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  • Most recent global cropping data (2010).
  • Broken down 6 cropping technologies and 4 variables (harvested area, physical area, yield, production, and value of production).
  • Poor ADMIN 1 coverage rates in Africa and Oceania.
  • Practically non-existent ADMIN 2 coverage in Africa, Middle East, Oceania, and Russia.


In an effort to better understand and manage the causes and effects of global environmental change, the late 2000s saw rise to multiple global agricultural data sets. Generalized land cover and land use data sets were commonplace throughout 1980-2000, however, these (mostly) remotely sensed data sets relegated agricultural land cover to “cropland,” and completely ignored descriptive agricultural systems. In order to address the impacts of agricultural practices, one must have a clear understanding of what is grown, where it’s grown, how it’s grown, and how much is produced.

The Farming the Planet 2 [FTP2; Monfreda, Ramankutty, and Foley (2008)] was the first major undertaking to address these concerns. This dataset is derived from sub-national agricultural surveys and census data on the areas and yields of 175 crops across more than 200 countries covered by the the Food and Agriculture Organization of the United Nations (FAO). Surveys, censuses, and reports are disaggregated and spatially assigned to global raster layers with the aid of remotely sensed imagery at a 5 arcminute resolution. This is one of the earliest attempts to provide a spatially explicit geo-database of extensive global cropping information. Moreover, it is the first geospatial agricultural product to present global yield data in addition to harvested areas. FTP2 offered one of the first comprehensive documentations of the earth’s agricultural footprint, and as such, its downstream environmental impacts. This dataset has been widely employed for research purposes. Portmann, Siebert, and Döll (2010) build upon FTP2 with the release of MIRCA2000. MIRCA2000 added 3 complimentary features to the FTP2 data products: estimating monthly time-steps, cropping calendars, the ability to distinguish between irrigated and rainfed crops (Siebert et al. 2010). As with FTP2, MIRCA2000’s crop harvested areas are also available at a 5 arcminute resolution.

The FTP2 and MIRCA2000 research groups have since added several additional agricultural data sets that compliment their initial releases. Several of the complimentary FTP2 data sets from Ramunkutty, Monfreda, and Sacks are available at the University of Wisconsin’s Center for Sustainability and the Global Environment website. The most applicable to this discussion discussion is the Crop Calendar Dataset [CCD; Sacks et al. (2010)]. The CCD presents global planting and harvest dates for 19 crops at 5 arcminute and 0.5 degree resolutions. CCD uses regional mean temperature to estimate planting and harvest dates based on physiological requirements for the corresponding crop. Similarly, the Cropping Periods List (CPL) and Condensed Crop Calendars (CCC) data sets are available at the MIRCA2000 website. The MIRCA2000 cropping calendar data present plant and harvest dates by month at a 5 arcminute resolution. The CCD, CPL, and CCC data sets are largely the same; the primary difference being the file formats. The majority of MIRCA2000 data are presented in the FTL format, which can be cumbersome and/or simply undreadable in several geospatial software platforms. Conversely, FTP2 data are typically available in GeoTIFF and NetCDF, which have more interoperability.

Both MIRCA2000 and FTP2 represent vast improvements upon prior global gridded agricultural data, however, they are snapshots in time of estimated agricultural systems in the year 2000. Moreover, they are largely academic exercises with limited prospects for regular updates. The Spatial Production Allocation Model [MapSPAM; You et al. (2014)] is the most recent foray into spatially explicit global agricultural mapping. SPAM is a sophisticated 5 arcminute gridded cropping dataset employing a cross-entropy approach that incorporates, sub-national production statistics, satellite imagery, agricultural suitability and irrigation maps, population density, potential for revenue, market accessibility, and prior crop presence to produce a multitude of estimates for 42 crops, 4 production systems, and 4 variables. These include estimates of harvested area, physical area, yield, production, and value of production. These estimates are provided for 6 cropping technologies that span a gradient of irrigated and rainfed management systems. In addition to implementing a more sophisticated model than its predecessors, MapSPAM released 2 updates to their original year 2000 allocation model. These are SPAM 2005 [v3.2; International Food Policy Research Institute and International Institute for Applied Systems Analysis (IIASA) (2016)] and the recently released SPAM 2010 [v1.1; International Food Policy Research Institute (2019)].

