An Africa-wide agricultural production database to support policy and satellite-based measurement systems

Agriculture remains a backbone of the African economy, contributing up to 70% of household income in rural areas. Yet crop yields across Africa are rising at a slower rate than the global average. Currently, strategies to improve agricultural productivity are limited by the availability of granular, accurate, and spatially-extensive data. These disaggregated statistics are required to understand how crop yields respond to climate variability, climate extremes, and agronomic practices. Here, we present GROW-Africa, a database that includes n = 535,844 georeferenced observations of crop yields across Africa focusing on 25 key crops including maize, sorghum, cassava, groundnuts, cowpeas, rice, yams, and millet. The database assimilates observations from a range of spatial scales, from regional government statistics, to household farmer surveys, to plot-level crop cuts. We use co-located observations to identify sources of bias and error in these varied data types. Finally, we demonstrate how the GROW-Africa database can be used to train remote sensing algorithms to produce continuous maps of crop yields across Africa.