Increasing Agricultural Resilience through Better Risk Management in Zambia Increasing Agricultural Resilience through Better Risk Management in Zambia Increasing Agricultural Resilience through Better Risk Management in Zambia Ademola Braimoh, Alex Mwanakasale, Antony Chapoto, Rhoda Rubaiza, Brian Chisanga, Ngao Mubanga, Paul Samboko, Asa Giertz, and Grace Obuya © 2018 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW, Washington, DC 20433 Telephone: 202-473-1000; Internet: www.worldbank.org Some rights reserved This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. 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Cover design: Progressive Publishing Services Contents Foreword ix Acknowledgments xi Acronyms and Abbreviations xiii Executive Summary xv Production Risks xv Market Risks xvi Enabling Environment Risks xvi Prioritizing Risks xvi Methodology for Risk Assessment xviii Agricultural Risk Management and the World Bank Agenda in Zambia xviii CHAPTER 1: Introduction and Context 1 CHAPTER 2: Zambia’s Agricultural Sector 7 The Potential for Agriculture 7 Historical Context of Agricultural Policies 7 Current Agricultural Policies 11 Agricultural Production System: Major Crops and Livestock 12 CHAPTER 3: Data and Methodology 15 Agricultural Risk 15 Analytical Approach 15 CHAPTER 4: Agricultural Risk Assessment 19 Production Risks 21 Market Risks 28 Exchange Rates, Interest Rates, and Inflation 34 Enabling Environment 34 The Role of Government in Risk Management 37 CHAPTER 5: Impacts of the Risks on the Agricultural Sector 41 Overall Agricultural Losses 41 The Impacts of Agricultural Risks on Different Stakeholders 47 Vulnerable Groups and Impact on Household Food Security 50 CHAPTER 6: Risk Prioritization and Management 53 Risk Prioritization 53 Risk Management Solutions 53 Examples of Projects Addressing Agricultural Risks 63 Bibliography 69 Appendix A: Risk Strategies for Crops Subsector 75 Appendix B: Risk Strategies for Livestock Subsector 77 Appendix C: Ranking of Importance of Risk Solutions 79 Appendix D: Focus Group Discussions: Farmer Profiles and Coping Strategies 83 Farmer Focus Group Discussions: Profile 83 Farmer Focus Group Discussions: Coping Strategies 83 Contentsv Boxes Box 4.1: Production Risks in the Northern versus the Southern Province 29 Box 4.2: Maize Price Volatility 32 Box 4.3: Cotton Price Volatility 33 Box 5.1: To What Do the Annual Losses per Hectare Translate? 43 Figures Figure ES.1: Timeline of Major Shocks to Agricultural Production in Zambia (1983–2015) xvii Figure 1.1: GDP Composition (2015) 2 Figure 1.2: Agricultural Growth versus GDP Performance 2 Figure 1.3: Agricultural Sector Risk Management Process Flow 4 Figure 2.1: Zambia’s Agroecological Zones 8 Figure 2.2: Maize Sales and FRA Purchases in Zambia 10 Figure 2.3: Share of the Agricultural Budget Spent on FRA/FISP (2001–17) 11 Figure 2.4: Main Crops Produced by Smallholder Farmers in Zambia 13 Figure 2.5: Livestock Ownership among Smallholder Farmers 13 Figure 3.1: Example of How Indicative Losses Are Calculated 17 Figure 3.2: Strategic Risk Instruments According to Risk Layers 18 Figure 4.1: Timeline of Major Shocks to Agricultural Production in Zambia (1983–2015) 20 Figure 4.2: Timeline of Major Shocks to Agricultural Production in Zambia (1995–2016) 22 Figure 4.3: Extreme Weather Events by Province (1981/82–2016/17) 23 Figure 4.4: Cassava Yields (MT/ha), 1982–2014 26 Figure 4.5: Disease Outbreaks 27 Figure B4.1.1: Maize Yields (t/ha) in the Northern and Southern Provinces (1987–2016) 29 Figure 4.6: Annual Price Changes for Selected Crops 31 Figure B4.2.1: Price Volatility of Maize 32 Figure B4.3.1: Price Volatility of Cotton 33 Figure 4.7: Real Beef Prices (ZMW/kg) 34 Figure 4.8: Trends in the Rates of Inflation, Exchange, and Lending 35 Figure 5.1: Cumulative Value and Frequency of Losses per Crop (1982–2016) 42 Figure 5.2: Loss Value per Hectare (1982–2016) 43 Figure 5.3: Average Annual Value and Frequency of Losses by Decade in Zambia (1982–2016) 44 Figure 5.4: Annual Value of Losses 44 vi Contents Figure 5.5: Cumulative Value and Frequency of Losses per Risk (1982–2016) 45 Figure 5.6: Livestock Losses 45 Figure 6.1: Priority Scores (%) for the Risk Management Options 62 Figure 6.2: The Maharashtra Climate-Resilient Agriculture Project Framework 63 Tables Table 1.1: Commodities Comprising the Top 80 Percent of the Gross Production Value (GPV) 4 Table 2.1: Estimates of Land Availability (2011–35) 8 Table 3.1: Risks in the Agricultural Sector 16 Table 4.1: Major Drought Incidents in Zambia 23 Table 4.2: Major Excess Rainfall and Flooding Incidents in Zambia 25 Table 4.3: Value of Government/Donor-Financed Agricultural Projects by Type of Activity, 2017 38 Table 5.1: Losses from Agricultural Production Risks (1982–2016) 42 Table 5.2: Estimated Cumulative Losses to the Livestock Sector by Risk Event, 1991–2015 46 Table 5.3: Value of Forgone Foreign Exchange Earnings as a Result of Limited Maize Exports (2008/09–2015/16) 47 Table 6.1: Risk Prioritization—Crop Subsector 54 Table 6.2: Risk Prioritization—Livestock Subsector 54 Table 6.3: Weather Risk Management Options 56 Table 6.4: Option for Managing Disease Outbreaks 59 Table 6.5: Managing Price Volatility 60 Table 6.6: Relevance of Risk Management Options to the Prioritized Risks 61 Table 6.7: Maharashtra Project Components and Costs 64 Table 6.8: Project Costs for Expanding Rural Finance in Mexico 65 Table 6.9: List of Institutions Working with FND 66 Table 6.10: Project Costs for Agricultural Diversification and Market Development in Burkina Faso 67 Table 6.11: Project Costs for Strengthening Social Protection in Rwanda 68 Contentsvii Foreword Better managing agricultural risks such as drought, floods, disease, and commodity price volatility offers opportunities to minimize losses and put agriculture on a stronger footing in Zambia. This report analyzes risks and identifies solutions to ensure greater food security for consumers, optimize the use of public resources, and promote income and investment among pro- ducers in an inherently risky sector. Climate-smart agriculture and investment in practices and technologies that increase agricultural resilience are natural starting points that rely largely on a diversification of commodities in production to enable farmers to be responsive to, and when possible, capitalize on changing conditions. Much will rely on their access to and ability to apply practical information. Early warning systems to identify developments that may imperil food security can be employed to both ensure public health and substantially reduce the costs of maintaining the necessary safety nets. A vital element of this climate-smart agriculture is placing production within a larger context of land use and conservation in which sources of live- lihoods become more diverse and soil and water resources are purposefully managed and preserved. Our hope is that the risk management options outlined in this report will increase the resilience of all actors in the agricultural value chain but espe- cially the most vulnerable, rural households with few coping mechanisms of their own. Protecting smallholder farmers from falling into poverty in the event of climatic and financial shocks and giving them the tools to thrive are important objectives in the work of the World Bank and its partners in Zambia. Juergen Voegele Paul Noumba Um Senior Director Country Director, Agriculture Global Practice Zambia The World Bank The World Bank Forewordix Acknowledgments This work drew from contributions from a range of experts working on agri- culture, food security, climate change, and disaster risk management. We thank everyone who contributed to its richness and multidisciplinary outlook. The study team comprised Ademola Braimoh, Alex Mwanakasale, Antony Chapoto, Rhoda Rubaiza, Brian Chisanga, Ngao Mubanga, Paul Samboko, Asa Giertz, and Grace Obuya. The analytical work was prepared under the overall guidance of Paul Noumba Um, Ina-Marlene E. Ruthenberg, and Mark Cackler. We are grateful for the contribution of the following colleagues: Mr. Julius Shawa, Mrs. Emma Malawo, Mr. Misheck Nyembe, and Mr Dingiswayo Banda (Ministry of Agriculture); Mr. Michael Isimwaa, Dr. Swithine Kabilika, Dr. Christine Yamba Yamba, and Dr. Gregory Bwalya (Ministry of Fisheries and Livestock); Mr. Patrick Chuni (Central Statistical Office); Mrs. Yizaso Musonda (Pensions and Insurance Authority); Mr. Dafulin Kaonga (Cotton Board of Zambia); Mr. Mweene Moonga (MayFair Insurance); Mr. Gilbert Kaimana (Zambia Sugar); Mr. Yotam Mkandawire (GTAZ); Mr. Humphrey Katotoka (Zambia National Farmers Union); Dr. Therese Gondwe (International Institute for Tropical Agriculture); and Mr. Joseph Intsiful (African Climate Policy Centre). We also acknowledge the input from traders, processors, and farmers interviewed in the Southern and Eastern Provinces that gave us practical insights on the various value chains, the participants in the Consultative Stakeholder Workshop for their constructive feedback on the preliminary findings, various government officials from the Zambia Metrological Department, Zambia Agriculture Research Institute, National Livestock Epidemiology and Information Centre, Disaster Management and Mitigation Unit, Zambian Commodity Exchange (ZAMACE), and devel- opment partners such as Consultative Group on International Agricultural Research, World Food Programme, and Food and Agriculture Organization. The report benefited greatly from invaluable suggestions from peer reviewers: Willem G. Janssen, Niels B. Holm-Nielsen, Pablo Benitez, Zano Mataruka, and Stephen D’Alessandro (Senior Agriculture Economist, GFAGE). We also thank Holger Kray and Catherine Tovey for their invaluable suggestions. Finally, we thank Sateh El-Arnaout, Christine Heumesser, Mercy Chimpokosera-Mseu, Hellen Mungaila, Marie Lolo Sow, Clarisse Livia Isaias Nhabangue, Sombo Rachel Samunete, and Srilatha Shankar (World Bank) for assistance rendered at various stages of the project. Gunnar Larson edited the document. Financial support for this work was provided by the Food Price Crisis Response (FPCR) Trust Fund. Acknowledgmentsxi Acronyms and Abbreviations AEZ agroecological zone ASIP Agricultural Sector Investment Programme ASRA Agriculture Sector Risk Assessment Cat DDO Catastrophe Deferred Drawdown Option CFS Crop Forecast Survey CSA Climate-Smart Agriculture CSO Central Statistical Office DMMU Disaster Management and Mitigation Unit FAO Food and Agriculture Organization (of the UN) FAOSTAT Food and Agriculture Organization Corporate Statistical Database FISP Farmer Input Support Programme FMD foot-and-mouth disease FND Financial Development Agency FRA Food Reserve Agency GDP gross domestic product GPV gross production value GTAZ Grain Traders Association of Zambia Ha hectare IAPRI Indaba Agricultural Policy Research Institute ICT information and communication technology IFC International Finance Corporation IMF International Monetary Fund MAL1 Ministry of Agriculture and Livestock MoA Ministry of Agriculture MoFL Ministry of Fisheries and Livestock MSMEs Micro, Small, and Medium Enterprises MT metric ton NAMBOARD National Agricultural Marketing Board NUSFAZ National Small-scale Farmers Association of Zambia OIE World Organisation for Animal Health (Office International des Epizooties) OPV open-pollinated variety PFIs Participating Financial Intermediaries PRODIVA Productive Diversification in African Agriculture RALS Rural Agricultural Livelihoods Survey 1Before September 2015, the Ministry of Agriculture (MoA) and Ministry of Fisheries and Livestock (MoFL) were both under the Ministry of Agriculture and Livestock (MAL). Acronyms and Abbreviationsxiii SAP Structural Adjustment Programme UN United Nations VAM Vulnerability Assessment and Mapping WDI World Development Indicators WFP World Food Programme WRS Warehouse Receipt System ZAMACE Zambia Commodity Exchange ZIFLP Zambia Integrated Forest Landscape Program ZMW Zambian Kwacha ZNFU Zambia National Farmers Union ZVAC Zambia Vulnerability Assessment Committee All dollar amounts are U.S. dollars unless otherwise indicated. xiv Acronyms and Abbreviations Executive Summary The objective of this report is to analyze the principal risks the agricultural sector faces in the Republic of Zambia and to identify pathways for how these risks are to be managed. Risk refers to the possibility that an adverse develop- ment will occur that negatively affects the performance of farms or the larger agricultural supply chain. A risk event refers to such a development when it actually occurs. Risk events were a major factor contributing to the decline in agriculture’s share of Zambia’s gross domestic product (GDP), which fell from 8.2 percent during the period between 2011 and 2015, to 5.3 percent in 2015 itself—a year that saw a variety of such events, including El Niño and attacks of fall armyworms. In terms of the severity and frequency of adverse impacts, and how they affected food security, rural livelihoods, and the broader economy, these varied somewhat between agricultural subsectors and between different regions in Zambia. Drought, floods, and price volatility appear to be the principal risks affecting crop agriculture in the country. Drought and out- breaks of animal disease are the principal risks affecting livestock. Exposure to the consequences of these and other risks can be effectively limited through risk management systems tailored to the conditions prevailing in a country’s agricultural sector. Agricultural risk in Zambia, as in other coun- tries, can be usefully divided into production, market, and enabling environ- ment risks. Production Risks Drought is the most significant risk facing Zambian agriculture. El Niño– related droughts in the 1990s led to severe agricultural losses, resulting in a 10 percent contraction in agricultural GDP. The worst drought took place in 1992 and led to crop losses worth $154 million, the highest recorded during the period studied. Drought events affected all commodities across the board except cassava and cotton. Severe droughts occur on average once every 20 years, whereas the smaller localized droughts and dry spells average once every 5 years. The rain-fed agriculture and high poverty rates characteristic of smallholders have increased their exposure to frequent weather shocks and limited their ability to cope with them. Excess rainfall and floods led to the second highest production losses recorded during the period studied. In 2002, for instance, floods led to a 68 percent fall in cotton production, and about a third of groundnut and maize production. The resulting losses amounted to nearly $100 million. Executive Summaryxv Pests and diseases also caused significant losses. Pests included the fall armyworm and the maize stock borer. Outbreaks of diseases such as cassava mosaic disease also caused significant losses in maize production and cas- sava in the key cassava growing areas of the Luapula, Central, Western, and Northern Provinces. Market Risks Price volatility was the most significant market-related risk facing farmers and other players in the agricultural value chains in Zambia. Investing in pro- ductivity-enhancing and income-raising technologies and practices—which can be instrumental in enabling smallholders to overcome poverty traps—is inordinately risky in contexts in which output prices are highly unpredictable. Reductions in international prices are often rapidly transmitted into the local cotton market and affect production the following year. The volatility of maize prices from one year to the next has lessened dramatically since the early 1990s, except in those years when the government intervenes in a market. This happened in the 2017–18 marketing season, for example, when maize prices crashed. The export ban introduced the previous year led to a large carryover stock of maize, whereas the current year’s harvest proved to be a bumper crop. This oversupply allowed farm-gate prices to collapse. The unpredictable involvement of the Food Reserve Agency in procuring and disposing of strategic maize reserves tends to cause price uncertainty as well. Enabling Environment Risks The Structural Adjustment Program of the late 1980s and early 1990s led to major macroeconomic changes (figure ES.1). Together with other policy changes during the period, including the disbandment of input and marketing subsidies and the privatization of parastatals, this had an enormous impact on Zambia’s agricultural GDP. The country’s civil service was retrenched, includ- ing extension workers, and this affected all levels of the various commodity chains. Yet precisely quantifying the losses that resulted from the policies is not possible given the changes occurring throughout the country’s macroeconomy, including dramatic fluctuations in the inflation rate and in exchange rates. Prioritizing Risks Prioritizing the risks that prevail in a certain country is the first step in man- aging them effectively based on the likelihood of a risk event taking place and the scale of the economic consequences that ensue when they do occur. In the xvi Executive Summary FIGURE ES.1  Timeline of Major Shocks to Agricultural Production in Zambia (1983–2015) 80 Pre-SAP SAP Post-SAP Macroeconomic changes Macroeconomic changes Macroeconomic changes 60 FRA mandate FRA established FISP Introduced increased 40 20 0 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 El Nino El Nino La Nina Prolonged Prolonged Floods El Nino Dry 1997–98 Floods Dry Spell Dry Spell Dry Spell Drought –20 Drought Spells Drought 2001–02 2004–05 2009–10 2012–13 2015–16 1993–95 1987 (South) El Nino –40 Drought Excessive 1991–92 rain (North) Removal of –60 fertiliser subsidy –80 Agriculture GDP (annual % growth) Executive Summaryxvii Source: Authors’ compilation. case of Zambia, the following three areas of risk management are found to warrant priority, with significant potential for synergizing actions undertaken across them: • Strengthen early warning system to detect threats to food security. • Develop climate-smart agriculture and increase resilience to climate-­ related shocks through diversification. • Develop the Zambian Commodity Exchange (ZAMACE) and build a shock-responsive safety net. Methodology for Risk Assessment This report focuses on the risks affecting agricultural commodities that together account for 80 percent of the value of farm production in Zambia. These are beef, maize, sugarcane, cassava, tobacco, cotton, groundnuts, veg- etables, chicken, and pork. Quantitative methods were used to estimate pro- duction losses and trade losses resulting from the export ban. Risks to the enabling environment were estimated qualitatively. Negative deviations from medium- to long-term yield trends that are greater than what can normally be expected in agricultural production are used to estimate production losses. The value of the losses is then estimated in local producer prices. Expert inter- views and published literature were used to validate the key findings, and addi- tional areas of risk management warranting further analysis were discussed at a workshop in the Chisamba District. The range of experts and stakeholders consulted in the study reflects the interdisciplinary set of issues at play in managing agricultural risk. These included public and private sector actors engaged in policy and planning, economics, livestock development, veteri- nary services, epidemiology and disease surveillance, agricultural research, irrigation and water, natural resource management, disaster risk manage- ment, meteorology, grain trading, and agricultural finance and insurance. Agricultural Risk Management and the World Bank Agenda in Zambia Agricultural risk management has been a focus of the World Bank’s work throughout much of the developing world, and many of the lessons gleaned from one region apply to others. This risk management is an integral part of the Bank’s larger program of work in the country, aimed at building resil- ience, principally through an agenda of climate-smart agriculture. This larger work program includes Productive Diversification in African Agriculture and Effects on Resilience and Nutrition (PRODIVA), which is designed to iden- tify the drivers of and constraints to productive diversification in agriculture xviii Executive Summary at household, landscape, and country levels. The Zambia Integrated Forest Landscape Program (ZIFLP) is another innovative project that seeks to increase forest cover as an instrument of climate-smart agriculture, improved livelihoods and resilience, and reduced greenhouse gas emissions. The Community Markets for Conservation nonprofit enterprise in Zambia is another World Bank point of contact for climate-smart agriculture (CSA) in Zambia, and one that is actively developing supply chains featuring prod- ucts that play a positive role in land management and rural income genera- tion. Climate-smart agriculture through agroforestry, integrated soil fertility management, and conservation agriculture is a focus of an important World Bank partnership with the International Center for Tropical Agriculture and other partners to support the incorporation of climate-smart agriculture into national planning. These together are part of the larger context of this work on agricultural risk management in Zambia. Executive Summaryxix CHAPTER 1 Introduction and Context Zambia is a landlocked, lower-middle income country in southern Africa with one of the highest economic growth rates among the world’s rapidly growing economies. Between 1960 and 1999, its gross domestic product (GDP) in real terms doubled from $4.6 billion to $9.5 billion. Between 2000 and 2015, its GDP nearly tripled, from $9.9 billion to $26 billion. Despite this growth, Zambia’s GDP at market prices (constant 2010 U.S. dollars) has remained consistently below the Sub-Saharan African average. GDP per cap- ita fell by nearly half between independence in 1964 and the mid-1990s, from $1,525 to $892. Since then, the country has recovered, and its estimated GDP per capita of $1,607 in 2015 is at par with the Sub-Saharan average of $1,660. Agriculture is the main source of livelihood for some 1.5 million, or 60 percent of all households in the country. Yet despite its significance for liveli- hoods and employment, its share of overall GDP in Zambia is small relative to that in other Sub-Saharan countries, and has been diminishing over time— very much in line with development theory. In 2015, agriculture accounted for only 5.3 percent of the GDP (figure 1.1), down from an average 8.2 percent during the period from 2011 to 2015, when the sector accounted for about 9.6 percent of national export earnings (CSO 2015; World Bank 2016). In spite of its proportionately small share of the economy compared with services and industry, agricultural performance wields important effects on the larger macroeconomy. In 