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Climate drivers provide valuable insights into late season prediction of Australian wheat yield

机译:气候司机为澳大利亚小麦产量的晚期预测提供了有价值的见解

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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.
机译:在收获之前的粮食产量远见将为许多来自农民的供应链提供许多利益相关者,通过批量处理程序,银行和保险公司。由于许多原因,在收获之前估计澳大利亚粮食产量很难,包括高度降雨量。收获前的最终几个月的降雨可能对最终收获总数至关重要。在这里,我们从9月1日探讨了降雨的重要性,这广泛对应于敷料肥料应用窗口的近距离确定最终产量的剩余种植季节。这是通过在APSIM作物模型中的历史气候的历史气氛的水有限小麦潜在产量总量的敏感性分析来评估。在降雨影响小麦产量的地方,比较三种方法预测基于气候数据输入的小麦产量:1)气候学方法,它使用30年的观察到气候数据,2)模拟气候,其使用来自气候的信息司机(El-Nino Southern振荡和印度洋偶极)创造模拟年; 3)来自一般循环模型(Access-S)的动态气候预测。我们发现,潜在的产量强烈依赖于季节植物可用水(PAW),其中几年高爪子未受欢迎的季节降雨。预测模拟气候学(气候司机)的潜在产量具有最大的技能,具有0.45吨/公顷的最小均方根误差。与气候学和接入-S预测方法相比,这种方法首先排名第42%的研究位置。这些知识可以帮助决策者提供有关纳入季节性气候预测和最合适的气候预测方法的必要性。

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