Data from 534 steers representing six sire breed groups were used to develop live animal ultrasound prediction equations for weight and percentage of retail product. Steers were ultrasonically measured for 12th-rib fat thickness (UFAT), rump fat thickness (URPFAT), longissimus muscle area (ULMA), and body wall thickness (UBDWALL) within 5 d before slaughter. Carcass measurements included in USDA yield grade (YG) and quality grade calculations were obtained. Carcasses were fabricated into boneless, totally trimmed retail products. Regression equations to predict weight and percentage of retail product were developed using either live animal or carcass traits as independent variables. Most of the variation in weight of retail product was accounted for by live weight (FWT) and carcass weight with R2 values of 0.66 and 0.69, respectively. Fat measurements accounted for the largest portion of the variation in percentage of retail product when used as single predictors (R2 = 0.54, 0.44, 0.23, and 0.54 for UFAT, URPFAT, UBDWALL, and carcass fat, respectively). Final models (P < 0.10) using live animal variables included FWT, UFAT, ULMA, and URPFAT for retail product weight (R2 = 0.84) and UFAT, URPFAT, ULMA, UBDWALL, and FWT for retail product percentage (R2 = 0.61). Comparatively, equations using YG variables resulted in R2 values of 0.86 and 0.65 for weight and percentage of retail product, respectively. Results indicate that live animal equations using ultrasound measurements are similar in accuracy to carcass measurements for predicting beef carcass composition, and alternative ultrasound measurements of rump fat and body wall thickness enhance the predictive capability of live animal-based equations for retail yield.

Five hundred thirty-four steers were evaluated over a 2-yr period to determine the accuracy of ultrasonic estimates of carcass 12th-rib fat thickness (CFAT) and longissimus muscle area (CLMA). Within 5 d before slaughter, steers were ultrasonically measured for 12th-rib fat thickness (UFAT) and longissimus muscle area (ULMA) using an Aloka 500V real-time ultrasound machine equipped with a 17.2-cm, 3.5-MHz linear transducer. Overall, correlation coefficients between ultrasound and carcass fat and longissimus muscle area were 0.89 and 0.86, respectively. Correlations for UFAT with CFAT were similar between years (0.86 and 0.90), whereas the relationship between ULMA and CLMA was stronger in yr 1 (r = 0.91; n = 282) than in yr 2 (r = 0.79; n = 252). Differences between ultrasonic and carcass measurements were expressed on both an actual (FDIFF and RDIFF) and absolute (FDEV and RDEV) basis. Mean FDIFF and RDIFF indicated that ultrasound underestimated CFAT by 0.06 cm and overestimated CLMA by 0.71 cm2 across both years. Overall mean FDEV and RDEV, which are indications of the average error rate, were 0.16 cm and 3.39 cm2, respectively. Analysis of year effects revealed that FDIFF, FDEV, and RDEV were greater (P < 0.01) in magnitude in yr 1. Further analysis of FDEV indicated that leaner (CFAT < 0.51 cm) cattle were overestimated and that fatter (CFAT > 1.02 cm) cattle were underestimated with ultrasound. Similarly, steers with small CLMA (< 71.0 cm2) were overestimated, and steers with large CLMA (> 90.3 cm2) were underestimated. The thickness of CFAT had an effect (P < 0.05) on the error of UFAT and ULMA measurements, with leaner animals being more accurately evaluated for both traits. Standard errors of prediction (SEP) adjusted for bias of ultrasound measurements were 0.20 cm and 4.49 cm2 for UFAT and ULMA, respectively. Differences in SEP were observed for ULMA, but not UFAT, by year. These results indicate that ultrasound can be an accurate estimator of carcass traits in live cattle when measurements are taken by an experienced, well-trained technician, with only small differences in accuracy between years.

The American Angus Association has sponsored a carcass evaluation since 1974. The carcass data collected as a part of this program are used by the association to conduct a biannual sire evaluation for carcass merit. This paper presents age-adjustment factors and genetic parameter estimates for carcass traits to be used in the Angus carcass genetic evaluation program. Because of the large range in slaughter ages, age classes were defined as all those animals slaughtered at an age of < or = 480 d and those with a slaughter age > 480 d. Linear and quadratic partial regressions on slaughter age for hot carcass weight (HCW), USDA marbling score (MS), 12th rib longissimus muscle area (LMA), and 12th-rib fat thickness (FT) were estimated within sex and age class. Quadratic age regressions were not significant, nor was the linear age regression coefficient for FT in steers in the > 480-d age class. Heritability estimates for age-constant HCW, MS, LMA, and FT were .31, .26, .32, and .26, respectively. The estimated genetic correlation (rg) between HCW and LMA was .47. The estimated rg between HCW and FT was .38 and between MS and FT was -.13. The linear genetic trends for CWT and LMA were significantly positive at .414 kg/yr and .075 cm2/yr, respectively. The genetic trends for FT and MS were very small but significantly negative at -.004 cm/yr and -.003 units/yr, respectively.

