Improving performance of index insurance using crop models and phenological monitoring
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Afshar MH, Foster T, Higginbottom TP, Parkes B, Hufkens K, Mansabdar S, Ceballos F, Kramer B. 2020. Improving performance of index insurance using crop models and phenological monitoring. CCAFS Working Paper no. 337. Wageningen, the Netherlands: CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS).
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Extreme weather events cause considerable damage to 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 analyze 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 (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.
Author ORCID identifiers
Thomas Higginbottom https://orcid.org/0000-0003-1150-1444
Ben Parkes https://orcid.org/0000-0002-5936-8331
Koen Hufkens https://orcid.org/0000-0002-5070-8109
Sanjay Mansabdar https://orcid.org/0000-0001-6166-4845
Francisco Ceballos https://orcid.org/0000-0001-8699-5114
Berber Kramer https://orcid.org/0000-0001-7644-6613