Genetic parameters for ultrasound and carcass measures of yield and quality among replacement and slaughter beef cattle.

To evaluate beef cows on their reproductive performance a maternal productive index (M.P.I.) was developed as an alternative to their evaluation in terms of simpler traits, or in terms of a conventional selection index based on simple traits. Data on M.P.I. were obtained from Hereford cows on three grazing treatments each containing three groups of cows differentiated by last breeding season (i.e. presence and age of calf at foot). The same cows were also scored for coat type on two occasions in late spring and early summer. The magnitude and change of coat score are explained by the hypothesis that nutritional stress delays the cycle of shedding of winter coat and its replacement by a sleek coat. Analysis of the correlations between coat score data and M.P.I. shows that low M.P.I. is also associated with a delay in change of coat type. This suggests that M.P.I. is an indication of adaptation of cows to their environment with poorly adapted animals suffering a greater stress. Because M.P.I. is a direct measure of a very important goal of beef cattle breeding it should be used widely in selection (or culling) of beef cows. Whether genetic progress will be faster than through the use of simpler traits, may be judged once genetic parameters have been estimated for M.P.I.

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Comparison of models for estimating genetic parameters and predicting breeding values for birth weight and calving ease in Czech Charolais cattle

Czech Journal of Animal Science ◽

10.17221/7529-cjas ◽

2014 ◽

Vol 59 (No. 7) ◽

pp. 302-309 ◽

Cited By ~ 2

Author(s):

L. Vostrý ◽

Z. Veselá ◽

A. Svitáková ◽

H. Vostrá Vydrová

Keyword(s):

Animal Model ◽

Birth Weight ◽

Beef Cattle ◽

Fixed Effects ◽

Genetic Parameters ◽

Genetic Correlations ◽

Genetic Effects ◽

Parameters Estimation ◽

Calving Ease ◽

Heritability Estimates

The most appropriate model for genetic parameters estimation for calving ease and birth weight in beef cattle was selected. A total of 27 402 field records were available from the Czech Charolais breed. For estimation of genetic parameters for calving ease and body weight, three bivariate models were tested: a linear-linear animal model (L-LM) with calving ease classified into four categories (1 – easy; 2–4 – most difficult), a linear-linear animal model (SC-LM) in which calving ease scores were transformed into Snell scores (Snell 1964) and expressed as percentage of assisted calving (ranging 0–100%), and a bivariate threshold-linear animal model (T-LM) with calving ease classified into four categories (1 – easy, 2–4 – most difficult). All tested models included fixed effects for contemporary group (herd × year × season), age of dam, sex and breed of a calf. Random effects included direct and maternal genetic effects, maternal permanent environmental effect, and residual error. Direct heritability estimates for calving ease and birth weight were, with the use of L-LM, SC-LM, and T-LM, from 0.096 ± 0.013 to 0.226 ± 0.024 and from 0.210 ± 0.024 to 0.225 ± 0.026, respectively. Maternal heritability estimates for calving ease and birth weight were, with the use of L-LM, SC-LM, and T-LM, from 0.060 ± 0.031 to 0.104 ± 0.125 and from 0.074 ± 0.041 to 0.075 ± 0.040, respectively. Genetic correlations of direct calving ease with direct birth weight ranged from 0.46 ± 0.06 to 0.50 ± 0.06 for all tested models; whereas maternal genetic correlations between these two traits ranged from 0.24 ± 0.17 to 0.25 ± 0.53. Correlations between direct and maternal genetic effects within-trait were negative and substantial for all tested models (ranging from –0.574 ± 0.125 to –0.680 ± 0.141 for calving ease and from –0.553 ± 0.122 to –0.558 ± 0.118 for birth weight, respectively), illustrating the importance of including this parameter in calving ease evaluations. Results indicate that any of the tested models could be used to reliably estimate genetic parameters for calving ease for beef cattle in the Czech Republic. However, because of advantages in computation time and practical considerations, genetic analysis using SC-LM (transformed data) is recommended.

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