-This study was conducted on 2049 eggs, collected from commercial white layer hybrids, with the purpose of predicting egg weight (EW) from egg quality characteristics such as shell weight (SW), albumen weight (AW), and yolk weight (YW). In the prediction of EW, ridge regression (RR), multiple linear regression (MLR), and regression tree analysis (RTM) methods were used. Predictive performance of RR and MLR methods was evaluated using the determination coefficient (R 2 ) and variance inflation factor (VIF). R 2 (%) coefficients for RR and MLR methods were found as 93.15% and 93.4% without multicollinearity problems due to very low VIF values, varying from 1 to 2, respectively. Being a visual, non-parametric analysis technique, regression tree method (RTM) based on CHAID algorithm performed a very high predictive accuracy of 99.988% in the prediction of EW. The highest EW (71.963 g) was obtained from eggs with AW > 41 g and YW > 17 g. The usability of RTM due to a very great accuracy of 99.988 (%R 2 ) in the prediction of EW could be advised in practice in comparison with the ridge regression and multiple linear regression analysis techniques, and might be a very valuable tool with respect to quality classification of eggs produced in the poultry science.
The selection objective for animal production is the highest income with the lowest production cost, while ensuring the highest animal welfare. A selection experiment for environmental variability of birth weight in mice showed a correlated response in the mean after 20 generations starting from a crossed panmictic population. The relationship between the birth weight and its environmental variability explained the correlated response. The scale effect represents a potential cause of this correlation. The relationship between the mean and the variability implies: the higher the mean, the higher the variability. The study was to quantify by simulation the genetic correlation between a trait and its environmental variability. This can be attributable to the scale effect in a range of coefficients of variation and heritabilities between 0.05 and 0.50. The resulting genetic correlation ranged from 0.1335 to 0.7021 being the highest for the highest heritability and the lowest CV. The scale effect for a trait with heritability between 0.25 and 0.35 and CV between 0.15 and 0.25 generated a genetic correlation between 0.43 and 0.57. The genetic coefficient of variation (GCV) affecting residual variability was modulated by the strength reducing the impact of the scale effect. GCV ranged from 0.0050 to 1.4984. The strength of the scale effect might be in the range between 0 and 1. The scale effect would explain many reported genetic correlation and the additive genetic variance for the variability. This is relevant when increasing the mean of a trait jointly with the reduction of its variability.
Bu araştırma, yetiştirme tipi elit ve taban olan İvesi ırkı kuzuların sütten kesim ağırlıkları üzerine CART, CHAID ve Exhausted CHAID algoritmalarının tahminleme performanslarını karşılaştırmak amacıyla yapılmıştır. Bu çalışmada Osmaniye Toprakkale ilçesinde bulunan farklı yetiştirme tipi uygulayan (elit ve taban) iki işletmeden elde edilen 2014-2015 yıllarında Kasım-Ocak aylarında doğmuş toplam 331 baş İvesi ırkı kuzu kaydı kullanılmıştır. Elit sürü tipinde doğan kuzuların doğum ağırlığı (DA) ortalaması 4.92±0.05 kg; taban sürüde doğan kuzuların DA ortalaması ise 4.11±0.07 kg; elit sürüde yetişen kuzuların sütten kesim ağırlığı (SKA) ortalaması 14.35±0.12 kg; taban sürüde yetişenlerin ise 13.89±0.16 kg olduğu görülmüştür. Her iki sürüde (elit ve taban) doğumdaki ana yaşı, doğum tipi, cinsiyet, doğum ayı, sütten kesim zamanı ve doğum ağırlığı bağımsız değişken olarak kullanılırken, sütten kesim ağırlığı bağımlı değişken olarak kullanılmıştır. CART, CHAID ve Exhausted CHAID algoritmalarını mukayese etmek için RMSE, MAPE, RAE, SDratio ve MAD uyum iyiliği kriterleri ile Pearson korelasyon katsayısı (r), R 2 Adj ve R 2 değerleri kullanılmıştır. Her iki sürüde en yüksek R 2 değeri CART algoritması ile elde edilmiştir. Bu çalışma ile yetiştirici koşullarında yapılan seleksiyon çalışmalarında CART algoritması iyi bir araç olarak değerlendirilebilir.
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