Heritability and repeatability of monthly egg production under fixed and random regression models were estimated. Adequacy of the models was examined as well. Six generations of two lines (A88 - 13950 recorded hens and 9351 - recorded hens) were used. The analysis was based on fixed regression models with covariates nested within hatch-year subclasses and nonnested covariates as well as random regression model with random coefficients modelling additive genetic and permanent environmental effects. In the fixed regression analysis the following functions were used to describe average production curves: exponential model, mixed log model, polynomial regression, Yang model, fourth order polynomial. In the random regression analysis Legendre polynomials were used. The computation was done using DFREML package. Low heritability coefficients were estimated from 0.02 to 0.06, 0.03 to 0.2, for A88 and K66 lines, respectively. The function of Ali and Schaeffer most adequately described the data however due to high number of parameters it is more computationally demanding. According to the most adequate model (including third order Legendre polynomials for fixed effects and fourth order for genetic and permanent environmental effects), relatively high heritabilities were estimated in first (0.5) and final (0.3) periods of production with substantial decrease during the peak. The heritability was more stable for models with lower orders of polynomials. Random regression models were concluded to be the most adequate. It was confirmed by literature studies, especially on dairy cattle. Therefore, the methodology based on random regression animal models can be recommended for genetic evaluation of laying hens.
Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume , , 07.29, 2006
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