MapSPAM2020_AdaptationAtlas_SSA
The MapSPAM 2020 SSA Adaptation Atlas Dataset is a regional adaptation and extension of IFPRI’s global MapSPAM 2020, specifically tailored for Sub-Saharan Africa (SSA) for the Africa Agriculture Adaptation Atlas project. It compiles sub-national crop production statistics for 42 major crops, harmoni...
| Autores principales: | , , , , , |
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| Formato: | Conjunto de datos |
| Lenguaje: | Inglés |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/179890 |
| _version_ | 1855523589950799872 |
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| author | Wood-sichra, Ulrike Steward, Peter Richard Rosenstock, Todd Stuart Youngberg, Brayden Guo, Zhe Zhou, Shuang |
| author_browse | Guo, Zhe Rosenstock, Todd Stuart Steward, Peter Richard Wood-sichra, Ulrike Youngberg, Brayden Zhou, Shuang |
| author_facet | Wood-sichra, Ulrike Steward, Peter Richard Rosenstock, Todd Stuart Youngberg, Brayden Guo, Zhe Zhou, Shuang |
| author_sort | Wood-sichra, Ulrike |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The MapSPAM 2020 SSA Adaptation Atlas Dataset is a regional adaptation and extension of IFPRI’s global MapSPAM 2020, specifically tailored for Sub-Saharan Africa (SSA) for the Africa Agriculture Adaptation Atlas project. It compiles sub-national crop production statistics for 42 major crops, harmonized with FAOSTAT country totals and supplemented by multiple national and sub-national sources. These production statistics are disaggregated into 6 production systems: irrigated, high-input rainfed, low-input rainfed, rainfed subsistence, all rainfed, and all.
The dataset includes the raw CSV data as well as rasterized GeoTIFF outputs processed for spatial analyses. It includes additional indicators derived from the raw model outputs in the form of rasterized GeoTIFF outputs.
The included variables are:
Physical Area (hectare)
Harvested Area (hectare)
Production (tonne)
Yield (kg/ha)
Value of Production (2015 International Dollars)
Value of Production (2021 Nominal USD)
While methodologically aligned with IFPRI’s MapSPAM2020 v2, this SSA version introduces adaptations for regional representation. Notably, it allows limited cropland expansion into pixels with zero initial cropland, employs suitability surfaces as allocation constraints, and does not allocate crops into protected areas. This results in an output that spreads production across more pixels and is less “clustered” than the IFPRI version.
This version also expands IFPRI’s MapSPAM2020 outputs by providing value of production data and the additional disaggregation of production systems for rainfed into high-input, low-input, and subsistence.
This dataset and model utilize the starting datasets and initial parameters from IFRPI’s MapSpam2017, and we want to acknowledge their contributions to this work.
The dataset is designed to support regional climate adaptation research and agricultural policy planning across SSA, while maintaining consistency and transparency with IFPRI’s original methods and documentation.
Methodology:Methodology is largely aligned with IFRPRI's MapSPAM2017. The methodology utilizes sub-national and nationally reported agricultural production values, cropland and protected area maps, and crop-suitability data. It uses a cross-entrophy optimization model to produce spatially explicit estimates of crop production across the modeled area.
More information can be found here: https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/DHXBJX/RUVTGA&version=9.4
The methodology is largely aligned with IFPRI’s MapSPAM. It integrates sub-national and national agricultural production statistics with cropland maps, protected area data, and crop-suitability information. A cross-entropy optimization model implemented in GAMS, is used to generate spatially explicit estimates of crop area and production across the modeled landscape. This approach is highly sensitive to the initial input parameters, and we utilize the initial parameters from IFPRI's MapSPAM2017 as a starting point. Other differences from the IFPRI version of MapSPAM are noted in the description.
