AUS5773 AGRICULTURE GLOBAL PRACTICE DISCUSSION PAPER 09 AGRICULTURAL RISK MANAGEMENT IN THE FACE OF CLIMATE CHANGE WORLD BANK GROUP REPORT NUMBER AUS5773 OCTOBER 2015 AGRICULTURE GLOBAL PRACTICE DISCUSSION PAPER 09 AGRICULTURAL RISK MANAGEMENT IN THE FACE OF CLIMATE CHANGE © 2015 World Bank Group 1818 H Street NW Washington, DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org Email: feedback@worldbank.org All rights reserved This volume is a product of the staff of the International Bank for Reconstruction and Development/The World Bank. The findings, interpretations, and conclusions expressed in this paper do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Rights and Permissions The material in this publication is copyrighted. Copying and/or transmitting portions or all of this work without permission may be a violation of applicable law. The International Bank for Reconstruction and Development/The World Bank encourages dissemination of its work and will normally grant permission to reproduce portions of the work promptly. For permission to photocopy or reprint any part of this work, please send a request with complete information to the Copyright Clear- ance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA, telephone 978-750-8400, fax 978-750-4470, http://www.copyright .com/. All other queries on rights and licenses, including subsidiary rights, should be addressed to the Office of the Publisher, The World Bank, 1818 H Street NW, Washington, DC 20433, USA, fax 202-522-2422, e-mail pubrights@worldbank.org. Cover photos from top left clock wise: Nonie Reyes/World Bank; Alex Proimos; Pablo Tosco/Oxfam; Darin. CONTENTS Acknowledgments v List of Abbreviations vii Glossary ix Executive Summary xi Chapter One: Conceptualizing Climate Change Implications for ARM 1 Chapter Two: Climate Change Risks in Agriculture 7 Production Risks 8 Temperature Fluctuations 8 Market Risk 15 Enabling Environment Risks 19 Chapter Three: Implications of Climate Change for ARM 23 References 31 Appendix A: Overview of the Impacts of Changing Climate Averages on Agriculture 37 Appendix B: Introduction to the World Bank’s Agricultural Risk Management Approach 41 BOXES Box 1.1: Key Clarifications 2 Box 1.2: Shifting Temperature Distribution 3 Box 2.1: Agriculture Is Part of the Problem and the Solution to Climate Change 8 Box 2.2: Impacts of Climate Change on Average Growing Conditions and the Supply of Food 17 Box 3.1: Making Robust Decision Despite Deep Uncertainties About the Future 29 FIGURES Figure ES.1: Illustration of Key Mutual Points of Relevance between Climate Change and Agriculture Risk Management xiii Figure ES.2: Overview of the Elements of the New Normal of Climate Change and Implications for Agricultural Risk Management xiv Figure 1.1: Illustration of the Evolution of a Temperature Distribution with a +4°C Change in Average Temperature 1 Figure B1.1.1: Changes in Climate Variability Trigger Changes in Weather and Climate Risks 2 Figure 1.2: Illustration of Climate Change Shifting the Mean of a Temperature Distribution 3 Figure B1.2.1: Shift in the Summer Temperatures on the Landmass of the Northern Hemisphere 3 Figure 1.3: Illustration of the Effect of Climate Change on the Tails of a Temperature Distribution 4 Figure 1.4: Illustration of the Effect of Climate Change on the Variability of a Temperature Distribution 5 Figure 1.5: Illustration of the Effect of Climate Change on Climatic Uncertainty 6 Agricultural Risk Management in the Face of Climate Change iii Figure 2.1: Percentile Change in the Number of Days Under Drought Conditions by the End of the 21st Century (2070–2099) 10 Figure 2.2: Observed Tropical Cyclone Tracks and Intensity for All Known Storms over the Period 1947–2008 13 Figure 2.3: Modeled Price Impacts of Extreme Weather Event Scenarios in 2030 16 Figure B2.2.1: World Price Effects for the Major Grains (in U.S. dollars 2000), Assuming No Carbon Dioxide Fertilization Effect under Two Different Models (CSIRO and NCAR) 17 Figure 2.4: Illustration of a Large Climate and Weather Event Disrupting Entire Producer Clusters 18 Figure 2.5: Time Dependence of FAO Food Price Index from January 2004 to May 2011 20 Figure 3.1: Illustration of Key Mutual Points of Relevance between Climate Change and Agriculture Risk Management 24 Figure 3.2: Schematic Illustrating How ARM Can Offer a Pathway to Achieving Resilience Focused CSA Outcomes 25 Figure 3.3: Example of a Prioritization Matrix from the Niger Country Agriculture Risk Assessment Using Option Filtering Approach (World Bank 2013a). 26 Figure 3.4: Risk Assessment and Management Cycle 27 Figure B3.1.1: An Iterative Process of Decision Making to Prompt Robust Action in the Face of Uncertainty 29 Figure B2.1: Agricultural Risk Management Framework 42 Figure B2.2: Illustration of Risk Layering Approach 42 TABLES Table 2.1: Definitions and Indices Most Commonly Used in Climate Literature to Describe Extreme Precipitation 11 Table A1.1: Direct and Indirect Impacts of Climate Change on Livestock Production Systems 39 iv Agricultural Global Practice Discussion Paper ACKNOWLEDGMENTS Agricultural Risk Management in the Face of Climate Change was prepared by a team led by Vikas Choudhary (Senior Economist, GFADR; Task Team Leader) and consisted of Tobias Baedeker (Agricultural Economist, GFADR), Aira Maria Htenas (Operations Officer, GFADR), Jitendra P. Srivastava (Consultant, GFADR), and Alona Gutman (Consultant, GFADR). The team is grateful to Marc Sadler (Lead Economist, GFADR) and Mark E. Cack- ler (Practice Manager, GFADR) for their valuable guidance and support. Ana Elisa Bucher (Climate Change Specialist, GCCPT), Diego Arias Carballo (Senior Agricul- ture Economist, GFADR), and Ademola Braimoh (Senior Natural Resource Manage- ment Specialist, GFADR) kindly peer reviewed the report and provided very insightful suggestions. This work was financed by the World Bank and a Multidonor Trust Fund supported by the Ministry of Foreign Affairs of the Government of the Netherlands and State Secretariat for Economic Affairs (SECO) of the Government of Switzerland. Agricultural Risk Management in the Face of Climate Change v LIST OF ABBREVIATIONS ARM agriculture risk management RVF Rift Valley Fever ARMT agriculture risk management team UNFCCC United Nations Framework Convention on CSA climate-smart agriculture Climate Change FAO Food and Agriculture Organization WB World Bank GHG greenhouse gas emissions WMO World Meteorological Organization IPCC Intergovernmental Panel on Climate Change IRRI International Rice Research Institute All dollar amounts are U.S. dollars unless otherwise indicated. PDSI Palmer Drought Severity Index Agricultural Risk Management in the Face of Climate Change vii GLOSSARY Adaptation: The process of adjustment to actual or Climate variability: Refers to variations in the mean state expected climate and its effects. In human systems, adapta- and other climate statistics (standard deviations, the occur- tion seeks to moderate or avoid harm or exploit beneficial rence of extremes, and so on) on all temporal and spatial opportunities. In some natural systems, human intervention scales beyond those of individual weather events. Variability may facilitate adjustment to expected climate and its effects. may result from natural internal processes within the climate Agricultural risk: The possibility of an event or events system (internal variability) or from variations in natural or that can create an unexpected, unplanned outcome, usually anthropogenic external forces (external variability). resulting in losses. There are three main attributes of risk: Extreme climate event: See Extreme weather event. event hazard, uncertainty, and losses (World Bank 2015). Extreme weather event: An extreme weather event is an Climate: Climate in a narrow sense is usually defined as the event that is rare within its statistical reference distribution at average weather, or more rigorously, as the statistical descrip- a particular place. Definitions of rare vary, but an extreme tion in terms of the mean and variability of relevant quanti- weather event would normally be as rare as or rarer than ties over a period of time ranging from months to thousands the 10th or 90th percentile. By definition, the characteristics or millions of years. The classical period is 30 years, as defined of what is called extreme weather may vary from place to by the World Meteorological Organization (WMO). These place. An extreme climate event is an average of a quantities are most often surface variables such as tempera- number of weather events over a certain period of time, ture, precipitation, and wind. Climate in a wider sense is the an average which is itself extreme (for example, rainfall over state, including a statistical description, of the climate system. a season). Climate change: Climate change refers to a statistically Mitigation: Mitigation has different definitions in the cli- significant variation in either the mean state of the climate mate change and risk management communities respectively. or in its variability, persisting for an extended period (typi- In the former, the mitigation of climate change is defined as cally decades or longer). Climate change may be due to anthropogenic interventions to reduce the sources or enhance natural internal processes or external forces, or to persistent the sinks of greenhouse gases. In the area of risk management anthropogenic changes in the composition of the atmos- and for the purposes of this report, risk mitigation is defined phere or in land use. In its Article 1, the Framework Conven- as activities designed to reduce the likelihood of an adverse tion on Climate Change (UNFCCC) defines climate change event or reduce the severity of actual losses. as: a change of climate which is attributed directly or indi- Rapid climate change: The non-linearity of the climate rectly to human activity that alters the composition of the system may lead to rapid climate change, sometimes global atmosphere and which, in addition to natural climate called abrupt events or even surprises. Some such abrupt variability, is observed over comparable time periods. events may be imaginable, such as a dramatic reorganiza- Climate prediction: A climate prediction or climate tion of the thermohaline circulation, rapid deglaciation, forecast is the result of an attempt to produce a most likely or massive melting of permafrost leading to fast changes description or estimate of the actual evolution of the climate in the carbon cycle. Others may be truly unexpected, as in the future, for example at seasonal, inter-annual, or long- a consequence of a strong, rapidly changing forcing of a term time scales. non-linear system. Climate projection: A projection of the response of the Resilience: The capacity of social, economic, and environ- climate system to emission or concentration scenarios of green- mental systems to cope with a hazardous event or trend or house gases and aerosols, or radiative forcing scenarios, often disturbance, responding or reorganizing in ways that main- based upon simulations by climate models. Climate projections tain their essential function, identity, and structure, while are distinguished from climate predictions in order to empha- also maintaining the capacity for adaptation, learning, and size that climate projections depend upon the emission/con- transformation. centration/radiative forcing scenario used, which are based on Uncertainty: An expression of the degree to which a assumptions, concerning, for example, future socio-economic value (for example, the future state of the climate system) is and technological developments, that may or may not be real- unknown. Uncertainty can result from lack of information or ized, and are therefore subject to substantial uncertainty. from disagreement about what is known or even knowable. Agricultural Risk Management in the Face of Climate Change ix It may have many types of sources, from quantifiable errors between climate change attributable to human activities in the data to ambiguously defined concepts or terminology, altering the atmospheric composition, and climate variability or uncertain projections of human behavior. Uncertainty can attributable to natural causes. therefore be represented by quantitative measures (for exam- Vulnerability: The propensity or predisposition to be ple, a range of values calculated by various models) or by adversely affected. Vulnerability encompasses a variety of qualitative statements (for example, reflecting the judgment concepts and elements including sensitivity or susceptibility of a team of experts). to harm and lack of capacity to cope and adapt. United Nations Framework Convention on Weather: is the state of the atmosphere with respect to Climate Change (UNFCCC): In its Article 1, the UNF- wind, temperature, cloudiness, moisture, pressure, and so on. CCC defines climate change as: “a change of climate which Weather refers to these conditions at a given point in time (for is attributed directly or indirectly to human activity that alters example, today’s high temperature), whereas Climate refers the composition of the global atmosphere and which is in to the “average” weather conditions for an area over a long addition to natural climate variability observed over compa- period of time (for example, the average high temperature for rable time periods.” The UNFCCC thus makes a distinction today’s date). (NOAA 2015). All definitions unless otherwise stated are from the Intergovernmental Panel on Climate Change (IPCC) (2014). x Agricultural Global Practice Discussion Paper EXECUTIVE SUMMARY While all sectors of economic activity experience hazards and unexpected events aris- ing from the “damaging whims of nature,” agriculture is one of the riskiest: Weather events and climate patterns directly cause significant production volatility, can often have indirect ripple effects in markets for agricultural inputs and outputs as well as ultimately lead to reactionary shifts in legal and policy frameworks. Few other sectors and their stakeholders are so immediately dependent on weather and climate. Climate change is becoming a source of significant additional risks for agriculture and food systems. Climate projections suggest that impacts will include shifting average growing conditions, increased climate and weather variability, and more uncertainty in predicting tomorrow’s climate and weather conditions. More concretely, these impacts will translate into an overall warming trend, an increasingly erratic distribution of precipitation, more frequent as well as far more devastating extreme events and spatial shifts in the occurrence of pests and diseases. Far from being a distant future reality, impacts are already being felt today. Research shows that many agricultural regions have already experienced declines in crop and livestock production due to climate change-induced stress (Lobell and Field 2007). Cli- mate disruptions to agricultural production have increased over the past 40 years and are projected to further increase over the next 25 years (Hartfield et al. 2014). While climate change is expected to produce both winners and losers overall, losses will far outweigh the gains ( Jarvis et al. 2011) and the poor will be disproportionally affected because of their dependence on agriculture and a lower capacity to adapt (World Bank 2008). The scale of projected impacts is alarming. For instance, each degree Celsius of global warming is projected to lead to an overall yield loss of about 5 percent (National Research Council 2011). As climate change progresses, it is increasingly likely that current cropping systems will no longer be viable in many locations. In Africa, for instance, under a range of scenarios progressing to 2050, 35 million farmers across 3 percent of the continent’s Agricultural Risk Management in the Face of Climate Change xi land area are anticipated to switch from mixed crop-live- of climatic and weather risks for agricultural production stock systems to livestock only ( Jones and Thornton 2008). and by highlighting trends as they emerge in the data. Agricultural risk management (ARM) is ideally placed Impacts from climate change on agriculture may be bro- to support stakeholders in building resilience to these ken into three categories: changes in average climate con- increased risks in short and medium term. While cli- ditions, climate variability, and climate uncertainty: mate change may introduce new types of extreme events » Average climate conditions may be defined in some locations, it most frequently will translate into as the expected temperature and precipitation in a “more (frequent and intense) of the same” hazards. ARM given location at a given time. Shifts in these aver- frameworks and approaches can point the way to identify ages and expected seasonal structural changes asso- optimal risk mitigation, transfer, and coping strategies— ciated with them are largely gradual in nature and and help identify appropriate actions for strengthening will require responses that may involve adjustments resilience and climate change adaptation. Please refer to in crop rotations, planting times, genetic selection, appendix A for a description of the ARM approach. fertilizer management, pest management, water management, and shifts in areas of production ARM can also play an important role in the transition to (Hartfield et al. 2014). Such change in long-term a climate-smarter agriculture system by offering a useful average growing conditions do not imply risk in the entry point for dialogue. The clear initial focus on the man- sense of exposure to sudden harmful impacts. agement of shorter term risks and their economic impact » Climate variability refers to variations in the mean can help create a sense of urgency and attract stakeholder state and other climate statistics (standard deviations, involvement that then paves the way for broader discus- the occurrence of extremes, and so on) on all temporal sions around climate-smart agriculture. and spatial scales beyond those of individual weather To understand the potential role of ARM in the global events. Increased climate variability will bring increas- response to climate change, two considerations are important. ingly frequent incidence of extreme weather events such as heat waves, droughts, and heavy precipitation. First, agricultural risks faced by hundreds of millions of In addition, risk of pest and disease events may in- farmers, traders, processors, retailers, and other stake- crease indirectly due to increases in climate variability. holders engaged in agricultural supply chains around the Since variability and extreme events are a key subject world can be usefully classified into production, market, of risk management, these impacts are the most rel- and enabling environment risks (World Bank 2013a). This evant in the context of this study. threefold categorization can help avoid the common fal- » Climate uncertainty is the degree to which lacy of exclusively situating climate change impacts in agri- we are currently unable to predict future climate. culture at producer level. Production losses due to climate While remaining challenging, projections of yearly and weather events, in concurrence with other factors, averages under climate change are currently sig- can have far wider reaching implications for entire supply nificantly more precise than projections of the im- chains and the food system as a whole. They can ultimately plied risk from extreme events and incidences of trigger government reactions such as export controls that pest and disease. In projecting the latter, significant can alter the enabling environment of the industry. error