. Risk in context of agriculture is often conceptualized based not only on its source (e.g., institutional, geopolitical, market, economic and climate risks) but also framed as the uncertainty of outcomes; it generally implies a likely negative consequence, often referred to as downside risks (Hardaker et al., 2004). Risk events can be both known and unknown in nature, and some risk events are unknown until they occur, and thereby having unknown outcomes and probabilities (commonly described as “unknown unknowns”) (Bond et al., 2015). Farming systems and communities manage the consequences of these risks and subsequently change as a response to these risks. In this context, I define response pathways as actions that systems or communities take to manage, mitigate, or navigate risks. Although diverse, these response pathways can be characterized as react, cope, and adapt (Green et al., 2021). Coping is defined as the usage of available skills and resources to address, manage, and overcome adverse conditions (as a consequence of risk exposure), and maintain basic functioning of the system in the short- to medium-term (Intergovernmental Panel on Climate Change (IPCC), 2023). Reaction is an unplanned response to a risk (often immediate) (Green et al., 2021). Adaptation is defined as processes of adjustment to actual or expected risks (like climate change), to minimize harm and capitalize on opportunities, wherever possible. In farming systems, an example of such adaptation maybe the change in planting dates (McDonald et al., 2022). I modify this response categorization by omitting “reaction” and adding “transformation” as another response pathway. There are two reasons for this—firstly, the research in this thesis focuses on long-term risks from climate change and therefore requires longer timescales of response pathways. Since reactive responses are often immediate and unplanned, their importance within the scope of this thesis is limited. Secondly, there is growing evidence that there are limits to adaptation which necessitate other response pathway, namely General introduction | 19 “transformation”. Transformation is a change in fundamental attributes of natural and human pathways (Intergovernmental Panel on Climate Change (IPCC), 2023). It can be a possible pathway to respond to long-term risks, large uncertainties, and the surprise element (“unknown unknown” risk elements described above) associated with risks such as climate change (Nelson, 2011). Some examples of such transformations are already underway such as plant-based meat alternatives (Kozicka et al., 2023). By framing response pathways in this manner, I introduce a forward-looking perspective in the theoretical framework. Finally, resilience is described as the capacity of interconnected social, economic, and ecological systems to cope with a hazardous event (risk) and reorganize in ways that maintain their essential function and structure. Additionally, resilience also has the capacity for adaptation, learning and transformation (Intergovernmental Panel on Climate Change (IPCC), 2023). Resilience is thus an outcome of the response pathways to risks. By focusing on the time element of risks, I combine the three response pathways described above with the time reference with regard to risk—before, during and after the risk event. Next, I frame resilience as an outcome of these two dimensions: (i) time reference with regard to risk (Y-axis of Figure 1.1) and (ii) the consequent response pathways (X-axis of Figure 1.1); and resilience framed as an overarching concept (Figure 1.1). With this background and the framework described, I position my research chapters and research objectives. Figure 1.1 Theoretical framework used in this thesis and positioning of research chapters therein based on their core focus. 20 | Chapter 1 I place my research Chapter 2 on climate policy, between the adaptation and transformation ‘boxes’. Chapter 2 looks at national climate policies, how they align with future climate risks, the readiness (in the form of economic and social capacities), and biophysical scope to implement them across different countries, transcending both the boxes of adaptation and transformation. The core focus of these climate policies is managing future risks through adaptive and transformative responses. For Chapter 3 on agricultural insurance, I frame this chapter under coping and adaptation. Agricultural insurance is often a coping mechanism to deal with current risks being faced by the farming systems. However, the payoffs from an insurance program can also catalyze adaptation actions, if designed well (Hellin et al., 2017; Linnerooth-Bayer & Mechler, 2006; Siebert, 2016). Further, insurance can also give incentives for risk prevention, hence it is placed across all the three risk timescales (before, during and after the event). Chapter 4 of this thesis looks at risk reducing effects of Climate-smart agriculture (CSA), and I frame this under adaptation. The type of adaptive strategies included in this chapter (like agro-advisories, agronomic practices like reduced tillage, improved cultivar, and precision water and nutrient management practices) are positioned to deal with risks before, during and after risk occurrence. Finally, my last research chapter (Chapter 5) focuses on recovery of maize and dairy milk systems from observed production shocks. This chapter looks at how these farming systems recover from production shocks and is placed under coping, because the response is focused on recovering upto (pre-shock) baseline production levels. While there may be long-term adaptive and transformative processes involved in the recovery of these systems, they are not the core focus of the chapter. Additionally, the categories defined in this framework have many interlinkages, synergies, feedback loops, and may not always be linear. I further highlight these interlinkages and connections in Chapter 6. 1.7 Thesis outline In this section I briefly summarize each research chapter, the key research objectives, the data and methods used, and contribution to the literature. Chapter 2 aims at developing a framework to monitor climate action in agriculture at a global scale. The research chapter contributes to the literature by illustrating how to measure and track adaptation in agriculture. The framework combines the need for adaptation (based on the projected risks faced by farming systems from climate change), the scope for adaptation General introduction | 21 in terms of biophysical limits, readiness to adapt based on different socio-economic macro indicators including GDP (Gross Domestic Product), governance indicators, among others (Sarkodie & Strezov, 2019) and finally, the intent for adaptation based on the NDC (Nationally determined contributions) commitments of different countries (pledges to adapt to climate change, as part of the Paris agreement to limit global warming)6. The results are illustrated for adaptation in agriculture and identifies the misalignment of need with other dimensions of adaptation, i.e., scope, intent, and readiness for adaptation. Chapter 3 builds on the adaptation gaps identified in the previous chapter and digs deeper into an important agricultural risk management and adaptation strategy—agricultural insurance. The objective of the study is to assess the extent to which agricultural insurance is a successful agricultural risk management option across sectors (crops and livestock), risk types (observed extreme weather events, biological risks like livestock epidemics, projected climate change) and geographies. The study is the first multi-sectoral, comprehensive review of agricultural insurance literature in the last two decades across different factors, sectors, and risks. Chapter 4 focuses on another important strategy—testing the benefits of climate-smart agriculture. Using a unique