2013 and 2015, when the agricultural sector experienced negative growth because of extreme weather events, the economy slowed down by about 2 percentage points (figure 1.2) (World Bank 2017a). The government of the Republic of Zambia has assigned priority to agricul- ture as one of sectors in which to diversify the economy and offset its over- dependence on copper, which accounts for 77 percent of national exports (World Bank 2017b). Within the sector, the 2014–18 National Agricultural Investment Plan identified inclusive agricultural growth as the key to facili- tating economic growth and poverty reduction for the 80 percent of Zambia’s population whose livelihoods depend on agriculture (MAL 2013). The gov- ernment’s recognition of agriculture’s significance is reflected in the increased budgetary allocations directed toward the sector, which now amount to nearly 10 percent of public expenditures. Approximately 80 percent of this amount is spent on input and marketing subsidies. Despite the relatively high level of public investment in agriculture, rural poverty rates have remained persistently high. The rural poverty rate was 76.6 percent in 2015 according to the Central Statistical Office (CSO 2015). CHAPTER 1—Introduction and Context1 FIGURE 1.1  GDP Composition (2015) 5% Services (% of GDP) 35% Industry (% of GDP) 60% Agriculture (% of GDP) Source: WDI 2017. FIGURE 1.2  Agricultural Growth versus GDP Performance 8% 6% 4% 2% 0% –2% –4% 2012 2013 2014 2015 2016e 2017f Agriculture Mining Non-mining industry Services GDP growth Source: World Bank 2017a. Note: e = estimate; f = forecast. Farmers also remain highly vulnerable to a myriad of agricultural risks, such as extreme weather events caused by El Niño and La Niña (World Bank 2017a). The Zambia Vulnerability Assessment Committee (ZVAC) reported that during the 2014–15 and 2015–16 seasons, there was a 38 percent and 41 percent reduction, respectively, in maize production. The committee also reported a decrease in water and pasture available for livestock, and increased incidence of disease outbreaks, particularly Newcastle disease in chicken. Responses to such matters tend to draw directly on scarce public 2 CHAPTER 1—Introduction and Context resources, diminishing what is available for public investment elsewhere. Nevertheless, the government and development partners draw on other resources to respond to crises. The Food Price Crisis Response (FPCR) Trust Fund was established in 2008 as part of a multidonor facility to provide grant funding for low-­income coun- tries negatively affected by the impact of rising food prices. Its role included supporting governments in the design of sustainable policies that mitigate the adverse impacts of high and more volatile food prices on poverty, while min- imizing long-term market distortions. With the support of the FPCR Trust Fund, supplemented by the Multidonor Trust Fund on Risk Management, the World Bank conducted a study titled Increasing Agricultural Resilience through Better Risk Management in Zambia. The study had three compo- nents: (a) Strengthening Agricultural Policies, (b) Agricultural Sector Risk Assessment (ASRA), and (c) Knowledge Exchange and Dissemination. This ASRA report is a combination of the first two components of the study. The third component will involve workshops and seminars to deliberate the find- ings of the study. An Agricultural Sector Risk Assessment is “an orderly process to analyze, identify, and prioritize risk, which serves as the basis for the design of risk man- agement strategies” (World Bank 2016). The objective of the Zambia ASRA is to identify, analyze, quantify, and prioritize risks of Zambia’s agricultural sector, as well as to identify the areas of risk management solutions that need further scaling up and strengthening. The methodology incorporates quanti- tative and qualitative tools such as analysis of primary and secondary data, a desk literature review, interviews, and focus group discussions. The findings were presented and discussed, and recommendations were made for risk-solu- tion interventions during an in-country stakeholder consultative workshop. Stakeholders consulted during this study included farmers, traders, proces- sors, public officials, development partners, and civil society representatives. To provide a sectorwide overview of the impacts of risk events, the com- modities that contribute the top 80 percent of Zambia’s agricultural pro- duction value (table 1.1) were assessed on three levels: production, market, and enabling environment. Although maize receives the most attention and has long been prioritized in agricultural public expenditures, beef contrib- utes more to gross production value. Besides beef and maize, which together account for 43 percent of agricultural value, the rest of the commodities con- tribute 6 percent or less. In the context of agricultural risk management, risks are defined as “uncertain events that have the probability to cause losses.” Constraints are “conditions that lead to suboptimal performance” (Choudhary et al. 2016). Figure 1.3 provides an overview of the World Bank’s agricultural sector risk management process. This study complements three key ongoing World Bank technical support operations designed to help build the resilience of the agricultural sector in CHAPTER 1—Introduction and Context3 TABLE 1.1 Commodities Comprising the Top 80 Percent of the Gross Production Value (GPV) Average GPV Cumulative total of (constant 2004–06, proportion of total Rank Commoditya US$, thousands)b GPV (%) agricultural value (%) 1 Meat indigenous, cattle 460,498 23 23 2 Maize 396,939 20 43 3 Sugarcane 124,781 6 49 4 Cassava 112,086 6 55 5 Tobacco, 97,599 5 60 unmanufactured 6 Meat, gamec 83,191 4 64 7 Cotton lint 82,365 4 68 8 Groundnuts, with shell 74,979 4 72 9 Vegetables, freshness 65,012 3 75 10 Meat indigenous, chicken 62,693 3 79 11 Meat indigenous, pig 56,277 3 81 Source: Food and Agriculture Organization Corporate Statistical Database (FAOSTAT). a. Although the government considers fisheries to be under the agricultural sector, it was not included in the assessment because no single fishery’s product falls within the top 80 percent production value. b. Based on average agricultural GPV for 2011–13 using 2004–06 constant International dollar (I$). For the sector as a whole, GPV = $1,986,261,000. c. Although game meat is in the FAOSTAT’s top 80 percent of GPV commodities, the government considers it to be under the tourism sector; therefore, it is not part of the study. FIGURE 1.3  Agricultural Sector Risk Management Process Flow Client demand Development of Implementation Risk Solution risk management and risk assessment assessment plan monitoring Desk review Desk review Development of Implementation plan by In-country In-country stakeholders assessment assessment Monitoring risks mission mission Incorporation into existing govt. Consultation Stakeholder programs and workshop workshop develpoment plans Finalize analysis Training Source: Choudhary et al. 2016. 4 CHAPTER 1—Introduction and Context Zambia. The first is the Productive Diversification1 in African Agriculture and Effects on Resilience and Nutrition (PRODIVA). The objective of PRODIVA is to analyze drivers of and constraints to productive diversification in agri- culture at household, landscape, and country levels; to assess its impact on nutrition and resilience outcomes; and to make institutional and policy rec- ommendations for agricultural diversification. The second initiative is the Zambia Climate-Smart Investment Plan (CSIP), designed to build capacity of the Ministry of Agriculture to opera- tionalize country climate commitments toward a productive, resilient, and low-­emissions agricultural sector. The CSIP builds on the climate-smart agriculture (CSA) country profile for Zambia, which offers the entry point for how CSA can help the agricultural sector adapt to and mitigate climate change while achieving agricultural sector growth and poverty reduction. It also aligns objectives and goals across Zambia’s agricultural and climate change strategies, policies, and tools and is expected to inform the prepa- ration of the Second National Agriculture Investment Plan, in addition to providing opportunities for leveraging global partnerships for climate-smart agriculture development. The third initiative is the Zambia Integrated Forest Landscape Program (ZIFLP), aimed at providing support to rural communities in the Eastern Province (EP) to allow them to better manage the resources of their land- scapes to (a) reduce deforestation and unsustainable agricultural expansion; (b) enhance benefits that communities derive from forestry, agriculture, and wildlife; and (c) reduce their vulnerability to climate change. The project is an innovative mix of funding: an IDA credit of $17 million, a GEF grant of $8.05 million, and a BioCarbon Fund (BioCF) grant of $7.75 million. Its design follows the successful Landscape Management Project under which EP com- munities recently received carbon payments for their efforts in reducing defor- estation and promoting climate-smart agriculture. ZIFLP implementation entails (a) creating an enabling environment to promote behavioral change in sustainable landscape management; (b) providing the incentives to shift from unsustainable farming and natural resources exploitation to sustainable alternatives; and (c) promoting climate-smart agriculture, sustainable forest management, improved wildlife management, biodiversity conservation, and sustainable livelihood options. The project will also prepare the groundwork for about $30 million of emissions reductions payments from the BioCF, to be processed as a World Bank operation within the next two years. The remainder of this report is organized as follows. Chapter 2 provides an overview of Zambia’s agricultural sector and the key constraints hampering 1Diversification typically refers to strategies and techniques to produce different agricultural products (horizontal diversification), engage in multiple value-added activities (vertical diversification), or exit the agricultural sector and engage in nonfarm activities. CHAPTER 1—Introduction and Context5 its growth. Chapter 3 describes the data and methodology, and the nature of risks in agriculture. Chapter 4 examines the risks at production, marketing, and enabling environment levels of the supply chain. Chapter 5 quantita- tively and qualitatively determines the impacts of the risks, and chapter 6 highlights stakeholders’ perceptions of the risks and priorities for risk management. 6 CHAPTER 1—Introduction and Context CHAPTER 2 Zambia’s Agricultural Sector The Potential for Agriculture Zambia has enormous agricultural growth potential. Of the estimated 74 mil- lion hectares (ha) of total land area, about 42 million ha (58 percent) are suitable for agriculture. Only 14 percent of the land suitable for agricultural production is being cultivated, and less than 30 percent of the land potentially suitable for irrigation has been developed. The available land per capita is higher than it is for most developing countries in southern Africa (table 2.1). About 6 hectares of land is available to each person, reflecting the country’s low population density of 19.2 persons per km2. The country also has suffi- cient water resources (ground, rain, and surface) available to support rain- fed and irrigated agriculture. Combined, these natural endowments uniquely position Zambia to be the breadbasket of the southern and central African regions (Chapoto and Sitko 2015). Zambia has three distinct agroecological zones (AEZs) that are distin- guished by temperature, rainfall, and soil type (figure 2.1). AEZ I covers the southern and southeastern margin of the country. Annual rainfall is less than 750 mm and is normally erratic and of high enough intensity that drought and moisture stress are frequent. The cropping season is 60–90 days. AEZ II stretches in a central band across the country, arching southwest from the Malawian border in the east to the Angolan border in the west. It has the most fertile soils. Rainfall in AEZ II ranges between 750 mm and 1,000 mm (medium rainfall) (figure 2.1). AEZ II has a growing season of 90–150 days. The zone is further subdivided into AEZ IIa and IIb based on differences in soil types. AEZ IIb has coarse, sandy soils with relatively low agroecological potential compared with AEZ IIa, but higher potential than AEZ I. AEZ III covers 41 percent of the country and comprises leached and acidic soils with rainfall of 1,000–1,500 mm per year. The zone covers the Northern, Luapula, Copperbelt, and Northwestern Provinces and parts of the Central Province. It has the longest plant-growing season at 140–200 days. Historical Context of Agricultural Policies Historically, agricultural policies in Zambia can best be understood in the context of the four political phases or “Republics” that define the country’s historical trajectory. Chapter 2—Zambia’s Agricultural Sector7 TABLE 2.1  Estimates of Land Availability (2011–35) Arable land-to-person ratio Land-to-person ratio Year Population (hectares/person) (hectares/person) 2011 13,100,000 3.1 5.7 2020 17,885,422 2.2 4.2 2025 19,900,000 2.0 3.8 2035 26,923,658 1.5 2.8 Source: Samboko, Kabisa, and Henley 2017. FIGURE 2.1  Zambia’s Agroecological Zones N W E S III IIb IIa I Agroecological Zones IIb I FEWSNET IIa III Source: Department of Meteorology. First Republic (1964–72). This period experienced two main agricultural policy decisions. The first was market support for maize at differential prices between farmers along the infrastructure corridor linking Livingstone in the Southern and Kitwe in the Southern Provinces and those in native reserves through the National Agricultural Marketing Board (NAMBOARD). The second agricultural policy entailed maize input subsidies through the Credit Organization of Zambia (COZ) established in 1966. The COZ was, however, marred with high default rates, whereas the administrative structure proved inadequate to efficiently allocate, distribute, and recover loans (Anderson 1968; Kydd 1986). Second Republic (1972–91). This period witnessed the implementation of pan-territorial maize pricing through NAMBOARD in 1974, the ushering 8 Chapter 2—Zambia’s Agricultural Sector in of the cooperative society movement, and increased provision of govern- ment-subsidized seeds and fertilizers through various schemes (Howard and Mungoma 1996). The government provided guaranteed markets for various crops, increasing the number of crops for which it set the producer and con- sumer prices (Kean and Wood 1992). Several models of financing small-scale farmers with loans were tried but all faced the familiar challenges of poor loan recovery rates and high overhead costs. Perhaps the best known among these was the Lima Bank (Dodge 1977). This period generally witnessed increased agricultural spending, which led to the higher uptake of maize hybrids by smallholders and rapid expansion of the area under maize cultivation. Input subsidy provision and market support through NAMBOARD accounted for the largest share of the national budget, averaging 60 percent by 1986, and 15 percent by the late 1980s (Govereh et al. 2006; Tembo et al. 2009). The government faced such severe budget deficits that with the arrival of the subsequent government in in 1991, budget alloca- tions to the agricultural sector declined by nearly 50 percent in 1992. The government dissolved NAMBOARD in 1989 and all its functions were transferred to the Zambia Cooperative Federation, which was previously an agent of NAMBOARD through its member cooperatives. However, the lifting of fertilizer subsidies was to be gradual. The government had learned this lesson the hard way. In 1986, when it attempted to completely remove maize subsidies, the action sparked major food riots. The government was forced to abandon the reforms and reintroduce the subsidies in 1987 (Simatele 2006). Other food-­ related riots occurred in 1991, elevating the perception of maize as a politically dangerous crop and so withdrawing public support from it. This has continued to shape overall agricultural policy in Zambia (Chapoto and Sitko 2015). Third Republic (1991–2001). The agricultural policy development during the first two years of the Third Republic was greatly influenced by the adop- tion of Structural Adjustment Programs (SAPs) of the International Monetary Fund (IMF) and the World Bank. This program fundamentally focused on three economic goals: (a) restore macroeconomic stability through monetary and fiscal reforms, (b) facilitate private sector growth by liberalizing price and exchange regulations and remove trade restrictions, and (c) remove the public monopolies in the industrial and agricultural sectors (Rakner 2003). The adoption of SAPs, coupled with climatic shocks such as the devastat- ing drought of 1991/92, resulted in a 39 percent drop in agricultural output (World Bank 1994). Furthermore, there was a sharp increase in the nomi- nal prices of agricultural commodities such that a 25-kg bag of maize meal increased from ZMW 225 to ZMW 1,800 (Seshamani 1996). Between 1996 and 2001, the development of the agricultural sector was coordinated through the Agricultural Sector Investment Program (ASIP). The ASIP acted as a tool for implementing the government policy of maize market liberalization and market reforms of 1994 (Tembo et al. 2009). The Chapter 2—Zambia’s Agricultural Sector9 overall objective of the ASIP was to provide improved and sustainable agri- cultural services by promoting free-market development, reducing the public sector’s role in commercial activity, and making the delivery of public services more efficient (MAFF 2001). In 1996, the government established the Food Reserve Agency (FRA), with an original mandate of administering a national food reserve. The govern- ment amended the FRA Act in 2005 to expand its crop-marketing activities, and the FRA has since increased its participation in maize marketing over time (see figure 2.2), with purchases significantly higher in 2011. Fourth Republic (2001–present). In 2002, the government replaced the pre- vious credit schemes with the Fertilizer Support Program, a subsidy program for maize seed and fertilizer aimed at improving access to inputs for viable but vulnerable smallholder farmers. Accordingly, the government increased the share of the agricultural budget spent on maize marketing and input subsidies from below 40 percent in 2002 to as high as 90 percent in 2013 (figure 2.3). The Fourth Republic has also witnessed a fair share of ad hoc trade poli- cies regarding maize. In December 2012, on the heels of a third consecutive bumper maize harvest, the government announced the suspension of maize exports because of rising mealie meal (maize flour) prices. In September 2013, Statutory Instrument No. 85 (SI No. 85) was signed once again to ban maize grain exports, although the ban was later lifted on maize bran through the 2014 Statutory Instrument No. 7 (SI No. 7). SI No. 85 was completely lifted in April 2014 through SI No. 3. The restriction of maize exports has been a recurring policy, with the most recent ban occurring in April 2016 FIGURE 2.2  Maize Sales and FRA Purchases in Zambia 100 2,500,000 90 80 2,000,000 70 % of total sales Metric Tons 60 1,500,000 50 40 1,000,000 30 20 500,000 10 0 – 20 /04 20 /05 20 /06 20 /07 20 /08 20 /09 20 /10 20 /11 20 /12 20 /13 20 /14 20 /15 20 /16 20 /17 8 /1 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 20 % large scale sales % smallholder sales Total anticipated maize sales (MT) FRA purchases (MT) Source: Central Statistical Office (CSO)/Ministry of Agriculture and Livestock (MAL), various years. 10 Chapter 2—Zambia’s Agricultural Sector FIGURE 2.3 Share of the Agricultural Budget Spent on FRA/FISP (2001–17) 100 80 Percentage 60 40 20 0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 FRA FISP OTHERS Source: Ministry of Finance and National Planning/MAL, various years. amid fears that the country would run out of maize following high regional demand because of the 2014/15 El Niño phenomenon. The government has, however, through a statement given by the finance minister, the Honorable Felix Mutati, recommitted to maintaining open-border maize-trade policies (Lusaka Times 2017). Current Agricultural Policies Input Subsidies The current agricultural policy framework in Zambia continues to be charac- terized by input subsidies and government intervention in maize marketing through the FRA. After several years of lobbying for Farmer Input Support Program (FISP) reform by various stakeholders in the country, in 2015 the government began piloting the e-voucher as a solution to the challenges in the traditional delivery of subsidies under the FISP. Initially, 13 districts were selected for the pilot during the 2015/16 agricultural season. This was expanded to 39 districts for the 2016/17 farming season (Kuteya and Chapoto 2017). In the 2017/18 agricultural season, the government intends to target 1 million farmers through the e-voucher. A major feature of the e-voucher is that it targets the poor associated with the traditional input subsidy program, giving farmers the freedom to choose inputs relevant to their farming opera- tions. This, in turn, encourages diversification into other crops and livestock, encourages private sector participation in input supply, and reduces the cost of implementation as government cedes its procurement and distribution roles to the private sector. Chapter 2—Zambia’s Agricultural Sector11 Agricultural Marketing Maize marketing policies have been erratic in recent times owing to the increased regional demand for maize resulting from El Niño impacts. Driven by fears of food insecurity, shortages in predominantly maize-produc- ing areas, and very high regional demand for the surplus maize produced (Chisanga and Chapoto 2016), the government imposed a temporary ban on maize and maize meal exports in April 2016. Although this was initially planned to last until September 2016, it continued to May 2017. The gov- ernment decided to open the borders upon realizing that most countries in the region produced enough maize in the La Niña year (that is, the 2016/17 agricultural season), with Zambia and South Africa producing record har- vests. In addition to the export ban, during the 2017 national budget speech the finance minister also announced the introduction of a 10 percent tax on maize exports. The government intended this to increase value addition and create employment. However, for both policy pronouncements, exports were still restricted because the bulk of the maize was held by the private sector. One positive development regarding commodity marketing warrants men- tion. The government provided support to fully operationalize the Zambia Commodity Exchange, or ZAMACE, as the agency responsible for imple- menting the Warehouse Receipt Systems (WRSs). ZAMACE became opera- tional when the government signed the Credit Act on November 4, 2014. The Ministry of Agriculture and Livestock (MAL)2 signed Statutory Instrument No. 59 (SI No. 59) authorizing ZAMACE to perform the functions of a Warehouse Licensing Authority (Chisanga and Chapoto 2016). ZAMACE has so far certified five warehouse operators with a total storage capacity of 425,000 metric tons (MT) for the WRS (ZAMACE 2017). Agricultural Production System: Major Crops and Livestock Ninety percent of smallholder farmers in Zambia grow maize as their main crop, reflecting the large subsidies that go into maize production (figure 2.4). Other major crops are groundnuts, cassava, mixed beans, and sweet potatoes produced by at least 15 percent of farmers. Other crops—seed cotton, sun- flower, soya beans, rice, sorghum, Bambara nuts, and cowpeas—are produced by not more than 10 percent of farmers. Figure 2.5 shows the livestock owned by smallholder farmers in Zambia. Village chickens are the largest livestock holding, followed by goats, cattle, the time, the Ministry of Agriculture (MoA) and the Ministry of Fisheries and Livestock (MoFL) were 2 At under one ministry. 