Reference soil groups map of Ethiopia based on legacy data and machine learning technique: EthioSoilGrids 1.0
Abstract. Up-to-date digital soil resources information, and its comprehensive understanding, is crucial to support crop production and sustainable agricultural development. Generating such information through conventional approaches consumes time and resources, which is difficult for developing cou...
| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Lenguaje: | Inglés |
| Publicado: |
Copernicus GmbH
2022
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/119860 |
| _version_ | 1855540866394882048 |
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| author | Ali, Ashenafi Erkossa, Teklu Gudeta, Kiflu Abera, Wuletawu Mesfin, Ephrem Mekete, Terefe Haile, Mitiku Haile, Wondwosen Abegaz, Assefa Tafesse, Demeke Belay, Gebeyhu Getahun, Mekonen Beyene, Sheleme Assen, Mohamed Regassa, Alemayehu Selassie, Yihenew G. Tadesse, Solomon Abebe, Dawit Walde, Yitbarek Hussien, Nesru Yirdaw, Abebe Mera, Addisu Admas, Tesema Wakoya, Feyera Legesse, Awgachew Tessema, Nigat Abebe, Ayele Gebremariam, Simret Aregaw, Yismaw Abebaw, Bizuayehu Bekele, Damtew Zewdie, Eylachew Schulz, Steffen Tamene, Lulseged D. Elias, Eyasu |
| author_browse | Abebaw, Bizuayehu Abebe, Ayele Abebe, Dawit Abegaz, Assefa Abera, Wuletawu Admas, Tesema Ali, Ashenafi Aregaw, Yismaw Assen, Mohamed Bekele, Damtew Belay, Gebeyhu Beyene, Sheleme Elias, Eyasu Erkossa, Teklu Gebremariam, Simret Getahun, Mekonen Gudeta, Kiflu Haile, Mitiku Haile, Wondwosen Hussien, Nesru Legesse, Awgachew Mekete, Terefe Mera, Addisu Mesfin, Ephrem Regassa, Alemayehu Schulz, Steffen Selassie, Yihenew G. Tadesse, Solomon Tafesse, Demeke Tamene, Lulseged D. Tessema, Nigat Wakoya, Feyera Walde, Yitbarek Yirdaw, Abebe Zewdie, Eylachew |
| author_facet | Ali, Ashenafi Erkossa, Teklu Gudeta, Kiflu Abera, Wuletawu Mesfin, Ephrem Mekete, Terefe Haile, Mitiku Haile, Wondwosen Abegaz, Assefa Tafesse, Demeke Belay, Gebeyhu Getahun, Mekonen Beyene, Sheleme Assen, Mohamed Regassa, Alemayehu Selassie, Yihenew G. Tadesse, Solomon Abebe, Dawit Walde, Yitbarek Hussien, Nesru Yirdaw, Abebe Mera, Addisu Admas, Tesema Wakoya, Feyera Legesse, Awgachew Tessema, Nigat Abebe, Ayele Gebremariam, Simret Aregaw, Yismaw Abebaw, Bizuayehu Bekele, Damtew Zewdie, Eylachew Schulz, Steffen Tamene, Lulseged D. Elias, Eyasu |
| author_sort | Ali, Ashenafi |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Abstract. Up-to-date digital soil resources information, and its comprehensive understanding, is crucial to support crop production and sustainable agricultural development. Generating such information through conventional approaches consumes time and resources, which is difficult for developing countries. In Ethiopia, the soil resource map that was in use is qualitative, dated (since 1984), and small-scale (1:2 M) which limits its practical applicability. Yet, a large legacy soil profile data accumulated over time and the emerging machine learning modelling approaches can help in generating a high-quality quantitative digital soil map that can provide accurate soil information. Thus, a group of researchers formed a coalition of the willing for soil and agronomy data sharing and collated about 20,000 soil profile data and stored them in a central database. The data were cleaned and harmonized using the latest soil profile data template and prepared 14,681 profile data for modelling. Random Forest was used to develop a continuous quantitative digital map of 18 WRB reference soil groups at 250 m resolution by integrating environmental variables-covariates representing major Ethiopian soil-forming factors. The validated map will have tremendous significance in soil management and other land-based development planning, given its improved spatial nature and quantitative digital representation. |
| format | Preprint |
| id | CGSpace119860 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Copernicus GmbH |
| publisherStr | Copernicus GmbH |
| record_format | dspace |
