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The aim of this study was to examine the direct, indirect and total effects of some body measurements on body weight of Saanen kids by using path analysis. For this aim, relationship between body weight (BW) and four morpho-biometrical traits [chest girth (CG), height at withers (HW), height at sacrum (HS) and body length (BL)]were studied in 75 Saanen kids at one month of age using path analysis. However HW trait was excluded from analyze because of its multicollinearity with HS. The effects of CG, HS and BL on BW were found statistically significant. BL was found as variable with highest indirect effect (0.521) on BW. The direct effect of CG was higher than HS and BL. The total effect of BL was higher than those of HS and CG. These results showed that chest girth could be used to estimate the body weight of Saanen kids for practical purposes as well as for selection purposes.

The aim of this study was to compare estimation methods: least squares method (LS), ridge regression (RR), Principal component regression (PCR) to estimate the parameters of multiple regression model in situations when the underlying assumptions of least squares estimation are untenable because of multicollinearity. For this aim, the effect of some body measurements on body weights (height at withers and rumps, body length, chest width, chest girth and chest depth, front, middle and hind rump width) obtained from totally 85 Karayaka lambs at weaning period raised at Research Farm of Ondokuz Mayis University was examined. Mean square error, R2 value and significance of parameters were used to evaluate estimator performance. The multicollinearity, between front and middle rump width which were used to estimate live weight, was eliminated by using RR and PCR. Although research findings showed that RR method had the smallest MSE and the highest R2 value, the estimates of PCR were determined to be more consistent when the importance tests of parameters were taken into account. The results showed that principal component regression approach should be used to estimate the live weight of Karayaka lambs at weaning period.

Least square (LS) method is a common method used to estimate the coefficients in multiple regression models. The least square multiple regression models produce biased regression coefficients when the multicollinearity is encountered in the studied data sets. Multicollinearity problem can be solved by using some methods. As one of the methods, Ridge Regression (RR) is a biased estimation method that enables to obtain models having more reliable coefficient of determination (R 2 ). This study was conducted on 40 Saanen kids in order to determine some morphological measurements (withers height, rump height, body length, chest width, chest girth and chest depth) affecting body weight. In this study, usability of ridge regression method in the presence of multicollinearity was evaluated. Variance Inflation Factor (VIF) values higher than 10 were detected for withers height and rump height. Coefficient of determination (R 2 ) was obtained as 0.88 from LS method and R 2 was obtained 0.875 with k=0.0136 from RR method. As a result, the model obtained from RR is more reliable than that obtained from LS.

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SummaryThis 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. Keywords: Random regression, Test day milk yield, Jersey, Genetic parameters Jersey Sığırlarında İlk Laktasyon Test Günü Süt Verimleri için Bazı Şansa Bağlı Regresyon Modellerinin Karşılaştırılması ve GenetikParametre Tahminleri ÖzetBu 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.

This study aims to compare the accuracy of pedigree-based and genomic-based breeding value prediction for different training population sizes. In this study, Bayes (A, B, C, Cpi) and GBLUP methods for genomic selection and BLUP method for pedigree-based selection were used. Genomic and pedigree-based breeding values were estimated for partial milk yield (158 days) of Holstein cows (400 individuals) from a private enterprise in the USA. For this aim, populations were created for indirect breeding value estimates as training (322–360) and test (78–40) populations. In animals genotyped with a 54k SNP, the marker file was encoded as –10, 0, and 10 for AA, AB, and BB marker genotypes, respectively. Bayes and GBLUP methods were performed using GenSel 4.55 software. A total of 50,000 iterations were used, with the first 5000 excluded as the burn-in. Pedigree-based breeding values were estimated by REML using MTDFREML software employing an animal model. Correlations between partial milk yield and estimated breeding values were used to assess the predictive ability for methods. Bayes B method gave the highest accuracy for the indirect estimate of breeding value.