Proposal for a new non-linear model to describe growth curves

Detalhes bibliográficos
Autor(a) principal: Santos, André Luiz Pinto dos
Data de Publicação: 2024
Outros Autores: Ferreira, Tiago Alessandro Espínola, Brito, Cícero Carlos Ramos de, Gomes-Silva, Frank, Moreira, Guilherme Rocha
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Bioscience journal (Online)
Texto Completo: https://seer.ufu.br/index.php/biosciencejournal/article/view/68936
Resumo: This study was developed with longitudinal data measurements of Norfolk rabbits from birth to 119 days of age to estimate the average growth curve, with the primary objective of proposing a non-linear model. It also selected the most appropriate sigmoidal model to describe the growth of Norfolk rabbits. The adjustments provided by the logistic, von Bertalanffy, Gompertz, Brody, Richards, and proposed models were compared. The parameters were estimated using the “nls” function of the “stats” package in R software, the least-squares method, and the Gauss-Newton convergence algorithm. The goodness-of-fit comparison was based on the following criteria: adjusted coefficient of determination (), mean square error (MSE), mean absolute deviation (MAD), Akaike information criterion (AIC), and Bayesian information criterion (BIC). Cluster analysis helped select and classify the non-linear growth models, considering the other goodness-of-fit criteria results. The proposed non-linear, von Bertalanffy, Gompertz, and Richards models described the growth curve of Norfolk rabbits satisfactorily, providing parameters with practical interpretations. The goodness-of-fit criteria showed that the proposed and von Bertalanffy models best represented the growth of rabbits.
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spelling Proposal for a new non-linear model to describe growth curvesAnimal growthBiological parametersCluster analysisGrowth curvesLongitudinal data. Agricultural SciencesThis study was developed with longitudinal data measurements of Norfolk rabbits from birth to 119 days of age to estimate the average growth curve, with the primary objective of proposing a non-linear model. It also selected the most appropriate sigmoidal model to describe the growth of Norfolk rabbits. The adjustments provided by the logistic, von Bertalanffy, Gompertz, Brody, Richards, and proposed models were compared. The parameters were estimated using the “nls” function of the “stats” package in R software, the least-squares method, and the Gauss-Newton convergence algorithm. The goodness-of-fit comparison was based on the following criteria: adjusted coefficient of determination (), mean square error (MSE), mean absolute deviation (MAD), Akaike information criterion (AIC), and Bayesian information criterion (BIC). Cluster analysis helped select and classify the non-linear growth models, considering the other goodness-of-fit criteria results. The proposed non-linear, von Bertalanffy, Gompertz, and Richards models described the growth curve of Norfolk rabbits satisfactorily, providing parameters with practical interpretations. The goodness-of-fit criteria showed that the proposed and von Bertalanffy models best represented the growth of rabbits.Universidade Federal de Uberlândia2024-02-15info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://seer.ufu.br/index.php/biosciencejournal/article/view/6893610.14393/BJ-v40n0a2024-68936Bioscience Journal ; Vol. 40 (2024): Continuous Publication; e40011Bioscience Journal ; v. 40 (2024): Continuous Publication; e400111981-3163reponame:Bioscience journal (Online)instname:Universidade Federal de Uberlândia (UFU)instacron:UFUenghttps://seer.ufu.br/index.php/biosciencejournal/article/view/68936/37987Brazil; Contemporary Copyright (c) 2024 André Luiz Pinto dos Santos, Tiago Alessandro Espínola Ferreira, Cícero Carlos Ramos de Brito, Frank Gomes-Silva, Guilherme Rocha Moreirahttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessSantos, André Luiz Pinto dosFerreira, Tiago Alessandro EspínolaBrito, Cícero Carlos Ramos deGomes-Silva, FrankMoreira, Guilherme Rocha2024-04-03T20:47:42Zoai:ojs.www.seer.ufu.br:article/68936Revistahttps://seer.ufu.br/index.php/biosciencejournalPUBhttps://seer.ufu.br/index.php/biosciencejournal/oaibiosciencej@ufu.br||1981-31631516-3725opendoar:2024-04-03T20:47:42Bioscience journal (Online) - Universidade Federal de Uberlândia (UFU)false
dc.title.none.fl_str_mv Proposal for a new non-linear model to describe growth curves
title Proposal for a new non-linear model to describe growth curves
spellingShingle Proposal for a new non-linear model to describe growth curves
Santos, André Luiz Pinto dos
Animal growth
Biological parameters
Cluster analysis
Growth curves
Longitudinal data.
