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Generating plausible crop distribution and performance maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach:

Author

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

Abstract

"Agricultural production statistics reported at country or sub-national geopolitical scales are used in a wide range of economic analyses, and spatially explicit (geo-referenced) production data are increasingly needed to support improved approaches to the planning and implementation of agricultural development. However, it is extremely challenging to compile and maintain collections of sub-national crop production data, particularly for poorer regions of the world. Large gaps exist in our knowledge of the current geographic distribution and spatial patterns of crop performance, and these gaps are unlikely to be filled in the near future. Regardless, the spatial scale of many sub-national statistical reporting units remains too coarse to capture the patterns of spatial heterogeneity in crop production and performance that are likely to be important from a policy and investment planning perspective. To fill these spatial data gaps, we have developed and applied a meso-scale model for the spatial disaggregation of crop production. Using a cross-entropy approach, our model makes plausible pixel-scale assessment of the spatial distribution of crop production within geopolitical units (e.g. countries or sub-national provinces and districts). The pixel-scale allocations are performed through the compilation and judicious fusion of relevant spatially explicit data, including production statistics, land use data, satellite imagery, biophysical crop “suitability” assessments, population density, and distance to urban centers, as wells as any prior knowledge about the spatial distribution of individual crops. The development, application and validation of a prior version of the model using data from Brazil strongly suggested that our spatial allocation approach shows considerable promise. This paper describes efforts to generate crop distribution maps for Sub-Saharan Africa for the year 2000 using this approach. Apart from the empirical challenge of applying the approach across many countries, the application includes three significant model improvements, namely (1) the ability to cope with production data sources that provided different degrees of spatial disaggregation for different crops within a single country; (2) the inclusion of a digital map of irrigation intensity as a new input layer; and (3) increased disaggregation of rainfed production systems. Using the modified spatial allocation model, we generated 5-minute (approximately 10-km) resolution grid maps for 20 major crops across Sub-Saharan Africa, namely barley, dry beans, cassava, cocoa, coffee, cotton, cowpeas, groundnuts, maize, millet, oil palm, plantain, potato, rice, sorghum, soybeans, sugar cane, sweet potato, wheat, and yam. The approach provides plausible results but also highlights the need for much more reliable input data for the region, especially with regard to sub-national production statistics and satellite-based estimates of cropland extent and intensity." from Author's Abstract

Suggested Citation

  • You, Liangzhi & Wood, Stanley & Wood-Sichra, Ulrike, 2007. "Generating plausible crop distribution and performance maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach:," IFPRI discussion papers 725, International Food Policy Research Institute (IFPRI).
  • Handle: RePEc:fpr:ifprid:725
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    References listed on IDEAS

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    Cited by:

    1. Kiselev, Sergey & Romashkin, Roman & Nelson, Gerald C. & Mason-D'Croz, Daniel & Palazzo, Amanda, 2013. "Russia's food security and climate change: Looking into the future," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 7, pages 1-66.
    2. 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.
    3. Takle, Eugene S. & Gustafson, David & Beachy, Roger & Neslon, Gerald C. & Mason-D'Croz, Daniel & Palazzo, Amanda, 2013. "US food security and climate change: Agricultural futures," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 7, pages 1-41.
    4. Foster, Vivien & Morella, Elvira, 2011. "Ethiopia's infrastructure: a continental perspective," Policy Research Working Paper Series 5595, The World Bank.
    5. Dube, Sikhalazo & Scholes, Robert J. & Nelson, Gerald C. & Mason-D'Croz, Daniel & Palazzo, Amanda, 2013. "South African food security and climate change: Agriculture futures," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 7, pages 1-54.
    6. Ye, Liming & Tang, Huajun & Wu, Wenbin & Yang, Peng & Nelson, Gerald C. & Mason-D'Croz, Daniel & Palazzo, Amanda, 2014. "Chinese food security and climate change: Agriculture futures," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 8, pages 1-39.
    7. Liang Zhi You, 2008. "Africa : Irrigation investment Needs in Sub-Saharan Africa," World Bank Other Operational Studies 7870, The World Bank.
    8. Tom Walker & Tom Hash & Fred Rattunde & Eva Weltzien, 2016. "Improved Crop Productivity for Africa’s Drylands," World Bank Publications, The World Bank, number 24818.
    9. 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.
    10. You, Liangzhi & Ringler, Claudia & Nelson, Gerald & Wood-Sichra, Ulrike & Robertson, Richard & Wood, Stanley & Guo, Zhe & Zhu, Tingju & Sun, Yan, 2010. "What is the irrigation potential for Africa?," IFPRI discussion papers 993, International Food Policy Research Institute (IFPRI).

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    Keywords

    Cross entropy; Spatial allocation; Agricultural production; crop suitability; Geographic information systems; Satellite image;

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