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 farmers cope with the financial consequences of extreme weather, a major challenge for index insurance is basis risk, where insurance payouts correlate poorly with act...
| Main Authors: | , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
MDPI
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/142880 |
| _version_ | 1855528922435813376 |
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| author | Afshar, Mehdi H. Foster, Tim Higginbottom, Thomas P. Parkes, Ben Hufkens, Koen Mansabdar, Sanjay Ceballos, Francisco Kramer, Berber |
| author_browse | Afshar, Mehdi H. Ceballos, Francisco Foster, Tim Higginbottom, Thomas P. Hufkens, Koen Kramer, Berber Mansabdar, Sanjay Parkes, Ben |
| author_facet | Afshar, Mehdi H. Foster, Tim Higginbottom, Thomas P. Parkes, Ben Hufkens, Koen Mansabdar, Sanjay Ceballos, Francisco Kramer, Berber |
| author_sort | Afshar, Mehdi H. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace142880 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1428802025-12-08T10:11:39Z Improving the performance of index insurance using crop models and phenological monitoring Afshar, Mehdi H. Foster, Tim Higginbottom, Thomas P. Parkes, Ben Hufkens, Koen Mansabdar, Sanjay Ceballos, Francisco Kramer, Berber insurance models data remote sensing leaf area index farmers crop yield yield forecasting smallholders extreme weather events livelihoods yields crop modelling 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. 2021-03-01 2024-05-22T12:11:14Z 2024-05-22T12:11:14Z Journal Article https://hdl.handle.net/10568/142880 en https://hdl.handle.net/10568/110712 https://doi.org/10.1016/j.deveng.2019.100042 https://doi.org/10.1016/j.agrformet.2018.11.002 https://doi.org/10.2499/p15738coll2.134751 Open Access MDPI Afshar, Mehdi H.; Foster, Tim; Higginbottom, Thomas P.; Parkes, Ben; Hufkens, Koen; Mansabdar, Sanjay; Ceballos, Francisco; and Kramer, Berber. 2021. Improving the performance of index insurance using crop models and phenological monitoring. Remote Sensing 13(5): 924. https://doi.org/10.3390/rs13050924 |
| spellingShingle | insurance models data remote sensing leaf area index farmers crop yield yield forecasting smallholders extreme weather events livelihoods yields crop modelling Afshar, Mehdi H. Foster, Tim Higginbottom, Thomas P. Parkes, Ben Hufkens, Koen Mansabdar, Sanjay Ceballos, Francisco Kramer, Berber Improving the performance of index insurance using crop models and phenological monitoring |
| title | Improving the performance of index insurance using crop models and phenological monitoring |
| title_full | Improving the performance of index insurance using crop models and phenological monitoring |
| title_fullStr | Improving the performance of index insurance using crop models and phenological monitoring |
| title_full_unstemmed | Improving the performance of index insurance using crop models and phenological monitoring |
| title_short | Improving the performance of index insurance using crop models and phenological monitoring |
| title_sort | improving the performance of index insurance using crop models and phenological monitoring |
| topic | insurance models data remote sensing leaf area index farmers crop yield yield forecasting smallholders extreme weather events livelihoods yields crop modelling |
| url | https://hdl.handle.net/10568/142880 |
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