MODELING OF NONLINEAR HYPSOMETRIC RELATION AND GROWTH OF DOMINANT AND CODOMINANT TREES OF Eucalyptus sp.
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Ciência Florestal (Online) |
Texto Completo: | https://periodicos.ufsm.br/cienciaflorestal/article/view/29895 |
Resumo: | This work aimed to evaluate linear and nonlinear, classical and generalized models with the addition of covariates for modeling the hypsometric relation and the height growth of dominant and codominant trees of eucalypt clones. Two linear models and two nonlinear models were fitted to estimate the hypsometric relationship and four nonlinear models to classify the site. Regarding the hypsometry, it was used the technique of inclusion of covariates in the model that provided the best statistics in order to obtain more precise estimates. The selection and quality of the fittings was based on the standard error “Syx (%)”, Akaike information criterion (AIC), Bayesian information criterion (BIC) and the test of maximum likelihood (LRATIO), in addition to the graphical analysis of the residuals. For handling, fitting and processing the data, it was used software ‘R’. According to the statistical analysis of the models, for the hypsometric relation the four parameters logistic model was proved to be superior when compared to the other adjusted models. The logistic model obtained better accuracy compared to the classical model. In relation to the estimates of height growth in dominant and codominant trees, the three parameter logistic model obtained the best statistical precision, and therefore it was used for the construction of site index curves. |
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Ciência Florestal (Online) |
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MODELING OF NONLINEAR HYPSOMETRIC RELATION AND GROWTH OF DOMINANT AND CODOMINANT TREES OF Eucalyptus sp.MODELAGEM NÃO LINEAR DA RELAÇÃO HIPSOMÉTRICA E DO CRESCIMENTO DAS ÁRVORES DOMINANTES E CODOMINANTES DE Eucalyptus sp.eucalyptregression modelsgeneralized modelscovariates.eucaliptomodelos de regressãomodelos generalizadoscovariáveis.This work aimed to evaluate linear and nonlinear, classical and generalized models with the addition of covariates for modeling the hypsometric relation and the height growth of dominant and codominant trees of eucalypt clones. Two linear models and two nonlinear models were fitted to estimate the hypsometric relationship and four nonlinear models to classify the site. Regarding the hypsometry, it was used the technique of inclusion of covariates in the model that provided the best statistics in order to obtain more precise estimates. The selection and quality of the fittings was based on the standard error “Syx (%)”, Akaike information criterion (AIC), Bayesian information criterion (BIC) and the test of maximum likelihood (LRATIO), in addition to the graphical analysis of the residuals. For handling, fitting and processing the data, it was used software ‘R’. According to the statistical analysis of the models, for the hypsometric relation the four parameters logistic model was proved to be superior when compared to the other adjusted models. The logistic model obtained better accuracy compared to the classical model. In relation to the estimates of height growth in dominant and codominant trees, the three parameter logistic model obtained the best statistical precision, and therefore it was used for the construction of site index curves.Este trabalho teve por objetivo avaliar modelos lineares e não lineares clássicos e generalizados com adição de covariáveis, para modelagem da relação hipsométrica e do crescimento em altura das árvores dominantes e codominantes de clones de eucalipto. Foram ajustados dois modelos lineares e dois não lineares para estimativa da relação hipsométrica e quatro modelos não lineares para classificação de sítio. Com relação à hipsometria, para o modelo que propiciou as melhores estatísticas, utilizou-se a técnica de inclusão de covariáveis para o ajuste, visando obter melhor precisão das estimativas. A seleção e a qualidade de ajuste dos modelos se deram com base no erro padrão percentual “Syx (%)”, critério de informação de Akaike (AIC), critério de informação Bayesiano (BIC) e no teste de razão da máxima verossimilhança (TRMV), além da análise gráfica dos resíduos. Para a manipulação, ajuste e processamento dos dados foi utilizado o software R. De acordo com as análises estatísticas dos modelos, para a relação hipsométrica, o logístico com quatro parâmetros mostrou-se superior em relação aos outros modelos ajustados. O modelo logístico com adição de covariáveis obteve uma melhor precisão em comparação ao modelo logístico clássico. Para a estimativa do crescimento em altura das árvores dominantes e codominantes, o modelo logístico com três parâmetros obteve as melhores estatísticas de precisão, sendo, então, utilizado para a construção das curvas de índice de sítio.Universidade Federal de Santa Maria2017-12-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.ufsm.br/cienciaflorestal/article/view/2989510.5902/1980509829895Ciência Florestal; Vol. 27 No. 4 (2017); 1325-1338Ciência Florestal; v. 27 n. 4 (2017); 1325-13381980-50980103-9954reponame:Ciência Florestal (Online)instname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMporhttps://periodicos.ufsm.br/cienciaflorestal/article/view/29895/pdf_1Copyright (c) 2017 Ciência Florestalinfo:eu-repo/semantics/openAccessMelo, Elliezer de AlmeidaCalegario, NatalinoMendonça, Adriano Ribeiro dePossato, Ernani LopesAlves, Joyce de AlmeidaIsaac Júnior, Marcos Antonio2017-12-12T00:30:07Zoai:ojs.pkp.sfu.ca:article/29895Revistahttp://www.ufsm.br/cienciaflorestal/ONGhttps://old.scielo.br/oai/scielo-oai.php||cienciaflorestal@ufsm.br|| cienciaflorestal@gmail.com|| cf@smail.ufsm.br1980-50980103-9954opendoar:2017-12-12T00:30:07Ciência Florestal (Online) - Universidade Federal de Santa Maria (UFSM)false |
