Repeatability of ultrasound-predicted percentage of intramuscular fat in feedlot cattle.

We used data from 144 bulls, heifers, and steers to determine the repeatability of ultrasound-predicted percentage of intramuscular fat and to study the effect of repeated measurements on the standard error of prediction. Animals were scanned at an average age of 433 d by a certified technician. Individual bulls, heifers, and steers were scanned five to six times each with two Aloka 500-V machines, and the percentage of intramuscular fat was predicted from two regions of interest within an image. Variance components and repeatability values were computed for the overall data and by machine, region of interest, and sex. Animals were broadly divided into two groups based on mean ultrasound-predicted percentage of intramuscular fat. Variance components and repeatability values were then estimated within each group. The overall repeatability of ultrasound-predicted percentage of intramuscular fat was .63 +/- .03. Differences in the repeatability values between machines and between regions of interest were not different from zero (P > .05). Bulls showed a lower within-animal SD of .82% as compared to .97 and 1.02% for steers and heifers, respectively. However, steer ultrasound-predicted percentage of intramuscular fat measures were more repeatable (P < .05) than those of bulls and heifers. The difference in repeatability between bull and heifer measures was not important (P > .05). Animals with mean ultrasound-predicted percentage of intramuscular fat less than 4.79% showed less repeatable measures (P < .05) than those with means above 4.79%. The image variance contributed to nearly 70% of the total variance of observations within an animal. Standard error of animal mean measures showed a 50% reduction when the number of images per animal increased to four. Therefore, we concluded that increasing the number of images per animal plays a more significant role in reducing the standard error of prediction than taking multiple measurements within a single image.