farm-level panel dataset collected from climate-smart villages in India from 2015-2020, the study investigates the response of crop yields towards heat stress under climate-smart management practices. It investigates whether wheat and soybean farm yields in India are robust to the effects of extreme heat stress during the crop growth period, contributing significantly to evidence which has previously focused on high-income countries and temperate regions (Tack et al., 2017). Chapter 6 further delves into production system resilience at a macro-level, focusing on maize and dairy milk production. Whereas several existing studies have analyzed the occurrence and impact of specific shocks, no study has assessed the recovery times and recovery likelihood to general production shocks. This study is the first to estimate the recovery likelihoods at a global scale for maize and dairy milk production systems, making an important contribution towards resilience research in agricultural production systems. Notably, it analyzes both maize and milk production, which are highly interdependent, with milk production generally being heavily understudied. It assesses recovery likelihoods from 6 https://unfccc.int/process-and-meetings/the-paris-agreement/nationally-determined-contributions-ndcs 22 | Chapter 1 production shocks in maize and dairy milk production systems at national scale using 60 years of data available from FAOSTAT. Table 1.2 provides an overview of the data sources for each of the research chapters, the time-period covered, and the methods used to address the research objectives in each chapter. Table 1.2 Data, time-period, and methods of the research chapters. Chapters Data sources Time-period Methods Chapter 2 (Climate policy) Previous research and globally available indicators (Aggarwal et al., 2019; Richards et al., 2015) 2050s (2041– 2060) Policy review and risk mapping Chapter 3 (Agricultural insurance)  Agricultural insurance research indexed in Scopus  International disaster database  Projected climate change impacts from Intergovernmental Panel on Climate Change (IPCC)  Food and Agriculture Organization (FAO) Emergency Prevention System for Transboundary Animal and Plant Pests and Diseases (the EMPRES project) 2000–2020, 2050s (2041– 2060) Systematic literature review and risk mapping Chapter 4 (Climate-smart agriculture) Farm-level panel data collected through surveys 2015–2020 Fixed effects regression with cubic splines Chapter 5 (Recovery) Production data available from Food and Agriculture Organization (FAO) 1961–2021 Shock estimation using LOESS regression and survival analysis The following four chapters (Chapter 2 to Chapter 5) focus on each of the research objectives described above. Next, I bring together the findings in a synthesis, and discuss limitations and opportunities for future research, scientific contribution, and recommendations in Chapter 6. The thesis ends with main conclusions in Chapter 6. General introduction | 23 1.8 References Acevedo, M., Pixley, K., Zinyengere, N., Meng, S., Tufan, H., Cichy, K., Bizikova, L., Isaacs, K., Ghezzi- Kopel, K., & Porciello, J. (2020). A scoping review of adoption of climate-resilient crops by small-scale producers in low- and middle-income countries. Nature Plants 2020 6:10, 6(10), 1231–1241. https://doi.org/10.1038/s41477-020-00783-z Aggarwal, P., Vyas, S., Thornton, P., & Campbell, B. M. (2019). How much does climate change add to the challenge of feeding the planet this century? Environmental Research Letters, 14(4), 043001. https://doi.org/10.1088/1748-9326/aafa3e Amundson, R., Berhe, A. A., Hopmans, J. W., Olson, C., Sztein, A. E., & Sparks, D. L. (2015). Soil and human security in the 21st century. Science, 348(6235), 1261071. https://doi.org/10.1126/science.1261071 Béné, C., Headey, D., Haddad, L., & von Grebmer, K. (2016). Is resilience a useful concept in the context of food security and nutrition programmes? Some conceptual and practical considerations. Food Security, 8(1), 123–138. https://doi.org/10.1007/S12571-015-0526-X Berrang-Ford, L., Siders, A. R., Lesnikowski, A., Fischer, A. P., Callaghan, M. W., Haddaway, N. R., Mach, K. J., Araos, M., Shah, M. A. R., Wannewitz, M., Doshi, D., Leiter, T., Matavel, C., Musah-Surugu, J. I., Wong-Parodi, G., Antwi-Agyei, P., Ajibade, I., Chauhan, N., Kakenmaster, W., … Abu, T. Z. (2021). A systematic global stocktake of evidence on human adaptation to climate change. Nature Climate Change 2021 11:11, 11(11), 989–1000. https://doi.org/10.1038/S41558-021-01170-Y Bijay-Singh, & Craswell, E. (2021). Fertilizers and nitrate pollution of surface and ground water: an increasingly pervasive global problem. SN Applied Sciences 2021 3:4, 3(4), 1–24. https://doi.org/10.1007/S42452-021-04521-8 Bond, A., Morrison-Saunders, A., Gunn, J. A. E., Pope, J., & Retief, F. (2015). Managing uncertainty, ambiguity and ignorance in impact assessment by embedding evolutionary resilience, participatory modelling and adaptive management. Journal of Environmental Management, 151, 97–104. https://doi.org/10.1016/J.JENVMAN.2014.12.030 Brück, T., & D’Errico, M. (2019). Food security and violent conflict: Introduction to the special issue. World Development, 117, 167–171. https://doi.org/10.1016/j.worlddev.2019.01.007 Ceballos, G., Ehrlich, P. R., Barnosky, A. D., García, A., Pringle, R. M., & Palmer, T. M. (2015). Accelerated modern human-induced species losses: Entering the sixth mass extinction. Science Advances, 1(5). https://doi.org/10.1126/SCIADV.1400253 Cheung, W. W. L., Frölicher, T. L., Lam, V. W. Y., Oyinlola, M. A., Reygondeau, G., Rashid Sumaila, U., Tai, T. C., I, L. C. L., & Wabnitz, C. C. C. (2021). Marine high temperature extremes amplify the impacts of climate change on fish and fisheries. Science Advances, 7(40). https://doi.org/10.1126/SCIADV.ABH0895 Cottrell, R. S., Nash, K. L., Halpern, B. S., Remenyi, T. A., Corney, S. P., Fleming, A., Fulton, E. A., Hornborg, S., Johne, A., Watson, R. A., & Blanchard, J. L. (2019). Food production shocks across land and sea. Nature Sustainability, 2(2), 130–137. https://doi.org/10.1038/s41893-018-0210-1 de Leeuw, J., Vrieling, A., Shee, A., Atzberger, C., Hadgu, K. M., Biradar, C. M., Keah, H., & Turvey, C. (2014). The potential and uptake of remote sensing in insurance: A review. Remote Sensing, 6(11), 10888–10912. https://doi.org/10.3390/rs61110888 24 | Chapter 1 Fischer, E. M., Sippel, S., & Knutti, R. (2021). Increasing probability of record-shattering climate extremes. Nature Climate Change 2021 11:8, 11(8), 689–695. https://doi.org/10.1038/s41558-021-01092-9 Freychet, N., Hegerl, G. C., Lord, N. S., Lo, Y. T. E., Mitchell, D., & Collins, M. (2022). Robust increase in population exposure to heat stress with increasing global warming. Environmental Research Letters, 17(6), 064049. https://doi.org/10.1088/1748-9326/AC71B9 Galluzzi, G., Halewood, M., Noriega, I. L., & Vernooy, R. (2016). Twenty-five years of international exchanges of plant genetic resources facilitated by the CGIAR genebanks: a case study on global interdependence. Biodiversity and Conservation, 25(8), 1421–1446. https://doi.org/10.1007/S10531-016-1109-7 Glotter, M., & Elliott, J. (2016). Simulating US agriculture in a modern Dust Bowl drought. Nature Plants 2016 3:1, 3(1), 1–6. https://doi.org/10.1038/nplants.2016.193 Grassini, P., Eskridge, K. M., & Cassman, K. G. (2013). Distinguishing between yield advances and yield plateaus in historical crop production trends. Nature Communications, 4, 1–11. https://doi.org/10.1038/ncomms3918 Green, K. M., Selgrath, J. C., Frawley, T. H., Oestreich, W. K., Mansfield, E. J., Urteaga, J., Swanson, S. S., Santana, F. N., Green, S. J., Naggea, J., & Crowder, L. B. (2021). How adaptive capacity shapes the Adapt, React, Cope response to climate impacts: insights from small-scale fisheries. Climatic Change, 164(1–2), 1–22. https://doi.org/10.1007/S10584-021-02965-W Hardaker, J. B., Huirne, R. B. M., Anderson, J. R., & Lien, G. (2004). Coping with Risk in Agriculture (Google eBook). In CABI Publishing, Wallingford. Hasegawa, T., Sakurai, G., Fujimori, S., Takahashi, K., Hijioka, Y., & Masui, T. (2021). Extreme climate events increase risk of global food insecurity and adaptation needs. Nature Food 2021 2:8, 2(8), 587–595. https://doi.org/10.1038/s43016-021-00335-4 Hasegawa, T., Wakatsuki, H., Ju, H., Vyas, S., Nelson, G. C., Farrell, A., Deryng, D., Meza, F., & Makowski, D. (2022). A global dataset for the projected impacts of climate change on four major crops. Scientific Data, 9(1), 58. https://doi.org/10.1038/s41597-022-01150-7 Hellin, J., Hansen, J., Rose, A., & Braun, M. (2017). Scaling up agricultural adaptation through insurance. 