Illinois Data Bank

Data for: Probabilistic global maps of crop-specific areas from 1961 to 2014

Cite this dataset:
Jackson, Nicole; Konar, Megan; Debaere, Peter; Estes, Lyndon (2019): Data for: Probabilistic global maps of crop-specific areas from 1961 to 2014. University of Illinois Urbana-Champaign. https://doi.org/10.13012/B2IDB-7439710_V1

Dataset Description

Agriculture has substantial socioeconomic and environmental impacts that vary between crops. However, information on how the spatial distribution of specific crops has changed over time across the globe is relatively sparse. We introduce the Probabilistic Cropland Allocation Model (PCAM), a novel algorithm to estimate where specific crops have likely been grown over time. Specifically, PCAM downscales annual and national-scale data on the crop-specific area harvested of 17 major crops to a global 0.5-degree grid from 1961-2014.

The resulting database presented here provides annual global gridded likelihood estimates of crop-specific areas. Both mean and standard deviations of grid cell fractions are available for each of the 17 crops. Each netCDF file contains an individual year of data with an additional variable ("crs") that defines the coordinate reference system used. Our results provide new insights into the likely changes in the spatial distribution of major crops over the past half-century. For additional information, please see the related paper by Jackson et al. (2019) in Environmental Research Letters (https://doi.org/10.1088/1748-9326/ab3b93).

Subject

Technology and Engineering

Keywords

 global; gridded; probabilistic allocation; crop suitability; agricultural geography; time series

License

 CC BY

Funder

 U.S. National Science Foundation (NSF)-Grant:ACI-1639529

Funder

 U.S. National Science Foundation (NSF)-Grant:EAR-1534544

Funder

 U.S. National Science Foundation (NSF)-Grant:CBET-1844773

Corresponding Creator

 Megan Konar

Downloaded

 6120 times
Version DOI Comment Publication Date
1 10.13012/B2IDB-7439710_V1 2019-09-01

6.09 KB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File
16.8 MB File

Contact the Research Data Service for help interpreting this log.

RelatedMaterial create: {"material_type"=>"Article", "availability"=>nil, "link"=>"https://doi.org/10.1038/s41597-025-05572-x", "uri"=>"10.1038/s41597-025-05572-x", "uri_type"=>"DOI", "citation"=>"Li, X., Yu, L., Du, Z. et al. Crop Statistic to Annual Map: Tracking spatiotemporal dynamics of crop-specific areas through machine learning and statistics disaggregating. Sci Data 12, 1249 (2025). https://doi.org/10.1038/s41597-025-05572-x", "dataset_id"=>1040, "selected_type"=>"Article", "datacite_list"=>"IsCitedBy", "note"=>nil, "feature"=>nil} 2025-07-21T16:04:18Z
Dataset update: {"publisher"=>["University of Illinois at Urbana-Champaign", "University of Illinois Urbana-Champaign"], "external_files_link"=>[nil, ""], "external_files_note"=>[nil, ""]} 2025-07-21T16:04:18Z
RelatedMaterial update: {"uri"=>["", "10.1088/1748-9326/ab3b93"], "uri_type"=>["", "DOI"], "datacite_list"=>["", "IsSupplementTo"], "note"=>[nil, ""], "feature"=>[nil, false]} 2024-01-26T17:49:52Z
Dataset update: {"publication_state"=>["file embargo", "released"]} 2019-09-28T06:00:07Z
Dataset update: {"publication_state"=>["released", "file embargo"]} 2019-09-27T20:36:38Z
Dataset update: {"publication_state"=>["file embargo", "released"]} 2019-08-28T15:46:00Z
Dataset update: {"publication_state"=>["released", "file embargo"]} 2019-08-28T15:45:59Z
RelatedMaterial update: {"uri"=>[nil, ""], "uri_type"=>[nil, ""], "datacite_list"=>[nil, ""]} 2019-08-28T15:45:59Z
Dataset update: {"version_comment"=>[nil, ""], "subject"=>[nil, "Technology and Engineering"]} 2019-08-28T15:45:59Z
Dataset update: {"publication_state"=>["file embargo", "released"]} 2019-08-28T14:34:20Z
Creator update: {"email"=>["lestes@princeton.edu", "lestes@clarku.edu"]} 2019-08-28T14:34:18Z
Dataset update: {"description"=>["Agriculture has substantial socioeconomic and environmental impacts that vary between crops. However, information on how the spatial distribution of specific crops has changed over time across the globe is relatively sparse. We introduce the Probabilistic Cropland Allocation Model (PCAM), a novel algorithm to estimate where specific crops have likely been grown over time. Specifically, PCAM downscales annual and national-scale data on the crop-specific area harvested of 17 major crops to a global 0.5-degree grid from 1961-2014. \r\n\r\nThe resulting database presented here provides annual global gridded likelihood estimates of crop-specific areas. Both mean and standard deviations of grid cell fractions are available for each of the 17 crops. Our results provide new insights into the likely changes in the spatial distribution of major crops over the past half-century. For additional information, please see the related paper by Jackson et al. (2019) in Environmental Research Letters (https://doi.org/10.1088/1748-9326/ab3b93).", "Agriculture has substantial socioeconomic and environmental impacts that vary between crops. However, information on how the spatial distribution of specific crops has changed over time across the globe is relatively sparse. We introduce the Probabilistic Cropland Allocation Model (PCAM), a novel algorithm to estimate where specific crops have likely been grown over time. Specifically, PCAM downscales annual and national-scale data on the crop-specific area harvested of 17 major crops to a global 0.5-degree grid from 1961-2014. \r\n\r\nThe resulting database presented here provides annual global gridded likelihood estimates of crop-specific areas. Both mean and standard deviations of grid cell fractions are available for each of the 17 crops. Each netCDF file contains an individual year of data with an additional variable (\"crs\") that defines the coordinate reference system used. Our results provide new insights into the likely changes in the spatial distribution of major crops over the past half-century. For additional information, please see the related paper by Jackson et al. (2019) in Environmental Research Letters (https://doi.org/10.1088/1748-9326/ab3b93)."]} 2019-08-28T14:34:18Z
Dataset update: {"data_curation_network"=>[false, true]} 2019-08-27T13:56:56Z
Dataset update: {"publication_state"=>["released", "file embargo"]} 2019-08-27T13:38:27Z
Research Data Service Illinois Data Bank
Access and Use Policies Web Privacy Notice Contact Us