margins persist, particularly at local scales. In addition, because climate change is anthropogenic Second, climate change impacts that lead to short-term risk or man-made, uncertainty over future emissions events—highly relevant to agriculture risk management— translates into uncertainty over future climate. For need to be differentiated from slow-onset changes in several reasons, uncertainty over both weather and average climatic conditions, which are most relevant to climate is hence increasing with climate change. agriculture policy planning more broadly. ARM can how- ever play a key role in enabling longer term adaptation Under a changing climate, the past will often no longer be planning by increasing the awareness of the importance the best guide to the (climatic) future—and climate change xii Agricultural Global Practice Discussion Paper FIGURE ES.1. ILLUSTRATION OF KEY MUTUAL POINTS OF RELEVANCE BETWEEN CLIMATE CHANGE AND AGRICULTURE RISK MANAGEMENT What ARM can contribute to meeting the climate challenge: A proven tool for building resilience to climate Climate and weather volatility change A key entry point for the operationalization of climate-smart agriculture where resilience is first priority Implications of climate change for ARM: Agriculture Increasing risks = increasing importance of ARM risk Need to adapt frameworks and approaches: management a. Incorporation of climate projections b. Decision making under uncertainty & capacity building to meet the unknown will therefore also require a paradigm shift in ARM. As cli- Chapter 3 assesses the impact of climate change on agri- mate change creates new and often uncertain risks, increas- culture, including the following: ingly sophisticated tools will be needed to understand and » Production risks: Including temperature fluc- manage them. Future projections will need to be incorpo- tuations, drought events, heavy rainfall (including rated in risk models and methodologies for decision mak- floods), and other direct weather events, such as ing under deep uncertainty will need to be deployed. cyclones and storms, as well as indirect implica- tions of climate change, such as pests and diseases. This study seeks to understand the climate change impacts » Risk repercussions at the market level: on agricultural risk—how do risks change? —and on agricultural For instance, increasingly averse growing condi- risk management—how can agricultural risk managers respond? tions will impact food price volatility and increased This response has two elements: First, what role can ARM play extreme events will impact increasingly complex in meeting the climate change challenge? Second, how will ARM need to global supply chains. adapt its methodology to the “New Normal” of climate change? » Risks on the enabling environment: Extreme weather events and associated price changes can The study limits itself to a discussion of crops and live- indirectly contribute to reactive trade and domestic stock. The principle audience for this report comprises support policies; natural resource constraints may practitioners working on agriculture risk management further exacerbate underlying tensions and lead to and other interested stakeholders. instability or even violence and conflict. Chapter 2 of this report sketches a conceptualization of Chapter 4 assesses the implications of climate change climate change impacts on agricultural risk. It introduces impacts for agricultural risk management. It asks what the more important concepts and definitions needed to ARM can contribute to climate change adaptation and frame the content. This includes a brief discussion on resilience building and enquires how ARM needs to adjust concepts of weather, climate, and climate change; of the its methodologies to reflect the “new normal” of climate ways in which climate change will impact agriculture; and change, offering four key recommendations summarized of the relevancy of these impacts to ARM. in figure ES.1. Agricultural Risk Management in the Face of Climate Change xiii FIGURE ES.2. OVERVIEW OF THE ELEMENTS OF THE NEW NORMAL OF CLIMATE CHANGE AND IMPLICATIONS FOR AGRICULTURAL RISK MANAGEMENT ELEMENTS OF THE TYPE IMPLICATIONS DEMANDS ON ARM NEW NORMAL Average/expected conditions change Supporting trend identification and change 1.a) Mean/ awareness building Average Limited, subject of medium- to longer-term agricultural development/adaptation planning Redefinition of ‘extreme events’, in Managing deteriorating conditions often with many locations likely: increased risks even under full adaptation 1. Distribution on APPROXIMATE More extreme heat Providing sensitivity analysis to identify threats the move KNOWNS without precedent in a given location More extreme precipitation events (more/less volume, intensity change, Important to avoid mal-adapting an existing system 1.b) Tails to fundamentally different conditions. type change) More pests & diseases Advent of types of climate and weather events without precedent in a given location (e.g., wildfires, floods) In the most frequent case of increasing Managing increased risks 2. Change in variability and variability: Strengthening overall system resilience volatility KNOWN More extreme events (most frequently: UNKNOWNS Less predictability increase) More pests & diseases Climate projections have limited Strengthening overall risk management precision, particularly at local scales capacity to prepare for the unknown/ 3. Increase in uncertainty Reduced predictability of future unexpected (Projection confidence UNKNOWN climate & weather conditions Uncertainty-proofing decision-making uncertainty over future UNKNOWNS processes emissions) xiv Agricultural Global Practice Discussion Paper CHAPTER ONE CONCEPTUALIZING CLIMATE CHANGE IMPLICATIONS FOR ARM As a starting point in conceptualizing the implications of climate change for ARM, consider the below illustrations of the different effects climate change will entail. Figure 1.1 shows two probabilistic distributions of average maximum temperatures in a given location on a given day of the year. The solid green line shows that on aver- age and before climate change, the weather on that day was most likely to exhibit a maximum of 25°C or fall into the range just around it (23–27°C). (See box 1.1.) With climate change of +4°C, this average would move to 29°C (dotted green line). Note that the distribution shows the different probabilities of a particular temperature maximum occurring. As temperatures move away from the mean and toward the flat- ter parts of the distribution (the tails) manifestations become much less likely. If such a very unlikely event occurs (for example, a temperature above 30°C in the original distribution), scientists speak of an “extreme event.” FIGURE 1.1. ILLUSTRATION OF THE EVOLUTION OF A TEMPERATURE DISTRIBUTION WITH A +4°C CHANGE IN AVERAGE TEMPERATURE Frequency Projected temperature Historic temperature distributions with climate change distribution Historic average New average Temperature 25°C 29°C: +4°C Agricultural Risk Management in the Face of Climate Change 1 BOX 1.1. KEY CLARIFICATIONS Weather describes the atmospheric conditions at a specific FIGURE B1.1.1. CHANGES IN CLIMATE place and time. Climate, in a narrow sense, is often defined as the average weather, or more rigorously, as the statistical VARIABILITY TRIGGER description in terms of the mean and variability of relevant CHANGES IN WEATHER quantities over a period of time ranging from months to AND CLIMATE RISKS thousands or millions of years. Thus climate change typically refers to any change in the climate conditions over time that Δ Climate Δ Weather & climate variability risks can be identified by changes in the mean or the variability of its properties (IPCC 2014). conditions, such as extremes occurring within a season or Weather and climate as well as their derivative terms such varying rainfall quantities across seasons. as “climate variability” or “weather risk” are not always used consistently and may carry different meanings across A change in climate variability entails a change in the inci- the climate change and agriculture risk management dence of weather and climate risks. For instance, higher rain- communities. fall variability can increase the risks of flooding and drought. Based on the IPCC’s distinction between extreme weather Climate variability refers to variations in the mean state and and climate events (2012), climate risks can be defined as risks other climate statistics (standard deviations, the occurrence of arising from events happening over a longer time scale, for extremes, and so on) on all temporal and spatial scales beyond instance low rainfall over a season. Weather risk on the other those of individual weather events. While this definition for hand would be risks associated with a single weather event instance includes natural inter-decadal climate variability such as a heavy precipitation event. The distinction between or long-term climate change, the term climate variability the two terms is not precise (IPCC 2012) and they are used most commonly refers to shorter term variation in climate almost interchangeably in the ARM literature. Shift in Climatic Means To illustrate, figure 1.2 depicts a 4°C increase in the aver- In the ARM literature, changes in climate averages are age temperature maximum for a given location on a given often referred to as “trends” and are described to have day of the year. Such a change would gradually, yet pro- limited implications for ARM. Such “climate trends” may foundly, alter the climatic context of the agriculture sector give rise to complex questions of adaptation planning but in the region in question. For instance, a 4°C shift cor- answers will mostly need to come from broader agricul- responds to the difference in yearly average temperature ture development planning. maximums between New York (17°C) and San Diego (21°C).1 ARM’s role would be to manage the risks aris- To simplify, ARM is concerned with risks arising in the ing from corresponding shifts of the distribution’s tails short run, treating the agricultural system as a given. (part of what the ARM literature refers to as “risks”), not Adjustments to shifts in average growing conditions, so much to plan for and design the new agricultural pro- however, involve the development of policy responses to duction system fit for the new climatic average. By infer- medium- to long-term challenges. ARM can help inform ence, ARM’s role would not be to enable ultimately futile these choices but does not take a primary role. attempts to practice “New York agriculture” in a “San Diego climate.”2 It is important to note that when ARM is misguidedly deployed to protect farmers from the results of a change in 1 Comparison for illustrative purposes only, every local climate context is com- average growing conditions, maladaptation may result. Mal- plex and many variables are needed to describe and compare contexts. adaptation in this case would describe a process where ARM 2 This illustration uses temperature because temperature distributions are well helps to maintain a status quo that will eventually become approximated using simple normal distributions. Other climate variables such as precipitation tend to follow more complex statistical patterns, please refer to non-viable. The longer ARM prolongs the situation, the box 2.2. Moreover, different locations are best approximated by different dis- more time and resources are lost that could have been used to tributions. However, the basic concepts of mean, variability, and tails are also support the sector in the transition to a new adapted system. applicable here. 2 Agricultural Global Practice Discussion Paper FIGURE 1.2. ILLUSTRATION OF CLIMATE CHANGE SHIFTING THE MEAN OF A TEMPERATURE DISTRIBUTION Frequency Projected temperature Historic temperature distributions with climate change distribution 1 (a): Mean shift Historic average New average Temperature 25°C 29°C: +4°C The implications of a change in climatic means for ARM are lim- BOX 1.2. SHIFTING TEMPERATURE ited, as gradual slow-onset temperature or precipitation shifts at or DISTRIBUTION around the mean are unlikely to affect agricultural risks in the short Figure B1.2.1 illustrates such a shift in temperature distri- or medium terms (in difference to the moving tails described in the bution in the case of the shift in average temperatures on next section). the landmass of earth’s northern hemisphere over the past 50 years (Hansen et al. 2012). Shifting Tails FIGURE B1.2.1. SHIFT IN THE SUMMER As temperature means can shift, so can the tails of distri- TEMPERATURES butions. Since tails of temperature or precipitation distri- ON THE LANDMASS butions house climate and weather extremes, they are of OF THE NORTHERN more immediate importance to ARM. HEMISPHERE Extreme events are commonly defined by the likeli- 0.6 NH Land, Jun–Jul–Aug Normal distribution hood of their occurrence. Even if a climatic condition is 1951–1961 0.5 1961–1971 “extreme” compared to global averages (say the dryness 1971–1981 1981–1991 of a desert), it may not be classified as an extreme event if 0.4 1991–2001 2001–2011 it is a common occurrence in the given context (such as a 0.3 drought in the desert). 0.2 Shifting tails imply that the definitions of “extreme” 0.1 events will change. What used to be an extreme event 0 –5 –4 –3 –2 –1 0 1 2 3 4 5 before the shift may become a common occurrence while Source: Hansen et al. 2012. more extreme or altogether new events will be classified as “extreme events.” Take the example in figure 1.3. Origi- Strictly speaking, even a shift of the tails and the altered nally, a day above 30°C only occurs with a probability of 5 frequencies of extreme events that come with it result percent. With 4°C climate change however, temperatures directly from the overall climate change trend, akin to the 30°C and above lay only 1°C above the average and will shift in averages. occur much more frequently. 30°C is no longer an extreme. The new extreme events, occurring only with a probability If complete adaptation of production systems to new sets of 5 percent, would be temperatures of 34°C and above. of climatic conditions (including mean and tails) could be Agricultural Risk Management in the Face of Climate Change 3 FIGURE 1.3. ILLUSTRATION OF THE EFFECT OF CLIMATE CHANGE ON THE TAILS OF A TEMPERATURE DISTRIBUTION Frequency Projected temperature Historic temperature distributions with climate change distribution 1 (a): Mean shift Historic average New average Temperature 25°C 29°C: +4°C 1 (b): Tail shifting 5/100 Chance of NEW: 5/100 Chance of temperature > 30°C temperature > 34°C assumed, there would be no unambiguous effect of cli- change trend will lead to a deterioration of agricultural mate change on agricultural risks within a given system. conditions overall (Jarvis et al. 2011). One very impor- To illustrate, take a semi-arid region with a production tant case in point are the critical temperature thresholds system centered around maize as an example. Say the that all members of the “big four”—corn, rice, soybeans, climate change trend were to bring fully arid conditions and wheat—exhibit. When temperatures during certain with diminished average rain and a strong increase in the stages of plant growth exceed these thresholds, severe risk of drought. Assuming full adaptation, the produc- yield losses occur (see the section on Increasing Climate tion system might switch entirely to, as an example, the Variability). These crops are of systemic importance for production of dates and pastoralist livestock. In this case, food security. Equally calorie-productive substitutes are agricultural risks may even have diminished as a result of often not available. Farmers may therefore often have no the adaptation to the climate change trend. choice but to continue to grow the same crop as condi- tions deteriorate, particularly those practicing subsistence However, there are several reasons why shifting tails will, farming. In such contexts and all others where growing in many cases of climate variables and contexts, increase conditions worsen overall, agricultural risks will increase agricultural risks and hence the need for ARM. significantly—and so will the need for ARM. First, adaptation will take time. It will often require struc- Finally, new kinds of extreme events, may also be part of tural changes and involve transitional phases. For instance, this “new normal.” New types of climatic hazards may new types of physical and human capital will need to be affect regions without previous experience in managing accumulated and access to new markets developed. Dur- the risks associated with them. For instance, this dynamic ing transition periods, parts of the production system is particularly relevant for pests and diseases, where rela- may be maladapted to prevailing climatic conditions and tively minor deviations from average weather patterns can hence require increased ARM capacity. lead to non-linear changes in disease and pest prevalence. Second, full adaptation may often remain elusive. Espe- The possibility of the appearance of new types of extreme events cially in many tropical contexts, the direction of the climate will pose a new challenge to ARM. ARM will be required to develop 4 Agricultural Global Practice Discussion Paper FIGURE 1.4. ILLUSTRATION OF THE EFFECT OF CLIMATE CHANGE ON THE VARIABILITY OF A TEMPERATURE DISTRIBUTION Frequency 2 Increasing variability Projected temperature Historic temperature distributions with climate distribution change 1 (a): Mean shift Historic average New average Temperature 25°C 29°C: +4°C 1 (b): Tail shifting 5/100 Chance of NEW: 5/100 Chance of temperature > 30°C temperature > 34°C the capacity to identify thresholds triggering potential new hazards Increased Climate Uncertainty and anticipate which novel extreme events may arise to help prepare Finally, climate change means that we know less about farmers and national, as well as regional, systems in dealing with what the climate will be like in future times than we the risks associated. This will be particularly critical as it can help used to (see dotted lines in figure 1.5). Natural variation avoid the often drastic losses associated with the first appearance of a (internal variability) has always created uncertainty over locally as yet unknown risk. future climate conditions and only parts of it could be explained by science. Increasing Climate Variability Science predicts that climate change will alter climate vari- Today, the additional layer of man-made climate change ability, with increases expected for most locations. More (a type of external variability) introduces additional variability translates into less predictability of climate and uncertainty from two sources. First the phenomenon is weather. Increased variability can be observed in figure not fully understood and projections include errors of 1.4: As the temperature curve flattens, variability increases. varying importance depending on scale and nature of The expected temperature at the mean is still the most the phenomenon projected. For instance, projections of likely outcome, but it is less dominant than it previously highly relevant climate extremes tend to contain larger was. That is, other temperatures have become more likely errors than projections of average climatic conditions. A and the tails have “fattened,” showing the increased likeli- second source of uncertainty stems from our limited abil- hood of extreme events. This flattening implies overall less ity to predict the human behavior that drives man-made predictable and therefore more “variable” climate. climate change. Projections hinge on