A total of 500 steers were used to develop models for prediction of percentage of intramuscular fat (PIMF) in live beef cattle. Prior to slaughter, steers were scanned across the 11th and 13th ribs using Aloka 500V and PIE scanner 200 machines. After slaughter, a cross-sectional slice of the longissimus dorsi muscle from the 12th rib facing was used for chemical extraction to determine carcass intramuscular fat measures (CIMF). Texture analysis software was used by two interpreters to define image parameters, which included Fourier, gradient, histogram, and co-occurrence parameters. A total of four prediction models were developed for each machine. These included, models developed without transformation of CIMF (model-I), models based on logarithmic transformation of CIMF (model-II), ridge regression (model-III), and principal components regression (model-IV) models. Model R2 and root mean square error (RMSE) of Aloka models I, II, III and IV were .72, .84%; .72, .86%; .69, .91%; and .71, .86%; respectively. The corresponding R2 and RMSE value of PIE models I, II, III, and IV were .68, .87%; .70, .85%; .64, .94%; and .65, .91%; respectively. All models were validated on an independent data set from 71 feedlot steers. The overall mean bias, standard error of prediction (SEP), and rank correlation coefficient across the four Aloka models were .42%, .84%, and .88, respectively. For PIE models the corresponding values were .67%, .81%, and .91, respectively. Both Aloka and PIE equipment can be used to accurately predict PIMF in live cattle. Further improvement in the accuracy of prediction could be achieved through increasing the development data set and the variation in PIMF of cattle used. Keywords ASL R1732 Disciplines Animal SciencesThis Craig Hays, CUP manager Summary A total of 500 steers were used to develop models for prediction of percentage of intramuscular fat (PIMF) in live beef cattle. Prior to slaughter, steers were scanned across the 11th and 13th ribs using Aloka 500V and PIE scanner 200 machines. After slaughter, a cross-sectional slice of the longissimus dorsi muscle from the 12th rib facing was used for chemical extraction to determine carcass intramuscular fat measures (CIMF). Texture analysis software was used by two interpreters to define image parameters, which included Fourier, gradient, histogram, and co-occurrence parameters. A total of four prediction models were developed for each machine. These included, models developed without transformation of CIMF (model-I), models based on logarithmic transformation of CIMF (model-II), ridge regression (model-III), and principal components regression (model-IV) models. Model R 2 and root mean square error (RMSE) of Aloka models I, II, III and IV were .72, .84%; .72, .86%; .69, .91%; and .71, .86%; respectively. The corresponding R 2 and RMSE value of PIE models I, II, III, and IV were .68, .87%; .70, .85%; .64, .94%; and .65, .91%; respectively. All models were validated on an independent data set from 71 feedlot steers. The overall mean bias...

The most widely used system to predict percentage of retail product from the four primal cuts of beef is USDA yield grade. The purpose of this study was to determine whether routine ultrasound measurements and additional rump measurements could be used in place of the carcass measurements used in the USDA yield grade equation to more accurately predict the percentage of saleable product from the four primals. This study used market cattle (n = 466) consisting of Angus bulls, Angus steers, and crossbred steers. Live animal ultrasound measures collected within 7 d of slaughter were 1) scan weight (SCANWT); 2) 12th- to 13th-rib s.c. fat thickness (UFAT); 3) 12th- to 13th-rib LM area (ULMA); 4) s.c. fat thickness over the termination of the biceps femoris in the rump (URFAT; reference point); 5) depth of gluteus medius under the reference point (URDEPTH); and 6) area of gluteus medius anterior to the reference point (URAREA). Traditional carcass measures collected included 1) HCW; 2) 12th-to 13th-rib s.c. fat thickness (CFAT); 3) 12th- to 13th-rib LM area (CLMA); and 4) estimated percentage of kidney, pelvic, and heart fat (CKPH). Right sides of carcasses were fabricated into subprimal cuts, lean trim, fat, and bone. Weights of each component were recorded, and percentage of retail product from the four primals was expressed as a percentage of side weight. A stepwise regression was performed using data from cattle (n = 328) to develop models to predict percentage of retail product from the four primals based on carcass measures or ultrasound measures, and comparisons were made between the models. The most accurate carcass prediction equation included CFAT, CKPH, and CLMA (R2 = 0.308), whereas the most accurate live prediction equation included UFAT, ULMA, SCANWT, and URAREA (R2 = 0.454). When these equations were applied to a validation set of cattle (n = 138), the carcass equation showed R2 = 0.350, whereas the ultrasound data showed R2 = 0.460. Ultrasound measures in the live animal were potentially more accurate predictors of retail product than measures collected on the carcass.