Reliability Analysis of Agricultural Foundation Models Under Distribution Shift

Recent geospatial foundation models (GFMs) have shown strong performance for crop yield prediction across regions and spatial scales, but operational deployment requires reliable performance under distribution drift. In agricultural settings, distribution drift may arise through temporal, label, concept, domain, or representation shifts, each of which can alter the relationship between incoming data and model behavior. This paper extends the previously published Fine-Tuning Agricultural Regression Models (FARM) framework, which adapts the Prithvi-EO-2.0-600M Vision Transformer for dense canola yield estimation over the Canadian Prairies, by introducing a systematic drift monitoring and reliability analysis pipeline. The proposed framework measures Mahalanobis, cosine, and Euclidean distances in both input and latent spaces. These distances characterize distributional shifts and allow us to evaluate their relationship with prediction error. Experiments on county-level and 30 m precision-agriculture datasets, spanning a normal growing season (2022) and a drift growing season (2021), show that both input-space and latent-space distances provide a useful signal of drift with latent-space metrics offering the clearest separation of anomalous conditions. At the same time, the results show that large drift scores do not necessarily correspond to large prediction errors. Some out-of-distribution samples are predicted accurately, while some in-distribution samples incur high error. These findings identify a critical gap between drift detection and uncertainty estimation and demonstrate that distributional distance alone is insufficient for operational reliability assessment. The results therefore motivate reliability-aware monitoring strategies for agricultural foundation models that combine drift detection with explicit uncertainty or error estimation in non-stationary environments.