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Production : Production Systems : Distribution
Disaggregated Statistical Production Data I; Selected SSA Food Staple Harvested Areas
Source: You, Wood and Wood-Sichra (2007)
Distribution
In order to evaluate the potential productivity, food security, growth and environmental impacts of agricultural production, it is critical to have reliable information on the spatial distribution and coincidence of people, agricultural production, and environmental services.For the past ten years the International Food Policy Research Institute (IFPRI) has been collecting sub-national crop production statistical and survey data. This has served as one input into the development of a spatial allocation model (SPAM) for generating spatially-disaggregated assessments of the distribution of crop production (e.g., pixel-based, crop-specific estimates of crop area, yield and production). The assessment approach (You and Wood 2006) involves triangulation and optimization across a broad range relevant spatial and tabular data including; national or sub-national crop production statistics, satellite data on land cover, maps of irrigated areas, biophysical crop suitability assessments, and population density, and secondary tabular data on irrigation and rainfed production systems, cropping intensity, and crop prices.
HarvestChoice has established procedures to validate its disaggregated results, primarily through collaboration with scientists at CGIAR centers, and continues to compile newer and higher resolution data and to re-generate successive versions of the SPAM data. Since May 2008 updates to the SPAM databaseare undertaken on a country-by-country basis as and when any significant new data input source is obtained. Typically this would be new sub-national crop production statistics or new land use data. While HarvestChoice maintains its own collection of sub-national production data, a major resource for such data is the Agro-Maps  site maintained by FAO. HarvestChoice scientists were involved in the establishment of Agro-Maps and continue to collaborate closely.
Disaggregated Statistical Production Data II; Global Irrigated Rice Production
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), and this information is used as a guideline of the actual crop productivity in the yield-gap analysis.
Progress
Tested in Latin America and sub-Saharan Africa, the spatial allocation model is currently applied 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.
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Key Papers
You, L., S. Wood, U. Wood-Sichra, J. Chamberlin. 2007. Generating plausible crop distribution maps for Sub-Sahara Africa using a spatial allocation model. Information Development, Vol.23, No.2/3, p.151-159
You, L., S. Wood, U. Wood-Sichra. 2007. Generating Plausible Crop Distribution and Performance Maps for Sub-Saharan Africa Using a Spatially Disaggregated Data Fusion and Optimization Approach. IFPRI Discussion Paper 720, International Food Policy Research Institute, Washington, DC, USA
You, L. and S. Wood. 2006. An entropy approach to spatial disaggregation of agricultural production. Agricultural Systems. Vol.90, Issues1-3, 329-347.
You, L., S. Wood, U. Wood-Sichra. 2006. Generating global crop distribution maps: from census to grid. Selected paper at IAEA 2006 Conference at Brisbane, Australia.
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Modeled pixel-scale crop area distribution – maize (2000) |
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Modeled pixel-scale crop area distribution – wheat (2000) |
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Modeled pixel-scale crop area distribution – rice (2000) |
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Modeled pixel-scale crop area distribution – barley (2000) |
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Modeled pixel-scale crop area distribution – millet (2000) |
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Modeled pixel-scale crop area distribution – potato (2000) |
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Modeled pixel-scale crop area distribution – sweet potato / yam (2000) |
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Modeled pixel-scale crop area distribution – cassava (2000) |
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Modeled pixel-scale crop area distribution – banana and plantain (2000) |
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Modeled pixel-scale crop area distribution – soybean (2000) |
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Modeled pixel-scale crop area distribution – beans (2000) |
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Modeled pixel-scale crop area distribution – other pulses (2000) |
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Modeled pixel-scale crop area distribution – sugar cane (2000) |
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Modeled pixel-scale crop area distribution – sugar beets (2000) |
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Modeled pixel-scale crop area distribution – coffee (2000) |
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Modeled pixel-scale crop area distribution – cotton (2000) |
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Modeled pixel-scale crop area distribution – other fiber (2000) |
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Modeled pixel-scale crop area distribution – groundnuts (2000) |
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Modeled pixel-scale crop area distribution – other oils (2000) |
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Modeled pixel-scale crop production distribution – maize (2000) |
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Modeled pixel-scale crop production distribution – wheat (2000) |
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Modeled pixel-scale crop production distribution – rice (2000) |
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Modeled pixel-scale crop production distribution – barley (2000) |
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Modeled pixel-scale crop production distribution – millet (2000) |
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Modeled pixel-scale crop production distribution – sorghum (2000) |
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Modeled pixel-scale crop production distribution – potato (2000) |
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Modeled pixel-scale crop production distribution – sweet potato / yam (2000) |
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Modeled pixel-scale crop production distribution – cassava (2000) |
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Modeled pixel-scale crop production distribution – banana and plantain (2000) |
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Modeled pixel-scale crop production distribution – soybean (2000) |
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Modeled pixel-scale crop production distribution – beans (2000) |
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Modeled pixel-scale crop production distribution – other pulses (2000) |
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Modeled pixel-scale crop production distribution – sugar cane (2000) |
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Modeled pixel-scale crop production distribution – sugar beets (2000) |
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Modeled pixel-scale crop production distribution – coffee (2000) |
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Modeled pixel-scale crop production distribution – cotton (2000) |
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Modeled pixel-scale crop production distribution – other fiber (2000) |
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Modeled pixel-scale crop production distribution – groundnuts (2000) |
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Modeled pixel-scale crop production distribution – other oils (2000) |
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