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Impact of Changing Seasonal Rainfall Patterns on Rainy-Season Crop Production in the Guinea Savannah of West Africa

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  • Müller, Marc
  • Sanfo, Safietou
  • Laube, Wolfram
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    Abstract

    Rainy-season farming is a major source of income for the rural population in the Guinea Savannah zone of West Africa. Farming systems in the region are dominated by rain-fed production of cereals, but include also leguminous crops and oilseeds. A recent World Bank study has identified high potentials for competitive agricultural production and agriculture-led growth in the Guinea Savannah zones of Sub-Saharan Africa. This optimistic outlook is conditional on appropriate investment strategies, policy reforms, and institutional changes. Furthermore, the World Bank warns that global climate change could pose a potential constraint for agricultural growth due to likely reductions in rainfall levels and significant increases in rainfall variability. This could lead to serious dry spells and a drop of crop yields. The study regions are the département Atakora in Benin, the région Sud-Ouest in Burkina Faso, and the Upper East Region in Ghana. Climate projections and trend estimates for these regions show very heterogeneous results for level and variability of monthly rainfall patterns. Therefore, we want to investigate which potential future developments pose the greater threat for agricultural production in the study regions. We develop a set of regional agricultural supply models, each representing 10-12 cropping activities and roughly 150.000 ha of agricultural area. We distinguish two stages of crop production: The planting stage from April to June and the yield formation stage between June and November. Preliminary results suggest that drought events during the planting stage have a more severe impact on the output of individual crops than drought events during the second stage. In contrast, the impact on total farm revenues appears to be more prominent during the second stage, when farmers have a limited capability to adjust their production plan. A clear if not surprising result is the larger vulnerability of crops with growth cycles ranging from the very beginning to the very end of the rainy season. The observed diversity of cropping activities serves the purpose to reduce the vulnerability to adverse rainfall events within a certain range. However, some extreme events are associated with very poor harvests of specific cash crops, thus severely affecting the income of the farming sector. A comprehensive picture will be obtained once the climate change scenarios are completed and the model results are tested and validated for various settings.

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    Bibliographic Info

    Paper provided by Agricultural and Applied Economics Association in its series 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. with number 151208.

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    Date of creation: 2013
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    Handle: RePEc:ags:aaea13:151208

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    Related research

    Keywords: Climate change; West Africa; agricultural production; stochastic production frontier; highest posterior density estimation; Crop Production/Industries; Environmental Economics and Policy; International Development; International Relations/Trade;

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    1. Ngeleza, Guyslain K. & Owusua, Rebecca & Jimah, Kipo & Kolavalli, Shashidhara, 2011. "Cropping practices and labor requirements in field operations for major crops in Ghana: What needs to be mechanized?," IFPRI discussion papers 1074, International Food Policy Research Institute (IFPRI).
    2. Just, Richard E. & Pope, Rulon D., 1978. "Stochastic specification of production functions and economic implications," Journal of Econometrics, Elsevier, vol. 7(1), pages 67-86, February.
    3. Leaver, Rosemary, 2004. "Measuring the supply response function of tobacco in Zimbabwe," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 43(1), March.
    4. john M. Antle, 2010. "Asymmetry, Partial Moments, and Production Risk," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(5), pages 1294-1309.
    5. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers 1488, Iowa State University, Department of Economics.
    6. Antle, John M, 1983. "Testing the Stochastic Structure of Production: A Flexible Moment-based Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(3), pages 192-201, July.
    7. Nin-Pratt, Alejandro & Johnson, Michael & Magalhaes, Eduardo & You, Liangzhi & Diao, Xinshen & Chamberlin, Jordan, 2011. "Yield gaps and potential agricultural growth in West and Central Africa:," Research reports alejandronin-pratt, International Food Policy Research Institute (IFPRI).
    8. Michael J. Roberts & Wolfram Schlenker, 2009. "World Supply and Demand of Food Commodity Calories," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 91(5), pages 1235-1242.
    9. Thomas Heckelei & Hendrik Wolff, 2003. "Estimation of constrained optimisation models for agricultural supply analysis based on generalised maximum entropy," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 30(1), pages 27-50, March.
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