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Improving performance of index insurance using crop models and phenological monitoring

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Title Improving performance of index insurance using crop models and phenological monitoring
 
Creator Afshar, Mehdi H
Foster, Timothy
Higginbottom, Thomas P
Parkes, Ben
Hufkens, Koen
Mansabdar, Sanjay
Ceballos, Francisco
Kramer, Berber
 
Subject crop production
crop yield
crop modelling
climate change
food security
agriculture
 
Description 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.
 
Date 2020-12-31
2021-01-05T15:24:22Z
2021-01-05T15:24:22Z
 
Type Working Paper
 
Identifier 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).
https://hdl.handle.net/10568/110712
 
Language en
 
Relation CCAFS Working Paper
 
Rights CC-BY-NC-4.0
Open Access
 
Format 38 p.
application/pdf
 
Publisher CGIAR Research Program on Climate Change, Agriculture and Food Security