emissions scenarios, The resulting demands on ARM of increased climate variability particularly for longer time scales. are as straightforward as they are critical. As it has been to date, ARM’s job will be to protect such weather fluctuations from impact- ARM will need to take account of uncertainty over future climate, for ing production and farmer incomes. More variability will entail more instance by developing strategies to take robust decisions under uncer- frequent risk events and an increase in the degree of difficulty and tainty and further emphasizing institutional capacity that enables importance of managing risk. successful risk management under many scenarios. Agricultural Risk Management in the Face of Climate Change 5 FIGURE 1.5. ILLUSTRATION OF THE EFFECT OF CLIMATE CHANGE ON CLIMATIC UNCERTAINTY Frequency 2 Increasing variability 3 Uncertainty Historic temperature distribution Projected temperature distributions with climate change 1 (a): Mean shift Historic average New average Temperature 25°C 29°C: +4°C 1 (b): Tail shifting 5/100 Chance of NEW: 5/100 Chance of temperature > 30°C temperature > 34°C Conclusion events (see figure 1.3) and because climate change will alter the inherent climate variability (see figure 1.4). In summary, climate change requires adjusting both Finally the remaining uncertainty over future climate to new average climatic conditions and preparing for change will lead to more climatic uncertainty over- more volatile weather with more frequent and intense all (see figure 1.5). Together, these effects combine to extreme events in most locations (see figure 1.2). This form the “new normal,” to which all stakeholders will is because changes in average conditions also impact need to adapt. the frequency of what today are considered extreme 6 Agricultural Global Practice Discussion Paper CHAPTER TWO CLIMATE CHANGE RISKS IN AGRICULTURE Weather and climate risks are pervasive in the agriculture sector.3 Agricultural risks driven by the vagaries of weather are a daily reality for hundreds of millions of farm- ers, traders, processors, retailers, and other stakeholders engaged in agricultural sup- ply chains around the world. Since the beginning of time, actors have been exposed and have found ways to mitigate, transfer, and cope with risks both before (ex-ante) and after (ex-post) they occurred (Hess et al. 2004). These risks can be classified primarily into production risks, market risks, and enabling environment risks. Climate change will have impacts at all three levels. This threefold categorization of agricultural risks has previously been shown to be useful (World Bank 2013a) and can help avoid the common fallacy of exclu- sively situating climate change impacts in agriculture at producer level. Production losses due to climate and weather events, in concurrence with other factors, can have far reaching implications for entire supply chains and the food system as a whole. They can ultimately trigger government reactions such as export controls or subsidies that can alter the enabling environment of the industry. It is therefore necessary to examine climate change impacts on agricultural risk beyond the pro- duction level. Among the many implications of climate change for agriculture, the following chapters will focus exclusively on the domain of agricultural risk management: changes directly affecting short- to medium-term agricultural risks. That is, the changes that entail additional variability of weather and climate, more frequent and intense extreme events and higher uncertainty overall. For a brief summary of the impacts of chang- ing average conditions on the global agriculture system, see appendix A. For a more detailed discussion, refer to the IPCC (2014) chapter on the impacts of climate change on food security and food production systems.4 3 In this report, agriculture consists of crops and livestock. 4 http://ipcc-wg2.gov/AR5/images/uploads/WGIIAR5-Chap7_FGDall.pdf Agricultural Risk Management in the Face of Climate Change 7 BOX 2.1. AGRICULTURE IS PART OF THE can be attributed to anthropogenic or man-made climate PROBLEM AND THE SOLUTION TO change (IPCC 2013). Both heat- and cold-day extremes have a detrimental impact on crops, but climate change CLIMATE CHANGE will have different impacts on the probability of the Agriculture is a very significant part of the climate change occurrence of these events in a given season. According to problem. Agriculture and associated land use change the IPCC (2013), the number of cold days and nights has account for up to one quarter of greenhouse gas (GHG) decreased over the past several decades, while globally the emissions globally. It is the largest single contributing sector after energy. For many developing countries, agriculture is number of warm days and nights has increased. Fewer the largest source of emissions. frost days over time have been found for every country in which they have been studied (Easterling 2000). Fur- At the same time, agriculture has the potential to become ther, extreme minimum temperatures have had a strong part of the solution. A number of agriculture practices are known to reduce emissions or enable the sequestration of increasing trend in each season over the last several dec- carbon in soils and biomass. Moreover, by increasing pro- ades. Significantly, the frequency of heat waves over a ductivity, agriculture can help to reduce deforestation pres- large part of Europe, Asia, and Australia has increased, sures. with the probability of heat wave occurrence more than One key strategy to achieve this directional shift of the sec- doubling in some locations (IPCC 2013). Daily tempera- tor, is climate-smart agriculture (CSA). CSA is an approach ture extremes in Africa and South America have less cer- for transforming and reorienting agricultural systems to tainly been affected by climate change, but in most regions support food security under the new realities of climate of the globe that have enough indicative data available, change (Lipper et al. 2014). It aims to achieve three simul- there is at least medium confidence that the duration or taneous outcomes: Increased productivity, enhanced resil- frequency of heat waves or warm spells has increased ience, and reduced emissions. Examples of tools that can (IPCC 2011). increase the climate-smartness of production include a wide range of practices and approaches from agroforestry to rangeland management to climate and weather informa- EXTREME HEAT DAYS AND NIGHTS; tion services. HEAT WAVES Short-term temperature extremes can be critical for plant growth, especially when coinciding with key stages of PRODUCTION RISKS plant development. Plant physiology can be significantly This chapter assesses production risks amplified by increased altered beyond key temperature thresholds, leading to the frequency and intensity of extreme events as well as higher potential for severe crop yield impacts from projected cli- climatic variability overall. Building on the information mate change (Gornall et al. 2010). provided by publications such as the World Bank’s “Turn Down the Heat” Series (2014a), temperature fluctuations, For many crops, when a plant enters its flowering stage drought events, heavy rainfall (including floods), and other (including right before and after), just a few days of direct weather events—such as cyclones and storms—as extreme temperatures (greater than 32°C) can drastically well as indirect implications of climate change—such as reduce yield (Wheeler et al. 2000). For rice, if tempera- pests and diseases are discussed. Understanding how these tures at flowering exceed 35°C for more than just one will occur and what the overall risk landscape will look like hour, high percentages of the grains become sterile (Luo will be critical in developing measures to manage risks and 2009). In one experiment, soybeans produced nearly a adapt agriculture to climate change.5 third less in seed yields after experiencing a 10°C tem- perature increase for 8 days during the late flowering stage TEMPERATURE and early pod filling (Luo 2009). FLUCTUATIONS 5 For a discussion of the impacts on climate change on average growing con- Change in the occurrence of temperature extremes has ditions rather than short- to medium-term agriculture risks, please refer to been observed since the mid-20th century, some of which appendix A. 8 Agricultural Global Practice Discussion Paper Short-durations of high temperatures can also impact and Roberts 2009). In very wet areas, rain-fed crops have crops in other ways. Despite being typically produced in a similar level of resilience to heat stress as irrigated crops. high temperatures, groundnuts for instance see severely reduced yields when temperatures exceed 42°C even for While the impact of extreme heat on livestock has not short periods of time during post-flowering (Prasad et al. been well studied, it is known to cause physiological harm. 2003). For maize, short periods above 36°C reduce its pol- The thermal comfort zone of temperate-region cattle is len’s viability. Plants that require seasonal cold tempera- 5–15°C, although tropical breeds have higher heat toler- tures to flower, such as winter wheat, are also impacted ance (Sirohi and Michaelowa 2007). Temperatures above by periods of high temperatures, impeding the flowering this range affect livestock in four significant ways: (1) process. Furthermore, in the United States, crop yields are causing mortality through heat-stress, (2) reducing feed negatively impacted by temperatures above 29°C for corn, intake, (3) reducing dairy yields, and (4) affecting repro- 30°C for soybean and 32°C for cotton (Gornall et al. 2010). duction (Thornton et al. 2009). In general, most livestock species have comfort zones between 10 and 30°C, and at Without adaptation, even mid-latitude crops could suffer temperatures above this, animals reduce their feed intake at very high temperatures during critical growth stages. 3–5 percent per additional degree of temperature, so Recent increases in climate variability may have affected temperature extremes may have a large impact. crop yields in countries across Europe since around the mid-1980s causing higher inter-annual variability in wheat Finally, extreme heat poses significant risks for farmers yields (Porter and Semenov 2005). Changes in annual and rural labor directly. Several recent studies have shown yield variability would make wheat a high-risk crop in that many rural areas of the world will likely be exposed some locations, such as Spain. In 1972, an extremely high to prolonged heat waves that impact rural populations’ average summer temperature in the former Soviet Union health disproportionately severely (see for instance Bur- (USSR) contributed to widespread disruptions in world gess et al. 2015). cereal markets and food security (Battisti and Naylor 2009). Similarly high temperatures and drought have had FEWER COLD DAYS AND NIGHTS, an impact in Russia in recent years, including 2010 and DECREASE IN FROST OCCURRENCE 2012, impacting global wheat prices and policies, further Decreased incidences of cold days and nights are, inde- discussed in the following sections on market and enabling pendently, likely to have a positive impact on crop produc- environment risks. tion. Frost occurrences typically have a negative impact on crop production, so a decrease in the incidence of The sensitivity of production systems to extreme temper- frost and similar cold stresses will improve crop produc- atures is partly the result of biophysical relationships but tion globally. One of the crops likely to benefit from this is also depends strongly on their individual characteristics wheat, as less occurrences of frost will reduce the potential and context. For instance, while irrigated systems also face for chilling and freezing injuries (Government of Western stress under extreme temperatures, it is typically expected Australia 2013). However, the positive impact of less frost that rain-fed systems will experience more harm, since days is not expected to outweigh the negative impacts of transpiration cools canopies and prevents direct tempera- more frequent high temperature extremes. ture damage (Lobell and Gourdji 2012). Crops that are more frequently irrigated such as rice and sugarcane may Crops experience different risks from frost. Although therefore be less sensitive to extreme temperatures. cold extremes are typically harmful, cold temperatures are often important for pre-flowering plant stages, so In some instances, rice may even benefit from moder- a decrease in cold temperatures can also have negative ately higher maximum temperatures, until direct heat impacts in certain cases. For non-grain crops such as damage occurs (Welch et al. 2010). Similarly, irrigated fruits, production risks may result from variability, as seen maize in the western United States is much less sensitive in reduced low-temperature nights and earlier start of to extreme heat than rain-fed maize elsewhere (Schlenker the warm season. If the temperature drops shortly after Agricultural Risk Management in the Face of Climate Change 9 a brief warm period, fruits such as cherries, apples, and more intense and longer droughts due to climate pears may flower too early, harming yields if temperatures change, in particular southern Europe and West Africa drop. (IPCC 2011). Figure 2.1 shows the relative increase in the occurrence of drought conditions for a 4°C world relative to the 1976–2005 baseline (Prudhomme Drought Events et al. 2013). Drought is a climatic occurrence characterized by tem- porary moisture availability significantly below average Many of the largest reductions in crop productivity his- over a specified period. It can thus occur even in wet and torically have been attributed to anomalously low precipi- humid regions. Arid areas are prone to drought because tation events (Kumar et al. 2004; Sivakumar et al. 2005). the amount of rainfall often critically depends on a small Since the 1960s, major growing areas of barley, maize, number of rainfall events (Dai 2011). rice, sorghum, soybean, and wheat globally have seen an increase in the percentage of area affected by drought, Droughts arise from combinations of five factors: (1) from approximately 5–10 percent to approximately 15–25 Delays in the onset of rain or rainy seasons; (2) early ces- percent as defined in terms of the PDSI (Gornall et al. sation of rain or the rainy season; (3) prolonged periods 2010). Anthropogenic increases in greenhouse gas and without rainfall resulting in an unusual rainfall distribu- aerosol concentrations have made a measurable contribu- tion; (4) a lack in the volume of cumulative rainfall over tion to the observed drying trend in PDSI (Burke  et al. the growing season; and (5) water and soil moisture def- 2006; IPCC 2007). icits during critical stages of crop growth (for example, Droughts are expected to intensify with medium con- flowering). fidence in the 21st century in regions including south- All of these factors are likely to be impacted by cli- ern Europe and the Mediterranean region, central mate change. Some areas have already experienced Europe, central North America, Central America and Figure 2.1.  Percentile Change in the Number of Days Under Drought Conditions by the End of the 21st Century (2070–2099) Source: Prudhomme et al. 2013. Note: White regions: Hyper-arid regions for which runoff is equal to zero more than 90 percent of the time in the reference and future periods. 10 Agricultural Global Practice Discussion Paper Mexico, northeast Brazil, and southern Africa (IPCC include: precipitation from very wet days, simple daily 2011; figure ES.2). Monsoon failures in South Asia intensity index, wettest day, and wettest consecutive are a possibility of non-negligible likelihood (Nelson day (IPCC 2013). et al. 2010). Other areas have overall low confidence for drought intensification as a result of inconsistent More regions have likely seen increases in the number drought change projections, dependent both on model of heavy precipitation events than decreases (IPCC and dryness index. 2011). In North America and Europe, the frequency and intensity of heavy precipitation events has likely At mid to high latitudes, drought impacts may com- increased. Most countries that experienced a signifi- plicate the potential benefits the regions may experi- cant increase or decrease in monthly or seasonal pre- ence due to average increased temperature and season cipitation also experienced a disproportionate change length. In Russia, for instance, while some losses may in the amount of precipitation falling during the heavy be offset by gains in other areas, many of the main crop and extreme precipitation events. In some areas the fre- growing areas may experience crop production short- quency of 1-day heavy precipitation events increased falls twice as often in the 2020s, and triple in the 2070s but the seasonal total did not; this can indicate a (Alcamo et al. 2007). deficiency in available water for some of the month, followed by a harmful heavy precipitation event (East- Droughts also affect livestock significantly either erling 2000). Increases in extreme precipitation events, through reduced length of the growing period (Krist- including major storms, are responsible for a dispropor- janson et al. 2004), reduced feed and fodder availability, tionate share of the observed 5 to 10 percent increase or through lack of water. In India for instance, reduced in total annual precipitation that the United States has feed and water availability due to a drought in 1987 experienced since the early 20th century. affected 168 million cattle. The state of Gujarat alone, lost more than half of its cattle (Sirohi and Michaelowa 2007). In Mongolia, summer droughts have been observed to cause delayed rather than immediate fatal- ities. There, a summer drought prevents cattle from TABLE 2.1. DEFINITIONS AND INDICES MOST obtaining enough calories to subsequently weather the COMMONLY USED IN CLIMATE harsh winters and spring windstorms, causing delayed LITERATURE TO DESCRIBE fatalities during these periods (Batima 2006). Finally, EXTREME PRECIPITATION when droughts are followed by high rainfall, there has Name Description been some observance of increased outbreaks of dis- Precipitation from very wet Amount of precipitation eases (Thornton et al. 2009). days from days greater than the 95th percentile (mm) INCREASED PRECIPITATION EVENTS Simple daily intensity index Ratio of annual total Rising temperatures generally lead to heavier precipi- precipitation to the number of wet days, which are those tation events for two reasons. First more evapotran- with 1 mm of rain or more spiration under higher temperatures results in more (mm day) water vapor present in the atmosphere. Second, simul- Wettest day Maximum 1-day taneously, a warmer atmosphere can hold a greater precipitation (mm) amount of moisture (UCSUSA 2011). Extreme and Wettest consecutive 5 days Maximum of consecutive heavy precipitation has had multiple definitions in the five days of precipitation literature due to the diversity of climates to which the (mm) descriptions apply. The most common four definitions Source: IPCC 2013. Agricultural Risk Management in the Face of Climate Change 11 As mean surface temperature increases, extreme precipi- dence in flood changes resulting from climate change (IPCC tation events are very likely to become more intense and 2011). Despite low overall confidence in flood predictions, more frequent by the end of the 21st century over most of the expectation of heavy rains and temperature changes in the mid-latitude areas, especially in winter, and over wet some regions