This study developed a rapid rice yield estimation workflow and customized yield prediction model by integrating remote sensing and meteorological data with machine learning (ML). Several issues need to be addressed while developing a crop yield estimation model, including data quality issues, data processing issues, selecting a suitable machine learning model that can learn from few available time-series data, and understanding the non-linear relationship between historical crop yield and remote sensing and meteorological factors. This study applied a series of data processing techniques and a customized ML model to improve the accuracy of crop yield estimation at the district level in Nepal. It was found that remote sensing-derived NDVI product alone was not sufficient for accurate estimation of crop yield. After incorporating other meteorological variables into the ML models, estimation accuracy improved dramatically. Along with NDVI, the meteorological variables of rainfall, soil moisture, and evapotranspiration also exhibited a strong association with rice yield. This study also found that stacking multiple tree-based regression models together could achieve better accuracy than benchmark linear regression or standalone ML models. Due to the unique and distinct physio-geographical setting of each district, a variation in estimation accuracy from district to district could be observed. Our data processing and ML model workflow achieved an average of 92% accuracy of yield estimation with RMSE 328.06 kg/ha and MAE 317.21 kg/ha. This methodological workflow can be replicated in other study areas and the results can help the local authorities and stakeholders understand the factors affecting crop yields as well as estimating crop yield before harvesting season to ensure food security and sustainability.

The increasing frequency of drought, heat stress and extreme precipitation is intensifying risks to agricultural systems in the Southern Slopes of the Himalaya (SSH) under climate change. Understanding crop loss responses across cropping systems is critical for regional food security. Yet, which extremes dominate productivity loss and how their risk patterns differ across systems remain unclear. Across the three climate extremes considered, loss probability dominated crop productivity risk in 77% of the study area. Double cropping showed a probability dominated vulnerability pattern, whereas single cropping exhibited a magnitude amplified pattern. Drought remained the dominant regional risk source, with its contribution declining as extreme precipitation and heat stress became more important. Furthermore, loss probability had lower thresholds than loss magnitude, indicating a nonlinear two stage process from risk triggering to damage amplification. Thus, agricultural risk in the SSH is structurally driven by cropping systems and multiple climate extremes.