Benefits From More Accurate Estimation of Saleable Meat Yield

Stephen T Morris
Institute of Veterinary, Animal & Biomedical Sciences
Massey University

INTRODUCTION

There is no doubt that an important determinant of the price per-kilogram value of a beef carcass to the beef processor is the percentage yield of saleable meat (SMY%) from that carcass, and because of this, most systems of beef carcass classification include assessments of this characteristic (Kempster et al 1982). Usually such assessments are indirect through the measurement of characteristics, such as fat depths, that are related to SMY%. The current export classification system for beef carcasses in New Zealand, for example, requires that carcasses be allocated to classes specified by fat depth ranges and by subjective muscling scores (NZMPB 1996), but results are not reported in terms of SMY%.

The terms "to grade" or "to classify" are largely synonymous. Systems which have attempted to sort carcasses into groups which differ in "quality" and give names to groups which imply differences in quality (first, versus second and third; choice and prime versus standard, utility and manufacturing) are regarded as grading systems. In contrast, systems that sort carcasses on the basis of more objective information (species, carcass weight, age, sex, fat thickness or some other measurements of fatness, conformation, post-mortem treatment etc) and where possible use measurements rather than subjective assessment, and label carcasses with symbols and names not implicit of quality, are regarded as classification systems.

The New Zealand beef carcass classification system has some elements of both classification and grading. However, world trends undoubtedly are moving towards the use of classification systems as it is becoming increasingly apparent that objective measurements can provide more useful information for both purchasers and suppliers of beef.

YIELD OF SALEABLE MEAT

The most important saleable component of the carcass is the muscle (red meat). A proportion of the fat is also considered edible - this proportion will vary from market to market and from consumer to consumer within any market. The muscle plus saleable fat comprise the proportion of the carcass that could be sold for consumption (the saleable meat). For beef this is the carcass yield. The remaining fat trim and bone are not saleable as food items and must be considered by-products. However, for sheepmeat and goatmeat, the bone is often sold to the consumer in the cut, although even for these meats the proportion sold boneless is likely to increase with time.

While all grading/classification systems which include fatness classes as one of the grading criteria have an element of yield grading in the system - as fat class increases the meat yield decreases - some systems have as their main objective the prediction of carcass muscle content or yield of saleable meat. While the New Zealand beef carcass classification system includes an element of saleable meat yield prediction these is still a tremendous amount of overlap between adjacent grades. A similar situation applies to New Zealand export lamb grading system.

There are likely to be benefits for both beef processors and producers if the saleable SMY% of carcasses are taken into account when determining the price paid per kilogram of carcass to the producer. For the average producer this will not lead to any change in the average price per kilogram of carcass weight, but the more accurate feedback of SMY% values for cattle processed should make the identification of the types of animals and management practices that lead to higher yielding carcasses more effective in the longer term. For processors the availability of more accurate SMY% information when establishing price schedules will permit the setting of large differentials between high and low carcasses, and over time this should result in an increase in the average SMY% of the carcasses being processed.

Before investing in improved systems to evaluate and report SMY%, it is important to determine how large the benefits from this information are, and how they are affected by various factors such as the accuracy with which SMY% of the carcass can be estimated, as it is unrealistic to assume yield can be exactly predicted for all individual carcasses.

SOURCES OF THE BENEFITS

The three main beneficiaries from an improved accuracy of SMY% estimation are the processor, the producer who is finishing the cattle, and the bull breeder. Possible benefits for each of these players in the industry include increased premiums for better yielding cattle, more accurate feedback on SMY% of animals to the owner, and management advantages within the processing plant. There appear to be few short-term "direct" benefits to the processor for knowing the SMY% of the carcass more accurately. However, longer-term benefits arise from the fact that carcasses with a high SMY% can be rewarded more precisely and with higher premiums, so that there will be a stronger incentive for producers to deliver cattle that excel in this trait. As a result the average SMY% of carcasses should improve over time.

