Yunan Lin, Sebastian Bathiany, Maha Badri, Maximilian Gelbrecht, Philipp Hess, Brian Groenke, Jens Heinke, Christoph Müller 等 9 位作者
Global gridded crop models (GGCMs) are crucial for projecting climate impacts on agricultural productivity and assessing associated risks to food security. However, state-of-the-art GGCMs are subject to substantial uncertainties arising from reduced representations of complex processes. Projected increases in the frequency and severity of climate extremes require more reliable agricultural yield simulations. Recent machine learning approaches trained on observational data provide alternatives to GGCMs, but remain under-constrained due to sparse observations and show poor out-of-distribution generalization. Here we introduce NeuralCrop, a differentiable hybrid GGCM that combines the strengths of an advanced process-based GGCM (LPJmL) with data-driven components. NeuralCrop is first trained to reproduce its underlying fully process-based version, before it is fine-tuned on observational flux data of physiological variables. Evaluation against held-out flux-tower observations from European wheat and US maize sites shows 32.6-45.2% reductions in mean normalized root-mean-square error for daily carbon and water fluxes. Although fine-tuned only on physiological variables, NeuralCrop improves temporal correlations with independent yield records across 71.4% of the cultivated area for European wheat and 84.0% for the US Corn Belt during 1981-2019. It performs highly competitively compared to GGCMI Phase~3 models. NeuralCrop reproduces yield anomalies more accurately across a range of soil-moisture conditions and especially for extreme drought conditions. For large-scale, long-term simulations, NeuralCrop runs nearly two orders of magnitude faster than the CPU-based LPJmL baseline model, mainly thanks to GPU acceleration. Our results highlight end-to-end hybrid crop modelling as a promising avenue for improving crop simulations and informing food security assessments under climate change.
Yunan Lin, Sebastian Bathiany, Maximilian Gelbrecht, Jens Heinke, Christoph Müller, Niklas Boers
Global gridded crop models (GGCMs) are crucial for assessing the performance of food producing systems, for example under climate change or their contribution to environmental challenges. However, systematic calibration of their parameters remains a long-standing challenge. Persisting uncertainties in these parameters propagate into future crop projections, thereby affecting and potentially biasing food security assessments. Here we present a fully process-based differentiable GGCM implementation that offers a fundamentally new approach to parameter calibration, uncertainty quantification, yield loss attribution, and management optimization. Using automatic differentiation, we optimize selected model parameters against site-scale carbon and water flux observations, reducing normalized root-mean-square errors at held-out sites by 24.8-36.2% compared to the default state-of-the-art model. Without calibration against yield data, the optimized model better captures detrended yield anomalies across 61.2-64.3% of global crop area and reduces low-yield errors by 3.4-43.5% for wheat, rice, maize and soybean. It also ranks among the best performers in comparison with GGCMI Phase 3 models. Future simulations show that calibration reduces the projected end-of-century rice production increase from 55.1% to 22.1% under SSP5-8.5. Parameter ensembles further reveal that parameterizations with comparable historical skill can produce markedly different future production responses. Beyond calibration, differentiability enables the attribution of crop losses under extreme events and supports the design of adaptive crop management strategies. For the 2018 European drought, we identify spring heat and water limitation as major contributors to wheat yield losses and develop more effective irrigation strategies for mitigating simulated regional yield losses.