16. https://cgspace.cgiar.org/bitstream/handle/10568/92977 Horton, R. M., de Sherbinin, A., Wrathall, D., & Oppenheimer, M. (2021). Assessing human habitability and migration. Science, 372(6548), 1279–1283. https://doi.org/10.1126/SCIENCE.ABI8603 Hsiang, S. M., Burke, M., & Miguel, E. (2013). Quantifying the influence of climate on human conflict. Science, 341(6151). https://doi.org/10.1126/science.1235367 Iizumi, T., Furuya, J., Shen, Z., Kim, W., Okada, M., Fujimori, S., Hasegawa, T., & Nishimori, M. (2017). Responses of crop yield growth to global temperature and socioeconomic changes. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-08214-4 Intergovernmental Panel on Climate Change (IPCC). (2023). Climate Change 2022 – Impacts, Adaptation and Vulnerability. In Climate Change 2022 – Impacts, Adaptation and Vulnerability. https://doi.org/10.1017/9781009325844 Ivanovich, C. C., Sun, T., Gordon, D. R., & Ocko, I. B. (2023). Future warming from global food consumption. Nature Climate Change 2023 13:3, 13(3), 297–302. https://doi.org/10.1038/s41558-023-01605-8 General introduction | 25 Jägermeyr, J., Müller, C., Ruane, A. C., Elliott, J., Balkovic, J., Castillo, O., Faye, B., Foster, I., Folberth, C., Franke, J. A., Fuchs, K., Guarin, J. R., Heinke, J., Hoogenboom, G., Iizumi, T., Jain, A. K., Kelly, D., Khabarov, N., Lange, S., … Rosenzweig, C. (2021). Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nature Food 2021 2:11, 2(11), 873–885. https://doi.org/10.1038/s43016-021-00400-y Jagermeyr, J., Robock, A., Elliott, J., Muller, C., Xia, L., Khabarov, N., Folberth, C., Schmid, E., Liu, W., Zabel, F., Rabin, S. S., Puma, M. J., Heslin, A., Franke, J., Foster, I., Asseng, S., Bardeen, C. G., Toon, O. B., & Rosenzweig, C. (2020). A regional nuclear conflict would compromise global food security. Proceedings of the National Academy of Sciences of the United States of America, 117(13), 7071–7081. https://doi.org/10.1073 Jasechko, S., & Perrone, D. (2021). Global groundwater wells at risk of running dry. Science, 372(6540), 418– 421. https://doi.org/10.1126/science.abc2755 Kaiser, J. (2004). Wounding Earth’s fragile skin. Science, 304(5677), 1616–1618. https://doi.org/10.1126/science.304.5677.1616 Kanter, D. R., Bartolini, F., Kugelberg, S., Leip, A., Oenema, O., & Uwizeye, A. (2019). Nitrogen pollution policy beyond the farm. Nature Food 2019 1:1, 1(1), 27–32. https://doi.org/10.1038/s43016-019-0001-5 Kozicka, M., Havlík, P., Valin, H., Wollenberg, E., Deppermann, A., Leclère, D., Lauri, P., Moses, R., Boere, E., Frank, S., Davis, C., Park, E., & Gurwick, N. (2023). Feeding climate and biodiversity goals with novel plant-based meat and milk alternatives. Nature Communications 2023 14:1, 14(1), 1–13. https://doi.org/10.1038/s41467-023-40899-2 Langridge, P., Braun, H., Hulke, B., Ober, E., & Prasanna, B. M. (2021). Breeding crops for climate resilience. Theoretical and Applied Genetics, 134(6), 1607–1611. https://doi.org/10.1007/S00122-021-03854-7 Lesk, C., Rowhani, P., & Ramankutty, N. (2016). Influence of extreme weather disasters on global crop production. Nature, 529(7584), 84–87. https://doi.org/10.1038/nature16467 Linnerooth-Bayer, J., & Mechler, R. (2006). Insurance for assisting adaptation to climate change in developing countries: A proposed strategy. Climate Policy, 6(6), 621–636. https://doi.org/10.1080/14693062.2006.9685628 Lipper, L., Thornton, P., Campbell, B. M., Baedeker, T., Braimoh, A., Bwalya, M., Caron, P., Cattaneo, A., Garrity, D., Henry, K., Hottle, R., Jackson, L., Jarvis, A., Kossam, F., Mann, W., McCarthy, N., Meybeck, A., Neufeldt, H., Remington, T., … Torquebiau, E. F. (2014). Climate-smart agriculture for food security. Nature Climate Change, 4(12), 1068–1072. https://doi.org/10.1038/nclimate2437 Lowder, S. K., Skoet, J., & Raney, T. (2016). The Number, Size, and Distribution of Farms, Smallholder Farms, and Family Farms Worldwide. World Development, 87, 16–29. https://doi.org/10.1016/j.worlddev.2015.10.041 Mach, K. J., Kraan, C. M., Adger, W. N., Buhaug, H., Burke, M., Fearon, J. D., Field, C. B., Hendrix, C. S., Maystadt, J.-F., O’Loughlin, J., Roessler, P., Scheffran, J., Schultz, K. A., & von Uexkull, N. (2019). Climate as a risk factor for armed conflict. Nature, 571(7764), 193–197. https://doi.org/10.1038/s41586- 019-1300-6 McDonald, A. J., Balwinder-Singh, Keil, A., Srivastava, A., Craufurd, P., Kishore, A., Kumar, V., Paudel, G., Singh, S., Singh, A. K., Sohane, R. K., & Malik, R. K. (2022). Time management governs climate 26 | Chapter 1 resilience and productivity in the coupled rice–wheat cropping systems of eastern India. Nature Food 2022 3:7, 3(7), 542–551. https://doi.org/10.1038/s43016-022-00549-0 Menegat, S., Ledo, A., & Tirado, R. (2022). Greenhouse gas emissions from global production and use of nitrogen synthetic fertilisers in agriculture. Scientific Reports 2022 12:1, 12(1), 1–13. https://doi.org/10.1038/s41598-022-18773-w Meuwissen, M. P. M., Feindt, P. H., Midmore, P., Wauters, E., Finger, R., Appel, F., Spiegel, A., Mathijs, E., Termeer, K. J. A. M., Balmann, A., de Mey, Y., & Reidsma, P. (2020). The Struggle of Farming Systems in Europe: Looking for Explanations through the Lens of Resilience. EuroChoices, 19(2), 4–11. https://doi.org/10.1111/1746-692X.12278 Mueller, N. D., Gerber, J. S., Johnston, M., Ray, D. K., Ramankutty, N., & Foley, J. A. (2012). Closing yield gaps through nutrient and water management. Nature, 490(7419), 254–257. https://doi.org/10.1038/nature11420 Mukherji, A. (2022a). Sustainable Groundwater Management in India Needs a Water-Energy-Food Nexus Approach. Applied Economic Perspectives and Policy, 44(1), 394–410. https://doi.org/10.1002/AEPP.13123 Mukherji, A. (2022b). The “water machine” of Bengal. Science, 377(6612), 1258–1259. https://doi.org/10.1126/SCIENCE.ADE0393 Nalau, J., & Verrall, B. (2021). Mapping the evolution and current trends in climate change adaptation science. Climate Risk Management, 32, 100290. https://doi.org/10.1016/J.CRM.2021.100290 Nelson, D. R. (2011). Adaptation and resilience: responding to a changing climate. Wiley Interdisciplinary Reviews: Climate Change, 2(1), 113–120. https://doi.org/10.1002/WCC.91 Niles, M. T., & Salerno, J. D. (2018). A cross-country analysis of climate shocks and smallholder food insecurity. PLOS ONE, 13(2), e0192928. https://doi.org/10.1371/journal.pone.0192928 Pimm, S. L., Jenkins, C. N., Abell, R., Brooks, T. M., Gittleman, J. L., Joppa, L. N., Raven, P. H., Roberts, C. M., & Sexton, J. O. (2014). The biodiversity of species and their rates of extinction, distribution, and protection. Science, 344(6187). https://doi.org/10.1126/SCIENCE.1246752 Rahimi, J., Mutua, J. Y., Notenbaert, A. M. O., Marshall, K., & Butterbach-Bahl, K. (2021). Heat stress will detrimentally impact future livestock production in East Africa. Nature Food 2021 2:2, 2(2), 88–96. https://doi.org/10.1038/s43016-021-00226-8 Richards, M., Bruun, T. B., Campbell, B. M., Gregersen, L. E., Huyer, S., Kuntze, V., Madsen, S. T. N., Oldvig, M. B., & Vasileiou, I. (2015). How countries