Generating global crop distribution maps: from census to grid

Liangzhi You, Stanley Wood and Ulrike Wood-Sichra

No 21299, 2006 Annual meeting, July 23-26, Long Beach, CA from American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association)

Abstract: In order to evaluate food security, technology potential and the environmental impacts of production in a strategic and regional context, it is critical to have reliable information on the spatial distribution and coincidence of people, agricultural production, and environmental services. This paper proposes a spatial allocation model for generating highly disaggregated, crop-specific production data by a triangulation of any and all relevant background and partial information. This includes national or sub-national crop production statistics, satellite data on land cover, maps of irrigated areas, biophysical crop suitability assessments, population density, secondary data on irrigation and rainfed production systems, cropping intensity, and crop prices. This information is compiled and integrated to generate "prior" estimates of the spatial distribution of individual crops. Priors are then submitted to an optimization model that uses cross-entropy principles and area and production accounting constraints to simultaneously allocate crops into the individual pixels of a GIS database. The result for each pixel (notionally of any size, but typically from 25 to 100 square km) is the area and production of each crop produced, split by the shares grown under irrigated, high-input rainfed, low-input rainfed conditions (each with distinct yield levels). Tested in Latin America and sub-Saharan Africa, the spatial allocation model is applied here to generate a global distribution of crop area and production for 20 major crops (wheat, rice, maize, barley, millet, sorghum, potato, sweet potato, cassava and yams, plantain and banana, soybean, dry beans, other pulse, sugar cane, sugar beets, coffee, cotton, other fibres, groundnuts, and other oil crops). The detailed spatial datasets represent a truly unique and extremely rich platform for exploring the social, economic and environmental consequences of agricultural production in a strategic policy context.

Keywords: Research; Methods/; Statistical; Methods (search for similar items in EconPapers)
Pages: 16
Date: 2006
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Citations: View citations in EconPapers (24)

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https://ageconsearch.umn.edu/record/21299/files/sp06wo01.pdf (application/pdf)

Related works:
Journal Article: Generating global crop distribution maps: From census to grid (2014)
Working Paper: Generating Global Crop Distribution Maps: From Census to Grid (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:ags:aaea06:21299

DOI: 10.22004/ag.econ.21299

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