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      Estimação de parâmetros genéticos utilizando-se a produção de leite no dia do controle em primeiras lactações de vacas da raça Jersey Translated title: Estimation of genetic parameters for test day milk yield of first lactation Jersey cows using repeatability and random regression models

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          Abstract

          Registros da produção de leite de 3.531 controles de 620 primíparas da raça Jersey foram utilizados para a estimação dos componentes de covariância e dos parâmetros genéticos para a produção de leite por meio dos modelos de repetibilidade e de regressão aleatória. O modelo de repetibilidade (MR) incluiu os efeitos fixos de rebanho-ano-mês do controle, estação do parto e idade da vaca ao parto como covariável, com termos linear e quadrático, e os efeitos aleatórios de animal, de ambiente permanente e residual. O modelo de regressão aleatória incluiu os mesmos efeitos do MR, com o uso da função de Wilmink para a modelagem dos efeitos fixo (estação de parto) e aleatórios (genético de animal e de ambiente permanente). As estimativas de variância genética aditiva, de ambiente permanente, residual e fenotípica, e de herdabilidade e repetibilidade para a produção de leite usando-se o MR foram, respectivamente, 7,08; 1,88; 7,79 e 16,76 kg² e 0,42 e 0,50. Pelo modelo de regressão aleatória, os valores de herdabilidade para a produção de leite decresceram de 0,81 a 0,20 ao longo da lactação. As estimativas de variância genética aditiva mantiveram-se praticamente constantes durante a lactação, com maiores valores no início e final da lactação. As correlações genéticas foram próximas à unidade para as produções de leite da fase intermediária da lactação e diminuíram com o aumento do período entre os controles leiteiros. O modelo de repetibilidade não é a melhor alternativa para a modelagem das produções de leite no dia do controle da primeira lactação de vacas Jersey no Brasil.

          Translated abstract

          Data consisting of 3,531 test day records of 620 first parity Jersey cows were used to estimate genetic parameters for milk yield. Covariance components were estimated by repeatability and random regression animal models. The Wilmink function was used in the random regression model to fit the fixed effect of season of calving and the additive genetic, permanent environment and residual random effects. The repeatability model included the fixed effects of herd-year-month of test, season of calving and age at calving as a covariable with linear and quadratic terms and animal, permanent environmental and residual random effects. The estimates of additive genetic, permanent environmental, residual and phenotypic variances, heritability and repeatability obtained by the repeatability model for milk yield were, respectively: 7.08; 1.88; 7.79 and 16.76 kg², 0.42 and 0.50. The estimates of heritability for milk yield obtained from the random regression model decreased from 0.81 in the beginning of the lactation to 0.20 in the end. Larger estimates of genetic variance were observed at the extremes of the lactation period, but they were practically constant in mid-lactation. The permanent environmental variances increased along lactation. Genetic correlations between milk yields were larger in close test days, particularly in mid-lactation and decreased for the most distant test days. Genetic parameters do not indicate the repeatability model as an alternative to use test day records in order to estimate breeding values for milk yield of Jersey cows in Brazil.

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          Application of random regression models in animal breeding

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            Genetic evaluation of dairy cattle using test-day models.

            Recently there has been considerable interest in modeling individual test-day records (TDR) for genetic evaluation of dairy cattle as a replacement for the traditional use of estimated accumulated 305-d yields. Some advantages of test-day models (TDM) include the ability to account for environmental effects of each test day, the ability to model the trajectory of the lactation for individual genotypes or groups of animals, and the possibility of genetic evaluations for persistency of production. Also, the use of test-day models avoids the necessity of extending short lactations on culled animals and animals with records in progress. The disadvantages of TDM include computational difficulties associated with analyzing much larger datasets and the need to estimate many more parameters than in a traditional 305-d lactation model. Several different models have been proposed to model the trajectory of the lactation, including so-called "biological functions," various polynomials and character process models. At present, there is not universal agreement on which models to use in routine prediction of breeding values and better methods to compare models are desirable. Obtaining accurate estimates of the dispersion parameters to use in TDM remains a challenge. Methods used include a two-step procedure in which the dispersion parameters are estimated in a series of multivariate models followed by a reduction in order of fit using covariance functions, and a one-step procedure in which the parameters of TDM are estimated using restricted maximum likelihood or Bayesian methods in a random regression model. Further research should focus on including multiple lactation data and accounting for heterogeneity variance.
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              The effect of test day models on the estimation of genetic parameters and breeding values for dairy yield traits.

              The present study estimated genetic parameters for yields of milk, fat, and protein applying REML procedures under test day animal models. The data consisted of 155,494 test day records from 15,756 Friesian cows in first lactation from one region in northern Germany. The models applied included a traditional herd-year-season model for the analysis of single test days and 305-d records and two test day models differing by the definition of contemporary groups, either as herd-year-season of calving or as herd-test day. For single test days, heritabilities were highest for midlactation yields. For test day models, estimates of heritability varied with the number of test day records included for each cow. Estimates of .32, .19, and .20 for yields of milk, fat, and protein, respectively, were highest when only test d 3 to 7 were included; the corresponding estimates for 305-d records were .39, .32, and .30. Estimates of residual variances were reduced when test day records were converted to records of average yield in standardized intervals of 30 d. Breeding values were estimated for 305-d and test day models. A comparison of both sets of breeding values indicates only minor changes in sire rank, but more drastic reranking for individual cows.
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                Author and article information

                Contributors
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Journal
                rbz
                Revista Brasileira de Zootecnia
                R. Bras. Zootec.
                Sociedade Brasileira de Zootecnia (Viçosa )
                1806-9290
                August 2006
                : 35
                : 4 suppl
                : 1646-1652
                Affiliations
                [1 ] Universidade Federal de Pelotas Brazil
                [2 ] Universidade Federal de Pelotas Brazil
                [3 ] Embrapa Gado de Leite
                [4 ] Universidade Federal do Rio Grande do Sul
                Article
                S1516-35982006000600010
                10.1590/S1516-35982006000600010
                07baf35d-d8be-4770-9993-ffef0a4911b1

                http://creativecommons.org/licenses/by/4.0/

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                SciELO Brazil

                Self URI (journal page): http://www.scielo.br/scielo.php?script=sci_serial&pid=1516-3598&lng=en
                Categories
                AGRICULTURE, DAIRY & ANIMAL SCIENCE
                VETERINARY SCIENCES

                Animal agriculture,General veterinary medicine
                dairy cattle,genetic evaluation,heritability,random regression,repeatability,selection,variance components,avaliação genética,bovinos de leite,componentes de variância,herdabilidade,regressão aleatória,repetibilidade,seleção

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