estimativas de parâmetros genéticos para produção e composição do leite de vacas da raça holandesa no estado do paraná estimates of genetic parameters for yield and composition of milk of holstein cows in paraná state

estimaciones de parámetros genéticos para la producción y composición de la leche de vacas de la raza holandesa en el estado de paraná estimaciones de parámetros genéticos para la producción y composición de la leche de vacas holstein en el estado de paraná

Clicks: 308
ID: 209616
2008
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Abstract
Estimaram-se as herdabilidades e as correlações genéticas para as produções de leite (PL), de gordura (PG) e de proteína (PP) e para os percentuais de gordura (%G) e de proteína (%P) no leite de vacas da raça Holandesa no estado do Paraná. Foram analisados 117.082 registros de lactações encerradas para estimativa das características PL, PG e PP e 116.747 registros de lactações para %G e de %P. Todos os animais foram controlados oficialmente pelo Serviço de Controle Leiteiro Mensal da Associação Paranaense de Criadores de Bovinos da Raça Holandesa entre janeiro de 1992 e dezembro de 2003. A estimação dos componentes de co-variância e dos parâmetros genéticos foi realizada por meio de inferência Bayesiana em análises uni e tricaracteres utilizando-se um modelo animal que incluiu os efeitos fixos de grupo de contemporâneos e número de ordenhas, além dos efeitos linear e quadrático da idade da vaca ao parto e os efeitos aleatórios genéticos aditivos, permanente de ambiente e residual. As médias e os respectivos desvios-padrãopara as PL, PG e PP, em kg, foram 8.181,23 ± 1.903,51; 270,88 ± 67,58 e 249,01 ± 55,65, respectivamente, e para os %G e %P foram 3,33 ± 0,45 e 3,06 ± 0,19, respectivamente. As estimativas de herdabilidade para PL, PG, PP, %G e %P obtidas nas análises unicaracteres foram 0,26; 0,28; 0,25; 0,60 e 0,58, respectivamente, e indicam que as características possuem variação genética aditiva que garante boa resposta à seleção. As estimativas das correlações genéticas entre PL e PG, PL e PP, PG e PP, PL e %G, PL e %P, %G e %P foram 0,56; 0,89; 0,67; -0,42; -0,39 e 0,56, respectivamente, portanto, a seleção realizada para aumentar a PL promoverá aumento na PG e PP e redução nos %G e %P.
The objectives of this study were to estimate heritabilities and genetic correlations for milk (MY), fat (FY) and protein (PY) yields and for percentages of fat (%F) and protein (%P) in milk of Holstein cows in Paraná State. Data consisted of 117,082 lactation records for MY, FY and PY and 116,747 records for %F and %P from herds supervised by the Milk Recording Service of the Holstein Breeders Association of Paraná from January 1992 to December 2003. Covariance components and genetic parameters were estimated by Bayesian inference using an animal model including the fixed effects of contemporary group, number of milking, linear and quadratic effects for calving age and the additive genetic, permanent environmental and residual random effects. Averages and respective standard deviations for MY, FY and PY (kg) were respectively 8,181.23 ± 1,903.51, 270.88 ± 67.58 and 249.01 ± 55.65 and for %F and %P they were 3.33 ± 0.45 and 3.06 0.19, respectively. Heritability estimates for MY, FY, PY, %G and %P obtained from single-trait analyses were 0.26, 0.28, 0.25, 0.60 and 0.58, respectively. They indicate that these traits may be improved by selection. Genetic correlation estimates between MY and FY, MY and PY, FY and PY, MY and %F, MY and %P and for %F and %P were 0.56, 0.89, 0.67, -0.42, -0.39 and 0.56, respectively. Selection for increasing MY will result in positive correlated genetic gains for kg of fat and protein but decrease their percentages in milk.
Reference Key
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Authors ;Meiby Carneiro de Paula;Elias Nunes Martins;Luiz Otávio Campos da Silva;Carlos Antonio Lopes de Oliveira;Altair Antônio Valotto;Eliane Gasparino
Journal european journal of lipid science and technology
Year 2008
DOI
10.1590/S1516-35982008000500007
URL
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982008000500007
Keywords
heritability bayesian inference gibbs sampler genetic correlation

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