Using agro-hydrological machine-learning to spatially target investments in sustainable groundwater irrigation

Groundwater irrigation supports over 40% of global crop production and stabilizes yields amidst climatic change. Yet, over-abstraction can cause water scarcity, disrupt ecosystems, and increase greenhouse gas emissions. Governments and international financial institutions have made significant investments in sustainable groundwater irrigation but require enhanced spatial targeting to increase impact. In response, this study employs an agro-hydrological machine-learning approach to analyze spatial patterns of (i) crop yield responses to increased irrigation and (ii) groundwater sustainability in South Asia – characterized by smallholder farming, increasing groundwater dependence, and post-green revolution sustainability challenges. We show that modestly increasing irrigation intensity in groundwater-rich areas with high yield responses could boost rice production by 2.22Mt annually – sufficient to feed over 33 million people with little anticipated risks of groundwater depletion. However, current investments overlook these areas. Our approach can be globally applied to catalyze sustainable irrigation through integrated use of expanding agricultural and hydrological datasets.