12 Chapter 2—Zambia’s Agricultural Sector FIGURE 2.4  Main Crops Produced by Smallholder Farmers in Zambia 100 % of Percentage 80 60 40 20 0 ts s r t t ze av a an es tto n we ille an s ce m nu ea s ai nu s to lo Ri ha M d as be ot a co nf M be or g ra ow p un d ed ya ba ro C ixe tp Su S m C G e Se So Ba M Swe Source: RALS 2015. FIGURE 2.5  Livestock Ownership among Smallholder Farmers 90 14 % of farmers ownimg livestock 80 12 70 Average number of livestock owned % owning livestock 10 Number of owned 60 50 8 40 6 30 4 20 2 10 0 0 Village Goats Cattle Pigs Ducks & Guinea Sheep Rabbits Donkeys chickens Geese Fowls Source: RALS 2015. and pigs. Other livestock shown in figure 2.5 are less common, with owner- ship of less than 10 percent. The 2015 Rural Agricultural Livelihoods Survey (RALS) indicates that the average smallholder household owns 13 village chickens, 7 goats, 8 cattle, and 4 pigs (IAPRI 2016) . Chapter 2—Zambia’s Agricultural Sector13 CHAPTER 3 Data and Methodology Agricultural Risk Agricultural risk can be usefully classified into three categories based on its scale of magnitude: micro-, meso-, and macrolevels. Microlevel risks apply to individual farms and farm households. Mesolevel risks apply to entire communities or groups of farms, and are more difficult to contain. These include local droughts, floods, and outbreaks of contagious livestock disease, some of which are zoonotic and can be trans- mitted between animals and humans. Macrolevel risks refer to those that affect entire countries or regions and may very well apply to multiple coun- tries. These often relate to the possibility of shocks that bring about sudden changes in global commodity prices and may result from policies that have unintended effects on markets. Examples of various types of risks in agriculture are summarized in table 3.1. Analytical Approach The approach used in conducting the agricultural risk assessment follows Choudhary and others (2016). Focus group discussions with smallholder farmers in the Kalomo and Chipata Districts were undertaken together with a literature review on the risks faced by Zambian farmers and the coping strat- egies they use to manage those risks. Interviews were also held with traders, processors, and others in rural Zambia, as well as with government and agri- cultural extension staff. The focus on the smallholder farming community (those cultivating 20 hectares or less) was based on a recommendation by the Ministry of Agriculture. Typically, these are the most vulnerable to agri- cultural shocks; nevertheless, the proposed risk solutions in this report cover all farmer categories. Quantitative analysis was used to estimate the value of losses from agricultural risk events, focusing on production and on trade losses caused by export bans. The most prominent agricultural risks are associated with events that lead to losses in production, such as droughts and disease or pest outbreaks—more so than market-related risks. It is useful to quantify the average annual loss for a crop over time to balance years in which yields meet or exceed expectations and years in which yields fall short of expectations. The following method was applied to calculate production losses in any given year: (a) A historical Chapter 3—Data and Methodology15 TABLE 3.1  Risks in the Agricultural Sector Microlevel Mesolevel Macrolevel (idiosyncratic risk) (covariant risk) (systemic risk) Individual/Household Groups/Communities Provinces/National Market or prices Side-marketing behavior Change in land price, National marketing or pricing by contracted individual new grades/standards, policy change, import/export Chief banning off-taker policy change, endogenous or agrochemicals variability, exchange rate, state buying/not buying a crop, for example, maize, cotton Farm production Personal hazards Localized weather, Floods, droughts, widespread affecting farm hailstorms, flooding, hail, red locust/fall armyworm households, for example, frost, whirlwind national epidemic, contagious illness or death of family diseases, for example, Contagious disease head, important relative cholera, AIDS killing livestock within for which head is the village materially responsible, livestock deaths or illness Financial Change in family savings Informal credit and Changes in Central Bank activities and income earnings savings club policies, rise in base lending from nonfarm sources, for membership, solidarity or savings rate, country risks, example, head getting a burial insurance macroeconomic situation and wage job, lobolaa cattle society policy, foreign exchange controls Institutional/ Change in social or legal Changes in local Change in national policies and legal liabilities policies, laws affecting regulations, for example, value internal savings and added tax policy, council levies group savings and lending schemes Examples Natural risks Weather risks: drought, flood, erratic rainfall, hailstorm, and temperature variations Other natural risks: landslides, earthquakes Biological/ Crop and livestock diseases and pests environmental risks Market risks and Difficulties in accessing quality inputs and remunerative output markets and price challenges volatility Risks from weak Weak or missing institutions for collection and timely dissemination of market-relevant or missing information; ineffective regulatory oversight of participants in markets providing institutional storage, insurance, and other finance-linked services infrastructure Policy risks Interventions in input markets and in output markets (including price setting and controls over exports/imports) Source: Adapted from Organisation for Economic Co-operation and Development (OECD) 2009. a. Lobola literally refers to “bride price.” It is property in cash or kind, which a prospective husband or head of his family gives to the head of the prospective wife’s family in consideration of a customary marriage. linear trend line for the yield of each crop was constructed. (b) A second lin- ear trend line was drawn, representing one-third of the standard deviation of the crop yields. (c) Years of significant loss were identified as those in which actual yields were lower than the second linear trend line. (d) Production losses were calculated using the difference between the predicted value (the 16 Chapter 3—Data and Methodology original trend line) and actual yield. (e) Losses were added and divided by the total number of years examined to determine the average annual loss rate for a particular crop. (f) The annual quantity lost was converted into value terms by using the producer price for each crop. (g) Because producer prices are in local currency, the value was converted to U.S. dollars using the average exchange rate. To make sure that these losses are from risks and not from a decrease in the size of cultivated land, the decrease in yield is first quantified and then multiplied by the area under production. The value is estimated at constant U.S. dollar prices. To determine how frequently production is affected by risk events, we look at a time series covering multiple years. The more years there are in the time series, the more reliably the frequency of production shortfalls can be estimated. For example, the production may be affected by negative impacts once in 3 years, or once in 5 years. The average cost of losses is esti- mated by adding up the value of losses over a given period and dividing by the total number of years in the given time series. Figure 3.1 shows the basis for estimating indicative losses. The orange curve is the yield, the blue dotted line is the long-term trend, and the grey line with triangular shapes marks one-third of the standard deviation. Losses are measured in years where they fall below this point (denoted by the arrows in figure 3.1). Results from the risk assessment were validated, and a risk prioritization exercise conducted with public and private sector participants at a consulta- tive stakeholder workshop in the Chisamba District. Stakeholders’ perceptions of how agricultural risks should be prioritized was based on the probability of an adverse event, and when it occurs, its expected impact on production FIGURE 3.1  Example of How Indicative Losses Are Calculated 20 18 16 14 12 10 8 6 y = 0.3033x + 5.5697 4 R2 = 0.7238 2 0 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 1980 2008 2010 2012 Yield (tonnes/ha) trend 0.3 trend Source: Giertz et al. 2015. Chapter 3—Data and Methodology17 FIGURE 3.2  Strategic Risk Instruments According to Risk Layers Probablity Layer 3 Very low frequency, Very high losses Layer 2 Risk mitigation Low frequency, + Risk transfer Medium losses + Risk copying Layer 1 Risk mitigation + Risk transfer High frequency, Low losses Risk mitigation Severity Source: Giertz et al. 2015. value, household food security and vulnerability, and the income of different stakeholders. The appropriate risk management instruments depend on the probability of the risk and the severity of its impacts (figure 3.2). Three categories of instru- ment apply: risk mitigation, risk transfer, and risk-coping, all of which are ele- ments of a larger risk management strategy. For risks associated with events that occur frequently but that have limited impacts, risk mitigation is the pre- ferred approach. Risk mitigation includes measures that reduce the likelihood that an adverse event occurs and that offset the severity of the event when it does occur—for instance, the installation of water-draining infrastructure and the subsequent diversification of crops produced. For risks associated with events that are less frequent but that have higher impacts, those exposed to risks may opt to transfer the risk. Risk transfer refers to mechanisms such as an insurance, reinsurance, or financial hedging, in which a willing third party assumes all or part of the risk. In the event of the risk of events that seldom occur but that have very large impacts when they do, risk coping mechanisms may be required to enable those affected to manage, despite the losses they incur. Risk coping includes mechanisms such as public assistance to produc- ers, debt restructuring, and scalable social safety nets. The mitigation, transfer, and coping all entail measures taken and budgeted for prior to the risk event. 18 Chapter 3—Data and Methodology CHAPTER 4 Agricultural Risk Assessment This chapter focuses on risks affecting production, marketing, and the enabling environment and their occurrence over the past 30 years. The study found that droughts, floods, diseases and pests, extreme price volatility, mac- roeconomic changes, and unexpected policy changes were the most import- ant risks facing the agricultural sector in Zambia. Other risks included input distribution delays, trade restrictions, political uncertainty, and ad hoc local government levies. As in many other African countries, Zambia’s structural adjustment pro- gram (SAP) was a defining event in its political economy. The study period can therefore be usefully categorized into (a) pre-SAP (1980s), (b) SAP (1990s), and (c) post-SAP (2000s) (figure 4.1). Pre-SAP. During this period from independence to the late 1980s, Zambia became a one-party state in which the government controlled almost all aspects of the economy through parastatals, price controls, and inflows and outflows of goods and services. Government expenditure exploded. The value of input and marketing subsidies, for instance, were eight times in 1974 what they were at independence. Parastatals incurred heavy losses. The marketing board, for example, had incurred losses valued at 17 percent of the national budget by the late 1980s (Govereh, Jayne, and Chapoto 2008). A downturn in government revenues resulting from falling copper prices led to negotiations with the International Monetary Fund (IMF) for assistance. During the pre- SAP period, the major risk event was an El Niño–related prolonged dry spell in 1987, which significantly affected agricultural production. SAP. Painful economic policy reforms were conditional for IMF loans. In 1989, the government finally agreed to the International Monetary Fund’s (IMF) conditions and the SAP officially began in 1991. The economy was lib- eralized, price controls removed, parastatals privatized, government expendi- tures reduced, and the civil service sharply downsized (Govereh, Jayne, and Chapoto 2008). Inflation spiraled almost out of control, reaching a high of 183 percent in 1993.3 In addition, exchange rates and interest rates were highly vol- atile. These macroeconomic changes coupled with extreme El Niño–related weather events. These included severe droughts in 1991–92 and 1993–95, as well as a drought that affected the south and excessive rains in the north in 1997–98. These crippled the agricultural sector during the 1990s. Following these risk events, recovery efforts were hampered by the private sector’s 3 WDI. http://databank.worldbank.org/data/reports.aspx?source=2&country=ZMB. Chapter 4—Agricultural Risk Assessment19 FIGURE 4.1  Timeline of Major Shocks to Agricultural Production in Zambia (1983–2015) 20 80 Pre-SAP SAP Post-SAP Macroeconomic changes Macroeconomic changes Macroeconomic changes 60 FRA mandate FRA established FISP Introduced increased 40 20 0 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 El Nino El Nino La Nina Prolonged Prolonged Floods El Nino Dry 1997–98 Floods Dry Spell Dry Spell Dry Spell Drought –20 Drought Spells Drought 2001–02 2004–05 2009–10 2012–13 2015–16 1993–95 1987 (South) El Nino –40 Drought Excessive 1991–92 rain (North) Removal of –60 fertiliser subsidy –80 Agriculture GDP (annual % growth) Chapter 4—Agricultural Risk Assessment Source: Authors’ compilation. measured response to filling the gap left after government provision of inputs and advisory services ceased. As the macroeconomic situation began to improve, a few of the policy reforms were rolled back. A number of these major policy decisions are linked to elections being held around that time. Post-SAP. Overall, the 2000s have had fewer and less severe shocks than the 1990s. The economy was stabilized, and growth took off. The major risk events during this period were weather-related: dry spells, excessive rain and floods, and the El Niño drought in 2014/15 and 2015/16. In addition to weath- er-related risks, exchange rate fluctuations in 2000 and 2016 also affected the agricultural economy. Production Risks Weather risks were the most frequent and impactful risks to the agricultural sector in Zambia (figure 4.2). Between 1981/82 and 2016/17, all but five agri- cultural seasons experienced extreme weather events affecting one or more provinces (table 4.1). Most of these events were local in scale and production in unaffected areas offset the effects so that national agricultural performance remained on track. However, for affected districts, especially those in remote areas with poor access to markets, these events had an enormous impact on household food security and income. Overall, excess rainfall occurs more frequently than drought, although the impacts of the latter are greater (figure 4.3). Looking at risk events at a national level conceals the disparities between agroecological zones (AEZs) and provinces. Although AEZ III receives the highest average rainfall, it is the most affected by extreme weather events, particularly the Northwestern and Luapula Provinces with 10 and 7 rainfall deficit years and 8 and 7 excess rainfall years, respectively. The Southern Province and AEZ I are often in the headlines because of drought. AEZ I has the lowest average rainfall (less than 750 mm annually), but it is a key maize production area for Zambia. Maize, the dominant crop in the zone, is highly susceptible to moisture stress (Chisanga et al. 2015). Rainfall deficits have a large impact on national maize production. Cassava, conversely, is drought tolerant and flood resistant and is the dominant crop in AEZ III. The choice of appropriate crop varieties has aided smallholders in the Northwestern and Luapula Provinces in mitigating and coping with weather risks. Drought Research suggests that Zambia is experiencing the effects of climate change. In the Southern, Lusaka, Eastern, and Northern Provinces, farmers’ percep- tion of increasing temperatures were corroborated by empirical evidence, indicating that between 1960 and 2003, temperature increased by 1.3°C. Chapter 4—Agricultural Risk Assessment21 22 FIGURE 4.2  Timeline of Major Shocks to Agricultural Production in Zambia (1995–2016) 10 Agriculture GDP growth rate 1996 Election 2001 Election 2006 Election 2016 Election 8 FRA established FISP introduced mandate increased Maize export ban 6 4 2 0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2 La Nina Prolonged Prolonged El Nino Floods Dry Spell Dry Spell 1997–98 –4 2001–02 2004–05 2009–10 Drought Floods (South) Dry Spell –6 2012–13 Excessive –8 rain El Nino (North) Drought –10 2015–16 Source: Central Statistical Office (CSO), author’s notes. Chapter 4—Agricultural Risk Assessment FIGURE 4.3  Extreme Weather Events by Province (1981/82–2016/17) 10 9 Number of extreme 8 weather events 7 6 5 4 3 2 1 0 l lt n a ka rn rn rn rn tra e er ul e te he te rb sa ap en st th es es pe ut Lu Ea or Lu C -w W So op N th C or N Provinces Severe drought Drought Excess rainfall Extremely excess rainfall Source: World Food Programme (WFP) Vulnerability Assessment and Mapping (VAM). TABLE 4.1  Major Drought Incidents in Zambia Crops affected October–April according to yield Year rainfall (mm) Provinces affected trends 1981/82 794 Copperbelt, Luapula, Northern, Sugarcane Northwestern, Southern, Western 1991/92 770 Central, Copperbelt, Eastern, Maize, sugarcane, Luapula, Lusaka, Northwestern, groundnuts Southern 1993/94 800 Central, Copperbelt, Eastern, Maize, groundnuts Luapula, Lusaka, Northwestern 1994/95 764 Central, Copperbelt, Eastern, Maize, groundnuts Luapula, Lusaka, Northwestern, Southern, Western 2001/02 848 Central, Lusaka, Northwestern, Cassava, maize, Southern groundnuts 2014/15 862 Copperbelt, Luapula, Northern, Groundnuts Northwestern, Western Source: WFP, Food and Agriculture Organization Corporate Statistical Database (FAOSTAT), CSO. Farmers’ perception of a decrease in rainfall in the Southern, Lusaka, and Northern Provinces was similarly backed up by empirical evidence that has shown a 2.3 percent decrease in mean monthly rainfall per decade since 1960. The future trends in the country are toward a higher average temperature, a possible decrease in total rainfall, and some indication of more intense rain- fall events. The government estimates losses between $4.3 billion–$5.4 billion over 10–20 years as a result of climate change, of which the agricultural sector alone is expected to lose $2.2 billion–$3.1 billion (Mulenga and Wineman 2014; World Bank 2017b). Chapter 4—Agricultural Risk Assessment23 In this report, drought refers to occurrences of rainfall deficit during the growing season. It also encompasses dry spells, shortened seasons as a result of delays in the onset of rainfall, or early cessation. Drought manifests in dif- ferent forms across the provinces. The Northwestern Province has the highest incidence of lower than average cumulative rainfall received during a season (1 in 4 years), whereas the Southern Province tends to experience more dry spells occurring during the season.4 Between 1981/82 and 2016/17, Zambia experienced two severe droughts and five droughts in which more than 40 percent of the country was affected. Overall, severe droughts occur 1 in 20 years, whereas localized droughts occur 1 in 5 years. The major drought inci- dences are linked to El Niño events of 1991/92, 1993/95, and 2014/15. Maize was the crop most susceptible to drought, losing over half of the expected national production in 1991/92. Farmers in the Kalomo District, Southern Province, reported losing, in some cases, their entire maize crop during the 2015/16 drought, although production was higher in most of the country in comparison to the previous year. The high losses experienced by maize can be attributed to its preponderance across the country, even in areas that are drought prone. Cotton and cassava are the only major crops that did not experience losses resulting from drought. Both hybrid and drought-tolerant, open-pollinated varieties (OPVs) are available on the market, but adoption has been limited by the limited avail- ability of OPVs, which smallholder farmers prefer over hybrids. Smallholders prefer OPVs because they can recycle the seed, which reduces the pressure to buy seed every year. More hybrid varieties have been widely available and for a longer period than OPVs. Additionally, seed companies prefer to market hybrid varieties. The government’s Zambia Agricultural Research Institute has, over the last decade, focused on developing and releasing OPVs to meet growing demand, but distribution is limited as seed companies are reluctant to promote them (CIMMYT 2015). The Ministry of Agriculture (MoA) is also promoting other crops, particularly drought-tolerant varieties of legumes such as beans and cowpeas, to improve household nutrition as well as miti- gate crop losses caused by drought. In the livestock sector, drought was the most significant production risk. The droughts in 1994, 1998, and 2015, and a prolonged dry spell in which the Southern Province was particularly hard hit, led to considerable losses. During the 2015/16 El Niño drought event, farmers in the Eastern and Southern Provinces had to travel with their animals about 7 km to 25 km away from their homesteads in search of water and pastures. In the Kalomo District, Southern Province, 21 percent of focus group discussion participants lost ani- mals to drought. One of the participants lost 22 animals because of lack of pastures. Abortions in goats were also reported by focus group participants. 