Machine effects on accuracy of ultrasonic prediction of backfat and ribeye area in beef bulls, steers and heifers

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1 Machine effects on accuracy of ultrasonic prediction of backfat and ribeye area in beef bulls, steers and heifers P. K. Charagu, D. H. Crews, Jr., R. A. Kemp, and P. B. Mwansa Research Centre, Agriculture and Agri-Food Canada, P. O. Box 3000, Lethbridge, Alberta Canada T1J 4B1 ( LRC contribution number , received 10 May 1999, accepted 21 November Charagu, P. K., Crews, Jr., D. H., Kemp, R. A. and Mwansa, P. B Machine effects on accuracy of ultrasonic prediction of backfat and ribeye area in beef bulls, steers and heifers. Can. J. Anim. Sci. 80: Pre-slaughter ultrasound and carcass measurements of ribeye area (REA) and backfat (FAT) were recorded on composite beef bulls (n = 60), heifers (n = 60) and steers (n = 60). Breed composition of the composite was: 0.44 British (Hereford, Angus and Shorthorn) 0.25 Charolais, 0.25 Simmental and 0.06 Limousin. The Aloka SSD-1100 (AL) and the Tokyo Keiki CS 3000 (TK) ultrasound machines were compared by evaluating the difference between ultrasound and carcass measurements (bias), and the standard error of prediction (SEP). AL underpredicted REA in all three sexes while TK overpredicted heifers and steers and underpredicted bulls. Both machines were similar in accuracy among bulls for REA. For FAT AL underpredicted all three sexes while TK underpredicted heifers and had very small bias for bulls and steers. SEP for FAT were similar for both machines. Both machines underpredicted REA in larger muscled cattle and overpredicted in smaller-muscled cattle. Both machines also underpredicted FAT in fatter animals and overpredicted FAT in leaner animals. Machines were similar in accuracy among cattle with larger REA but differed significantly (P < 0.05) among smaller-muscled cattle. Machines were comparable in accuracy among animals of all FAT sizes. This study demonstrates that there is an important relationship between machine and the size and depth of muscle and backfat, respectively, and consequently between machine and sex, in accuracy of ultrasound prediction. Key words: Beef cattle, ultrasound, accuracy, back fat, ribeye area Charagu, P. K., Crews, Jr., D. H., Kemp, R. A. et Mwansa, P. B Effets dus à l appareil sur l exactitude des prédictions par ultrasons de l épaisseur du gras dorsal et de la surface de la noix de côte chez des taurillons, des bouvillons et des génisses à viande. Can. J. Anim. Sci. 80: Des évaluations par ultrasons des animaux sur pied et des mesures sur carcasse de la surface de la noix de côte (NSC) et de l épaisseur du gras dorsal (EGD) ont été prises sur des taurillons, des génisses (taures) et des bouvillons à viande de race composite. La composition génétique de la race était 44 % races britanniques : Hereford, Angus et Shorthorn, 25 % Charolais, 25 % Simmental et 6 % Limousin. Nous comparions les appareils à ultrasons Aloka SSD (AL) et Tokyo Keiki CS 3000 (TK) d après l écart entre les valeurs ultrasoniques et les valeurs mesurées sur carcasse et d après l erreur type de prédiction (ETP). L appareil AL produisait une sous-prédiction de la NSC pour les trois types sexuels, tandis que TK la surprédisait pour les génisses et les bouvillons et la sous-prédisait dans le cas des taurillon, les deux appareils démontrant chez ces derniers une exactitude comparable. L épaisseur du gras de couverture était sous-prédite sur les trois types sexuels par AL et sur les génisses par TK lequel, pour les taurillons et les bouvillons, fournissait des prédictions très proches des valeurs mesurées. L ETP pour EGD était de même amplitude avec les deux appareils qui, par ailleurs, sous-prédisaient SNC dans les bovins de grand format et la surprédisaient dans ceux de petit format. Ils sous-prédisaient aussi EGD chez les sujets plus gras et la surprédisaient sur les animaux plus maigres. Le degré d exactitude des appareils était comparable parmi les animaux de grand format, mais il variait de façon significative (P < 0,05) parmi ceux de petit format. Enfin il était le même pour les sujets de toutes catégories de gras de couverture. Ces observations mettent en évidence l important rapport existant, d une part, entre les appareils, et d autre part, entre le volume du muscle et l épaisseur du gras de couverture et, par conséquent, entre appareil et type sexuel, pour ce qui est de la prédiction par ultrasons. Mots clés: Bovin à viande, ultrason, exactitude, gras de couverture, surface de la noix de côte Ultrasound technology has been used in the North American beef industry since the 1950s, and researchers are now increasingly interested in the use of real-time ultrasound as a tool for selection of replacement breeding animals. The efficacy of ultrasound in selection programs, however, is dependent on its accuracy