Genetic parameters for milk production by using random regression models with different alternatives of fixed regression modeling.
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , |
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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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) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
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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1794503525473976320 |