| spelling | CGSpace1198602025-12-08T09:54:28Z Reference soil groups map of Ethiopia based on legacy data and machine learning technique: EthioSoilGrids 1.0 Ali, Ashenafi Erkossa, Teklu Gudeta, Kiflu Abera, Wuletawu Mesfin, Ephrem Mekete, Terefe Haile, Mitiku Haile, Wondwosen Abegaz, Assefa Tafesse, Demeke Belay, Gebeyhu Getahun, Mekonen Beyene, Sheleme Assen, Mohamed Regassa, Alemayehu Selassie, Yihenew G. Tadesse, Solomon Abebe, Dawit Walde, Yitbarek Hussien, Nesru Yirdaw, Abebe Mera, Addisu Admas, Tesema Wakoya, Feyera Legesse, Awgachew Tessema, Nigat Abebe, Ayele Gebremariam, Simret Aregaw, Yismaw Abebaw, Bizuayehu Bekele, Damtew Zewdie, Eylachew Schulz, Steffen Tamene, Lulseged D. Elias, Eyasu soil profiles machine learning modelling digital records perfil del suelo aprendizaje electrónico modelización Abstract. Up-to-date digital soil resources information, and its comprehensive understanding, is crucial to support crop production and sustainable agricultural development. Generating such information through conventional approaches consumes time and resources, which is difficult for developing countries. In Ethiopia, the soil resource map that was in use is qualitative, dated (since 1984), and small-scale (1:2 M) which limits its practical applicability. Yet, a large legacy soil profile data accumulated over time and the emerging machine learning modelling approaches can help in generating a high-quality quantitative digital soil map that can provide accurate soil information. Thus, a group of researchers formed a coalition of the willing for soil and agronomy data sharing and collated about 20,000 soil profile data and stored them in a central database. The data were cleaned and harmonized using the latest soil profile data template and prepared 14,681 profile data for modelling. Random Forest was used to develop a continuous quantitative digital map of 18 WRB reference soil groups at 250 m resolution by integrating environmental variables-covariates representing major Ethiopian soil-forming factors. The validated map will have tremendous significance in soil management and other land-based development planning, given its improved spatial nature and quantitative digital representation. 2022-05-23 2022-06-16T09:51:00Z 2022-06-16T09:51:00Z Preprint https://hdl.handle.net/10568/119860 en Open Access text/plain Copernicus GmbH Ali, A.; Erkossa, T.; Gudeta, K.; Abera, W.; Mesfin, E.; Mekete, T.; Haile, M.; Haile, W.; Abegaz, A.; Tafesse, D.; Belay, G.; Getahun, M.; Beyene, S.; Assen, M.; Regassa, A.; Selassie, Y.G.; Tadesse, S.; Abebe, D.; Walde, Y.; Hussien, N.; Yirdaw, A.; Mera, A.; Admas, T.; Wakoya, F.; Legesse, A.; Tessema, N.; Abebe, A.; Gebremariam, S.; Aregaw, Y.; Abebaw, B.; Bekele, D.; Zewdie, E.; Schulz, S.; Tamene, L.; Elias, E. (2022) Reference soil groups map of Ethiopia based on legacy data and machine learning technique: EthioSoilGrids 1.0. 40 p. Soil (23 May 2022) ISSN: 2199-3971 |
| spellingShingle | soil profiles machine learning modelling digital records perfil del suelo aprendizaje electrónico modelización Ali, Ashenafi Erkossa, Teklu Gudeta, Kiflu Abera, Wuletawu Mesfin, Ephrem Mekete, Terefe Haile, Mitiku Haile, Wondwosen Abegaz, Assefa Tafesse, Demeke Belay, Gebeyhu Getahun, Mekonen Beyene, Sheleme Assen, Mohamed Regassa, Alemayehu Selassie, Yihenew G. Tadesse, Solomon Abebe, Dawit Walde, Yitbarek Hussien, Nesru Yirdaw, Abebe Mera, Addisu Admas, Tesema Wakoya, Feyera Legesse, Awgachew Tessema, Nigat Abebe, Ayele Gebremariam, Simret Aregaw, Yismaw Abebaw, Bizuayehu Bekele, Damtew Zewdie, Eylachew Schulz, Steffen Tamene, Lulseged D. Elias, Eyasu Reference soil groups map of Ethiopia based on legacy data and machine learning technique: EthioSoilGrids 1.0 |
| title | Reference soil groups map of Ethiopia based on legacy data and machine learning technique: EthioSoilGrids 1.0 |
| title_full | Reference soil groups map of Ethiopia based on legacy data and machine learning technique: EthioSoilGrids 1.0 |
| title_fullStr | Reference soil groups map of Ethiopia based on legacy data and machine learning technique: EthioSoilGrids 1.0 |
| title_full_unstemmed | Reference soil groups map of Ethiopia based on legacy data and machine learning technique: EthioSoilGrids 1.0 |
| title_short | Reference soil groups map of Ethiopia based on legacy data and machine learning technique: EthioSoilGrids 1.0 |
| title_sort | reference soil groups map of ethiopia based on legacy data and machine learning technique ethiosoilgrids 1 0 |
| topic | soil profiles machine learning modelling digital records perfil del suelo aprendizaje electrónico modelización |
| url | https://hdl.handle.net/10568/119860 |
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