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Generating Plausible Crop Distribution Maps For Sub-Sahara Africa Using Spatial Allocation Model

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  • You, Liangzhi
  • Wood, Stanley
  • Wood-Sichra, Ulrike

Abstract

Spatial data, which are data that include the coordinates (either by latitude/longitude or by other addressing methods) on the surface of the earth, are essential for agricultural development. As fundamental parameters for agriculture policy research agricultural production statistics by geopolitical units such as country or sub-national entities have been used in many econometric analyses. However, collecting sub-national data is quite difficult in particular for developing countries. Even with great effort and only on regional scales, enormous data gaps exist and are unlikely to be filled. On the other hand, the spatial scale of even the subnational unit is relatively large for detailed spatial analysis. To fill these spatial data gaps we proposed a spatial allocation model. Using a classic cross-entropy approach, our spatial allocation model makes plausible allocations of crop production in geopolitical units (country, or state) into individual pixels, through judicious interpretation of all accessible evidence such as production statistics, farming systems, satellite image, crop biophysical suitability, crop price, local market access and prior knowledge. The prior application of the model to Brazil shows that the spatial allocation has relative good or acceptable agreement with actual statistic data. The current paper attempts to generate crop distribution maps for Sub-Sahara Africa for the year 2000 using the spatial allocation model. We modified the original model in the following three aspects: (1) Handle partial subnational statistics; (2) Include the irrigation map as another layer of information in the model; (3) Add subsistence portion of crops in addition to the existing three input and management levels (irrigated, high-input rainfed and low-input rainfed). With the modified spatial allocation model we obtain 5 by 5 minutes resolution maps for the following 20 major crops in Sub-Sahara Africa: Barley, Beans, Cassava, Cocoa, Coffee, Cotton, Cow Peas, Groundnuts, Maize, Millet, Oil Palm, Plantain, Potato, Rice, Sorghum, Soybeans, Sugar Cane, Sweet Potato, Wheat, Yam. This approach demonstrates that remote sensing technology such as satellite imagery could be quite useful in improved understanding of the spatial variation of land cover, agricultural production, and natural resources.

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Bibliographic Info

Paper provided by American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association) in its series 2004 Annual meeting, August 1-4, Denver, CO with number 19965.

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Date of creation: 2004
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Handle: RePEc:ags:aaea04:19965

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Related research

Keywords: Sub-Sahara Africa; cross entropy; satellite image; spatial allocation; agricultural production; crop suitability; Crop Production/Industries; C60; Q15; Q24;

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  1. Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
  2. Nelson, Gerald C., 2002. "Introduction to the special issue on spatial analysis for agricultural economists," Agricultural Economics, Blackwell, vol. 27(3), pages 197-200, November.
  3. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers 1488, Iowa State University, Department of Economics.
  4. Shen, Edward Z. & Perloff, Jeffrey M., 2001. "Maximum entropy and Bayesian approaches to the ratio problem," Journal of Econometrics, Elsevier, vol. 104(2), pages 289-313, September.
  5. Miller, Douglas & Lence, Sergio H., 1998. "Estimation of Multi-Output Production Functions with Incomplete Data: A Generalized Maximum Entropy Approach," Staff General Research Papers 1219, Iowa State University, Department of Economics.
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Cited by:
  1. Nin-Pratt, Alejandro & Johnson, Michael & Magalhaes, Eduardo & Diao, Xinshen & You, Liang & Chamberlin, Jordan, 2009. "Priorities for realizing the potential to increase agricultural productivity and growth in Western and Central Africa:," IFPRI discussion papers 876, International Food Policy Research Institute (IFPRI).
  2. Lall, Somik V. & Schroeder, Elizabeth & Schmidt, Emily, 2009. "Identifying spatial efficiency-equity tradeoffs in territorial development policies : evidence from Uganda," Policy Research Working Paper Series 4966, The World Bank.
  3. 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).
  4. Carole Megevand, 2013. "Deforestation Trends in the Congo Basin : Reconciling Economic Growth and Forest Protection," World Bank Publications, The World Bank, number 12477, October.
  5. You, Liangzhi & Johnson, Michael, 2008. "Exploring strategic priorities for regional agricultural R&D investments in East and Central Africa:," IFPRI discussion papers 776, International Food Policy Research Institute (IFPRI).
  6. Ulimwengu, John & Funes, Jose & Headey, Derek & You, Liangzhi, 2009. "Paving the way for development?: The impact of transport infrastructure on agricultural production and poverty reduction in the Democratic Republic of Congo," IFPRI discussion papers 944, International Food Policy Research Institute (IFPRI).
  7. Xavier, Antonio & Martins, Maria de Belem & Fragoso, Rui Manuel de Sousa, 2011. "Recovery of Incomplete Data of Statistical Livestock Number Applying an Entropy Approach," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 115790, European Association of Agricultural Economists.

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