Retrieval-Augmented Multi-scale Framework for County-Level Crop Yield Prediction Across Large Regions

TL;DR

This paper introduces a multi-scale, retrieval-augmented framework for county-level crop yield prediction that effectively captures temporal patterns and spatial variability, leading to more reliable forecasts across large regions and years.

Contribution

It presents a novel backbone architecture for multi-scale temporal modeling combined with a retrieval-based adaptation strategy to enhance spatial generalization in crop yield prediction.

Findings

Outperforms baseline models on US county-level corn yield data

Improves robustness across diverse spatial regions and years

Effectively captures both short-term and long-term crop growth dynamics

Abstract

This paper proposes a new method for crop yield prediction, which is essential for developing management strategies, informing insurance assessments, and ensuring long-term food security. Although existing data-driven approaches have shown promise in this domain, their performance often degrades when applied across large geographic regions and long time periods. This limitation arises from two key challenges: (1) difficulty in jointly capturing short-term and long-term temporal patterns, and (2) inability to effectively accommodate spatial data variability in agricultural systems. Ignoring these issues often leads to unreliable predictions for specific regions or years, which ultimately affects policy decisions and resource allocation. In this paper, we propose a new predictive framework to address these challenges. First, we introduce a new backbone model architecture that captures both short-term daily-scale crop growth dynamics and long-term dependencies across years. To further improve generalization across diverse spatial regions, we augment this model with a retrieval-based adaptation strategy. Recognizing the substantial yield variation across years, we design a novel retrieval-and-refinement pipeline that adjusts retrieved samples by removing cross-year bias not explained by input features. Our experiments on real-world county-level corn yield data over 630 counties in the US demonstrate that our method consistently outperforms different types of baselines. The results also verify the effectiveness of the retrieval-based augmentation method in improving model robustness under spatial heterogeneity.