can imply, through physical reasoning, possible tropical regions (IPCC 2013). increases in flood risk in those locations (IPCC 2011). Heavy rainfalls associated with tropical cyclones are likely Flooding can have significant negative impacts on crop to increase with continued warming. In some regions, production. Heavy rainfall events that result in flooding increases in heavy precipitation will occur despite projected can wipe out entire crops over wide areas. For instance decreases in total precipitation in those regions. Multiple in Jamaica, flooding causes large-scale damage to sug- emissions scenarios suggest that in many regions, a cur- arcane, for which a high water table, about one foot rent once-in-20-year annual maximum daily precipitation below the surface is detrimental (IDB 2013). In addi- amount is likely to become a once-in-5 to once-in-15-year tion to high water tables, in coastal communities flood- event by the end of the 21st century. The proportion of ing can cause damage by increasing salinity, through total rain falling in heavy rainfall events appears to be saline water intrusion (IDB 2013). Flooding can also increasing, and this trend is expected to continue as the cli- have duplicitous harmful effects in countries with win- mate continues to warm. For instance, a doubling of car- ters if it occurs prior to winter freezes. In 2011, the bon dioxide is projected to lead to an increase in intense areas along the largest rivers in the United States—Mis- rainfall over much of Europe (Gornall et al. 2010). souri, Ohio, and Mississippi—experienced $3.4 billion of direct damage, including significant crop loss, due to Heavy rainfall can severely impact crop production. Over- flash flooding. The flash flooding occurred after heavy abundant water can result in reduced plant growth due to spring snowmelt was induced by heavy precipitation in poor seed distribution, germination and emergence, soil the Northern Plains in the summer and fall of the year and nutrient erosion, soil water logging, siltation of water prior (NOAA 2011). storage areas, and floods. For rice, it is especially harmful when heavy rain falls on freshly seeded fields, and is worse Flooding also harms livestock through multiple chan- if the field has been wet direct seeded. Heavy textured soils nels. Significant flooding, particularly in the form of flash tend to have a worse result (IRRI 2009). Heavy rainfall floods, can lead to significant livestock losses. In India at the crop maturity stage may be linked to crop lodging, alone, flooding has caused average losses of nearly 94 delayed harvest, higher grain moisture content, potentially thousand cattle annually (Sirohi and Michaelowa 2007). lower grain quality and increased frequency of fungal dis- In the year 2000, one state alone lost 84 of a total of ease infections of the grain (Kettlewell et al. 1999). In one 93 thousand cattle during Southwest monsoon floods case, due to the poor quality of the product, the amount (Sirohi and Michaelowa 2007). Furthermore flooding can of milling wheat exported from the UK decreased signifi- increase the spread of pests and diseases (see subsequent cantly (Kettlewell et al. 1999). If agricultural machinery is section). Finally flooding also affects feedstocks with pos- not appropriately adapted to wetter soil conditions, planting sible negative effects on availability and price. may also be delayed, leading to huge potential crop losses. EXTREME PRECIPITATION AND DROUGHT FLOODS RISK FOR IRRIGATED CROPLAND Changes in the magnitude and frequency of floods associated Water requirements for irrigation imply that deviations in with climate change are somewhat difficult to ascertain due climate patterns even in areas far from agricultural fields to limited instrumental records taken by gauge stations and can affect irrigated agricultural production. Agriculture complicating factors such as the simultaneous impact of land along the Nile in Egypt, for instance, depends on rain- use change and engineering, both of which have a signifi- fall in the upriver areas of the Nile such as the Ethiopian cant effect on flood occurrence. Therefore there is low confi- Highlands (Döll and Siebert 2002). 12 Agricultural Global Practice Discussion Paper Climate change may increase river flow for a number of in the future with stronger winds and heavier precipitation years due to a higher rate of glacier melt. However, this (IPCC 2007). High-resolution models indicate a possible may not always be beneficial. In central Asia for instance, decrease in the frequency of future global tropical cyclones the increased flow in Amu Daria comes in early spring (McDonald et al. 2005; Bengtsson et al. 2007; Gualdi et when crops do not require water and often causes harm- al. 2008). The models do not all agree on projections of ful floods. the regional variations in tropical cyclone frequency. Heavy and low precipitation events in areas besides pri- The implications of tropical cyclones for agriculture mary agricultural land may therefore have a significant can be important, particularly in developing countries impact. Despite overall increases in annual water avail- with high population growth rates in vulnerable tropi- ability, insufficient storage of peak season flow may lead cal and subtropical regions. Tropical cyclone tracks for to water scarcity that could affect irrigated crop produc- all known storms over the period 1945–2008 are shown tion, while overabundant rainfall could lead to flooding, in figure 2.2. indicating the critical importance of extreme or low pre- cipitation events outside river-irrigated croplands for agri- The agricultural regions found most vulnerable to tropi- cultural productivity. cal cyclones include the United States, China, Vietnam, India, Bangladesh, Myanmar, and Madagascar. The river deltas of countries along the North Indian Ocean are OTHER DIRECT WEATHER EVENTS especially vulnerable because farming in coastal regions Increase in Heavy Tropical Cyclone Activity most at risk from flooding has increased due to high popu- The degree and direction of global change in tropical lation growth (Webster 2008). In October 2010 typhoon cyclone frequency and intensity under a warming climate Megi damaged $44 million of agricultural products and is uncertain. Tropical cyclones may become more intense facilities in the Philippines, while typhoon Ketsana caused FIGURE 2.2. OBSERVED TROPICAL CYCLONE TRACKS AND INTENSITY FOR ALL KNOWN STORMS OVER THE PERIOD 1947–2008 Tracks are produced from the IBTrACS dataset of NOAA/NCDC. Source: Knapp et al. 2010. Agricultural Risk Management in the Face of Climate Change 13 $130 million damage in the agriculture sector in 2009 long summer heat wave Italy saw a prolonged period of (CGIAR 2013). Tropical cyclones may result in mixed very high temperatures which caused large fires and a 36 benefits to agriculture in some cases, including providing percent drop in maize production (IPCC, SREX 2011). relief from droughts and abating water shortage, wild- Other conditions leading to fires include droughts follow- fires, and saltwater intrusion. For instance, in February ing rainy seasons which can turn vegetation into fuel for 2000 cyclone Eline devastated agriculture in Madagascar, wildfires (IPCC, SREX 2011). Additionally, in the west- but later contributed significantly to beneficial rainfall in ern U.S. rangelands, droughts can promote the growth of southern Namibia (Gornall et al. 2010). invasive fire-fueling grasses (Walthall et al. 2012) Storm surge events can cause great devastation, even if Windstorms, storms with winds typically exceeding 34 miles land is not permanently lost. Relatively little work has per hour, but classified separately from cyclones and torna- been done to assess the impacts of either mean sea-level does, are expected to increase in intensity and frequency rise or storm surges on agriculture. with climate change (IPCC 2013). Livestock can be severely damaged by windstorms, as has been the case during a dzud Hail, Bushfire, Windstorm in 2009–10 in Mongolia. A dzud is an unusual weather con- dition combining heavy windstorms with heavy snowfalls. It Hailstorms are an extreme event very frequently associ- affected 50 percent of the livestock from the households of the ated with risk for agriculture. Hail has been known to country’s herders and by April, 75 thousand herder families prevent wheat flowering in Eastern European and Scan- had lost more than half or all of their livestock. dinavian countries, and has a great impact in some parts of the Middle East. It is typically considered a localized event, so most climate models’ resolutions are too coarse INDIRECT EFFECTS OF CLIMATE to simulate hailstorms explicitly. Therefore it has been AND WEATHER EVENTS—PESTS unclear whether such events will become more likely AND DISEASES through intensified thunderstorms or less likely as a result Climate change will have significant impacts on the of overall warmer conditions. Recent simulations of hail occurrence of pests and diseases because weather exerts generation and maintenance during extreme precipita- an influence on all stages of host and pathogen life cycles tion events in one area have indicated a near-elimination and the development of disease. Increasing climate vari- of hail at the surface in the future, despite more intense ability, higher average temperatures, warmer winter mini- future storms and significantly larger amounts of in-cloud mum temperatures, changes in precipitation patterns, and hail (Mahoney et al. 2012). The main reason for the disap- water shortages are all climate factors that may favor pest pearance of surface hail appears to be an increase in the and disease invasions. height of the environmental melting level due to higher temperatures increasing the melting of frozen precipita- Active debate is ongoing and significant uncertainty tion. A decrease in future surface hail at high-elevation remains regarding the likely effects of climate change on locations may imply potential changes in both hail dam- pests and diseases. Some argue that while the distribution age and flood risk (Mahoney et al. 2012). of diseases may be affected by some climate-related shifts in the areas suitable for vector-borne diseases—such as Predicted changes in the climate are expected to increase malaria and bluetongue—impacts in the shorter term are the frequency of fires, as a combination of earlier snow- not expected to be significant (Woolhouse 2006). melts, droughts, and long heat waves that create the con- ditions for their spread. One such example occurred in Other studies indicate that increases in climate variability 2009 in Victoria, Australia, where drought, record heat, and average conditions may extend the geographic range and a 35-day period without rain, created a high-risk fire of some insect pests. For instance, with a 1°C increase in location from an area normally considered low to medium temperature a northward shift in distribution of between risk (IPCC SREX 2011). This combination of conditions 165 and 500 km is indicated for the European corn is not limited to Australia. In 2003, for instance, during a borer, a major pest of grain maize. La Roya coffee rust 14 Agricultural Global Practice Discussion Paper has attacked coffee plants in Central and South America precipitation, and could exacerbate potential spread of at higher altitudes as the climate warms (Oxfam 2013). these diseases. Increased movements of people and live- Over the next 10–20 years, oilseed rape disease could stock resulting from drought impacts could expose them both become more severe in its current area and spread to environments with new or increased health risks. to more northern regions (Evans et al. 2008). Tempera- ture increases may also advance invasions in the grow- Overall, it is clear that the potential impacts of climate ing season, when the crop is at early development and change on pest and disease could be of major significance. susceptible. Precipitation increases are also likely to favor While the debate on the immediacy of some of the effects the development of fungal and bacterial pathogens (Parry continues, significant knowledge gaps concerning many 1990). Similar developments are already ongoing, for existing diseases of livestock and their relation to climate instance with the coffee berry borer (Hypothenemus hampei) remain, and it is crucial to continue pursuing the topic having become more prevalent in East Africa due to exist- (King et al. 2006). ing warming ( Jaramillo et al. 2011). Some pests, includ- ing aphids and weevil larvae, respond positively to higher Conclusion levels of atmospheric CO2 (Staley and Johnson 2008; Climate change brings predominantly negative impacts Newman 2004). Aphids may also benefit from increased on agricultural production of both crops and livestock. temperatures, which prevent them from dying in large Increased climate variability, more frequent and intense numbers during the winter and may allow the species to extreme events of different types and more uncertainty disperse earlier and more widely (Zhou et al. 1995). As a overall will lead to increased production risks. result of rainfall-based migration patterns, precipitation variability due to climate change may affect locust occur- As discussed in the following chapter, these risks will often rences in sub-Saharan Africa (Cheke and Tratalos 2007). transmit into markets for agricultural commodities, creat- ing additional risks at the market level. Climate change impacts have had profound effects on the distribution of animal diseases, and will further transform the ecology of numerous pathogens. The current trend MARKET RISK regarding the ever-increasing globalization of the trade Markets are directly affected by agricultural production of animals and animal products ensures that agricultural risks from climate and weather events. As a result of this diseases will continue to follow legal and illegal trade pat- transmission, climate change will indirectly amplify price terns with increasing rapidity. In recent years, many agri- volatility of agricultural commodities and increase supply cultural diseases have given cause for concern regarding disruptions. This section examines challenges posed by changes in distribution or severity. Foot-and-mouth dis- climate change beyond the farm, as agricultural products ease, avian influenza, and African swine fever continue to travel from farms to consumers through markets utilizing cause serious problems (Arzt 2010). a variety of infrastructure. Risk of water-associated diseases may be further exacer- Interactions between climate change and other trends are bated by the increased potential for flooding in some areas likely to have particularly significant implications for risks and complicated by inadequate water access. For instance, at the market level. From one side, population growth, the Rift Valley Fever’s (RVF) vectors are mosquitos whose shifting diets, and competing demands on biological raw population grows with period of desiccation and flood- materials all contribute to increasing demand pressure ing, even if the flooding period is short. The disease is while climate change will negatively impact the supply side highly detrimental to livestock, as well as humans—in both through production and supply chain disruptions. 1997/98 over 100,000 animals died and 90,000 humans were infected (World Bank 2014b). The challenges arising from these interactions could well result in significant additional market volatility and risk Higher infection can result from more malnourished beyond even what the impacts of climate change seen in animals, which may be an indirect result of lowered isolation would already suggest. Indeed, in some cases Agricultural Risk Management in the Face of Climate Change 15 these factors may be a bigger constraint on availability long secular decline from 1974 onward, the World Bank and driver of rising food prices than direct impacts of cli- Food Price Index rose by 62 percent over the course of mate change on food production (Oxfam 2013). just a few months in 2008. International prices of maize, rice, and wheat increased in nominal terms by 70 percent, FOOD PRICE VOLATILITY 180 percent, and 120 percent respectively, compared to Variations in food (and more broadly agricultural) prices mid-2007. After declining by 30 percent from mid-2008 over time are problematic when they are large, unexpected, to mid-2010, it rose sharply again and in February 2011 and when they create uncertainty that increases risks for regained its 2008 peak. Throughout 2012 food prices players along the supply chain including producers, trad- remained high and in July 2012 they spiked again, espe- ers, consumers, and governments (FAO et al. 2011). cially for maize and wheat, with world food prices being 65 percent higher than their mid-2007 levels (53 percent Both low and high price levels are sources of concern. in real terms) (World Bank 2013b). Future food prices are Low prices benefit consumers, but reduce incentives for expected to remain higher than pre-2007 levels (World production and investment for producers. High prices Bank 2012). hurt consumers, but benefit producers who can respond to this signal. The majority of producers in developing Climate change has already acted as a significant driver of countries, however, does not have the capacity to do so, supply pressure and resulting price spikes of recent years and moreover, few smallholder households are net pro- and will do so even more going forward. See box 3.2 for ducers of food. The net effect of high prices therefore an indicative collection of partly climate change driven depends on a number of factors (FAO et al. 2011). extreme events that contributed to food price volatility to date. Figure 2.3 shows the projected effects of a number Global food price volatility has sharply increased over the of possible climate change driven extreme climate and past decade. World food prices have spiked thrice. After a weather events on global commodity markets. FIGURE 2.3. MODELED PRICE IMPACTS OF EXTREME WEATHER EVENT SCENARIOS IN 2030 A drought in East Africa A drought in North on a similar scale to that America, on a similar experienced in 1992 could scale to the historical increase average drought of 1988, could consumer maize prices in increase world market the region by ~50%. The export prices for maize simultaneous by ~140%, and world occurrence of market prices for wheat poor harvests in by ~33%. India and South East Asia could have a major impact on processed rice, with the global average export price increasing by ~25%. A bad-harvest year across South America similar to the severe A drought in West droughts and Africa on a similar major flooding scale to that A drought and flooding in experienced in experienced in 1992 Southern African Regions on 1990 could could increase a similar scale to that increase world average consumer experienced in 1995 could market prices maize prices in the increase average consumer for maize by region by ~50%. maize prices in the region by ~12%. ~120%. Source: Willenbockel 2012. 16 Agricultural Global Practice Discussion Paper BOX 2.2. IMPACTS OF CLIMATE CHANGE ON AVERAGE GROWING CONDITIONS AND THE SUPPLY OF FOOD In the longer run, changes in average growing conditions FIGURE B2.2.1. WORLD PRICE EFFECTS resulting from climate change are expected to lead to con- siderable price increases under all models. Figure 2.4 illus- FOR THE MAJOR GRAINS trates that climate change impacts could cause 2050 prices to (IN U.S. DOLLARS 2000), rise by 94–111 percent for wheat, 32–37 percent for rice, and ASSUMING NO CARBON 52–55 percent for maize, based on models from two mod- DIOXIDE FERTILIZATION eling groups—CSIRO (Commonwealth Scientific and Indus- trial Research Organisation) and NCAR (National Center EFFECT UNDER TWO for Atmospheric Research) both using the A2-SRES scenario DIFFERENT MODELS from the IPCCa incorporating assumptions of lower and (CSIRO AND NCAR) higher land precipitation, respectively (World Bank 2010). 