4 Dry spells occurrence based on anecdotal evidence received in interviews during the field mission. 24 Chapter 4—Agricultural Risk Assessment Excess Rainfall and Flooding Owing to Zambia’s extensive river network, excess rainfall floods large areas of the country (table 4.2). The situation is worsened by insufficient flood control infrastructure. Excess rainfall and flood risk events are linked to La Niña, such as the 2001/02 floods. Cotton is highly susceptible to flooding because it is primarily grown in valleys and floodplains. As a result of the excess rain and flooding in 2002 and 2007, nearly 70 percent and 40 percent, respec- tively, of anticipated production were lost. Maize and groundnuts are also susceptible, and in 2001 more than a third of production was lost. Cassava and tobacco are more tolerant but still lost 10 percent and 17 percent of pro- duction, respectively, in 2001. Sugarcane, conversely, was virtually unaffected. Similarly, although the livestock sector was affected by excessive rainfall and flooding, the losses were not below the three standard deviation trend thresh- old and thus were not considered to be significant. Excess rainfall and flooding risk events occur more frequently than drought. Localized events occur 1 in 5 years, whereas extreme events (large scale) occur on average 1 out of 10 years. However, not all localized events result in crop losses. The risk factors are related to farmer behavior, planting in flood-prone areas, and the management of flood-control infrastructure on private land. Pests and Diseases Agricultural pests and diseases also have large impacts on production. Farmers reported fall armyworm (Spodoptera frugiperda) and maize stalk borer (Busseola fusca) to be among the key pests affecting crop yields. A fall army- worm outbreak that started in Nigeria in early 2016 swept across Africa in 2017. By April 2017, it had been confirmed in 11 countries, and there were uncon- firmed reports of the outbreak in 15 countries. The Centre for Agriculture and TABLE 4.2  Major Excess Rainfall and Flooding Incidents in Zambia Crops affected October–April according to yield Year rainfall (mm) Provinces affected trends 1988/89 1,058 Eastern, Lusaka, Southern, Western Sugarcane 1992/93 1,069 Central, Copperbelt, Northwestern, Tobacco Western 1997/98 1,010 Luapula, Northern Sugarcane, groundnuts 2000/01 1,125 Central, Eastern, Luapula, Lusaka, Cassava, maize, cotton Southern, Northern, Copperbelt 2006/07 1,068 Eastern, Luapula, Northern, Cotton, groundnuts Northwestern 2007/08 824 Central, Lusaka, Western, Southern Cotton Source: WFP VAM, FAOSTAT, CSO. Chapter 4—Agricultural Risk Assessment25 Biosciences International estimates the outbreak to have caused a 20 percent and 8 percent loss in maize and sorghum, respectively, and production valued at $3 billion and $827 million, respectively (Abrahams et al. 2017). In the last quarter of 2016, a fall armyworm outbreak was reported in Zambia and the International Plant Protection Convention was officially notified in February 2017. At the time, the pest was estimated to have affected more than 130,000 hectares in six provinces. The government declared the outbreaks a national disaster and swiftly mobilized and spent more than $3 million to respond to them (FAO 2017). This swift response appeared to have curtailed the spread of the pest. However, Zambia still needs to remain vigilant as fall armyworm continues to ravage the rest of the continent and could thus easily attack again. The outbreak revealed gaps in the national early warning and extension systems that should be rapidly filled (Braimoh and others, 2018; Indaba Agricultural Policy Research Institute, IAPRI 2017). Other notable pests include maize stalk borer, African armyworm (partic- ularly the outbreak in 1996), and cowpea aphids. For the former two pests, farmers in Southern Province reported that they received pesticides from the government to manage the outbreaks. Cassava mosaic disease is the most significant risk to the cassava value chain in Zambia. The disease was first reported in Zambia in the 1990s. As the resistant disease spread, cassava yields fell sharply (figure 4.4), but as disease-­ cultivars became available, yields recovered, although below previous levels. In a countrywide survey, it was found that adoption rates of disease-­ resistant cultivars were low and farmers preferred local cultivars even though they were susceptible to the disease (Chikoti et al. 2014). The study found that cassava mosaic disease affected fields in seven provinces, with the highest FIGURE 4.4  Cassava Yields (MT/ha), 1982–2014 6.3 6.1 5.9 5.7 5.5 5.3 5.1 4.9 4.7 4.5 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Source: FAOSTAT. Note: MT = metric ton; ha = hectare. 26 Chapter 4—Agricultural Risk Assessment incidence (71.2 percent) in the Northwestern Province. The disease has there- fore evolved from a risk into a constraint that is affecting cassava productivity. It was noted that the disease symptoms were more severe in areas along major highways, suggesting that disease-affected cuttings might be transported from bordering countries affected by the disease (Chikoti et al. 2014). According to the World Organization for Animal Health (Office International des Epizooties, OIE), 20 percent of livestock productivity losses are the result of disease. In this study, diseases were found to be the most significant cause of loss in each year over the period covered. Foot-and-mouth disease (FMD), contagious bovine pleuropneumonia, anthrax, lumpy skin disease, African swine fever, and Newcastle disease are the key OIE-listed diseases leading to outbreaks. East Coast fever, also known as Corridor disease, and other tick- borne diseases such as babesiosis and anaplasmosis, as well as trypanosomi- asis (sleeping sickness, also known as nagana in cattle), which are endemic in Zambia, were constraints rather than risks because farmers deal with them on a day-to-day basis. They are also classified as management diseases by the Ministry of Fisheries and Livestock (MoFL) and, as such, government sup- port to their control is not available, except for vaccinations and communal disease control infrastructure, that is, dips. The number of outbreaks reported to the OIE has declined remarkably since 2005 (figure 4.5). However, this may be because of a change in the OIE’s reporting system, which, since 2005, has given emphasis to notifiable diseases FIGURE 4.5  Disease Outbreaks Number of annual disease outbreaks, 1996–2017 Number of annual disease outbreaks, 2005–2017 1,200 9 8 1,000 7 800 6 600 5 4 400 3 200 2 1 0 0 19 6 19 7 19 8 20 9 20 0 20 1 20 2 20 3 20 4 20 5 20 6 20 7 20 8 20 9 20 0 20 1 20 2 20 3 20 4 20 5 20 6 17 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 9 9 9 9 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 19 Number of outbreaks by disease, 1996–2017 Outbreaks by species, 1996--2017 700 3,500 600 3,000 500 2,500 400 2,000 300 1,500 200 1,000 100 500 0 0 Pa k’s piro ... . vi e lac ... -.. er sis os R sis la An sis is th P fe a ow F ) bu pic lera ra d sis rc se et s tle ta s: gs ts se en s lo s os rax cy be leg at o C i s rf a us se ho D M ept sea a ar os se ke Bo vin B (M (ts ie og al se os ph BP ve lmi L di mi oa po u ffa s M be a st sio o at Pi or ck o ... is ab im co po am lu ne ba k sm th tu ise ul D ra ae yo is C G Bu a H hi an ip t c om C H opo ic l ild l (li tin p W e In orrh F ip Ac m s io c om op to iou ct gi H Bo yc os D tag m an on n em yp is ia C Tr Av ha Source: OIE. Chapter 4—Agricultural Risk Assessment27 rather than all disease outbreaks. Also, with the liberalization of the livestock extension services, there are several vacant positions at the veterinary camp level. It is important to note that veterinary camps (that is, areas covered by livestock extension workers) are, on average, six times larger than agricul- tural (crop) camps.5 Their more extensive size means less reporting capac- ity. Moreover, government support is limited to notifiable diseases and thus they tend to be emphasized in the OIE reports. Nevertheless, livestock farmer focus group discussions revealed disease to be the second-highest risk fac- tor after drought. Cattle are the livestock animals most affected by outbreaks; 81 percent of OIE-reported outbreaks are attributed to diseases of cattle. Market Risks Price and production risks are highly interrelated because variability in pro- duction can result in high food price instability. Abrupt deviations in a com- modity’s price or production are generally manifestations of some form of underlying risk, which may include production risks, exchange rate volatil- ity, or market interventions by the government. Production responsiveness is low for annual crop commodities because planting decisions are made before prices for the new crop are known. These decisions depend on expected prices and not price realizations (Dana and Gilbert 2008). The vulnerability to price risks also depends on how integrated a market is with other markets. The less a market is integrated with others, the higher is the price instability stemming from variability in local production. In well-integrated markets on the other hand, price risks are easily transferred from one area to another. The down- side of a well-integrated market is that price risks could affect producers more extensively (Antonaci, Demeke, and Vezzani 2014). Zambia’s agricultural price risks emanate from excessive market interven- tions by the government as well as from production risks. The interventions are mainly short-term measures aimed at maintaining stock levels within the country. This is more so for maize compared with any other crop and they include export bans/restrictions, strategic stockpiling, and price controls. These short-term measures are popularly used to mitigate against produc- tion-related risks but end up worsening the situation by increasing price vol- atility in the market (Chapoto and Jayne 2009). Price Volatility Price volatility in the agricultural sector affects different actors in differ- ent ways, depending on where they are positioned on the supply chain. For 5A camp is the area covered by an extension officer. It may consist of several villages (the smallest admin- istrative unit). 28 Chapter 4—Agricultural Risk Assessment BOX 4.1 Production Risks in the Northern versus the Southern Province The Northern Province was severely affected by the removal of the input subsidies in 1989, mostly because it was not a high-production area for maize at the time. Over the past 10 years, maize productivity has been increasing in the Northern Province, whereas it has been on a downward trend in the Southern Province. Productivity in the Southern Province has been affected by the high frequency of weather-related risk events, drought averaging every 3 years, and excessive rainfall and flooding averaging every 5 years. Conversely, macroeco- nomic changes seem to have a higher impact on yields in the Northern Province, as seen in 1989 when yields fell nearly 274 percent, from 3 MT per ha to 0.8 MT per ha. The frequency of weather-related events in the Northern Province was lower, with drought every seven years; excessive rain and flooding is not an important hazard for the Province. Given the risks, gov- ernment efforts should focus on diversifying into drought- and flood-tolerant crops in the Southern Province and on ensuring macroeconomic stability for the Northern Province. FIGURE B4.1.1 Maize Yields (t/ha) in the Northern and Southern Provinces (1987–2016) 4.0 Excessive Excessive Excessive 3.5 Severe Severe rain and Prolonged rain and rain and drought drought Drought floods dry spell floods floods Drought 3.0 2.5 2.0 1.5 1.0 0.5 Macroeconomic changes Macroeconomic changes 0 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Zambia Southern Northern Linear (Southern) Linear (Northern) Source: Crop Forecast Surveys (CFSs). example, a farmer’s income is negatively affected by a sharp fall in producer prices, but a consumer sees lower food prices. A trader or a processor may lose out on a fall in retail prices or profit from an increase, depending on the timing of the price change. Although certain levels of intra- and inter-sea- sonal price volatility are acceptable in the market, it is the unpredictability of this volatility that presents a major price risk especially for smallholder farmers. Price volatility is a manifestation of the unpredictability in the pol- icy space, including trade restrictions that can lead to extreme price shifts over and above what is expected and predictable, leading to huge unexpected Chapter 4—Agricultural Risk Assessment29 losses. For smallholders, extreme price volatility is a risk that affects house- hold income and food security. Household income is directly affected when prices fall; however, for net food-buying households, sharp increases in food prices have significant impacts on food security. A study by IAPRI shows that a third of Zambian rural households are net maize buyers (Kuteya 2016). Additionally, sharp price drops negatively affect planting area and input investments, leading to yield drops and losses in production. Farmers plant a smaller area for the affected crop and reduce their level of investment in fertil- izer and improved seed, thus reducing production of the crop the next season. Cotton price volatility from one year to the next is among the highest among agricultural commodities in Zambia. Domestic cotton seed prices are determined by local supply conditions, as well as by international prices because most of the cotton lint is exported. The export of cotton lint trans- mits international prices directly into the local market, leaving cotton farmers susceptible to international cotton price movements. High international and domestic cotton prices in 2011 led to an increase in area planted and volume produced for the 2011/12 season. This led to a major price drop of 50 percent in 2012, and the losses incurred saw the burning of a number of trucks owned by cotton companies across the country. The government has had limited involvement in the cotton sector since its liberalization in 1994. However, when the sector faced major price declines between 1999 and 2000, the sector almost collapsed, prompting government interventions. The Cotton Act was enacted in 2005, and the government also created a Cotton Fund aimed at stimulating production and trying to reduce incidences of side selling. The Cotton Fund provides support to cotton pro- duction through loans offered to both large and small ginning companies. The Cotton Association of Zambia was also created in 2005 to promote the interests of cotton farmers. In 2009, the Cotton Board of Zambia was cre- ated using the Cotton Act to regulate the sector. With the crop diversification agenda and a need to increase cotton production, the government decided to put cotton under the Farmer Input Support Programme (FISP). It distributed about 153 MT of fertilizer to cotton farmers in the 2016/17 season. In the case of maize, the main sources of price volatility were local produc- tion conditions and international prices during the period when Zambia was a net importer of maize. In 2007–08, the world food price crisis was another driver of a maize price increase globally. In Zambia, the effect of the rising global food prices was not felt until late 2008 and early 2009. Failure by gov- ernment and other stakeholders to quickly respond to the crisis was the lead- ing cause for maize price escalation in the country rather than international developments (Chapoto 2012). Although price volatility in maize lessened between 2011 and 2014, when there were large surpluses, weather shocks caused large price swings again in 2015 and 2016. With a more resilient production system, Zambia still 30 Chapter 4—Agricultural Risk Assessment produced surplus maize even in 2016, which was an El Niño year. Yet high regional demand prompted the government to take drastic policy measures such as the imposition of an export ban that likely exacerbated the price vol- atility. Prices plummeted by almost 40 percent in 2017 mainly caused by the export ban that coincided with a bumper harvest. The country’s huge carry- over stock of 569,317 MT, plus the production of more than 3 million MT caused prices to crash. Such price swings disadvantage farmers and affect their ability to respond to shocks in the future as their income earning capac- ity is markedly reduced. In the case of groundnuts, major price drops took place in 2000 and 2007, with 35 percent and 20 percent declines, respectively. These were associated with high levels of production in each of those years for the respective commod- ities. In comparison with the rest, cassava is relatively stable. We hypothesize that groundnuts are largely grown for consumption by smallholders but are not as widely consumed as is maize. Groundnut yield, moreover, is relatively stable because it is tolerant to most of the risks that affect maize and some other crops. In comparison with crops, prices are relatively stable for livestock com- modities, although farmers have reported them to have been depressed since 2008. Although beef price data were not available between 2000 and 2015, a downward trend for real beef prices had already been seen between 1993 and 1999, as shown in figure 4.6. FIGURE 4.6  Annual Price Changes for Selected Crops Maize: Annual price change (%) Cotton: Annual price change (%) 120 100.00 100 80.00 80 60.00 60 40.00 40 20.00 20 0 0 –20 –20.00 –40 –40.00 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 –60 –60.00 Groundnuts: Annual price change (%) Cassava: Annual price change (%) 150 200 150 100 100 50 50 0 0 –50 –50 –100 –100 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Source: Central Statistical Office Monthly Average Price Data. Chapter 4—Agricultural Risk Assessment31 BOX 4.2  Maize Price Volatility Although maize was one of the commodities studied with the highest price volatility, since the 1990s, its inter-annual price volatility has lessened. In addition, a gradual reduction in prices over time was observed, which has been attributed to an increase in maize production as a result of several good years. For example, falling prices in 2000 and 2009 were associ- ated with high production levels. A decline in prices one year influences farmers’ production decisions the following year, including the area cultivated, which can decline by 59 percent. There was also a strong negative (60 percent) correlation between maize prices and yields. Anecdotal evidence suggests that price drops lead to reductions in investments in improving productivity such as input purchases and irrigation, resulting in lower yields. For staples such as maize, this has household income and food security implications. FIGURE B4.2.1  Price Volatility of Maize Real annual average wholesale price (ZMW/kg) Annual % price change 6.0 80.0 5.0 60.0 4.0 Price 3.0 40.0 2.0 20.0 Percent 1.0 0 0 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 –20.0 Real wholesale price –40.0 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 Linear (Real wholesale price) –60.0 Price vs area Price vs yield 1,800,000 6.0 6.0 2.5 1,600,000 5.0 1.0 2 1,400,000 1,200,000 4.0 1.0 1.5 1,000,000 Price Price Area 3.0 1.0 800,000 Yield 1 600,000 2.0 1.0 400,000 0.5 1.0 1.0 200,000 0 0.0 0 0 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 97 00 03 06 09 12 15 19 20 20 20 20 20 20 Area (Ha) Real wholesale price Yield (MT/Ha) Real wholesale price Source: MoA; Grain Traders Association of Zambia (GTAZ), and CSO. 32 Chapter 4—Agricultural Risk Assessment BOX 4.3  Cotton Price Volatility Cotton prices are highly volatile. However, since the liberalization of cotton’s production and marketing, prices have come down considerably. As prices have fallen, cotton has become more closely aligned to the direction of interna- tional prices. However, farmers seem to bear the cost of any declines in international prices, and when prices go up, they do not seem to benefit as much as would have been hoped. As seen earlier with maize, when prices rise, the area under cultivation increases and vice versa. When farmers were asked about why they continue to grow cotton even though prices are depressed, they said it was because of the input loans they receive from ginning companies. The farmers use many of the inputs received on other crops. The depressed prices are one reason why counterparty risk is a big issue for cotton traders. FIGURE B4.3.1  Price Volatility of Cotton Cotton: Interannual price changes (%) Domestic price vs international prices 0.60 350 0.16 International price |cents/kg| 0.40 Domestic price |ZMW/kg| 300 0.14 0.20 0.12 250 0 0.10 200 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 –0.20 0.08 150 Price –0.40 0.06 100 0.04 –0.60 50 0.02 –0.80 0 0.00 –1.00 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 –1.20 –1.40 Area harvested Domestic price |ZMW/kg| Price vs area Cotton annual price changes: Domestic price vs international 350,000 0.05 50% 0.05 300,000 0.04 Area harvested Price |ZMW/kg| 250,000 0.04 0% 0.03 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 200,000 0.03 150,000 0.02 –50% 100,000 0.02 0.01 –100% 50,000 0.01 0 0.00 –150% 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Domestic annual International annual Area harvested Domestic price |ZMW/kg| price changes (%) price changes (%) Source: IAPRI, FAOSTAT, and World Bank. Chapter 4—Agricultural Risk Assessment33 FIGURE 4.7  Real Beef Prices (ZMW/kg) 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0 1993 1994 1995 1996 1997 1998 1999 Source: CSO. Exchange Rates, Interest Rates, and Inflation Rapid, unpredictable changes in interest rates, exchange rates, and inflation are major risk factors affecting the agricultural sector (figure 4.8). Some may result from economic policy decisions in Zambia or abroad, such as a change in monetary policy from the U.S. Federal Reserve that affects the dollar exchange rate. After the liberalization of the Zambian economy, inter- est rates began to rise, exceeding 100 percent by 1993. From 2001 to 2007, Zambia underwent economic stabilization so that lending rates had declined significantly, well below 20 percent by 2007. Lending rates, however, started increasing in 2015 and were approximately 30 percent in 2017. After the lib- eralization and removal of the exchange control in 1991, the Zambian kwacha experienced significant depreciation and only stabilized between 2002 and 2008, after which it began depreciating again. The Zambian kwacha further underwent depreciation in 2015 and 2016 but stabilized in 2017. Enabling Environment Macroeconomic Changes For most of the 1980s, negotiations between the Zambian government and the IMF were on-again, off-again as economic conditions deteriorated until the need for IMF support outweighed the government’s concerns about the loan conditions and terms. The structural adjustment program (SAP) offi- cially commenced in 1991. The macroeconomic environment was turbulent in the 1990s as a result of the policy reforms implemented as part of the SAP. The impacts of some of the reforms made at the time are still being felt today. 