Estimation of the basic wood density of native species using mixed linear models

dc.creatorJeferson Pereira Martins Silva
dc.creatorMárcia Rodrigues de Moura Fernandes
dc.creatorAnny Francielly Ataide Gonçalves
dc.creatorIsáira Leite e Lopes
dc.creatorGilson Fernandes da Silva
dc.creatorChristian Dias Cabacinha
dc.date.accessioned2022-07-14T13:11:24Z
dc.date.accessioned2025-09-08T23:48:09Z
dc.date.available2022-07-14T13:11:24Z
dc.date.issued2019
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
dc.description.sponsorshipOutra Agência
dc.identifier.doihttps://doi.org/10.1590/2179-8087.038718
dc.identifier.issn21798087
dc.identifier.urihttps://hdl.handle.net/1843/43258
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofFloresta e Ambiente (Floram)
dc.rightsAcesso Aberto
dc.subjectPlantas dos cerrados
dc.subjectInteligência artificial
dc.subjectMadeira -- Qualidade
dc.subjectMadeira -- Densidade
dc.subjectModelos lineares (Estatística)
dc.titleEstimation of the basic wood density of native species using mixed linear models
dc.typeArtigo de periódico
local.citation.epage10
local.citation.spage1
local.citation.volume26
local.description.resumoThis paper aimed to estimate the basic density (DB) of the wood of Cerrado species using mixed linear models. For performing the DBH measurement, the sampling of 334 individuals was carried out. By keeping the Pilodyn apparatus in the DBH position, two measurements were made on opposite sides. Further, for determining DB, the trees were knocked down, followed by removal of five wood discs at different height of stem positions. For this purpose, two sets of modeling alternatives were proposed, which take into account with and without random effects, employing species as a random effect grouping variable. Thus, it was elucidated that, for the estimation of DB, the mixed model that considered the random effects performed better as compared to the alternative model without random effects. The inclusion of random effects leads to the estimation of DB with high accuracy.
local.identifier.orcidhttps://orcid.org/0000-0002-8148-083X
local.publisher.countryBrasil
local.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
local.publisher.initialsUFMG
local.url.externahttps://www.scielo.br/j/floram/a/X54hzCKyFFxL9s7yDH34rMm/

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