Genetic Relationship between Carcass Traits and Carcass Price of Korean Cattle

The objectives of this study were to estimate genetic parameters for the carcass price and carcass traits contributing to carcass grading and to investigate the influence of each carcass trait on the carcass price using multiple regression and path analyses. Data for carcass traits and carcass prices were collected from March 2003 to January 2009 on steers of Korean cattle raised at private farms. The analytical mixed animal model, including slaughter house-year-month combination, linear and quadratic slaughter age as fixed effects and random animal and residual effects, was used to estimate genetic parameters. The effects of carcass traits on the carcass price were evaluated by applying multiple regression analyses. Heritability estimates of carcass traits were 0.20±0.08 for carcass weight (CWT), 0.33±0.10 for back fat thickness (BFT), 0.07±0.05 for eye-muscle area (EMA) and 0.25±0.10 for marbling score (MS), and those of carcass prices were 0.21±0.10 for auction price per 1 kg of carcass weight (AP) and 0.13±0.07 for total price (CP). Genetic correlation coefficients of AP with CWT and MS were -0.35±0.29 and 0.99±0.04, respectively, and those of CP with CWT and MS were 0.59±0.22 and 0.39±0.29 respectively. If an appropriate adjustment for temporal economic value is available, the moderate heritability estimates of AP and CP might suggest their potential use as the breeding objectives for improving the gross incomes of beef cattle farms. The large genetic correlation estimates of carcass price variables with CWT and MS implied that simultaneous selection for both CWT and MS would be also useful in enhancing income.


INTRODUCTION
cattle raised at private farms in Kangwon province, South Korea. Animals were castrated at approximately six months of age and fed commercial concentrate feed with rice straw during the fattening period. Finished animals were transported from fattening farms to one of two slaughter houses at a distance within 3 hours by truck. Slaughtering and data collections were carried out according to the standard industrial procedures suggested by the Ministry for Food, Agriculture, Forestry and Fisheries (MIFAFF, 2007).
Animals were weighed on arrival, and held over-night with free access to drinking water. On the next morning, animals were stunned, bled by cutting the jugular vein, dressed using the standard procedures (removal of head, skin, viscera, and foot), and cut into halves. Two-sided carcasses were chilled for one night in a 0°C storage room, and then CWT, BFT, EMA, and MS were evaluated by official graders. The left side of the carcass was crosssectioned at a position between the last thoracic vertebra and the first lumbar vertebra, and EMA (cm 2 ), BFT (mm), and MS were measured on the cross-section. MS was classified into 1 (poor) to 9 (best) according to the Korean Beef Marbling Standard. Both sides of carcass were weighed and summed as CWT. After the carcass traits were measured and graded, the carcasses were transferred to an auction hall where carcass unit auction price per 1 kg of CWT (AP, monetary unit; Korean won(KRW), 1 US$≒1,200 KRW) was decided by auctioneers, and total carcass price for each carcass (CP) was calculated by multiplying AP with CWT.
Data were collected on 1,892 animals produced from 62 sires and 1,638 dams. They were raised on 480 farms, and each farm raised an average of 4 animals. Some observations for BFT and AP were lost during data collection, and the final data set for statistical analyses included observations of 1,892 for CWT, EMA and MS, 1,856 for BFT, and 1,233 for AP and CP. Pedigree information was additionally included for 3,064 animals, and the total of 4,956 animals, of which 266 were sires and 2,798 were dams, were included in the genetic parameter estimation.

Genetic parameter estimation
The analytical model included slaughter house-yearmonth combination fixed effect with 62 levels for CWT, EMA, BFT, and MS and 55 levels for AP and CP, and age at slaughter (days) effects were fitted as linear and quadratic covariates. Direct additive genetic effects and residuals were fitted as random effects. Variance and covariance components were estimated using the WOMBAT package of Meyer (2006). Single trait analyses were conducted to estimate heritability and breeding values, and two-trait analyses were conducted to estimate genetic and phenotypic correlation coefficients between the traits.

