Input data
SPAM relies on a collection of relevant spatially explicit input data, including crop production statistics, land cover and land use data, biophysical crop "suitability" assessments, population density, distance to urban (i.e. market) centers, as well as any prior knowledge about the spatial distribution of specific crops or crop systems.
Crop production statistics
While crop production data at the national level are reported by Food and Agriculture Organization of United Nations (FAO), similar data within sub-national boundaries are rarely available on a global scale. To satisfy an increasing necessity to have better crop production and land use data to support their respective programs, FAO, IFPRI (International Food Policy Research Institute) and SAGE (Center for Sustainability and the Global Environment, University of Wisconsin-Madison) started, in 2002, an informal collaborative consortium titled Agro-MAPS (Mapping of Agricultural Production Systems).
The goal of Agro-MAPS is to compile a consistent global spatial database based upon selected sub-national agricultural statistics. Agro-MAPS holds not only tabular statistical data but also links to maps of administrative districts (http://www.fao.org/landandwater/agll/agromaps/interactive/index.jsp). As input into SPAM, we started with Agro-MAPS data, and made a great effort to add more sub-national data, paying particular attention to developing countries in Africa, Latin America, and Asia. We established a network of data resources from various local subnational offices in many countries throughout the world. Currently most of the data used are from World Food Programme (WFP) crop and food supply assessment mission surveys, agricultural performance surveys, national bureaus of statistics, regional agricultural centers, ministries of agriculture, rural and extension services, regional NGOs, agricultural censuses, ministries of the environment, and water resource groups.
Taking advantage of these national partners and the institutes of the CGIAR (http://www.cgiar.org), we were able to compile a robust database with crop production data for more crops, longer time series, and smaller administrative units than any single collection of subnational production data currently available. These data were compiled in HarvestChoice's new information database system which allows for the collection of time series data as well as data at varying administrative levels for all countries of the world. This database now serves as the feeder of crop production data into SPAM.
Below is a table showing a regional overview of data available in SPAM 2000 by administrative level:
| Region | Number of Admin Units Level 1 | Number of Admin Units Level 2 | Data Availability (%) |
| Asia | 403 | 4,698 | 57.41 |
| Canada | 12 | 12 | 50.00 |
| Europe | 740 | 737 | 25.29 |
| LAC | 422 | 8,517 | 23.12 |
| Meast | 150 | 140 | 24.67 |
| Nafrica | 284 | 283 | 22.16 |
| Oceania | 30 | 75 | 71.95 |
| Russia | 75 | 76 | 62.28 |
| SSA | 591 | 3,862 | 43.54 |
| USA | 51 | 3,096 | 96.29 |
| World Total | 2,758 | 21,496 | 49.80 |
Land cover / Land use
Satellite-based land cover datasets serve to provide detailed spatial information on cropland extent – distinguishing cropland from other forms of land cover such as forest, grassland, and water bodies and, therefore, delineating the geographical extents within which crop production must be allocated. The reliability of the land cover data in terms of measuring cropland can have significant implications for the overall reliability of the allocation.
At the time that the model was finalized for SPAM v3 there were two global land cover datasets for the year 2000, BU-MODIS Land Cover, and JRC's GLC2000 and one for 1992/93 (USGS's GLCC) that were used as input into the model. Each dataset has its own pros and cons so based on an evaluation of the three for Africa, we chose to use aggregates of all three. The merger of three different satellite derived products allows the model to identify, and prioritize, areas that were classified as cultivated in any of the three input datasets. The shortcomings of individual datasets in certain areas can thus potentially be overcome by the strengths of the others. The source data were all at a resolution of 30 arc seconds (approx 1x1km at the equator) but were aggregated to a 5 minute (approximately 10x10km at the equator) resolution for input to the SPAM allocation. More information on the various land cover datasets used for SPAM and other HarvestChoice projects can be found on the HarvestChoice site.
Information on actual land use is even more difficult to find than that on agricultural land cover. One key factor to successfully allocating production statistics is to know what areas are irrigated. The Land and Water Division of FAO and the University of Frankfurt are working together to develop the Global Map of Irrigated Areas (GMIA) which provides GIS coverage of areas equipped for irrigation at a 5 minute resolution. Using these data we were able to identify areas that were most likely irrigated and thus allocated the irrigated area and production to these locations.
Crop suitability
Different crops have different thermal, moisture, and soil requirements, particularly under rainfed conditions. FAO, in collaboration with the International Institute for Applied Systems Analysis (IIASA), has developed the agro-ecological zones (AEZ) methodology based on an evaluation of existing land resources and biophysical limitations and potentials for specific crops (FAO/IIASA). This methodology provides maximum potential and biophysically attainable crop yields and suitable crop areas. For SPAM we utilized three production system types from the FAO/IIASA suitability datasets: Irrigated; rainfed - high input/commercial; rainfed - low input/subsistence. For each crop by the three input levels, we define our suitable land as the sum of the four suitability classes in the AEZ model: very suitable, suitable, moderately suitable, and marginally suitable. These data were made available at a 5 minute resolution. Maps of crop suitability for the 20 crops allocated at the three input levels are available through the HarvestChoice data portal.
Population density (coming soon)
Accessibility (coming soon)
Input Systems-- Three Levels
Each crop can grow in any of 3 input systems:
- Irrigated (I)
- Rainfed, high-input/commercial (H)
- Rainfed, low-input/subsistence (L)
The definition of these input systems (/management levels) more or less follows FAO/IIASA’s GAEZ project (http://www.iiasa.ac.at/Research/LUC/GAEZ/index.htm) since we use those suitability surfaces.
The rainfed, high input/commercial crop system is rainfed-based agriculture, but uses high-yield varieties and some animal traction and mechanization. It at least applies some fertilizer, chemical pest, disease or weed controls.
The rainfed, low-input/subsistence crop system refers to rainfed crop production which uses traditional varieties and mainly manual labor without (or with little) application of nutrients or chemicals for pest and disease control.
In contrast, irrigated crop system refers to the crop area equipped with either full or partial control irrigation. Normally the crop production on the irrigated fields uses high level of inputs such as modern varieties and fertilizer as well as advanced management such as soil/water conservation measures.
Example: Rice Harvested Area for Three Input Levels
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Rainfed, high-input/commercial
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Rainfed, low-input/subsistence
Area harvested is allocated to at least one of these systems according to further information available or expert judgement. Physical area, production and yield are also calculated for each input system and crop allocation results follow the same subdivision. Final results are presented in disaggregated (per system) and aggregated form (sum of all systems).





