Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

• Main Authors: Maria Gabriela Campolina Diniz Peixoto, Daniel Jordan de Abreu Santos, Rusbel Raul Aspilcueta Borquis, Frank Ângelo Tomita Bruneli, João Cláudio do Carmo Panetto, Humberto Tonhati
• Format: Article
• Language: English
• Published: Embrapa Informação Tecnológica 2014-05-01
• Series: Pesquisa Agropecuária Brasileira
• Subjects: Bos indicus; funções de covariância; curva de lactação; modelos de dia do controle
• Online Access: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2014000500372&lng=en&tlng=en

The objective of this work was to compare random regression models for the estimation of genetic parameters for Guzerat milk production, using orthogonal Legendre polynomials. Records (20,524) of test-day milk yield (TDMY) from 2,816 first-lactation Guzerat cows were used. TDMY grouped into 10-monthly classes were analyzed for additive genetic effect and for environmental and residual permanent effects (random effects), whereas the contemporary group, calving age (linear and quadratic effects) and mean lactation curve were analized as fixed effects. Trajectories for the additive genetic and permanent environmental effects were modeled by means of a covariance function employing orthogonal Legendre polynomials ranging from the second to the fifth order. Residual variances were considered in one, four, six, or ten variance classes. The best model had six residual variance classes. The heritability estimates for the TDMY records varied from 0.19 to 0.32. The random regression model that used a second-order Legendre polynomial for the additive genetic effect, and a fifth-order polynomial for the permanent environmental effect is adequate for comparison by the main employed criteria. The model with a second-order Legendre polynomial for the additive genetic effect, and that with a fourth-order for the permanent environmental effect could also be employed in these analyses.