Abstract Foresight of grain yields prior to harvest would be empowering for many stakeholders along the supply chain from farmers through to bulk handlers, banks and insurance companies. Estimating Australian grain production ahead of harvest is difficult for many reasons including the highly variable year to year rainfall. The rainfall in the final months prior to harvest, can be crucial to final harvest totals. Here we explore the importance of rainfall from September 1, which broadly corresponds to the close of the top-dressing fertilizer application window, for the remaining cropping season in determining final yield. This is assessed via sensitivity analysis of water-limited wheat potential yield totals from historical climate in the APSIM crop model. At locations where the rainfall influences wheat yield, we compare three methods to forecast wheat yields that differ based on the climate data input: 1) climatology approach, which uses 30 years of observed climate data, 2) analogue climatology, which uses information from climate drivers (El-Nino Southern Oscillation and Indian Ocean Dipole) to create analogue years; and 3) dynamical climate forecasts from a general circulation model (ACCESS-S). We find that potential yields strongly depend on in-season plant available water (PAW) where years with high PAW are unaffected by the late season rainfall. Predicting the potential yield from analogue climatology (climate drivers) had the greatest skill, with smallest Root Mean Squared Error of 0.45 t/ha. This approach ranked first for 42% of the study locations compared to the climatology and ACCESS-S forecasting methods. This knowledge can help inform decision makers about the need to incorporate seasonal climate forecasts and the most appropriate climate forecasting method.

中文翻译：

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气候驱动因素为澳大利亚小麦产量的晚季预测提供了宝贵的见解

摘要 收获前粮食产量的预见将为供应链上的许多利益相关者赋权，从农民到散装处理商、银行和保险公司。由于许多原因，包括每年降雨量的变化很大，在收获之前估计澳大利亚的粮食产量是很困难的。收获前最后几个月的降雨对最终收获总量至关重要。在这里，我们探讨了 9 月 1 日（大致对应于追肥施肥窗口的关闭）的降雨对剩余作物季节确定最终产量的重要性。这是通过对 APSIM 作物模型中历史气候的限水小麦潜在产量总量的敏感性分析来评估的。在降雨影响小麦产量的地方，我们比较了三种根据气候数据输入而不同的小麦产量预测方法：1) 气候学方法，使用 30 年的观测气候数据，2) 模拟气候学，使用来自气候驱动因素（厄尔尼诺南方涛动和印度Ocean Dipole）创建模拟年；3) 大气环流模型 (ACCESS-S) 的动态气候预报。我们发现潜在产量在很大程度上取决于当季植物可用水 (PAW)，其中高 PAW 的年份不受晚季降雨的影响。预测模拟气候学（气候驱动因素）的潜在产量具有最高的技能，最小均方根误差为 0.45 吨/公顷。与气候学和 ACCESS-S 预测方法相比，这种方法在 42% 的研究地点排名第一。