2000 2050 no climate change Another analysis projects price rises of 54 percent for both 2050 CSIRO NoCF 2050 NCAR NoCF 450 rice and wheat and 101 percent for maize by 2050 under cli- 400 mate change (PwC 2013). The exception is soybeans, where 350 Dollars per metric ton most estimates predict minimal impacts. Grain price increases 300 resulting from climate change also indirectly result in higher 250 meat prices due to higher feed prices for livestock. Beef prices 200 are estimated to be 33 percent higher by 2050 with no cli- 150 mate change and 60 percent higher with climate change, with 100 similar numbers for pork and poultry (World Bank 2010). As 50 a general average, the expected effect of climate change on 0 crop prices is a 20 percent increase—an average that masks Rice Wheat Maize Soybeans Other grains significant variation across crops and regions. Source: World Bank 2008. a Roughly corresponding to a path where emission growth continues and results in an increase of average temperature of more than +4°C by 2100. In addition to extreme climate and weather-event-driven SUPPLY CHAIN DISRUPTION RISK disruptions, climate change will put pressure on the global Significant gaps remain in the research on climate change food supply overall. Since its impacts on average growing impacts on global supply chains. The following is there- conditions are negative overall, supply will come under fore only a first approximation of the kind of disruption significant pressure to achieve the yield gains required risks climate change impacts could give rise to. to feed 9 billion increasingly wealthy customers in 2050 even in the absence of extreme events. See appendix A Production Disruptions and Repercussions for more details. Agriculture supply chains are increasingly exposed to Together, continued rising demand, slow-onset impacts disruption risk from localized, regional and even global of gradually rising temperatures or reduced precipita- climate and weather risks. Commodities such as wheat, tion combined with increased frequency and intensity of maize, and especially rice will face a greater magnitude extreme events will cause food price volatility to persist of supply chain disruption risks going forward (Gledhill and amplify it into the future. et al. 2013). Importantly, these estimates do not incorporate the impact As climate change brings more frequent and more of increased or intensified extreme events resulting from intense extreme events coupled with overall less condu- climate change, discussed in the preceding chapter. cive growing conditions (see previous chapter), produc- Agricultural Risk Management in the Face of Climate Change 17 FIGURE 2.4. ILLUSTRATION OF A LARGE Transport and Infrastructure CLIMATE AND WEATHER A number of extreme climate and weather events induced EVENT DISRUPTING ENTIRE by climate change can impact both transportation and PRODUCER CLUSTERS infrastructure. Pressure on prices may result from delays, Existing supply chains will not be able to destruction of commodities, and quality impacts. For cope with climate change instance, floods and landslides can disrupt the distribution Trader of crops by damaging roads and bridges between fields and factories where the crops are processed (Doyle 2012). Retailer Ports or transport routes may temporarily close due to Trader extreme weather. High temperatures cause rail tracks to expand and buckle. More frequent and severe heat waves Bigger shocks eliminate whole supplier clusters/regions may require track repairs or speed restrictions to avoid derailments. Tropical storms and hurricanes can also tion disruptions of previously unknown scales are likely to leave debris on railways, disrupting rail travel and freight occur. The schematic in figure 2.4 illustrates this process. transport. Heavy precipitation could also lead to delays and disruption. For example, the June 2008 U.S. midwest With climate change, the risk of supply chain disruptions floods closed major east-west rail lines for several days of entire clusters of producers is increasing dramatically. (EPA 2013). In addition, coastal infrastructure and distri- Extreme events at novel scales will put traditional supply bution facilities may be exposed to flood damage (PWC chain structures into question to the point where global 2013). For instance, rain-induced landslides on transport commodities of the scale of coffee or cacao may encoun- roads in Colombia caused the price of green beans to ter shortages and food retailers may temporarily have increase in every market (Oxfam 2013). sourcing difficulties and be unable to offer them. Although the effects of weather on transport are visibly As supply chains become more and more globally inte- evident, there have not been many integrated assessments grated, the potential for worldwide shocks and price at either national or global levels of the impacts on trans- spikes further increases (PWC 2013). Highly locally con- portation of changes in frequency, severity, and seasonality centrated commodities are particularly relevant to global of extreme weather events. The effects of infrastructure supply chain disruption risks because climate and weather disruption on food availability are widely recognized, but phenomena of limited geographic scope can already be they remain a “known unknown” in the context of under- sufficient to disrupt significant portions of global supply. standing potential future climate change impacts on food security. This is an area of major vulnerability that war- Supply disruptions that affect significant parts of global pro- rants further attention and research (Oxfam 2013). duction also have the potential to increase counterparty risk. As disruptive climate and weather events trigger price vola- Storage tility and risk factors across different members of the supply Another major aspect of commodity supply chains that chain become more strongly positively correlated, counter- may be impacted by climate change is storage infrastruc- party risks can spread and endanger entire sub-sectors. ture and process. Food storage infrastructures such as ware- houses may be damaged or destroyed by extreme weather In a first of its kind public announcement, the UK retailer events such as flooding and storms (Doyle 2012). Storage ASDA published results of a study which found that 95 costs may rise due to strains on electricity grids, air con- percent of all fresh produce on offer is already at risk from ditioning, and refrigeration from increasing temperatures. climate change (Guardian 2014). The study was the first attempt by a food retailer to put hard figures against the Higher temperatures will significantly affect food safety, impacts climate change will have on the food it buys from with perishable foods such as fruits and vegetables espe- across the world. cially vulnerable. Storage life is constrained by temperature, 18 Agricultural Global Practice Discussion Paper as increased bacterial growth rates halve storage for of a given agriculture sector. It has distinct characteris- every 2–3°C increase up to 10°C. Communities may be tics at global, regional, and national levels and plays a exposed to unsafe levels of aflatoxin from stored maize, key role in shaping the supply chain and the sector as a as throughout a season farmers sell and buy back maize whole. Enabling environmental risks include political devel- locally (Vermuelen et al. 2012). Late rains during crop opments, changes in regulation, arising conflict, or trade harvests increase moisture content in grains and increase restrictions that lead to financial losses. costs of drying. Production and market risks resulting from more frequent, Under heavy rain conditions, produce in storage may rot extreme, and uncertain climate and weather events have due to low capacity, leading to a decreased supply that can the potential to lead to, and be complicated by, enabling result in sudden rising prices. Insufficient storage capacity environment factors. Due to the complex nature of the and rain exposure and extreme weather may cause grains social, economic, and political motivations behind the to rot. Rains during the wheat harvest elsewhere may lead governmental and individual decision making involved to grain spoilage due to a lack of capacity for drying and are even more difficult to predict and identify with cer- storage, affecting the price and quality of crops. tainty than risks at production and market levels. Climate change will increase the unpredictability of rain An active area of research, a number of potential indi- patterns, indicating increasing disparities in resilience rect channels of climate change’s influence on the between nations with sufficient storage capacity and those enabling environment have been identified in literature. without. As with transportation impacts, storage impacts For instance, there is ample historic precedent that sup- are likely to hit developing nations hardest, due to the lack ply shocks triggered by climate and weather events have of resilient infrastructure. However, there is little research resulted in reactive trade and domestic support policies, as to date on the impacts of increasing climate variability recently as the 2008 food price shock. and longer-term climatic trends on major food storage facilities or on the performance of more traditional food Climate change is one of many variables influencing storage systems, such as home-built granaries. enabling environments. While causal impacts can often be plausibly argued between climate change and devel- opments at the enabling environment level, attribution Conclusion will often remain elusive. Moreover, amid a concert of The interaction between high food prices overall and other factors, the scale of importance of climate change additional price volatility induced by production shocks impacts is hard to assess and its impacts should therefore fueled by climate change will likely lead to socially and not be overstated. politically explosive dynamics on the consumer side, fur- ther explored in the following chapter. Political and regulatory risks. In times of uncertainty countries may resort to ad hoc, isolationist measures. Price Additional research is required to fully understand the volatility—particularly when concerning basic food com- relevance of climate change impacts on future food price modities—can bring significant uncertainty. Common volatility and agricultural supply chains. responses to supply and price shocks include panic-buying, hoarding, subsidizing imports, and placing export controls on impacted commodities (Oxfam 2012). Protectionist ENABLING ENVIRONMENT measures such as these may amplify risks (Ahmed and Mar- RISKS tin 2009). Trade-restricting policy responses to higher food This section focuses on the implications of climate change prices exacerbated price increases; for example, changes in risks on the enabling environment. border protection measures accounted for an estimated 45 percent of the world price increase for rice and 30 percent The enabling environment is composed of the regulatory, of the increase for wheat in 2006–08 (World Bank 2014c). political, conflict, macroeconomic, and trade environments Through increases in weather and climate shock driven Agricultural Risk Management in the Face of Climate Change 19 FIGURE 2.5. TIME DEPENDENCE OF FAO FOOD PRICE INDEX FROM JANUARY 2004 TO MAY 2011 Algeria (4), Saudi Arabia (1) Haiti (5), Egypt (3), Mauritania (1), Sudan (1), Yemen (300+) 260 240 Cote d’lvoire (1) Somalia (5) Oman (2), Morocco (5) 220 200 Sudan (3) Tunisia (1) Egypt (800+) Iraq (29), Bahrain (31) 180 Libya (10000+) Syria (900+) 240 Cameroon (40) 160 Tunisia (300+) Uganda (5) Yemen (12) 140 120 India (1), 220 100 Mozambique (6) Sudan (1) 50 Mozambique (13) Food price index 1990 1995 2000 2005 2010 Mauritania (2) 200 India (4) Somalia (5) 180 160 140 Burundi (1) 120 2004 2006 2008 2010 2012 Source: Lagi et al. 2011. production disruptions, climate change may contribute to Conflict over scarce natural resources. Tempera- more volatile market environments and ultimately indi- ture and rainfall events have a complex relationship with rectly contribute to increasing regulatory risk. the potential for local resource conflict. Local disputes over grazing lands for livestock may be influenced by resource Red dashed vertical lines correspond to beginning dates scarcity partially caused by climate change impacts, espe- of “food riots” and protests associated with the major cially heat waves and droughts. Temperature extremes are recent unrest in North Africa and the Middle East. The associated with stock losses for pastoralists, which could overall death toll is reported in parentheses. Inset shows increase the potential for associated conflicts. Lack of rain FAO Food Price Index from 1990 to 2011. may also decrease forage in the areas where herding is common, forcing herders to gather in temporary homes, Throughout history, food price spikes have triggered polit- competing for the same limited grazing land and forage ical instability, for instance in the form of riots. Figure 2.5 resources for their livestock (Stark 2011). Farmers and shows a measure of global food prices, the UN Food and cattle keepers requiring water during the dry season have Agriculture Organization (FAO) Food Price Index and increased potential for conflict over water resources when the timing of reported food riots in recent years. In 2008 long-term drought further limits rainfall. more than 60 food riots occurred worldwide in 30 dif- ferent countries, 10 of which resulted in multiple deaths, Results of a study on rainfall, temperature, and con- as shown in the figure. After an intermediate drop, even flict correlations in East Africa indicated that temper- higher prices at the end of 2010 and the beginning of ature increases had more influence in raising violence 2011 coincided with additional food riots (in Mauritania than precipitation variability (O’Loughlin et al. 2012). and Uganda), as well as contributing to the broader pro- Greater precipitation decreased conflict but the study tests and government changes in North Africa and the found that drier than normal conditions had no signifi- Middle East known as the Arab Spring. Conversely, there cant effect. Overall, both temperature and precipitation are comparatively fewer food riots when the global food were only modest indicators of conflict relative to other prices are lower (Lagi et al. 2011). factors. 20 Agricultural Global Practice Discussion Paper Impacts from climate change are projected to signifi- Conclusion cantly increase the numbers and the permanency of As climate change brings more frequent and intense extreme migratory movements as a result of extreme events, events, it is likely to have a negative impact on enabling potentially leading to considerable population redistri- environment risks, for instance increasing the probability of bution. Some areas at risk of migration are connected to adverse interventionist trade policies, food price spike related existing conflicts; climate change-induced natural disas- political instability and conflict over natural resources. At ters could exacerbate existing enabling environment the same time, climate change is only one of many variables conditions that, combined, may lead to sudden migra- driving such developments and should be seen in proportion. tion events (Friedman 2014). Agricultural Risk Management in the Face of Climate Change 21 CHAPTER THREE IMPLICATIONS OF CLIMATE CHANGE FOR ARM This section examines the implications of climate change for agriculture risk manage- ment. For an introduction into the basic concepts of ARM, please refer to appendix B. WHAT ARM CAN CONTRIBUTE TO MEETING THE CHALLENGES OF CLIMATE CHANGE A proven tool for managing risks today and building resilience to climate and weather variability tomorrow: Agricultural risk management is ideally placed to support all actors in dealing with the increased agricultural risks climate change will bring. ARM was designed to help production, market, and enabling environment risks. While climate change may intro- duce new types of extreme events in some locations, it most frequently will trans- late into “more frequent and intense—of the same” hazards. ARM frameworks and approaches can point the way to the identification of optimal mitigation, transfer, and coping strategies—and have a track record of successfully accomplishing the task. Agriculture risk management therefore needs to be seen as a key part to identify short and medium term solutions to the challenges climate change poses to agriculture and food systems. ARM tools are proven, tested, and readily available: Many countries have risk management frameworks and systems in place that can be further devel- oped and “climate-proofed.” For example, cutting edge risk management approaches already integrate important principles of effective extreme event risk management, including taking an integrated systems approach, community-level participation and the use of local and community knowledge in synergy with national and international policies and actions (IPCC 2012). Agricultural risk assessment and RM strategies can therefore provide crucial support to food systems during the structural transitions that will be part of adaptation pro- cesses, but ARM is no substitute for longer term strategic adaptation planning. Agricultural Risk Management in the Face of Climate Change 23 FIGURE 3.1. ILLUSTRATION OF KEY MUTUAL POINTS OF RELEVANCE BETWEEN CLIMATE CHANGE AND AGRICULTURE RISK MANAGEMENT What ARM can contribute to meeting the climate challenge: A proven tool for building resilience to climate Climate and weather volatility change A key entry point for the operationalization of climate-smart agriculture where resilience is first priority Implications of climate change for ARM: Agriculture Increasing risks = increasing importance of ARM risk Need to adapt frameworks and approaches: management a. Incorporation of climate projections b. Decision making under uncertainty & capacity building to meet the unknown Adaptation to climate change will take time. Agricultural spends resources in a misguided attempt to adapt existing production and the broader food system are highly fine- production systems to the changing climate without hope tuned instruments closely adapted to and shaped by local for longer term sustainability. For instance, if a production conditions that exhibit significant levels of inertia: human area is projected to lose suitability for a given crop in the capacity, productive infrastructure, or market access are medium term but ARM tools such as a government subsi- often all specific to a production system comprising of a dized agricultural insurance schemes are deployed to extend defined set of crop and livestock products. the life of production in the face of ever-increasing risks and reduced yields, resources may go to waste. Instead, the opti- In some contexts, climate change will affect growing mal adaptive response may be to change production sys- conditions in such a way, that important crops or live- tems entirely and invest the available resources support this stock species are pushed beyond what they can toler- transition rather than extending the lifetime of a lost cause. ate. During the processes of structural adjustment that Similarly, climate change impacts in the medium turn can adaptation in such contexts will require, ARM can be an affect the (cost-)effectiveness of risk management interven- important tool to manage volatility during transition and tions implemented today, such as irrigation infrastructure. cushion the effect on those able to adapt least rapidly. For Therefore, periodic risk assessments become quite impor- instance, production of some crops will continue even as tant for reprioritizing risk and interventions in a changing growing conditions are increasingly far from optimal. As risk context overtime to avoid the risk of maladaptation. production becomes increasingly risky, ARM can help manage those risks and support the process of assigning ARM needs to work hand-in-hand with adaptation plan- roles in risk mitigation, coping, and transfer to different ning to avoid the risk of maladaptation and to ensure an stakeholders. optimal flow of information from ARM to adaptation planners. Through its periodic production data analysis It is important to note, however, that ARM’s role is mainly and risk profile update, ARM will often be well placed to to help manage risks around the trend, not the trend itself. spot the risk of suitability loss far in advance and needs That is, ARM has to be careful in avoiding contributing to to ensure that it makes this knowledge available to the maladaptation. Maladaptation can occur when a system broader agriculture planning and adaptation community. 