Bayesian inference of genetic parameters for test-day milk yield, milk quality traits, and somatic cell score in Burlina cows

Abstract

The aim of the study was to infer (co)variance components for daily milk yield, fat and protein contents, and somatic cell score (SCS) in Burlina cattle (a local breed in northeast Italy). Data consisted of 13,576 monthly test-day records of 666 cows (parities 1 to 8) collected in 10 herds between 1999 and 2009. Repeatability animal models were implemented using Bayesian methods. Flat priors were assumed for systematic effects of herd test date, days in milk, and parity, as well as for permanent environmental, genetic, and residual effects. On average, Burlina cows produced 17.0 kg of milk per day, with 3.66 and 3.33 percent of fat and protein, respectively, and 358,000 cells per mL of milk. Marginal posterior medians (highest posterior density of 95%) of heritability were 0.18 (0.09-0.28), 0.28 (0.21-0.36), 0.35 (0.25-0.49), and 0.05 (0.01-0.11) for milk yield, fat content, protein content, and SCS, respectively. Marginal posterior medians of genetic correlations between the traits were low and a 95 percent Bayesian confidence region included zero, with the exception of the genetic correlation between fat and protein contents. Despite the low number of animals in the population, results suggest that genetic variance for production and quality traits exists in Burlina cattle.