Modelo Logístico Bayesiano no estudo do crescimento de tomates
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFLA |
Texto Completo: | http://repositorio.ufla.br/jspui/handle/1/49408 |
Resumo: | Knowing the growth of tomato and its fruits, as measured by biomass accumulation over time is essential for the proper handling and detection of problems in the development of crops. This growth can be studied using various models of non-linear regression that can be used to facilitate interpretation of the processes involved in plant production system. Among the empirical models often used to estimate the growth of plants and their components is the function Logistic. One method used to estimate the parameters of the growth rate is the Bayesian method. The study objective to apply the Bayesian approach in describing the data – real and simulated – the diameter growth of tomatoes, using the model Logistic. The algorithms for the Gibbs Sampler and Metropolis – Hastings were implemented using the R language. The condition of convergence of the chains was checked using the criteria suggested by Nogueira, Sáfadi and Ferreira (2004) available on the R software package BOA. The Bayesian approach was efficient, since it was evaluated and verified by the simulation process, with very close estimates of the parametric value, and estimates were shown to be consistent with the values reported in literature. |
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Modelo Logístico Bayesiano no estudo do crescimento de tomatesModelo não linearCurva de crescimentoMetodologia BayesianaNonlinear modelGrowth curveBayesian methodologyKnowing the growth of tomato and its fruits, as measured by biomass accumulation over time is essential for the proper handling and detection of problems in the development of crops. This growth can be studied using various models of non-linear regression that can be used to facilitate interpretation of the processes involved in plant production system. Among the empirical models often used to estimate the growth of plants and their components is the function Logistic. One method used to estimate the parameters of the growth rate is the Bayesian method. The study objective to apply the Bayesian approach in describing the data – real and simulated – the diameter growth of tomatoes, using the model Logistic. The algorithms for the Gibbs Sampler and Metropolis – Hastings were implemented using the R language. The condition of convergence of the chains was checked using the criteria suggested by Nogueira, Sáfadi and Ferreira (2004) available on the R software package BOA. The Bayesian approach was efficient, since it was evaluated and verified by the simulation process, with very close estimates of the parametric value, and estimates were shown to be consistent with the values reported in literature.Conhecer o crescimento do tomateiro e de seus frutos, medido através do acúmulo de biomassa ao longo do tempo, são fundamentais para o manejo adequado e a detecção de problemas no desenvolvimento das culturas. Este crescimento pode ser estudado por meio de vários modelos de regressão não linear que podem ser usados para facilitar a interpretação dos processos envolvidos no sistema de produção vegetal. Entre os modelos empíricos usados frequentemente para estimar o crescimento de plantas, e seus componentes, encontra-se a função logística. Um dos métodos utilizados para estimar os parâmetros das curvas de crescimento é o método bayesiano. Este estudo teve como objetivo aplicar a metodologia bayesiana na descrição dos dados –reais e simulados -de crescimento do diâmetro de tomates, utilizando o modelonão linear logístico. Os algoritmos para o amostrador de Gibbs e o Metropolis-Hastings foram implementados utilizando-se a linguagem R. A condição de convergência das cadeias foi verificada por meio do critério de Raftery & Lewis, que está disponível no pacote BOA (“Bayesian Output Analysis”) do software R. A metodologia bayesiana mostrou-se eficiente na estimação dos parâmetros da curva de crescimento, e as estimativas mostraram-se coerentes com os valores relatados na literatura.2022-02-21T20:21:18Z2022-02-21T20:21:18Z2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfMENDES, P. N. et al. Modelo Logístico Bayesiano no estudo do crescimento de tomates. Research, Society and Development, [S.l.], v. 10, n. 3, 2021.http://repositorio.ufla.br/jspui/handle/1/49408Research, Society and Developmentreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessMendes, Patrícia NevesMuniz, Joel AugutoSavian, Taciana VillelaSáfadi, ThelmaJerônimo, Gabriel da Costa Cantospor2023-05-26T19:38:07Zoai:localhost:1/49408Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2023-05-26T19:38:07Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
Modelo Logístico Bayesiano no estudo do crescimento de tomates |
title |
Modelo Logístico Bayesiano no estudo do crescimento de tomates |
spellingShingle |
Modelo Logístico Bayesiano no estudo do crescimento de tomates Mendes, Patrícia Neves Modelo não linear Curva de crescimento Metodologia Bayesiana Nonlinear model Growth curve Bayesian methodology |
title_short |
Modelo Logístico Bayesiano no estudo do crescimento de tomates |
title_full |
Modelo Logístico Bayesiano no estudo do crescimento de tomates |
title_fullStr |
Modelo Logístico Bayesiano no estudo do crescimento de tomates |
title_full_unstemmed |
Modelo Logístico Bayesiano no estudo do crescimento de tomates |
title_sort |
Modelo Logístico Bayesiano no estudo do crescimento de tomates |
author |
Mendes, Patrícia Neves |
author_facet |
Mendes, Patrícia Neves Muniz, Joel Auguto Savian, Taciana Villela Sáfadi, Thelma Jerônimo, Gabriel da Costa Cantos |
author_role |
author |
author2 |
Muniz, Joel Auguto Savian, Taciana Villela Sáfadi, Thelma Jerônimo, Gabriel da Costa Cantos |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Mendes, Patrícia Neves Muniz, Joel Auguto Savian, Taciana Villela Sáfadi, Thelma Jerônimo, Gabriel da Costa Cantos |
dc.subject.por.fl_str_mv |
Modelo não linear Curva de crescimento Metodologia Bayesiana Nonlinear model Growth curve Bayesian methodology |
topic |
Modelo não linear Curva de crescimento Metodologia Bayesiana Nonlinear model Growth curve Bayesian methodology |
description |
Knowing the growth of tomato and its fruits, as measured by biomass accumulation over time is essential for the proper handling and detection of problems in the development of crops. This growth can be studied using various models of non-linear regression that can be used to facilitate interpretation of the processes involved in plant production system. Among the empirical models often used to estimate the growth of plants and their components is the function Logistic. One method used to estimate the parameters of the growth rate is the Bayesian method. The study objective to apply the Bayesian approach in describing the data – real and simulated – the diameter growth of tomatoes, using the model Logistic. The algorithms for the Gibbs Sampler and Metropolis – Hastings were implemented using the R language. The condition of convergence of the chains was checked using the criteria suggested by Nogueira, Sáfadi and Ferreira (2004) available on the R software package BOA. The Bayesian approach was efficient, since it was evaluated and verified by the simulation process, with very close estimates of the parametric value, and estimates were shown to be consistent with the values reported in literature. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 2022-02-21T20:21:18Z 2022-02-21T20:21:18Z |
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 |
MENDES, P. N. et al. Modelo Logístico Bayesiano no estudo do crescimento de tomates. Research, Society and Development, [S.l.], v. 10, n. 3, 2021. http://repositorio.ufla.br/jspui/handle/1/49408 |
identifier_str_mv |
MENDES, P. N. et al. Modelo Logístico Bayesiano no estudo do crescimento de tomates. Research, Society and Development, [S.l.], v. 10, n. 3, 2021. |
url |
http://repositorio.ufla.br/jspui/handle/1/49408 |
dc.language.iso.fl_str_mv |
por |
language |
por |
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.source.none.fl_str_mv |
Research, Society and Development 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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1807835159941087232 |