24 Agricultural Global Practice Discussion Paper FIGURE 3.2. SCHEMATIC ILLUSTRATING HOW ARM CAN OFFER A PATHWAY TO ACHIEVING RESILIENCE FOCUSED CSA OUTCOMES CHALLENGE Operationalizing Climate-Smart Agriculture Prioritizing Resilience ENTRY POINT OBJECTIVES OUTCOMES Agriculture risk assessment Short Term: Increased resilience Risk management to climate change Plus: Evidence-based & systematic prioritization Increased productivity (and) Longer Term: Climate-Smart Reduced emissions Agriculture (CSA) (depending on context) Policy dialogue & government co-ownership with resilience emphasis In this way, although not always made explicit, agriculture from data gathering to option evaluation on to solution risk management tools in effect help build resilience to climate development. Risk data from the past are systematically change. While the term “resilience” may refer to somewhat collected and synthesized with climate projection data different concepts in the climate change and risk manage- to identify future risks from climate and weather events, ment communities, many of the tools ARM commonly particularly possible trend changes in key variables such deploys also appear in adaptation or resilience building proj- as precipitation or temperature. The assessments take ects. These include early warning systems, irrigation infra- a holistic, integrated view including both direct (pro- structure or improved agronomic or climate-smart agriculture duction level) and indirect risks (markets and enabling practices such as agroforestry or conservation agriculture. environment level). A key entry point for the operationalization of Key risks identified are then matched with potential solu- climate-smart agriculture where resilience is the tions. Solutions are processed through a prioritization first priority: matrix, allowing to simultaneously consider a range of goals starting with resilience, productivity, and environ- Climate-smart agriculture (CSA) is an approach that aims mental and sustainability goals. The approach can be fur- to achieve three outcomes simultaneously: increased pro- ther expanded to include additional dimensions such as ductivity, enhanced resilience, and reduced emissions. nutrition, gender, or value chain approaches. CSA has generated significant interest in recent times and attempts to operationalize the concept are currently The results of this process are highly contextualized prior- under development. ities, including at local level (community, district, region), and can be designed to cover the full potential “triple Where resilience is the main focus of CSA, agricultural win” of CSA (see figure 3.3). In continuation to the risk risk assessments present a proven and attractive entry assessment, prioritized potential solutions are then further point for operationalization, with two advantageous key developed through solution assessments. features that stand out (see figure 3.2). The second key feature is the attractiveness of agricul- First, agriculture risk assessments offer a well-estab- ture risk assessments as a vantage point for government lished systematic risk prioritization process, starting dialogue. Agricultural Risk Management in the Face of Climate Change 25 FIGURE 3.3. EXAMPLE OF A PRIORITIZATION MATRIX FROM THE NIGER COUNTRY AGRICULTURE RISK ASSESSMENT USING OPTION FILTERING APPROACH (WORLD BANK 2013a) Potential Adverse Impact on Relative Ease of Return Impact on Poverty Scalability Cost Implementation Time Envirnoment Alleviation Drought tolerant/improved High Medium Medium Short Low High seed varieties (M) Soil and water conservation High Medium Medium Medium Low High (M) Irrigation (M) Low High Low Short- Moderate High medium Early detection and High Medium High Short Moderate Low destruction of locusts (M) Community-level food and High Medium Medium Short Low High fodder banks (M, C) Vaccination programs (M) High Medium Medium Medium Low High Contingent financing (C) High Low High Short Low Low Shortening emergency Medium Low Medium Short Low Low response time (C) Strategic de-stocking (C) Low Medium Low Medium Low Low Insurance (T) Low Low Medium Medium Low Low Source: Authors. Note: M is Mitigation, C is Coping, and T is Transfer. Across all steps, the agriculture risk assessment process is Finally, ARM as it is understood here, is a continuous pro- highly collaborative and involves strong country co-own- cess rather than a one-off investment. As with best adap- ership. For instance, risk assessment processes are initiated tation building practice, regular activities under an ARM based on country demand. Government representatives are umbrella can help monitor climate risks as they develop involved at every stage of the process. This close involve- and maintain momentum over time. Risk assessment is an ment enables a process of prioritizing investment, policy, iterative and dynamic process which needs to be incorpo- and technical assistance opportunities as well as the develop- rated as a periodic exercise to gauge from time to time the ment of longer term action plans for operationalizing CSA. risk profile of agricultural sector and principal commodities (see figure 3.4). The context and structure of agricultural The quantification and monetization of agricultural risks sector changes over time and risks needs to be re-assessed is an ideal tool to generate interest not only from Minis- periodically to situate old risk in a new context; identify tries of Agriculture, but from Ministries of Finance and new risks; adapt old solutions or develop new solutions in Planning due to the often unexpectedly large losses to gov- response to evolving risk profile of agricultural sector. ernment budgets and country trade balances caused by agricultural risks. Monetization helps generate a sense of In summary, agriculture risk assessments represent a immediacy and urgency, given its focus on past and cur- useful entry point that enables managing the short term rently ongoing losses. Once the relevance of the problem (risks) while building a key bridge to the longer term (resil- and the need for action are established, doors are opened ience and climate-smart agriculture with resilience focus) toward leveraging further political support for action on if it is adopted as a periodic exercise to gauge changing more future-facing issues, such as climate change. scenarios over time. 26 Agricultural Global Practice Discussion Paper FIGURE 3.4. RISK ASSESSMENT AND baseline. The past is no longer always the best guide for MANAGEMENT CYCLE the future, as discussed in the next section. Even volatility becomes more dynamic over time. The entire risk land- scape starts to shift. For ARM to protect production systems and supply chains Evaluation Risk Assessment struggling with the impacts of climate change around the world, global ARM capacity will need to be signifi- cantly strengthened at all levels. Many countries have only rudimentary ARM systems and their food systems remain Assessing highly vulnerable even to today’s more moderate threats. Risk Monitoring Solutions ARM in its modern form is still a relatively recent element of the development toolbox and has yet to be fully scaled up. Finally, as risks increase, so do the costs of mitigating, Implementation coping with, or transferring them. In the same vein, as risks increase, so will the return on investment for ARM. The benefits of successful ARM are well known: Every risk mitigated, success- fully coped with, or transferred in efficient ways helps HOW ARM NEEDS TO ADJUST UNDER avoid losses, protects food security, reduces the cost of THE “NEW NORMAL” credit for farmers and creates incentives for investment As risks in agricultural systems increase with cli- in the medium- to longer-term. Benefits often add up mate change, so will the importance, as well as to significant sums. As risks of potential losses increase, the challenge, of managing them: so does the value of avoidable losses for producers and other members of the value chain. Put differently, the Under risky growing conditions, ARM can be the differ- more risks there are, the more value can be added—and ence between an agriculture sector that accumulates capi- protected—through ARM. tal and improves productivity and one that stagnates or even dwindles. For instance, risky agriculture sectors with Farmers are particularly vulnerable to climate change and poorly managed risk suffer from a lack of incentives for ARM can play a key role in protecting them. All risks are investment, see stakeholders forced to diminish their asset eventually transmitted across the agriculture sector (and base to absorb shocks and see investment opportunities in along the various supply chains), but production risks such prevention go to waste. With ever greater shares of pro- as weather shocks and pests and diseases generally affect duction at risk, it will become increasingly critical to have farmers the most. Farmers are also the members of the optimal systems in place to manage them and avoid unsus- supply chain with the lowest adaptive capacity, the high- tainable loss levels. Please refer to appendix B for back- est incidence of poverty and are therefore highly vulner- ground on the World Bank’s approach to ARM. able. In particular, they often still lack access to effective risk transfer solutions and are forced to rely on traditional The more variable climate and weather conditions are, community-based mechanisms that will be overwhelmed the more diverse and frequent risks arise and the more with the kind of weather and climate shocks climate challenging effective risk management will become. The change will bring. key parameter is variability. Higher variability means more frequent and extreme deviations from the norm To maximize its positive contributions in meeting and therefore less predictable risks. In addition, climate the climate challenge, ARM will need to incorpo- change introduces the added complexity of a moving rate a number of adjustments to its frameworks Agricultural Risk Management in the Face of Climate Change 27 and approaches to accommodate the “new nor- existing trend information with future projections. This mal” of climate change into the RM strategies: will be particularly important and challenging where pro- jections indicate a trend reversal or non-linear changes a. Need to incorporate projections of future cli- compared to historic trends. Also, timescales for ARM mate and weather conditions will need to be more clearly defined than in the past. Since under climate change the past is no longer the only, Climate projections should for instance be consulted: or necessarily best, guide for the future, agriculture risk » Early on in a risk assessment, when the broader management will need to adjust its methodologies. context is defined, the climate context could be routinely examined in addition to the client, pro- To date, historic records over several decades have pro- grammatic, risk, and agricultural contexts. This vided risk managers with high quality information and may help flag countries where climate change may enabled them to create precise risk profiles for given be of particular relevance from the go.6 locations, activities, and hazards. They were also able » During the risk prioritization phase as part of the to accommodate some internal variability or natural cli- initial assessment, to ensure prioritization is “cli- mate variation through the observation and integration mate-smart.” of trends. Both historic averages and trends remain key » During the solutions assessment, it is important to pieces of information in the “New Normal” because assess the climate-smartness of options being de- of their precision and their continued strong predictive veloped. For instance, where physical infrastruc- power in the short to medium term. ture investment options such as irrigation networks are considered as a means to reduce vulnerability Going forward however, climate change projections will to risk events such as droughts, it would be essen- become an additional required element. As discussed tial to consult projections on future precipitation. above, climate change is expected to increase climate vari- Since these investments have long lifetimes of over ability and the frequency of extreme climate and weather 20 years, climate conditions could be significantly events. Since these are at the core of many of the risks different from today and directly impact the viabil- ARM manages, projections will be an important tool of ity of the project in question. predicting risk profiles. Climate change projections can supply important information for decision making in b. Decision making under uncertainty and capac- ARM contexts. ity building to meet the unknown Unfortunately, climate projections still suffer from a set of Climate change brings uncertainty. Climate projections deficiencies that complicate their use. Climate modeling always come with point estimates (“best guess”) surrounded results contain sometimes large amounts of uncertainty by sometimes large confidence intervals (“ranges”). Deriv- due to an only partial scientific understanding of the ing policy conclusions can therefore be difficult. Moreover hugely complex global climate system and due to uncer- different climate models can sometimes disagree starkly in tain future human carbon emissions. Models are much their projections of elementary climate variables. better at predicting certain variables at certain timescales (average seasonal temperature or yearly rainfall) than There are a number of available methodologies derived others (frequency of temperature extremes or intensity from statistical decision theory that can help reach of daily rainfall events). Model precision also declines as “robust” decisions under uncertainty. Robust here scale becomes more local. describes options that perform “reasonably well” under a These complexities will require ARM to update and mod- ify its tools and approaches to accommodate future data 6 Tools to support this process are available, for instance in the form of the and to integrate uncertainty into decision making. Each Climate Risk Screening Tools developed by the World Bank, available under context will require careful weighing of historic data and https://climatescreeningtools.worldbank.org/ 28 Agricultural Global Practice Discussion Paper BOX 3.1. MAKING ROBUST DECISIONS DESPITE DEEP UNCERTAINTIES ABOUT THE FUTURE Governments invest billions of dollars annually in long- FIGURE B3.1.1. AN ITERATIVE PROCESS term projects. Physical structures like irrigation infrastruc- ture, roads and dams often last for decades and need to be OF DECISION MAKING useful throughout their lifetimes (Kalra et al. 2014). Simi- TO PROMPT ROBUST larly, structural decisions in agriculture, such as introducing ACTION IN THE FACE OF irrigation or shifting cropping systems can shape the sector UNCERTAINTY for many years to come. Yet deep uncertainties pose formi- dable challenges to making near-term decisions that make Multistakeholder analysis of uncertainties and long-term sense. Climate change and other socio-economic possible scenarios uncertainties can have serious consequences on develop- What are the possible scenarios? ment efforts. Traditional decision approaches have been asking, “Which Identification of the investment option best meets our goals given our beliefs about the future?” Implementation vulnerabilities of existing plans Such approaches, sometimes called “Predict-then-Act,” rely and learning In what scenarios does my plan fail? on our accurately predicting and then reaching consensus on what the future will bring (Bonzanigo and Kalra 2014). But, disagreement about the future can lead to gridlocks. Worse, if Adjustment of plans and introduction of monitoring systems one project is designed for a future that then does not materi- alize, losses will be high. Can I change my plan to aviod failure in these scenarios? When will I know that my plan is at risk of failure? Methods that identify robust decisions have been recom- What will I be able to do at that time to correct course? mended for investment lending but are not yet widely used. These methods, sometimes called “Deliberation-With-Analy- Source: WDR 2014 team. sis,” ask different questions: How do options perform across a wide range of potential future conditions? Under what specific conditions does These methods have been mainly applied in the United States the leading option fail to meet decision makers’ goals? Are those conditions and Europe. Recently, the World Bank has begun to test them sufficiently likely that decision makers should choose a different option? in the developing context. A number of ongoing World Bank (Lempert et al. 2013). These methods do not seek to suggest projects are applying these state-of-the-art methods to water an optimal investment, but rather one that performs well no management in Lima, Peru; urban wetland management for matter what the future may bring. These investments are gen- flood protection in Colombo, Sri Lanka; and hydropower erally called robust choices. investments in Nepal. range of different potential scenarios (Anton et al. 2012). robust investments because their strengths can come to While not maximizing usefulness for any particular sce- play independent of the future climate scenario that actu- nario, they maximize safety and flexibility. Some method- ally materializes. ologies help in dealing with knowledge gaps (Ben-Haim 2006), others propose frameworks to determine likely These methodologies could find application in a number “weights” of different options (Etner et al. 2011), attempt of agriculture risk management decisions. For instance: to identify “no regret” options (Stakhiv 1998) or develop » During the risk prioritization phase as part of the models where decision can be taken without first having initial assessment, it will be important to assess dif- to agree on the probabilities of different scenarios (Kalra ferent options’ sensitivity to climate projections. et al. 2014; see box 3.1 and figure B3.1.1). This could help prevent deprioritizing certain options that are deemed somewhat unlikely but Options that fulfill the robustness criteria often tend to would have heavy impacts if they occurred. involve capacity building in different shapes and forms. » During the solutions assessment, if there is signifi- Early warning systems or an empowered extension service cant uncertainty over what the future climate will be for instance contribute to adaptive capacity and are often like while investment with long lifetimes are being Agricultural Risk Management in the Face of Climate Change 29 considered. In such cases, it would be beneficial to tomorrow. It offers a pragmatic take on the often all too assess the sensibility of the options considered to theoretical concept of resilience based on a holistic sys- different climate scenarios and deploy robust deci- tems approach to avoiding losses and building risk man- sion making methods if the decision does appear agement capacity at production, market, and enabling sensitive to the range of uncertainty at hand. environment levels. As such, agriculture risk assessments can serve as a key entry point to the operationalization Conclusion of climate-smart agriculture where resilience is the first Climate change is a reality today and presents a significant priority. threat to the global agriculture and food systems tomor- row. Impacts on agriculture risks in particular are mani- The conceptual framework presented in this chapter fold, clearly negative overall and downright alarming for hopes to make progress on the incorporation of climate some agricultural production systems and commodities. change implications into agriculture risk management approaches. Ultimately, it aspires to contribute to the Agriculture risk assessments are a key process to identify mainstreaming of ARM in client countries as a way to risk management strategies today and to build resilience help them thrive in the face of climate change. 