Leveraging browse and grazing forage estimates to optimize index-based livestock insurance
African pastoralists suffer recurrent droughts that cause high livestock mortality and vulnerability to climate change. The index-based livestock insurance (IBLI) program offers protection against drought impacts. However, the current IBLI design relying on the normalized difference vegetation index...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Springer
2024
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| Online Access: | https://hdl.handle.net/10568/148937 |
| _version_ | 1855528529203036160 |
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| author | Kahiu, Njoki Anchang, J. Alulu, Vincent Fava, Francesco P. Jensen, Nathaniel D. Hanan, N.P. |
| author_browse | Alulu, Vincent Anchang, J. Fava, Francesco P. Hanan, N.P. Jensen, Nathaniel D. Kahiu, Njoki |
| author_facet | Kahiu, Njoki Anchang, J. Alulu, Vincent Fava, Francesco P. Jensen, Nathaniel D. Hanan, N.P. |
| author_sort | Kahiu, Njoki |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | African pastoralists suffer recurrent droughts that cause high livestock mortality and vulnerability to climate change. The index-based livestock insurance (IBLI) program offers protection against drought impacts. However, the current IBLI design relying on the normalized difference vegetation index (NDVI) may pose limitation because it does not consider the mixed composition of rangelands (including herbaceous and woody plants) and the diverse feeding habits of grazers and browsers. To enhance IBLI, we assessed the efficacy of utilizing distinct browse and grazing forage estimates from woody LAI (LAIW) and herbaceous LAI (LAIH), respectively, derived from aggregate leaf area index (LAIA), as an alternative to NDVI for refined IBLI design. Using historical livestock mortality data from northern Kenya as reference ground dataset, our analysis compared two competing models for (1) aggregate forage estimates including sub-models for NDVI, LAI (LAIA); and (2) partitioned biomass model (LAIP) comprising LAIH and LAIW. By integrating forage estimates with ancillary environmental variables, we found that LAIP, with separate forage estimates, outperformed the aggregate models. For total livestock mortality, LAIP yielded the lowest RMSE (5.9 TLUs) and higher R2 (0.83), surpassing NDVI and LAIA models RMSE (9.3 TLUs) and R2 (0.6). A similar pattern was observed for species-specific livestock mortality. The influence of environmental variables across the models varied, depending on level of mortality aggregation or separation. Overall, forage availability was consistently the most influential variable, with species-specific models showing the different forage preferences in various animal types. These results suggest that deriving distinct browse and grazing forage estimates from LAIP has the potential to reduce basis risk by enhancing IBLI index accuracy. |
| format | Journal Article |
| id | CGSpace148937 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1489372026-01-25T12:03:12Z Leveraging browse and grazing forage estimates to optimize index-based livestock insurance Kahiu, Njoki Anchang, J. Alulu, Vincent Fava, Francesco P. Jensen, Nathaniel D. Hanan, N.P. animal feeding climate change drought insurance pastoralism forage African pastoralists suffer recurrent droughts that cause high livestock mortality and vulnerability to climate change. The index-based livestock insurance (IBLI) program offers protection against drought impacts. However, the current IBLI design relying on the normalized difference vegetation index (NDVI) may pose limitation because it does not consider the mixed composition of rangelands (including herbaceous and woody plants) and the diverse feeding habits of grazers and browsers. To enhance IBLI, we assessed the efficacy of utilizing distinct browse and grazing forage estimates from woody LAI (LAIW) and herbaceous LAI (LAIH), respectively, derived from aggregate leaf area index (LAIA), as an alternative to NDVI for refined IBLI design. Using historical livestock mortality data from northern Kenya as reference ground dataset, our analysis compared two competing models for (1) aggregate forage estimates including sub-models for NDVI, LAI (LAIA); and (2) partitioned biomass model (LAIP) comprising LAIH and LAIW. By integrating forage estimates with ancillary environmental variables, we found that LAIP, with separate forage estimates, outperformed the aggregate models. For total livestock mortality, LAIP yielded the lowest RMSE (5.9 TLUs) and higher R2 (0.83), surpassing NDVI and LAIA models RMSE (9.3 TLUs) and R2 (0.6). A similar pattern was observed for species-specific livestock mortality. The influence of environmental variables across the models varied, depending on level of mortality aggregation or separation. Overall, forage availability was consistently the most influential variable, with species-specific models showing the different forage preferences in various animal types. These results suggest that deriving distinct browse and grazing forage estimates from LAIP has the potential to reduce basis risk by enhancing IBLI index accuracy. 2024 2024-07-05T06:15:28Z 2024-07-05T06:15:28Z Journal Article https://hdl.handle.net/10568/148937 en Open Access Springer Kahiu, N., Anchang, J., Alulu, V., Fava, F.P., Jensen, N. and Hanan, N.P. 2024. Leveraging browse and grazing forage estimates to optimize index-based livestock insurance. Scientific Reports 14:14834. |
| spellingShingle | animal feeding climate change drought insurance pastoralism forage Kahiu, Njoki Anchang, J. Alulu, Vincent Fava, Francesco P. Jensen, Nathaniel D. Hanan, N.P. Leveraging browse and grazing forage estimates to optimize index-based livestock insurance |
| title | Leveraging browse and grazing forage estimates to optimize index-based livestock insurance |
| title_full | Leveraging browse and grazing forage estimates to optimize index-based livestock insurance |
| title_fullStr | Leveraging browse and grazing forage estimates to optimize index-based livestock insurance |
| title_full_unstemmed | Leveraging browse and grazing forage estimates to optimize index-based livestock insurance |
| title_short | Leveraging browse and grazing forage estimates to optimize index-based livestock insurance |
| title_sort | leveraging browse and grazing forage estimates to optimize index based livestock insurance |
| topic | animal feeding climate change drought insurance pastoralism forage |
| url | https://hdl.handle.net/10568/148937 |
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