PSVII-16 Prediction equations of the carcass physical composition of water buffaloes

This study determined equations to estimate the physical components of the buffalo carcass using biometric measurements performed in vivo, carcass traits performed postmortem, and the set of both measurements. Seventy-five non-castrated males (25 of each genetic group: Jafarabadi, Mediterranean and Murrah), with means of initial body weight and age of 314 ±117 kg and 390 ±58 days, respectively, were used. The animals were allocated in collective pens and received feeding ad libitum for 240 days. Ultrasound assessments [(backfat thickness (BFT); Ribeye area (REA)], weight, hip height (HH) measurements, and body condition score were performed every 28 days, and tested as independent variables. The postmortem measurements tested as independent variables were carcass length (CL, cm), carcass depth (CD, cm), cold carcass weight (CCW), hot carcass weight (HCW), and carcass compactness index (CCI, kg/cm). The empty body weight (EBW) was also obtained and subsequently used in the equations. The percentage of tissues which comprise the carcass was determined by the physical separation. The equations to estimate the physical composition of the carcass were developed using the stepwise regression method and Mallows’ Cp criterion, and processed by the REG procedure in SAS. The best prediction equations were those which used both independent variables obtained in vivo and postmortem, namely: Muscle, kg = 16.17 - 0,042×EBW + 0.211×CCW + 49.309×CD + 0.210×HCW - 35.570×HH (R² =0.91; RMSE =3.448; Cp =4.864); Fat, kg = 29.471 + 0.07×EBW - 33.716×CL + 0.285×BFT – 11.080×CD (R² =0.77; RMSE =2.151; Cp =2.568); Bone, kg = -34.261 + 20.380×CL + 18.356×HH + 0.011×EBW (R² =0.87; RMSE =1.181; Cp =2.218). In conclusion, the equations obtained from models with both in vivo and postmortem measurements present high prediction and can be used efficiently in the estimate of the physical components of the water buffalo carcass. Supported by FAPESP (#2014/05473-7).