NeuralCrop: Combining physics and machine learning for improved crop yield projections

ArXi:2512.20177v2 Announce Type: replace Global gridded crop models (GGCMs) are crucial to project the impacts of climate change on agricultural productivity and assess associated risks for food security. Despite decades of development, state-of-the-art GGCMs retain substantial uncertainties stemming from process representations. Recently, machine learning approaches trained on observational data provide alternatives in crop yield projections.