Assessing countrywide soil organic carbon stock using hybrid machine learning modelling and legacy soil data in Cameroon

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dc.creatorSilatsa, F.B.
dc.creatorYemefack, Martin
dc.creatorTabi, F.O.
dc.creatorHeuvelink, G.B.
dc.creatorLeenaars, J.G.
dc.date2020-05
dc.date2020-09-23T14:22:05Z
dc.date2020-09-23T14:22:05Z
dc.date.accessioned2026-06-27T15:54:49Z
dc.identifierhttps://hdl.handle.net/10568/109604
dc.identifier.urihttp://hdl.handle.net/123456789/117238
dc.languageen
dc.publisherElsevier
dc.rightsLimited Access
dc.sourceSilatsa, F.B., Yemefack, M., Tabi, F.O., Heuvelink, G.B. & Leenaars, J.G. (2020). Assessing countrywide soil organic carbon stock using hybrid machine learning modelling and legacy soil data in Cameroon. Geoderma, 367, 114260: 1-13.
dc.subjectsoil
dc.subjectsoil organic carbon
dc.subjectland management
dc.subjectregression analysis
dc.subjecthybridization
dc.subjectcameroon
dc.subjectclimate change
dc.titleAssessing countrywide soil organic carbon stock using hybrid machine learning modelling and legacy soil data in Cameroon
dc.typeJournal Article

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Instituto Internacional de Agricultura Tropical (IITA)