In most crops, genetic and environmental factors interact in complex ways giving rise to substantial genotype-by-environment interactions (G×E). We propose that computer simulations leveraging field trial data, DNA sequences, and historical weather records can be used to tackle the longstanding problem of predicting cultivars’ future performances under largely uncertain weather conditions. We present a computer simulation platform that uses Monte Carlo methods to integrate uncertainty about future weather conditions and model parameters. We use extensive experimental wheat yield data (n = 25,841) to learn G×E patterns and validate, using left-trial-out cross-validation, the predictive performance of the model. Subsequently, we use the fitted model to generate circa 143 million grain yield data points for 28 wheat genotypes in 16 locations in France, over 16 years of historical weather records. The phenotypes generated by the simulation platform have multiple downstream uses; we illustrate this by predicting the distribution of expected yield at 448 cultivar-location combinations and performing means-stability analyses.

在大多数作物中，遗传和环境因素以复杂的方式相互作用，导致大量的基因型-环境相互作用 (G×E)。我们建议利用田间试验数据、DNA 序列和历史天气记录的计算机模拟可用于解决在很大程度上不确定的天气条件下预测品种未来表现的长期问题。我们提出了一个计算机模拟平台，该平台使用蒙特卡罗方法来整合有关未来天气条件和模型参数的不确定性。我们使用广泛的实验小麦产量数据（n = 25,841) 来学习 G×E 模式并使用左试验交叉验证来验证模型的预测性能。随后，我们使用拟合模型为法国 16 个地点的 28 个小麦基因型生成了大约 1.43 亿个谷物产量数据点，超过 16 年的历史天气记录。模拟平台产生的表型有多种下游用途；我们通过预测 448 个品种-位置组合的预期产量分布并进行均值稳定性分析来说明这一点。