30 Agricultural Global Practice Discussion Paper REFERENCES ADB (Asian Development Bank). 2012. “Addressing Climate Change and Migration in Asia and the Pacific.” Agriculture and Migration, 27–38. http://www.adb.org /sites/default/files/pub/2012/addressing-climate-change-migration.pdf. Alcamo, J., N. Dronin, M. Endejan, G. Golubev, and A. Kirilenko. 2007. “A New Assessment of Climate Change Impacts on Food Production Shortfalls and Water Availability in Russia.” Global Environmental Change 17 (3): 429–44. Alexandratos, N., and J. Bruinsma. 2012. “World agriculture towards 2030/2050: the 2012 revision.” ESA Working Paper No. 12-03, FAO, Rome. http://www.fao.org /docrep/016/ap106e/ap106e.pdf Antón, J., et al. 2012. “A Comparative Study of Risk Management in Agriculture under Climate Change.” OECD Food, Agriculture and Fisheries Papers, No. 58. http:// dx.doi.org/10.1787/5k94d6fx5bd8-en. Arzt, J., W. R. White, B. V. Thomsen, and C. C. Brown. 2010. “Agricultural Diseases on the Move Early in the Third Millennium.” Veterinary Pathology Online 47(1): 15–27. Batima, P. 2006. “A Final Submitted to Assessments of Impacts and Adaptations to Climate Change (AIACC).” Project No. AS 06. The International START Secre- tariat, Washington, DC. http://ipcc-wg2.gov/njlite_download.php?id=5854. Battisti, D. S., R. L. Naylor. 2009. “Historical Warnings of Future Food Insecurity with Unprecedented Seasonal Heat.” Science 323: 240–44. Bengtsson, L., K. I. Hodges, M. Esch, N. Keenlyside, L. Kornblueh, J. Luo, and T. Yamagata. (2007). “How May Tropical Cyclones Change in a Warmer Climate?” Tellus 59: 539–61. Ben-Haim, Y. 2006. Info-Gap Decision Theory: Decisions under Severe Uncertainty. Academic Press, Oxford, UK. Bonzanigo, L., and R. N. Kalra. 2014. “Making Informed Investment Decisions in an Uncertain World: A Short Demonstration.” World Bank Policy Research Working Paper No. 6,765, World Bank, Washington, DC. Burgess, R., O. Deschenes, D. Donaldson, and M. Greenstone. 2013. “The Unequal Effects of Weather and Climate Change: Evidence from Mortality in India.” Unpublished working paper. Burke, E. J., S. J. Brown, N. Christidis. 2006. “Modeling the Recent Evolution of Global Drought and Projections for the Twenty-first Century with the Hadley Centre Climate Model.” J. Hydrometeorol 7: 1113–25. Byerlee, D., X. Diao, and C. Jackson. 2005. “Agriculture, Rural Development, and Pro-poor Growth Country Experiences in the Post-Reform Era.” Agriculture and Rural Development Discussion Paper 21, World Bank, Washington, DC. CGIAR 2013. “Adapting to extreme events in Southeast Asia through sustainable land management systems.” https://ccafs.cgiar.org/research/projects/adapting -extreme-events-southeast-asia-through-sustainable-land-management-syste-0# .VYRl0DJdXQQ. Agricultural Risk Management in the Face of Climate Change 31 Confino, Jo. 2014. “Asda: 95% of our fresh produce is Hatfield, J., et al. 2014. “Climate Change Impacts in the already at risk from climate change.” Guardian News- United States: The Third National Climate Assess- paper, April 25, 2014. http://www.theguardian.com ment.” In U.S. Global Change Research Program, edited /sustainable-business/asda-food-waste-risk-climate by J. M. Melillo, Terese (T. C.) Richmond, and G. W. -change. Yohe: 150–74. doi:10.7930/J02Z13FR. Dai, A. 2011. “Drought under Global Warming: A Hess, U., K. Richter, and A. Stoppa. 2004. Weather Risk Review.” Wiley Interdisciplinary Reviews: Climate Change, Management for Agriculture and Agri-Business in Develop- 2(1): 45–65. ing Countries. IFC, Washington, DC. https://www Easterling, D., et al. 2000. “Climate Extremes: Observations, .agriskmanagementforum.org/sites/agriskmanage Modeling and Impacts.” Science Magazine 289 (5,487): mentforum.org/files/Documents/Weather_Risk 2068–74. http://www.sciencemag.org/content _Management.pdf. /289/5487/2068.full. Inter-American Development Bank (IDB) and FAO. 2013. Etner, J., M. Jeleva, and J. M. Tallon. 2011. “Decision Climate Change and Agriculture in Jamaica: Agricultural Sector Theory under Ambiguity.” Journal of Economic Surveys. Support Analysis. FAO, Rome, Italy. http://www.fao doi: 10.1111/j.1467-6419.2010.00641.x .org/docrep/018/i3417e/i3417e.pdf. FAO (Food and Agriculture Organization). 2006. “Food IPCC. 2012. “Summary for Policymakers.” In Managing Security.” Policy Brief, Number 2 ( June). FAO, Rome, the Risks of Extreme Events and Disasters to Advance Cli- Italy. mate Change Adaptation, edited by C. B. Field, V. Barros, ———. 2013. “The State of Food Insecurity (SOFI). T. F. Stocker, D. Qin, D. J. Dokken, K. L. Ebi, M. D. The multiple dimensions of food security.” FAO, Mastrandrea, K. J. Mach, G.-K. Plattner, S. K. Allen, Rome, Italy. http://www.fao.org/publications/sofi M. Tignor, and P. M. Midgley. A Special Report of Work- /2013/en/. ing Groups I and II of the Intergovernmental Panel on Cli- FAO, IFAD, IMF, OECD, UNCTAD, WFP, World mate Change. Cambridge, U.K., and New York, U.S.A. Bank, WTO, IFPRI, and UN HLTF. 2011. “Price Cambridge University Press. 1–19. Volatility in Food and Agricultural Markets: Policy ———. 2013. “Summary for Policymakers.” In Cli- Responses.” http://www.oecd.org/tad/agricultural mate Change 2013: The Physical Science Basis. Contribu- -trade/48152638.pdf. tion of Working Group I to the Fifth Assessment Report of Friedman, L. 2014. Heat Stress, Not Flooding, Drives Most Cli- the Intergovernmental Panel on Climate Change, edited by mate Migrants. E & E Publishing. http://www.eenews T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, .net/stories/1059993474?utm_source=hootsuite& S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and utm_campaign=hootsuite. P. M. Midgley. Cambridge, U.K., and New York, Gornall, J., et al. 2010. “Implications of Climate Change U.S.A. Cambridge University Press for Agricultural Productivity in the Early Twenty-first ———. 2014. “Summary for Policymakers.” In Climate Century.” doi:10.1098/rstb.2010.0158. Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Government of Western Australia. 2013. “Potential Frost Global and Sectoral Aspects. Contribution of Working Group Changes Due to Climate Change.” Department of II to the Fifth Assessment Report of the Intergovernmental Agriculture and Food. http://grains.agric.wa.gov.au Panel on Climate Change, edited by C. B. Field, V. R. /node/frost-%E2%80%94-science. Barros, D. J. Dokken, K. J. Mach, M. D. Mastrandrea, Gualdi, S., E. Scoccimarro, A. Navarra. 2008. “Changes T. E. Bilir, M. Chatterjee, K. L. Ebi, Y. O. Estrada, in Tropical Cyclone Activity Due to Global Warming: R. C. Genova, B. Girma, E. S. Kissel, A. N. Levy, Results from a High-Resolution Coupled General S. MacCracken, P. R. Mastrandrea, and L. L. White. Circulation Model.” Journal of Climate (21): 5204–28. Cambridge, U.K., and New York, U.S.A. Cambridge Hansen, J., M. Sato, and R. Ruedy. 2012. “Perception University Press. 1–32. of climate change.” Proceedings of the National Academy Jarvis, A., C. Lau, S. Cook, E. Wollenberg, J. Hansen, of Sciences (PNAS) 109 (37). http://pubs.giss.nasa.gov O. Bonilla, and A. Challinor. 2011. “An integrated /docs/2012/2012_Hansen_etal_1.pdf. adaptation and mitigation framework for developing 32 Agricultural Global Practice Discussion Paper agricultural research: synergies and trade-offs.” Exper- natural resource management in West Africa.” ILRI, imental Agriculture 47(02): 185–203. Nairobi. https://www.ilri.org/InfoServ/Webpub Jaramillo, J., E. Muchugu, F. E. Vega, A. Davis, C. Borge- /fulldocs/SustainableCropLivestock/Pg028_044%20 meister, A. Chabi-Olaye. 2011. “Some Like It Hot: Kristjanson%20and%20Thornton.pdf. The Influence and Implications of Climate Change Lempert, R. J., S. W. Popper, D. G. Groves, N. Kalra, J. R. on Coffee Berry Borer (Hypothenemus hampei) and Fischbach, S. C. Bankes, B. P. Bryant, M. T. Collins, Coffee Production in East Africa.” PLoS ONE 6(9): K. Keller, A. Hackbarth, L. Dixon, T. LaTourrette, e24528. doi:10.1371/journal.pone.0024528. T. Reville, J. W. Hall, C. Mijere, and D. J. McInerney. Jones, P. G., and P. K. Thornton. 2008. “Croppers to 2013. Making Good Decisions without Predictions. Research Livestock Keepers: Livelihood Transitions to 2050 Brief 9701. RAND Corporation. in Africa Due to Climate Change.” Environ. Sci. Policy. Lipper, L., et al. 2014. “Climate-Smart Agriculture for doi:10.1016/j.envsci.2008.08.006. Food Security.” Nature Climate Change 4: 1068–72. International Rice Research Institute (IRRI). 2009. Rice doi:10.1038/nclimate2437. Doctor—Rice Knowledge Bank. http://www.knowledgebank Lobell, D. B. and C. B. Field. 2007. “Global Scale .irri.org/RiceDoctor/information-sheets-main Climate–Crop Yield Relationships and the Impacts menu2730/in-the-field-mainmenu-2736/heavy-rain of Recent Warming.” Environmental Research Letters fall-mainmenu-2783.html. 2 014002 doi:10.1088/1748-9326/2/1/014002. Kalra, N., S. Hallegatte, R. Lempert, C. Brown, A. Foz- Lobell, D. B., and S. M. Gourdji. 2012. “The Influence of zard, G. Stuart, A. Shah. 2014. “Agreeing on robust Climate Change on Global Crop Productivity.” Plant decisions : new processes for decision making under Physiology 160(4): 1686–97. deep uncertainty.” Policy Research Working Paper No. Luo, Q. 2011. “Temperature Thresholds and Crop Pro- 6906, World Bank, Washington, DC. duction: A Review.” Climatic Change 109(3–4): 583–98. Kastner, T., M. J. Ibarrola Rivas, W. Koch, and S. Nonhe- Martin, W. and K. Anderson. 2011. “Export Restric- bel. 2012. “Global Changes in Diets and the Conse- tions and Price Insulations during Commodity Price quences for Land Requirements for Food.” Proceedings Booms.” American Journal of Agricultural Economics 94(2): of the National Academy of Sciences of the United States 422–27. of America 109(18): 6868–72. http://www.pnas.org McDonald, R. E., D. G. Bleaken, D. R. Cresswell, V . D. Pope, /content/109/18/6868.abstract. C. A. Senior. 2005. “Tropical Storms: Representation Kettlewell, P. S., R. B. Sothern, W. L. Koukkari. 1999. and Diagnosis in Climate Models and the Impacts of “U.K. Wheat Quality and Economic Value Are Climate Change.” Climate Dynamics 25: 19–36. Dependent on the North Atlantic Oscillation.” Journal Mueller, V., C. Gray, and K. Kosec. 2014. “Heat Stress Cereal Science 29: 205–209. Increases Long-Term Human Migration in Rural Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, Pakistan.” Nature Climate Change 4(3): 182–85. and C. J. Neumann. 2010. “The International Best National Oceanic and Atmospheric Administration (NOAA). Track Archive for Climate Stewardship (IBTrACS): 2015. Glossary. http://w1.weather.gov/glossary/ Unifying Tropical Cyclone Best Track Data.” Bulletin National Research Council. 2011. Climate Stabilization of the American Meteorological Society 91: 363–76. Targets: Emissions, Concentrations, and Impacts over Dec- Kumar, K. K., K. R. Kumar, R. G. Ashrit, N. R. Desh- ades to Millennia. The National Academies Press, pande, and J. W. Hansen. 2004. “Climate Impacts on Washington, DC. Indian Agriculture.” International Journal of Climatol- Nelson, G. C., M. W. Rosegrant et al. 2010. Food Security, ogy 24: 1375–93. Farming, and Climate Change to 2050: Scenarios, Results, Kristjanson, P. M., P. K. Thornton, R. L. Kruska, R. S. Policy Options 172. International Food Policy Research Reid, N. Henninger, T. O. Williams, and P. Hiernaux. Institute: Washington, DC. 2004. “Mapping livestock systems and changes to OECD. 2009. Managing Risk in Agriculture: A Holistic Approach. 2050: Implications for West Africa. Sustainable crop– OECD Publishing, Paris.doi: 10.1787/ 9789264075313 livestock production for improved livelihoods and -en. Agricultural Risk Management in the Face of Climate Change 33 O’Loughlin, J., et al. 2012. “Climate Variability and Con- Union of Concerned Scientists (UCSUSA). 2011. “Heavy flict Risk in East Africa, 1990–2009.” PNAS 109 (45). Flooding and Global Warming: Is There a Con- Oxfam. 2012. “Extreme Weather, Extreme Prices.” Oxfam nection?” http://www.ucsusa.org/global_warming Issue Briefing. https://www.oxfam.org/sites/www /science_and_impacts/impacts/heavy-flooding-and .oxfam.org/files/file_attachments/20120905-ib -global-warming.html#.VYR2DzJdXQQ. -extreme-weather-extreme-prices-en_3.pdf. Vara Prasad, P. V ., K. J. Boote, L. Hartwell Allen, and Porter, J. R., M. A. Semenov. 2005. “Crop Responses to J. M. Thomas. 2003. “Super-Optimal Temperatures Are Climatic Variation.” Philosophical Transactions of the Detrimental to Peanut (Arachis hypogaea L.) Reproductive Royal Society B 360: 2021–35. Processes and Yield at Both Ambient and Elevated Car- Prudhomme, C., et. al. (2013). “Hydrological Droughts in bon Dioxide.” Global Change Biology 9(12): 1775–87. the 21st Century, Hotspots, and Uncertainties from a Walthall, C. L., et al. 2012. “Climate Change and Agri- Global Multimodel Ensemble Experiment.” Proceed- culture in the United States: Effects and Adaptation.” ings of the National Academy of Sciences. USDA Technical Bulletin 1935. USDA, Washington, DC. Schlenker, W., and M. J. Roberts. 2009. “Nonlinear Warner, K., and T. Afifi. 2014. “Where the Rain Falls: Evi- Temperature Effects Indicate Severe Damages to dence from 8 Countries on How Vulnerable Households U.S. Crop Yields under Climate Change.” Pro- Use Migration to Manage the Risk of Rainfall Variability ceedings of the National Academy of Sciences 106(37): and Food Insecurity.” Climate and Development 6(1): 1–17. 15594–98. Webster, P. J. 2008. “Myanmar’s Deadly Daffodil.” Nature Schubert, R., and H. J. Schellnhuber. 2009. Climate Change Geoscience 1(8): 488–90. as a Security Risk. Routledge, London, U.K. Welch, J. R., J. R. Vincent, M. Auffhammer, P. F. Moya, Sirohi, S., and A. Michaelowa. 2007. “Sufferer and A. Dobermann, and D. Dawe. 2010. “Rice Yields in Cause: Indian Livestock and Climate Change.” Cli- Tropical/Subtropical Asia Exhibit Large but Oppos- matic Change 85 (3–4): 285–98. ing Sensitivities to Minimum and Maximum Tem- Sivakumar, M. V. K., H. P. Das, and O. Brunini. 2005. peratures.” Proceedings of the National Academy of Sciences “Impacts of Present and Future Climate Variability 107(33): 14562–67. and Change on Agriculture and Forestry in the Arid Werz, M., and L. Conley. 2012. “Climate Change Migra- and Semi-arid Tropics.” Climate Change 70: 31–72. tion and Conflict in Northwest Africa: Rising Dan- Stakhiv, E. Z. 1998. “Policy Implications of Climate gers and Policy Options across the arc of Tension.” Change Impacts on Water Resources Management.” 2012/04. Center for American Progress. http:// Water Policy 1(2): 159–75. doi: 10.1016/S1366-7017 www.americanprogress.org (98)00018-X. Wheeler, T. R., P. Q. Craufurd, R. H. Ellis, J. R. Porter, Stark, J. 2011. “Climate Change and Conflict in Uganda: and P. V. Prasad. 2000. “Temperature Variability and The Cattle Corridor and Karamoja.” USAID Office of the Yield of Annual Crops.” Agriculture, Ecosystems & Conflict Management and Mitigation (No. 3). Discussion Environment 82(1): 159–67. paper. U.S. Agency for International Development, Willenbockel, D. 2012. “Extreme Weather Events and Washington, DC. Crop Price Spikes in a Changing Climate: Illustrative Thornton, P. K. 2010. “Livestock Production: Recent Global Simulation Scenarios.” Oxfam Policy and Prac- Trends, Future Prospects.” Philosophical Transactions of tice: Climate Change and Resilience 8(2): 15-74. the Royal Society of London B: Biological Sciences 365(1,554): Wollenweber, B., J. R. Porter, and J. Schellberg. 2003. 2853–67. “Lack of Interaction between Extreme High—Tem- Thornton, P. K., J. van de Steeg, A. Notenbaert, M. Her- perature Events at Vegetative and Reproductive rero. 2009. “The Impacts of Climate Change on Live- Growth Stages in Wheat.” Journal of Agronomy and Crop stock and Livestock Systems in Developing Countries: Science 189(3): 142–50. A Review of What We Know and What We Need to World Bank. 2008. Agriculture for Development. World Know.” Agricultural Systems 101(3): 113–27. Development Report 2008. Washington, DC: World Bank. 34 Agricultural Global Practice Discussion Paper ———. 2011. Philippines—Typhoons Ondoy and Pepeng: Post- /implementing-agriculture-development-world-bank Disaster Needs Assessment—Main Report. Washington, -group-agriculture-action-plan-2013-2015. DC: World Bank. https://openknowledge.worldbank ———. 2014a. Turn Down the Heat: Confronting the New .org/handle/10986/2778?show=full. Climate Normal. Washington, DC: World Bank. ———. 2012. Turn Down the Heat: Why a 4°C Warmer https://openknowledge.worldbank.org/handle/ World Must Be Avoided. Washington, DC: World 10986/20595. Bank. http://documents.worldbank.org/curated/en ———. 2014b. Reducing Climate Sensitive Disease Risks. /2012/11/17097815/turn-down-heat-4%C2%B0c Report Number 84956-GLB. http://documents -warmer-world-must-avoided. .worldbank.org/curated/en/2014/04/19567115 ———. 2013a. Agricultural Sector Risk Assessment in Niger: /reducing-climate-sensitive-disease-risks. Moving from Crisis Response to Long-Term Risk Manage- ———. 2014c. Food Price Watch. 5 (17). http://www.world ment. Washington, DC: World Bank. http://hdl bank.org/content/dam/Worldbank/document/ .handle.net/10986/13260. Poverty%20documents/FPW_May%202014_final ———. 2013b. Implementing Agriculture for Development: .pdf. World Bank Group Agriculture Action Plan (2013–2015). ———. 