Analyzing Pork Carcass Evaluation Technologies in a Swine Bioeconomic Model

Inaccurate pork (Sus scrofa) carcass evaluation technologies have the potential to send inaccurate economic signals to producers regarding leanness. The objective of this study was to estimate the difference in the optimal level of returns to management and operator labor under alternative assumptions about the carcass evaluation technology employed and the actual returns based on carcass dissection data. Two genotypes of barrows and gilts reflecting significant genetic variation were analyzed. The carcass evaluation technologies examined were: an optical probe (PROBE), electromagnetic scanner (EMSCAN), and a combination of both technologies (BOTH). A deterministic bioeconomic model of swine growth was formulated to measure the effect of these technologies on pork producer profitability. Relationships between biological variables for feed efficiency, live weight, lean weight, fat weight, carcass weight, and backfat depth were estimated as functions of time for two genotypes of barrows and gilts. Economic variables included production costs and revenues from a component pricing model with separate payments for lean, fat, and byproducts. Error was defined as the optimal return to management and operator labor derived from the bioeconomic optimization model minus the actual return as determined from carcass dissection. The range of error was $‐5.41 (lean genotype gilts) to $0.23 per pig (fat genotype barrows) for the PROBE model. For the EMSCAN model this range was $‐2.63 (lean genotype barrows) to $5.46 (fat genotype barrows) while the BOTH model had a range of $‐3.54 (fat genotype gilts) to $1.54 (fat genotype barrows). The results indicated that the absolute error (sum of errors across genotype and sex) for each technology was 40% higher for the PROBE model than the EMSCAN or BOTH models. Optimal marketing weights were lowest for the PROBE model and highest for the EMSCAN model. Research Question Inaccurate pork carcass evaluation technologies have the potential to send inaccurate economic signals to producers regarding leanness. The objective of this study was to estimate the difference in the optimal level of returns to management and operator labor under alternative assumptions about the carcass evaluation technology employed and the actual returns based on carcass dissection data. An optimization routine that maximizes the average daily return was used to examine these effects for two alternative genotypes. The carcass evaluation technologies chosen to predict lean were an optical probe (PROBE), electromagnetic scanner (EMSCAN), and a combination of both technologies (BOTH). Literature Summary Research by animal and industry scientists has analyzed different carcass evaluation technologies and their potential for use in packing plants. Various carcass merit pricing systems have been adopted as alternatives to live weight pricing of hogs. However, little research has been conducted to determine whether the evaluation technologies used in these systems provide accurate estimates of carcass value that can be translated into price signals for rewarding leanness. The marketing weight is a critical decision variable for a producer and is dependent on the price signals. Inaccurate estimates of the optimal marketing weight lead to lower producer returns. Error was defined as the optimal return to management and operator labor derived from the bioeconomic optimization model minus the actual return as determined from carcass dissection. Examination of two genotypes of barrows and gilts (representing a lean and less lean genotype) within the context of a bioeconomic optimization model indicated that the absolute errors (sum of all error across technologies) was greatest for the PROBE. The optimal marketing weight was lowest for the PROBE model and highest for the EMSCAN model. Study Description Data for two genotypes from the Purdue Cooperative Swine Lean Growth Project were used in the analysis. Average costs for an all‐in, all‐out feeder pig finishing producer were used in conjunction with a swine growth model that estimated biological relationships including feed efficiency, live weight, lean weight, fat weight, carcass weight, backfat depth, and feed intake. A deterministic bioeconomic model was employed to determine the marketing weight which maximized the return to management and operator labor where the revenue is based on the technology's prediction of pounds of lean. Applied Questions What are the relative impacts on returns to management and operator labor of using alternative carcass evaluation technologies to predict lean? The range of differences in returns from optimal value using the carcass evaluation technology and the actual value was $‐5.41 (fat genotype gilts) to $0.23 (fat genotype barrows) for PROBE. For the EMSCAN model, this range was $‐2.63 (lean genotype barrows) to $5.46 (fat genotype barrows). The BOTH model had a range of $‐3.54 (fat genotype gilts) to $1.54 (fat genotype barrows). The results indicated that the absolute error (sum of errors across genotype and sex) of the PROBE was 40% higher than the absolute errors from the EMSCAN and BOTH models. Does the choice of carcass evaluation technology affect producer decisions on the choice of marketing system used? The results clearly demonstrate the necessity to coordinate the marketing system with the genetics being used on the farm. For the fat genotype, returns per head are greatest under the EMSCAN scenario regardless of sex. However for the lean genotype, the barrows have higher returns under the assumption of BOTH, while returns are highest for gilts in EMSCAN. This suggests that producers with a large number of animals may find it advantageous to market barrows and gilts under different programs. However, the bioeconomic model does not account for additional costs that may be associated with marketing under different systems. These costs may reduce some of the differences between marketing systems. The results also suggest that electronic technologies can be combined with component pricing to provide incentives for producers to adopt leaner genetics depending on the cost of these changes. The estimated carcass value of the leaner genetics greatly exceeded that of the fat genotype regardless of which technology was used.