Estimation of genetic parameters for daily milk yield of primiparous Ayrshire cows by random regression test-day models

Test-day data comprising of 63,331 test-day milk records of 6310 primiparous Finnish Ayrshire cows were used to estimate genetic parameters for daily milk production. Two alternative random regression (RR) sub-models were used to describe bleeding values for the shape of lactation curves of individual cows: a five parameter logarithmic polynomial (ASM) or a normalised third order orthogonal polynomial. Permanent environment (PE) of a cow was described by either a common PE effect (ASM and OPMPE1) or a normalised third order orthogonal polynomial (OPMPE4). Variance components were estimated with an animal model using EM-REML. A multitrait (MT) approach together with continuous covariance function (CF) was used to derive reference for RR estimates. Heritability estimates obtained by ASM (0.41-0.60) and OPMPE1 (0.28-0.53) were higher than those derived from CF analysis (0.20-0.28). Fitting the RR sub-model for PE effects strongly influenced the magnitude of heritability estimates (0.23-0.36). Estimates of heritability were found to be highest during early and late lactation when estimated by ASM and OPMPE1 models, while the converse was true for those derived by CF. Estimates obtained by the OPMPE4 model were highest at the beginning of lactation and between 183 and 256 days in milk. Genetic correlations were high between consecutive test days, but decreased when intervals between test days increased. Where models ASM and OPMPE1 indicated a negative correlation between distant test days, OPMPE4 estimates were consistent with those of CF. Due to the statistical complexity of RR test-day models use of MT is a more feasible approach for the estimation of (co)variance components for CF coefficients. (C) 2000 Elsevier Science B.V. All rights reserved.