34 Chapter 4—Agricultural Risk Assessment 150 Perc Perce 100 100 50 50 0 0 Jan–75 Jan–78 Jan–81 Jan–84 Jan–87 Jan–90 Jan–93 Jan–96 Jan–99 Jan–02 Jan–05 Jan–08 Jan–11 Jan–14 FIGURE 4.8  Trends in the Rates of Inflation, Exchange, and Lending Jan–75 Jan–78 Jan–81 Jan–84 Jan–87 Jan–90 Jan–93 Jan–96 Jan–99 Jan–02 Jan–05 Jan–08 Jan–11 Jan–14 Inflation rate Exchange rates Exchange rates 250 14 14 12 200 12 10 10 150 Percent Percent 8 Percent 8 100 6 6 4 4 50 2 2 0 0 0 Jan–75 Apr–78 Jul–81 Oct–84 Jan–88 Apr–91 Jul–94 Oct–97 Jan–01 Apr–04 Jul–07 Oct–10 Jan–14 Jan–75 Jan–78 Jan–81 Jan–84 Jan–87 Jan–90 Jan–93 Jan–96 Jan–99 Jan–02 Jan–05 Jan–08 Jan–11 Jan–14 Jan–75 Apr–78 Jul–81 Oct–84 Jan–88 Apr–91 Jul–94 Oct–97 Jan–01 Apr–04 Jul–07 Oct–10 Jan–14 Exchange rates Lending rates (%) Lending rates (%) 14 120 120 12 100 100 10 80 Percent 80 Percent Percent 8 60 60 6 40 40 4 20 20 2 0 0 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 Jan–75 Apr–78 Jul–81 Oct–84 Jan–88 Apr–91 Jul–94 Oct–97 Jan–01 Apr–04 Jul–07 Oct–10 Jan–14 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 Lending rates (%) Lending rates (%) Lending rates (%) Source: CSO/Bank of Zambia. 120 100 80 Extension services have yet to recover from the budget cuts and staff losses Percent 60 that occurred at the time. Livestock farmers have been particularly hard hit 40 because veterinary camps are larger than agricultural camps. In some dis- 20 tricts such as Chipata in the Eastern Province, livestock camps are six times 0 larger, and not fully staffed. Private veterinarians have not picked up the slack, 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 largely because they are concentrated in urban areas. Zambia has remained Lending rates (%) politically stable and its stability makes it attractive to investors, including foreign direct investment. Changes in government in the recent past have been the result of the death of sitting presidents. The transi- tions from one administration to the next have been peaceful. Through the subsidy programs, namely, FISP and the Food Reserve Agency (FRA), the area under maize cultivated has been expanded and Zambia has moved from being a net importer to a surplus maize producer. By recently implementing an e-voucher, the government has signaled to farmers that their production systems need to be diversified. The amendment of the Credit Act, which assigned the Zambia Commodity Exchange (ZAMACE) the implementation of Warehouse Receipt Systems (WRSs), implies that the government is willing to work with the private sector in developing credible, Chapter 4—Agricultural Risk Assessment35 structured markets as opposed to serving as the main player in agricultural marketing—with particular reference to maize. The unintended consequences of government interventions in the mar- ket are at times an important source of agricultural risk. These interventions include ad hoc export bans and other export restrictions; the storage of large, strategic grain reserves, price controls through subsidies to millers; and gov- ernment spending that is skewed toward maize, the main staple crop. During the El Niño weather risk event, Zambia banned exports of maize to neigh- boring countries at the very time when demand for maize was highest. The lifting of the export ban upon the announcement that Zambia would produce a bumper maize crop was untimely because the region was also expecting a good harvest. The resulting depression of local maize prices was a major dis- incentive for maize producers. Input Delivery Delays The input subsidies program in Zambia has been characterized by input delays. In 1997, input delivery delays coupled with disease outbreaks in livestock led to a 3 percent reduction in agricultural gross national product (GDP). The rains started earlier and occurred in the Western Province instead of the Northern Province as would be expected (FAO and WFP 1998). The gov- ernment and farmers were unprepared. Even with seasons that start on time and in the areas anticipated, there have been delays in the delivery of inputs. The government therefore decided to restructure the program, piloting the e-voucher in the 2015/16 and 2016/17 seasons. However, the e-voucher also faced challenges, including delays. Farmers interviewed stressed input delays as one of the key risks they face, both under the traditional and e-voucher systems of FISP delivery. Some of the issues reported included missing names of eligible farmers who had regis- tered and paid their contribution, and so having to register two or more times; the government’s contribution not deposited on registered eligible farmers’ accounts (for two seasons running in some cases); and deposits being made late by the government, after the growing season had commenced. These issues contributed to late planting and resulting lower yields (Chisanga et al. 2015). Counterparty Risk Counterparty risk affects produce buyers, especially in the cotton supply chain. In the early 1990s, the cotton industry was liberalized and several play- ers entered that could purchase cotton anywhere in the country. This led to intense competition between buyers. They provide inputs to farmers at the beginning of the season with the understanding that the farmers will sell them seed cotton at the end of the season. However, owing to intense competition 36 Chapter 4—Agricultural Risk Assessment among buyers, farmers often end up selling to the highest bidder. Although the ginners often recoup their input loans in kind (seed cotton), they do not usu- ally receive as much seed cotton as anticipated from their input investments. The Role of Government in Risk Management Policies, including agricultural support, trade, and social, fiscal, and macro- economic policies, are the principal instruments government uses to man- age agricultural risk (Antón 2008). A consensus surrounds the notion that governments should focus on managing catastrophic risks. Catastrophic risk events such as the global financial meltdown, pest and disease attacks, and extreme weather events occur infrequently, but have far-reaching con- sequences on the sector and people when they do. Facilitating information flows to the farmer requires public resources, as do investments in key drivers of agricultural growth such as feeder roads, research and development, and pest and disease control. More often, funds to finance the management of such catastrophic shocks come from international sources such as the World Bank’s Catastrophe Deferred Drawdown Option (Cat DDO). A Cat DDO is a contingent credit line that provides immediate liquidity to countries in the aftermath of a nat- ural disaster, a time when liquidity constraints are usually highest. It is part of a broad spectrum of risk-financing instruments available from the World Bank Group to help borrowers plan efficient responses to natural disasters. Cat DDO is a comprehensive and proactive approach to manage disaster and climate risks. It helps to improve the capacity to effectively reduce disaster risks and improve management of the socioeconomic and fiscal impacts of disasters. Contingency financing provides important access to postdisaster liquidity to meet emergency and recovery needs. Cat DDO has three key features. The first is the drawdown trigger for the loan, which is usually the declaration of a state of emergency resulting from a disaster. The second feature of the option is its revolving nature, which allows for amounts repaid prior to the closing date to be made available for subse- quent drawdown. The last feature is the number of renewals that may be made up to four times for a total of 15 years. Renewals require that the adequacy of the macroeconomic framework and a disaster risk management program be reconfirmed and updated upon renewal. Because risk management typically takes up public resources, the gains from utilizing a Cat DDO must be weighed against alternative government expenditures. A general equilibrium analysis can be used to assess the various alternatives. The government recognizes the importance of the agricultural sector in driving economic development for the poor. To that effect, it has put in Chapter 4—Agricultural Risk Assessment37 place several programs to help deal with declining agricultural growth, per- sistently high rural poverty, and low agricultural productivity. These include the Farmer Input Subsidy Program (FISP), the Food Reserve Agency (FRA), and social protection programs including the social cash transfers and food security pack. Having realized the impact of risk events on its investments, and the greater exposure to those risks by smallholders, the government created the Disaster Management and Mitigation Unit (DMMU) under the office of the vice pres- ident to spearhead the coordination, preparation, and response to disaster events. Additionally, the Zambia Vulnerability Assessment Committee, a technical platform that carries out seasonal assessments to identify food-­ insecure populations and communities and prepares appropriate responses, consists of various stakeholders, including government departments, civil society organizations, United Nations agencies, and bilateral and multilateral development organizations. In 2011, the government established the Zambia National Climate Change Secretariat with the mandate to design and implement climate change mitiga- tion and adaption initiatives such as the Pilot Program for Climate Resilience. Other existing agricultural risk management initiatives include the Integrated Production and Pest Management Project, focused on the cotton commodity chain, and the Conservation Agriculture Scaling Up (CASU) project under the auspices of the UN Food and Agriculture Organization (FAO); Livestock Development and Animal Health Project; Irrigation Development and Support Project; and Agriculture Productivity Program for Southern Africa under World Bank support. Table 4.3 shows the 2017 budget allocations for various activities aimed at developing the agricultural sector. Most of the funds are for input subsidy pro- visions at $296 million. Irrigation development was allocated at $44 million in 2017; however, since 2011, an estimated $245 million has been allocated TABLE 4.3 Value of Government/Donor-Financed Agricultural Projects by Type of Activity, 2017 Activity Allocation (US$, millions) Grain storage 18 Livestock disease control 4 Irrigation development 44 Social cash transfers 56 FISP through e-voucher 296 Extension services (crop and livestock) 3 Research 4 Source: 2018 National Budget (Ministry of Finance). Note: FISP = Farmer Input Subsidy Program. 38 Chapter 4—Agricultural Risk Assessment toward irrigation development. Besides irrigation, the other priority areas of the agriculture development expenditure are through social cash transfers, research, extension, and disease control. The government also embarked on a project to expand grain storage from about 800,000 MT in 2011 to 2,000,000 MT at a cost of about $18 million. The expenditure on social cash transfers is an important intervention for households vulnerable to agricultural risks. Effective targeting of vulnerable households is key, so that households with productive capacity are targeted through other means such as e-voucher FISP or other mechanisms. The government also has a unique role in increasing farmers’ awareness of risks and risk management through communication channels at its disposal. Using Information and Communication Technologies (ICTs) such as mobile phones, which many farmers use, government can communicate early warn- ing information and raise awareness about effective methods to mitigate risks. The government can also catalyze private investment in cost-effective irri- gation systems that are well-suited to communal farmers (Ngoma et al. 2017). Cost-effective irrigation technologies help smallholder farmers improve their livelihoods by allowing for a more efficient use of inputs in the following ways: (a) use less water to grow the same amount of crops; (b) reduce the amount of fertilizer needed per plant, dissolving nutrients in the irrigation water for uniform application; (c) reduce energy use through lower water use; and (d) decrease the amount of time required to provide water to a crop area by reg- ulating the flow of water in the irrigation operation. Although Zambia has witnessed a number of large irrigation projects, it is largely smallholder farmers who irrigate their gardens, and these are located mostly in the dambos (wetlands). Field crop irrigation is almost nonexistent. Participation in communal smallholder irrigation schemes is limited by poor organization of farmers and the very small size of such schemes (Ngoma et al. 2017). Smallholders can benefit from cost-effective irrigation technologies. Several factors are necessary to unleash these positive impacts: (a) Farmers’ initial awareness of and knowledge of how to use irrigation resources. This includes the suitability of the farmers’ land, their choice of crops, the level of intensity of cropping practices, and proper maintenance of the equipment. (b) Access to water, the availability of reliable roads to transport crops to mar- kets, and access to storage facilities. (c) The government’s role in ensuring that appropriate regulations are in place to support smallholder agriculture (without crowding out the private sector) and to ensure farmers’ access to technology. (d) Availability and quality of the other agricultural inputs used by the farmer, such as seeds, fertilizer, pesticides, and machinery. (e) Access to markets. (f) Access to finance to purchase efficient irrigation equipment. Chapter 4—Agricultural Risk Assessment39 CHAPTER 5 Impacts of the Risks on the Agricultural Sector Overall Agricultural Losses This chapter attempts to quantify the impacts of risk events in terms of the magnitude and frequency of the losses and the stakeholders affected. The impacts are expressed in terms of losses in crop and livestock production and trade losses resulting from export bans. Table 5.1 shows the cumulative and average annual losses from produc- tion risks for selected crops. Between 1982 and 2016, the crop subsector experienced a total of $1.3 billion of risk-related losses. This translates to $38 million in 2004–06 constant prices on average annually, or 2.43 percent of the total annual agricultural production value in Zambia. Of the crops analyzed, 35  percent of losses are from maize, suggesting a high impact of agricultural production risks on smallholder food security. Similarly, groundnuts and cotton account for 22 percent and 20 percent, respectively, of total annual losses. Sugarcane is an important part of the agricultural economy; however, because it is almost exclusively commercially grown under irrigation, smallholders are shielded from sugarcane production losses, which account for 13 percent of total agricultural losses. Although the average annual losses are high, the impacts of the individual shocks are even more devastating. Average figures are useful to understand the aggregate costs of production risk, yet they tend to conceal the cata- strophic impacts that some shocks have on participants in the sector at the time they occur. Shocks have considerable impact on household and national food security, exhibit important fiscal repercussions, reduce the availability of foreign exchange, and have an overall macroeconomic destabilizing effect. Figure 5.1 shows the magnitude of losses for individual years, where the size of the circle depicts the losses as a share of total agricultural production value. The figure depicts the magnitude of the losses on the larger agricultural sector. Figure 5.2, conversely, shows the annual losses per hectare (ha) per commod- ity, illustrating the impact on smallholder income. Cotton, groundnuts, and tobacco are primarily grown for sale as commodities, and therefore macro- economic changes such as inflation, exchange rates, and interest rates have a more significant impact on them than on crops such as maize and cassava that are primarily grown for household consumption. This has implications for policy makers about the types of risk management interventions that would be undertaken for the various commodities, as is described herein. Chapter 5—Impacts of the Risks on the Agricultural Sector41 TABLE 5.1  Losses from Agricultural Production Risks (1982–2016) Annual % loss Average annual Average annual of agricultural Total losses Total losses Crop losses (tons) losses (US$) GDP (2011–13) (tons) (US$) Cassava 10,246 1,030,125 0.06 358,613 36,054,360 Maize 89,469 13,460,611 0.84 3,131,422 471,121,397 Vegetables 1,423 427,008 0.03 49,818 14,945,283 Cotton 7,882 7,925,165 0.50 275,862 277,380,787 Sugarcane 22,128 4,952,933 0.31 774,483 173,352,661 Groundnuts 6,118 8,517,495 0.53 214,146 298,112,338 Tobacco 2,864 2,378,292 0.15 100,257 83,240,235 Total 140,131 38,691,630 2.43 4,904,601 1,354,207,060 Source: Authors’ analysis from Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) and Crop Forecast Survey (CFS) Central Statistical Office (CSO). FIGURE 5.1 Cumulative Value and Frequency of Losses per Crop (1982–2016) 600 Losses (US$, millions) 500 Maize 400 300 Cotton Groundnuts 200 Sugarcane 100 Cassava Tobacco Vegetables 0 0 0.00 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 Frequency Source: Authors’ compilation from FAOSTAT and CFS (CSO) databases. Note: FAOSTAT data were not available for 2015 and 2016, whereas 20-plus-year CFS data were not available for all commodities. Therefore, FAOSTAT data were used for 1982–2014, whereas CFS data were used for 2015–16 to capture the impact of the recent El Niño event. The sizes of the balloons indicate the relative value of the losses across crops. The losses, as a proportion of gross production value (GPV), were extreme for important crops such as maize, tobacco, and cotton, implying disastrous impacts on household incomes, food security, and well-being. Losses for maize as the national staple crop had particularly devastating impacts on household food security, whereas tobacco and cotton impacts had more effect on house- hold income. Maize, groundnuts, and cotton have the highest average annual and the most frequent losses, making the farmers growing the crops highly exposed to shocks (figure 5.1). Cassava and cotton are drought tolerant; their losses were caused mainly by disease (cassava mosaic) and flooding especially in 2000 and 2002, respectively. Groundnuts have frequent but relatively small losses that add up over time. Sugarcane losses, although relatively small as a 42 Chapter 5—Impacts of the Risks on the Agricultural Sector FIGURE 5.2  Loss Value per Hectare (1982–2016) 50 45 40 35 US$/Ha/Year 30 25 20 15 10 5 0 Cotton Groundnuts Tobacco Cassava Maize Sugarcane Vegetables Source: Authors’ compilation from FAOSTAT and CFS (CSO) databases. BOX 5.1  To What Do the Annual Losses per Hectare Translate? Annual maize losses per hectare are US$4.8, which is equivalent to ZMW 48. According to farmers in the Kalomo District, Southern Province, this would translate to a bucket of mealie meal (about 20 kg), which would feed an average family for three weeks for net food buying households. In terms of grain, this is a loss of about a 50-kg bag of maize (0.7 ZMW/kg to 1.5 ZMW/kg) for net food selling households. However, most Zambian smallholder households are net food buyers (Chapoto and Sitko 2015). proportion of its production value, becomes sizable because of its contribu- tion to GPV when compared with other crops. With the exception of maize, crops with relatively high frequency of losses also tend to have high losses per hectare. The highest frequency of major shocks to the agricultural sector were experienced in the 1990s, followed by the 2000s. The 1980s and 2010s expe- rienced the least frequency of risk events. The highest frequency and magni- tude of losses occurred in the 1990s and 2000s, which were a result of severe droughts and excessive rainfall and floods, respectively (figure 5.3). Clearly, the observed value of losses across the four decades is directly related to the frequency of exposure to risk events. Notable is the drought in 1992 in which $154 million was lost, equivalent to 10 percent of agricultural gross domes- tic product (GDP) (figure 5.4). There was consensus among the farmers and other stakeholders involved in the sector at the time that it was the worst risk event that Zambia had ever experienced. The government reached out to the Chapter 5—Impacts of the Risks on the Agricultural Sector43 FIGURE 5.3 Average Annual Value and Frequency of Losses by Decade in Zambia (1982–2016) 80 70 Losses (US$, millions) 60 1990s 50 40 2000s 30 20 2010s 10 1980s 0 0 0.05 0.10 0.15 0.20 0.25 0.30 0.35 Frequency of losses Source: Authors’ compilation from FAOSTAT and CFS (CSO) databases. Note: The sizes of the balloons reflect the relative value of the losses across decades. FIGURE 5.4  Annual Value of Losses 180 160 Loss (US$, millions) 140 120 100 80 60 40 20 0 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 Source: Authors’ compilation from FAOSTAT and CFS (CSO) databases. international community for humanitarian assistance, and the country was supported with supplies of yellow maize.6 In 2001, excessive rainfall led to flooding in most parts of the country and thus a $98 million loss, equivalent to 6 percent of agricultural GDP. The 2010s have so far not seen many losses, though only six years were analyzed, and thus the probability that a major risk event(s) may still occur is very real. Steps to prepare for that eventuality are discussed in the next chapter. Overall, drought, macroeconomic changes, and excessive rainfall and floods had the highest impact on the agricultural sector with $438 million, $361 mil- lion, and $172 million, respectively (figure 5.5). As previously discussed, the definition of drought includes dry spells, localized events, and severe events, 6 Zambians produce and consume white maize, and so the yellow maize distributed in the form of human- itarian assistance in 1992 is particularly notable. 