We introduce a novel forecasting model for crop yields that explicitly accounts for spatio-temporal dependence and the influence of extreme weather and climatic events. Our approach combines Bayesian Structural Time Series for modeling marginal crop yields, ensuring a more robust quantification of uncertainty given the typically short historical records. To capture dynamic dependencies between regions, we develop a time-varying conditional copula model, where the copula parameter evolves over time as a function of its previous lag and extreme weather covariates. Unlike traditional approaches that treat climatic factors as fixed inputs, we incorporate dynamic Generalized Extreme Value models to characterize extreme weather events, enabling a more accurate reflection of their impact on crop yields. Furthermore, to ensure scalability for large-scale applications, we build on the existing Partitioning Around Medoids clustering algorithm and introduce a novel dissimilarity measure that integrates both spatial and copula-based dependence, enabling an effective reduction of the dimensionality in the dependence structure.

翻译：本文提出了一种新颖的作物产量预测模型，该模型明确考虑了时空依赖性以及极端天气和气候事件的影响。我们的方法结合了贝叶斯结构时间序列来建模边际作物产量，鉴于历史记录通常较短，这确保了更稳健的不确定性量化。为了捕捉区域间的动态依赖性，我们开发了一种时变条件Copula模型，其中Copula参数随时间演变，是其先前滞后项和极端天气协变量的函数。与将气候因素视为固定输入的传统方法不同，我们引入了动态广义极值模型来刻画极端天气事件，从而能更准确地反映其对作物产量的影响。此外，为确保大规模应用的可扩展性，我们在现有的围绕中心点划分聚类算法基础上，提出了一种新颖的相异性度量，该度量融合了空间和基于Copula的依赖性，从而能够有效降低依赖结构中的维度。