Cluster analyses to explore the genetic curve pattern for milk yield of Holstein

Detalhes bibliográficos
Autor(a) principal: Savegnago, Rodrigo Pelicioni [UNESP]
Data de Publicação: 2016
Outros Autores: Nascimento, Guilherme Batista do [UNESP], Rosa, Guilherme Jordão de Magalhães, Carneiro, Raul Lara Resende de, Sesana, Roberta Cristina, El Faro, Lenira, Munari, Danísio Prado [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.livsci.2015.11.010
http://hdl.handle.net/11449/172318
Resumo: Animal selection in dairy cattle can vary depending on the objectives of the breeding programs. The objective of this study was to explore the genetic curve pattern of EBVs for test day milk yields (TDMY) in Holstein cows using cluster analyses to identify the most suitable animals for selection based on their genetic curve for milk yield. A data set with 29,477 monthly TDMY records from 3543 first lactations of Brazilian Holstein cows were used to predict the breeding values for TDMY with random regression model. Hierarchical and non-hierarchical cluster analyses were performed based on the EBVs for 30, 60, 90, 120, 150, 180, 210, 240, 270, and 305 days in milk (DIM) to explore the genetic curve patterns of milk production of animals within the population. At first moment, the population was divided into three groups based on animals' genetic curve pattern for milk yield using hierarchical cluster analysis. According to non-hierarchical cluster analysis, one of those groups had EBVs along the lactation curve above the population average. Further cluster analysis done only with those animals with genetic curve pattern above the population mean showed specific subgroups of animals with different genetic curves for milk yield despite of all of those animals had EBVs above the population average, along the lactation curve. It indicated that specific subgroup of animals with a specific genetic curve pattern for milk yield can be chosen depending on the objectives of the breeding program. It was concluded that the cluster analyzes could be used to select animals based on the shapes of the genetic curve for milk production together with the EBV for milk yield at 305 days in milk. Thus, it can be possible to select at the same time more productive animals with genetic curves that met the goals of breeding programs that take into account the milk production in other parts along the milk production curve.
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spelling Cluster analyses to explore the genetic curve pattern for milk yield of HolsteinBreeding valueDairy cattleMultivariate analysisPersistencyAnimal selection in dairy cattle can vary depending on the objectives of the breeding programs. The objective of this study was to explore the genetic curve pattern of EBVs for test day milk yields (TDMY) in Holstein cows using cluster analyses to identify the most suitable animals for selection based on their genetic curve for milk yield. A data set with 29,477 monthly TDMY records from 3543 first lactations of Brazilian Holstein cows were used to predict the breeding values for TDMY with random regression model. Hierarchical and non-hierarchical cluster analyses were performed based on the EBVs for 30, 60, 90, 120, 150, 180, 210, 240, 270, and 305 days in milk (DIM) to explore the genetic curve patterns of milk production of animals within the population. At first moment, the population was divided into three groups based on animals' genetic curve pattern for milk yield using hierarchical cluster analysis. According to non-hierarchical cluster analysis, one of those groups had EBVs along the lactation curve above the population average. Further cluster analysis done only with those animals with genetic curve pattern above the population mean showed specific subgroups of animals with different genetic curves for milk yield despite of all of those animals had EBVs above the population average, along the lactation curve. It indicated that specific subgroup of animals with a specific genetic curve pattern for milk yield can be chosen depending on the objectives of the breeding program. It was concluded that the cluster analyzes could be used to select animals based on the shapes of the genetic curve for milk production together with the EBV for milk yield at 305 days in milk. Thus, it can be possible to select at the same time more productive animals with genetic curves that met the goals of breeding programs that take into account the milk production in other parts along the milk production curve.Departamento de Ciências Exatas Faculdade de Ciências Agrárias e Veterinárias Universidade Estadual Paulista FCAV/UNESPDepartment of Animal Sciences University of WisconsinCRV Lagoa SertãozinhoAgência Paulista de Tecnologia dos Agronegócios (APTA Centro Leste/Secretaria de Agricultura e Abastecimento (SAA)Departamento de Ciências Exatas Faculdade de Ciências Agrárias e Veterinárias Universidade Estadual Paulista FCAV/UNESPUniversidade Estadual Paulista (Unesp)University of WisconsinSertãozinhoCentro Leste/Secretaria de Agricultura e Abastecimento (SAA)Savegnago, Rodrigo Pelicioni [UNESP]Nascimento, Guilherme Batista do [UNESP]Rosa, Guilherme Jordão de MagalhãesCarneiro, Raul Lara Resende deSesana, Roberta CristinaEl Faro, LeniraMunari, Danísio Prado [UNESP]2018-12-11T16:59:41Z2018-12-11T16:59:41Z2016-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article28-32application/pdfhttp://dx.doi.org/10.1016/j.livsci.2015.11.010Livestock Science, v. 183, p. 28-32.1871-1413http://hdl.handle.net/11449/17231810.1016/j.livsci.2015.11.0102-s2.0-849501050072-s2.0-84950105007.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLivestock Science0,730info:eu-repo/semantics/openAccess2024-01-14T06:22:51Zoai:repositorio.unesp.br:11449/172318Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-01-14T06:22:51Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Cluster analyses to explore the genetic curve pattern for milk yield of Holstein
