Abstract
Records of test-day milk yields of the first three lactations of 25,500 Holstein cows were used to estimate genetic parameters for milk yield by using two alternatives of definition of fixed regression of the random regression models (RRM). Legendre polynomials of fourth and fifth orders were used to model regression of fixed curve (defined based on averages of the populations or multiple sub-populations formed by grouping animals which calved at the same age and in the same season of the year) or random lactation curves (additive genetic and permanent enviroment). Akaike information criterion (AIC) and Bayesian information criterion (BIC) indicated that the models which used multiple regression of fixed lactation curves of lactation multiple regression model with fixed lactation curves had the best fit for the first lactation test-day milk yields and the models which used a single regression of fixed curve had the best fit for the second and third lactations. Heritability for milk yield during lactation estimates did not vary among models but ranged from 0.22 to 0.34, from 0.11 to 0.21, and from 0.10 to 0.20, respectively, in the first three lactations. Similarly to heridability estimates of genetic correlations did not vary among models. The use of single or multiple fixed regressions for fixed lactation curves by RRM does not influence the estimates of genetic parameters for test-day milk yield across lactations.
Highlights
IntroductionOfficial genetic evaluations of dairy cows in many countries (Strabel et al 2004). The use of random regression models with parametric functions as covariates, allows to split the shape of lactation curve into two parts: a general part (fixed) for assessing similarities of the lactation curves within specific groups of animals (i.e.: similar age, stage of lactation, parity and season of birth) and a second part specific for each individual animal (random) (Bormann et al, 2003)
The use of more precise model definitions in genetic evaluations contributes to increase efficiency of selection programs
Legendre polynomials of fourth and fifth orders were used to model regression of fixed curve or random lactation curves
Summary
Official genetic evaluations of dairy cows in many countries (Strabel et al 2004). The use of random regression models with parametric functions as covariates, allows to split the shape of lactation curve into two parts: a general part (fixed) for assessing similarities of the lactation curves within specific groups of animals (i.e.: similar age, stage of lactation, parity and season of birth) and a second part specific for each individual animal (random) (Bormann et al, 2003). Choosing the most appropriate model depends on decisions concerning the effects that are to be included in the model, especially those accounting for similarities of lactation curves of groups of animals under the influence of a common environmental effect (fixed part) or individual lactation curves of animals (random part). One or multiple regressions of lactation fixed curves have been evaluated (Strabel et al, 2004, Costa et al, 2008, Muir et al, 2007).
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