Agricultural Sciences
title_short Proposal for a new non-linear model to describe growth curves
title_full Proposal for a new non-linear model to describe growth curves
title_fullStr Proposal for a new non-linear model to describe growth curves
title_full_unstemmed Proposal for a new non-linear model to describe growth curves
title_sort Proposal for a new non-linear model to describe growth curves
author Santos, André Luiz Pinto dos
author_facet Santos, André Luiz Pinto dos
Ferreira, Tiago Alessandro Espínola
Brito, Cícero Carlos Ramos de
Gomes-Silva, Frank
Moreira, Guilherme Rocha
author_role author
author2 Ferreira, Tiago Alessandro Espínola
Brito, Cícero Carlos Ramos de
Gomes-Silva, Frank
Moreira, Guilherme Rocha
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Santos, André Luiz Pinto dos
Ferreira, Tiago Alessandro Espínola
Brito, Cícero Carlos Ramos de
Gomes-Silva, Frank
Moreira, Guilherme Rocha
dc.subject.por.fl_str_mv Animal growth
Biological parameters
Cluster analysis
Growth curves
Longitudinal data.
Agricultural Sciences
topic Animal growth
Biological parameters
Cluster analysis
Growth curves
Longitudinal data.
Agricultural Sciences
description This study was developed with longitudinal data measurements of Norfolk rabbits from birth to 119 days of age to estimate the average growth curve, with the primary objective of proposing a non-linear model. It also selected the most appropriate sigmoidal model to describe the growth of Norfolk rabbits. The adjustments provided by the logistic, von Bertalanffy, Gompertz, Brody, Richards, and proposed models were compared. The parameters were estimated using the “nls” function of the “stats” package in R software, the least-squares method, and the Gauss-Newton convergence algorithm. The goodness-of-fit comparison was based on the following criteria: adjusted coefficient of determination (), mean square error (MSE), mean absolute deviation (MAD), Akaike information criterion (AIC), and Bayesian information criterion (BIC). Cluster analysis helped select and classify the non-linear growth models, considering the other goodness-of-fit criteria results. The proposed non-linear, von Bertalanffy, Gompertz, and Richards models described the growth curve of Norfolk rabbits satisfactorily, providing parameters with practical interpretations. The goodness-of-fit criteria showed that the proposed and von Bertalanffy models best represented the growth of rabbits.
publishDate 2024
dc.date.none.fl_str_mv 2024-02-15
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://seer.ufu.br/index.php/biosciencejournal/article/view/68936
10.14393/BJ-v40n0a2024-68936
url https://seer.ufu.br/index.php/biosciencejournal/article/view/68936
identifier_str_mv 10.14393/BJ-v40n0a2024-68936
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://seer.ufu.br/index.php/biosciencejournal/article/view/68936/37987
dc.rights.driver.fl_str_mv https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.coverage.none.fl_str_mv Brazil; Contemporary
dc.publisher.none.fl_str_mv Universidade Federal de Uberlândia
publisher.none.fl_str_mv Universidade Federal de Uberlândia
dc.source.none.fl_str_mv Bioscience Journal ; Vol. 40 (2024): Continuous Publication; e40011
Bioscience Journal ; v. 40 (2024): Continuous Publication; e40011
1981-3163
reponame:Bioscience journal (Online)
instname:Universidade Federal de Uberlândia (UFU)
instacron:UFU
instname_str Universidade Federal de Uberlândia (UFU)
instacron_str UFU
institution UFU
reponame_str Bioscience journal (Online)
collection Bioscience journal (Online)
repository.name.fl_str_mv Bioscience journal (Online) - Universidade Federal de Uberlândia (UFU)
repository.mail.fl_str_mv biosciencej@ufu.br||
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