Cluster analyses to explore the genetic curve pattern for milk yield of Holstein
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , , , , |
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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Repositório Institucional da UNESP |
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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/openAccess2024-06-06T13:43:59Zoai:repositorio.unesp.br:11449/172318Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T22:57:12.352352Repositório 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 |
|
_version_ |
1808129477266374656 |