From Simulation to Discovery: AI Enabled Probabilistic Emulation of Mechanistic Crop Systems
For crop scientists and agricultural modelers, this provides a computationally efficient way to explore large genotype-environment-management spaces that were previously intractable with process-based models.
This work develops a probabilistic neural emulator of the APSIM crop model that reproduces key maize growth processes across 13 outputs with high fidelity (R²=0.93) while reducing simulation time by orders of magnitude, enabling scalable exploration of genotype-environment-management interactions. Applying the emulator across 100,000 trait configurations and future climate scenarios, they identify 181 maize trait combinations that maintain high yield under all tested conditions, an analysis infeasible with the mechanistic model alone.
Global food security depends on predicting crop responses to climate variability, yet process based crop models remain too computationally expensive for large scale exploration of genotype and environment interactions. Here we develop a probabilistic neural emulator of APSIM that reproduces key maize growth processes across 13 outputs with high fidelity (with R^2 of 0.93) while reducing simulation time by several orders of magnitude. Trained on two million simulations spanning diverse genetic, soil, and management conditions, and augmented with a convolutional synthetic weather generator that produces physically consistent climate sequences, the framework enables scalable exploration of crop responses under realistic and diverse environmental inputs while providing calibrated predictive uncertainty without costly Bayesian inference. Applying this framework across 100,000 trait configurations, six soil environments in Iowa and Illinois, and climate projections through the year 2100 under two emissions scenarios, we identify 181 maize trait combinations that consistently maintain high yield across all tested conditionsan analysis infeasible with the mechanistic model alone. We further show that radiation use efficiency and temperature driven root dynamics are dominant drivers of yield resilience. Notably, projected yield distributions vary substantially across locations, with some lower productivity sites exhibiting yield increases under future climate scenarios, indicating that climate change may reshape regional yield potential in nonintuitive ways. These results demonstrate how uncertainty aware emulation transforms mechanistic crop simulation from a computational bottleneck into an on demand discovery engine, one capable of interrogating the full genotype, environment and management space at a scale no process-based model can match.