The ability of ‘digital agriculture’ to support on-farm decision making is predicated on the real-time combination of observations and prior knowledge into an integrated digital environment. The mathematical discipline that seeks to provide this integration is known as model data assimilation (DA), with demonstrated benefits including improved predictive reliability, and the capacity to identify unexpected changes in field conditions and potential measurement errors. Despite routine adoption in other fields, the delayed adoption of DA in agriculture is due to the need to express end-of-season outcomes such as yield, update forecasts of these outcomes throughout the growing season as data become available, and enhance forecast reliability. To overcome these challenges, three guiding principles are introduced, providing a means to operationalize crop model DA for robust on-farm decision support. We apply the guiding principles using a South Australian viticulture case study. Our case study involves application of an iterative form of a widely used DA algorithm (ensemble Kalman filter) to dynamically update both static parameters and states associated with a grapevine simulation model. Daily weather data as well as fortnightly ground-based leaf area index (LAI) data are used for assimilation. It is shown how crop model DA can lead to not only significant improvements in forecasts of LAI but also to forecasts of end-of-season yield. The guiding principles also enable observations of greatest value to be identified throughout the season. This study highlights the role that formal crop model DA can play in agricultural decision support through enhancing situational awareness in real time.

“数字农业”支持农场决策的能力取决于将观察结果和先验知识实时结合到一个集成的数字环境中。寻求提供这种集成的数学学科被称为模型数据同化 (DA)，其优势包括提高预测可靠性，以及识别现场条件和潜在测量误差的意外变化的能力。尽管在其他领域常规采用，但在农业中延迟采用 DA 是由于需要表达季末结果（例如产量），在数据可用时更新整个生长季节对这些结果的预测，并提高预测的可靠性。为了克服这些挑战，引入了三个指导原则，提供一种方法来实施作物模型 DA，以获得强大的农场决策支持。我们通过南澳大利亚葡萄栽培案例研究应用指导原则。我们的案例研究涉及应用广泛使用的 DA 算法（集成卡尔曼滤波器）的迭代形式来动态更新与小道消息仿真模型相关的静态参数和状态。每日天气数据以及每两周一次的地面叶面积指数 (LAI) 数据用于同化。它显示了作物模型 DA 如何不仅可以显着改善 LAI 的预测，还可以预测季末产量。指导原则还可以在整个季节中识别出最有价值的观察结果。