Abstract
We used actual and adjusted weights to 120 d and 210 d of age of 72,731 male and female Nellore calves born in 40 PMGRN - Nellore Brazil herds from 1985 to 2005 aiming to compare the effect of different definitions of contemporary groups on estimates of (co)variance and genetic parameters. Four models, each one with a different structure of contemporary group (CG), were compared using the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and the Consistent Akaike Information Criterion (CAIC). (Co)variance estimates were obtained using a derivative-free restricted maximum likelihood procedure. Estimates of (co)variances and genetic parameters were similar for the four models considered. However, the BIC and CAIC indicated that the most appropriate model for this Nellore population was the one that considered CG to be random, and sex of calf to be fixed and separate from CG, in which CG was defined as the group of calves born in the same herd, year, season of birth (trimester), and undergone the same management.KEYWORDS: beef cattle; contemporary groups; information criteria.
Highlights
IntroductionContemporary groups have usually been considered as fixed effects in beef cattle genetic evaluations
The structure of contemporary groups (CG) is of primary importance for genetic evaluation of animals under selection; they are crucial to avoid potential biases in genetic evaluations due to differential treatment of animals in a population (VAN VLECK, 1987).Contemporary groups have usually been considered as fixed effects in beef cattle genetic evaluations
This has been based on HENDERSON (1973) statement that in sire models, genetic predictions of sires would be associated to contemporary group effects, and to eliminate this bias, CG needed to be defined as fixed effects
Summary
Contemporary groups have usually been considered as fixed effects in beef cattle genetic evaluations. BABOT et al (2003) managed to estimate genetic values for litter size in herds with insufficient number of animals per CG using simulated data, whereas VASCONCELOS et al (2005) estimated genetic values for milk production in dairy cattle in Portugal using contrast models. Treating CG as random effects was found to be advantageous by GONZÁLEZ-RECIO & ALENDA (2005) when analyzing binary reproductive traits in Spanish dairy cattle, by WOLF et al (2005) for growth and litter size in swine utilizing a multi-trait animal model, and by LEGARRA et al (2005) for milk production in ewes using a Bayesian approach
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