This GCFSI-funded innovation will combine crop models and plot-level soil sample information into an app that will let farmers test new fertilizer combinations to quickly and without cost learn how fertilizer combinations interact with their plot's characteristics.

Project Implementers: Travis J. Lybbert, University of California, Davis, and Emilia Tjernström, University of Wisconsin, Madison

Advanced agricultural technologies hold great promise for unlocking the agricultural production potential found in smallholder plots in the developing world. However, these improved technologies work best when matched to plot-level agronomic conditions. Typically, farmers experiment with technologies over several seasons, slowly adapting new ideas into their plot-level management plan. This is slowed further by adverse weather events, which limit what a farmer can learn. This GCFSI-funded innovation will combine crop models and plot-level soil sample information into an app that will let farmers test new fertilizer combinations to quickly and without cost learn how fertilizer combinations interact with their plot's characteristics. This team will 1) develop the app-based game, which can be calibrated with plot-level soil test information, 2) test how playing the app affects farmer learning, and 3) evaluate the app predictions and results.