Bayesian modeling of the coffee tree growth curve

Detalhes bibliográficos
Autor(a) principal: Pereira,Adriele Aparecida
Data de Publicação: 2022
Outros Autores: Silva,Edilson Marcelino, Fernandes,Tales Jesus, Morais,Augusto Ramalho de, Sáfadi,Thelma, Muniz,Joel Augusto
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Ciência Rural
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782022000900203
Resumo: ABSTRACT: When modeling growth curves, it should be considered that longitudinal data may show residual autocorrelation, and, if this characteristic is not considered, the results and inferences may be compromised. The Bayesian approach, which considers priori information about studied phenomenon has been shown to be efficient in estimating parameters. However, as it is generally not possible to obtain marginal distributions analytically, it is necessary to use some method, such as the weighted resampling method, to generate samples of these distributions and thus obtain an approximation. Among the advantages of this method, stand out the generation of independent samples and the fact that it is not necessary to evaluate convergence. In this context, the objective of this work research was: to present the Bayesian nonlinear modeling of the coffee tree height growth, irrigated and non-irrigated (NI), considering the residual autocorrelation and the nonlinear Logistic, Brody, von Bertalanffy and Richard models. Among the results, it was found that, for NI plants, the Deviance Information Criterion (DIC) and the Criterion of density Predictive Ordered (CPO), indicated that, among the evaluated models, the Logistic model is the one that best describes the height growth of the coffee tree over time. For irrigated plants, these same criteria indicated the Brody model. Thus, the growth of the non-irrigated and irrigated coffee tree followed different growth patterns, the height of the non-irrigated coffee tree showed sigmoidal growth with maximum growth rate at 726 days after planting and the irrigated coffee tree starts its development with high growth rates that gradually decrease over time.
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spelling Bayesian modeling of the coffee tree growth curveresidual autocorrelationnonlinear modelsLogistic modelBrody modelVon Bertalanffy modelRichards model.ABSTRACT: When modeling growth curves, it should be considered that longitudinal data may show residual autocorrelation, and, if this characteristic is not considered, the results and inferences may be compromised. The Bayesian approach, which considers priori information about studied phenomenon has been shown to be efficient in estimating parameters. However, as it is generally not possible to obtain marginal distributions analytically, it is necessary to use some method, such as the weighted resampling method, to generate samples of these distributions and thus obtain an approximation. Among the advantages of this method, stand out the generation of independent samples and the fact that it is not necessary to evaluate convergence. In this context, the objective of this work research was: to present the Bayesian nonlinear modeling of the coffee tree height growth, irrigated and non-irrigated (NI), considering the residual autocorrelation and the nonlinear Logistic, Brody, von Bertalanffy and Richard models. Among the results, it was found that, for NI plants, the Deviance Information Criterion (DIC) and the Criterion of density Predictive Ordered (CPO), indicated that, among the evaluated models, the Logistic model is the one that best describes the height growth of the coffee tree over time. For irrigated plants, these same criteria indicated the Brody model. Thus, the growth of the non-irrigated and irrigated coffee tree followed different growth patterns, the height of the non-irrigated coffee tree showed sigmoidal growth with maximum growth rate at 726 days after planting and the irrigated coffee tree starts its development with high growth rates that gradually decrease over time.Universidade Federal de Santa Maria2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782022000900203Ciência Rural v.52 n.9 2022reponame:Ciência Ruralinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM10.1590/0103-8478cr20210275info:eu-repo/semantics/openAccessPereira,Adriele AparecidaSilva,Edilson MarcelinoFernandes,Tales JesusMorais,Augusto Ramalho deSáfadi,ThelmaMuniz,Joel Augustoeng2022-03-09T00:00:00ZRevista
dc.title.none.fl_str_mv Bayesian modeling of the coffee tree growth curve
title Bayesian modeling of the coffee tree growth curve
spellingShingle Bayesian modeling of the coffee tree growth curve
Pereira,Adriele Aparecida
residual autocorrelation
nonlinear models
Logistic model
Brody model
Von Bertalanffy model
Richards model.
title_short Bayesian modeling of the coffee tree growth curve
title_full Bayesian modeling of the coffee tree growth curve
title_fullStr Bayesian modeling of the coffee tree growth curve
title_full_unstemmed Bayesian modeling of the coffee tree growth curve
title_sort Bayesian modeling of the coffee tree growth curve
author Pereira,Adriele Aparecida
author_facet Pereira,Adriele Aparecida
Silva,Edilson Marcelino
Fernandes,Tales Jesus
Morais,Augusto Ramalho de
Sáfadi,Thelma
Muniz,Joel Augusto
author_role author
author2 Silva,Edilson Marcelino
Fernandes,Tales Jesus
Morais,Augusto Ramalho de
Sáfadi,Thelma
Muniz,Joel Augusto
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Pereira,Adriele Aparecida
Silva,Edilson Marcelino
Fernandes,Tales Jesus
Morais,Augusto Ramalho de
Sáfadi,Thelma
Muniz,Joel Augusto
dc.subject.por.fl_str_mv residual autocorrelation
nonlinear models
Logistic model
Brody model
Von Bertalanffy model
Richards model.
topic residual autocorrelation
nonlinear models
Logistic model
Brody model
Von Bertalanffy model
Richards model.
description ABSTRACT: When modeling growth curves, it should be considered that longitudinal data may show residual autocorrelation, and, if this characteristic is not considered, the results and inferences may be compromised. The Bayesian approach, which considers priori information about studied phenomenon has been shown to be efficient in estimating parameters. However, as it is generally not possible to obtain marginal distributions analytically, it is necessary to use some method, such as the weighted resampling method, to generate samples of these distributions and thus obtain an approximation. Among the advantages of this method, stand out the generation of independent samples and the fact that it is not necessary to evaluate convergence. In this context, the objective of this work research was: to present the Bayesian nonlinear modeling of the coffee tree height growth, irrigated and non-irrigated (NI), considering the residual autocorrelation and the nonlinear Logistic, Brody, von Bertalanffy and Richard models. Among the results, it was found that, for NI plants, the Deviance Information Criterion (DIC) and the Criterion of density Predictive Ordered (CPO), indicated that, among the evaluated models, the Logistic model is the one that best describes the height growth of the coffee tree over time. For irrigated plants, these same criteria indicated the Brody model. Thus, the growth of the non-irrigated and irrigated coffee tree followed different growth patterns, the height of the non-irrigated coffee tree showed sigmoidal growth with maximum growth rate at 726 days after planting and the irrigated coffee tree starts its development with high growth rates that gradually decrease over time.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782022000900203
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782022000900203
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0103-8478cr20210275
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
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 Rural v.52 n.9 2022
reponame:Ciência Rural
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 Rural
collection Ciência Rural
repository.name.fl_str_mv
repository.mail.fl_str_mv
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