1 to 10 of 25 Results
Jun 9, 2025
International Food Policy Research Institute (IFPRI), 2024, "Global Spatially-Disaggregated Crop Production Statistics Data for 2020 Version 2.0", https://doi.org/10.7910/DVN/SWPENT, Harvard Dataverse, V4
The 2020 SPAM (Spatial Production Allocation Model) products, encompassing crop area, yield, and production at a 5-minute grid resolution, have been developed by Zhe Guo, Shuang Zhou, and Liangzhi You. Employing a diverse range of inputs, IFPRI's Spatial Production Allocation Model (SPAM) utilizes a cross-entropy method to generate plausible estima... |
Dec 22, 2020
International Food Policy Research Institute (IFPRI), 2020, "Spatially-Disaggregated Crop Production Statistics Data in Africa South of the Sahara for 2017", https://doi.org/10.7910/DVN/FSSKBW, Harvard Dataverse, V3
Using a variety of inputs, IFPRI's Spatial Production Allocation Model (SPAM, also known as MapSPAM) uses a cross-entropy approach to make plausible estimates of crop distribution within disaggregated units. Moving the data from coarser units such as countries and sub-national provinces, to finer units such as grid cells, reveals spatial patterns o... |
Jul 15, 2020
International Food Policy Research Institute (IFPRI), 2019, "Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0", https://doi.org/10.7910/DVN/PRFF8V, Harvard Dataverse, V4
Using a variety of inputs, IFPRI's Spatial Production Allocation Model (SPAM) uses a cross-entropy approach to make plausible estimates of crop distribution within disaggregated units. Moving the data from coarser units such as countries and sub-national provinces, to finer units such as grid cells, reveals spatial patterns of crop performance, cre... |
Jan 22, 2020
International Food Policy Research Institute, 2019, "Global Spatially-Disaggregated Crop Production Statistics Data for 2000 Version 3.0.7", https://doi.org/10.7910/DVN/A50I2T, Harvard Dataverse, V2, UNF:6:ikCh6KeB4r2u/p2tkbFDOw== [fileUNF]
Using a variety of inputs, IFPRI's Spatial Production Allocation Model (SPAM) uses a cross-entropy approach to make plausible estimates of crop distribution within disaggregated units. Moving the data from coarser units such as countries and sub-national provinces, to finer units such as grid cells, reveals spatial patterns of crop performance, cre... |
Nov 16, 2018
International Food Policy Research Institute (IFPRI), 2018, "Ghana Feed the Future Harmonized Dataset", https://doi.org/10.7910/DVN/DXMARV, Harvard Dataverse, V3, UNF:6:U5L6MtwnE/NhiNqdEo2hOg== [fileUNF]
This dataset was created by re-compiling available open, gender/sex-disaggregated Feed the Future data for Ghana and applying standard processing methods to enhance their accessibility and interoperability. This process entailed the standardization of variable names and labels, the creation of derived socio-economic indicators such as dietary diver... |
Dec 5, 2017
HarvestChoice, International Food Policy Research Institute (IFPRI); University of Minnesota, 2017, "CELL5M: A Multidisciplinary Geospatial Database for Africa South of the Sahara", https://doi.org/10.7910/DVN/G4TBLF, Harvard Dataverse, V5
Spatially-explicit data is increasingly becoming available across disciplines, yet they are often limited to a specific domain. In order to use such datasets in a coherent analysis, such as to decide where to target specific types of agricultural investment, there should be an effort to make such datasets harmonized and interoperable. For Africa So... |
Dec 5, 2017
International Food Policy Research Institute (IFPRI); International Institute for Applied Systems Analysis (IIASA), 2016, "Global Spatially-Disaggregated Crop Production Statistics Data for 2005 Version 3.2", https://doi.org/10.7910/DVN/DHXBJX, Harvard Dataverse, V9
Using a variety of inputs, IFPRI's Spatial Production Allocation Model (SPAM) uses a cross-entropy approach to make plausible estimates of crop distribution within disaggregated units. Moving the data from coarser units such as countries and sub-national provinces, to finer units such as grid cells, reveals spatial patterns of crop performance, cre... |
International Food Policy Research Institute (IFPRI), 2017, "BIHS Harmonized Dataset", https://doi.org/10.7910/DVN/PUK1P7, Harvard Dataverse, V3, UNF:6:d5u9Yp/+SdhwUO8E9ZfU1Q== [fileUNF]
This dataset is was created by re-compiling available open, gender/sex-disaggregated Feed the Future datasets for Bangladesh and applying standard processing methods to enhance their accessibility and interoperability. This process entailed the standardization of variable names and labels, the creation of derived socio-economic indicators such as d... |
Jul 11, 2017 - IFPRI Dataverse
Koo, Jawoo; Dimes, John, 2013, "HC27 Generic Soil Profile Database", https://doi.org/10.7910/DVN/90WJ9W, Harvard Dataverse, V5
The HC27 soil profile database consists of generic soil profiles developed by John Dimes and Jawoo Koo. The 27 soil profiles were generated based on three criteria that crop models are most responsive to: texture, rooting depth (proxy of water availability), and organic carbon content (proxy of fertility). Three levels for each category were classi... |
Apr 18, 2017
HarvestChoice, International Food Policy Research Institute, 2017, "Segmentation Data for Nigeria and India States of Bihar, Odisha, and Uttar Pradesh", https://doi.org/10.7910/DVN/K5NSAF, Harvard Dataverse, V2, UNF:6:qgWv6+TKzcJ9f5nf0W2j/w== [fileUNF]
This dataset was compiled by processing and harmonizing multiple secondary datasets, covering Nigeria and three states in India (Bihar, Odisha, and Uttar Pradesh), to help those working in the agricultural development sector identify and characterize groups of smallholder farmers that have the highest potential leverage in terms of increasing agric... |