plan to address agricultural adaptation and mitigation An analysis of Intended Nationally Determined Contributions. Richardson, K., Steffen, W., Lucht, W., Bendtsen, J., Cornell, S. E., Donges, J. F., Drüke, M., Fetzer, I., Bala, G., von Bloh, W., Feulner, G., Fiedler, S., Gerten, D., Gleeson, T., Hofmann, M., Huiskamp, W., Kummu, M., Mohan, C., Nogués-Bravo, D., … Rockström, J. (2023). Earth beyond six of nine planetary boundaries. Science Advances, 9(37). https://doi.org/10.1126/SCIADV.ADH2458 Rippke, U., Ramirez-Villegas, J., Jarvis, A., Vermeulen, S. J., Parker, L., Mer, F., Diekkrüger, B., Challinor, A. J., & Howden, M. (2016). Timescales of transformational climate change adaptation in sub-Saharan African agriculture. Nature Climate Change, 6(6), 605–609. https://doi.org/10.1038/nclimate2947 General introduction | 27 Rosa, L., Chiarelli, D. D., Rulli, M. C., Dell’Angelo, J., & D’Odorico, P. (2020). Global agricultural economic water scarcity. Science Advances, 6(18). https://doi.org/10.1126/SCIADV.AAZ6031 Rosenzweig, C., Elliott, J., Deryng, D., Ruane, A. C., Müller, C., Arneth, A., Boote, K. J., Folberth, C., Glotter, M., Khabarov, N., Neumann, K., Piontek, F., Pugh, T. a M., Schmid, E., Stehfest, E., Yang, H., & Jones, J. W. (2014). Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proceedings of the National Academy of Sciences, 111(9), 3268–3273. https://doi.org/10.1073/pnas.1222463110 Sarkodie, S. A., & Strezov, V. (2019). Economic, social and governance adaptation readiness for mitigation of climate change vulnerability: Evidence from 192 countries. Science of The Total Environment, 656, 150– 164. https://doi.org/10.1016/j.scitotenv.2018.11.349 Sarr, B., Atta, S., & Kafando, L. (2012). Review of climate indices used in agricultural index insurance systems in Africa . Science et Changements Planetaires – Secheresse, 23(4), 255–260. https://doi.org/10.1684/sec.2012.0348 Siebert, A. (2016). Analysis of Index Insurance Potential for Adaptation to Hydroclimatic Risks in the West African Sahel. Weather, Climate, and Society, 8(3), 265–283. https://doi.org/10.1175/WCAS-D-15-0040.1 Tack, J., Barkley, A., & Hendricks, N. (2017). Irrigation offsets wheat yield reductions from warming temperatures. Environmental Research Letters, 12(11), 114027. https://doi.org/10.1088/1748-9326/aa8d27 Thornton, P., Nelson, G., Mayberry, D., & Herrero, M. (2021). Increases in extreme heat stress in domesticated livestock species during the twenty-first century. Global Change Biology, 27(22), 5762–5772. https://doi.org/10.1111/GCB.15825 Wang, X. S., He, L., Ma, X. H., Bie, Q., Luo, L., Xiong, Y. C., & Ye, J. S. (2022). The emergence of prolonged deadly humid heatwaves. International Journal of Climatology, 42(16), 8607–8618. https://doi.org/10.1002/JOC.7750 Warren, D. D. and D. C. and N. R. and J. P. and R. (2014). Global crop yield response to extreme heat stress under multiple climate change futures. Environmental Research Letters, 9(3), 34011. http://stacks.iop.org/1748-9326/9/i=3/a=034011 Wuepper, D., Borrelli, P., & Finger, R. (2019). Countries and the global rate of soil erosion. Nature Sustainability 2019 3:1, 3(1), 51–55. https://doi.org/10.1038/s41893-019-0438-4 Yang, Y., Jin, Z., Mueller, N. D., Driscoll, A. W., Hernandez, R. R., Grodsky, S. M., Sloat, L. L., Chester, M. V., Zhu, Y. G., & Lobell, D. B. (2023). Sustainable irrigation and climate feedbacks. Nature Food 2023 4:8, 4(8), 654–663. https://doi.org/10.1038/s43016-023-00821-x 28 | Chapter 1 Chapter 2 The gap between intent and climate action in agriculture This chapter is adapted from Vyas, S., Khatri-Chhetri, A., Aggarwal, P., Thornton, P., Campbell, B.M., 2022. Perspective: The gap between intent and climate action in agriculture. Global Food Secur. 32,100612. http://dx.doi.org/10.1016/j.gfs.2022.100612 Abstract Following the UNFCCC Paris Agreement, most nations made commitments within their Nationally Determined Contributions (NDCs) to adaptation and mitigation in agriculture. However, these commitments need to be assessed in relation with ground truth, including biophysical and socio-economic limits to climate action. We propose a new framework for monitoring climate action by countries/regions, based on four dimensions—intent, need, scope and readiness for implementing adaptation and mitigation in agriculture. While “intent” reflects intended climate action by countries such as those mentioned in NDCs or NAPs and NAMAs, “need” highlights vulnerability of a country’s agriculture to climate change and historical GHG emissions. The third dimension, “scope”, is related to the biophysical opportunities and limits to adapt or to mitigate. Finally, the “readiness” dimension considers a country’s current ability to implement various adaptation/mitigation actions and policies. The framework is illustrated with a global analysis, using selected indicators for each of these dimensions. Results indicate that 61 countries globally (including key food producers) should consider corrective action. The framework presented in this chapter can serve as a monitoring and evaluation mechanism for NDC implementation and tracking progress. Keywords: Climate change, NDC, adaptation, mitigation, Paris agreement, readiness, climate hotspots, agriculture, climate action 30 | Chapter 2 2.1 Introduction Recent studies project a significant impact of climate change including more frequent extreme weather events, on food production, distribution, and consumption across the world (Cottrell et al., 2019; Godde et al., 2021). Food production, agriculture, and other land-use activities also account for 23% of anthropogenic emissions (Rivera et al., 2019). Rising to these challenges requires adaptation and mitigation actions at different scales by stakeholders (Bapna et al., 2019; UNFCC, 2017). Nationally Determined Contributions (NDCs), submitted by member nations under “The Paris Agreement” outline individual country pledges to climate action7. Agriculture is one of the critical sectors in prioritizing national mitigation and adaptation plans across the NDCs for 148 and 131 countries respectively (FAO, 2016). This intent is very encouraging, however implementing these actions is highly contingent upon the alignment of critical drivers which affect their feasibility. In this chapter, we propose a new framework for monitoring and tracking climate action in agriculture. The framework can help assess the types and feasibility of climate actions required in agriculture, and in understanding suitable pathways to achieve the goals of the Paris Agreement. Framework for monitoring climate action in agriculture The proposed framework examines four inter-related dimensions for climate action in agriculture—the intent, the need for action, the scope for action and the readiness to implement (Figure 2.1). Intended climate action should be aligned with a country’s need and scope for adaptation and mitigation in agriculture; and its readiness to implement activities, which is influenced by its policy landscape and enabling conditions. We focus on climate action (adaptation and mitigation) required until the 2050s, as this period is critical to keep planetary changes within environmental limits (Rogelj et al., 2016; Steffen et al., 2015) and supporting sustainable development. Each dimension and the potential data and indicators are discussed below: Intent It is critical to understand a country’s intent to undertake action for climate adaptation and mitigation independent of its capacity for implementation. This intent can be judged by the policy actions and/or budgets allocated for this purpose. Inclusion of agricultural adaptation 7 https://unfccc.int/documents/9176 The gap between intent and climate action in agriculture | 31 and mitigation actions in NDC documents, National Action Plans (NAPs) and Nationally Appropriate Mitigation Actions (NAMAs) submitted to