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Generating global crop distribution maps: From census to grid

Author

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

Abstract

We describe a new crop allocation model that adds further methodological and data enhancements to the available crop downscaling modeling. The model comprises the estimates of crop area, yield and production for 20 major crops under four rainfed and irrigated production systems across a global 5arc minute grid. The new model builds on prior work by the authors (and published in this journal) in developing regional downscaled databases for Latin America and the Caribbean (LAC) and sub-Saharan Africa (SSA) and encompasses notions of comparative advantage and potential economic worth as factors influencing the geographic distribution of crop production. This is done through a downscaling approach that accounts for spatial variation in the biophysical conditions influencing the productivity of individual crops within the cropland extent, and that uses crop prices to weigh the gross revenue potential of alternate crops when considering how to prioritize the allocation of specific crops to individual grid cells. The proposed methodology also allows for the inclusion of partial, existing sources of evidence and feedback on local crop distribution patterns through the use of spatial allocation priors that are then subjected to an entropy-based optimization procedure that imposes a range of consistency and aggregation constraints. We compare the global datasets and summarize factors that give rise to systematic differences amongst them and how such differences might influence the fitness for purpose of each dataset. We conclude with some recommendations on priorities for further work in improving the reliability, utility and periodic repeatability of generating crop production distribution data.

Suggested Citation

  • You, Liangzhi & Wood, Stanley & Wood-Sichra, Ulrike & Wu, Wenbin, 2014. "Generating global crop distribution maps: From census to grid," Agricultural Systems, Elsevier, vol. 127(C), pages 53-60.
  • Handle: RePEc:eee:agisys:v:127:y:2014:i:c:p:53-60
    DOI: 10.1016/j.agsy.2014.01.002
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    References listed on IDEAS

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    1. You, Liangzhi & Wood, Stanley, 2006. "An entropy approach to spatial disaggregation of agricultural production," Agricultural Systems, Elsevier, vol. 90(1-3), pages 329-347, October.
    2. Genti Kostandini & Bradford F. Mills & Steven Were Omamo & Stanley Wood, 2009. "Ex ante analysis of the benefits of transgenic drought tolerance research on cereal crops in low‐income countries," Agricultural Economics, International Association of Agricultural Economists, vol. 40(4), pages 477-492, July.
    3. You, Liangzhi & Ringler, Claudia & Wood-Sichra, Ulrike & Robertson, Richard & Wood, Stanley & Zhu, Tingju & Nelson, Gerald & Guo, Zhe & Sun, Yan, 2011. "What is the irrigation potential for Africa? A combined biophysical and socioeconomic approach," Food Policy, Elsevier, vol. 36(6), pages 770-782.
    4. 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.
    5. Bruno Losch & Sandrine Fréguin-Gresh & Eric Thomas White, 2012. "Structural Transformation and Rural Change Revisited : Challenges for Late Developing Countries in a Globalizing World
      [Transformations rurales et développement : Les défis du changement structurel
      ," World Bank Publications, The World Bank, number 12482, September.
    6. 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.
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    Citations

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    Cited by:

    1. repec:eee:ecomod:v:392:y:2019:i:c:p:212-225 is not listed on IDEAS
    2. repec:wbk:wbpubs:27526 is not listed on IDEAS
    3. Van Dijk, M. & You, L. & Havlik, P. & Palazzo, A. & Mosnier, A., 2018. "Generating high-resolution national crop distribution maps: Combining statistics, gridded data and surveys using an optimization approach," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 276038, International Association of Agricultural Economists.
    4. repec:eee:rensus:v:107:y:2019:i:c:p:250-263 is not listed on IDEAS
    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. Kazi Ahmed & Guiling Wang & Miao Yu & Jawoo Koo & Liangzhi You, 2015. "Potential impact of climate change on cereal crop yield in West Africa," Climatic Change, Springer, vol. 133(2), pages 321-334, November.
    7. repec:eee:agisys:v:153:y:2017:i:c:p:212-220 is not listed on IDEAS
    8. repec:eee:agiwat:v:193:y:2017:i:c:p:251-264 is not listed on IDEAS
    9. Iimi,Atsushi & You,Liangzhi & Wood-Sichra,Ulrike & Humphrey,Richard Martin, 2015. "Agriculture production and transport infrastructure in east Africa : an application of spatial autoregression," Policy Research Working Paper Series 7281, The World Bank.
    10. Fjelde, Hanne, 2015. "Farming or Fighting? Agricultural Price Shocks and Civil War in Africa," World Development, Elsevier, vol. 67(C), pages 525-534.

    More about this item

    Keywords

    Global; Cross entropy; Spatial allocation; Agricultural production; Farming system; Crop suitability;

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • 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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