Data products that aggregate and assign sub-national statistics, surveys, and censuses to internationally spatially explicit pixels or polygons are exciting developing fields in socio-geospatial sciences, however, the quality of the outputs are relient upon the quality, or even presence, of their input data. Researchers should be cognisant of the quality of the primary data sources for their areas of interest and address any potential biases that may have resulted from limitations in data availability for their given region. For a final technical note, although these data sets are all available at 5 arcminutes, their spatial extents can vary by 0.001. On certain software and package platforms, this may occasionally result in frustrating experiences using the data sets in conjunction for analysis.

Screenshot or Representative Figure:

Figure 1: Sample data from MapSPAM depicting global harvested area of banana crops under all cropping systems.

Figure 1: Sample data from MapSPAM depicting global harvested area of banana crops under all cropping systems.


International Food Policy Research Institute. 2019. “Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 1.0.” Harvard Dataverse.
International Food Policy Research Institute, and International Institute for Applied Systems Analysis (IIASA). 2016. “Global Spatially-Disaggregated Crop Production Statistics Data for 2005 Version 3.2.” Harvard Dataverse. Harvard Dataverse.
Monfreda, Chad, Navin Ramankutty, and Jonathan A. Foley. 2008. “Farming the Planet: 2. Geographic Distribution of Crop Areas, Yields, Physiological Types, and Net Primary Production in the Year 2000: Global Crop Areas and Yields in 2000.” Global Biogeochemical Cycles 22 (1): 1–19.
Portmann, Felix T., Stefan Siebert, and Petra Döll. 2010. Mirca2000-Global Monthly Irrigated and Rainfed Crop Areas Around the Year 2000: A New High-Resolution Data Set for Agricultural and Hydrological Modeling: Monthly Irrigated and Rainfed Crop Areas.” Global Biogeochemical Cycles 24 (1): 1–24.
Sacks, William J., Delphine Deryng, Jonathan A. Foley, and Navin Ramankutty. 2010. “Crop Planting Dates: An Analysis of Global Patterns.” Global Ecology and Biogeography 19 (5): 607–20.
Siebert, S., J. Burke, J. M. Faures, K. Frenken, J. Hoogeveen, P. Döll, and F. T. Portmann. 2010. “Groundwater Use for Irrigation a Global Inventory.” Hydrology and Earth System Sciences 14 (10): 1863–80.
You, Liangzhi, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu. 2014. “Generating Global Crop Distribution Maps: From Census to Grid.” Agricultural Systems 127 (May): 53–60.

Citation Information:

Dataset Contact Information:

Liangzhi You, College of Economics and Management, Huazhong Agricultural University, Wuhan, Hubei 430070, China 
Stanley Wood, International Food Policy Research Institute, 2033 K Street, NW, Washington, DC 20006, USA 
Ulrike Wood-Sichra, Bill & Melinda Gates Foundation, 500 Fifth Avenue North, Seattle, WA 98109, USA 
Wenbin Wu, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China 

Use Constraints:

The data are freely available for academic use and other non-commercial use. Redistribution, or commercial use, is not allowed without prior permission. Using the data to create maps for academic publishing is allowed.


Informative abstract inside of quotation marks.


Additional Metadata

Spatial Information:

Bounding Coordinates:

  • West Bounding Coordinate: -180.00
  • East Bounding Coordinate: 179.9986
  • North Bounding Coordinate: 90.00
  • South Bounding Coordinate: -89.99928

Spatial Reference Information:

  • Coordinate System: geographic
  • Resolution: 5
  • Units: arcminute
  • Geodetic Model: WGS1984

Time Period Information:

  • Beginning Date: 2010
  • Ending Date: 2010

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