UNFCCC, along with other domestic policy measures can be used to represent the intent dimension of the monitoring framework (Kuramochi et al., 2020). There are multiple studies available to assess the intent for climate action through NDCs (Richards et al., 2015b) (FAO, 2020), but most are limited to specific regions or sectors. Need Climate action is needed by many countries to achieve collective global objectives of the Paris agreement, but the specific need for adaptation is also influenced by vulnerability at national and sub-national scales. Similarly, for mitigation, focus on land-use change and emission targets becomes important. Risk is often characterized as an intersection of climate hazards, exposure and vulnerability (consisting of socio-economic factors, among others) (Collins et al., 2019). In addition, understanding the balance between food demand and supply and its exposure to climatic risk is also critical to identify potential food insecure regions. For example, if a country is food self-sufficient or produces surplus with high projected climatic impacts, it may be less likely to prioritize an increase in food production, but rather focus on maintaining growth and implementing risk management interventions. On the other hand, if a country has a food deficit coupled with high projected climatic impacts, it may need to prioritize adaptation actions, even though trade can modify its response. Similarly, mitigation needs might be appropriately linked to a country’s historical emissions, land-use systems (IPCC, 2019), food production priorities and capabilities to implement mitigation in agriculture. The need for mitigation and allocation of mitigation targets is context-dependent and there are various methods and tools available to estimate mitigation targets in agriculture (Frank et al., 2017; Richards et al., 2018). Scope The growth in crop production seen since the Green Revolution can be attributed mainly to an increase in productivity (yield gap closure) and crop area expansion (Bren d’Amour et al., 2017). The magnitude of the crop yield gap can be used as an indicator of scope—larger the gap, higher the scope for change. There are some studies and data available to measure crop yield gaps like (Mueller et al., 2012) and (http://www.yieldgap.org/), but these are limited by the number of countries analyzed. Diversification opportunities to expand livestock and fish 32 | Chapter 2 culture could be additional indicators of scope. Expansion of the arable area for crop cultivation can be another criterion to assess the scope of adaptation through land-use change. Emission intensity in terms of food production (CO2 equivalent emissions from croplands and livestock production per calorie or per unit of production) is a potentially useful criterion to understand the scope for mitigation in agriculture. It is better than absolute emissions per ha of land because it reflects both emissions and food production, an important consideration for countries to meet their national food security targets. Readiness There are many indicators which can be chosen to represent readiness. Ideally, the readiness index to analyze the framework presented in this chapter should a) combine both biophysical and socio-political dimensions which adequately represent the readiness to implement climate action and b) be able to represent most of the countries and should not be limited in its spatial scale. Available indicators which can be considered are global adaptation index (https://gain.nd.edu/about), change readiness index (https://home.kpmg/xx/en/home/insights/2019/06/2019-change-readiness-index.html) and World Bank’s enabling the business of agriculture (https://eba.worldbank.org/). Figure 2.1 Framework for analyzing climate action for adaptation and mitigation in agriculture. The gap between intent and climate action in agriculture | 33 2.2 Data and methods The framework outlined above allows for monitoring climate action in agricultural adaptation and mitigation. We apply the framework using publicly available indicators and data, to represent the need, scope, readiness, and intent for adaptation in agriculture. We have chosen global agriculture NDC data (Richards et al., 2015a) to represent the intent for adaptation. Indicator for the need dimension is based on a recent analysis of the gap between national future food demand and supply, assessed along with projected impacts of climate change on food production in the 2050s (Aggarwal et al., 2019). The scope for adaptation is envisaged as potential for adaptation in agriculture (we have limited the analysis here to crops and not included livestock due to limited data availability), based on a country’s biophysical limits. It includes increasing crop production by reducing crop yield gaps and increasing cultivated area. To represent this, yield gap as % of attainable yields for cereal crops (maize, wheat and rice) was calculated (Mueller et al., 2012) and arable land as fraction of total agricultural land was estimated using land statistics from FAO (year 2019). Notre Dame-Global Adaptation Index for the year 2019 (ND-GAIN) (Sarkodie and Strezov, 2019) is a generic readiness indicator. In the absence of another suitable global indicator for agriculture, we have assumed that ND-GAIN also represents the differences in readiness (for implementing climate action) among countries for agriculture sector as well. Although we have chosen indicators that we believe adequately represent the various dimensions of the framework, there could be other suitable indicators that can be used. Future research on developing a specific readiness index for climate action in agriculture would be useful. 2.3 Results Figure 2.2 shows results for countries based on the four dimensions of the framework. The scope for adaptation, however, is represented by two variables—cereal yield gap and available arable land. Most of the higher-income countries are in the upper left corner of the graph, indicating high scope (due to possibility of expanding arable area despite having low yield gaps) and high readiness despite low-medium need. On the other hand, lower-income countries, especially those of the African continent, are in the right lower quadrant of the graph indicating high scope and low readiness despite high need and intent. Many key food producers (like India, China, Brazil) have medium scope and readiness. The framework illustrated here is dynamic—the indicator used for readiness is publicly available and updated 34 | Chapter 2 every year (the ND-GAIN Index is available since 1995), which allows for tracking the progress of each country over a period, based on its position. Figure 2.2 Illustration of framework with a scatterplot of intent, need, scope and readiness of different countries for adaptation in agriculture. Focus on adaptation in agriculture of Nationally Determined Contributions (NDCs) of countries is taken as an indicator of intent. Need for adaptation is represented by traffic light color of the symbols. High need countries are those with a projected future food production deficit and high negative impacts of climate change (more than 10% loss), medium need countries also have similar food production deficit but low negative climate impacts (less than 10%), and low need countries have negligible food production deficit and no negative climate impacts. Scope for adaptation is represented by cereal yield gap (percentage), and by the available arable land as symbol size. Higher the yield gap or available arable land, higher is the scope. Readiness for adaptation is illustrated by Notre Dame Global Adaptation Initiative’s (ND- GAIN) Country Index. For ease of interpretation and visualization these results are grouped into twelve distinct classes—combinations of three classes of need (high, medium, and low), and two classes for each of scope and readiness (high and low) (Figure 2.3). Alignment of need with intent, scope and readiness is the key objective of the clustering analysis. Results show that most of the countries need to act urgently on adaptation. Countries (and regions) like Brazil, most of Sub- Saharan Africa and Central Asia, Bangladesh and Indonesia require focus on adaptation actions in agriculture, as their needs are high whereas scope and/or readiness are low. A few The gap between intent and climate action in agriculture | 35 higher-income countries of northern and eastern Europe are also hotspots due to projected climate change impacts (Iglesias and Rosenzweig, 2009; Parry et al., 2004), and limited scope for yield gap closure and cropland expansion, despite high readiness. For these countries, food imports from other countries may be an effective adaptation pathway. In comparison, most of the higher-income countries of Western Europe, North America and Australia have high readiness to adapt and variable scope, but their needs are low to medium due to limited food security concerns. Such countries have by and large not committed to adaptation actions in their NDCs. Globally, most of the countries have high to medium need for adaptation, and concerted efforts are required to align adaptation initiatives in agriculture with ground realities. Figure 2.3 Global assessment based on intent, need, scope and readiness in adaptation for agriculture. For details of indicators, please refer to Figure 2.2. Thresholds for high, medium, and low yields are also given in Figure 2.2. High scope denotes yield gap more than 50% of the attainable yield and/or current arable land is less than 50% of the total agricultural land. Readiness for climate action of a country is considered high when Notre Dame Global Adaptation Initiative’s (ND-GAIN) Country Index is greater than 0.5. Countries which intend to undertake climate action in agriculture in the Nationally Determined Contributions (NDCs) are shown by hatching. 36 | Chapter 2 2.4 Discussion and conclusion We highlight several crucial takeaways from this study. First, the framework presented in this chapter can serve as an important monitoring and evaluation mechanism for NDC implementation. The framework serves as a starting point to develop a comprehensive monitoring mechanism to track NDC progress. Similar mechanisms are already developed for other collective global goals such as the Sustainable Development Goals (SDG) (https://sdg- tracker.org/). Most indicators which can be used for this framework are reported annually, thus enabling a temporal analysis. Future research integrating synergies and trade-offs between different components of the framework through modelling can further help enhance the current work. Second, results for adaptation show a mismatch between the four dimensions of climate action—particularly amongst developing nations. We found that 61 countries (52% of the total reviewed) have high need for adaptation but a mismatch between scope, intent and/or readiness. On the contrary, 11% of the countries have low needs in adaptation, and a focus on adaptation in the NDC. Adaptation finance today accounts for only 5% of global climate finance, of which only 23% is invested in agriculture, forestry, land-use and natural resource management (CPI, 2018), and is well below what is required (Campbell et al., 2018; Odhong’ et al., 2019). For developing countries with limited financial resources, alignment of policy initiatives with need, scope and readiness is essential, so that their fast-depleting financial resources are used to support what they need at priority. The framework presented in this analysis would need periodic updating as its dimensions are likely to change with development and climate change scenarios. For example, the need for adaptation based on projected climate impacts for the 2050s (and future food security) may change based on actual emissions reduction achieved (which will affect the projected climate impacts). The trajectories countries chose for adaptation will likely affect their mitigation results and vice-versa (Deng et al., 2017). Dietary changes in future may drive feed expansion at the expense of food production (The Eat-Lancet Commission, 2019). Projected land-use changes will influence the area available for farming (and can also cause deforestation and peatland degradation), and it should also be included in the scope. Policymakers across the world are also focusing on transforming food systems using sustainable and climate-smart pathways, through innovations in technology (Godde et al., 2021; Herrero et al., 2020). Once successful, these innovations would affect all dimensions of the framework. To conclude, the Paris The gap between intent and climate action in agriculture | 37 Agreement is widely viewed as an important policy and institutional framework for collective global climate action, especially for agriculture (Chand, 2020). The proposed framework provides a holistic way to contextualize and align climate change strategies with existing conditions and to help identify future trajectories. As countries learn to adjust to the new realities of climate change, scaling adaptation and mitigation will play a key role in changing the landscape of climate action across regions. 38 | Chapter 2 2.5 References Aggarwal, P., Vyas, S., Thornton, P., Campbell, B.M., 2019. How much does climate change add to the challenge of feeding the planet this century? Environ. Res. Lett. 14, 043001. https://doi.org/10.1088/1748- 9326/aafa3e Bapna, M., Brandon, C., Chan, C., Patwardhan, A., Dickson, B., 2019. Adapt now: a global call for leadership on climate resilience. Bren d’Amour, C., Reitsma, F., Baiocchi, G., Barthel, S., Güneralp, B., Erb, K.-H., Haberl, H., Creutzig, F., Seto, K.C., 2017. Future urban land expansion and implications for global croplands. Proc. Natl. Acad. Sci. 114, 8939–8944. https://doi.org/10.1073/pnas.1606036114 Campbell, B.M., Hansen, J., Rioux, J., Stirling, C.M., Twomlow, S., (Lini) Wollenberg, E., 2018. Urgent action to combat climate change and its impacts (SDG 13): transforming agriculture and food systems. Curr. Opin. Environ. Sustain. 34, 13–20. https://doi.org/10.1016/j.cosust.2018.06.005 Chand, A., 2020. Paris Agreement needs food system change. Nat. Food 1, 772–772. https://doi.org/10.1038/s43016-020-00205-5 Collins M., M. Sutherland, L. Bouwer, S.-M. Cheong, T. Frölicher, H. Jacot Des Combes, M. Koll Roxy, I. Losada, K. McInnes, B. Ratter, E. Rivera-Arriaga, R.D. Susanto, D. Swingedouw, and L. Tibig, 2019: Extremes, Abrupt Changes and Managing Risk. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press. Cottrell, R.S., Nash, K.L., Halpern, B.S., Remenyi, T.A., Corney, S.P., Fleming, A., Fulton, E.A., Hornborg, S., Johne, A., Watson, R.A., Blanchard, J.L., 2019. Food production shocks across land and sea. Nat. Sustain. 2, 130–137. https://doi.org/10.1038/s41893-018-0210-1 CPI, 2018. Global Climate Finance: An Updated View. https://doi.org/10.1007/978-3-642-39564-2_4 Deng, H.-M., Liang, Q.-M., Liu, L.-J., Anadon, L.D., 2017. Co-benefits of greenhouse gas mitigation: a review and classification by type, mitigation sector, and geography. Environ. Res. Lett. 12, 123001. https://doi.org/10.1088/1748-9326/aa98d2 FAO, 2020. A common framework for agriculture and land use in the nationally determined contributions, A common framework for agriculture and land use in the nationally determined contributions. https://doi.org/10.4060/cb1589en FAO, 2016. The agriculture sectors in the Intended Nationally Determined Contributions: Analysis. Frank, S., Havlík, P., Soussana, J.