Selection of nonlinear mixed models for growth curves of dairy buffaloes (Bubalus bubalis)

Detalhes bibliográficos
Autor(a) principal: Araujo Neto, F. R.
Data de Publicação: 2020
Outros Autores: Oliveira, D. P. [UNESP], Aspilcueta-Borquis, R. R., Vieira, D. A., Guimarães, K. C., Oliveira, H. N. [UNESP], Tonhati, H. [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1017/S0021859620000325
http://hdl.handle.net/11449/201763
Resumo: The determination of livestock growth patterns is important for meat or milk production systems, and nonlinear models are used to summarize and interpret the information. The aim of this study was to more accurately estimate growth curve parameters in buffalo cows by evaluating and selecting nonlinear mixed models that employ different types of residuals and include or not contemporary groups (CG) as a covariate. Weight records from 720 animals obtained over a period of 60 months were used. The growth curves were fit using nonlinear mixed-effects models. The Bertalanffy, Gompertz and Logistic models were evaluated. Modelling residuals using four structures (constant, combined, exponential and proportional) and the inclusion or not of CG in the models were also evaluated. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to select the model. In addition to estimating the parameters of the nonlinear growth models and their correlations, the instantaneous growth rate and inflection point were obtained. The Bertalanffy model with a combined residual structure and CG exhibited the lowest AIC and BIC values. Asymptotic weight (A) estimates ranged from 621.8 to 742.1 kg, and the maturity rate (k) ranged from 0.068 to 0.115 kg/month. The correlation between A and k ranged from -0.32 to -0.82 among the models evaluated. The selection criteria indicated that the Bertalanffy model was the most suitable for growth curve analysis in buffaloes.
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spelling Selection of nonlinear mixed models for growth curves of dairy buffaloes (Bubalus bubalis)Model comparisonrandom effectSAEM algorithmThe determination of livestock growth patterns is important for meat or milk production systems, and nonlinear models are used to summarize and interpret the information. The aim of this study was to more accurately estimate growth curve parameters in buffalo cows by evaluating and selecting nonlinear mixed models that employ different types of residuals and include or not contemporary groups (CG) as a covariate. Weight records from 720 animals obtained over a period of 60 months were used. The growth curves were fit using nonlinear mixed-effects models. The Bertalanffy, Gompertz and Logistic models were evaluated. Modelling residuals using four structures (constant, combined, exponential and proportional) and the inclusion or not of CG in the models were also evaluated. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to select the model. In addition to estimating the parameters of the nonlinear growth models and their correlations, the instantaneous growth rate and inflection point were obtained. The Bertalanffy model with a combined residual structure and CG exhibited the lowest AIC and BIC values. Asymptotic weight (A) estimates ranged from 621.8 to 742.1 kg, and the maturity rate (k) ranged from 0.068 to 0.115 kg/month. The correlation between A and k ranged from -0.32 to -0.82 among the models evaluated. The selection criteria indicated that the Bertalanffy model was the most suitable for growth curve analysis in buffaloes.Insituto Federal de Educação Ciência e Tecnologia Goiano IF Goiano - Campus Rio VerdeUniversidade Estadual Paulista UNESP Campus JaboticabalUniversidade Federal de Grande DouradosUniversidade Estadual Paulista UNESP Campus JaboticabalIF Goiano - Campus Rio VerdeUniversidade Estadual Paulista (Unesp)Universidade Federal de Grande DouradosAraujo Neto, F. R.Oliveira, D. P. [UNESP]Aspilcueta-Borquis, R. R.Vieira, D. A.Guimarães, K. C.Oliveira, H. N. [UNESP]Tonhati, H. [UNESP]2020-12-12T02:41:08Z2020-12-12T02:41:08Z2020-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1017/S0021859620000325Journal of Agricultural Science.1469-51460021-8596http://hdl.handle.net/11449/20176310.1017/S00218596200003252-s2.0-85084847812Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of Agricultural Scienceinfo:eu-repo/semantics/openAccess2024-06-07T18:40:13Zoai:repositorio.unesp.br:11449/201763Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:45:29.687667Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Selection of nonlinear mixed models for growth curves of dairy buffaloes (Bubalus bubalis)
title Selection of nonlinear mixed models for growth curves of dairy buffaloes (Bubalus bubalis)
spellingShingle Selection of nonlinear mixed models for growth curves of dairy buffaloes (Bubalus bubalis)
Araujo Neto, F. R.
