SONER ÇANKAYA, ÇİĞDEM TAKMA, SAMET HASAN ABACI, Mehmet ÜLKER

A comparison of some random regression models for first lactation test day milk yields in Jersey cows and estimating of genetic parameters

Bu çalışma Jersey sığırlarında ilk laktasyon süt verimleri için genetik parametrelerinin tahmini üzerine Ali Schaeffer, Wilmink ve Legendre polinomlarının 3 farklı uyum sırasında şansa bağlı regresyon modellerini karşılaştırmak için yürütülmüştür. Bu amaçla, çalışmada Samsun Karaköy Tarım İşletmesi’ndeki 1996-2011 yılları arasındaki 686 ilk laktasyonun 6387 adet aylık süt verim kaydı kullanılmıştır. Çalışmada ilk laktasyon test günü süt verimleri (TGSV) için kovaryans bileşenleri, kalıtım dereceleri ve TGSV arasındaki genetik korelasyonlar DFREML istatistik paket programı içerisindeki DXMRR opsiyonu kullanılarak tahmin edilmiştir. Modelleri karşılaştırmak için -2LogL, Akaike bilgi kriteri (AIC), Bayesian bilgi kriteri (BIC), Hata varyansı (RV) ve Log olabilirlik değerleri kullanılmıştır. En küçük AIC ve BIC değerlerine sahip AS(4,4) modeli ile kalıtım derecesi değerleri (0.08 - 0.28), eklemeli genetik korelasyonlar (0.68 - 0.99) ve fenotipik korelasyonlar (0.21 - 0.66) tahmin edilmiştir. Sonuç olarak, AS(4,4) modelinin Jersey sığırlarının genetik değerlendirmesi ve süt üretimi açısından işletme yönetim kararları için kullanılabilir olduğuna karar verildi.

Jersey sığırlarında ilk laktasyon test günü süt verimleri için bazı şansa bağlı regresyon modellerinin karşılaştırılması ve genetik parametre tahminleri

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.

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