[2512.20177] NeuralCrop: Combining physics and machine learning for improved crop yield projections

Computer Science > Machine Learning arXiv:2512.20177 (cs) [Submitted on 23 Dec 2025 (v1), last revised 30 Mar 2026 (this version, v2)] Title:NeuralCrop: Combining physics and machine learning for improved crop yield projections Authors:Yunan Lin, Sebastian Bathiany, Maha Badri, Maximilian Gelbrecht, Philipp Hess, Brian Groenke, Jens Heinke, Christoph Müller, Niklas Boers View a PDF of the paper titled NeuralCrop: Combining physics and machine learning for improved crop yield projections, by Yunan Lin and 8 other authors View PDF HTML (experimental) Abstract: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. However, these models have not demonstrated improved performance over traditional GGCMs and are not suitable for projecting crop yields under a changing climate due to their poor out-of-distribution generalization. Here we introduce NeuralCrop, a differentiable hybrid GGCM that combines the strengths of an advanced process-based GGCM, resolving important processes explicitly, with data-driven machine learning components. NeuralCrop is first trained to emulate a competitive GGCM before it is fine-tuned on observational da...