More than 30% of population in Saharan Africa is undernourished and the demand for food is projected to increase due to high population growth rate. Recent agronomic research has generated innovative technologies to increase food production and quality. Development programs and extension services has invested significant effort to promote adoption and scaling out of best-bet agronomic technologies. Achievement of the potential impacts of these innovative technologies is hindered by low adoption by farmers. Proper targeting of sites to scale out agricultural technologies is a key determinant of the rate of adoption. Targeting locations with similar biophysical and socio-economic characteristics significantly increase probability of adoption. Recommendation domains (RDs) are homogeneous areas with similar biophysical and socio-economic characteristics. We demonstrate the potential of geospatial analysis in identifying RDs for scaling best-bet agronomic technologies. We use GIS and remote sensing generated data to generate homogeneous clusters that exhibit similar biophysical and socio-economic characteristics in the FtF zone in Tanzania. Results identifies 20 sustainable recommendation domains (SRDs) and the main bio-socio-economic gradients that discriminate them. These SRDs were generated after excluding critical ecosystems and non-agricultural land. We propose an Impact Based Spatial Targeting Index (IBSTI) as an evidence-based tool for guiding spatial targeting of agricultural technologies based on their estimated potential impact(s). The utilized data-driven clustering method is suitable for identifying SRDs in regions with limited technology trials data but can be replicated in different ecologies and technologies. Results demonstrate the potential of geospatial tools in generating evidence-based policies for scaling-out innovative agricultural technologies.
Key words: Agricultural technologies, cluster analysis, Feed the Future, Impact Based Spatial Targeting Index, GIS, recommendation domains, scaling out, spatial targeting, Tanzania