Effects of estimation accuracy on potential payment premiums for superior beef carcasses

TitleEffects of estimation accuracy on potential payment premiums for superior beef carcasses
Publication TypeJournal Article
Year of Publication1999
AuthorsPurchas, R.W., Garrick D.J., and Lopez-Villalobos N.
JournalNew Zealand Journal of Agricultural Research
Pagination305 - 314
Date Published1999
ISBN Number00288233 (ISSN)
KeywordsCarcass classification, Saleable meat yield, Simulation model

Knowledge of saleable meat yield (SMY%) at the time of carcass classification enables payments to be made on the basis of carcass saleable meat content. This study determined the extent to which improved accuracy of estimating SMY% enabled larger premiums to be paid for better yielding carcasses. A population of 1000 carcasses was simulated for true SMY% values (275 kg carcass weight; mean SMY% of 66 with a standard deviation of 3.0) as well as estimated SMY% values when accuracies in terms of residual standard deviation (RSDs) ranged from 0 to 2.5. A consequence of the requirement that estimates of SMY% be unbiased was that variability of the estimates was less than that of the true SMY% when estimation was imperfect. Premiums that could be paid to the top 5% of carcasses relative to the average increased by 5.8, 9.2, and 9.7 c kg-1 as the RSD decreased from 2.5 to 1.0 for five-step, eight-step, and smooth payment systems, respectively. The rate at which potential premiums increased with decreases in RSD values was low at high levels of accuracy (RSD values below about 1.0). The potential premiums for the top 5% were highest for the smooth payment system with an advantage over the five-step system of 24% at an RSD of 1.0 and 28% at an RSD of 2.0, but the size of the advantage varied with the proportion of carcasses considered. It is concluded that the benefits that may be derived from using a smooth payment system rather than a stepwise system will often be more easily attained than those from improving the accuracy of estimating SMY% and may be as great.

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