Probabilistic Crop Yields Forecasts With Spatio-Temporal Conditional Copula Using Extreme Weather Covariates
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ID: 282052
2025
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Abstract
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.
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| Authors | Marie Michaelides; Mélina Mailhot; Yongkun Li |
| Journal | arXiv |
| Year | 2025 |
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