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

Mehdi H. Afshar,Timothy Foster,Francisco Ceballos,Koen Hufkens,Berber Kramer,Thomas P. Higginbottom,Ben Parkes,Sanjay Mansabdar

doi:10.3390/rs13050924

Mehdi H. Afshar, Timothy Foster + Show 6 more

Open Access

https://doi.org/10.3390/rs13050924

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Abstract

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

Highlights

Agriculture plays a critical role in supporting livelihoods and food security for rural households across the developing world [1]
To evaluate the value provided by using crop models to generate larger synthetic yield training datasets, we compared the performance of our models reported in Section 3.2 with plot and GP level yield estimates derived using statistical vegetation index (VI)-based models developed by using observed yield data from crop cutting experiments (CCEs)
Similar to the trends observed in the previous analysis of statistical yield models that were derived based on Agricultural Production System sIMulator (APSIM) simulated yield data, we found an improvement in the performance of VI-based models when aggregating yield estimation from plot to the GP level (Table 2)

Summary

Introduction

Agriculture plays a critical role in supporting livelihoods and food security for rural households across the developing world [1]. Designing strategies to protect farmers against crop losses caused by adverse weather conditions, such as droughts or floods, has become a key priority for governments and donors, given expected increases in the frequency or intensity of extreme weather events in the coming decades due to climate change [2,3]. One of these strategies is to provide smallholder farmers with agricultural insurance, which offers financial protection from losses associated with extreme weather. Amongst different types of insurance frameworks (such as whole-farm revenue insurance [4,5] and bancassurance [6,7]), index-based insurance

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