title: Predicting the yield and composition of mature cow carcasses.
abstract: Cow carcasses (n = 60) were selected based on conformation and external fat to develop more current and useful prediction equations for estimating yield and composition. Adjusted preliminary yield grade was highly correlated to percentage of the carcass as fat (.91), percentage fat in the total lean (.89), and percentage fat in the lean trimmings (.88) of carcasses from non-grain-fed mature cows. Equations for predicting percentage of the carcass as chemical fat had higher -R2 values than equations predicting other compositional end points. The "best" regression equation for predicting total yield (i.e., whole muscle cuts plus lean trimmings adjusted to 10% chemical fat) included hot carcass weight (HCWT), adjusted preliminary yield grade (APYG), longissimus area (LMA), and marbling (MARB), with R2 = .75 and residual standard deviation (RSD) = 2.47. A similar equation predicting total yield from unribbed carcass data included HCWT, APYG, and conformation (CONF) with R2 = .69 and RSD = 3.11. These two equations were applied to a test group of cow carcasses (n = 20), and the average difference between the actual and predicted total yield values from ribbed data and unribbed data was .45 and .83% of HCWT; simple correlations between the actual and predicted values were .74 and .69, respectively. These equations contain relatively simple independent variables to identify and more nearly represent current industry processing practices than equations previously available.
authors.affiliation: Department of Animal Science, University of Florida, Gainesville 32611, USA.
authors.forename: D D
authors.initials: DD
authors.lastname: Johnson
authors.forename: A L
authors.initials: AL
authors.lastname: Rogers
mesh_terms: D000375:Aging / Q000502:physiology*; D000818:Animals; D001823:Body Composition / Q000502:physiology*; D002417:Cattle / Q000502:physiology*; D005260:Female; D005524:Food Technology / Q000379:methods* / Q000592:standards / Q000706:statistics & numerical data; D005525:Food-Processing Industry / Q000379:methods / Q000592:standards / Q000706:statistics & numerical data; D005544:Forecasting; D008460:Meat / Q000592:standards*; D015233:Models, Statistical; D011336:Probability; D012044:Regression Analysis