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: Repositório Institucional da UFLA
Texto Completo: http://repositorio.ufla.br/jspui/handle/1/50672
Resumo: 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 curveModelagem bayesiana da curva de crescimento do cafeeiroResidual autocorrelationNonlinear modelsLogistic modelBrody modelVon Bertalanffy modelRichards modelAutocorrelação residualModelos não linearesModelo LogísticoModelo BrodyModelo Von BertalanffyModelo RichardsWhen 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.Na modelagem de curvas de crescimento deve-se considerar que dados longitudinais podem apresentar autocorrelação residual, sendo que, se tal característica não é considerada, os resultados e inferências podem ser comprometidos. A abordagem bayesiana, que considera informações à priori sobre o fenômeno em estudo tem se mostrado eficiente na estimação de parâmetros. No entanto, como geralmente não é possível obter as distribuições marginais de forma analítica, faz-se necessário a utilização de algum método, como o método de reamostragem ponderada, para gerar amostras dessas distribuições e assim obter uma aproximação para as mesmas. Dentre as vantagens desse método, destaca-se a geração de amostras independentes e o fato de não ser necessário avaliar convergência. Diante desse contexto, o objetivo deste trabalho foi apresentar a modelagem não linear bayesiana do crescimento em altura de plantas do cafeeiro, irrigadas e não irrigadas (NI), considerando a autocorrelação residual e os modelos não lineares Logístico, Brody, von Bertalanffy e Richards. Em vista dos resultados, verificou-se que, para as plantas NI, o DIC e CPOc, indicaram que, dentre os modelos avaliados, o modelo Logístico é o que melhor descreve o crescimento em altura do cafeeiro ao longo do tempo. E, para as plantas irrigadas, esses mesmos critérios indicaram o modelo Brody. Assim, o crescimento da planta do cafeeiro não irrigado e irrigado seguiram padrões de crescimento distintos, a altura do cafeeiro não irrigado apresentou crescimento sigmoidal com taxa máxima de crescimento aos 726 dias após o plantio, já o cafeeiro irrigado inicia seu desenvolvimento com altas taxas de crescimento que vão diminuindo aos poucos com o tempo.Universidade Federal de Santa Maria2022-07-20T22:40:42Z2022-07-20T22:40:42Z2022-03info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfPEREIRA, A. A. et al. Bayesian modeling of the coffee tree growth curve. Ciência Rural, Santa Maria, v. 52, n. 9, e20210275, 2022. DOI: https://doi.org/10.1590/0103-8478cr20210275.http://repositorio.ufla.br/jspui/handle/1/50672Ciência Ruralreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessPereira, Adriele AparecidaSilva, Edilson MarcelinoFernandes, Tales JesusMorais, Augusto Ramalho deSáfadi, ThelmaMuniz, Joel Augustoeng2022-07-20T22:41:01Zoai:localhost:1/50672Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2022-07-20T22:41:01Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false
dc.title.none.fl_str_mv Bayesian modeling of the coffee tree growth curve
Modelagem bayesiana da curva de crescimento do cafeeiro
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
Autocorrelação residual
Modelos não lineares
Modelo Logístico
Modelo Brody
Modelo Von Bertalanffy
Modelo Richards
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
Autocorrelação residual
Modelos não lineares
Modelo Logístico
Modelo Brody
Modelo Von Bertalanffy
Modelo Richards
topic Residual autocorrelation
Nonlinear models
Logistic model
Brody model
Von Bertalanffy model
Richards model
Autocorrelação residual
Modelos não lineares
Modelo Logístico
Modelo Brody
Modelo Von Bertalanffy
Modelo Richards
description 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 2022-07-20T22:40:42Z
2022-07-20T22:40:42Z
2022-03
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv PEREIRA, A. A. et al. Bayesian modeling of the coffee tree growth curve. Ciência Rural, Santa Maria, v. 52, n. 9, e20210275, 2022. DOI: https://doi.org/10.1590/0103-8478cr20210275.
http://repositorio.ufla.br/jspui/handle/1/50672
identifier_str_mv PEREIRA, A. A. et al. Bayesian modeling of the coffee tree growth curve. Ciência Rural, Santa Maria, v. 52, n. 9, e20210275, 2022. DOI: https://doi.org/10.1590/0103-8478cr20210275.
url http://repositorio.ufla.br/jspui/handle/1/50672
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
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 Rural
reponame:Repositório Institucional da UFLA
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Repositório Institucional da UFLA
collection Repositório Institucional da UFLA
repository.name.fl_str_mv Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv nivaldo@ufla.br || repositorio.biblioteca@ufla.br
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