WPS4602 Policy ReseaRch WoRking PaPeR 4602 Long-Term Adaptation: Selecting Farm Types across Agro-Ecological Zones in Africa Niggol Seo Robert Mendelsohn Ariel Dinar Pradeep Kurukulasuriya Rashid Hassan The World Bank Development Research Group Sustainable Rural and Urban Development Team April 2008 Policy ReseaRch WoRking PaPeR 4602 Abstract Using economic data from more than 8,500 household current decisions against future decisions as if the only surveys across 10 African countries, this paper examines change were climate change. They focus on two climate whether the choice of farm type depends on the climate scenarios from existing climate models: the Canadian and agro-ecological zone of each farm. The paper also Climate Centre scenario, which is hot and dry, and studies how farm type choice varies across farmers in each the Parallel Climate Model scenario, which is mild zone, using a multinomial logit choice model. Farmers and wet. The results indicate that the change in farm are observed to choose from one of the following five types varies dramatically by climate scenario but also by types of farms: rainfed crop-only, irrigated crop-only, agro-ecological zone. Policy makers must be careful to mixed rainfed (crop and livestock), mixed irrigated, encourage the appropriate suite of measures to promote and livestock-only farming. The authors compare the most adapted farm type to each location. This paper--a product of the Sustainable Rural and Urban DevelopmentTeam, Development Research Group--is part of a larger effort in the department to mainstream research on climate change. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at Niggol.seo@yale.edu, Robert.mendelsohn@ yale.edu, Adinar@worldbank.org, Rashid.hassan@up.ac.za, and Pradeep.kurukulasuriya@undp.org. 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Produced by the Research Support Team LONG-TERM ADAPTATION: SELECTING FARM TYPES ACROSS AGRO- ECOLOGICAL ZONES IN AFRICA1 Niggol Seo2, Robert Mendelsohn3, Ariel Dinar4, Pradeep Kurukulasuriya5, and Rashid Hassan6 1This paper is one of the product of a study "Measuring the Impact of and Adaptation to Climate Change Using Agroecological Zones in Africa" funded by the KCP Trust Fund and conducted in DECRG at the World Bank. We benefited from comments by Richard Adams, Brian Hurd, and Robert Evenson on an earlier draft. 2School of Forestry and Environmental Studies, Yale University, and consultant to the World Bank; 230 Prospect St. , New Haven, CT06511; phone 203-432-9771; email Niggol.seo@yale.edu. 3School of Forestry and Environmental Studies, Yale University; 230 Prospect St, New Haven, CT06511 and a consultant to the World Bank; phone 203-432-5128; email Robert.mendelsohn@yale.edu. 4Development Research Group, World Bank, 1818 H St. NW, Washington DC 20433; phone 202-473-0434; email adinar@worldbank.org. 5 Energy and Environment Group, Bureau of Development Policy, United Nations Development Programme, New York; phone 212-217 2512; email: pradeep.kurukulasuriya@undp.org. 6Department of Agricultural Economics, University of Pretoria, and Center for Environmental Economics for Africa; email Rashid.hassan@up.ac.za . 1. Introduction There is strong scientific evidence that the earth is warming due to greenhouse gas emissions into the atmosphere (IPCC 2007). Agriculture is expected to be one of the most vulnerable economic sectors to this climate change (Pearce et al. 1996; Reilly 2006; Tol 2002; Mendelsohn and Williams 2007). Agriculture in low-latitude developing countries is especially vulnerable because the majority of poor households depend on local farming and because the high current temperatures of many developing countries makes farming challenging. Consequently, the impact of climate change on agriculture has been one of the most studied impacts of climate change (Adams et al. 1990; Rosenzweig and Parry 1994; Mendelsohn et al. 1994; Reilly et al. 1996; Schlenker et al. 2005; Kurukulasuriya et al. 2007a, 2007b, 2008a, 2008b; Deschenes and Greenstone 2007; Seo and Mendelsohn 2008a, 2008b, 2008c, 2008d; Wang et al 2008). Researchers agree that climate change will damage agriculture in developing countries, such as countries in Africa and South America (Rosenzweig and Parry 1994; Reilly et al. 1996; Kurukulasuriya et al. 2007; Seo and Mendelsohn 