FACTORS INFLUENCING THE ACCURACY OF ESTIMATION

The accuracy of estimating SMY% will be largely determined by the predictor or group of predictors used. Note that the New Zealand beef carcass classification system contains indirect assessment of saleable meat yield through the measurement of fat depths and subjective muscling scores but results are not reported in terms of SMY%. Other systems use eye muscle area and hot side carcass weight to give more accurate predictions of SMY% than fat depth alone. The Japanese grading system estimates SMY% by using a multiple reggression equation which includes four carcass measurements, namely rib eye area, rib thickness, cold left side carcass weight, and subcutaneous fat thickness. A detailed description of the strengths and weaknesses of alternative predictors of SMY% can be found in Morgan Jones 1995.

A standard definition for saleable meat is required in order to effectively evaluate alternative predictors of SMY%, and also as a common language between processors and producers. The definition needs to be tight in terms of fat-trim levels and anatomical descriptions of the cuts, but it need not correspond exactly to normal commercial practice. In fact it could not always match commercial practice because cuts are prepared to many different specifications, but it would be desirable if all specifications used could be calibrated in terms of the standard. High levels of SMY% estimation accuracy will not be attainable if the item being estimated is not closely defined.

PRICE DIFFERENTIALS

To determine how the accuracy of SMY % estimation influenced the premiums that could be paid for superior carcasses, a population of 1,000 carcasses all having the same weight (275kg), but with varying SMY% (average = 66%; SD = 3) was simulated, and then 5 predicted SMY% values were calculated for each carcass corresponding to those expected when the linear regression relationship between predicted and actual SMY% had an RSD (a measure of accuracy) of 2.5, 2, 1.5, 1, and 0.5 (Purchas et al 1997). The characteristics of the actual SMY% values obtained (ie when RSD = 0), together with the predicted SMY% values for the 5 levels of accuracy are shown in Table 1, for one simulated population of 1,000 carcasses.

Table 1. Characteristics of the actual SMY% and the predicted values of SMY% at five different levels of accuracy (different RSD values) for a simulated population of one thousand 275kg carcasses (Purchas et al 1997).

The most important effect of a lower accuracy of SMY% estimation (ie higher RSD values) is the decreased variation and lower range of the predicted values. The effect of this is that more carcasses will be classed as being close to the average and fewer will be classed as having particularly low or particularly high SMY% values, thereby reducing the opportunity to reward producers for superior SMY%. This is demonstrated in Table 2 where the number of carcasses that fall into various predicted SMY% ranges as shown. Values are shown for a situation where predicted SMY% is divided up into four percentage-point ranges so that there are a total of 5 steps and where the division is into 2 percentage point ranges so that there are 8 steps. The price values given in the last column of Table 2 were calculated based on the assumptions that there was fixed killing and processing cost per carcass of $150 and that the price paid per kilogram of saleable meat was $4.03. (Purchas et al 1997). These did not vary appreciable with changes in RSD value used except that the total money paid out for the 1,000 carcasses would have been the same regardless of the accuracy of the classification system, but with more accurate estimation of SMY% (ie lower RSD values) all carcasses would have been paid for at the extremes of the price range, and the price differential between the top and bottom group of carcasses with regard to actual SMY% would have been greater. Larger price differentials give stronger market signals for breeders and producers.

Table 2. The distribution of carcasses between predicted SMY% classes when the accuracy of prediction in terms of RSD values ranged from 0 to 2.5 percentage points. Results are shown for when the predicted SMY% range is divided into 5 steps (4 percentage points per step) or 8 steps (2 percentage points per step) (Purchas et al 1997).

Price per kg of carcass assuming a fixed processing cost of $150/carcass and returns of $4.03/kg of saleable meat.

Table 3 gives an indication of the size of the price differentials that could be offered to producers who produce carcasses at either end of the SMY% range. The average price per kilogram of carcass has been calculated for the best 5% and poorest 5% of carcasses sorted according to their predicted SMY%. These results are compared with price differentials for the top and bottom 10% and the top and bottom 20%. The calculations were made separately for situations where carcasses were sorted into five or eight step wise classes and also where the carcasses were not sorted into groups (ie a "smooth" system operated). The premium that could be offered for the top 5% relative to the average is one of half the differential between the top and bottom 5%. The results presented in Table 3 indicate that a producer consistently finishing cattle in the top 5% should be receiving an average premium of $68.75 per head (25c/kg) over the average producers for carcasses of 275kg when the accuracy (RSD = 0.5) is very high and a smooth payout system is used. For a moderate level of accuracy (RSD = 2.5) the premium would be about $38/head (14 cent/kg premium).