Model comparison
random effect
SAEM algorithm
title_short Selection of nonlinear mixed models for growth curves of dairy buffaloes (Bubalus bubalis)
title_full Selection of nonlinear mixed models for growth curves of dairy buffaloes (Bubalus bubalis)
title_fullStr Selection of nonlinear mixed models for growth curves of dairy buffaloes (Bubalus bubalis)
title_full_unstemmed Selection of nonlinear mixed models for growth curves of dairy buffaloes (Bubalus bubalis)
title_sort Selection of nonlinear mixed models for growth curves of dairy buffaloes (Bubalus bubalis)
author Araujo Neto, F. R.
author_facet Araujo Neto, F. R.
Oliveira, D. P. [UNESP]
Aspilcueta-Borquis, R. R.
Vieira, D. A.
Guimarães, K. C.
Oliveira, H. N. [UNESP]
Tonhati, H. [UNESP]
author_role author
author2 Oliveira, D. P. [UNESP]
Aspilcueta-Borquis, R. R.
Vieira, D. A.
Guimarães, K. C.
Oliveira, H. N. [UNESP]
Tonhati, H. [UNESP]
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv IF Goiano - Campus Rio Verde
Universidade Estadual Paulista (Unesp)
Universidade Federal de Grande Dourados
dc.contributor.author.fl_str_mv Araujo Neto, F. R.
Oliveira, D. P. [UNESP]
Aspilcueta-Borquis, R. R.
Vieira, D. A.
Guimarães, K. C.
Oliveira, H. N. [UNESP]
Tonhati, H. [UNESP]
dc.subject.por.fl_str_mv Model comparison
random effect
SAEM algorithm
topic Model comparison
random effect
SAEM algorithm
description The determination of livestock growth patterns is important for meat or milk production systems, and nonlinear models are used to summarize and interpret the information. The aim of this study was to more accurately estimate growth curve parameters in buffalo cows by evaluating and selecting nonlinear mixed models that employ different types of residuals and include or not contemporary groups (CG) as a covariate. Weight records from 720 animals obtained over a period of 60 months were used. The growth curves were fit using nonlinear mixed-effects models. The Bertalanffy, Gompertz and Logistic models were evaluated. Modelling residuals using four structures (constant, combined, exponential and proportional) and the inclusion or not of CG in the models were also evaluated. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to select the model. In addition to estimating the parameters of the nonlinear growth models and their correlations, the instantaneous growth rate and inflection point were obtained. The Bertalanffy model with a combined residual structure and CG exhibited the lowest AIC and BIC values. Asymptotic weight (A) estimates ranged from 621.8 to 742.1 kg, and the maturity rate (k) ranged from 0.068 to 0.115 kg/month. The correlation between A and k ranged from -0.32 to -0.82 among the models evaluated. The selection criteria indicated that the Bertalanffy model was the most suitable for growth curve analysis in buffaloes.
publishDate 2020
dc.date.none.fl_str_mv 2020-12-12T02:41:08Z
2020-12-12T02:41:08Z
2020-01-01
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 http://dx.doi.org/10.1017/S0021859620000325
Journal of Agricultural Science.
1469-5146
0021-8596
http://hdl.handle.net/11449/201763
10.1017/S0021859620000325
2-s2.0-85084847812
url http://dx.doi.org/10.1017/S0021859620000325
http://hdl.handle.net/11449/201763
identifier_str_mv Journal of Agricultural Science.
1469-5146
0021-8596
10.1017/S0021859620000325
2-s2.0-85084847812
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Journal of Agricultural Science
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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