in predicting corresponding carcass measurements. It has been shown that measurements of ribeye area and backfat thickness obtained using real-time ultrasound technology are moderately accurate (Perkins et al. 1992a; Bergen et al. 1997) and are further improved by the use of digitized image analysis software (Sather et al. 1996). There are, however, factors that introduce variation and inaccuracy, both in the collection and interpretation of 19 Abbreviations: AL, Aloka SSD-1100 ultrasound machine; CFAT, carcass backfat thickness; CREA, ribeye area; FAT, backfat thickness; FATDEV, magnitude of the difference between carcass and ultrasound-measured back fat thickness; FATDIFF, difference between carcass and ultrasound measured back fat thickness; LD, longissimus dorsi; REA, area of longissimus dorsi muscle; READEV, magnitude of the difference between carcass and ultrasound-measured rib eye area; READIFF, difference between carcass and ultrasound measured rib eye area; SEP, standard error of prediction; TK, Tokyo-Keiki CS 3000 ultrasound machine; UFAT, real-time ultrasound measures of backfat

2 20 CANADIAN JOURNAL OF ANIMAL SCIENCE images. Perkins et al. (1992b), Robinson et al. (1992), Herring et al. (1994) and Moeller and Christian (1998) demonstrated that different machines and operators affect the accuracy of measurements. Cost and availability considerations have resulted in a variety of different makes of ultrasound equipment being used to collect data for genetic evaluation and management use. There is a consequent need to investigate the effect of ultrasound equipment differences on animal ranking and accuracy of prediction for use in genetic improvement programs. MATERIALS AND METHODS Composite (0.25 Charolais, 0.25 Simmental 0.06 Limousin and 0.44 British (Hereford/Angus/Shorthorn) beef animals (n = 60 bulls, n = 60 heifers and n = 60 steers) used in this study were born in 1997 at the Onefour Research Substation of Agriculture and Agri-Food Canada at Manyberries in Southern Alberta. All animals involved in this study were cared for in accordance with the standards set by the Canadian Council on Animal Care (1993). Following weaning (average age 257 d), bulls and heifers were reared on a growing ration for about 200 d to achieve a mean gain of approximately 0.75 kg d 1 for heifers and 1.0 kg d 1 for bulls. Thereafter they were put on a finishing ration (mean gain of approximately 1.25 kg d 1 for bulls and 1.0 kg d 1 for heifers) for approximately 90 d and then shipped for slaughter. The average age at slaughter was 523 d. Steers were reared on a typical (barley silage, barley grain) feedlot diet from weaning (average age 257 d) until slaughter at a pen average of 500 kg and 10 mm backfat. Steers reached the designated slaughter endpoint earlier than bulls and heifers and were slaughtered at an average age of 438 ± 13 d in three groups approximately 2 wk apart. Real-time ultrasound measures of backfat (UFAT) and ribeye area (UREA) were taken between 3 and 17 d prior to slaughter. All bulls and heifers were slaughtered 17 d after scanning, while the steers were slaughtered in three groups, 3, 4 and 15 d after scanning. All animals were scanned on the left side between the 12th and 13th rib, perpendicular to the spine. Each animal was consecutively scanned by one technician using two ultrasound machines, the Aloka SSD-1100 Flexus (Aloka Co. Ltd., Tokyo, Japan) (AL) with a 17.5 cm linear 3.5- MHz probe and the Tokyo Keiki CS 3000 (Tokyo Keiki Co. Ltd., Tokyo, Japan) (TK) with a 10.1 cm linear 3.5-MHz probe. The longer probe on the AL allows for capturing the entire LD muscle in one image, while with the TK two images, one of each half must be taken and the technician must match the image halves at the time of scanning to capture the entire LD. A standoff guide (Animal Guide Fabrication, Lansing, New York) was used with the AL, but not with TK, to establish good transducer-animal contact. Digitized images from both machines were analyzed by one technician using Jandel SigmaScan (Jandel Scientific, San Rafael, CA) software. Ultrasonic backfat thickness (UFAT) was the mean of three equally spaced measures, at approximately the 0.25, 0.50 and 0.75 positions over the ribeye. The area of the longissimus dorsi (UREA) was traced from the digitized image and UREA computed by the software. Following routine slaughter and processing procedures, carcass backfat thickness (CFAT) and ribeye area (CREA) were measured by a certified beef grader on each carcass after chilling for 24 h. Statistical Analysis The sources of variation in carcass and ultrasound measurements were evaluated using the GLM procedure of the SAS Institute, Inc. (1985). For carcass measurements the model included sex and the covariate of age at slaughter nested within sex (Model 1). Ultrasound measurements (UREA and UFAT) were evaluated using a fixed effects