More information can be found here: https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/DHXBJX/RUVTGA&version=9.4 |
| format | Conjunto de datos |
| id | CGSpace179890 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| record_format | dspace |
| spelling | CGSpace1798902026-01-15T12:47:02Z MapSPAM2020_AdaptationAtlas_SSA Wood-sichra, Ulrike Steward, Peter Richard Rosenstock, Todd Stuart Youngberg, Brayden Guo, Zhe Zhou, Shuang crop production spatial analysis The MapSPAM 2020 SSA Adaptation Atlas Dataset is a regional adaptation and extension of IFPRI’s global MapSPAM 2020, specifically tailored for Sub-Saharan Africa (SSA) for the Africa Agriculture Adaptation Atlas project. It compiles sub-national crop production statistics for 42 major crops, harmonized with FAOSTAT country totals and supplemented by multiple national and sub-national sources. These production statistics are disaggregated into 6 production systems: irrigated, high-input rainfed, low-input rainfed, rainfed subsistence, all rainfed, and all. The dataset includes the raw CSV data as well as rasterized GeoTIFF outputs processed for spatial analyses. It includes additional indicators derived from the raw model outputs in the form of rasterized GeoTIFF outputs. The included variables are: Physical Area (hectare) Harvested Area (hectare) Production (tonne) Yield (kg/ha) Value of Production (2015 International Dollars) Value of Production (2021 Nominal USD) While methodologically aligned with IFPRI’s MapSPAM2020 v2, this SSA version introduces adaptations for regional representation. Notably, it allows limited cropland expansion into pixels with zero initial cropland, employs suitability surfaces as allocation constraints, and does not allocate crops into protected areas. This results in an output that spreads production across more pixels and is less “clustered” than the IFPRI version. This version also expands IFPRI’s MapSPAM2020 outputs by providing value of production data and the additional disaggregation of production systems for rainfed into high-input, low-input, and subsistence. This dataset and model utilize the starting datasets and initial parameters from IFRPI’s MapSpam2017, and we want to acknowledge their contributions to this work. The dataset is designed to support regional climate adaptation research and agricultural policy planning across SSA, while maintaining consistency and transparency with IFPRI’s original methods and documentation. Methodology:Methodology is largely aligned with IFRPRI's MapSPAM2017. The methodology utilizes sub-national and nationally reported agricultural production values, cropland and protected area maps, and crop-suitability data. It uses a cross-entrophy optimization model to produce spatially explicit estimates of crop production across the modeled area. More information can be found here: https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/DHXBJX/RUVTGA&version=9.4 The methodology is largely aligned with IFPRI’s MapSPAM. It integrates sub-national and national agricultural production statistics with cropland maps, protected area data, and crop-suitability information. A cross-entropy optimization model implemented in GAMS, is used to generate spatially explicit estimates of crop area and production across the modeled landscape. This approach is highly sensitive to the initial input parameters, and we utilize the initial parameters from IFPRI's MapSPAM2017 as a starting point. Other differences from the IFPRI version of MapSPAM are noted in the description. More information can be found here: https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/DHXBJX/RUVTGA&version=9.4 2026 2026-01-15T12:45:42Z 2026-01-15T12:45:42Z Dataset https://hdl.handle.net/10568/179890 en Open Access Wood-sichra, U.; Steward, P.R.; Rosenstock, T.S.; Youngberg, B.; Guo, Z.; Zhou, S. (2026) MapSPAM2020_AdaptationAtlas_SSA. https://doi.org/10.7910/DVN/Z0HK7R |
| spellingShingle | crop production spatial analysis Wood-sichra, Ulrike Steward, Peter Richard Rosenstock, Todd Stuart Youngberg, Brayden Guo, Zhe Zhou, Shuang MapSPAM2020_AdaptationAtlas_SSA |
| title | MapSPAM2020_AdaptationAtlas_SSA |
| title_full | MapSPAM2020_AdaptationAtlas_SSA |
| title_fullStr | MapSPAM2020_AdaptationAtlas_SSA |
| title_full_unstemmed | MapSPAM2020_AdaptationAtlas_SSA |
| title_short | MapSPAM2020_AdaptationAtlas_SSA |
| title_sort | mapspam2020 adaptationatlas ssa |
| topic | crop production spatial analysis |
| url | https://hdl.handle.net/10568/179890 |
| work_keys_str_mv | AT woodsichraulrike mapspam2020adaptationatlasssa AT stewardpeterrichard mapspam2020adaptationatlasssa AT rosenstocktoddstuart mapspam2020adaptationatlasssa AT youngbergbrayden mapspam2020adaptationatlasssa AT guozhe mapspam2020adaptationatlasssa AT zhoushuang mapspam2020adaptationatlasssa |