Predicting body and carcase composition in Nellore heifers and their cross-breeds - Souza, Evaristo Jorge; Valadares Filho, Sebastião; Torres, Thaysa; da Silva, Daniel; Silva, José Ricardo (Revista Ciência Agronômica, 2018)
The aim of this study was to predict the body and carcase composition of Nellore heifers and their crosses with Angus and Simmental bulls. Sixty heifers from three genetic groups were used, 20 Nellore, 20 Nellore x Angus and 20 Nellore x Simmental; of these, 12 (four from each genetic group) were slaughtered at the beginning of the experiment (reference group). The heifers were randomly divided into nine treatments (completely randomised design), in a 3 x 3 factorial scheme of three genetic groups and three diets (30 and 50% of the dry matter of the concentrate feed, in addition to the maintenance group) in feedlot. Twelve heifers (four from each genetic group) were fed at maintenance level (1.1% of body weight in dry matter) with a diet containing 30% concentrate, and 36 heifers (12 animals from each genetic group) were fed ad libitum, with 30% (six from each group) or 50% (six from each group) of the dry matter (DM) from the concentrate feed. After slaughtering, the right-side half-carcase was completely dissected, and the 9 th, 10 th and 11 th ribs removed, from the left-side half-carcase, were cut. The 9 th, 10 th and 11 th rib cut satisfactorily estimated the fat and bone content; however, the muscle content was underestimated by 5.32%. The 9 th, 10 th and 11 th rib cut also satisfactorily estimated the chemical composition of the body and carcase, but not the physical composition of the carcase of Nellore heifers or their crosses with Angus and Simmental. The chemical constituents of the empty body and carcase are adequately estimated from the 9 th, 10 th and 11 th rib cut when the equations proposed by Valadares Filho, Paulino and Magalhães (2006) are used. The percentage of macrominerals can be estimated from the rib cut, however, the levels of calcium, phosphorus and magnesium are the most accurate.