Global crop yield forecasting using seasonal climate information from a multi-model ensemble
Global crop yield forecasting using seasonal climate information from a multi-model ensemble
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DOI：
10.1016/j.cliser.2018.06.003
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发表时间：
2018-08
期刊：
影响因子：
3.2
通讯作者：
T. Iizumi;Yonghee Shin;Wonsik Kim;Moosup Kim;Jaewon Choi
中科院分区：
文献类型：
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作者：
T. Iizumi;Yonghee Shin;Wonsik Kim;Moosup Kim;Jaewon Choi
预测全球主要作物产量的年度变化，预计将有助于加强社会更好地应对极端气候事件引发的粮食生产冲击和粮食价格飙升的能力。然而，要实现可靠的业务服务，就需要对全球作物预测所用的方法作出重大改进。在这里，我们评估的可靠性，全球的季节内和季前预测的产量变化，通过应用统计产量模型的季节性温度和降水后报数据来自多模式集成（MME）。这种分析进行了五个单独的大气-海洋耦合环流模式（GCM）和两个MME数据集使用平均法和镶嵌法。研究了玉米、水稻、小麦和大豆四种主要农作物。镶嵌方法可靠地预测了收获前三个月全球收获面积的大部分（25-38%）的产量变异性。镶嵌方法显示良好预测技能的区域比使用平均方法获得的区域大2 - 3倍（9-15%）。使用镶嵌方法，可靠的季节内预测的国家产量变异可以产生在36%，24%，25%和30%的玉米，大豆，水稻和小麦生产国，分别。在23-32%的作物生产国中，季前预测是可靠的。由于商品和粮食安全专家对国家一级的产量变异性感兴趣，镶嵌方法为发展全球产量预测业务提供了基础。
Forecasting year-to-year variations in the yields of major crops globally is expected to have utility in strengthening the ability of societies to better respond to food production shocks and food price spikes triggered by climate extremes. However, substantial improvements to the methodology used in global crop forecasting are required to realize a reliable operational service. Here, we assess the reliability of global within-season and pre-season predictions of yield variability obtained by applying statistical yield models to seasonal temperature and precipitation hindcast data derived from a multi-model ensemble (MME). This analysis is performed for five individual atmosphere-ocean coupled general circulation models (GCMs) and the two MME datasets generated using the average method and the mosaic method. Four major crops, maize, rice, wheat and soybean are studied. The mosaic method reliably predicts the yield variability over a large portion (25–38%) of the global harvested area three months before harvesting. The areas where the mosaic method displays good prediction skill are two to three times larger than those achieved using the average method (9–15%). Using the mosaic method, reliable within-season predictions of national yield variability can be produced in 36%, 24%, 25% and 30% of the maize-, soybean, rice- and wheat-producing countries, respectively. The pre-season predictions are found to be reliable in 23–32% of the crop-producing countries. As yield variability at the national level is of interest to commodity and food security specialists, the mosaic method provides a basis for developing an operational global yield forecasting service.