model (Model 2) that included sex, machine, the interaction between sex and machine, and the covariate of age at ultrasound nested within sex. Product-moment correlation coefficients between ultrasound and carcass measurements were estimated within machine. For both UREA and UFAT, two measures of accuracy were derived: bias and magnitude of the bias (deviation). The difference (bias) between pre-slaughter ultrasound and carcass measurements was defined as: READIFF = (UREA CREA) FATDIFF = (UFAT CFAT) for ribeye area and backfat, respectively. These terms give the value and direction of bias. The absolute value of bias (deviation) was defined as: READEV = ( UREA CREA ) FATDEV = ( UFAT CFAT ) for ribeye area and backfat, respectively. These measures of accuracy were analyzed using the model previously described for ultrasound measurements (Model 2). Among steers the effect of time period from scanning to slaughter, which was 3, 4 or 15 d was evaluated and was not significant (P > 0.05) for any of the bias and deviation measures. It was therefore omitted in subsequent analyses. This seems to indicate deposition of muscle and fat up to 2 wk prior to slaughter is not significant, and that ultrasound measurements within this period are similar. The other measure of accuracy for ultrasound prediction assessed was the standard error of prediction (SEP) as proposed by Robinson et al. (1992). Standard error of prediction measures the degree of variability that occurs in the prediction of carcass measurements from the measurements by ultrasound. It allows for the correction of bias since each measurement is deviated from its mean, and is therefore considered more suitable than a correlation coefficient, which does not account for bias (Houghton and Turlington 1992). The standard error of prediction was calculated using the method described by Robinson et al. (1992) and Herring et al. (1994) as follows: SEP = ( ) 2 Ultrasoundi Carcassi Bias Ni 1

3 CHARUGU ET AL. ACCURACY OF ULTRASONIC PREDICTION OF BACK FAT AND RIBEYE AREA 21 Table1. Least squares means and standard errors of REA and FAT for carcass and ultrasound measurements by sex and machine REA z (cm 2 ) FAT z (mm) CARCASS ALOKA TOKYO CARCASS ALOKA TOKYO Steers 76.8 ± 5.4a 69.4 ± 2.7a 97.8 ± 2.7ab 15.0 ± 1.6a 10.4 ± 0.7a 15.1 ± 0.7a Heifers 83.0 ± 4.0a 78.8 ± 2.1b 93.1 ± 2.1a 11.3 ± 1.2b 8.1 ± 0.6b 9.0 ± 0.6b Bulls ± 3.7b 98.8 ± 1.9c 99.6 ± 1.9b 6.8 ± 1.1c 6.1 ± 0.5c 7.2 ± 0.5c z ALOKA = measurements using Aloka SSD-1100, TOKYO = measurements using Tokyo-Keiki CS 3000, CARCASS = carcass measurements a,c Means on one column having different superscript are significantly different (P < 0.05) Table 2. Correlation (r) between carcass and ultrasound measurements REA z (cm 2 ) FAT z (mm) AL/CARC TK/CARC AL/TK AL/CARC TK/CARC AL/TK Bulls Heifers a Steers a z AL = measurements using Aloka SSD-1100, TK = measurements using Tokyo-Keiki CS 3000, CARC = carcass measurements a Correlation coefficient not significant (P > 0.05) where ultrasound i and carcass i were the ultrasound and carcass measures on animal i, bias was the mean difference between ultrasound and carcass measurements within the group being analyzed, and N i the number of observations in the group. The SEP was calculated within sex, machine and sex-machine subclasses. Previous studies (Perkins et al. 1992a; Smith et al. 1992; Herring et al. 1993; Moeller and Christian 1998) have shown that the magnitude of FAT and REA has an effect on the accuracy of the respective ultrasound measurements. Ultrasound backfat tends to be overpredicted (positive bias) on animals with relatively less FAT and underpredicted (negative bias) on animals with more FAT. Similarly ultrasound ribeye area is overpredicted on animals with relatively smaller carcass REA and underpredicted on those with larger carcass REA. Given these findings and the wide variation in carcass measurements in this study the decision was made to investigate the effects of size of carcass measurement on the accuracy of the two machines. An initial analysis was done to investigate the relationship between carcass measurements and the accuracy measures of bias and deviation by regressing bias and deviation on carcass measurements (CREA and CFAT). Results indicated trends agreeing with the findings of the studies quoted above. To further quantify these trends, animals were assigned to one of three categories of CREA and CFAT using the phenotypic standard deviations for these traits. Animals within one standard deviation of the mean were put into one category and those greater or less than one standard deviation were placed in separate categories. With respect to these groupings the effect of sex was interpreted as being its influence on the size of both CREA and CFAT. A similar approach was used by Moeller and Christian ( 1998) working with swine of different sexes and