RANDOM FOREST MODEL TO PREDICT THE HEIGHT OF EUCALYPTUS

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.contributorEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)-
Autor(es): dc.creatorLima, Elizeu de S.-
Autor(es): dc.creatorSouza, Zigomar M. de-
Autor(es): dc.creatorOliveira, Stanley R. de M.-
Autor(es): dc.creatorMontanari, Rafael [UNESP]-
Autor(es): dc.creatorFarhate, Camila V. V. [UNESP]-
Data de aceite: dc.date.accessioned2022-08-04T21:58:37Z-
Data de disponibilização: dc.date.available2022-08-04T21:58:37Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1590/1809-4430-Eng.Agric.v42nepe20210153/2022-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/218601-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/218601-
Descrição: dc.descriptionEucalyptus (Eucalyptus urograndis) production has significantly advanced over the past few years in Brazil, especially with regard to acreage and productivity. Machine learning has made significant advances in most varied fields of agrarian sciences. In this context, this study aimed to use physicochemical variables of the soil as well as climatic and dendrometric variables of eucalyptus to predict its height using the random forest algorithm. The study was conducted in the municipality of Tres Lagoas, in Mato Grosso do Sul, Brazil. The original database consisted of 49 soil physicochemical variables collected at 0-0.20 m and 0.20-0.40 m, two dendrometric and four climatic variables, and one response variable related to the height of eucalyptus. A correlation matrix was applied to select variables. Furthermore, modeling was performed using the random forest algorithm, which performed well (r = 0.98, R-2 = 0.96) in predicting the height of eucalyptus. Overall, the most important variables to predict the eucalyptus plant height included diameter at breast height (DBH), phosphorus content (P1), gravimetric moisture (GM1) at a soil depth between 0.00 m and 0.20 m, and exchangeable aluminum content (Al2) between 0.20 m to 0.40 m of soil.-
Descrição: dc.descriptionUniv Estadual Paulista, Fac Ciencias Agr & Vet, Jaboticabal, SP, Brazil-
Descrição: dc.descriptionUniv Estadual Campinas, Fac Engn Agr, Campinas, SP, Brazil-
Descrição: dc.descriptionEmbrapa Agr Digital, Campinas, SP, Brazil-
Descrição: dc.descriptionUniv Estadual Paulista, Fac Engn, Ilha Solteira, SP, Brazil-
Descrição: dc.descriptionUniv Estadual Paulista, Fac Ciencias Agr & Vet, Jaboticabal, SP, Brazil-
Descrição: dc.descriptionUniv Estadual Paulista, Fac Engn, Ilha Solteira, SP, Brazil-
Formato: dc.format11-
Idioma: dc.languageen-
Publicador: dc.publisherSoc Brasil Engenharia Agricola-
Relação: dc.relationEngenharia Agricola-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectPhysicochemical variables of soil-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectsoil phosphorus content-
Palavras-chave: dc.subjectsoil moisture-
Palavras-chave: dc.subjectexchangeable aluminum-
Título: dc.titleRANDOM FOREST MODEL TO PREDICT THE HEIGHT OF EUCALYPTUS-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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