Table 3. Changes in the price differential (c/kg) between groups of carcasses at the top and bottom end of the predicted SMY% range when RSD values ranged from 0 to 2.5. Differentials are shown for two systems of stepwise payment and for a smooth system (Purchas et al 1997).

The above simulation study indicates that as accuracy of estimation of SMY% increases, the benefit in terms of the size of the premiums that could be paid for superior carcasses increased, but at a decreasing rate. In specific terms, the improvement as the residual standard deviation (a measure of accuracy) decreased from 2.5 to 1 was marked, but there was little further improvement as it was reduced further from 1 to 0. Potential premiums for superior carcasses were higher from smooth payment system than for step wise systems, in other words the step payment systems effectively lower the accuracy. The latter system means that all carcasses within a range of SMY% value receive the same price per kilogram. Generally payment on a step wise basis had an effect on premiums that were similar to the effect of using less accurate systems of measurement. The current beef carcass classification system in New Zealand is based on steps in fat depth, muscling and carcass weight, however, the results of the study by (Purchas et al 1997) suggest that significant improvements could be achieved by moving to some form of smooth payment system.

GENETIC SELECTION FOR SALEABLE MEAT YIELD

One method of improving the SMY% of cattle within a breed is by genetic selection, whereby animals used for breeding purposes are those with the highest merit for that trait. Such selection is primarily carried out within the registered or bull-breeding sector of the beef cattle industry so that overall genetic change is largely determined by the direction and rate of change within this sector which includes about 4% of the national beef breeding cow herd (Charteris and Garrick 1996). Within a small fraction of bull-breeding herds ultrasonic scanning procedures are used to evaluate potential sires for eye muscle area at the 12/13th rib (EMA), along with fat depths at this site and over the rump. The data is then combined with pedigree records and information from other traits to provide estimates of the genetic merit of animals in the form of estimated breeding values (EBV's). Thus, the methods of measurement and of analysing data are ready available to bull breeders, but currently there is little in the way of monetary incentive for them to select for SMY% because producers who are buying the bulls seldom see any clear benefits from paying more for bulls that excel in this trait. Provided appropriate recompense were forthcoming, the study of (Purchas et al 1997) indicated that on the basis of current technology (ultrasonic scanning), within breed genetic improvement in SMY% is possible and is likely to be particularly important in the longer term.

CONCLUSIONS

Investment and improvement in the accuracy of prediction of SMY% will be in the best interests of the entire industry.

Appreciable benefits may be obtained by relatively simple modifications to the current system, such as the use of a smooth payment system, and predicting SMY% values of individual carcasses, in terms of a standard SMY% (for feed back purposes).

The benefits of improved accuracy should pass to all sectors of the industry, so ways of covering the costs involved in a fair manner need to be considered.

Improved accuracy of estimating SMY% may lead to public-relations benefits.

Failure to actively monitor, evaluate, (and where appropriate) implement new developments in the area of beef carcass evaluation is likely to have a negative impact on the competitive status of the New Zealand beef industry.

REFERENCES

Charteris, P.L. and Garrick, D.J. 1996. Characterisation of beef cattle industry structure. Proceedings of the New Zealand Society of Animal Production 56:386-389.

Kempster, A.J., Cutherbertson, A. and Harrington, G. 1982. Carcass evaluation in livestock breeding, production and marketing.Granada, London.

Morgan Jones, S.D. (ed.) 1995. Quality and grading of carcasses of meat animals. CRC Press, Boca Raton.

NZMPB. 1996. New Zealand meat: Guide to beef carcass classification. (Pamphlet). New Zealand Meat Producers Board, Wellington.

Purchas, R.W,. Garrick, D.J., Charteris, P.L., Lopez-Villalobos, N.J. and Dake, D.K. 1997. Benefits from more accurate estimation of saleable meat yield for beef carcass classification purposes. Report for the Beef Carcass Classification Review Group, Massey University, Palmerston North, New Zealand.