dc.title.none.fl_str_mv |
MODELING OF NONLINEAR HYPSOMETRIC RELATION AND GROWTH OF DOMINANT AND CODOMINANT TREES OF Eucalyptus sp. MODELAGEM NÃO LINEAR DA RELAÇÃO HIPSOMÉTRICA E DO CRESCIMENTO DAS ÁRVORES DOMINANTES E CODOMINANTES DE Eucalyptus sp. |
title |
MODELING OF NONLINEAR HYPSOMETRIC RELATION AND GROWTH OF DOMINANT AND CODOMINANT TREES OF Eucalyptus sp. |
spellingShingle |
MODELING OF NONLINEAR HYPSOMETRIC RELATION AND GROWTH OF DOMINANT AND CODOMINANT TREES OF Eucalyptus sp. Melo, Elliezer de Almeida eucalypt regression models generalized models covariates. eucalipto modelos de regressão modelos generalizados covariáveis. |
title_short |
MODELING OF NONLINEAR HYPSOMETRIC RELATION AND GROWTH OF DOMINANT AND CODOMINANT TREES OF Eucalyptus sp. |
title_full |
MODELING OF NONLINEAR HYPSOMETRIC RELATION AND GROWTH OF DOMINANT AND CODOMINANT TREES OF Eucalyptus sp. |
title_fullStr |
MODELING OF NONLINEAR HYPSOMETRIC RELATION AND GROWTH OF DOMINANT AND CODOMINANT TREES OF Eucalyptus sp. |
title_full_unstemmed |
MODELING OF NONLINEAR HYPSOMETRIC RELATION AND GROWTH OF DOMINANT AND CODOMINANT TREES OF Eucalyptus sp. |
title_sort |
MODELING OF NONLINEAR HYPSOMETRIC RELATION AND GROWTH OF DOMINANT AND CODOMINANT TREES OF Eucalyptus sp. |
author |
Melo, Elliezer de Almeida |
author_facet |
Melo, Elliezer de Almeida Calegario, Natalino Mendonça, Adriano Ribeiro de Possato, Ernani Lopes Alves, Joyce de Almeida Isaac Júnior, Marcos Antonio |
author_role |
author |
author2 |
Calegario, Natalino Mendonça, Adriano Ribeiro de Possato, Ernani Lopes Alves, Joyce de Almeida Isaac Júnior, Marcos Antonio |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Melo, Elliezer de Almeida Calegario, Natalino Mendonça, Adriano Ribeiro de Possato, Ernani Lopes Alves, Joyce de Almeida Isaac Júnior, Marcos Antonio |
dc.subject.por.fl_str_mv |
eucalypt regression models generalized models covariates. eucalipto modelos de regressão modelos generalizados covariáveis. |
topic |
eucalypt regression models generalized models covariates. eucalipto modelos de regressão modelos generalizados covariáveis. |
description |
This work aimed to evaluate linear and nonlinear, classical and generalized models with the addition of covariates for modeling the hypsometric relation and the height growth of dominant and codominant trees of eucalypt clones. Two linear models and two nonlinear models were fitted to estimate the hypsometric relationship and four nonlinear models to classify the site. Regarding the hypsometry, it was used the technique of inclusion of covariates in the model that provided the best statistics in order to obtain more precise estimates. The selection and quality of the fittings was based on the standard error “Syx (%)”, Akaike information criterion (AIC), Bayesian information criterion (BIC) and the test of maximum likelihood (LRATIO), in addition to the graphical analysis of the residuals. For handling, fitting and processing the data, it was used software ‘R’. According to the statistical analysis of the models, for the hypsometric relation the four parameters logistic model was proved to be superior when compared to the other adjusted models. The logistic model obtained better accuracy compared to the classical model. In relation to the estimates of height growth in dominant and codominant trees, the three parameter logistic model obtained the best statistical precision, and therefore it was used for the construction of site index curves. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-12-11 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.ufsm.br/cienciaflorestal/article/view/29895 10.5902/1980509829895 |
url |
https://periodicos.ufsm.br/cienciaflorestal/article/view/29895 |
identifier_str_mv |
10.5902/1980509829895 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://periodicos.ufsm.br/cienciaflorestal/article/view/29895/pdf_1 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2017 Ciência Florestal info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2017 Ciência Florestal |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria |
dc.source.none.fl_str_mv |
Ciência Florestal; Vol. 27 No. 4 (2017); 1325-1338 Ciência Florestal; v. 27 n. 4 (2017); 1325-1338 1980-5098 0103-9954 reponame:Ciência Florestal (Online) instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
instname_str |
Universidade Federal de Santa Maria (UFSM) |
instacron_str |
UFSM |
institution |
UFSM |
reponame_str |
Ciência Florestal (Online) |
collection |
Ciência Florestal (Online) |
repository.name.fl_str_mv |
Ciência Florestal (Online) - Universidade Federal de Santa Maria (UFSM) |
repository.mail.fl_str_mv |
||cienciaflorestal@ufsm.br|| cienciaflorestal@gmail.com|| cf@smail.ufsm.br |
_version_ |
1799944132568809472 |