Accounting for correlation among environmental covariates improves delineation of extrapolation suitability index for agronomic technological packages
This paper generates an extrapolation suitability index (ESI) to guide scaling-out of improved maize varieties and inorganic fertilizers. The best-bet technology packages were selected based on yield gap data from trial sites in Tanzania. A modified extrapolation detection algorithm was used to gene...
| Autores principales: | , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Informa UK Limited
2019
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/89935 |
| _version_ | 1855531416529403904 |
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| author | Muthoni, Francis K. Baijukya, Frederick P. Bekunda, Mateete A. Sseguya, H. Kimaro, Anthony A. Alabi, T. Mruma, S. Hoeschle-Zeledon, Irmgard |
| author_browse | Alabi, T. Baijukya, Frederick P. Bekunda, Mateete A. Hoeschle-Zeledon, Irmgard Kimaro, Anthony A. Mruma, S. Muthoni, Francis K. Sseguya, H. |
| author_facet | Muthoni, Francis K. Baijukya, Frederick P. Bekunda, Mateete A. Sseguya, H. Kimaro, Anthony A. Alabi, T. Mruma, S. Hoeschle-Zeledon, Irmgard |
| author_sort | Muthoni, Francis K. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This paper generates an extrapolation suitability index (ESI) to guide scaling-out of improved maize varieties and inorganic fertilizers. The best-bet technology packages were selected based on yield gap data from trial sites in Tanzania. A modified extrapolation detection algorithm was used to generate maps on two types of dissimilarities between environmental conditions at the reference sites and the outlying projection domain. The two dissimilarity maps were intersected to generate ESI. Accounting for correlation structure among covariates improved estimate of risk of extrapolating technologies. The covariate that highly limited the suitability of specific technology package in each pixel was identified. The impact based spatial targeting index (IBSTI) identified zones that should be prioritized to maximize the potential impacts of scaling-out technology packages. The proposed indices will guide extension agencies in targeting technology packages to suitable environments with high potential impact to increase probability of adoption and reduce risk of failure. |
| format | Journal Article |
| id | CGSpace89935 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Informa UK Limited |
| publisherStr | Informa UK Limited |
| record_format | dspace |
| spelling | CGSpace899352025-11-12T06:52:08Z Accounting for correlation among environmental covariates improves delineation of extrapolation suitability index for agronomic technological packages Muthoni, Francis K. Baijukya, Frederick P. Bekunda, Mateete A. Sseguya, H. Kimaro, Anthony A. Alabi, T. Mruma, S. Hoeschle-Zeledon, Irmgard maize food security sustainable agriculture big data novel correlation priority setting risk of failure spatial targeting This paper generates an extrapolation suitability index (ESI) to guide scaling-out of improved maize varieties and inorganic fertilizers. The best-bet technology packages were selected based on yield gap data from trial sites in Tanzania. A modified extrapolation detection algorithm was used to generate maps on two types of dissimilarities between environmental conditions at the reference sites and the outlying projection domain. The two dissimilarity maps were intersected to generate ESI. Accounting for correlation structure among covariates improved estimate of risk of extrapolating technologies. The covariate that highly limited the suitability of specific technology package in each pixel was identified. The impact based spatial targeting index (IBSTI) identified zones that should be prioritized to maximize the potential impacts of scaling-out technology packages. The proposed indices will guide extension agencies in targeting technology packages to suitable environments with high potential impact to increase probability of adoption and reduce risk of failure. 2019-03-21 2018-01-08T09:46:08Z 2018-01-08T09:46:08Z Journal Article https://hdl.handle.net/10568/89935 en Open Access application/pdf Informa UK Limited Muthoni, F.K., Baijukya, F., Bekunda, M., Sseguya, H., Kimaro, A., Alabi, T., ... and Hoeschle-Zeledon, I. 2017. Accounting for correlation among environmental covariates improves delineation of extrapolation suitability index for agronomic technological packages. Geocarto International, 1-23. |
| spellingShingle | maize food security sustainable agriculture big data novel correlation priority setting risk of failure spatial targeting Muthoni, Francis K. Baijukya, Frederick P. Bekunda, Mateete A. Sseguya, H. Kimaro, Anthony A. Alabi, T. Mruma, S. Hoeschle-Zeledon, Irmgard Accounting for correlation among environmental covariates improves delineation of extrapolation suitability index for agronomic technological packages |
| title | Accounting for correlation among environmental covariates improves delineation of extrapolation suitability index for agronomic technological packages |
| title_full | Accounting for correlation among environmental covariates improves delineation of extrapolation suitability index for agronomic technological packages |
| title_fullStr | Accounting for correlation among environmental covariates improves delineation of extrapolation suitability index for agronomic technological packages |
| title_full_unstemmed | Accounting for correlation among environmental covariates improves delineation of extrapolation suitability index for agronomic technological packages |
| title_short | Accounting for correlation among environmental covariates improves delineation of extrapolation suitability index for agronomic technological packages |
| title_sort | accounting for correlation among environmental covariates improves delineation of extrapolation suitability index for agronomic technological packages |
| topic | maize food security sustainable agriculture big data novel correlation priority setting risk of failure spatial targeting |
| url | https://hdl.handle.net/10568/89935 |
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