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.

翻译：全球粮食安全依赖于预测作物对气候变异的响应，然而基于过程的作物模型因计算成本过高，难以大规模探索基因型与环境的相互作用。本文开发了APSIM的概率神经仿真器，该模型在13个输出变量上以高保真度（R²=0.93）复现了玉米关键生长过程，同时将模拟时间降低了数个数量级。该框架基于覆盖多样化遗传、土壤和管理条件的200万次模拟训练，并辅以基于卷积的合成天气生成器（可产生物理一致的气候序列），从而能够在现实且多样的环境输入下实现作物响应的可扩展探索，同时无需昂贵的贝叶斯推断即可提供校准后的预测不确定性。通过将该框架应用于10万种性状配置、爱荷华州和伊利诺伊州的六种土壤环境以及两种排放情景下直至2100年的气候预测，我们识别出181种在所有测试条件下均能持续保持高产的玉米性状组合——这一分析无法单独通过机理模型实现。进一步研究表明，辐射利用效率和温度驱动的根系动态是产量恢复力的主要驱动因素。值得注意的是，预测的产量分布在各地之间存在显著差异，部分低产地在未来气候情景下呈现产量增长，表明气候变化可能以非直观的方式重塑区域产量潜力。这些结果展示了不确定性感知的仿真如何将机理作物模拟从计算瓶颈转变为按需发现的引擎——其能以任何基于过程的模型无法企及的规模，全面审视基因型、环境与管理的空间。