2015. Effective Agricultural Risk Management: Nuanced Washington, DC: World Bank. http://documents World Bank Assistance. Forthcoming. Washington, DC: .worldbank.org/curated/en/2013/01/17747135 World Bank. Agricultural Risk Management in the Face of Climate Change 35 APPENDIX A OVERVIEW OF THE IMPACTS OF CHANGING CLIMATE AVERAGES ON AGRICULTURE Temperature Rise on Crops Different types of crops have different responses to increased temperatures. Wheat, rice, maize, soybeans, barley, and sorghum are the six most widely grown crops in the world, which are produced in over 40 percent of global cropland area, and pro- vide 55 percent of non-meat calories and over 70 percent of animal feed (Lobell and Field 2007). Increased temperatures resulting from climate change since 1981 can be estimated to have resulted in annual combined production losses of 40 million tons ($5 billion) due to the negative impact these temperature changes have overall on these major cereal crops, in some areas offsetting a significant portion of yield gains from technology improvements (Lobell et al. 2012). Wheat and maize in particular experienced production decreases of 5.5 and 3.8 percent respectively, while soybeans and rice averaged no loss or gain from temperature increases (Lobell and Field 2007). Although the repercussions of climate change on food production will vary enor- mously from region to region, higher average growing season temperatures have the potential to significantly impact agricultural productivity. A significant increase in mean seasonal temperature could shift harvest times for many crops, requiring agri- cultural adaptation to these average changes (Gornall et al. 2010). In warmer areas such as seasonally arid and tropical regions, where some crops are already growing in maximum temperatures at which they can survive, increased temperatures can lead to extended heat stress and water loss. These areas could be expected to experience severe losses even with only a 2°C temperature change, partially due to cereal harvest reduction as well as a potential lack of adaptive capacity. Most, but not all, middle and higher latitude locations would be more likely to experience an increase in agricultural production under a similar level of average warming, with the potential to increase wheat production by nearly 10 percent, counter to a similar percentage loss in low lati- tude areas under 2°C warming (World Bank 2010; figure 2). However, if mean global temperature warms by 2–4°C, agricultural productivity is likely to decline worldwide, in every region. Extreme negative impacts on agricultural production globally can be expected from an average temperature rise of 4°C or more. Agricultural Risk Management in the Face of Climate Change 37 Developing countries fare especially poorly in these pro- agricultural production. Future precipitation changes will jections, worse for all crops under multiple scenarios influence the magnitude and direction of climate impacts compared to developed country production (World Bank on crop production. Even small changes in mean annual 2010). Negative effects of temperature change on agri- rainfall in a single year can impact productivity. A change cultural productivity are especially pronounced in Sub- in growing season precipitation by one standard deviation Saharan Africa and South Asia, in which all major crops can be associated with as much as a 10 percent change in are expected to experience yield reductions under climate production (Lobell and Burke 2008). change, while East Asia and the Pacific have more mixed results dependent on crop and climate models. However, Average rising temperatures could also lead to an increase rice production will be negatively affected by tempera- in crop irrigation needs, due to increased evapotranspira- ture increases, while wheat and maize are mixed. In high tion and longer growing seasons. Water needs for agricul- latitude countries such as the Russian Federation, more ture could increase by 5 to 20 percent or more by the end favorable temperatures and longer planting periods com- of the century, thus placing extra water stress on crops. bined with improved technology could result in significant Regionally, irrigation requirements in the Middle East, gains in potential agricultural land (Fisher et al. 2005). North Africa, and Southeast Asia could increase by at However, extreme events are likely to reduce these ben- least 15 percent (Fisher et al. 2006). However, precipita- efits, with significant impacts from temperature extremes. tion changes also indicate decreased water needs in some areas, such as China, though uncertainties about these Precipitation Change on Crops variances make such projections difficult to estimate. Higher temperatures will increase evaporation, and even- Due to the combined impacts of the expansion of warm- tually will also increase average rainfall (Nelson 2014). It is ing oceans and increased water from melting ice, sea-level difficult to project exact changes in average precipitation rise is one of the most consistent climate impact projections. regionally because regional precipitation depends strongly Increases in mean sea level threaten to inundate agricultural on changes in atmospheric circulation, which depends on lands and salinize groundwater in the coming decades to the relative rate of warming in different regions. centuries. Sea-level rise is expected to eventually inundate There are often a number of complicated climate factors many small islands and coastal land in areas with low capac- influencing precipitation change projections specific to a ity to respond through adaptive measures such as sea walls. given location, such as monsoon circulation and evapora- Agricultural crop vulnerability is clearly greatest where tion potential (Meehl et al. 2007). Nonetheless, there is large sea-level rise occurs in conjunction with low-lying increasing confidence in projections of an overall increase coastal agriculture. Sea-level rise would likely impact many in precipitation in high latitudes. Simultaneously, many mid-latitude coastal areas and increase seawater penetra- parts of the tropics and sub-tropics are expected to expe- tion into coastal aquifers used for irrigation of coastal plains rience an overall decrease in precipitation (IPCC 2007). (World Bank 2012). In Bangladesh, 40 percent of produc- For instance, large increases have been projected in the tive land is projected to be lost in the southern region of southern United States, while low-latitude tropics would Bangladesh for a 65 cm sea level rise by the 2080s. While experience decreasing average rainfall. In some of these the largest impacts from sea level rise may not be seen for models, India is expected to experience increasing pre- many centuries, relatively little work has been done to assess cipitation, while others do not predict this, illustrating the the impacts of mean sea-level rise on agriculture. wide range of precipitation change projections from dif- ferent climate scenarios (Christensen et al. 2007). Impacts of Changing Climate Averages Increased water stress will occur both in rain-fed and irrigated agricultural lands. Mean precipitation change on Livestock is especially important to identify for rain-fed areas, Livestock production systems will be affected in direct and however, which account for over 80 percent of total indirect ways (see table A1.1) and changes in productivity 38 Agricultural Global Practice Discussion Paper are inevitable. Increasing climate variability will undoubt- TABLE A1.1. DIRECT AND INDIRECT edly increase livestock production risks as well as reduce IMPACTS OF CLIMATE CHANGE the ability of farmers to manage these risks. Direct impacts ON LIVESTOCK PRODUCTION include changes on quantity and quality of feed crops and SYSTEMS grazing systems (Thornton et al. 2009). Current evidence suggests that grazing areas in lowland sites with low rainfall Grazing systems Non-grazing systems see the largest reduction in yield during dry seasons (Sirohi Direct impacts and Michaelowa 2007). Increases in temperature and Extreme weather events Water availability changes in rainfall and its variability can lead to feed scar- Drought and floods Extreme weather events city and consequently reduced feed intake that can have an Productivity losses (physiological stress) owing impact on productivity (milk production and weight gain) to temperature increase and even mortality (Thornton and Cramer 2012). In addi- Water availability tion to affecting livestock directly on their physiological pro- Indirect impacts cesses, and indirectly on crop and rangeland resources, heat Agro-ecological changes: Increased resource price, for stress can also have an effect on livestock vector-borne dis- example feed and energy ease (Nelson et al. 2014) through changes in the distribution Fodder quality and quality Disease epidemics of ticks, mosquitos, flies, and others (Thornton and Cramer Host–pathogen Increased cost of animal 2012). Increasing temperatures are also expected to amplify interactions housing, for example cooling the water needs of livestock. Taking into account poten- systems tial reductions in water availability, this need is expected to Disease epidemics curtail livestock development (Thornton and Cramer Eds. Source: Thornton 2010. 2012). Extreme events will also impact livestock. Droughts, heavy rains, flooding, and cyclones have all been found to have effects on livestock. In India alone, flooding has caused with a 1°C increase in temperature a northward shift in losses of nearly 94 thousand cattle annually on average distribution of between 165 and 500 km is indicated for (Sirohi and Michaelowa 2007). Droughts are even more the European corn borer, a major pest of grain maize. serious in the country. In one particularly large drought in La Roya coffee rust has attacked coffee plants in Cen- 1987, one state lost more than half of its 34 million cattle tral and South America at higher altitudes as the climate (Sirohi and Michaelowa 2007). warms (Oxfam 2013). Over the next 10–20 years, oilseed rape disease could both become more severe in its cur- rent area and spread to more northern regions (Evans et Pests and Diseases on Crops and Livestock al. 2008). Temperature increases may also advance inva- Weather exerts an influence on all stages of host and sions in the growing season, when the crop is at early pathogen life cycles, and the development of disease, and development and is susceptible. Precipitation increases climate change threatens the control of pest and disease are also likely to favor the development of fungal and invasions, including insects, plant diseases, and invasive bacterial pathogens (Parry 1990). Some pests, including weeds. Increasing average temperatures, warmer winter aphids and weevil larvae, respond positively to higher minimum temperatures, changes in precipitation pat- levels of atmospheric carbon dioxide (Staley and John- terns, and water shortages are all climate factors that favor son 2008; Newman 2004). Aphids may also benefit from pest and disease invasions. The impacts of climate change increased temperatures, which prevent them from dying on the spread and incidences of crop pests are complex in large numbers during the winter and may allow the and as yet the full implications in terms of crop yield are species to disperse earlier and more widely (Zhou et al. uncertain, but could be substantial. 1995). As a result of rainfall-based migration patterns, precipitation variability due to climate change may Studies indicate that temperature increases may extend affect locust occurrences in sub-Saharan Africa (Cheke the geographic range of some insect pests. For instance, and Tratalos 2007). Agricultural Risk Management in the Face of Climate Change 39 Climate change impacts have had profound effects on the dis- Food Quality tribution of animal diseases, and will further transform the Climate change affects nutrition by disrupting supply ecology of numerous pathogens. The current trend regarding of food (like yields). There is new evidence, however, the ever-increasing globalization of the trade of animals and that higher atmospheric carbon dioxide concentration animal products ensures that agricultural diseases will con- changes the nutrient value of crops and might also change tinue to follow legal and illegal trade patterns with increas- the variety of foods available (Nelson 2014). Studies have ing rapidity. In recent years, many agricultural diseases have found that elevated carbon dioxide is associated with given cause for concern regarding changes in distribution or lower concentrations of zinc and iron in wheat, rice, field severity. Foot-and-mouth disease, avian influenza, and African peas, and soybeans, as well as lower protein content in swine fever continue to cause serious problems (Arzt 2010). wheat and rice (Myers et al. 2014). The International Rice In the next twenty years, while the distribution of these Research Institute (IRRI) is also expecting rice quality to diseases may be affected by some climate-related shifts in decrease due to higher temperatures (Nelson 2014). Pro- the areas suitable for vector-borne diseases such as malaria tein content of cereals such as wheat and rice may have and bluetongue, these are not expected to have as much of already declined in the past century due to atmospheric an impact in the short term (Woolhouse 2006). However, changes (Burns et al. 2010). Leaves will also contain up to in the United Kingdom, change in the extent, amount, 20 percent less protein affecting the nutritional intake of and seasonal timing of helminthes (parasites) has resulted grazing animals (Burns et al. 2010). from climate change impacts, especially higher tempera- tures (Van Dijk et al. 2010). These kinds of shifting disease In addition to protein and carbohydrates, plants contain patterns resulting from climate change will require aware- other chemicals too that protect them from herbivores and ness and preparedness as well as early detection and diag- disease-causing pathogens. Depending on factors such as nosis of livestock parasitic disease, and may become more the amount consumed at one time, a person’s health, age, prevalent as temperatures rise (Gornall et al. 2010). and weight, and the accompanying amount of protein ingested, the consumer can tolerate these natural toxins. Although the direct impacts of climate change on live- At higher carbon dioxide levels, however, plant resources stock disease over the next two to three decades may be that would have otherwise been directed toward powering relatively muted, knowledge gaps concerning many exist- photosynthesis, are directed instead toward the chemicals ing diseases of livestock and their relation to climate and which protect the plants but might harm those that con- other factors make this a very important topic to pursue sume them. The effects on human nutrition are not fully (King et al. 2006). known (Burns et al. 2010). 40 Agricultural Global Practice Discussion Paper APPENDIX B INTRODUCTION TO THE WORLD BANK’S AGRICULTURAL RISK MANAGEMENT APPROACH Risks arising from the “damaging whims of nature, including pestilence, and diseases” are nothing new for agriculture. All of the previously discussed hazards, climate, and weather related risks that climate change will bring have existed and created challenges ever since agriculture was practiced. One of the key tools developed to help build resilience toward and reduce vulnerability to these (and other) risks, is agricultural risk management. ARM as practiced by the Agricultural Risk Management Team (ARMT) at the World Bank typically involves the following sequence: 1. Risks assessment and prioritization: Analysis of the three principle types of agricultural risks and their prioritization, based on probability of oc- currence and severity of losses. a. Production risks: Weather events (droughts, floods, hurricanes, cyclones, sud- den drops or increases in temperature, frost, and so on), pest and disease outbreaks, bush fires, windstorms, and so on are major risks that lead to pro- duction volatility. b. Market risks: Risks like commodity and input price volatility, exchange rate and interest rate volatility, and counterparty and default risks usually materi- alize at the market level but have backward linkages to the farm gate, thereby affecting all stakeholders. c. Enabling environment risk: Changes in government or business regulations, macro-economic environment, political risks, conflict, trade restrictions, and so on are all major enabling environment risks that lead to financial losses. 2. Stakeholders’ assessment: This entails analysis of the role of different stakeholders across the agricultural sector and understanding of their risk man- agement capacity. For simplicity, the sector is analyzed across three layers: a. Producers (micro): Marginal, small, and medium sized farmers are the back- bone of the agricultural sector in most developing countries. Agricultural Risk Management in the Face of Climate Change 41 FIGURE B2.1. AGRICULTURAL RISK FIGURE B2.2. ILLUSTRATION OF RISK MANAGEMENT FRAMEWORK LAYERING APPROACH Instruments Risk layering Investments Probability LAYER 2 Technical assistance very low frequency, Policy very high losses LAYER 2 risk mitigation low frequency, + risk transfer medium losses + risk coping Stakeholders LAYER 1 risk mitigation Producers + risk transfer Commercial sector high frequency, Risks Strategies low losses Public sector Production Mitigate Risk mitigation Market Transfer Enabling environment Cope Severity b. Commercial sector stakeholders (meso): Com- to select an appropriate risk management strategy. mercial stakeholders, including traders, middle- » All standard text may be set in lowercase (e.g. “high men, wholesalers and retailers, financial institu- frequency, low losses – risk mitigation”). tions, input providers, and so on. » In the last column, add spaces in “very low frequency.” c. Public sector (macro): Public sector institutions, parastatals, government, and donors. Implementation Instruments: Translating risk 3. Risk Management Strategies: The principle management strategies into concrete action requires strategies to manage agricultural risks can be clas- deployment of several instruments which can be classi- sified into: fied under: a. Mitigation: Activities designed to reduce the 1. Agricultural investments: Financial investments in likelihood of an adverse event or reduce the se- irrigation infrastructure, drought and pest tolerant verity of actual losses. Risk mitigation options seed varieties, soil and water conservation, weather are numerous and varied (for example, irriga- infrastructure, or investment in improving systems tion; use of resistant seeds; improved early warn- (for example, agriculture extension systems or dis- ing systems; and adoption of better agronomic ease surveillance systems). practices). 2. Technical assistance: This is geared toward build- b. Transfer: This entails the transfer of risk to a ing capacity of local stakeholders (for example, willing party, for a fee or premium. Commer- training in price risk management; feasibility stud- cial insurance and hedging are the well-known ies for various instruments; flood risk modeling forms of risk transfer. work; developing early warning systems). c. Coping: This involves improving resilience to 3. Policy support: Improved risk management might withstand and cope with events, through ex-ante entail policy reform (for example, changes in policy preparation. Examples include social safety net to improve access to agricultural inputs; changes in programs, buffer funds, savings, strategic re- information policy to make weather information serves, contingent financing, and so on. easily accessible to all; government procurement, 4. A risk layering approach, based on the prob- storage, and grain release policies to manage stra- ability of occurrence and potential losses, is used tegic reserves). 42 Agricultural Global Practice Discussion Paper A G R I C U LT U R E G L O B A L P R A C T I C E D I S C U S S I O N P A P E R 0 9 W O R L D B A N K G R O U P R E P O R T N U M B E R AUS5773 1818 H Street, NW Washington, D.C. 20433 USA Telephone: 202-473-1000 Internet: www.worldbank.org/agriculture Twitter: @WBG_agriculture