An Autoregressive Repeatability Animal Model for Test-Day Records in Multiple Lactations

Test-day (TD) models are becoming a standard for genetic evaluation of production traits in dairy cattle. Various approaches to model covariances between TD records include random regression, autoregressive repeatability, orthogonal polynomials, and models based on character processing. The applicab...

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Bibliographic Details
Published in:Journal of dairy science 2002-08, Vol.85 (8), p.2040-2045
Main Authors: Carvalheira, J., Pollak, E.J., Quaas, R.L., Blake, R.W.
Format: Article
Language:English
Subjects:
Animal models
Animal productions
Animals
autoregression
Biological and medical sciences
Breeding
Cattle - genetics
Cattle - physiology
dairy cattle
Environment
Female
Fundamental and applied biological sciences. Psychology
genetic evaluation
Lactation - genetics
Male
Mathematics
Models, Biological
Pedigree
Terrestrial animal productions
test-day animal model
Vertebrates
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Summary:Test-day (TD) models are becoming a standard for genetic evaluation of production traits in dairy cattle. Various approaches to model covariances between TD records include random regression, autoregressive repeatability, orthogonal polynomials, and models based on character processing. The applicability of these models is mainly associated with the number of parameters to estimate, incorporation of multiple lactations, and the accuracy of correlations generated by the cow's repeated expression of milking performance (TD yields) within and across lactations. We define and evaluate a multiple-lactation, autoregressive-repeatability model that disentangles environmental effects due to cow within and between lactations. Simulated records either included or ignored a long-term environmental effect between lactations. Our autoregressive TD animal model correctly detected presence and the absence of this effect and accurately recovered the assumed variance components and correlations underlying the data (10 parameters for three lactations). Estimates of variance components and autocorrelation coefficients were obtained using DFREML-simplex methodology. Given the value of this approach to reduce the size of residual variance components, autoregressive animal models are a preferable alternative to classical methods based on cumulative lactation yield to improve milk production in dairy cattle.
ISSN:0022-0302
1525-3198
DOI:10.3168/jds.S0022-0302(02)74281-1