Sustainable recommendation domains for scaling agricultural technologies in Tanzania
Low adoption of sustainable intensification technologies hinders achievement of their potential impacts on increasing agricultural productivity. Proper targeting of locations to scale-out particular technologies is a key determinant of the rate of adoption. Targeting locations with similar biophysic...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Elsevier
2017
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| Online Access: | https://hdl.handle.net/10568/80938 |
| _version_ | 1855541596339044352 |
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| author | Muthoni, Francis K. Guo, Zhe Bekunda, Mateete A. Sseguya, Haroon Kizito, Fred Baijukya, Frederick P. Hoeschle-Zeledon, Irmgard |
| author_browse | Baijukya, Frederick P. Bekunda, Mateete A. Guo, Zhe Hoeschle-Zeledon, Irmgard Kizito, Fred Muthoni, Francis K. Sseguya, Haroon |
| author_facet | Muthoni, Francis K. Guo, Zhe Bekunda, Mateete A. Sseguya, Haroon Kizito, Fred Baijukya, Frederick P. Hoeschle-Zeledon, Irmgard |
| author_sort | Muthoni, Francis K. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Low adoption of sustainable intensification technologies hinders achievement of their potential impacts on increasing agricultural productivity. Proper targeting of locations to scale-out particular technologies is a key determinant of the rate of adoption. Targeting locations with similar biophysical and socio-economic characteristics significantly increases the probability of adoption. Areas with similar biophysical and socio-economic characteristics are referred to as recommendation domains (RDs). This study used geospatial analysis to delineate sustainable recommendation domains (SRDs) for scaling improved crop varieties and good agronomic practices in Tanzania. The study uses K-means clustering to identify relatively similar clusters from grid raster’s representing biophysical and socio-economic environments. Critical ecosystems are masked-out from the clusters to generate the SRDs. The potential impacts of scaling technologies in the generated SRDs were assessed and a spatial targeting index developed. Results identify 20 SRDs and the bio-socio-economic gradients that delineate them. This study proposes an Impact Based Spatial Targeting Index (IBSTI) as an objective tool for priority setting when scaling agricultural technologies. IBSTI identified priority areas within each SRD that should be targeted to maximize potential impacts of a scaling intervention. The data-driven clustering method is recommended for regions with limited technology trials. Results demonstrate the potential of geospatial tools in generating evidence-based policies on scaling of sustainable intensification technologies. |
| format | Journal Article |
| id | CGSpace80938 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace809382025-04-08T18:36:14Z Sustainable recommendation domains for scaling agricultural technologies in Tanzania Muthoni, Francis K. Guo, Zhe Bekunda, Mateete A. Sseguya, Haroon Kizito, Fred Baijukya, Frederick P. Hoeschle-Zeledon, Irmgard spatial information información espacial geographical information systems sistemas de información geográfica cluster sampling intensification ecosystems sustainable agriculture Low adoption of sustainable intensification technologies hinders achievement of their potential impacts on increasing agricultural productivity. Proper targeting of locations to scale-out particular technologies is a key determinant of the rate of adoption. Targeting locations with similar biophysical and socio-economic characteristics significantly increases the probability of adoption. Areas with similar biophysical and socio-economic characteristics are referred to as recommendation domains (RDs). This study used geospatial analysis to delineate sustainable recommendation domains (SRDs) for scaling improved crop varieties and good agronomic practices in Tanzania. The study uses K-means clustering to identify relatively similar clusters from grid raster’s representing biophysical and socio-economic environments. Critical ecosystems are masked-out from the clusters to generate the SRDs. The potential impacts of scaling technologies in the generated SRDs were assessed and a spatial targeting index developed. Results identify 20 SRDs and the bio-socio-economic gradients that delineate them. This study proposes an Impact Based Spatial Targeting Index (IBSTI) as an objective tool for priority setting when scaling agricultural technologies. IBSTI identified priority areas within each SRD that should be targeted to maximize potential impacts of a scaling intervention. The data-driven clustering method is recommended for regions with limited technology trials. Results demonstrate the potential of geospatial tools in generating evidence-based policies on scaling of sustainable intensification technologies. 2017-07 2017-05-09T20:06:55Z 2017-05-09T20:06:55Z Journal Article https://hdl.handle.net/10568/80938 en Open Access Elsevier Muthoni, Francis K.; Guo, Zhe; Bekunda, Mateete; Sseguya, Haroon; Kizito, Fred; Baijukya, Frederick; Hoeschle-Zeledon, Irmgard. 2017. Sustainable recommendation domains for scaling agricultural technologies in Tanzania. Land Use Policy 66: 34-48. |
| spellingShingle | spatial information información espacial geographical information systems sistemas de información geográfica cluster sampling intensification ecosystems sustainable agriculture Muthoni, Francis K. Guo, Zhe Bekunda, Mateete A. Sseguya, Haroon Kizito, Fred Baijukya, Frederick P. Hoeschle-Zeledon, Irmgard Sustainable recommendation domains for scaling agricultural technologies in Tanzania |
| title | Sustainable recommendation domains for scaling agricultural technologies in Tanzania |
| title_full | Sustainable recommendation domains for scaling agricultural technologies in Tanzania |
| title_fullStr | Sustainable recommendation domains for scaling agricultural technologies in Tanzania |
| title_full_unstemmed | Sustainable recommendation domains for scaling agricultural technologies in Tanzania |
| title_short | Sustainable recommendation domains for scaling agricultural technologies in Tanzania |
| title_sort | sustainable recommendation domains for scaling agricultural technologies in tanzania |
| topic | spatial information información espacial geographical information systems sistemas de información geográfica cluster sampling intensification ecosystems sustainable agriculture |
| url | https://hdl.handle.net/10568/80938 |
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