S. R. Miraei Ashtiani, P. Zamani, A. Nikkhah, M. Moradi Shahrbabak, A. Naserian, F. Akbari,
Volume 9, Issue 4 (1-2006)
Abstract
Improvement of feed efficiency in dairy farming economically has a great importance. In this study, the genetic parameters of net energy efficiency and its relationships with milk yield, 3.2% fat corrected milk, body weight, gross income and income over feed costs were investigated, by the 2589 monthly records collected from the 723 lactating cows in the 3 herds. The different requirements of energy were estimated, by the National Research Council (NRC) models. A general linear model was employed for determining significant factors affecting each trait. The genetic parameters were estimated by a multivariate analysis with the derivative free approach of restricted maximum likelihood algorithm. The animal models contained the fixed effects of herd-year-season, parity number and stage of lactation, the regression coefficients of each trait on the dietary levels of rumen undegradable protein and metabolizable energy, and the random effects of animal additive genetic, permanent environment and residuals. The heritability of milk yield, 3.2 percent fat corrected milk, body weight, net energy efficiency, gross income and income over feed cost, were estimates 0.31, 0.32, 0.30, 0.34, 0.24 and 0.29 respectively. The results of this study indicated that the direct selection for net energy efficiency might genetically improve the feed efficiency. It also seems that the selection for fat corrected milk may be effective for the indirect improvement of feed efficiency and economic performance of dairy cows.
H. Farhangfar, H. Naeemipour , R. Lotfi,
Volume 12, Issue 43 (4-2008)
Abstract
This study was undertaken to estimate genetic trend and parameters of Holstein cattle in Khorasan province for milk yield using a spline random regression test day animal model. A total of 32854 monthly test day milk records (twice and thrice a day milking) obtained from 3842 Holstein heifers (progeny of 466 sires) distributed in 125 herds and calved from 2001 to 2005 was used to predict breeding value of individual animals. In the model, fixed effects of herd including year-month of recording, milking times, age at calving (linear and quadratic covariables), Holstein gene percentage (linear covariable) as well as random effects of additive genetic and permanent environment were studied. To take account of the shape of the lactation curve at genetic and environmental levels, cubic spline polynomials were also included in the test day model. Bayesian method by applying Gibbs sampling technique (100000 chains applying RRGIBBS software) was utilized to obtain posterior means of predicted breeding value of animals for milk yield at individual month of lactation. The results showed that mean of breeding value for 305-day milk yield was 52.90 kg (p<0.05). Spearman rank correlations between predicted breeding values at different months of lactation decreased as the interval between them increased. The highest and lowest rank correlations were found between months 8 and 9 (0.998) and between months 1 and 10 (0.312), respectively. Predicted breeding value of 305-day milk had the lowest and highest rank correlations with predicted breeding value at months 1 (0.553) and 6 (0.990), respectively. Regression analysis of average predicted breeding value of progenies in their birth year showed that the amount of genetic trend for 305 day milk yield was 17.75 kg per year, statistically no different from zero (p value=0.165).
