V. L. Cardoso

Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows

We analyzed 152,145 test-day records from 7317 first lactations of Holstein cows recorded from 1995 to 2003. Our objective was to model variations in test-day milk yield during the first lactation of Holstein cows by random regression model (RRM), using various functions in order to obtain adequate and parsimonious models for the estimation of genetic parameters. Test-day milk yields were grouped into weekly classes of days in milk, ranging from 1 to 44 weeks. The contemporary groups were defined as herd-test-day. The analyses were performed using a single-trait RRM, including the direct additive, permanent environmental and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cow at calving were included as fixed effects. The mean trend of milk yield was modeled with a fourth-order orthogonal Legendre polynomial. The additive genetic and permanent environmental covariance functions were estimated by random regression on two parametric functions, Ali and Schaeffer and Wilmink, and on B-spline functions of days in milk. The covariance components and the genetic parameters were estimated by the restricted maximum likelihood method. Results from RRM parametric and B-spline functions were compared to RRM on Legendre polynomials and with a multi-trait analysis, using the same data set. Heritability estimates presented similar trends during mid-lactation (13 to 31 weeks) and between week 37 and the end of lactation, for all RRM. Heritabilities obtained by multi-trait analysis were of a lower magnitude than those estimated by RRM. The RRMs with a higher number of parameters were more useful to describe the genetic variation of test-day milk yield throughout the lactation. RRM using B-spline and Legendre polynomials as base functions appears to be the most adequate to describe the covariance structure of the data.

Indicadores de desempenho em rebanho da raça Holandesa: curvas de crescimento e altura, características reprodutivas, produtivas e parâmetros genéticos

Data on weight, height, productive and reproductive performance of 1,272 Holstein heifers, born from 1990 to 1998, were used to evaluate the growth and height patterns of these females from birth to first calving, and the following traits: weight at first calving (PP), age at first calving (IP), milk production in the first lactation (PL) and first calving interval (IEP), and to estimate genetic parameters of these traits. The von Bertalanffy fitted models were: Yi=802.10 (1-0.630EXP (-0.0022t)) for weight, and Yi=1.40 (1-0.208EXP (-0.0038t)) for height. The average performance of heifers were: height 140cm, weight 602kg and 27 month of age at first calving. The total milk yield was 8,026kg and first IEP was 420 days. The (co)variance components estimates were obtained by a derivative-free algorithm with the DFREML. The estimates of heritabilities were 0.31, 0.35, 0.34, respectively for PP, IP and PL, and 0.10 for IEP. The genetic correlations estimates between PP and PL, PL and IP and IEP and PL were low. The correlation between IEP and PP was 0.49 and between IEP and IP was, -0.69, sugesting an antagonism between the two last traits.

Genetic parameters for first lactation test-day milk flow in Holstein cows

Genetic parameters for test-day milk flow (TDMF) of 2175 first lactations of Holstein cows were estimated using multiple-trait and repeatability models. The models included the direct additive genetic effect as a random effect and contemporary group (defined as the year and month of test) and age of cow at calving (linear and quadratic effect) as fixed effects. For the repeatability model, in addition to the effects cited, the permanent environmental effect of the animal was also included as a random effect. Variance components were estimated using the restricted maximum likelihood method in single- and multiple-trait and repeatability analyses. The heritability estimates for TDMF ranged from 0.23 (TDMF 6) to 0.32 (TDMF 2 and TDMF 4) in single-trait analysis and from 0.28 (TDMF 7 and TDMF 10) to 0.37 (TDMF 4) in multiple-trait analysis. In general, higher heritabilities were observed at the beginning of lactation until the fourth month. Heritability estimated with the repeatability model was 0.27 and the coefficient of repeatability for first lactation TDMF was 0.66. The genetic correlations were positive and ranged from 0.72 (TDMF 1 and 10) to 0.97 (TDMF 4 and 5). The results indicate that milk flow should respond satisfactorily to selection, promoting rapid genetic gains because the estimated heritabilities were moderate to high. Higher genetic gains might be obtained if selection was performed in the TDMF 4. Both the repeatability model and the multiple-trait model are adequate for the genetic evaluation of animals in terms of milk flow, but the latter provides more accurate estimates of breeding values.

Análise econômica de esquemas alternativos de controle leiteiro

Costs of six different milk recording schemes based on the interval between controls (with the possibility of a three-month interval supervision control and the use of the farmersx92 milk recording data), the number of milking recorded and milk quality analyses (milk composition and/or somatic cell counting) were compared. The ratio milk recording costs over gross income (CL/RB) and over net income (CL/RL) were also estimated. The traditional scheme (monthly recording) presented the highest monthly costs and ratios CL/RB and CL/RL. Schemes with longer intervals between controls and alternate sampling presented the lowest costs due to the reduction of travel costs. Monthly milk recording costs over milk gross income and over net income ranged from 0.68% to 1.8% and from 6.6% to 17.0%, respectively.

Selection strategies for dairy buffaloes: economic and genetic consequences

Buffaloes are generally raised in Brazil without milk-recording programs, and thus without genetic evaluations of any of their traits. This study evaluated the economic impacts of three different selection strategies on buffalo populations and the evolution of genetic trends, genetic variances and inbreeding coefficients resulting from each of them. The selection strategies used were as follows: (i) random selection; (ii) phenotypic selection; and (iii) progeny testing (PT). As the numbers of herds enrolled in milk-recording programs increased, phenotypic selection and PT strategies increased both monetary benefits and genetic trends. The extra costs of implementing milk recording (MR) and PT procedures were exceeded by the income resulting from better buffalo performance. Progeny testing is known to result in beneficial genetic trends and the use of artificial insemination promoted better distributions of genetic material into herds that were not enrolled in milk-recording programs. Phenotypic selection and PT increased mean milk production--a key factor in profitability. Inbreeding levels remained stable with phenotypic selection, even as the numbers of MR herds increased. Increases in the numbers of sires that were evaluated reduced the mean inbreeding coefficient in PT. Increasing the number of herds enrolled in milk-recording programs resulted in increased numbers of sires needed for PT, but this did not increase the inbreeding coefficient. In summary, phenotypic selection and PT strategies appear to be economically viable for buffalo husbandry in south-eastern Brazil under current (2007-2008) economic conditions and should be encouraged.

Random regression models using different functions to model milk flow in dairy cows

We analyzed 75,555 test-day milk flow records from 2175 primiparous Holstein cows that calved between 1997 and 2005. Milk flow was obtained by dividing the mean milk yield (kg) of the 3 daily milking by the total milking time (min) and was expressed as kg/min. Milk flow was grouped into 43 weekly classes. The analyses were performed using a single-trait Random Regression Models that included direct additive genetic, permanent environmental, and residual random effects. In addition, the contemporary group and linear and quadratic effects of cow age at calving were included as fixed effects. Fourth-order orthogonal Legendre polynomial of days in milk was used to model the mean trend in milk flow. The additive genetic and permanent environmental covariance functions were estimated using random regression Legendre polynomials and B-spline functions of days in milk. The model using a third-order Legendre polynomial for additive genetic effects and a sixth-order polynomial for permanent environmental effects, which contained 7 residual classes, proved to be the most adequate to describe variations in milk flow, and was also the most parsimonious. The heritability in milk flow estimated by the most parsimonious model was of moderate to high magnitude.