Prediction of the chemical body composition of Nellore and crossbreed bulls

Abstract

Young Nellore and crossbreed bulls were comparatively slaughtered to generate equation models for predicting the chemical composition of the empty body and carcass from the chemical composition of the Hankins and Howe section (; ). Data were collected from 236 animals from different genetic groups: Nellore, one-half Canchim + one-half Nellore, one-half Angus + one-half Nellore, and one-half Simmental + one-half Nellore, with 48 baseline animals (BW range from 218 to 433 kg) and 188 animals finished in the feedlot (BW range from 356 to 618 kg). The chemical composition prediction equation model was developed for all genetic groups using stepwise regression analysis. Across all animals, the percentages of water and ether extract in the HH section were highly correlated ( < 0.001) with the percentages in the carcass ( = 0.911 and = 0.901, respectively, for water content of the carcass [HOC] and = 0.921 and = 0.921, respectively, for ether extract content of the carcass [EEC]) and empty body ( = 0.937 and = 0.926, respectively, for water content of the empty body [HOEB] and = 0.935 and = 0.939, respectively, for ether extract content of the empty body [EEEB]). The best prediction models were for the traits of empty body weight, HOEB, EEEB, HOC, and EEC. Determination coefficients for predicting the dependent variables obtained from the carcass composition were lower than those obtained from the empty body composition. It was concluded that the chemical composition of the empty body and the carcass can be predicted from the composition of the HH section, using a general equation for different genetic groups.

Estimation of empty body and carcass chemical composition of lactating and growing cattle: comparison of imaging, adipose cellularity, and rib dissection methods

“…The estimation of the carcass’ composition based on the dissection of one rib together with slaughter measurements provided a simple and accurate method, as previously outlined by Robelin et al (1976) . Fiems et al (2005) with the 8th rib dissection and Berndt et al (2017) with the 9th to 11th ribs dissections concluded similarly to Robelin et al (1976) , who outlined the effect of the breed on the intercept, similar to the effect of the animal type (cow/calf) in the present study. The interest is that when only a part of the carcass is used to estimate the whole composition, it saves time and alleviates the destruction of the whole carcass.…”

“…To be fully recognized as an accurate method of interest, imaging techniques need to be compared with the established reference method (i.e., “gold standard”), that is for chemical composition, the chemical analysis of homogenate of the EB or carcass after post mortem grinding. Their results need also to be compared to those from other well-established methods, like the dissection of a single ( Geay and Béranger, 1969 ; Robelin and Geay, 1976 ; Fiems et al, 2005 ) or multiple rib cut ( Hankins and Howe, 1946 ; Mitchell et al, 1997 , Ribeiro et al, 2011 ; Berndt et al, 2017 ), which are often used to estimate the composition of the whole carcass. Another classical method to estimate the EB composition is the measurement of adipose cell size (ACS; Hood and Allen, 1973 , Waltner et al, 1994 ).…”

Pasture intensification in beef cattle production can affect methane emission intensity

“…After slaughter, the hot carcass weight (HCWf) was recorded. The HCW at the beginning of the experiment (HCWi) was predicted by regression analysis using the SAS PROC REG's stepwise tool (SAS Institute, Inc., Cary, NC), as described by Berndt et al (2017). The data set used to develop the prediction equation consisted of 64 Nellore animals with an average LW of 396 kg (Berndt et al, 2017).…”

“…The HCW at the beginning of the experiment (HCWi) was predicted by regression analysis using the SAS PROC REG's stepwise tool (SAS Institute, Inc., Cary, NC), as described by Berndt et al (2017). The data set used to develop the prediction equation consisted of 64 Nellore animals with an average LW of 396 kg (Berndt et al, 2017). Using the abovementioned approach, the prediction equation was built as HCWi = −23.4847 + (0.6350 × LW), which provided an R 2 of 0.96 and Cp of −0.74.…”