Global Within-Season Yield Anomaly Prediction for Major Crops Derived Using Seasonal Forecasts of Large-Scale Climate Indices and Regional Temperature and Precipitation
Global Within-Season Yield Anomaly Prediction for Major Crops Derived Using Seasonal Forecasts of Large-Scale Climate Indices and Regional Temperature and Precipitation
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DOI：
10.1175/waf-d-20-0097.1
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发表时间：
2021-02-01
影响因子：
2.9
通讯作者：
Maeda, Shuhei
中科院分区：
文献类型：
--
作者：
Iizumi, Toshichika;Takaya, Yuhei;Maeda, Shuhei
与主要气候模式相关的天气和气候变异性是全球农田地区商品作物产量年际变异的主要驱动因素。目前处于试运行阶段的全球作物预测服务是以温度和降水预测为基础的，而最近的文献表明，作物预测服务可能受益于使用气候指数预测。然而，没有一致的比较是依赖于温度和降水预测和气候指数的产量模型之间的预测技巧。在这里，我们提出了一个全球性的评估26年（1983-2008年）的玉米，水稻，小麦和大豆的季节内产量异常后报使用不同类型的统计产量模型。一种模型利用个别种植区的温度和降水量（TP模型类型）来代表当前的服务，而另一种类型依赖于大尺度气候指数（CI模型）。对于TP模型，比较了三种具有不同模型复杂度的规范。结果表明，CI模型的特点是从再分析模型到后报模型的技术面积略有减少，并显示了水稻和大豆的最大技术面积。在TP模型中，简单模型的技能与更复杂的模型的技能相当。我们的研究结果表明，除了温度和降水预测外，气候指数预测用于全球作物预测服务可能会增加作物和国家的总数，其中熟练的产量异常预测是可行的。
Weather and climate variability associated with major climate modes is a main driver of interannual yield variability of commodity crops in global cropland areas. A global crop forecasting service that is currently in the test operation phase is based on temperature and precipitation forecasts, while recent literature suggests that crop forecasting services may benefit from the use of climate index forecasts. However, no consistent comparison is available on prediction skill between yield models relying on forecasts from temperature and precipitation and from climate indices. Here, we present a global assessment of 26-yr (1983-2008) within-season yield anomaly hindcasts for maize, rice, wheat, and soybean derived using different types of statistical yield models. One type of model utilizes temperature and precipitation for individual cropping areas (the TP model type) to represent the current service, whereas the other type relies on large-scale climate indices (the CI model). For the TP models, three specifications with different model complexities are compared. The results show that the CI model is characterized by a small reduction in the skillful area from the reanalysis model to the hindcast model and shows the largest skillful areas for rice and soybean. In the TP models, the skill of the simple model is comparable to that of the more complex models. Our findings suggest that the use of climate index forecasts for global crop forecasting services in addition to temperature and precipitation forecasts likely increases the total number of crops and countries where skillful yield anomaly prediction is feasible.