Bayesian estimation of genetic parameters for test day records in dairy cattle using linear hierarchical models

Abstract

Hierarchical models were fitted to first lactation test day milk yields of Canadian Holstein cows. Models included Wilmink’s lactation curve and herd-test date effects in the first stage, and herd–year–season of calving, region–age–season of calving and animal genetic effects in the second stage of the hierarchy, using various combinations of these factors. Functions of Wilmink’s function trajectory parameters were analysed jointly with the original parameters in selected models. Genetic and environmental variances, and heritabilities for lactation curve coefficients, daily and 305-day yields, and persistency of lactation were inferred by Bayesian methodology via Gibbs sampling. Accounting for the between herd–year–season variation in trajectory parameters resulted in more stable estimates of genetic variances over lactation, more sensible heritabilities for extreme days in milk, larger genetic correlations between yields at distant days of lactation, but lower heritabilities of lactation curve coefficients. Joint modelling of lactation curve parameters and selected functions thereof allowed for estimation of heritabilities of time-dependent traits, and reduced seemingly anomalous estimates of genetic parameters obtained with current implementations of certain random regression models.