44 Chapter 5—Impacts of the Risks on the Agricultural Sector FIGURE 5.5 Cumulative Value and Frequency of Losses per Risk (1982–2016) 600 Losses (US$, millions) 500 Drought 400 Macroeoonomic changes 300 200 Input distribution Excessive rainfall and delays floods 100 Political instability 0 0 0.05 0.10 0.15 0.20 0.25 0.30 0.35 Frequency Source: Authors’ compilation from FAOSTAT, CFS (CSO), and FEWSNET databases. Note: The sizes of the balloons reflect the relative size of the losses by source of risk. whereas macroeconomic changes include volatile inflation, exchange rate fluctuations, and volatile interest rates. Excessive rainfall and floods tend to go hand in hand. They were quite frequent but localized and therefore resulted in relatively small aggregate losses. Political uncertainty exemplified by riots and an attempted coup that led to multiparty democracy had a $45 million impact on crop production. Input delays were highlighted in 1997 in what would otherwise have been a good production year. Rains started early in the Western Province instead of in the North and South Provinces, as would be usual, but the government was unprepared for the early rains, resulting in late input distribution and a $90 million loss in production value. For the livestock sector, the period studied was 1991–2015 because of data limitations (figure 5.6). During this period, a total of $1.3 billion is estimated FIGURE 5.6  Livestock Losses Diseases (Multiple) 400 350 Drought & Losses (US$, millions) Drought & Diseases 300 Macroeconomis Diseases (FMD) 250 changes & Diseases (Multiple) Drought Disease (FMD) (FMD) 200 Drought 150 100 50 0 1994 1998 1999 2003 2005 2012 2015 Source: Authors’ compilation from World Organisation for Animal Health (Office International des Episooties) (OIE) and Post Harvest Survey. Chapter 5—Impacts of the Risks on the Agricultural Sector45 to have been lost, of which 99 percent was attributed to the beef commodity chain. The hypotheses for the small losses in pigs and chicken are the follow- ing: (a) Pigs and chicken have significantly lower risks than cattle; (b) outflows from pig and chicken herds/flocks are not significantly different between risk event years and normal years; and (c) herd dynamics and livestock produc- tion (products) are not sufficiently captured with the available data. Because of data limitations, it was not possible to test these hypotheses. Because of farmers’ coping behavior in the event of drought, it is assumed that drought was the cause of the loss in years when droughts were accompa- nied by disease outbreaks. Communal grazing practices, where animals con- gregate around watering points during droughts, tend to lead to outbreaks (Hamoonga et al. 2014). Foot-and-mouth disease (FMD) was the single most important disease economically. It is estimated that the government spends $2.7 million annually on controlling FMD (Sinkala et al. 2014). Cumulatively, drought had the most significant impact, closely followed by disease. The other notable risk event is reported to have been inflation in 1999. The high losses in the livestock sector are attributed to the high value of the animals. Trade Losses Caused by the Export Bans Maize has the potential to boost Zambia’s export revenue, especially when the economy is struggling from reduced export revenues as a result of falling copper prices. In 2015, Zambia earned $210 million from the exports of maize and related products. Zambia can increase its maize exports without under- mining its own food security. Unfortunately, export restrictions imposed on the 2016 harvest meant that Zambia missed any opportunity to capitalize on its potential to maximize export earnings from maize. In 2015, this opportu- nity was lost to imports by countries such as South Africa, Zimbabwe, and Malawi. Table 5.2 shows trade losses in terms of potential export earnings that could not be earned because of export bans and restrictions between 2008 and 2016. Note that this assumes that Zambia maintains a strategic grain reserve of 500,000 metric tons (MT) and that there is regional maize demand. Given the high export parity prevailing in the 2016/17 maize marketing season, Zambia had to forgo $270 million in export revenue. Over the period 2008 and 2016, Zambia may have forgone nearly $1.36 billion dollars because of export restrictions (table 5.3). TABLE 5.2 Estimated Cumulative Losses to the Livestock Sector by Risk Event, 1991–2015 Risk Estimated losses (US$) Drought 686,105,072 Disease 521,496,781 Macroeconomic changes 173,743,381 46 Chapter 5—Impacts of the Risks on the Agricultural Sector TABLE 5.3  Value of Forgone Foreign Exchange Earnings as a Result of Limited Maize Exports (2008/09–2015/16) Forgone Not Foreign Production Expected Exported Exported Exchange + Carryover Domestic Exports Formally Formally Earnings Production Production Stocks Consumption (1,000 (1,000 (1,000 through Trade Year (1,000 MT) (1,000 MT) (1,000 MT) MT) MT) MT) (US$) G = F × Export A B C D=B−C E F=D−E Parity Price 2008/09 1,887 1,950 1,700 250 173 77 16,940,000 2009/10 2,795 3,094 2,000 1,094 3 1,091 240,020,000 2010/11 3,020 3,450 2,500 950 30 920 202,400,000 2011/12 2,853 3,550 2,500 1,050 358 692 152,240,000 2012/13 2,532 2,988 2,500 488 73 415 91,300,000 2013/14 3,351 3,948 2,500 1,448 231 1,217 267,746,329 2014/15 2,618 3,964 2,500 1,464 895 568 116,528,765 2015/16 2,873 3,541 2,500 1,041 221 820 270,567,279 Total 1,357,742,373 Source: Ministry of Agriculture and Livestock (MAL)/CFS, Common format for Transient Data Exchange (calculations of forgone foreign exchange based on authors’ computation). The Impacts of Agricultural Risks on Different Stakeholders The impact of the risks is a function of variation in vulnerability, that is, sensi- tivity, adaptive capacity, and exposure. Sensitivity is described as “the degree of impact of the initial shock”—in other words, the changes in consump- tion levels in response to the shock. Adaptive capacity is “the ability of the household to access ex post coping strategies that helps it return to pre-shock welfare levels,” whereas exposure is “the probability of a given shock material- izing and affecting the household’s assets” (Choudhary et al. 2016). The more the value chain progresses from producers to exporters and processors, the higher the capacity to cope with the shocks described ear- lier. High poverty rates in rural areas leave smallholder farmers vulnerable and with low adaptive capacity to risks, especially to weather risks and price volatility. Most smallholders are not organized despite the best efforts by the government and nongovernmental actors. In cases in which farmer groups and association have been formed, this tends to result from them wanting to access to benefits from either a government or nongovernmental program. Hence, they are very weak and unable to advocate on behalf of their members. The Zambia National Farmers Union (ZNFU) is the largest farmer represen- tative body in the country; however, it is largely subscribed to by commercial and medium-scale farmers and large agribusinesses. Although ZNFU has branches in virtually all districts in Zambia, the influence of smallholders Chapter 5—Impacts of the Risks on the Agricultural Sector47 remains negligible. In response, another union to represent the smallholder farmers, the National Small-scale Farmers Association of Zambia (NUSFAZ) was formed in 2014. The impact of NUSFAZ has yet to be felt because they have yet to fully establish themselves countrywide. Commodity-specific farmer organizations have also been created, such as the Cotton Association of Zambia and the Tobacco Association of Zambia, but they are also too weak to represent the interests of the thousands of smallholder farmers. Smallholders grow crops for various purposes. Staples such as maize and groundnuts are dual purpose. More than 50 percent is sold and the rest con- sumed by producing households. Cotton and tobacco are almost exclusively grown for market (99 percent), whereas cassava and vegetable production is primarily consumed by the household (only 22 and 36 percent, respectively, is sold) (Chapoto and Mbata-Zulu 2016). Smallholder livestock production also has multiple purposes—consumption, insurance, savings, income, and prestige. However, unlike crop farmers, livestock farmers will sell only if they must. In bad seasons, the proportion of dual-purpose crops that is consumed by households increases. Therefore, the impact of the losses is felt both in terms of household food security and income. Own-produced food stocks in the drought-affected 2015/16 season were expected to last 3 months or less for most respondents, whereas in a good year, they would have been expected to last 11 months or more. In addition, access to the market to supplement own production is limited by lower income given the lack of surplus food for sale, thus reducing the adaptive capacity of smallholder households (Zambia Vulnerability Assessment Committee [ZVAC] 2016). The ZVAC assessment of 2015/16 also found that the Food Consumption Scores were lower than those of the previous season as a result of adverse coping mechanisms such as reducing the number of meals consumed per day and the consumption of nutritionally less preferred foods. Risk solutions targeting smallholders should be a combination of increasing productivity and mitigating production losses. Furthermore, the level of commercial- ization at the provincial level differs. The Central, Eastern, Muchinga, and Copperbelt Provinces are more commercialized than the Western, Luapula, and Lusaka. Therefore, the former would have more market-oriented inter- ventions, whereas the latter would benefit more from social protection and other interventions that protect household food security and increase their adaptive capacity. Livestock is primarily a vehicle for savings and insurance among small- holder producers, who tend to sell off more animals during severe droughts. However, higher volumes in the market as well as poorer physical condi- tion scores also tend to lead to lower prices. Livestock sales therefore tend to become a coping mechanism of last resort. The higher incidence of dis- ease outbreaks during moderate and severe droughts further compounds the 48 Chapter 5—Impacts of the Risks on the Agricultural Sector adverse impacts of the risk events. Therefore, social protection interventions as well as increasing access to water, feed, and animal health services during severe droughts would be beneficial. Medium-scale farmers produce primarily for the market. For example, results from the Rural Agricultural Livelihoods Survey (RALS) 2015 show that households that cultivated 10 hectares or more had the highest house- hold commercialization index—65 percent. These households are highly exposed to market and enabling environment shocks such as price volatil- ity and export bans, respectively. Interviews with stakeholders indicated that commercial and medium-scale farmers had better access to markets, finance, inputs, and technology and therefore had a higher adaptive capacity. Given their exposure to market risks, commercial and medium-scale farmers would benefit more from interventions that would bring about more stability on the market, for example, predictable and stable trade policies, capitalization of the Zambia Commodity Exchange (ZAMACE) and more robust Warehouse Receipt System (WRS). Traders are usefully classified into small-, medium-, and large-scale oper- ators. Small-scale traders buy directly from farmers at farm gate prices and operate at minimal margins. Their lack of storage facilities means they must sell commodities within a short period of time, making them less capable of taking advantage of seasonal price variations. This predisposes them to higher price risks. Medium-scale traders have access to some storage facilities and have higher stock turnover, and therefore have better risk coping mechanisms than do small-scale traders. Both small-scale and medium-scale traders are constrained in terms of access to credit for investing in storage facilities. Large-scale traders consist of corporations, including multinational firms such as Cargill, NWK Agri-Services, and AFGRI Corporation. They have access to capital and credit, which allows them to invest in storage and to pur- chase large stocks of commodities in the market. Access to storage facilities allows them to take advantage of spatial and temporal price differences. In this regard, large-scale traders have well-developed risk-coping mechanisms. In addition, large-scale traders also sign forward contracts with large-scale farmers and processors as a way of mitigating price and other risks. However, these traders are more vulnerable to government policy interventions includ- ing ad-hoc import and export bans. They are also negatively affected when- ever the Food Reserve Agency (FRA) offloads large quantities of maize stocks at below-market prices to millers because they cannot compete with the FRA at these prices. Policy consistency would help moderate the market and price risks. A fully functional commodity exchange would benefit these large trad- ers, especially if the FRA utilizes such an exchange to procure or sell the stra- tegic reserve stocks. Traders and exporters are highly exposed to macroeconomic changes and government interventions in markets. Exporters though have greater adaptive Chapter 5—Impacts of the Risks on the Agricultural Sector49 capacity than traders because of their access to risk transfer instruments such as hedging, insurance, and contingency loans (trade finance) and their abil- ity to lobby and influence governments to reverse decisions that may have adverse impacts on their businesses through organizations such as ZNFU and the Grain Traders Association of Zambia (GTAZ). Processors include maize millers producing mealie meal and stock feed. Most urban consumers depend on large commercial millers, whereas rural consumers use mostly hammer millers. Large-scale millers have significant leverage with government and throughout entire value chains because of the sensitivity that government attaches to retail food prices. With the increased maize purchases by government, millers have become increasingly dependent on maize from the FRA, often at below-market prices. Therefore, the market and price risks borne by millers are much lower than traders and producers. Unlike most of the other actors in the value chain, input distributors did not face major risks. Input delays in the government program have been a boon for input distributors. Their main constraint was poor infrastructure, which increases their transport costs, and poor access to finance, especially for the small-scale distributors. Financial institutions’ role in the agricultural sector is changing. As agricul- ture’s share of GDP has decreased, lending to agriculture has also decreased. It fell from 30 percent in 2004 to 13 percent in 2014 (Simpasa 2016). A survey by the African Development Bank found that the larger the financial institution, the more likely it is to lend to the agricultural sector. Commercial farmers and agribusinesses are most likely to have access because they are more likely to have sufficient collateral and the transactions costs are lower per loan than for those of medium scale and smallholders. The impact of agricultural risks has led to the sector having the highest proportion of nonperforming loans. The authors of the report observed that “47% of total loans by large banks in the agriculture sector were classified as doubtful or in default” (Simpasa 2016). Interventions to improve risk management in the agricultural sector as a whole are anticipated to have a positive impact on agriculture lending. With nearly 50 percent of farmers financially excluded, there is still room for growth in agricultural finance, particularly in developing appropriate prod- ucts for agricultural producers whose income is seasonal. Vulnerable Groups and Impact on Household Food Security Though agricultural shocks were less frequent and had less impact in the past 10 years than in the two decades prior, high rates of rural poverty, among other factors, limit Zambia’s capacity to cope with risk events. According to the Rural Agricultural Livelihoods Survey, most smallholders rely on their 50 Chapter 5—Impacts of the Risks on the Agricultural Sector own production for household food consumption, which is supplemented by income from cash crops such as tobacco and cotton. Production is rain fed, leaving rural households highly exposed to food insecurity in the event of a weather shock. Small landholdings, inability to expand the holdings, and low levels of fertilizer and manure use (25 percent and 6 percent, respectively) mean that optimal productivity cannot be achieved. With an average of 2.1 hectares cultivated per household using hand hoe or draft animals for tillage, individual production is often insufficient to meet household consumption needs. Therefore, price volatility and depressed prices for cash crops as well as poor transmission of marketing subsidies to consumers hamper food access for smallholders (Indaba Agricultural Policy Research Institute, 2016). Poverty is another contributor to vulnerability. Decline in well-being is higher for poor than nonpoor households experiencing the same shocks, making them more vulnerable to food insecurity (Giertz et al. 2015). The sale of productive assets such as livestock is a key coping mechanism. From focus group discussions with farmers, the study noted that in cat- tle-keeping communities where cattle are a source of draft power, selling off cattle was the last resort in the face of a shock. Goats, pigs, chickens, and other household assets were more easily sold. Other notable coping mechanisms were the consumption of fewer meals or maize bran, remittances from rela- tives in urban areas, borrowing from other farmers, working on other farmers’ fields in exchange for food, taking children out of school, and prostitution. It is worth noting that smallholder profiles and their levels of exposure vary widely, depending on their cropping system, location, landholding size, gender, and household size, among other factors. Based on the livelihood profiles developed by the FAO and the Indaba Agricultural Policy Research Institute (IAPRI), five livelihood profiles or clusters have been identified based on income or the size of landholdings. The livelihood profiles provide a more nuanced picture of the typology of smallholder farming households in Zambia. Most of the 1.5 million smallholder farming households in Zambia fall into the cluster of poor accessible households (57 percent) followed by poor remote households (13 percent). Wage-earning households make up 4  percent, whereas outgrowing households make up 26 percent. Market- participating households make up less than 1 percent (Subakanya et al. 2017). About 70 percent of rural households belong to the “poor” clusters; typically, these have high rates of poverty and food insecurity. The other three house- hold clusters are generally better off, either because they participate in wage employment, obtain credit for agricultural purposes or are market oriented (Subakanya et  al. 2017). This means that interventions aimed at building climate resilience must take into account heterogeneity among smallholder farmers in Zambia. Chapter 5—Impacts of the Risks on the Agricultural Sector51 CHAPTER 6 Risk Prioritization and Management Agricultural risks vary in the severity, frequency, and distribution of their impacts on different agroecological zones, commodities, and the broader economy. Food security and rural livelihoods need to be considered at each of these levels.7 To better utilize scarce resources, it is important to under- stand which risks cause major shocks to the sector in terms of losses and to observe the frequency with which they occur. This chapter summarizes the risks faced by the agricultural sector in Zambia and the possible solutions identified during the study. These were validated through a stakeholders’ con- sultative workshop held in the Chisamba District in June 2017. Risk Prioritization Tables 6.1 and 6.2 summarize stakeholders’ opinions regarding how agri- cultural risks should be prioritized, defined based on the probability of the event, and its expected impact in terms of production value, household food security, vulnerability, and income of different stakeholders. The tables list the most significant risks based on their potential to cause significant losses to the agricultural sector and the frequency of their occurrence. These corroborate in large measure the results presented in chapter 5. In terms of prioritization, the following emerged as the most important risks to Zambia’s crop subsector: (a) drought, (b) excessive rainfall and floods, and (c) price volatility. In the livestock subsector, the top risks are (a) drought and (b) disease outbreaks. Risk Management Solutions A long list of solutions was developed from stakeholder interviews, focus group discussions, and published literature on Zambia’s agricultural sec- tor (see appendixes A and B). The proposed strategies are a combination of risk-mitigation, risk-transfer, and risk-coping instruments. For risks that are frequent but with limited impacts, the best approach is to try to mitigate them. 7Source: Mozambique: Agricultural Sector Risk Assessment (World Bank 2015). http://p4arm.org/app/ uploads/2015/02/Mozambique000A00risk0prioritization.pdf. Chapter 6—Risk Prioritization and Management53 TABLE 6.1  Risk Prioritization—Crop Subsector Impact (Losses) Moderately High High Critical Crops Low (< 10%) (10%–30%) (30%–50%) (> 50%) Highly Probable (1 year in 3) • Crop levies — • Price volatility — ad hoc Probable (1 year in 5) — — • Localized — drought and dry spells Probability of Event Occasional (1 year in 10) — • Inflation • Floods — • Exchange rate fluctuation • Macroeconomic changes • Trade restrictions Remote (1 year in 20) • Input • Political • Postharvest • Severe distribution instability losses drought delays • Pests • Disease Note: — = Not available. TABLE 6.2  Risk Prioritization—Livestock Subsector Impact (Losses) Moderately High Crops Low (< 10%) (10%–30%) High (30%–50%) Critical (> 50%) Highly Probable — • Ad hoc levies • Price volatility — Probability of Event (1 year in 3) • Input supply delay Probable — — — — (1 year in 5) Occasional • Floods • Macroeconomic • Disease • Drought (1 year in 10) changes Remote • Inflation • Political instability — — (1 year in 20) Note: — = Not available. Risk Management Options In this section, we provide risk management options followed by examples of their implementation in other countries. Implementing the risk management options involves coping with a number of constraints, including investment costs, transaction costs, operations and maintenance costs, scalability, and the knowledge-intensive nature of some of the solutions. For example, strength- ening climate resilience for smallholder farming systems and improving early 54 Chapter 6—Risk Prioritization and Management warning systems (EWSs)9 8 require substantial up-front investments (Braimoh and others, 2018). Financing agricultural risk in remote areas entails high transaction costs of reaching remote populations. It