Strategies to improve market value of pig carcases

Selection for profitability

Genetic improvement of pig performance is a primary tool in enhancing profitability in pig enterprises. The first step of genetically improving livestock is the development of a breeding objective which combines all economically important traits that can be improved by selection. These traits are given economic values that are defined as the increase in profit with a single unit change in each trait while keeping all other traits constant. Breeding objectives should include all traits that affect profitability even if they cannot be directly measured. This can be achieved by using knowledge of genetic parameters for traits that that breeders wish to improve (breeding objective traits) and traits that have no economic importance but are genetically correlated with traits in the breeding objective (selection criteria). Some traits may be prohibitively difficult or expensive to measure such as feed conversion ratios while others like carcase attributes can only be measured on animals after slaughter.

Historically, only average daily gain and backfat depth have been recorded on farm. However, there are many other traits that influence the profitability of both the production enterprise and subsequent processing and sales facilities. The economic benefits of considering weight of primal cuts were discussed at the last workshop (Mérour and Hermesch, 2008) and since then genetic parameters have been obtained for these traits from the analyses of the French data (Mérour and Hermesch, 2009). Belly composition is important for some markets providing a further avenue to increase the market value of the carcase in addition to backfat at a given carcase weight which is the basis of current payment systems in Australia.

It was the aim of these index calculations to evaluate the benefits of including measures of muscle depth, belly fat and length on live animals as well as measures of carcass and meat quality attributes on related animals of the selection candidate by comparing response to selection and accuracies for different scenarios. Recommendations are provided based on results from these index calculations.