Improving performance of index insurance using crop models and phenological monitoring
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...
| Main Authors: | , , , , , , , |
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| Format: | Artículo preliminar |
| Language: | Inglés |
| Published: |
CGIAR Research Program on Climate Change, Agriculture and Food Security
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/110712 |
| _version_ | 1855541462358294528 |
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| author | Afshar, Mehdi H Foster, Timothy Higginbottom, Thomas P Parkes, Ben Hufkens, Koen Mansabdar, Sanjay Ceballos, Francisco Kramer, Berber |
| author_browse | Afshar, Mehdi H Ceballos, Francisco Foster, Timothy Higginbottom, Thomas P Hufkens, Koen Kramer, Berber Mansabdar, Sanjay Parkes, Ben |
| author_facet | Afshar, Mehdi H Foster, Timothy 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 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. |
| format | Artículo preliminar |
| id | CGSpace110712 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | CGIAR Research Program on Climate Change, Agriculture and Food Security |
| publisherStr | CGIAR Research Program on Climate Change, Agriculture and Food Security |
| record_format | dspace |
| spelling | CGSpace1107122025-11-06T07:41:11Z Improving performance of index insurance using crop models and phenological monitoring Afshar, Mehdi H Foster, Timothy Higginbottom, Thomas P Parkes, Ben Hufkens, Koen Mansabdar, Sanjay Ceballos, Francisco Kramer, Berber capacity development crop yield crop production agriculture food security crop modelling climate change 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. 2020-12-31 2021-01-05T15:24:22Z 2021-01-05T15:24:22Z Working Paper https://hdl.handle.net/10568/110712 en https://doi.org/10.3390/rs13050924 https://doi.org/10.2499/p15738coll2.134941 Open Access application/pdf CGIAR Research Program on Climate Change, Agriculture and Food Security 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). |
| spellingShingle | capacity development crop yield crop production agriculture food security crop modelling climate change Afshar, Mehdi H Foster, Timothy Higginbottom, Thomas P Parkes, Ben Hufkens, Koen Mansabdar, Sanjay Ceballos, Francisco Kramer, Berber Improving performance of index insurance using crop models and phenological monitoring |
| title | Improving performance of index insurance using crop models and phenological monitoring |
| title_full | Improving performance of index insurance using crop models and phenological monitoring |
| title_fullStr | Improving performance of index insurance using crop models and phenological monitoring |
| title_full_unstemmed | Improving performance of index insurance using crop models and phenological monitoring |
| title_short | Improving performance of index insurance using crop models and phenological monitoring |
| title_sort | improving performance of index insurance using crop models and phenological monitoring |
| topic | capacity development crop yield crop production agriculture food security crop modelling climate change |
| url | https://hdl.handle.net/10568/110712 |
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