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

Listed author(s):
  • You, Liangzhi
  • Wood, Stanley
  • Wood-Sichra, Ulrike

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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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
Handle: RePEc:ags:aaea04:19965
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  1. Lence, Sergio H & Miller, Douglas J, 1998. "Estimation of Multi-output Production Functions with Incomplete Data: A Generalised Maximum Entropy Approach," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 25(2), pages 188-209.
  2. 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.
  3. Staal, S. J. & Baltenweck, I. & Waithaka, M. M. & deWolff, T. & Njoroge, L., 2002. "Location and uptake: integrated household and GIS analysis of technology adoption and land use, with application to smallholder dairy farms in Kenya," Agricultural Economics, Blackwell, vol. 27(3), pages 295-315, November.
  4. Anselin, Luc, 2002. "Under the hood Issues in the specification and interpretation of spatial regression models," Agricultural Economics of Agricultural Economists, International Association of Agricultural Economists, vol. 27(3), November.
  5. 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.
  6. 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.
  7. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
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