High-resolution global maps of yield potential with local relevance for targeted crop production improvement

Abstract

Identifying untapped opportunities for crop production improvement in current cropland is crucial to guide food availability interventions. Here we integrated an agronomically robust bottom-up approach with machine learning to generate global maps of yield potential of high resolution (ca. 1 km2 at the Equator) and accuracy for maize, wheat and rice. These maps serve as a robust reference to benchmark farmers’ yields in the context of current cropping systems and water regimes and can help to identify areas with large room to increase crop yields. High-resolution global maps of yield potential were created through crop modelling and machine learning. These maps can help orient agricultural research and development programmes and assess food security and land use from local to regional levels.