Remote sensing has been widely employed to identify crop types and monitor crop yields on farms. Here, we combine successive seasons of these products to identify crop rotations in each field across 20 million hectares of the Western Australian Wheatbelt. We used the APSIM crop model to define the starting soil water, temperature stresses, biomass, and crop yield to characterize the prevailing agro-environment of that field. These remote sensing data and APSIM crop modeling outputs were then combined, with machine learning, to predict the effect of the complex interaction between agro-environment and crop rotation on wheat yield. Predictions from machine learning are employed to evaluate the benefits or otherwise of crop rotation across Western Australia for every field in the study region. In general, if fields subjected to a wheat-cereal rotation were instead subjected to a wheat-canola rotation, then 68% of these fields were predicted to experience a yield increase of between 0 and 1850 kg ha^-1. However, only 28% of fields planted to canola were predicted to have a yield benefit of 200 kg ha^-1 or more on the following wheat crops. On average, annual pastures generated a slight yield penalty of 47 kg ha^-1 to the following wheat crop. The findings from this study, using crop models, remote sensing, and machine learning, indicate that the benefits of break crops and pastures to farmers is less than the 400 to 600 kg ha^-1 benefit commonly reported from field experiments. These management insights could underpin the development of future decision aids or agricultural digital twins for crop management decisions such as crop rotation planning. The approach provides farmers with tangible insights about their production using outputs from crop-based remote sensing and crop modeling.

遥感已被广泛用于识别作物类型和监测农场的作物产量。在这里，我们结合这些产品的连续季节来确定西澳大利亚小麦带 2000 万公顷每个田地的作物轮作。我们使用 APSIM 作物模型来定义起始土壤水分、温度胁迫、生物量和作物产量，以表征该田地的主要农业环境。然后将这些遥感数据和 APSIM 作物建模输出与机器学习相结合，以预测农业环境和作物轮作之间复杂的相互作用对小麦产量的影响。机器学习的预测被用来评估西澳大利亚州作物轮作对研究区域每个领域的好处或其他方面。一般来说，^-1。然而，只有 28% 的油菜种植田预计对随后的小麦作物产生 200 kg ha ^{-1或更多的产量效益。}平均而言，一年生牧场对下一季小麦作物产生了 47 kg ha ^-1的轻微减产。这项研究使用作物模型、遥感和机器学习得出的结果表明，间歇作物和牧场对农民的好处小于 400 至 600 kg ha ^-1实地实验中普遍报告的益处。这些管理见解可以支持未来决策辅助工具或农业数字双胞胎的发展，用于作物管理决策，例如轮作计划。该方法使用基于作物的遥感和作物建模的输出，为农民提供有关其生产的切实见解。