Comparison of test-day models for genetic evaluation of production traits in dairy cattle

Abstract

A multiple-trait reduced rank random regression test-day model was applied for the breeding value estimation for first parity milk, protein, and fat yield of Finnish dairy cattle. This model was compared with three other models: a similar multiple-trait random regression test-day model without rank reduction, a multiple-trait repeatability test-day model, and a multiple-trait 305-d lactation yield model. Required (co)variance parameters were derived from the same covariance functions for all four models. For both random regression models, standard deviations of breeding values were the same and correlations between breeding values were between 0.995 and 0.998, resulting in only slight differences in the ranking of animals. Genetic trends were identical for the random regression test-day models and very similar to those estimated by the 305-d lactation yield model. The repeatability test-day model gave a slightly different genetic trend and inflated standard deviations for breeding values of cows with lactations in progress. Reduction of rank in the random regression test-day model decreased memory requirements and improved convergence in iteration when solving the mixed model equations.