CropFusionNet: an interpretable deep learning framework for uncertainty-aware crop yield forecasting across Germany

Escalating climate fluctuations and the increasing frequency of compound extreme weather events pose severe threats to global food security. Current operational crop yield forecasting systems, which predominantly rely on process-based models or traditional statistical approaches, often underestimate yield losses during climatic extremes due to their inability to capture complex, non-linear climate-yield dynamics. While deep learning (DL) offers a transformative alternative, its widespread adoption remains limited by its “black-box” nature and the difficulty of processing long agro-meteorological time series without losing critical signals from climatic anomalies. To address these gaps, we propose CropFusionNet, a novel and interpretable architecture inspired by the Temporal Fusion Transformer (TFT), designed for district-level (NUTS-3) crop yield forecasting. CropFusionNet integrates daily time-varying climatic variables with static agro-environmental covariates and offers a more interpretable deep learning framework, enabling attribution of predictions to key environmental drivers, though not fully mechanistic. Applied to Germany's principal crop commodities, CropFusionNet consistently outperformed established deep learning baselines and operational frameworks such as MARS and ABSOLUT. It achieved high predictive accuracy for silage maize (R 2 = 0.75; MAPE = 8.66%), winter wheat (R 2 = 0.60; MAPE = 8.06%), and a moderate accuracy for winter barley (R 2 = 0.45; MAPE = 10%), successfully capturing inter-annual variability and accurately reproducing extreme negative yield anomalies during severe drought years (2003, 2018, and 2022). Lead-time analyses further demonstrated that reliable forecasts for winter cereals (wheat and barley) can be made 40–60 days before harvest, whereas spring crops (silage maize) can be predicted up to 70 days in advance. By disentangling key environmental drivers and effectively representing non-linear system dynamics, CropFusionNet represents a fundamental transformation towards scalable, accurate, uncertainty-aware, and interpretable agricultural forecasting, providing essential support for proactive food security management. The model implementation is available at https: // github.com/geonextgis/CropFusionNet .