Abstract Early and reliable seasonal crop yield forecasts are crucial for both farmers and decision-makers. Commonly-used methods for seasonal yield forecasting are based on process-based crop models or statistical regression-based models. Both have limitations, particularly in regard to accounting for growth stage-specific climate extremes (such as drought, heat, and frost). In this study, we firstly developed a hybrid yield forecasting approach by blending of multiple growth stage-specific indicators, i.e. APSIM (a process-based crop model)-simulated biomass, and climate extremes, NDVI (Normalized Difference Vegetation Index), and SPEI (Standardized Precipitation and Evapotranspiration Index) before forecasting dates, using a regression model (random forest or multiple linear regression). Plot-scale wheat yield (2008–2017) in the southeastern Australian wheat belt was dynamically forecasted at the end of several targeted growth stages as the growing season progressed to harvest. Results showed that the forecasting accuracy increased significantly for both systems as forecast time approached harvest time. The forecasting system based on random forest outperformed the forecasting system based on multiple linear regression at each forecasting event. Satisfactory yield forecasts occurred at one month (~35 days) prior to harvest (r = 0.85, LCCC = 0.81, MAPE = 17.6%, RMSE = 0.70 t ha−1, and ROC score = 0.90), and at two months before harvest (r = 0.62, LCCC = 0.53, MAPE = 27.1%, RMSE = 1.01 t ha−1, and ROC score = 0.88). In addition, drought events throughout the growing season were identified as the main factor causing yield losses in the wheat belt during the past decade. With the increasing availability of farming-related data, we expect that the yield forecasting system proposed in our study may be widely extended to other comparable cropping regions to produce sufficiently accurate wheat yield forecasts for stakeholders to develop strategic decisions in their respective roles.

中文翻译：

---

使用生物物理模型和机器学习技术的混合方法改进了动态小麦产量预测

摘要 早期可靠的季节性作物产量预测对农民和决策者都至关重要。季节性产量预测的常用方法基于基于过程的作物模型或基于统计回归的模型。两者都有局限性，特别是在考虑特定生长阶段的极端气候（如干旱、高温和霜冻）方面。在这项研究中，我们首先通过混合多个特定生长阶段的指标，即 APSIM（基于过程的作物模型）模拟生物量、气候极端情况、NDVI（标准化差异植被指数）和 SPEI 开发了一种混合产量预测方法（标准化降水和蒸散指数）在预测日期之前，使用回归模型（随机森林或多元线性回归）。随着生长季节进入收获期，在几个目标生长阶段结束时动态预测了澳大利亚东南部小麦带的田块规模小麦产量（2008-2017）。结果表明，随着预报时间接近收获时间，两个系统的预报准确度都显着提高。基于随机森林的预测系统在每个预测事件上都优于基于多元线性回归的预测系统。令人满意的产量预测发生在收获前一个月（~35 天）（r = 0.85，LCCC = 0.81，MAPE = 17.6%，RMSE = 0.70 t ha−1，以及 ROC 得分 = 0.90）和收获前两个月（r = 0.62，LCCC = 0.53，MAPE = 27.1%，RMSE = 1.01 t ha−1，ROC 分数 = 0.88）。此外，在过去十年中，整个生长季节的干旱事件被确定为造成小麦带产量损失的主要因素。随着农业相关数据的可用性不断增加，我们预计我们研究中提出的产量预测系统可能会广泛扩展到其他可比较的种植区，为利益相关者提供足够准确的小麦产量预测，以制定各自角色的战略决策。