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

Author

Listed:
  • 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.

Suggested Citation

  • You, Liangzhi & Wood, Stanley & Wood-Sichra, Ulrike, 2004. "Generating Plausible Crop Distribution Maps For Sub-Sahara Africa Using Spatial Allocation Model," 2004 Annual meeting, August 1-4, Denver, CO 19965, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  • Handle: RePEc:ags:aaea04:19965
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    File URL: http://purl.umn.edu/19965
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    References listed on IDEAS

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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.
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    4. 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.
    5. 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.
    6. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
    7. 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.
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    Citations

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    Cited by:

    1. Carole Megevand, 2013. "Deforestation Trends in the Congo Basin : Reconciling Economic Growth and Forest Protection
      [Dynamiques de déforestation dans le bassin du Congo : Réconcilier la croissance économique et la protect
      ," World Bank Publications, The World Bank, number 12477.
    2. 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).
    3. Somik V. Lall & Elizabeth Schroeder & Emily Schmidt, 2014. "Identifying Spatial Efficiency-Equity Trade-offs in Territorial Development Policies: Evidence from Uganda," Journal of Development Studies, Taylor & Francis Journals, vol. 50(12), pages 1717-1733, December.
    4. 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).
    5. Xavier, Antonio & Martins, Maria de Belem Costa Freitas & 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.
    6. Liangzhi You & Michael Johnson, 2010. "Exploring strategic priorities for regional agricultural R&D investments in East and Central Africa," Agricultural Economics, International Association of Agricultural Economists, vol. 41(2), pages 177-190, March.
    7. 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).

    More about this item

    Keywords

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

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • Q15 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment
    • Q24 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Land

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