Extreme weather events are recognized as major drivers of crop yield losses, which threaten food security and farmers’ incomes. Given the increasing frequency and intensity of extreme weather under climate change, it is crucial to quantify the related future yield damages of important crops to inform prospective climate change adaptation planning. In this study, we present a statistical modeling approach to project the changes in crop yields under climate change for eight majorly cultivated field crops in Germany, estimating the impacts of nine types of extreme weather events. To select the most relevant predictors, we apply the least absolute shrinkage and selection operator (LASSO) regression to district-level yield data.

中文翻译：

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使用 LASSO 回归预测极端天气事件对作物产量的影响


极端天气事件被认为是作物产量损失的主要驱动因素，威胁着粮食安全和农民的收入。鉴于气候变化下极端天气的频率和强度不断增加，量化重要作物的相关未来产量损失以为未来的气候变化适应计划提供信息至关重要。在这项研究中，我们提出了一种统计建模方法，用于预测德国八种主要种植的大田作物在气候变化下作物产量的变化，估计了九种极端天气事件的影响。为了选择最相关的预测因子，我们将最小绝对收缩和选择运算符 （LASSO） 回归应用于地区级别的产量数据。