-F., Levesque, A., Valin, H., Wollenberg, E., Kleinwechter, U., Fricko, O., Gusti, M., Herrero, M., Smith, P., Hasegawa, T., Kraxner, F., Obersteiner, M., 2017. Reducing greenhouse gas emissions in agriculture without compromising food security? Environ. Res. Lett. 12, 105004. https://doi.org/10.1088/1748-9326/aa8c83 Godde, C.M., Mason-D’Croz, D., Mayberry, D.E., Thornton, P.K., Herrero, M., 2021. Impacts of climate change on the livestock food supply chain; a review of the evidence. Glob. Food Sec. 28, 100488. https://doi.org/10.1016/j.gfs.2020.100488 Herrero, M., Thornton, P.K., Mason-D’Croz, D., Palmer, J., Benton, T.G., Bodirsky, B.L., Bogard, J.R., Hall, The gap between intent and climate action in agriculture | 39 A., Lee, B., Nyborg, K., Pradhan, P., Bonnett, G.D., Bryan, B.A., Campbell, B.M., Christensen, S., Clark, M., Cook, M.T., de Boer, I.J.M., Downs, C., Dizyee, K., Folberth, C., Godde, C.M., Gerber, J.S., Grundy, M., Havlik, P., Jarvis, A., King, R., Loboguerrero, A.M., Lopes, M.A., McIntyre, C.L., Naylor, R., Navarro, J., Obersteiner, M., Parodi, A., Peoples, M.B., Pikaar, I., Popp, A., Rockström, J., Robertson, M.J., Smith, P., Stehfest, E., Swain, S.M., Valin, H., van Wijk, M., van Zanten, H.H.E., Vermeulen, S., Vervoort, J., West, P.C., 2020. Innovation can accelerate the transition towards a sustainable food system. Nat. Food 1, 266–272. https://doi.org/10.1038/s43016-020-0074-1 Iglesias, A., Rosenzweig, C., 2009. Effects of Climate Change on Global Food Production from SRES Emissions and Socioeconomic Scenarios. Kuramochi, T., Roelfsema, M., Hsu, A., Lui, S., Weinfurter, A., Chan, S., Hale, T., Clapper, A., Chang, A., Höhne, N., 2020. Beyond national climate action: the impact of region, city, and business commitments on global greenhouse gas emissions. Clim. Policy 20, 275–291. https://doi.org/10.1080/14693062.2020.1740150 Mueller, N.D., Gerber, J.S., Johnston, M., Ray, D.K., Ramankutty, N., Foley, J.A., 2012. Closing yield gaps through nutrient and water management. Nature 490, 254–257. https://doi.org/10.1038/nature11420 Odhong’, C., Wilkes, A., van Dijk, S., Vorlaufer, M., Ndonga, S., Sing’ora, B., Kenyanito, L., 2019. Financing Large-Scale Mitigation by Smallholder Farmers: What Roles for Public Climate Finance? Front. Sustain. Food Syst. 3, 3. https://doi.org/10.3389/fsufs.2019.00003 P.R. Shukla, J. Skea, E. Calvo Buendia, V. Masson-Delmotte, H.- O. Pörtner, D.C.R., P. Zhai, R. Slade, S. Connors, R. van Diemen, M. Ferrat, E. Haughey, S. Luz, S. Neogi, M. Pathak, J. Petzold, J.P., Pereira, P. Vyas, E. Huntley, K. Kissick, M. Belkacemi, J.M., 2019. IPCC: Summary for policymakers, Summary for Policymakers. In: Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. https://doi.org/http://www.ipcc.ch/publications_and_data/ar4/wg2/en/spm.html Parry, M., Rosenzweig, C., Iglesias, A., Livermore, M., Fischer, G., 2004. Effects of climate change on global food production under SRES emissions and socio-economic scenarios. Glob. Environ. Chang. 14, 53–67. https://doi.org/10.1016/j.gloenvcha.2003.10.008 Richards, M., Bruun, T.B., Campbell, B.M., Gregersen, L.E., Huyer, S., Kuntze, V., Madsen, S.T.N., Oldvig, M.B., Vasileiou, I., 2015a. How countries plan to address agricultural adaptation and mitigation An analysis of Intended Nationally Determined Contributions, How countries plan to address agricultural adaptation and mitigation. Richards, M., Gregersen, L., Kuntze, V., Madsen, S., Oldvig, M., Vasileiou, I., 2015b. Agriculture’s prominence in the INDCs strategies. Richards, M.B., Wollenberg, E., van Vuuren, D., 2018. National contributions to climate change mitigation from agriculture: allocating a global target. Clim. Policy 18, 1271–1285. https://doi.org/10.1080/14693062.2018.1430018 Rivera, A., Bravo, C., Buob, G., 2019. Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems, in: IPCC. https://doi.org/10.1002/9781118786352.wbieg0538 Rogelj, J., den Elzen, M., Höhne, N., Fransen, T., Fekete, H., Winkler, H., Schaeffer, R., Sha, F., Riahi, K., Meinshausen, M., 2016. Paris Agreement climate proposals need a boost to keep warming well below 2 °C. Nature 534, 631–639. https://doi.org/10.1038/nature18307 40 | Chapter 2 Sarkodie, S.A., Strezov, V., 2019. Economic, social and governance adaptation readiness for mitigation of climate change vulnerability: Evidence from 192 countries. Sci. Total Environ. 656, 150–164. https://doi.org/10.1016/j.scitotenv.2018.11.349 Steffen, W., Richardson, K., Rockstrom, J., Cornell, S.E., Fetzer, I., Bennett, E.M., Biggs, R., Carpenter, S.R., de Vries, W., de Wit, C.A., Folke, C., Gerten, D., Heinke, J., Mace, G.M., Persson, L.M., Ramanathan, V., Reyers, B., Sorlin, S., 2015. Planetary boundaries: Guiding human development on a changing planet. Science (80-. ). 347, 1259855–1259855. https://doi.org/10.1126/science.1259855 The Eat-Lancet Commission, 2019. Healthy Diets From Planet; Food Planet Health, The Lancet. UNFCC, 2017. United Nations Climate Change Annual Report 2017. https://doi.org/10.1016/j.parkreldis.2015.02.017 The gap between intent and climate action in agriculture | 41 Supplementary information Supplementary table S2.1 Definition, indicators used and data source (including year) for results shown in Figures 2.2 and 2.3. Definition Indicator Source Intent for adaptation Commitment for agricultural adaptation in the Nationally Determined Contributions (NDC) of a country Presence or absence of agriculture in adaptation targets and actions, in the Nationally Determined Contributions (NDC) of a country (Richards et al., 2015) Need for adaptation Need or requirement of adaptation in agriculture based on national food security and climate change Future food production gap and projected impacts of climate change on cereal yields in 2050s (Aggarwal et al., 2019) Scope for adaptation Potential for adaptation in agriculture, based on a country’s biophysical limits. It includes increasing crop production by reducing crop yield gaps and increasing cultivated area through expansion of available arable land Yield gap, as a percentage of attainable yields for cereal crops and available arable area for cultivation (arable land as ratio of total agricultural land) (Mueller et al., 2012) FAO (2019) Readiness for adaptation An index to assesses the enabling environment and preparedness for scaling out technologies, practices, and services for adaptation and mitigation in agriculture Composite index of different enabling factors for climate action—economic, social, governance, financial, information and physical indicators Notre Dame Global Adaptation Initiative’s (ND-GAIN index (2019) 42 | Chapter 2 Supplementary table S2.2 Descriptive statistics for each of the indicators used to represent different dimensions. Indicator Count Type Mean Standard deviation Minimum Maximum Intent for adaptation 118 Categorical 0=no focus in Nationally Determined Contributions (NDC), 1= adaptation focus in Nationally Determined Contributions (NDC) .66 .475 0 1 Need for adaptation 118 Categorical 1= low need, 2 = medium need, 3= high need 2.34 .861 1 3 Scope for adaptation Yield gap 118 Percentage 47.69 21.73 1.54 89.37 Scope for adaptation Arable land as ratio of total agricultural land 118 Ratio .40 .26 .005 .98 Readiness index Notre Dame Global Adaptation Initiative’s (ND-GAIN index (2019) 118 Index 50.53 12.06 30.77 76.70 Supplementary references Aggarwal, P., Vyas, S., Thornton, P., Campbell, B.M., 2019. How much does climate change add to the challenge of feeding the planet this century? Environ. Res. Lett. 14, 043001. https://doi.org/10.1088/1748- 9326/aafa3e Mueller, N.D., Gerber, J.S., Johnston, M., Ray, D.K., Ramankutty, N., Foley, J.A., 2012. Closing yield gaps through nutrient and water management. Nature 490, 254–257. https://doi.org/10.1038/nature11420 Richards, M., Bruun, T.B., Campbell, B.M., Gregersen, L.E., Huyer, S., Kuntze, V., Madsen, S.T.N., Oldvig, M.B., Vasileiou, I., 2015. How countries plan to address agricultural adaptation and mitigation An analysis of Intended Nationally Determined Contributions. The gap between intent and climate action in agriculture | 43 44 Chapter 3 