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

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.

See it grow: Monitoring the use of stress-tolerant varieties and seed performance: Picture-based analyses of drought-tolerant seed use and performance in a Kenyan crop insurance project

“…Our results leave us with opportunities for further analysis. As we did not observe the performance of crops over an extended period, with variation within sites of both drought and non-drought years, one way to test the robustness of our findings would be to use crop simulations to contrast the performance of STVs with regular varieties under a variety of environmental circumstances (Afshar et al 2021). Using such models, which allow for different weather conditions and management practices to simulate yield data for both regular and STVs, would enable the researcher to test the assumption that these varieties perform better in a drought year.…”

Weather Based Index Insurance and Its Role in Agricultural Production

“…The contract's criteria are designed to coincide as closely as feasible with the policyholder's loss of a certain particular crop. Afshar (2021) claims that all policyholders within a specific region get rewards based on the similar contract and assessment at the same station, removing the requirement for in-field evaluation. A weather-based index insurance contract typically includes a reference station, a trigger, a payout, a limit, and an insurance duration.…”

“…Weather-based index insurance, in addition to moving risks away from the farmers, can focus on providing accessibility to large markets, sophisticated technology and inputs, agricultural knowledge, and loans and other banking services. Weather-based index insurance appears to have the most potential for assisting households, FSPs, and input providers in managing low-to-mediumfrequency covariate risks such as droughts, large insect outbreaks caused by weather extremes, and excessive precipitation (Afshar et al, 2021). As compared to typical agriculture insurance, weather-based index insurance reduces the insurability requirement (the economic size of an insurance transaction that can be reasonably serviced by an insurer).…”

Weather Index Insurance Can Offset Heat‐Induced Rice Losses Under Global Warming

“…More importantly, WII is a sustainable risk‐transfer tool because its direct and immediate financial payments can promote other technological adaptations to sustain agricultural systems (Jensen & Barrett, 2017; Panda et al., 2013). Therefore, increasing studies have focused on how to upscale WII in practice, such as developing new models to generate superior WII contracts (Afshar et al., 2021; Benami et al., 2021; Shirsath et al., 2019) and incorporating phenology or satellite information to reduce basis risk (Benami et al., 2021; Dalhaus et al., 2018; Masiza et al., 2022). But, our knowledge of the efficiency of WII in the warmer future is still very limited.…”