Multiple regression and path analyses
The effects of CWT, EMA, BFT, and MS on AP and CP were evaluated by multiple regression analyses. Partial regression coefficient for an independent variable means response on a dependent variable (AP or CP) when all other fitted independent variables (CWT, EMA, BFT, and MS) were held constant. Use of the variables standardized by the respective mean and standard deviation led to standardized partial regression coefficients equal to the path coefficients. Path coefficients could explain the change of standard deviation in a dependent variable which occurred by the change of standard deviation in an independent variable when all other fitted independent variables were held constant.
Path coefficients from independent to dependent variables and correlation coefficients among independent variables were used to partition the variation in price, the dependent variable. The variance was proportionally partitioned by all of the direct and indirect paths of influence from independent variables. Direct contribution of one independent variable to the dependent variable was simply the square of the path coefficient, and indirect contribution was estimated by summing up the product of the correlation coefficients and path coefficients along the indirect path (Lynch and Walsh, 1998). The correlation coefficients between traits were calculated using the following standardized breeding values from single trait analyses.
where SBV i is a standardized breeding value, BV i is an individual breeding value, BV is the mean of the individual breeding values, and SD(BV i ) is standard deviation of breeding value.
Partial-and standardized partial regression coefficients of CWT, EMA, BFT, and MS on AP or CP were estimated using the following multiple regression model.
where SBVAP (or SBVCP) is the standardized breeding value of AP (or CP), SBV1, SBV2, SBV3, and SBV4 are the standardized breeding values for CWT, BFT, EMA, and MS, respectively, b1, b2, b3, and b4 are their corresponding regression coefficients, and e is the residual.
Contribution of carcass trait to the variation in the dependent variable was calculated as follows; , and CC ij is correlation coefficient between traits i and j.