Mapping global research on agricultural insurance This chapter is adapted from Vyas, S., Dalhaus, T., Kropff, M., Aggarwal, P., Meuwissen, M.P.M., 2021. Mapping global research on agricultural insurance. Environ. Res. Lett. 16, 103003. http://dx.doi.org/10.1088/1748-9326/ac263d Abstract With a global market of 30 billion USD, agricultural insurance plays a key role in risk finance and contributes to climate change adaptation by achieving Sustainable Development Goals (SDGs) including no poverty, zero hunger, and climate action. The existing evidence in agricultural insurance is scattered across regions, topics and risks, and a structured synthesis is unavailable. To address this gap, we conducted a systematic review of 796 peer-reviewed papers on agricultural insurance published between 2000 and 2019. The goal of this review was twofold: 1) categorizing agricultural insurance literature by agricultural product insured, research theme, geographical study area, insurance type and hazards covered, and 2) mapping country-wise research intensity of these indicators vis-à-vis historical and projected risk and crisis events—extreme weather disasters, projected temperature increase under SSP5 (Shared Socioeconomic Pathways) scenario and livestock epidemics. We find that insurance research is focused on high-income countries while crops are the dominating agricultural product insured (33% of the papers). Large producers in production systems like fruits and vegetables (South America), millets (Africa) and fisheries and aquaculture (Southeast Asia) are not focused upon in the literature. Research on crop insurance is taking place where historical extreme weather disasters are frequent (correlation coefficient of 0.75), while we find a surprisingly low correlation between climate change induced temperature increases in the future and current research on crop insurance, even when sub-setting for papers on the research theme of climate change and insurance (-0.04). There is also limited evidence on the role of insurance to scale adaptation and mitigation measures to de-risk farming. Further, we find that the study area of livestock insurance papers is weakly correlated to the occurrence of livestock epidemics in the past (-0.06) and highly correlated to the historical drought frequency (0.51). For insurance to play its relevant role in climate change adaptation as described in the Sustainable Development Goals (SDGs), we recommend governments, insurance companies and researchers to better tune their interest to risk-prone areas and include novel developments in agriculture which will require major investments, and, hence, insurability, in the coming years. Keywords: Agricultural insurance, climate change, systematic review, mapping, livestock epidemics, extreme weather disasters 46 | Chapter 3 3.1 Introduction Agricultural insurance is a global billion-dollar industry growing at a fast rate. In 2019 alone, the insurance market was worth 30 billion USD (Wang et al., 2020). Climate change is an important driver of agricultural system instability and is expected to increase the frequency and intensity of risks in many regions across the globe (IPCC, 2018). Among different on- farm risk management tools available, one important strategy to manage these risks is agricultural insurance. State-supported insurance subsidies are common in many countries, amounting to over 20 billion USD annually (Hazell and Varangis, 2020). Effective insurance policies stabilize farm income, reduce poverty (SDG 1) and ensure a climate safety net for food producers (SDG 13). The welfare effects gained by insurance pay-offs can have multiple spill-over effects, including hunger reduction (SDG 2) (Siwedza and Shava, 2020). Therefore, insurance is a key element in agricultural adaptation to climate change, among other risk management tools. A synthesis of current agricultural insurance research can help in assessing the current work and in reshaping the future research agenda. However, evidence from existing reviews is scattered across different regions and sectors and is limited in scope. In fact, most systematic reviews on agricultural insurance are focused on index-based insurance only (Benami et al., 2021; Jan de Leeuw et al., 2014; Marr et al., 2016; Vroege et al., 2019). Furthermore, no study has compared the literature with existing risks and historical crisis events. This chapter addresses this gap by focusing on two objectives: 1) categorizing agricultural insurance literature by agricultural product insured, research theme, geographical study area, insurance product type and hazards covered, and 2) mapping research intensity by country for these indicators vis-à-vis historical and projected risk and crisis events—extreme weather disasters, projected temperature increase under SSP5 (Shared Socioeconomic Pathways) scenario and livestock epidemics. We first describe the data and methods, followed by an overview of global insurance research and a comparison of research intensity with risks. The results contribute to our understanding of different indicators of agricultural insurance dynamics, including the role of insurance in dealing with likely environmental change and alignment with risk hotspots. Agricultural systems today face myriad risks, both biotic and abiotic in nature. Losses from pests and diseases in agriculture and livestock are significant, especially among smallholder farming systems in the LMICs (De Groote et al., 2020; Mason-D’Croz et al., 2020). At the Mapping global research on agricultural insurance | 47 same time, climate change and weather extremes drive major food shocks across the globe (Cottrell et al., 2019). Extreme weather events (including heatwaves, drought, floods and cold waves) cause an average loss of 10% in cereal production alone (Lesk et al., 2016), and reduce the food quality of many other crops (Dalhaus et al., 2020; Kawasaki and Uchida, 2016). Climate change (gradual change in temperature and precipitation over time) reduces global consumable food calories by 1% every year (Ray et al., 2019), with additional losses in other sectors like livestock and fisheries (Godde et al., 2021; Lam et al., 2020). Weather extremes are increasing in magnitude, especially in the food-deficit, developing regions, which has major ramifications on food prices (Malesios et al., 2020) and international trade (Burkholz and Schweitzer, 2019). The magnitude and likelihood of extreme events are further expected to increase under projected climate change scenarios in many breadbasket regions (Kharin et al., 2018). These risks and crisis events enlarge the need for farm risk management, which can include multiple strategies including crop and livestock management (improved nutrient and water management), diversification, using seasonal weather forecasts as decision support and ultimately, risk financing tools (including insurance). These farm management tools complement each other, and insurance solutions are often used if other risk management tools reach their limits (Meuwissen et al., 2019). With the increasing severity and frequency of risk events in agriculture (Fischer et al., 2021), there is an additional focus on viable insurance solutions to de-risk agriculture from weather and disease/pest risks. Comparing insurance research intensity with risks and crisis events can help in understanding this mismatch and can reshape the research agenda. 3.2 Data and methods Selection of literature A systematic review was conducted using a combination of search terms related to agricultural insurance in Scopus, a widely used scientific database for published research. The literature review was done based on the PRISMA guidelines (http://www.prisma- statement.org/), allowing a replicable list of results (also provided as a Supplementary file). We focus on the peer-reviewed literature and thus excluded grey l