Bayesian modeling of the coffee tree growth curve
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , |
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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Ciência rural (Online) |
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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 |
|
_version_ |
1749140557015285760 |