also requires robust tech- nical and institutional capacity to design and deliver effective agricultural risk products and services. Tables 6.3, 6.4, and 6.5 summarize the various risk management options for the agricultural sector in Zambia. The government can easily act on some risk solutions with few financial implications. For example, the risks emanating from price volatility can be addressed through improved agricultural trade policies, such as avoiding export bans, and by fostering private sector partic- ipation in the maize market. Production risk and management options. Several options exist for address- ing production risks affecting both the crop and livestock subsectors. Essentially, the proposed risk management solutions seek to improve access to and the quality of early warning systems to enhance decision making for both subsectors and improving the climate resilience of smallholder farming systems. Specific components of each risk management option and how it could work are presented in table 6.3. Disease outbreaks and mitigation options. Managing the risk of disease out- breaks requires addressing existing gaps in livestock information systems to strengthen decision making for disease control. Table 6.4 provides details on how the proposed risk solution could work and the issues to consider. Price and market risk and mitigation options. The main cause of price vol- atility among staple crops is unpredictability in the policy space, excessive government involvement in the maize markets, and the limited diversifica- tion of Zambia’s agriculture. Proposed risk management options thus focus on (a) diversification to other crops such as cashews, soya beans, cassava, and rice and to a combination of crop–livestock production; and (b) trade policy stability to allow better private sector participation. Risk management options also include capitalization of the Zambia Commodity Exchange (ZAMACE) to ensure its sustainability. Specific components of the risk management options and how it could be operationalized are presented in table 6.5. Table 6.6 summarizing the risk prioritization exercise suggests that risk management solutions tend to be more specific to the prioritized risks. Improved access to early warning information helps to improve decision making for managing weather risks and diseases. However, improved access to early warning information is a necessary but not sufficient condition for managing weather risks. Agricultural diversification and adoption of other 8 An early warning system (EWS) is an integrated system of hazard monitoring, forecasting and prediction, disaster risk assessment, communication, and preparedness activities systems and processes that enables individuals, communities, governments, businesses, and others to take timely action to reduce disaster risks in advance of hazardous events (United Nations Office for Disaster Risk Reduction). Chapter 6—Risk Prioritization and Management55 TABLE 6.3  Weather Risk Management Options 56 Risk Impact of the management Elements requiring Current state and challenges with How to overcome intervention and Relative solution investments Why is it needed? the proposed risk solution the challenges scalability costs Strengthening -Automated weather -Enhance quality of -Limited meteorological and hydrological -Strengthen the technical -Highly scalable. Low Early Warning stations, weather decision making. weather stations (only 108 stations in 45 capacity for data analysis, -Contributes toward Systems information system, and out of 107 districts). machinery operation at -Increase food security and (EWS) (M, C) supporting various levels in the ZMD awareness. -Early warning information is not poverty reduction, infrastructure and the DMMU. translated into the local language and particularly among -Agricultural extension may not be as useful at the subnational -Install additional smallholder farmers, level as it is at the provincial level. infrastructure and and the farming -Capacity building leverage ICT. community in -These intervention general through elements target services disaster risk that will benefit reduction. smallholder farmers. Flood control -Promote of the use of -To alleviate flood -Low uptake among farmers because of -Enhance knowledge -Scalable. Moderate infrastructure soil bunds, reduced risk, protect areas limited finance to meet upfront transfer via extension and -Positive impacts on modernization tillage, agroforestry, and and transport water. investments and limited knowledge on smart subsidies to sustainable natural any such farming how to appropriately implement the enhance adoption of flood resource systems to attenuate farming systems mitigating farming management. flood risk at farm level systems at farm level -Limited investments in dams and (directly targeting the -Potential to diversion canals to direct water flows -Investments in dams/ farmers). increase food away from agricultural lands reservoirs at community security, reduce Facilities that do not level to adapt flood poverty through directly target farmers mitigating farming reductions in crop include the following: systems to local contexts loss from flooding. -Retention basins -Dams/reservoirs -Diversion channels Chapter 6—Risk Prioritization and Management farmers Risk Impact of the management Elements requiring Current state and challenges with How to overcome intervention and Relative solution investments Why is it needed? the proposed risk solution the challenges scalability costs Strengthen -Awareness (extension) -To minimize yield -Poor adoption rates of conservation -Understand the barriers Highly scalable with High the climate for smallholder farmers shocks in moisture agriculture practices despite promotion to adoption. positive impacts on resilience of stress years. for more than 15 years. food security among -Seed systems -Consider CSA benefits in smallholder smallholders and development and -Sustainable -Farmers in the drought-prone areas are form of avoided farming within the country. dissemination (targets agricultural susceptible to weather shocks. greenhouse gas systems both smallholder production. emissions a public good. (M, C) -Weak seed systems, with limited public farmers and services -Minimize distance finance toward research and -Strengthen extension that are indirectly linked traveled by development. service delivery. to the smallholder). livestock in drought years. Increase Smallholder financial -Enable farmers to -Low adoption rates of insurance products. -Segment the smallholder Highly scalable with High access to risk inclusion by expanding adopt new farmers, identify their expected high -Agricultural financing remains low, with financing the availability of technologies. financial needs, and impacts on high interest rates and poor access to (T, C) finance to the rural design products tailored profitable -Increase uptake of credit by farmers. economy. insurance. to the needs. investments among -High transaction costs of reaching remote smallholder farmers - Strengthen rural -Reduce the risk in -Enable DMMU and rural populations. that could lead to financial institutions. agricultural finance by other related increased food -Higher perceptions of nonrepayment addressing both individual -Provide adequate government security and poverty because of sector-specific risks, such as risks and systemic risks.  predictable contingency ministries and reduction. production, price, and market risks. funding. agencies to -Raise awareness on -Financial institutions’ lack of knowledge of -High impact and Low respond to extreme insurance and other agriculture-specific risks, effective means scalability that will risk events in a financial products. of managing transaction costs, marketing improve timeliness timely manner. - Partner with appropriate of response and financial services to agricultural clients. institutions and delivery reduce impact for -Government policies could create channels to reduce the affected disincentives for private sector lending and costs of serving communities. provision of financial services to the agricultural clients. agricultural sector. - Utilize World Bank’s Cat - DMMU and other government agencies DDO option for extreme scramble for resources in the event of risk events to enable a large-scale events because of limited rapid response while allocation to emergency fund, thus mobilizing additional Chapter 6—Risk Prioritization and Management57 response takes longer and so increases resources. the impact. Risk Impact of the 58 management Elements requiring Current state and challenges with How to overcome intervention and Relative solution investments Why is it needed? the proposed risk solution the challenges scalability costs Expand social -Integrate humanitarian -To help - There is need to better integrate social -Reform the input and High impact and Low protection relief and disaster risk households cope protection, humanitarian and disaster risk output subsidy programs scalability, with program I management with with exposure to management. in ways that lead to cost positive impacts on national safety nets. agricultural risks. savings that can be poverty reduction -There is insufficient social protection at channeled toward social and food security. - Integrated the expense of prioritization of ineffectual protection. beneficiaries’ registry. maize input and output subsidies. - Provide food but also -The budget for SCTs was increased in seeds and other inputs the 2017 national budget, but is still very for next season. small in comparison with the input subsidy program. - SCT scheme. - Build shock-responsive safety nets. -The intervention directly benefits the smallholder famers. Improve -Watering points for -Increase -Animals graze in communal pastures. -Invest in watering points High scalability with Low management livestock. production of at the community level. a relatively high of rangeland drought tolerant impact on food -Production and -Enhance extension and livestock pastures. security and dissemination of drought service delivery. resources resilience. tolerant pasture seeds. -Raise awareness (M, C) on drought-tolerant -Awareness and training. pastures and -The proposed increase their interventions directly adoption. and indirectly benefit the smallholder farmers. Source: Authors. Chapter 6—Risk Prioritization and Management Note: M, T, and C stand for Risk mitigation, Transfer, and Coping, respectively; Project costs: Low implies < $100 million; Moderate, between $100 and $300 million; and High > $300 million. The cost estimates are based on similar World Bank interventions. CSA = climate-smart agriculture; Cat DDO = Catastrophe Deferred Drawdown Option; DMMU = Disaster Management and Mitigation Unit; ICT = information and communication technology; SCT = scale-up social cash transfer; ZMD =Zambia Meteorological Department. TABLE 6.4  Option for Managing Disease Outbreaks Current state and challenges with the Impact of the Risk management Elements requiring proposed risk How to overcome the intervention and solution investments Why is it needed? solution challenges scalability Relative costs Strengthening -Technical support in -To enable the Ministry -Lack of sufficient -Ensure periodic -High scalability, and Low animal health form of disease of Fisheries and information on the collection of livestock a high impact on food systems (M) forecasting, monitoring, Livestock to assess the livestock subsector information. security. and control. levels of productivity, (for example, -Increase budgetary -Support should be losses, impacts, and population, disease -Enhanced Animal allocations to the targeted toward the possible mitigation incidences). Health Information subsector. Ministry of Fisheries measures in the systems for improved -Limited financial for disease livestock sector. decision making. support to the sector. forecasting, -To expand the monitoring and -Improved extension. coverage of vaccination control, and Livestock, -These interventions programs, and access and the Ministry of target services that will to dip services to more Agriculture for benefit the smallholder farmers. extension. farmer. -To increase access to animal health information, and other agricultural information that promotes the use of climate-smart livestock practices and other improved technologies. Source: Authors. Note: M, T, and C stand for Risk mitigation, Transfer, and Coping, respectively; Project costs: Low implies < $100 million; Moderate, between $100 and $300 million; and High > $300 million. Cost estimates are based on similar World Bank interventions. Chapter 6—Risk Prioritization and Management59 TABLE 6.5  Managing Price Volatility 60 Current state and Impact of the Risk management Elements requiring challenges with the How to overcome the intervention and solution investments Why is it needed? proposed risk solution challenges scalability Relative costs -Agricultural -Investment in -Stabilize -The political economy of -Increase confidence -High scalability -Agricultural production climate-resilient commodity prices. staple foods (maize) is the in stocks monitoring. with a high production diversification. crops other than greatest challenge in impact on food diversification can be Increase private Purchase the FRA food maize. achieving policy security. implemented as part -Strengthen sector participation reserves through the consistency. of climate-resilient ZAMACE and -Capitalization of in markets. commodity exchange. farming (see the warehouse ZAMACE. -Some positive steps that Minimize -Consumption and table 6.3). receipts system. could contribute to -Warehouse receipt consumption and production production diversification -Strengthening systems. production shocks. diversification. through the electronic ZAMACE and WRS -Storage facilities. delivery system of input -Raise awareness can be implemented subsidies. But hybrid seeds among policy makers as part of increased -Awareness other than maize need to be on the importance of access to risk -These services will open borders. made available. financing or safety indirectly benefit the -Agricultural marketing nets (see table 6.3). smallholder farmers. -The WRS is operational; ZAMACE is also operational reforms, including with two banks, six brokers, conducting a WRS and five certified storage needs assessment, operators, with 800,000 MT increasing accessibility in capacity and covering 18 of WRS to districts. However, low trade smallholders, and volumes threaten encouraging more sustainability. financial institutions to participate. Source: Authors. Note: M, T, and C stand for Risk mitigation, Transfer, and Coping, respectively. Project costs: Low implies < $100 million; Moderate, between $100 and $300 million; and High > $300 million. Cost estimates based on similar World Bank interventions. FRA = Food Reserve Agency; MT = metric ton; WRS = Warehouse Receipt System; ZAMACE = Zambia Commodity Exchange. Chapter 6—Risk Prioritization and Management TABLE 6.6 Relevance of Risk Management Options to the Prioritized Risks Price Priority Drought Floods Diseases volatility score (%) Early Warning System 5 5 4 2 80 Flood control infrastructure 1 5 1 1 40 Climate-resilient farming 5 4 4 3 80 Risk financing 3 4 1 3 55 ZAMACE and warehouse receipt system 4 2 3 5 70 Safety net programs 4 4 2 4 70 Agricultural diversification 4 3 3 4 70 Animal health systems 3 1 4 2 50 Rangeland and livestock management 4 2 3 2 55 Note: Relevance of management options is rated as 1 = Very low; 2 = Low; 3 = Moderate; 4 = High; and 5 = Very high. Priority score is the sum of the ratings of options over the prioritized risks divided by 20, the maximum score, and expressed as a percentage. It is an indication of the ability of a risk management option to address the range of agricultural risks in Zambia. ZAMACE = Zambia Commodity Exchange. climate-resilient farming practices are also crucial. The importance of diver- sification as a form of self-insurance to mitigate production, market, or enabling environment risks is reflected across the prioritized risks in table 6.6. Diversification functions as an ex post strategy to cope with shocks and to prompt agricultural households to reallocate labor to other opportuni- ties. Climate-resilient farming is vital for building resilience by enhancing the absorptive, adaptive, and transformative capacities of the agricultural systems,9 and an example for India is provided in the section “Examples of Projects Addressing Agricultural Risks.” Strengthening ZAMACE and the Warehouse Receipt System is the most effective means of addressing price volatility. It can potentially help to sta- bilize commodity prices, encourage private sector participation in markets, and minimize consumption and production shocks. Increasing access to risk financing can empower farmers to adopt climate-smart technologies to effectively manage price volatility and weather risks. The scaling-up of social safety net programs targeting the most vulnerable farmers who cannot pro- duce enough to feed themselves is crucial for managing risks faced by this group. Such scaling-up will entail building a “shock-responsive safety net”— an adaptive social protection approach aimed at increasing the efficiency of social programs to deal with current and future risks climate protection and preventive measures. A shock-responsive safety net in Zambia can be 9 Absorptive capacity refers to the ability to survive climate shocks; adaptive capacity is the ability to adjust in anticipation of climate shocks, without radically changing livelihood systems; and transformative capac- ity refers to the ability to prevent the impact of climate shocks through major transformation of livelihood systems. Chapter 6—Risk Prioritization and Management61 purposefully designed to integrate existing safety net, disaster risk manage- ment, and humanitarian responses that are often fragmented in the coun- try. The implementation of the shock-responsive safety net could focus on increasing the coverage of the vulnerable populations, enhancing the insti- tutionalization of the program with stronger focus on systems building, and improving delivery mechanisms. Animal health systems and improved range- land management are specific to deploying improved management practices to enhance the productivity and resilience of the livestock subsector. Table 6.6 and figure 6.1 indicate that five solutions can be prioritized to effectively manage agricultural risk in Zambia. These are an early warn- ing system, climate-resilient farming, strengthening ZAMACE and the Warehouse Receipt System, a shock-responsive safety net, and agricul- tural diversification. The risk solutions have the most potential to address the prioritized risks confronting the agricultural sector in the country. As stated in tables 6.3–6.5, there is potential for synergy when some of the risk management interventions are combined—for instance, strengthening ZAMACE can be implemented together with risk financing to address price volatility and incentivize smallholder farmers to adopt climate-smart agri- culture, or with shock-responsive safety net to address the needs of the most vulnerable. Given the potential for synergy for implementing the prioritized solutions, the government may find it useful to prioritize the following options: • Strengthen early warning system for food security. • Develop climate-smart agriculture and increase resilience to climate-re- lated shocks through diversification. • Develop ZAMACE and build a shock-responsive safety net. FIGURE 6.1  Priority Scores (%) for the Risk Management Options 0 10 20 30 40 50 60 70 80 90 Early warning system Climate-resilient farming ZAMACE and warehouse receipt system Safety net programs Agricultural diversification Risk financing Rangeland and livestock management Animal health systems Flood control system 62 Chapter 6—Risk Prioritization and Management Examples of Projects Addressing Agricultural Risks This section provides examples of risk management solutions that address agricultural risks similar to those in Zambia. The four examples taken from India, Mexico, Burkina Faso, and Rwanda are more reflective of the com- prehensive investment needs in Zambia because they address the technical, financial, and institutional needs for effective risk management. India: Maharashtra Project on Climate-Resilient Agriculture ($600 million) The state of Maharashtra is one of the economic growth engines of India. Agriculture in Maharashtra grew at an annual average of 6.4 percent from 2004 to 2012, but growth in the smallholders-dominated sector fluctuates heavily because of highly erratic rainfall and rainfall variability over time. Severe consecutive droughts experienced in large parts of Maharashtra in recent years have considerably affected the state’s agricultural performance and social fabric in rural areas, and have prompted the highest-level state authorities to declare “drought-proofing” of agriculture a key development priority for Maharashtra. To address climate change vulnerabilities, Maharashtra is developing a project covering 18,700 villages, 12.5 million hectares (ha) of arable land, and an estimated 25.5 million beneficiaries (figure 6.2). The project seeks to (a) introduce transformational changes in the agricultural sector by promoting FIGURE 6.2  The Maharashtra Climate-Resilient Agriculture Project Framework Climate change and Climate-resilient agriculture Development outcomes climate variablity in MH PoCRA and overarching goals RESEARCH & EXTENSION WEATHER ADVSORY SERVICES SAUs, SDAO, ATMA, KVKs, FIGs A more climate-resilient Increase in average Ca utri ed s r agriculture sector em te N nag rb ent sa M temperatures st a t on m lin a sy e w men Enhanced agricultural se an ity/ n u Increase in avg. io b atl qu ag so at . re productivity ig vs t es em dic monsoon rainfall BUT irr ter rea tra en ity o a a Household food and income icr w nt tio t n High rainfall variablity M en me Water W Soil security re h across space and time G atc security health C Agricultural sector growth Higher rainfall OR INS intensity GHG emission reduction and Climate-resilient climate co-benefits ER CA CT CHA ST NT Delays in onset of RA PA IC PAR farming systems CE TE monsoon rainfall Contribution to India’s KN Y D S (SA TE LUE G ION CI TNE Intented Nationally T OW EV Us, C AT More frequent SE IVA VA V Determined Contribution NO LE ELO RIDA extreme precipitation mpprov Improved E IN (COP21) R PR S & DG PM , ICR events and floods eed se seeds AT Contribution to the WBG’s ET E IM CL Prolonged dry spells RK commitment to increase the S, within monsoon PC EN ISAT d diver Crop diversification climate-related share of its MA ,F period Shortt maturity m matu cycle T ) ES portfolio (CCAP) SM Drought/salt/heat salt/h t/s tolerant m productivity Farm pro p PoCRA = Project on Climate Resilient Agriculture, CCAP = Climate Change Action Plan, GHG = Greenhouse Gas, MH = Maharashtra Chapter 6—Risk Prioritization and Management63 short-term solutions at farm and catchment levels, (b) and provide lon- ger-term solutions at institutional and policy levels to ensure the sustainabil- ity of the outcomes generated in the field. To enhance the adaptive capacity of farming systems, the project promotes the transfer of already proven and field-tested agricultural technologies and agronomic practices that enhance climate resilience at farm and catchment levels (shorter-term solutions). To increase the absorptive capacity of com- modity value chains for crops relevant to the climate agenda, the project strengthens Farmer Producer Companies and supports the seed supply chain for climate-resilient crop varieties. Finally, to improve the transformative capacity of institutions in rural areas, the project supports the mainstreaming of climate resilience in rural institutions as well as the generation and transfer of cutting-edge knowledge on climate change and its impact on key sectors (for example, agriculture, water) to provide strong analytical underpinnings for strategies and policies on climate adaptation and mitigation (longer-term solutions). The project components and costs are indicated in table 6.7. TABLE 6.7  Maharashtra Project Components and Costs Components Key activities Cost (US$, millions) 1 Promoting • Participatory development of mini- 457.60 climate-resilient watershed plans agricultural • Promote transfer of on-farm climate- systems resilient technologies and agronomic practices • Climate-resilient development of catchment areas 2 Postharvest • Promote Farmer Producer Companies 54.92 management • Strengthen emerging value chains for and value chain climate-resilient commodities promotion • Improve the performance of the supply chain for climate-resilient seed varieties 3 Institutional • Sustainability and institutional capacity 33.51 development, development knowledge and • Establish Climate Innovation Center policies • Generate and disseminate cutting edge knowledge 4 Project • Incremental operating costs 53.98 Management • Project communication and public awareness • Integrated ICT for M and E • Weather advisories Total 600.00 Note: ICT = information and communication technology; M and E = monitoring and evaluation. 