breeds. The respective categories (REA- CAT, FATCAT) for REA and FAT were: REACAT 1: CREA 76.8 cm 2 2: CREA > 76.8 and cm 2 3: CREA > cm 2 FATCAT 1: FAT 6.5 mm 2: FAT > 6.5 and 13.9 mm 3: FAT > 13.9 mm The effect of size category on bias and deviation was then evaluated using a fixed effects model (Model 3) including machine, category, interaction between machine and category and the covariate of age at ultrasound. Standard error of prediction was computed for each category by machine subgroup. RESULTS AND DISCUSSION Least squares means and standard errors for carcass (from Model 1) and for ultrasound measurements (from Model 2) by sex and machine are shown in Table 1. At slaughter, bulls had the largest CREA and least CFAT, with little difference between heifers and steers in CREA. Similar results were reported for REA by Hassen et al. (1998b). Though heifers and steers had comparable CREA, steers had significantly more (P < 0.05) FAT. Pearson product moment correlations between carcass and ultrasound measurements for the two machines are given in Table 2. Correlations were generally higher among FAT measures than among REA measures for both machines. This was consistent with the results of Hassen et al. (1998a) and Griffin et al. (1999) who also reported higher correlations among FAT than among REA measures. Another general observation is that AL shows slightly higher correlations than TK, particularly for REA. The consistently lower correlations among REA measurements with TK might be explained by inaccuracies introduced in the

4 22 CANADIAN JOURNAL OF ANIMAL SCIENCE Table 3. Least squares means and standard errors of bias and deviation, plus standard errors of prediction, for REA by sex and machine READIFF x READEV w SEP y READIFF x READEV w SEP v (cm 2 ) (cm 2 ) (cm 2 ) (cm 2 ) (cm 2 ) (cm 2 ) Steers 8.2 ± ± ± ± Heifers 4.2 ± ± ± ± Bulls 12.9 ± ± ± ± x Difference between ultrasound-measured and carcass ribeye area. w Magnitude of difference between ultrasound-measured and carcass ribeye area. Table 4. Least squares means and standard errors of bias and deviation, plus standard errors of prediction for FAT by sex and machine FATDIFF x FATDEV w SEP v FATDIFF x FATDEV w SEP v (mm) (mm) (mm) (mm) (mm) (mm) Steers 4.1 ± ± ± ± Heifers 3.4 ± ± ± ± Bulls 0.8 ± ± ± ± x Difference between ultrasound-measured and carcass backfat thickness. w Magnitude of difference between ultrasound-measured and carcass backfat thickness. matching of image halves. These correlation coefficients, however, should only be considered as preliminary indicators of accuracy. Bias (READIFF and FATDIFF) and deviation (READEV and FATDEV) were first analyzed using a model including the fixed effects of sex, machine and their interaction (Model 2). The interaction was significant for all four measures of accuracy. The resulting least squares means for bias and deviation for sex ( machine subclasses are shown in Tables 3 and 4 for REA and FAT, respectively. Among bulls both machines tended to underpredict REA (negative READIFF), and with the same magnitude. READEV and SEP were also comparable for both machines among bulls. All three measures of accuracy (READIFF, READEV and SEP) show that the two machines are comparable for REA measurement of bulls. The underprediction of REA among bulls by both machines is consistent with other reports (Perkins et al. 1992a; Smith et al. 1992; Herring et al. 1993; Moeller and Christian 1998), which show a negative association between bias and carcass REA. However, these studies did not compare directions of bias across genders. The two machines differed in their accuracy of predicting REA among the steers and heifers, both of which had smaller REA than bulls. AL underpredicted CREA in both steers and heifers while TK overpredicted both genders. Though biases for the two machines were in different directions, the mean READEV was higher (P < 0.05) for TK than AL among both heifers and steers. The SEP were also lower for AL, indicating higher accuracy of prediction for AL than TK. The differences in accuracy between the two machines in the measurement of REA among the heifers and steers would most likely be attributable to the use of split images with the TK. Among animals with a relatively smaller REA (heifers and steers) it appears that there might have been a tendency to overmatch the two halves, that is, matching them along a plane further away from the true median, resulting in an overprediction. The opposite would appear to be the case with the animals with larger REA (bulls). Technician error occurs at two sources: during image capture and during image analysis. The latter would most likely occur consistently across images from both machines. However, there is a higher likelihood for error in the measurement of REA during image capture with the TK since the matching of the split images is subject to technician