A Comparison of Some Random Regression Models for First Lactation Test Day Milk Yields in Jersey Cows and Estimating of Genetic Parameters

ÇANKAYA S., TAKMA Ç., Abacı S. H., Ulker M.

KAFKAS UNIVERSITESI VETERINER FAKULTESI DERGISI, cilt.20, sa.1, ss.5-10, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 20 Sayı: 1
  • Basım Tarihi: 2014
  • Doi Numarası: 10.9775/kvfd.2013.9234
  • Dergi Adı: KAFKAS UNIVERSITESI VETERINER FAKULTESI DERGISI
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.5-10
  • Anahtar Kelimeler: Random regression, Test day milk yield, Jersey, Genetic parameters, LEGENDRE POLYNOMIALS, HOLSTEIN COWS, SELECTION, CURVES, CATTLE
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

This study was conducted to compare random regression models for third order Ali Schaeffer (AS), Wilmink (W) and Legendre polynomials (L) on estimation of genetic parameters for first lactation milk yield in Jersey cows. For this aim, data used in this study were 6387 official milk yield records from monthly recording of 686 first lactations between 1996 and 2011 in Karakoy Agricultural State Farm, Samsun (Turkey). In this study, (co)variance components, heritability for first lactation test day milk yields (TDMY) and genetic correlations among these TDMYs were estimated by using DFREML statistical package under DXMRR option. To compare the models, -2LogL, Akaike's information criterion (AIC), Bayesian information criterion (BIC), Residual variances (RV) and Log likelihood values were used. Heritabilities (0.08 to 0.28), additive genetic correlations (0.68 to 0.99) and phenotypic correlations (0.21 to 0.66) were estimated by AS(4,4) random regression model which had the lowest AIC and BIC values. As a result, it was decided that the AS(4,4) random regression model can be used for management decisions and genetic evaluation of Jersey cows for milk production.