A deep-learning framework SECSF is presented that emulates the process-based ECroPS model for maize and barley using only daily temperatures and precipitation. Trained on ERA5 data, it reproduces crop growth dynamics and harvest timing while reducing computational cost by ~10^4 times, enabling probabilistic risk assessment for large seasonal and climate ensembles.
Important for scalable forecasting in agriculture, where fast and accurate climate risk estimates are crucial.
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