Comparing alternative random regression models to analyse first lactation daily milk yield data in Holstein–Friesian cattle
2003
Lopez-Romero, P. | Carabaño, M.J.
Test-day (TD) milk yields from Spanish Holstein cows were analysed in three independent data sets (35 615, 35 209 and 27 272 TD records, respectively) with a set of random regression models. Wilmink and Ali-Schaeffer lactation functions and, Legendre polynomials (RRL) of varying order (up to six coefficients) on additive genetic (AG) and permanent environmental (PE) effects were used. The analysis of the eigenvalues and eigenvectors of the AG and PE random regression (co)variance matrices revealed the possibility of reducing the dimension of the RRL submodels, particularly for the AG effects. Lactational submodels provided the largest daily AG variance estimates at the onset of lactation, as well as low or even negative genetic correlations between peripheral TD. Polynomials of higher order (four or above) showed oscillatory patterns with larger variances and lower genetic correlations predicted for the extremes of lactation. Model performance was assessed using a broad range of criteria. The results showed a strong consistency among data sets in terms of models ranking. Lactational models showed a worse performance than the RRL models with the same number of parameters. For RRL models, all criteria except the Bayesian information criterion, favoured the most complex model. This criterion selected a model with 2-3 coefficients for the AG effects and 5–6 coefficients for the PE effects.
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