The Promise of Time-Series Foundation Models for Agricultural Forecasting: Evidence from Marketing Year Average Prices

Forecasting agricultural markets remains a core challenge in business analytics, where nonlinear dynamics, structural breaks, and sparse data have historically limited the gains from increasingly complex econometric and machine learning models. As a result, a long-standing belief in the literature is that simple time-series methods often outperform more advanced alternatives. This paper provides the first systematic evidence that this belief no longer holds in the modern era of time-series foundation models (TSFMs). Using USDA ERS data from 1997-2025, we evaluate 17 forecasting approaches across four model classes, assessing monthly forecasting performance and benchmarking against Market Year Average (MYA) price predictions. This period spans multiple agricultural cycles, major policy changes, and major market disruptions, with substantial cross-commodity price volatility. Focusing on five state-of-the-art TSFMs, we show that zero-shot foundation models (with only historical prices and without any additional covariates) consistently outperform traditional time-series methods, machine learning models, and deep learning architectures trained from scratch. Among them, Time-MoE delivers the largest accuracy gains, improving forecasts by 45% (MAE) overall and by more than 50% for corn and soybeans relative to USDA benchmarks. These results point to a paradigm shift in agricultural forecasting: while earlier generations of advanced models struggled to surpass simple benchmarks, modern pre-trained foundation models achieve substantial and robust improvements, offering a scalable and powerful new framework for highstakes predictive analytics.