RESUMEN
The prediction of carcass composition in Hanwoo steers is very important for value-based marketing, and the improvement of prediction accuracy and precision can be achieved through the analyses of independent variables using a prediction equation with a sufficient dataset. The present study was conducted to develop a prediction equation for Hanwoo carcass composition for which data was collected from 7,907 Hanwoo steers raised at a private farm in Gangwon Province, South Korea, and slaughtered in the period between January 2009 and September 2014. Carcass traits such as carcass weight (CWT), back fat thickness (BFT), eye-muscle area (EMA), and marbling score (MAR) were used as independent variables for the development of a prediction equation for carcass composition, such as retail cut weight and percentage (RC, and %RC, respectively), trimmed fat weight and percentage (FAT, and %FAT, respectively), and separated bone weight and percentage (BONE, and %BONE), and its feasibility for practical use was evaluated using the estimated retail yield percentage (ELP) currently used in Korea. The equations were functions of all the variables, and the significance was estimated via stepwise regression analyses. Further, the model equations were verified by means of the residual standard deviation and the coefficient of determination (R(2)) between the predicted and observed values. As the results of stepwise analyses, CWT was the most important single variable in the equation for RC and FAT, and BFT was the most important variable for the equation of %RC and %FAT. The precision and accuracy of three variable equation consisting CWT, BFT, and EMA were very similar to those of four variable equation that included all for independent variables (CWT, BFT, EMA, and MAR) in RC and FAT, while the three variable equations provided a more accurate prediction for %RC. Consequently, the three-variable equation might be more appropriate for practical use than the four-variable equation based on its easy and cost-effective measurement. However, a relatively high average difference for the ELP in absolute value implies a revision of the official equation may be required, although the current official equation for predicting RC with three variables is still valid.
RESUMEN
The frequency of eye infections in the Finnish blue fox population has increased during the past decade. Eye infection may incur economic losses to producers due to reduced selection intensity, but ethical aspects need to be considered as well because eye infection can be quite painful and reduce animal well-being. The purpose of this study was to determine the potential for genetic selection against susceptibility to eye infection. The data were collected from 2076 blue foxes at the MTT fur animal research station. Genetic parameters were estimated using single- and multiple-trait animal models. The heritability estimate for eye infection was analysed as a binary trait (EYE) and was moderate (0.24 ± 0.07). EYE had a moderate antagonistic genetic correlation (-0.49 ± 0.20) with grading density (thick underfur). The genetic correlation of EYE with grading size or body condition score was estimated without precision, but all size traits had a low antagonistic phenotypic correlation with EYE. Our results suggest that there is genetic variance in susceptibility to EYE, indicating that eye health can be improved through selection. The current recommendation is that the sick animals should be culled immediately. If more efficient selection is needed, the selection index and multiple-trait animal models can be applied in breeding for better eye health.