Estimation of variance components of milk, fat, and protein yields of Tunisian Holstein dairy cattle using Bayesian and REML methods

Hafedh Ben Zaabza,Mohamed Amine Ferchichi,Boulbaba Rekik,Abderrahmen Ben Gara,Hedi Hammami

doi:10.5194/aab-59-243-2016

Abstract

Abstract. A multi-trait repeatability animal model under restricted maximum likelihood (REML) and Bayesian methods was used to estimate genetic parameters of milk, fat, and protein yields in Tunisian Holstein cows. The estimates of heritability for milk, fat, and protein yields from the REML procedure were 0.21 ± 0.05, 0.159 ± 0.04, and 0.158 ± 0.04, respectively. The corresponding results from the Bayesian procedure were 0.273 ± 0.02, 0.198 ± 0.01, and 0.187 ± 0.01. Heritability estimates tended to be larger via the Bayesian than those obtained by the REML method. Genetic and permanent environmental variances estimated by REML were smaller than those obtained by the Bayesian analysis. Inversely, REML estimates of the residual variances were larger than Bayesian estimates. Genetic and permanent correlation estimates were on the other hand comparable by both REML and Bayesian methods with permanent environmental being larger than genetic correlations. Results from this study confirm previous reports on genetic parameters for milk traits in Tunisian Holsteins and suggest that a multi-trait approach can be an alternative for implementing a routine genetic evaluation of the Tunisian dairy cattle population.

Highlights

In the past decades, dairy cattle management in Tunisia has been oriented toward increased milk yield
Values obtained in this study for heritabilities for 305day milk and fat yields are comparable to those found by Carabaño et al (1989) for Spanish data using the restricted maximum likelihood (REML) procedure
Variance components of 305-day milk, fat, and protein yields were investigated by REML and Bayesian procedures using a multi-trait animal model

Summary

Introduction

Dairy cattle management in Tunisia has been oriented toward increased milk yield. Estimates of genetic parameters for milk yield in dairy cows are abundant in the literature (Ben Gara et al, 2006, 2012; Hammami et al, 2008a, 2009a). Multivariate models are of fundamental importance in applied and theoretical quantitative genetics (Gianola and Sorensen, 2004). Two major methods were applied, restricted maximum likelihood (REML) and Bayesian methods. Bayesian methods were broadly used to solve many of the difficulties faced by conventional statistical methods and extend the applicability of statistics on animal breeding data. Markov chain Monte Carlo (MCMC) has an important impact in applied statistics, especially from a Bayesian perspective for the estimation of genetic parameters in the linear mixed effect model (Sorensen and Gianola, 2002; Hallander et al, 2010). The aim of this research was to use a multi-trait

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Discussion

Conclusion