Four species of grass generate half of all human-consumed calories. However, abundant biological data on species that produce our food remain largely inaccessible, imposing direct barriers to understanding crop yield and fitness traits. Here, we assemble and analyse a continent-wide database of field experiments spanning 10 years and hundreds of thousands of machine-phenotyped populations of ten major crop species. Training an ensemble of machine learning models, using thousands of variables capturing weather, ground sensor, soil, chemical and fertilizer dosage, management and satellite data, produces robust cross-continent yield models exceeding R² = 0.8 prediction accuracy. In contrast to ‘black box’ analytics, detailed interrogation of these models reveals drivers of crop behaviour and complex interactions predicting yield and agronomic traits. These results demonstrate the capacity of machine learning models to interrogate large datasets, generate new and testable outputs and predict crop behaviour, highlighting the powerful role of data in the future of food.

四种草产生的热量占人类消耗热量的一半。然而，关于生产我们食物的物种的丰富生物学数据在很大程度上仍然无法获得，这直接阻碍了了解作物产量和适应性状。在这里，我们收集并分析了跨越 10 年的全大陆田间实验数据库和十种主要作物物种的数十万个机器表型种群。训练一组机器学习模型，使用数千个捕获天气、地面传感器、土壤、化学品和肥料剂量、管理和卫星数据的变量，生成超过R ²的稳健的跨大陆产量模型 = 0.8 预测准确度。与“黑匣子”分析相比，对这些模型的详细询问揭示了作物行为的驱动因素以及预测产量和农艺性状的复杂相互作用。这些结果展示了机器学习模型查询大型数据集、生成新的和可测试的输出以及预测作物行为的能力，突出了数据在未来食品中的强大作用。