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 South of the Sahara (SSA) region, the HarvestChoice CELL5M Database was developed in this spirit of moving multidisciplinary data into one harmonized, geospatial database. The database includes over 750 biophysical and socio-economic indicators, many of which can be easily expanded to global scale. The CELL5M database provides a platform for cross-cutting spatial analyses and fine-grain visualization of the mix of farming systems and populations across SSA. It was created as the central core to support a decision-making platform that would enable development practitioners and researchers to explore multi-faceted spatial relationships at the nexus of poverty, health and nutrition, farming systems, innovation, and environment. The database is a matrix populated by over 350,000 grid cells covering SSA at five arc-minute spatial resolution. Users of the database, including those conduct researches on agricultural policy, research, and development issues, can also easily overlay their own indicators. Numerical aggregation of the gridded data by specific geographical domains, either at subnational level or across country borders for more regional analysis, is also readily possible without needing to use any specific GIS software. See the HCID database (http://dx.doi.org/10.7910/DVN/MZLXVQ) for the geometry of each grid cell. The database also provides standard-compliant data API that currently powers several web-based data visualization and analytics tools.
Countries: 5 arc-minute grid cell
Spatially-Disaggregated Crop Production Statistics Data in Africa South of the Saharan for 2017
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 of crop performance, creating Africa South of the Sahara-wide grid-scape at the confluence between geography and agricultural production systems. Improving spatial understanding of crop production systems allows policymakers and donors to better target agricultural and rural development policies and investments, increasing food security and growth with minimal environmental impacts.
Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0
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, creating a global grid-scape at the confluence between geography and agricultural production systems. Improving spatial understanding of crop production systems allows policymakers and donors to better target agricultural and rural development policies and investments, increasing food security and growth with minimal environmental impacts.