Effect of Different Residual Variances on Genetic Parameters of Test Day Milk Yields

Bu çalışmada Siyah Alaca'ların denetim günü süt verimine ait genetik parametre tahminine heterojen hata varyanslarının etkisi incelenmiştir. Bu amaçla sabit etkileri, şansa bağlı genetik ve kalıcı çevre etkilerini içeren üçüncü dereceden şansa bağlı regresyon modelleri kullanılmıştır. Bu modellerden RV10 modelinde hata varyansları her bir denetim gününde farklı kabul edilmiştir. RV1 modelinde ise hata varyansları tüm denetim günlerinde sabit kabul edilmiştir. Varyans bileşenleri tahminlerinin karşılaştırılmasında ardışık (RV2-RV9) ve ardışık olmayan (NRV2-NRV9) hata varyansı gruplarını içeren modeller kullanılmıştır. Söz konusu grupların belirlenmesinde denetim günü süt verimlerinin her biri için tek değişkenli analiz uygulanmıştır. Ardışık hata varyansı tanımlanan modellerde hata varyansı tahmini 5.62 ile 11.75 arasında ve ardışık olmayan hata varyansı tanımlanan modellerde ise 5.61 ile 11.71 arasında değişmiştir. Ardışık ve ardışık olmayan hata varyanslı modellerde eklemeli genetik varyanslar sırasıyla 0.55 ile 6.76 ve 0.08 ile 2.46 arasında tahminlenmiştir. Kalıcı çevre varyansları ise ardışık modellerde 2.36 ile 18.60 ve ardışık olmayanlarda 6.92 ile 18.85 arasında tahminlenmiştir. Kalıtım derecesi tahminleri de ardışık modeller için 0.02 ile 0.43 arasında ve ardışık olmayan modeller için 0.01 ile 0.13 arasında elde edilmiştir. Sonuç olarak, hata varyanslarının kontrol altına alınmasıyla daha güvenilir genetik parametre tahminlerine ulaşılmıştır. Her bir denetim günündeki hata varyansının tanımlamasında RV10 modelinin kullanımı tavsiye edilmiştir. Bunun yanı sıra, RV5 modelinin RV10 modeline göre alternatif bir model olduğu belirlenmiştir

Denetim Günü Süt Veriminin Genetik Parametre Tahminine Farklı

Heterogeneous residual variance effects on genetic parameters were examined for test day milk yields of Turkish Holsteins. A third order random regression models including the fixed, random additive genetic and permanent environmental effects were used. One of these models, RV10, residual variances is assumed to be different for each test day milk yields. The RV1 model has constant residual variance for each test day. Sequential (RV2 to RV9) and non-sequential (NRV2 to NRV9) groups of residual variances were also described in the models in order to compare estimates of variance components. The univariate analysis of milk yields for each test days was performed to define variance groups. The predicted residual variances ranged from 5.62 to 11.75 and from 5.61to 11.71 for RV and NRV models, respectively. Estimates of additive genetic variances changed between 0.55-6.76 for RV and 0.08-2.46 for NRV models. Permanent environmental variances were found between 2.36 and 18.60 for RV and 6.92 to 18.85 for NRV models. Heritability estimates varied from 0.02 to 0.43 for RV and 0.01 to 0.13 for NRV models. As a result, more accurate genetic parameter estimates are achieved by controlling the residual variances. RV10 model should be preferred to define details of the milk yield residual variances for each test day. However, RV5 model has been determined that an alternative model as compared with RV10

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