Trajectory of body weight of performance tested dual-purpose bulls

The objective of this study was to estimate genetic parameters for the results of classifying of carcass traits by the SEUROP method in beef cattle in the Czech Republic using linear and linear-threshold models. Genetic parameters were calculated and evaluated in a set of 4276 animals of eleven beef breeds and crosses with dairy and dual-purpose breeds (Aberdeen Angus – 1376, Hereford – 994, Simmental – 651, Charolais – 524, Piemontese – 185, Galloway – 162, Blonde d’Aquitaine – 147, Limousine – 106, Highland – 53, Gasconne – 44, Belgian Blue – 34) in 2005–2008. Aberdeen Angus, Hereford, Charolais and beef Simmental were the most numerous breeds. Fixed effect of a classifier, fixed regression on age at slaughter by means of Legendre polynomial of the second degree separately for the each breed and sex and fixed regression on heterosis coefficient were included in a model equation. Genetic parameters were estimated by a multi-trait animal model using a linear model and a linear-threshold model in which carcass weight (CW) was considered as the linear trait and carcass conformation (CC) and carcass fatness (CF) grading as threshold traits. The heritability coefficient for CW differed only moderately according to the method of the genetic parameter estimation (0.295 in linear model and 0.306 in linear-threshold model). The heritability coefficient for CC was 0.187 in linear model and 0.237 in linear-threshold model. The heritability coefficient for CF grading was 0.089 in linear model and 0.146 in linear-threshold model. Genetic correlation between CW and CC was high (0.823 in linear model and 0.959 in linear-threshold model), the correlation between CW and CF was intermediate (0.332 and 0.328, respectively) and it was low between CF and CC (0.071 and 0.053). If CW was included in the model equation as fixed regression using Legendre polynomial, lower heritability coefficients for CC (0.077 and 0.078) and CF (0.086 and 0.123) were calculated and the correlation between CC and CF was negative (–0.430 and –0.429).

Section: Methodsmentioning

confidence: 99%

Linear and linear-threshold model for genetic parameters for SEUROP carcass traits in Czech beef cattle

Veselá¹,

Vostrý²,

Šafus³

2011

“…In the total pedigree file 44% of animals have known Table 1. Přibyl et al (2007) describes the methodology of evaluation. The growth of animals and variability of traits in relation to age were fitted by functions (f) of orthogonal Legendre Polynomials (LP) and Linear Spline (SP).…”

Section: Methodsmentioning

confidence: 99%

“…Rasch and Mašata (2006) compared several methods of estimating variance components, the essential part of evaluation. Přibyl et al (2007) describes RR methodology of growth evaluation for dual-purpose CF bulls at test stations. Legendre Polynomials (LP) were used to express the dependence of live weight on age for a fixed part of the equation (function within stationyear-period (HYS) classes) and Linear Splines (SP) for random parts (the animal and its permanent environmental effects).…”

mentioning

confidence: 99%

Models for evaluation of growth of performance tested bulls

Přibyl¹,

Krejcová²,

Přibylová³

2008

Self Cite

Before being used for insemination, young bulls of Czech Fleckvieh (CF) are tested for growth at performance-test stations. While at stations, the bulls are weighed monthly. Evaluation included 7 448 bulls with 82 676 records of weight measured from 6 to 520 days of life. In the station-year-period (HYS), which can be prolonged up to 3 months, different groups were tested according to the beginning of growth curve and according to test-days of weighing. Weight analyses were used to handle heterogeneous variability based on age. Legendre Polynomials (LP) with 5 parameters described the average growth curve for HYS classes. Deviations from average curves were decomposed into genetic (G), animal's permanent environment (PE) and residual (RES) components. Functions of (G) and (PE) were tested using LP random regression (RR) methodology with 5 or 3 parameters and Linear Spline (SP) function with 5 knots. Variance increases with the age of the animals. From 100 to 400 days, heritability was nearly the same with a mild depression in the middle of the period. The average was h 2 = 0.31 and ended with h 2 = 0.36. Results were similar for variance components, heritability, genetic, environmental and phenotype correlations from different models with different LP and SP functions. Higher RES variability occurred only for LP with 3 parameters. For traits like live weight, the RR should have at least 3 parameters and SP function should be used.

“…Records of body weights at different ages show high correlations between each other because weight at age (x) includes the previous weight at age (x-1) (Bouška et al, 2003;Přibyl et al, 2007a). These values must not, therefore, be overestimated.…”

mentioning

confidence: 99%

“…Bogdanovic et al (2002) estimated variance components for weights and gains in different periods for Simmental bulls. The evaluation of live weight of Czech Fleckvieh bulls at performance-test stations by RR was described by Přibyl et al (2007aPřibyl et al ( , 2008. Gains over 50-day intervals were studied by Krejčová et al (2007a,b) and Mielenz et al (2007), comparing RR and MT models at the same time.…”

mentioning

confidence: 99%

Genetic evaluation of daily gains of dual-purpose bulls using a random regression model

Krejcová¹,

Přibyl²,

Přibylová³

et al. 2008

Self Cite

Daily gains of 8 243 dual-purpose bulls from 100 to 400 days of age during the years 1971 to 2007 were analyzed by random regression models. Orthogonal Legendre polynomials (LP) of degree 4 were applied to daily gains calculated at 30-day intervals over the test period. Fixed curves were estimated within the station-year-season of birth. The models also included a fixed station-year-season of weighing, animal additive genetic effects and animal permanent environmental effects. The peak daily gain was attained between 230 and 280 days of age, which corresponded to the period of the lowest variance in daily gains. Heritability estimates of daily gain were in the range of 0.014 to 0.043. The reliability of composite trait -cumulative gains over the entire period was 0.87. Genetic correlations between gains at different ages were high for adjacent ages and decreased with increasing difference in ages. Correlations of permanent environmental effects were high for adjacent ages, but became negative for ages that were far apart, indicating the possibility of compensatory growth. The phenotypic correlations were close to zero. The correlations for cumulative daily gains were higher than those for individual daily gains.