Estimation of genetic parameters for partial egg production periods by means of random regression models

Abstract

ABSTRACT. We estimated genetic parameters for egg production in different periods by means of random regression models, aiming at selection based on partial egg production from a generation of layers. The production was evaluated for each individual by recording the number of eggs produced from 20 to 70 weeks of age, with partial records taken every three weeks for a total of 17 periods. The covariance functions were estimated with a random regression model by the restricted maximum likelihood method. A model composed of third-order polynomials for the additive effect, ninth-order polynomials for the permanent environment, and a residual variance structure with five distinct classes, was found to be most suitable for adjusting the egg production data for laying hens. The heritability estimates varied from 0.04 to 0.14. The genetic correlations were all positive, varying from 0.10 to 0.99. Selection applied in partial egg production periods will result in greater genetic profit for the adjacent periods. However, as the distance in time between periods increases, selection becomes less efficient. Selection based on the second period (23 to 25 weeks of age), where greater heritability was estimated, would note benefit the final egg-laying cycle periods.

Genotypic parameters for egg production in pure breed hens by using random regression model

“…TONGSIRI et al (2014) working with birds of the 11th generation of hens selected from the Plymouth Rock White and Rod Island Red breeds observed a heritability of 0.30 and 0.33 respectively for the characteristic egg production at 17 weeks of age. SHADPARVAR & ENAYATI (2012), analyzing data from six generations of Mazandaran native laying hens selected at the eighth week of age, reported a heritability of 0.156 for the number of eggs produced, and reported a negative correlation for egg numbers and the age of the first egg.…”

“…For all 24 combinations R-G that converged, the best values of -2LMR, AIC, BIC, and MSR were obtained when the third order polynomial regression was used to model the fixed curve. VENTURINI et al (2012) used random regressions to assess the production of eggs in laying hens and defined the fixed trajectory of the curve by a cubic Legendre polynomial.…”

“…Additionally, it provides estimates of genetic parameters at any point along the egg production curve in the interval in which the measurements have been obtained. The prediction of these values for egg production is of great economic importance, and should be considered when selecting laying hens (VENTURINI, 2012).…”

Modelos de regressão aleatória na seleção de codornas de corte para produção de ovos

“…A predição de valores genéticos quanto à persistência de postura em aves de corte é uma característica de grande impacto econômico que deve ser considerada no processo de seleção, em melhoramento genético (Teixeira et al, 2012;Venturini et al, 2012). Os modelos animais tradicionais de repetibilidade e o multicaracterístico fornecem predições de valores genéticos para determinados pontos da curva de crescimento, enquanto que os modelos de regressão aleatória (MRA) permitem essas estimativas para quaisquer pontos da curva (Sarmento et al, 2008).…”

Utilização de dados parciais na seleção de codornas de corte para produção de ovos

“…A determinação da produção de ovos em períodos parciais permite reduzir o intervalo de geração e possibilita a seleção de animais superiores mais cedo (Rossi et al, 2010). Assim, é possível aumentar a intensidade de seleção e reduzir os custos de produção, conforme se verifica em trabalhos com galinhas poedeiras (Al-Samarai et al, 2008;Venturini et al, 2012). No entanto, recomendações quanto a esses períodos parciais, para codornas de corte, não foram encontradas na literatura.…”