Genetic parameters for milk production by using random regression models with different alternatives of fixed regression modeling.

Detalhes bibliográficos
Autor(a) principal: COBUCI, J. A.
Data de Publicação: 2011
Outros Autores: COSTA, C. N., BRACCINI NETO, J., FREITAS, A. F. de
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/902188
https://doi.org/10.1590/S1516-35982011000300013
Resumo: Records of test-day milk yields of the first three lactations of 25,500 Holstein cows were used to estimate genetic parameters for milk yield by using two alternatives of definition of fixed regression of the random regression models (RRM). Legendre polynomials of fourth and fifth orders were used to model regression of fixed curve (defined based on averages of the populations or multiple sub-populations formed by grouping animals which calved at the same age and in the same season of the year) or random lactation curves (additive genetic and permanent enviroment). Akaike information criterion (AIC) and Bayesian information criterion (BIC) indicated that the models which used multiple regression of fixed lactation curves of lactation multiple regression model with fixed lactation curves had the best fit for the first lactation test-day milk yields and the models which used a single regression of fixed curve had the best fit for the second and third lactations. Heritability for milk yield during lactation estimates did not vary among models but ranged from 0.22 to 0.34, from 0.11 to 0.21, and from 0.10 to 0.20, respectively, in the first three lactations. Similarly to heridability estimates of genetic correlations did not vary among models. The use of single or multiple fixed regressions for fixed lactation curves by RRM does not influence the estimates of genetic parameters for test-day milk yield across lactations.
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spelling Genetic parameters for milk production by using random regression models with different alternatives of fixed regression modeling.Test-day milk yieldLegendre polynomialSelectiongenetic correlationheritabilityRecords of test-day milk yields of the first three lactations of 25,500 Holstein cows were used to estimate genetic parameters for milk yield by using two alternatives of definition of fixed regression of the random regression models (RRM). Legendre polynomials of fourth and fifth orders were used to model regression of fixed curve (defined based on averages of the populations or multiple sub-populations formed by grouping animals which calved at the same age and in the same season of the year) or random lactation curves (additive genetic and permanent enviroment). Akaike information criterion (AIC) and Bayesian information criterion (BIC) indicated that the models which used multiple regression of fixed lactation curves of lactation multiple regression model with fixed lactation curves had the best fit for the first lactation test-day milk yields and the models which used a single regression of fixed curve had the best fit for the second and third lactations. Heritability for milk yield during lactation estimates did not vary among models but ranged from 0.22 to 0.34, from 0.11 to 0.21, and from 0.10 to 0.20, respectively, in the first three lactations. Similarly to heridability estimates of genetic correlations did not vary among models. The use of single or multiple fixed regressions for fixed lactation curves by RRM does not influence the estimates of genetic parameters for test-day milk yield across lactations.JAIME ARAÚJO COBUCI, UFRGS / CNPq; CLAUDIO NAPOLIS COSTA, CNPGL; JOSÉ BRACCINI NETO, UFRGS.; ARY FERREIRA DE FREITAS, Pesquisador aposentado do CNPGL.COBUCI, J. A.COSTA, C. N.BRACCINI NETO, J.FREITAS, A. F. de2022-07-01T10:19:41Z2022-07-01T10:19:41Z2011-10-042011info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleRevista Brasileira de Zootecnia, v. 40, n. 3, p. 557-567, 2011.http://www.alice.cnptia.embrapa.br/alice/handle/doc/902188https://doi.org/10.1590/S1516-35982011000300013enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2022-07-01T10:19:51Zoai:www.alice.cnptia.embrapa.br:doc/902188Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542022-07-01T10:19:51falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542022-07-01T10:19:51Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv Genetic parameters for milk production by using random regression models with different alternatives of fixed regression modeling.
title Genetic parameters for milk production by using random regression models with different alternatives of fixed regression modeling.
spellingShingle Genetic parameters for milk production by using random regression models with different alternatives of fixed regression modeling.
COBUCI, J. A.
Test-day milk yield
Legendre polynomial
Selection
genetic correlation
heritability
title_short Genetic parameters for milk production by using random regression models with different alternatives of fixed regression modeling.
title_full Genetic parameters for milk production by using random regression models with different alternatives of fixed regression modeling.
title_fullStr Genetic parameters for milk production by using random regression models with different alternatives of fixed regression modeling.
title_full_unstemmed Genetic parameters for milk production by using random regression models with different alternatives of fixed regression modeling.
title_sort Genetic parameters for milk production by using random regression models with different alternatives of fixed regression modeling.
author COBUCI, J. A.
author_facet COBUCI, J. A.
COSTA, C. N.
BRACCINI NETO, J.
FREITAS, A. F. de
author_role author
author2 COSTA, C. N.
BRACCINI NETO, J.
FREITAS, A. F. de
author2_role author
author
author
dc.contributor.none.fl_str_mv JAIME ARAÚJO COBUCI, UFRGS / CNPq; CLAUDIO NAPOLIS COSTA, CNPGL; JOSÉ BRACCINI NETO, UFRGS.; ARY FERREIRA DE FREITAS, Pesquisador aposentado do CNPGL.
dc.contributor.author.fl_str_mv COBUCI, J. A.
COSTA, C. N.
BRACCINI NETO, J.
FREITAS, A. F. de
dc.subject.por.fl_str_mv Test-day milk yield
Legendre polynomial
Selection
genetic correlation
heritability
topic Test-day milk yield
Legendre polynomial
Selection
genetic correlation
heritability
description Records of test-day milk yields of the first three lactations of 25,500 Holstein cows were used to estimate genetic parameters for milk yield by using two alternatives of definition of fixed regression of the random regression models (RRM). Legendre polynomials of fourth and fifth orders were used to model regression of fixed curve (defined based on averages of the populations or multiple sub-populations formed by grouping animals which calved at the same age and in the same season of the year) or random lactation curves (additive genetic and permanent enviroment). Akaike information criterion (AIC) and Bayesian information criterion (BIC) indicated that the models which used multiple regression of fixed lactation curves of lactation multiple regression model with fixed lactation curves had the best fit for the first lactation test-day milk yields and the models which used a single regression of fixed curve had the best fit for the second and third lactations. Heritability for milk yield during lactation estimates did not vary among models but ranged from 0.22 to 0.34, from 0.11 to 0.21, and from 0.10 to 0.20, respectively, in the first three lactations. Similarly to heridability estimates of genetic correlations did not vary among models. The use of single or multiple fixed regressions for fixed lactation curves by RRM does not influence the estimates of genetic parameters for test-day milk yield across lactations.
publishDate 2011
dc.date.none.fl_str_mv 2011-10-04
2011
2022-07-01T10:19:41Z
2022-07-01T10:19:41Z
dc.type.driver.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv Revista Brasileira de Zootecnia, v. 40, n. 3, p. 557-567, 2011.
http://www.alice.cnptia.embrapa.br/alice/handle/doc/902188
https://doi.org/10.1590/S1516-35982011000300013
identifier_str_mv Revista Brasileira de Zootecnia, v. 40, n. 3, p. 557-567, 2011.
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/902188
https://doi.org/10.1590/S1516-35982011000300013
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
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instname_str Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
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institution EMBRAPA
reponame_str Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
collection Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
repository.name.fl_str_mv Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
repository.mail.fl_str_mv cg-riaa@embrapa.br
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