interpretation. Additionally, since no standoff guide was used with TK it means images could have been subject to longitudinal movement along the animal s back. This could result in a muscle image that is not a fair representation of the actual cross-section. These two possible sources of inaccuracy might partially explain the relatively higher SEPs for the TK compared to the AL for REA measurements. Among bulls FAT was underpredicted (P < 0.05) by AL but bias for TK was not significantly different from zero. Mean bias estimates of less than 3 mm probably have little practical significance. Both machines underpredicted fat among heifers with AL showing a larger mean bias. Among the steers, AL underpredicted FAT while the mean bias for TK was less than a millimeter. The underprediction of FAT by AL among heifers and steers, which had more backfat than bulls, is consistent with the findings of other researchers (Herring et al. 1994; Moller and Christian 1998) who reported that animals with more FAT tend to be under-

5 CHARUGU ET AL. ACCURACY OF ULTRASONIC PREDICTION OF BACK FAT AND RIBEYE AREA 23 Table 5. Regression of bias and deviation on carcass traits within machine CREA x CFAT w CREA x CFAT w READIFF v u u READEV t u u FATDIFF s u u u FATDEV r u u x CREA, carcass ribeye area. w CFAT carcass backfat thickness. v Difference between ultrasound-measured and carcass ribeye area. u Regression coefficient significant (P < 0.05). t Magnitude of difference between ultrasound-measured and carcass ribeye area. s Difference between ultrasound-measured and carcass backfat thickness. r Magnitude of difference between ultrasound-measured and carcass back fat thickness. predicted by ultrasound. The magnitude of the bias (FAT- DEV) was similar among bulls for both machines but higher in the AL for both heifers and steers. The SEP, however, were very comparable across both machines with an average difference of 0.1 mm. This similarity in accuracy was also reflected in the higher correlations between carcass and ultrasound measurements of FAT and in the correlations between machine predictions. The correlations between machines were much higher in FAT than in REA. This might be attributable to the fact that in the measurement of FAT the problem of matching images is less of a factor. Research has also shown that there is less operator variability in the measurement of FAT than in the measurement of REA (McLaren et al. 1991). Both READIFF and READEV were independently regressed on carcass REA measurements (CREA) and then on carcass FAT measurements (CFAT). Likewise, FATD- IFF and FATDEV were regressed on CFAT and CREA. All regressions were done within machine. This was done to get a preliminary indication of the trends in accuracy of prediction (bias and deviation) when carcass values of the two traits increased. Results of this analysis are given in Table 5. For both machines the coefficients obtained from regressing READIFF on CREA indicate that an increase in carcass muscle size results in a negative trend in measurement bias. This implies that ultrasonic measurements of REA are more likely to overpredict smaller REA and underpredict larger REA. Similarly, in FAT, the coefficients indicate a negative slope for FATDIFF, implying that leaner animals are more likely to be overpredicted and fatter ones more likely to be underpredicted. The regression of READIFF on CFAT was not significant (P > 0.05) for either machine, indicating that the amount of backfat cover did not influence the accuracy of ultrasonic prediction of REA. Similarly the regression of FATDIFF on CREA was not significant (P > 0.05). To further examine the effect of REA size, and backfat depth, on the accuracy of ultrasonic measurements of REA and FAT, respectively, the animals were assigned to size categories as explained earlier. The results are shown in Tables 6 and 7 for REA and FAT, respectively. Table 6. Least squares means and standard errors of bias and deviation, plus standard errors of prediction, for REA by size category and machine READIF x READEV w SEP v READIFF x READEV w SEP v REACAT u (cm 2 ) (cm 2 ) (cm 2 ) (cm 2 ) (cm 2 ) (cm 2 ) 1. REA 76.8 cm ± ± ± ± REA > 76.8 and cm ± ± ± ± REA > cm ± ± ± ± x Difference between ultrasound-measured and carcass ribeye area. w Magnitude of difference between ultrasound-measured and carcass ribeye area. u REACAT, ribeye area size category. Table 7. Least squares means and standard errors of bias and deviation, plus standard errors of prediction, for FAT by sex and machine FATDIFF x FATDEV w SEP v FATDIFF x FATDEV w SEP v FATCAT u (mm) (mm) (mm) (mm) (mm) (mm) 1 FAT 6.5 mm 1.2 ± ± ± ± FAT > 6.5 and 13.9 mm 2.7 ± ± ± ± FAT > 13.9 mm 6.0 ± ± ± ± x Difference between ultrasound-measured and carcass backfat. w Magnitude of difference between ultrasound-measured and carcass backfat. u FATCAT, backfat thickness category.

