| Abstrakt: |
The objectives were to develop equations for predicting fat-free lean in swine carcasses and to estimate the prediction bias that was due to genetic group, sex, and dietary lysine level. Barrows and gilts (n = 1,024) from four projects conducted by the National Pork Board were evaluated by six procedures, and their carcass fat-free lean was determined. Pigs of 16 genetic groups were fed within weight groups one of four dietary regimens that differed by 0.45% in lysine content and slaughtered at weights between 89 and 163 kg. Variables in equations included carcass weight and measures of backfat depth and LM. Fat-free lean was predicted from measures of fat and muscle depth measured with the Fat-O-Meater (FOM), Automated Ultrasonic System (AUS), and Ultrafom (UFOM) instruments, carcass 10th-rib backfat and LM area (C10R), carcass last-rib backfat (CLR), and live animal scan of backfat depth and LM area with an Aloka 500 instrument (SCAN). Equations for C10R (residual standard deviation, RSD = 2.93 kg) and SCAN (RSD = 3.06 kg) were the most precise. The RSD for AUS, FOM, and UFOM equations were 3.46, 3.57, and 3.62 kg, respectively. The least precise equation was CLR, for which the RSD was 4.04 kg. All procedures produced biased predictions for some genetic groups (P< 0.01). Fat-free lean tended to be overestimated in fatter groups and underestimated in leaner ones. The CLR, FOM, and AUS procedures overestimated fat-free lean in barrows and underestimated it in gilts (P< 0.01), but other procedures were not biased by sex. Bias due to dietary lysine level was assessed for the C10R, CLR, FOM, and SCAN procedures, and fat-free lean in pigs fed the low-lysine dietary regimen was overestimated by CLR, FOM, and SCAN (P< 0.05). Positive regressions of residuals (measured fat-free lean minus predicted fat-free lean) on measured fat-free lean were found for each procedure, ranging from 0.204 ± 0.013 kg/kg for C10R to 0.605 ± 0.049 kg/kg for UFOM, indicating that all procedures overestimated fat-free lean in fat pigs and underestimated it in lean pigs. The pigs evaluated represent the range of variation in pigs delivered to packing plants, and thus the prediction equations should have broad application within the industry. Buying systems that base fat-free lean predictions on measures of carcass fat depth and muscle depth or area will overvalue fat pigs and undervalue lean pigs. |
