Alternative pork carcass evaluation techniques: I. Differences in predictions of value.

Dissected and predicted wholesale and lean boneless values for 154 pork carcasses representing seven genotypes with substantial variation in carcass composition and percentage of lean were determined. Dissected carcass value was determined using a component pricing model, and four alternative models were specified to predict that value. The models included measurements from a ruler (RULER) and two carcass evaluation technologies, Hennessy probe (PROBE) and electromagnetic scanner (EMS1). A combination of the PROBE and EMS1 models (EMS2) was also used. For wholesale value, R2 were .40, .70, .59, and .74, and the RSD were 8.18, 5.77, 6.76, and 5.38 ($/100 kg of carcass value) for RULER, PROBE, EMS1, and EMS2, respectively. For lean boneless value, the R2 were .41, .73, .59, and .74, and the RSD were 8.34, 5.67, 6.99, and 5.51 ($/100 kg of carcass value) for RULER, PROBE, EMS1, and EMS2, respectively. The results indicate that a combination of probe and electromagnetic scanner measurements provided the best fit to dissected value.

Alternative pork carcass evaluation techniques: II. Statistical analysis of error attributable to sex, genotype, and weight.

Carcasses of 154 hogs representing seven genotypes with substantial variation in carcass composition and percentage of lean were completely dissected and analyzed. Measurements from a ruler, Hennessy probe, and electromagnetic scanner were each used to predict wholesale and lean boneless carcass value. Error, defined as dissected value minus predicted value, due to the omission of sex, genotype, weight, and their interactions was estimated for each model. The errors were significantly different from zero for the models using ruler and electromagnetic scanning measurements separately (P < .01). Errors due to sex, genotype, weight, and their interactions were greatest for the less lean barrows. A combination of probe and electromagnetic scanner measurements resulted in the least error. The value of barrows with low percentage of lean was consistently overpredicted, whereas the value of leaner gilts was underpredicted for the models using ruler and electromagnetic scanning separately (P < .001).