title Cluster analyses to explore the genetic curve pattern for milk yield of Holstein
spellingShingle Cluster analyses to explore the genetic curve pattern for milk yield of Holstein
Savegnago, Rodrigo Pelicioni [UNESP]
Breeding value
Dairy cattle
Multivariate analysis
Persistency
title_short Cluster analyses to explore the genetic curve pattern for milk yield of Holstein
title_full Cluster analyses to explore the genetic curve pattern for milk yield of Holstein
title_fullStr Cluster analyses to explore the genetic curve pattern for milk yield of Holstein
title_full_unstemmed Cluster analyses to explore the genetic curve pattern for milk yield of Holstein
title_sort Cluster analyses to explore the genetic curve pattern for milk yield of Holstein
author Savegnago, Rodrigo Pelicioni [UNESP]
author_facet Savegnago, Rodrigo Pelicioni [UNESP]
Nascimento, Guilherme Batista do [UNESP]
Rosa, Guilherme Jordão de Magalhães
Carneiro, Raul Lara Resende de
Sesana, Roberta Cristina
El Faro, Lenira
Munari, Danísio Prado [UNESP]
author_role author
author2 Nascimento, Guilherme Batista do [UNESP]
Rosa, Guilherme Jordão de Magalhães
Carneiro, Raul Lara Resende de
Sesana, Roberta Cristina
El Faro, Lenira
Munari, Danísio Prado [UNESP]
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
University of Wisconsin
Sertãozinho
Centro Leste/Secretaria de Agricultura e Abastecimento (SAA)
dc.contributor.author.fl_str_mv Savegnago, Rodrigo Pelicioni [UNESP]
Nascimento, Guilherme Batista do [UNESP]
Rosa, Guilherme Jordão de Magalhães
Carneiro, Raul Lara Resende de
Sesana, Roberta Cristina
El Faro, Lenira
Munari, Danísio Prado [UNESP]
dc.subject.por.fl_str_mv Breeding value
Dairy cattle
Multivariate analysis
Persistency
topic Breeding value
Dairy cattle
Multivariate analysis
Persistency
description Animal selection in dairy cattle can vary depending on the objectives of the breeding programs. The objective of this study was to explore the genetic curve pattern of EBVs for test day milk yields (TDMY) in Holstein cows using cluster analyses to identify the most suitable animals for selection based on their genetic curve for milk yield. A data set with 29,477 monthly TDMY records from 3543 first lactations of Brazilian Holstein cows were used to predict the breeding values for TDMY with random regression model. Hierarchical and non-hierarchical cluster analyses were performed based on the EBVs for 30, 60, 90, 120, 150, 180, 210, 240, 270, and 305 days in milk (DIM) to explore the genetic curve patterns of milk production of animals within the population. At first moment, the population was divided into three groups based on animals' genetic curve pattern for milk yield using hierarchical cluster analysis. According to non-hierarchical cluster analysis, one of those groups had EBVs along the lactation curve above the population average. Further cluster analysis done only with those animals with genetic curve pattern above the population mean showed specific subgroups of animals with different genetic curves for milk yield despite of all of those animals had EBVs above the population average, along the lactation curve. It indicated that specific subgroup of animals with a specific genetic curve pattern for milk yield can be chosen depending on the objectives of the breeding program. It was concluded that the cluster analyzes could be used to select animals based on the shapes of the genetic curve for milk production together with the EBV for milk yield at 305 days in milk. Thus, it can be possible to select at the same time more productive animals with genetic curves that met the goals of breeding programs that take into account the milk production in other parts along the milk production curve.
publishDate 2016
dc.date.none.fl_str_mv 2016-01-01
2018-12-11T16:59:41Z
2018-12-11T16:59:41Z
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/10.1016/j.livsci.2015.11.010
Livestock Science, v. 183, p. 28-32.
1871-1413
http://hdl.handle.net/11449/172318
10.1016/j.livsci.2015.11.010
2-s2.0-84950105007
2-s2.0-84950105007.pdf
url http://dx.doi.org/10.1016/j.livsci.2015.11.010
http://hdl.handle.net/11449/172318
identifier_str_mv Livestock Science, v. 183, p. 28-32.
1871-1413
10.1016/j.livsci.2015.11.010
2-s2.0-84950105007
2-s2.0-84950105007.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Livestock Science
0,730
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 28-32
application/pdf
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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