Communicating pork value to the retailer

Abstract

Boxed pork was obtained to represent four different purchasing specifications common in the industry to conduct a study on yields, labor requirement, and value and profit assessment. Boxes of bone-in loins (n = 180), boneless loins (n = 94), Boston butts (n = 148), fresh hams (n = 28), and boneless hams (n = 23) were shipped to the Rosenthal Meat Science and Technology Center at Texas A&M University to perform cutting yields and time tests in a simulated retail cutting room. Subprimals within each purchasing specification were allotted randomly to cutting styles. Processing times (sec) and retail weights (kg) were obtained to determine relative value and retail yields (%). Retail yields ranged from 71.12 to 95.28% for bone-in loins, 94.92 to 98.57% for boneless loins, 88.45 to 98.59% for Boston butts, 59.54 to 60.24% for fresh hams, 99.97 to 100.12% for inside fresh pork leg, 99.36 to 100-01% for outside fresh pork leg, and 98.88 to 100.45% for tenderloins. Cutting times were increased up to three-fold when bone-in subprimals were taken to boneless endpoints. Cutting style affected (P [ .05) retail yield, total processing time, and value differential (US $/.4536 kg) for bone-in loins. Cutting time and value for boneless loins differed (P [ .05) by cutting style. Boston butt retail yield and cutting time were affected (P [ .05) by cutting style. When cutting styles within subprimals were pooled, bone-in loin, boneless loin, Boston butt, and outside fresh pork leg percentage of retail yield were influenced (P [ .05) by purchasing specification. Purchasing specification affected (P [ .05) processing time for bone-in loins, boneless loins, fresh hams, inside fresh pork leg, and outside fresh pork leg. Value differential was impacted (P [ .05) by purchasing specification for bone-in loins, boneless loins, Boston butts, and inside fresh pork leg. With cutting style and purchasing specification influencing retail yield and cutting time the following linear regression model was developed for each subprimal with more than one cutting style: y g + si +pj +spij + eijk where y is the predicted percentage retail yield or processing time, g is the intercept, si is the effect of cutting style, pj is the effect of purchasing specification, spij is the effect of the interaction of cutting style and purchasing specification, and eijk is the error term which is approximately normally distributed with a mean of 0 and variance of a2. This model explained between 21 and 93% of the variability associated with percentage of retail yield and between 81 and 91% of the variability associated with cutting time for subprimals with more than one cutting style. Due to the numerous combination of factors that can affect retail yield, cutting time, and retail value of pork the computer program generated from this data will aid the retailer in making marketing and purchasing decisions that have been time consuming in the past.