64 Chapter 6—Risk Prioritization and Management Mexico: Expanding Rural Finance ($405 million) Sound economic policies in Mexico during the past two decades have con- tributed to the attainment of stable macroeconomic conditions and resilience during the global financial crisis. However, like many Latin American coun- tries, Mexico faces a productivity growth challenge. Over the past decade, the economy grew at 2.4 percent annually, well below the regional average of 4 percent. Low productivity growth depressed income growth and Mexico’s per capita income has remained at about 30 percent of that of the United States. By comparison, East Asia Tigers’ per capita income tripled over the past three decades and is currently about 60 percent of that of the United States. Poverty rates are much higher in rural than urban areas of Mexico. In 2012, extreme income poverty at 30.9 percent in rural areas was more than twice the 12.9 percent in urban areas. Despite a stable macroeconomic frame- work and a series of market-enhancing reforms, the financial market fails to provide adequate access to key segments in Mexico. A vibrant financial sector that identifies and funds viable business opportunities is an important micro- economic foundation for shared prosperity by supporting increased incomes while helping manage risks. Credit in general and (rural) agricultural credit is underdeveloped in Mexico, and the lack of credit is associated with limited rural economic activity. Credit constraints have been found to be pervasive in rural Mexico, limiting the investments and growth of rural enterprises. To address this shortcoming, the Expanding Rural Finance Project (table  6.8) was launched to increase the availability of finance to the rural economy. The project beneficiaries are rural Financial Development Agencies (FND), Participating Financial Intermediaries (PFIs), and Micro, Small, and Medium Enterprises (MSMEs) borrowing from the PFIs. The project helps FND expand its activities and loan portfolio, reduce its operating costs rela- tive to its portfolio size, and to strengthen its IT systems. The project supports lending for productive activities as opposed to consumption credit, helping PFIs reach more clients and grow their activities in rural areas. The capacity TABLE 6.8  Project Costs for Expanding Rural Finance in Mexico Components Subcomponents Cost (US$, millions) 1 Expanding credit for Credit line through PFIs to A.  365 rural MSMEs MSMEs 10 Supporting FND Pilots for B.  Rural Finance 2 Strengthening Modernization of banking A.  25 institutional capacity for systems 5 sustainable rural finance Strengthening rural financial B.  institutions Total 405 Note: FND = Financial Development Agency; MSME = Micro, Small, and Medium Enterprises; PFI = Participating Financial Intermediaries. Chapter 6—Risk Prioritization and Management65 of PFIs is developed, enabling them to offer sustainable finance in rural areas. Rural MSMEs benefit from improved access to finance and expanded eco- nomic activity. The project is a financial intermediary loan, consisting of an IBRD credit line intermediated by FND through eligible PFIs serving rural borrowers. FND, the borrower and implementing agency for the project in turn on-lend/ finance PFIs. The selected PFIs in turn subfinance private MSMEs in the rural economy. FND selects PFIs following well-established criteria; defines the characteristics of the loans to be provided from FND to PFIs; defines eligibility criteria for final borrowers; defines the characteristics of subloans eligible to receive funds; defines and implements a communication strategy; and defines and implements a monitoring and evaluation strategy between FND and the Bank. Table 6.9 summarizes the types of institutions with which FND works. TABLE 6.9  List of Institutions Working with FND Supervised institutions Unsupervised institutions Banks: Including both full banks and niche banks SOFOMES: Public, limited liability companies offering SOFIPOS: Public limited liability companies offering both lending services to the lending and deposit services. population. Only SOFOMES Cooperative societies of savings and credit (Cajas): with ownership links to banks Both lend and take deposits and they are only supervised are supervised and FND has if their assets exceed about US$1 million. Currently, FND not traditionally worked with only works with supervised cooperatives. these. Credit unions: Member-based companies able to offer Producer associations credit only to their members and can operate only in the industry group to which their members belong. Almacenas generales de deposito (warehouse deposit financing): Serve to both store agricultural products and also lend to those using the warehousing facilities with stored products as collateral. Note: FND = Financial Development Agency. Burkina Faso: Agricultural Diversification and Market Development Project ($150 million) Burkina Faso’s agriculture-based economy is still dominated by subsistence production systems characterized by low crop and livestock productivity, low diversification, and limited participation of formal private businesses in the development of agropastoral value chains. To take advantage of poten- tial sources of growth, Burkina Faso needed to adequately address a series of constraints: (a) inadequate policy and institutional framework (trade policy, market efficiency); (b) poor infrastructure and high cost of public services and utilities; (c) limited capacity in the public and private sectors; and (d) weak producer/professional associations. The Agricultural Diversification and Market Development Project was set up to increase the competitiveness of selected agricultural subsectors that 66 Chapter 6—Risk Prioritization and Management TABLE 6.10 Project Costs for Agricultural Diversification and Market Development in Burkina Faso Components Activities Cost (US$, millions) Improvement of agro-silvo- • Capacity building for 65.5 pastoral supply chains professional organizations performance and agricultural trade 1 associations • Investment for supply chain development Development of irrigation • Irrigation infrastructure 60.6 2 and marketing infrastructure • Marketing infrastructure Improving the business • Improvement of the 24.3 environment, regulatory regulatory, legal, and framework, and provision of financial framework advisory services 3 • Capacity building for service providers • Project management and monitoring and evaluation Total 150.4 target national, subregional, and international markets, thereby contributing to shared agricultural growth for the country. The project also promoted busi- nesses in rural Burkina Faso, where access to credit from commercial banks and microfinance institutions is markedly limited. The project benefited more than 385,000 people, of whom 30 percent are women. It developed four targeted value-chains (meat/livestock, poultry, onion, and mango) whose professional organizations are now well structured and fully operational. Agricultural exports for the supply chains reached 206,000 tons, up from 6,500 tons, and 275,000 tons, up from 17,500 tons for subregional and international markets, respectively. The total amount of loans secured through local banks and microfinance institutions to support the financing of microprojects reached $4.3 million, linking smallholders to the banking system. About 162 successful microprojects were also transformed into small and medium enterprises and are fully operational. Rwanda: Strengthening Social Protection ($80 million) Social protection remains one of the government of Rwanda’s main priori- ties for meeting its ambitious poverty reduction and human capital develop- ment goals. To further this agenda, Rwanda has started building an integrated social protection system to ensure a minimum standard of living and access to core public services, boost resilience to shocks, promote equitable growth, and strengthen opportunity through increased human capital development. The Strengthening Social Protection Project supports three key innovations: enhancing livelihoods package through skills training, asset transfers (pro- ductive and livestock), and referrals to other productive and social services; Chapter 6—Risk Prioritization and Management67 TABLE 6.11 Project Costs for Strengthening Social Protection in Rwanda Components Key activities Cost ($ million) 1 Improving coverage, • Direct support cash transfer 68.5 adequacy and effectiveness • Classic public works of the Vision 2020 Umurenge Program cash • Expanded public works transfers • Refurbishment of infrastructure for quality community and home- based childcare 2 Enhancing access to human • Nationwide sensitization 6.5 capital and economic and community mobilization inclusion services • Improving parenting and childcare services for vulnerable groups • Enhancing livelihoods 3 Delivery Systems, Policy, • Evidence-based policy and 5.0 and Program Management program development • Institutional strengthening • Delivery systems Total 80.0 expanded public works (ePW) childcare for moderately labor-constrained households; and nutrition support grants. 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Strengthen the climate Strengthen extension and advisory — Improve FISP e-voucher resilience of smallholder system. management. systems. Improve climate-smart water and — Promote tree-based soil management. cropping systems (agroforestry). Increase awareness and promote — — use of improved varieties and technologies. Increase access to risk — Upscale crop Increase allocation to financing. insurance emergency fund–input safety nets. — Hedging and — WRSs Medium- to long-term Limit FRA’s role in maize — — policy options for the marketing. Capitalize the maize sector commodity exchange by buying strategic reserves through ZAMACE. Government should promote — — private sector storage by eliminating pan territorial and seasonal pricing. Enact Agricultural Marketing Act. — — Moderate price volatility through — — trade. Maintain an open border maize policy to make Zambia a reliable supplier. Enact Agricultural Marketing Act. — — Note:— = Not available; FISP = Farmer Input Support Programme; FRA = Food Reserve Agency; WRS = Warehouse Receipt System; ZAMACE = Zambia Commodity Exchange. APPENDIX A—Risk Strategies for Crops Subsector75 APPENDIX B Risk Strategies for Livestock Subsector Risk Solution Mitigation Transfer Coping Improve early Climate-sensitive disease outlooks: — Early warning systems: warning systems. Provide long-term projections of Provide short- to disease trends so that disease control medium-term disease and mitigation efforts can be integrated forecasting for into long-term planning. appropriate interventions and mitigation efforts. Risk mapping: Identify areas of greatest — — threat and disease mitigation measures. Conservation of livestock feed — — resources: Increase awareness and provide training on conservation of animal feed resources, for example, hay and silage preparation. Strengthen Drought-resistant fodder varieties: — Access to water: management of Distribution, awareness raising, and Increase number of rangeland and training on pasture management using watering points in livestock resources. drought-resistant fodder varieties. drought-prone areas. — — Promote silvo-pastoral systems (integrating trees and shrubs in pastures with animals). Strengthen animal Expand access to dips: Increase the — — health systems. number of dips and spray races in livestock keeping communities. Expand existing vaccination programs: — — Coverage should be expanded to cover farmers who are not being reached. Increase access to animal health — — information: Fill veterinary camp level positions that are not filled. Increase access to inputs: Create and — — support programs that encourage the establishment of veterinary drugs shops/livestock kits. Strengthen extension and advisory — — system. table continues next page APPENDIX B—Risk Strategies for Livestock Subsector77 (continued) Risk Solution Mitigation Transfer Coping Increase capacity Ensure consistent collection, analysis, Promote Encourage farmers to and support to and dissemination of livestock statistics. livestock use flexible e-voucher. policy development. insurance. Support private veterinarians to offer — — animal health services in rural areas. Consistent budgetary allocation for — — animal health emergency fund. Improve monitoring of vaccination — — program, especially Newcastle disease vaccination. Centralize and harmonize livestock — — movement levies. Note:— = Not available. 78 APPENDIX B—Risk Strategies for Livestock Subsector Appendix C Ranking of Importance of Risk Solutions Crops Average Strengthen early warning Improve weather infrastructure. 4.40 systems. Improve access to early warning information for improved 4.60 decision making. Provide flood control/protection infrastructure to flood-prone 3.60 areas. Average of risk solutions 4.20 Strengthen the climate Improve climate-smart water and soil management. 4.30 resilience of smallholder systems. Promote tree-based cropping systems (agroforestry). 3.80 Increase awareness of and promote use of improved varieties 4.50 and technologies. Improve FISP e-voucher management. 4.10 Strengthen extension and advisory system. 4.70 Average of risk solutions 4.28 Increase access to risk Upscale weather index insurance. 4.10 financing. Hedging and WRSs. 4.00 Increase allocation to emergency fund–input safety nets. 3.70 Average of risk solutions 3.93 Medium- to long-term policy Enact Agricultural Marketing Act. 4.00 options for the maize sector Limit FRA’s role in maize marketing. Capitalize the commodity 4.20 exchange by buying strategic reserves through ZAMACE. Government should promote private sector storage by 4.10 eliminating pan territorial and seasonal pricing. Moderate price volatility through trade. Maintain an open 4.00 border maize policy to make Zambia a reliable supplier. Average of risk solutions 4.08 Livestock Improve early warning Risk mapping: Identify areas of greatest threat and disease 4.40 systems. mitigation measures. Climate-sensitive disease outlooks: Provide long-term 4.20 projections of disease trends so that disease control and mitigation efforts can be integrated into long-term planning. Early warning systems: Provide short- to medium-term disease 4.50 forecasting for appropriate interventions and mitigation efforts. table continues next page APPENDIX C—Ranking of Importance of Risk Solutions79 (continued) Crops Average Average of risk solutions 4.37 Strengthen management of Access to water: Increase number of watering points in 4.50 rangeland and livestock drought-prone areas. resources. Conservation of livestock feed resources: Increase awareness 4.00 and provide training on conservation of animal feed resources, for example, hay and silage preparation. Drought-resistant fodder varieties: Distribution, awareness 3.90 raising, and training on pasture management using drought- resistant fodder varieties. Promote silvo-pastoral systems (integrating trees and shrubs 3.80 in pastures with animals). Livestock stocking and restocking. 4.50 Average of risk solutions 4.14 Strengthen animal health Expand access to dips: Increase the number of dips and 4.30 systems. spray races in livestock-keeping communities. Expand existing vaccination programs: Coverage should be 4.40 expanded to farmers who are not being reached. Increase access to animal health information: Fill empty 4.20 veterinary camp–level positions. Increase access to inputs: Create and support programs that 3.90 encourage the establishment of veterinary drugs shops/ livestock kits. Strengthen extension and advisory system. 4.30 Average of risk solutions 4.22 Increase capacity and Ensure consistent collection, analysis, and dissemination of 4.40 support to policy livestock statistics. development. Promote livestock insurance. 3.90 Consistent budgetary allocation for animal health emergency 4.00 fund. Improve monitoring of vaccination program. 3.80 Centralize and harmonize livestock movement levies. 3.20 Encourage farmers to use flexible e-voucher. 4.00 Support private veterinarians to offer animal health services in 4.10 rural areas. Average of risk solutions 3.91 Policy Recommendations Policies that promote modernization of the agricultural sector (for example, mechanization, 4.60 irrigation, increased use of ICT). Openness to trade in food and investments led by the private sector (especially food staples). 4.30 Policy stability: To attract private sector investment. Government funds alone are not enough 4.60 to meet the rising demand. Crowd in private sector, both local and international. 4.10 Regulations that promote competition and more innovation. 4.30 table continues next page 80 APPENDIX C—Ranking of Importance of Risk Solutions (continued) Crops Average Consumption diversification provides a key to helping vulnerable households deal with food 3.80 price shocks. Move away from maize centric policies. 4.50 Focus public investment into areas that stimulate growth instead of private goods: Subsidies 4.10 should not crowd out private sector participation. Provide investment incentives (for example, tax breaks) to both local and international 3.90 investors. Average of risk solutions 4.24 Note: Importance of management/solution options is rated as 1 = Very low; 2 = Low; 3 = Moderate; 4 = High; and 5 = Very high. FISP = Farmer Input Support Programme; FRA = Food Reserve Agency; ICT = information and communication technology; WRS = Warehouse Receipt System; ZAMACE = Zambia Commodity Exchange. APPENDIX C—Ranking of Importance of Risk Solutions81 APPENDIX D Focus Group Discussions: Farmer Profiles and Coping Strategies Farmer Focus Group Discussions: Profile Kalomo District Chipata District Participants 14 15 Farmer type Smallholders (< 10 acres), except for two Smallholders, except for one emerging medium-sized farmers farmer (15 acres) Farming system Mixed crop–livestock systems Mixed crop–livestock systems Risks Drought, fall armyworm (1996, 2012), Drought, fall armyworm (2016/17), aphids spike in input prices in 2015 Ranking of risks Drought Drought, price volatility Worst drought 1992 1992 Effects of drought Maize dried up (in one case, at knee height) In 2014, harvested 10–15 bags of maize less than average • Pastures dried up • Streams dried up • 3 of 14 lost their animals (one lost 22 animals because of lack of pasture) • Goats aborting because of hunger Farmer Focus Group Discussions: Coping Strategies Kalomo District (Southern Province) Chipata District (Eastern Province) Moved their animals to the Kalomo River 1.  1. Took animals to dam, 15 km away Sold livestock, primarily goats 2.  2. Sold livestock, especially goats and pigs Bought maize meal 3.  3. Tilled other farmers fields Consumed fewer meals a day 4.  4. Bought mealie meal (maize flour) and maize Dug boreholes and fenced off their land 5.  5. Consumed bran Dug shallow wells near Kalomo River 6.  6. Consumed vegetables only Fetched water in 240-liter drums 7.  7. Depended on remittances Conservation agricultural practices 8.  helped reduce crop losses because of 8. Borrowed from within the village dry spells. 9. Input credit from tobacco companies, inputs spread to other crops 10. Took children out of school 11. Engaged in prostitution APPENDIX D—Focus Group Discussions: Farmer Profiles and Coping Strategies83 A proper understanding of the risks faced by the agricultural sector and effective strategies to manage those risks is vital to creating a diversified and resilient economy for sustained growth and economic transformation. Increasing Agricultural Resilience through Better Risk Management in Zambia provides a rigorous analysis of the production, marketing, and enabling environment risks faced by Zambia’s agricultural sector and prioritizes solu- tions to manage the risks. In terms of the severity and frequency of adverse impacts, the analysis shows that droughts, floods, price volatilities, and trade restrictions are the principal risks affecting agriculture in the country. Exposure to the consequences of these and other risks can be effectively limited through risk management systems tailored to the country’s context. Three areas of risk management are found to warrant priority, with significant potential for synergizing actions undertaken across them: • Strengthen early warning system to detect threats to food security; • Develop climate-smart agriculture and increase resilience to climate-related shocks through diversification; and • Develop the Zambian Commodity Exchange (ZAMACE) and build a shock-responsive safety net.