The objective was to evaluate the effects of feeding two different commercially available blends of essential oils to finishing steers to replace conventional feed additives in feedlot diets. Angus-based crossbred steers (N=43; starting BW=466±31 kg) were used in a randomized complete block design. Steers were randomly assigned to four different dietary treatments for the 100-day finishing period in which steers were fed high moisture corn/alfalfa silage/soybean meal diets. Dietary treatments included a negative control (no additives; CON), a positive control (33 mg/kg monensin and 11 mg/kg tylosin added to the diet; M/T), and two different proprietary blends of essential oils [EO-1: 1 g/steer/day Victus Liv (DSM Nutritional Products) and EO-2: 4 g/steer/day Fortissa Fit 45 (Provimi Canada ULC)]. Growth performance, carcass characteristics, meat quality, sensory attributes (excluding juiciness), most fatty acid parameters, and shelf-life (color and lipid oxidative stability) were generally unaffected by the inclusion of two different commercially available essential oil blends when compared with both a negative control (CON) and a positive control (M/T). This indicates that commercially available essential oil blends may show promise as a replacement to conventional feed additives like antibiotics without causing negative effects to meat quality, storage stability, and eating experience.

The objective was to update the equation used for prediction of pork carcass leanness with the Destron PG-100 optical grading probe. A recent cutout study (completed in 2020-2021) consisting of 337 pork carcasses was used for this research. An updated equation was generated using a calibration dataset (n = 188 carcasses) and prediction precision and prediction accuracy of the new equation was evaluated using a validation dataset (n = 149 carcasses). The updated equation was generated using forward stepwise selection multiple regression techniques in PROC REG of SAS, and the same parameters as the existing equation were used to fit the model. The updated Destron equation [89.16298 – (1.63023 × backfat thickness) – (0.42126 × muscle depth) + (0.01930 × backfat thickness2) + (0.00308 × muscle depth2) + (0.00369 × backfat thickness × muscle depth)] and the existing Destron equation [68.1863 – (0.7833 × backfat thickness) + (0.0689 × muscle depth) + (0.0080 × backfat thickness2) – (0.0002 × muscle depth2) + (0.0006 × backfat thickness × muscle depth)] were similar in their prediction precision for determination of carcass lean yield (LY), with the updated equation R 2 = 0.75 and root mean square error (RMSE) = 1.97 and the existing equation R 2 = 0.75 and RMSE = 1.94. However, when prediction accuracy was evaluated using the variance explained by predictive models based on cross-validation (VEcv) and Legates and McCabe’s efficiency coefficient (E1), the updated equation (VEcv = 67.97%; E1 = 42.41%) was much more accurate compared with the existing equation (VEcv = -117.53%; E1 = -69.24%). Furthermore, when accuracy was evaluated by separating carcasses into 3% carcass LY groupings ranging from less than 50% LY to greater than 62% LY, the existing equation correctly estimated carcass LY 8.1% of the time, while the updated equation correctly estimated carcass LY 47.7% of the time. In an effort to further compare the abilities of the updated equation, comparisons were made with an advanced automated ultrasonic scanner (AutoFom III), which scans the entire carcass. The prediction precision of the AutoFom III was R 2 = 0.83 and RMSE = 1.61, while the AutoFom III correctly estimated carcass LY 38.2% of the time and prediction accuracy calculations for the AutoFom III were VEcv = 44.37% and E1 = 21.34%). Overall, refinement of the Destron PG-100 predicted LY equation did not change prediction precision, but substantially improved prediction accuracy.

This study compared accuracy of two methods for predicting carcass leanness (i.e., predicted lean yield) with fat-free lean yields obtained by manual carcass side cut-out and dissection of lean, fat, and bone components. The two prediction methods evaluated in this study estimated lean yield by measuring fat thickness and muscle depth at one location with an optical grading probe (Destron PG-100) or by scanning the entire carcass with advanced ultrasound technology (AutoFom III). Pork carcasses (166 barrows and 171 gilts; head-on hot carcass weights ranging from 89.4 to 138.0 kg) were selected based on their fit within desired hot carcass weight ranges, their fit within specific backfat thickness ranges, and sex (barrow or gilt). Data (n = 337 carcasses) were analyzed using a 3 × 2 factorial arrangement in a randomized complete block design including the fixed effects of method for predicting lean yield, sex, and their interaction, and random effects of producer (i.e., farm) and slaughter date. Linear regression analysis was then used to examine the accuracy of the Destron PG-100 and AutoFom III data for measuring backfat thickness, muscle depth, and predicted lean yield when compared with fat-free lean yields obtained with manual carcass side cut-out and dissections. Partial least squares (PLS) regression analysis was used to predict the measured traits from image parameters generated by the AutoFom III software. There were method differences (P ˂ 0.01) for determining muscle depth and lean yield with no method differences (P = 0.27) for measuring backfat thickness. Both optical probe and ultrasound technologies strongly predicted backfat thickness (R  2 ≥ 0.81) and lean yield (R  2 ≥ 0.66), but poorly predicted muscle depth (R  2 ≤ 0.33). The AutoFom III improved accuracy [R 2 = 0.77, root mean square error (RMSE) = 1.82] for determination of predicted lean yield versus the Destron PG-100 (R 2 = 0.66, RMSE = 2.22). The AutoFom III was also used to predict bone-in/boneless primal weights, which is not possible with the Destron PG-100. The cross-validated prediction accuracy for the prediction of primal weights ranged from 0.71 to 0.84 for bone-in cuts and 0.59 to 0.82 for boneless cut lean yield. The AutoFom III was moderately (r ≤ 0.67) accurate for the determination of predicted lean yield in the picnic, belly, and ham primal cuts and highly (r ≥ 0.68) accurate for the determination of predicted lean yield in the whole shoulder, butt, and loin primal cuts.

Objectives of this research were to compare carcass characteristics, carcass cutting yields, and meat quality for market barrows and market gilts. Commercially-sourced carcasses from 168 market barrows and 175 market gilts weighing an average of 107.44 ± 7.37 kg were selected from 17 different slaughter groups representing approximately 3,950 carcasses. Each group was sorted into percentiles based on hot carcass weight with an equal number of barrows and gilts selected from each quartile so that weight minimally confounded parameters of interest. Carcass lean yield was determined for carcasses following fabrication (i.e., dissection of lean, fat, and bone tissue components) and meat quality measurements were evaluated at the time of fabrication (24-72 hours post-mortem) and following 14-days of post-mortem storage. Data were analyzed as a randomized complete block design with carcass serving as the experimental unit, sex (barrow or gilt), the three hot carcass weight quantiles [Light (< 104 kg); Average (104 – 110 kg); Heavy (> 110 kg)], and the interaction between sex and hot carcass weight quantile serving as fixed effects, and producer nested within slaughter event serving as a random effect. Results from the study demonstrated that gilt carcasses were leaner (3 mm less backfat thickness; 3.5 cm 2 greater loin muscle area, 1.52% greater merchandized-cut yield, and 2.92% greater dissected carcass lean yield; P < 0.01) than barrow carcasses, while loins from barrows were higher quality (0.43% more intramuscular fat and slightly less shear force; P < 0.01) than loins from gilts. While this study confirms the well-known biological principle that barrow carcasses have greater levels of fat deposition and lower levels of carcass leanness when compared with gilt carcasses, this study provides a much-needed quantification of these differences for the commercial industry that will undoubtedly be useful as new technologies emerge in upcoming years.