Data-driven similar response units for agricultural technology targeting: An example from Ethiopia
Ethiopia has heterogeneous topographic, climatic and socio-ecological systems. Recommendations of agricultural inputs and management practices based on coarse domains such as agro-ecological zones (AEZ) may not lead to accurate targeting, mainly due to large intra-zone variations. The lack of well-t...
| Main Authors: | , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Cambridge University Press
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/120934 |
| _version_ | 1855535676097822720 |
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| author | Tamene, Lulseged D. Abera, Wuletawu Bendito, Eduardo Erkossa, Teklu Tariku, Meklit Sewnet, Habtamu Tibebe, Degefie Sied, Jema Feyisa, Gudina Wondie, Menale Tesfaye, Kindie |
| author_browse | Abera, Wuletawu Bendito, Eduardo Erkossa, Teklu Feyisa, Gudina Sewnet, Habtamu Sied, Jema Tamene, Lulseged D. Tariku, Meklit Tesfaye, Kindie Tibebe, Degefie Wondie, Menale |
| author_facet | Tamene, Lulseged D. Abera, Wuletawu Bendito, Eduardo Erkossa, Teklu Tariku, Meklit Sewnet, Habtamu Tibebe, Degefie Sied, Jema Feyisa, Gudina Wondie, Menale Tesfaye, Kindie |
| author_sort | Tamene, Lulseged D. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Ethiopia has heterogeneous topographic, climatic and socio-ecological systems. Recommendations of agricultural inputs and management practices based on coarse domains such as agro-ecological zones (AEZ) may not lead to accurate targeting, mainly due to large intra-zone variations. The lack of well-targeted recommendations may contribute to the underperformance of promising technologies. Therefore, there is a need to define units where similar environmental and biophysical features prevail, based on which specific recommendations can be made for similar response units (SRUs). We used unsupervised machine learning algorithms to identify areas of high similarity or homogeneous zones called ‘SRUs’ that can guide the targeting of agricultural technologies. SRUs are landscape entities defined by integrating relevant environmental covariates with the intention to identify areas of similar responses. Using environmental spatial data layers such as edaphic and ecological variables for delineation of the SRUs, we applied K- and X-means clustering techniques to generate various granular levels of zonation and define areas of high similarity. The results of the clustering were validated through expert consultation and by comparison with an existing operational AEZ map of Ethiopia. We also augmented validation of the heterogeneity of the SRUs by using field-based crop response to fertiliser application experimental data. The expert consultation highlighted that the SRUs can provide improved clustering of areas of high similarity for targeting interventions. Comparison with the AEZ map indicated that SRUs with the same number of AEZ units captured heterogeneity better with less within-cluster variability of the former. In addition, SRUs show lower within-cluster variability to optimal crop response to fertiliser application compared with AEZs with the same number of classes. This implies that the SRUs can be used for refined agricultural input and technology targeting. The work in this study also developed an operational framework that users can deploy to fetch data from the cloud and generate SRUs for their areas of interest. |
| format | Journal Article |
| id | CGSpace120934 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Cambridge University Press |
| publisherStr | Cambridge University Press |
| record_format | dspace |
| spelling | CGSpace1209342025-11-12T06:51:16Z Data-driven similar response units for agricultural technology targeting: An example from Ethiopia Tamene, Lulseged D. Abera, Wuletawu Bendito, Eduardo Erkossa, Teklu Tariku, Meklit Sewnet, Habtamu Tibebe, Degefie Sied, Jema Feyisa, Gudina Wondie, Menale Tesfaye, Kindie appropriate technology machine learning policies agriculture farming systems tecnología apropiada aprendizaje electrónico políticas technology transfer fertilizer applications ethiopia agroecology Ethiopia has heterogeneous topographic, climatic and socio-ecological systems. Recommendations of agricultural inputs and management practices based on coarse domains such as agro-ecological zones (AEZ) may not lead to accurate targeting, mainly due to large intra-zone variations. The lack of well-targeted recommendations may contribute to the underperformance of promising technologies. Therefore, there is a need to define units where similar environmental and biophysical features prevail, based on which specific recommendations can be made for similar response units (SRUs). We used unsupervised machine learning algorithms to identify areas of high similarity or homogeneous zones called ‘SRUs’ that can guide the targeting of agricultural technologies. SRUs are landscape entities defined by integrating relevant environmental covariates with the intention to identify areas of similar responses. Using environmental spatial data layers such as edaphic and ecological variables for delineation of the SRUs, we applied K- and X-means clustering techniques to generate various granular levels of zonation and define areas of high similarity. The results of the clustering were validated through expert consultation and by comparison with an existing operational AEZ map of Ethiopia. We also augmented validation of the heterogeneity of the SRUs by using field-based crop response to fertiliser application experimental data. The expert consultation highlighted that the SRUs can provide improved clustering of areas of high similarity for targeting interventions. Comparison with the AEZ map indicated that SRUs with the same number of AEZ units captured heterogeneity better with less within-cluster variability of the former. In addition, SRUs show lower within-cluster variability to optimal crop response to fertiliser application compared with AEZs with the same number of classes. This implies that the SRUs can be used for refined agricultural input and technology targeting. The work in this study also developed an operational framework that users can deploy to fetch data from the cloud and generate SRUs for their areas of interest. 2022 2022-08-24T13:43:38Z 2022-08-24T13:43:38Z Journal Article https://hdl.handle.net/10568/120934 en Open Access application/pdf Cambridge University Press Tamene, L., Abera, W., Bendito, E., Erkossa, T., Tariku, M., Sewnet, H., Tibebe, D., Sied, J., Feyisa, G., Wondie, M., & Tesfaye, K. (2022). Data-driven similar response units for agricultural technology targeting: An example from Ethiopia. Experimental Agriculture, 58. https://doi.org/10.1017/s0014479722000126 |
| spellingShingle | appropriate technology machine learning policies agriculture farming systems tecnología apropiada aprendizaje electrónico políticas technology transfer fertilizer applications ethiopia agroecology Tamene, Lulseged D. Abera, Wuletawu Bendito, Eduardo Erkossa, Teklu Tariku, Meklit Sewnet, Habtamu Tibebe, Degefie Sied, Jema Feyisa, Gudina Wondie, Menale Tesfaye, Kindie Data-driven similar response units for agricultural technology targeting: An example from Ethiopia |
| title | Data-driven similar response units for agricultural technology targeting: An example from Ethiopia |
| title_full | Data-driven similar response units for agricultural technology targeting: An example from Ethiopia |
| title_fullStr | Data-driven similar response units for agricultural technology targeting: An example from Ethiopia |
| title_full_unstemmed | Data-driven similar response units for agricultural technology targeting: An example from Ethiopia |
| title_short | Data-driven similar response units for agricultural technology targeting: An example from Ethiopia |
| title_sort | data driven similar response units for agricultural technology targeting an example from ethiopia |
| topic | appropriate technology machine learning policies agriculture farming systems tecnología apropiada aprendizaje electrónico políticas technology transfer fertilizer applications ethiopia agroecology |
| url | https://hdl.handle.net/10568/120934 |
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