Reference soil groups map of Ethiopia based on legacy data and machine learning technique: EthioSoilGrids 1.0

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dc.creatorAli, Ashenafi
dc.creatorErkossa, Teklu
dc.creatorGudeta, Kiflu
dc.creatorAbera, Wuletawu
dc.creatorMesfin, Ephrem
dc.creatorMekete, Terefe
dc.creatorHaile, Mitiku
dc.creatorHaile, Wondwosen
dc.creatorAbegaz, Assefa
dc.creatorTafesse, Demeke
dc.creatorBelay, Gebeyhu
dc.creatorGetahun, Mekonen
dc.creatorBeyene, Sheleme
dc.creatorAssen, Mohamed
dc.creatorRegassa, Alemayehu
dc.creatorSelassie, Yihenew G.
dc.creatorTadesse, Solomon
dc.creatorAbebe, Dawit
dc.creatorWalde, Yitbarek
dc.creatorHussien, Nesru
dc.creatorYirdaw, Abebe
dc.creatorMera, Addisu
dc.creatorAdmas, Tesema
dc.creatorWakoya, Feyera
dc.creatorLegesse, Awgachew
dc.creatorTessema, Nigat
dc.creatorAbebe, Ayele
dc.creatorGebremariam, Simret
dc.creatorAregaw, Yismaw
dc.creatorAbebaw, Bizuayehu
dc.creatorBekele, Damtew
dc.creatorZewdie, Eylachew
dc.creatorSchulz, Steffen
dc.creatorTamene, Lulseged D.
dc.creatorElias, Eyasu
dc.date2022-05-23
dc.date2022-06-16T09:51:00Z
dc.date2022-06-16T09:51:00Z
dc.date.accessioned2026-06-27T13:36:51Z
dc.descriptionAbstract. 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.
dc.formattext/plain
dc.identifierhttps://hdl.handle.net/10568/119860
dc.identifier.urihttp://hdl.handle.net/123456789/64911
dc.languageen
dc.publisherCopernicus GmbH
dc.rightsOpen Access
dc.sourceAli, 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
dc.subjectsoil profiles
dc.subjectmachine learning
dc.subjectmodelling
dc.subjectdigital records
dc.subjectperfil del suelo
dc.subjectaprendizaje electrónico
dc.subjectmodelización
dc.titleReference soil groups map of Ethiopia based on legacy data and machine learning technique: EthioSoilGrids 1.0
dc.typePreprint

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