Improving the Performance of Index Insurance Using Crop Models and Phenological Monitoring

Extreme weather events cause considerable damage to the livelihoods of smallholder farmers globally. Whilst index insurance can help cope with financial consequences extreme weather, a major challenge for is basis risk, where payouts correlate poorly actual crop losses. We analyse what extent use simulation models and phenology monitoring reduce risk in insurance. Using biophysical process-based model (Agricultural Production System sIMulator (APSIM)) applied rice producers Odisha, India, we simulate synthetic yield dataset train non-parametric statistical predict yields as function meteorological phenological conditions. find that performance depends on whether or conditions are used predictors one aggregates these by season growth stage. Validating preferred observed data, explains around 54% variance at village cluster (Gram Panchayat) level, outperforming vegetation index-based were trained directly data. Our methods findings guide efforts design smart phenology-based target resources farming environments.