Prediction of tissue composition of live dairy calves and carcasses by computed tomography

Abstract

Computed tomography (CT) is a non-destructive technique, based on X-rays, that has been used in several livestock species to evaluate carcass composition. The objective of this study was to construct predictive equations to estimate carcass and viscera composition for preweaning calves using CT. For this purpose, 24 Holstein male calves (4 ± 0.9 d of age; 40 ± 2.2 kg of body weight) were fed a milk replacer (MR; 23% CP; 15% fat) either 4 L/d or 8 L/d of MR at the rate of 125 g/L of water to ensure different levels of fat and protein accretion and generate sufficient variation to obtain the equations of calibration. Then, at 30 ± 2.4 d of age, 3 calves from each feeding program, and at 50 ± 1.9 d of age, 9 calves from each feeding program were CT-scanned, and humanly sacrificed. Carcasses were also CT scanned 24 h post mortem. Images from CT were analysed and used to predict content of protein and fat of carcasses, red and white viscera. The models rendered a residual predictive deviation between 1.1 (protein red viscera) and 2.6 (fat white viscera) in live animal images and between 1.1 (carcass moisture) and 4.5 (fat white viscera) in carcass images. The root mean square error of prediction relative to the mean ranged between 1.32 (carcass moisture) and 17.3% (fat white viscera) in live animal images and between 1.38 (carcass moisture) and 17.3 (fat red viscera) in carcass images. The coefficient of determination ranged between 0.19 (protein red viscera) and 0.88 (fat white viscera) in images from live calves and between 0.26 (carcass protein) and 0.98 (fat white viscera) in carcass images. In conclusion, it is possible to predict body composition of calves using a non-destructive technique by means of computed tomography images and this prediction could be used in studies were the estimation of this content would be relevant.