6 24 CANADIAN JOURNAL OF ANIMAL SCIENCE For REA, both machines overpredicted animals with the smallest REA (REACAT1) and underpredicted animals with the largest REA (REACAT3), which was in agreement with the findings of Herring et al. (1994) and Moller and Christian (1998). Both machines had similar bias, deviation and SEP for REACAT3. This would indicate that among animals with larger muscle size the two machines had comparable accuracy of prediction. This corroborates the results observed earlier when looking at accuracy by sex and machine whereby the two machines had similar accuracy among the bulls, which had the highest mean CREA. Among the smaller muscled category (REACAT1), though both machines overpredicted REA, the degrees of accuracy were different as evidenced by different READIFF, READ- EV and SEP. The machine differences were more pronounced in the middle category where AL underpredicted while TK overpredicted. In the two smaller categories (REACAT1 and REACAT2) READEV and SEP were lower for AL, indicating that it had higher accuracy. For the TK, SEP reduced with increasing CREA, further supporting the observation that there might have been more error arising from the matching of images among animals with smaller REA than among those with larger REA. Results for influence of carcass backfat depth on accuracy of prediction (Table 7) showed that both machines overpredicted animals with less FAT (FATCAT1) and underpredicted those with more FAT (FATCAT3), the same trend documented by Herring et al. (1994) and Moller and Christian (1998). Both machines also underpredicted animals in FATCAT2, indicating that FAT greater than 6.5 mm was underpredicted. Differences in SEP between the two machines were less than 1 mm in all FAT categories, again implying greater similarity in accuracy of FAT measurement than in the measurement of REA. CONCLUSIONS From the observed results it can be concluded that the two machines had more differences in accuracy in the prediction of ribeye area than that of backfat. The differences in accuracy in ribeye area prediction, however, appear to be particularly more influenced by the size of muscle. This was demonstrated by the fact that the two machines had similar accuracy predicting ribeye area among bulls and size category REACAT3, among which muscle size was largest. Accuracy was much more different among animals with smaller muscle size. This was attributed to the problem of matching images with Tokyo Keiki, which appears to be more pronounced among the smaller muscled animals. Accuracy of backfat prediction, where matching of images was not an issue, was more comparable between the two machines. The results from this study demonstrate that though ultrasound technology has been shown to be relatively accurate in predicting carcass traits, differences in accuracy among machine types can be large. Variability in machine accuracy could be compounded by variability among ultrasound technicians, which has been shown to exist (Perkins et al. 1992b; Robinson et al. 1992, Hassen et al. 1998a). The implications, for example, in the use of ultrasound in sire evaluation, would be that some sires could be unfairly penalized as a result of the interaction between machine and technician. To minimize such bias would require inclusion of these effects in evaluation models. ACKNOWLEDGEMENTS The authors wish to thank Norm Shannon, Ronda Crews, Allan Ross and the Beef Cattle Staff at Onefour. Bergen, R. D., McKinnon, J. J., Christensen, D. A., Kohle, N. and Belanger, A Use of real-time ultrasound to evaluate live animal carcass traits in young performance-tested beef bulls. J. Anim. 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