RESULTS AND DISCUSSION
The averages of CWT, BFT, EMA and MS with the slaughtering age of 889.84 d were 420.89 kg, 12.09 mm, 88.15 cm 2 , and 5.79, respectively ( Table 1). All of these values were larger than those of the corresponding traits with a smaller slaughtering age. Moon et al. (2007) obtained 368.03 kg, 10.10 mm, 80.64 cm 2 , and 4.19 from the data of 85,441 steers with unknown slaughter ages, and Hwang (2008) obtained 321.01 kg, 8.27 mm, 75.72 cm 2 , and 2.91 with the slaughtering age of 726.33 d. The coefficients of variations (CV) for BFT and MS were larger than those for CWT and EMA in this study. This concurred with the results from other Korean cattle Hwang et al., 2008) and from Japanese Brown cattle (Kahi et al., 2007). On the other hand, a small CV for MS was obtained in Brahman (15.80%, Smith, 2007) and in various US sires (15.69%, Van Vleck et al., 2007). This might be because the US sires are put out to pasture more frequently in management and that genetics differ among the breeds.
Furthermore, this might be also due to a different measuring system, giving especially wide ranges of MS from 200 to 899 (Smith et al., 2007) and from 20 to 109 (Van Vleck et al., 2007). This was supported by the large CV estimate of 62.2% with the MS from 1 to 7 in Hereford cattle (Galli et al., 2008).
Heritability estimates for CWT, BFT, EMA, and MS in the current study (0.20, 0.33, 0.07, and 0.25, Table 2) were smaller than those in the study of Hwang et al. (2008) where the estimates were obtained from Hanwoo steers raised in a progeny test station (0.30, 0.44, 0.37, and 0.44). This implied that the smaller heritability estimates were caused mainly by various management systems in private farms. Nevertheless, the heritability estimate of 0.20 for CWT in this study concurred with the weighted average heritability (0.23) in the review study of Koots et al. (1994). Our low heritability estimate (0.07) for EMA agreed with the estimate (0.07) obtained in steer progeny from composite, Angus, and Simmental sires in the study of Hassen et al. (1999). However, many studies showed moderate to large estimates in Australian Angus and Hereford (0.26 and 0.38, Reverta et al., 2000), Angus (0.45, Kemp et al., 2002), in Simmental (0.26-0.27, Rumph et al., 2007, Japanese Black cattle (0.43, Osawa et al., 2006), and in Brahman (0.44, Riley et al., 2002;0.50, Smith et al., 2007). The small estimate in the current study might be due  to the higher slaughter age of 889d as well as the genetic difference, considering another Korean cattle study in which 0.37 was obtained with the age of 726 d (Hwang et al., 2008). Heritability estimates (0.21 and 0.19) of AP and CP in the current study were also small comparing to other studies. Kahi et al. (2007) reported heritability estimates of 0.41 for carcass price and 0.62 for net income per year in Japanese Brown cattle, and Ibi et al. (2006) reported the heritability ranges from 0.32 to 0.42 for AP and from 0.33 to 0.46 for CP in Japanese Black cattle. On the other hand, there were small heritability estimates for carcass market value of Gelbvieh bulls using different data sources (0.10 and 0.19 from slaughterhouse data and programmed field test data, Engellandt et al., 1999).
Genetic correlation between AP and MS was considerably high (0.99), which concurred with the results from Japanese cattle (0.96 to 0.98 for Japanese Black cattle, Ibi, 2006;0.98 for Japanese Brown cattle, Kahi, 2007). These results reflected that the marbling degree was a critical trait for deciding carcass value both in Korean and Japanese carcass markets.
Our estimate of genetic correlation between CP and MS (0.39, p>0.05)) was smaller than the estimates (0.71 to 0.96) reported by Ibi et al. (2006). This could have resulted from the negative genetic relationship between CWT and MS. Ultimately, either AP or CP would be used as a breeding objective because the final goal of beef producers is improving their farm profitability. A caution should be attached to the single trait selection for higher AP because it would lead to small carcass weight with the negative genetic correlation between AP and CWT. This is undesirable because increasing body weight was another important breeding goal in Korea (MAFF, 2008).
Multiple regression analyses revealed that the analytical model with the independent variables of CWT, BFT, EMA, and MS accounted for 72.6% and 73.7% of the variability of AP and CP, respectively (Table 4). Partial regression coefficients of MS and BFT on AP were 0.6233 and -0.0737. Partial regression coefficient would be an indicator of economic value for each independent trait, and 1 unit increase of breeding value for MS (BFT) raised the breeding value for AP by 0.6233 KRW (-0.0737 KRW). Standardized partial regression coefficients would be a relative economic weight of each individual trait, and larger standardized partial regression coefficients on AP for MS and BFT than those for CWT and EMA suggested that MS and BFT were more influential on the carcass value of Korean cattle. A negative value of the standardized partial multiple regression coefficient for CWT revealed that a heavy carcass was less preferable in the Korean market. This agreed with the study of Ibi et al. (2006) where a negative economic weight was estimated for CWT of Japanese Black cattle. The largest standardized partial regression coefficient on CP was estimated for CWT (743.1 KRW) with the greatest relative importance on CP, and the smallest was for EMA (-2.8 KRW). While the relative importance of MS on the total carcass value was smaller than that of CWT for the Korean cattle, the standardized economic weight of MS was larger than that of CWT for Japanese black cattle (Ibi et al., 2006). The negative values of BFT on AP and CP in the current study were also observed for Japanese black cattle (Ibi et al., 2006). Path analyses partitioned the variability of AP and CP by each carcass trait, and path diagrams of relationships between carcass traits and AP (CP) are presented in Figure  1 Pyatt et al. (2005) where CWT and MS accounted for 51 and 10% of the variability of carcass values for Simmental steers. The final goal of beef cattle breeding would be to increase the gross income of beef cattle farms. The current study showed that AP and CP have the potential as a breeding objective due to their moderate heritability estimates. However, this should satisfy the assumption of a reasonable adjustment for temporal economic value. The moderate to high genetic correlation of carcass prices with CWT and MS implied that simultaneous selection for both CWT and MS would be also useful for raising income. Diagonal element indicates direct contribution of the carcass variable on each price variable, and off-diagonal element indicates joint contribution resulting from correlations between carcass traits and path coefficients of carcass traits. AP = Auction price; CP = Carcass price; CWT = Carcass weight; BFT = Back fat thickness; EMA = Eye-muscle area; MS = Marbling score; TC = Total contribution of each carcass trait on the variation of dependent price variable. The TC was calculated by summing over all the direct and indirect contributions of each carcass trait.