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: | 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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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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1815438994593284096 |