Section 5
Chapter 4,279

Prediction of retail product and trimmable fat yields from the four primal cuts in beef cattle using ultrasound or carcass data

Tait, R.G.; Wilson, D.E.; Rouse, G.H.

Journal of Animal Science 83(6): 1353-1360


ISSN/ISBN: 0021-8812
PMID: 15890812
DOI: 10.2527/2005.8361353x
Accession: 004278562

Download citation:  

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 (R superscript 2 = 0.308), whereas the most accurate live prediction equation included UFAT, ULMA, SCANWT, and URAREA (R superscript 2 = 0.454). When these equations were applied to a validation set of cattle (n = 138), the carcass equation showed R superscript 2 = 0.350, whereas the ultrasound data showed R superscript 2 = 0.460. Ultrasound measures in the live animal were potentially more accurate predictors of retail product than measures collected on the carcass.

PDF emailed within 0-6 h: $19.90