2007). But the magnitude of such damage will depend on how efficiently farmers adapt to the new climates (Mendelsohn 2000). As farmers make adjustments that increase their net revenue through efficient adaptations, they will reduce the potential damages from climate change. Initial research indicates that farmers are likely to make many changes including changing irrigation, crop species choice, and livestock species choice (Kurukulasuriya and Mendelsohn 2006; 2007, 2008; Seo and Mendelsohn 2008a; 2008b). This study focuses on the choice of farm type as an adaptation to climate change (Mendelsohn and Seo 2007, Hassan and Nhemachena 2008). We extend earlier studies on adapting through farm type selection by looking at how this choice varies by Agroecological Zones (AEZs). Tying the choice to AEZs illustrates how the choice varies across the landscape and also allows us to extrapolate from our sample of farms to the continent. The paper examines five farm types: crop-only rainfed, crop-only irrigated, mixed (crop and livestock) rainfed, mixed irrigated, and livestock-only farms. A multinomial logit model is estimated to predict the probability each farm type is chosen in each AEZ in Africa. The analysis uses a dataset of 8500 household farms collected across 10 countries in Africa (Dinar et al., 2008). This paper differs from previous adaptation papers in that it quantifies adaptation measures appropriate for each of the 16 Agro-Ecological Zones in Africa. Based on the AEZ classification by the FAO (1978), the paper examines the likelihood each farm type is adopted in each AEZ. Since a single adaptation policy is never relevant to all AEZs, it is important to quantify how adaptation measures vary across AEZs depending on agro- climatic conditions. In the next section, a multinomial logit model of choice of farm type is provided. Data used in the study are described in the third section. The fourth section provides the empirical results. The paper then simulates climate change impacts on the distribution of farm types across AEZs for two climate scenarios in 2100, a hot and dry scenario and a mild and wet scenario. The paper concludes with discussions and policy implications. 2. Theory We assume that farmers manage their farms to maximize the net revenue from various farming activities, taking the existing climate as given. We define net revenue broadly to include the value of own consumption. So the model provides an appropriate description of both commercial and household farms. Farmers first consider a type of farm and then which combination of crops or livestock species, inputs, and timing that would maximize the net revenue they obtain. We assume that farmers have five choices about the type of farm they can select: crop-only rainfed, crop-only irrigated, mixed (crop and livestock) rainfed, mixed irrigated, and livestock-only farms7. Farmers will examine exogenous factors relevant to their farm but beyond their control when making these decisions. Most specifically, they will consider climate but also soils and elevation. Because these factors determine which AEZ a farmer is in, we hypothesize that the farmers' choices will vary by AEZ. Let the net revenue from these five alternative farm types, j, be written in the following 7The number of farms that specialized in livestock is too small to divide them into rainfed livestock only farms and irrigated livestock only farms. Most of them are, however, raise livestock on dryland. 4 form: =V (Zjw)+ where j=1,...,5and w =1,...,16. (1) jw jw where Z is a vector of exogenous variables that affect profitability of any of the five types of farms and w reflects the 16 AEZs in Africa.. For example, Z includes climate, soils, water availability, market access variables, electricity provision, and education of the farmer. Note that farmers choose farm type j from the five alternatives, but they do not choose AEZ w. The profit function is composed of two components: the observable component V and an error term . The decision of a farmer who is located in AEZ w, is to choose one farm type from the available choices j that yields the highest net revenue given the external conditions: arg max{ *1w,*2w,...,*Jw} (2) j Suppressing the subscript w, the farmer will choose farm type j over all other farm types if: >k fork j. [orif k -