Persistency of lactation using random regression models and different fixed regression modeling approaches

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Autor(a) principal: Cobuci, Jaime Araújo
Data de Publicação: 2012
Outros Autores: Costa, Claudio Napolis
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFRGS
Texto Completo: http://hdl.handle.net/10183/98474
Resumo: Milk yield test-day records on the first three lactations of 25,500 Holstein cows were used to estimate genetic parameters and predict breeding values for nine measures of persistency and 305-d milk yield in a random regression animal model using two criteria to define the fixed regression. Legendre polynomials of fourth and fifth orders were used to model the fixed and random regressions of lactation curves. The fixed regressions were adjusted for average milk yield on populations (single) or subpopulations (multiple) formed by cows that calved at the same age and in the same season. Akaike Information (AIC) and Bayesian Information (BIC) criteria indicated that models with multiple regression lactation curves had the best fit to test-day milk records of first lactations, while models with a single regression curve had the best fit for the second and third lactations. Heritability and genetic correlation estimates between persistency and milk yield differed significantly depending on the lactation order and the measures of persistency used. These parameters did not differ significantly depending on the criteria used for defining the fixed regressions for lactation curves. In general, the heritability estimates were higher for first (0.07 to 0.43), followed by the second (0.08 to 0.21) and third (0.04 to 0.10) lactation. The rank of sires resulting from the processes of genetic evaluation for milk yield or persistency using random regression models differed according to the criteria used for determining the fixed regression of lactation curve.
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spelling Cobuci, Jaime AraújoCosta, Claudio Napolis2014-07-23T02:07:54Z20121516-3598http://hdl.handle.net/10183/98474000899846Milk yield test-day records on the first three lactations of 25,500 Holstein cows were used to estimate genetic parameters and predict breeding values for nine measures of persistency and 305-d milk yield in a random regression animal model using two criteria to define the fixed regression. Legendre polynomials of fourth and fifth orders were used to model the fixed and random regressions of lactation curves. The fixed regressions were adjusted for average milk yield on populations (single) or subpopulations (multiple) formed by cows that calved at the same age and in the same season. Akaike Information (AIC) and Bayesian Information (BIC) criteria indicated that models with multiple regression lactation curves had the best fit to test-day milk records of first lactations, while models with a single regression curve had the best fit for the second and third lactations. Heritability and genetic correlation estimates between persistency and milk yield differed significantly depending on the lactation order and the measures of persistency used. These parameters did not differ significantly depending on the criteria used for defining the fixed regressions for lactation curves. In general, the heritability estimates were higher for first (0.07 to 0.43), followed by the second (0.08 to 0.21) and third (0.04 to 0.10) lactation. The rank of sires resulting from the processes of genetic evaluation for milk yield or persistency using random regression models differed according to the criteria used for determining the fixed regression of lactation curve.application/pdfengRevista brasileira de zootecnia= Brazilian journal of animal science [recurso eletrônico]. Viçosa, MG. Vol. 41, n. 9 (set. 2012), p. 1996-2004Gado leiteiroGenética animalMelhoramento genetico animalBreeding valueLegendre polynomialSelectionTest-day milk yieldPersistency of lactation using random regression models and different fixed regression modeling approachesinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/otherinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSORIGINAL000899846.pdf000899846.pdfTexto completo (inglês)application/pdf497509http://www.lume.ufrgs.br/bitstream/10183/98474/1/000899846.pdf51a1d0de6983c485cfe7ef3e868bd5c6MD51TEXT000899846.pdf.txt000899846.pdf.txtExtracted Texttext/plain40597http://www.lume.ufrgs.br/bitstream/10183/98474/2/000899846.pdf.txt7e5b1c717cf6b7de83214ad2b73b068eMD52THUMBNAIL000899846.pdf.jpg000899846.pdf.jpgGenerated Thumbnailimage/jpeg2170http://www.lume.ufrgs.br/bitstream/10183/98474/3/000899846.pdf.jpgdb051e301fe21e8a3959466ae3aae755MD5310183/984742021-07-09 04:38:00.382332oai:www.lume.ufrgs.br:10183/98474Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2021-07-09T07:38Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false
dc.title.pt_BR.fl_str_mv Persistency of lactation using random regression models and different fixed regression modeling approaches
title Persistency of lactation using random regression models and different fixed regression modeling approaches
spellingShingle Persistency of lactation using random regression models and different fixed regression modeling approaches
Cobuci, Jaime Araújo
Gado leiteiro
Genética animal
Melhoramento genetico animal
Breeding value
Legendre polynomial
Selection
Test-day milk yield
title_short Persistency of lactation using random regression models and different fixed regression modeling approaches
title_full Persistency of lactation using random regression models and different fixed regression modeling approaches
title_fullStr Persistency of lactation using random regression models and different fixed regression modeling approaches
title_full_unstemmed Persistency of lactation using random regression models and different fixed regression modeling approaches
title_sort Persistency of lactation using random regression models and different fixed regression modeling approaches
author Cobuci, Jaime Araújo
author_facet Cobuci, Jaime Araújo
Costa, Claudio Napolis
author_role author
author2 Costa, Claudio Napolis
author2_role author
dc.contributor.author.fl_str_mv Cobuci, Jaime Araújo
Costa, Claudio Napolis
dc.subject.por.fl_str_mv Gado leiteiro
Genética animal
Melhoramento genetico animal
topic Gado leiteiro
Genética animal
Melhoramento genetico animal
Breeding value
Legendre polynomial
Selection
Test-day milk yield
dc.subject.eng.fl_str_mv Breeding value
Legendre polynomial
Selection
Test-day milk yield
description Milk yield test-day records on the first three lactations of 25,500 Holstein cows were used to estimate genetic parameters and predict breeding values for nine measures of persistency and 305-d milk yield in a random regression animal model using two criteria to define the fixed regression. Legendre polynomials of fourth and fifth orders were used to model the fixed and random regressions of lactation curves. The fixed regressions were adjusted for average milk yield on populations (single) or subpopulations (multiple) formed by cows that calved at the same age and in the same season. Akaike Information (AIC) and Bayesian Information (BIC) criteria indicated that models with multiple regression lactation curves had the best fit to test-day milk records of first lactations, while models with a single regression curve had the best fit for the second and third lactations. Heritability and genetic correlation estimates between persistency and milk yield differed significantly depending on the lactation order and the measures of persistency used. These parameters did not differ significantly depending on the criteria used for defining the fixed regressions for lactation curves. In general, the heritability estimates were higher for first (0.07 to 0.43), followed by the second (0.08 to 0.21) and third (0.04 to 0.10) lactation. The rank of sires resulting from the processes of genetic evaluation for milk yield or persistency using random regression models differed according to the criteria used for determining the fixed regression of lactation curve.
publishDate 2012
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dc.relation.ispartof.pt_BR.fl_str_mv Revista brasileira de zootecnia= Brazilian journal of animal science [recurso eletrônico]. Viçosa, MG. Vol. 41, n. 9 (set. 2012), p. 1996-2004
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