Spatial frameworks for prioritization of agricultural research and development
Abstract Food security interventions and policies need reliable estimates of actual crop production and the scope to enhance production on existing cropland. We assess the performance of two widely used “top-down” gridded frameworks (GAEZ and AgMIP) versus an alternative “bottom-up” approach that estimates extra production potential locally, for a number of representative sites, and then upscales the results to larger spatial scales (GYGA). Our results show that estimates from top-down frameworks are alarmingly unlikely, with estimated potential production being lower than current production at some locations. The consequences of using these coarse estimates to predict food security are illustrated by an example from sub-Saharan Africa. Our study shows that current foresights on food security, land use, and climate change and associate priority setting on AR&D based on yield potential and yield gaps derived from top-down approaches are subject to a high degree of uncertainty and would benefit from incorporating estimates from bottom-up approaches.