From Simulation to Discovery: AI Enabled Probabilistic Emulation of Mechanistic Crop Systems

2026-05-15

AI Summary

This study addresses the high computational cost of traditional process-based crop models, which hinders efficient exploration of genotype-by-environment interactions. The authors develop a probabilistic neural network–based surrogate model that accurately emulates key maize growth processes in APSIM and integrates it with a convolutional weather generator to enable rapid, uncertainty-calibrated simulations across diverse environmental conditions. This approach facilitates, for the first time, large-scale, high-fidelity, and uncertainty-aware simulations of complex cropping systems. Applied across 100,000 trait combinations, six soil types, and future climate scenarios, the framework identifies 181 stable-yielding maize genotypes and reveals that radiation use efficiency and temperature-driven root dynamics are critical determinants of yield resilience.

Abstract

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.

Problem

• crop modeling
• computational efficiency
• genotype-environment interaction
• climate variability
• food security

Innovation

• probabilistic neural emulator
• crop modeling
• climate resilience
• synthetic weather generation
• genotype-environment interaction