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

We used data from 144 bulls, heifers, and steers to determine the repeatabi lity of ultrasound-predicted percentage of intramuscular fat and to study t he effect of repeated measurements on the standard error of prediction. Ani mals were scanned at an average age of 433 d by a certified technician. Ind ividual bulls, heifers, and steers were scanned five to six times each with two Aloka 500-V machines, and the percentage of intramuscular fat was pred icted from two regions of interest within an image. Variance components and repeatability values were computed for the overall data and by machine, re gion of interest, and sex. Animals were broadly divided into two groups bas ed on mean ultrasound-predicted percentage of intramuscular fat. Variance c omponents and repeatability values were then estimated within each group. T he overall repeatability of ultrasound-predicted percentage of intramuscula r fat was .63 +/- .03. Differences in the repeatability values between mach ines 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 a nd heifer measures was not important (P >.05). Animals with mean ultrasound -predicted percentage of intramuscular fat less than 4.79% showed less repe atable measures (P <.05) than those with means above 4.79%. The image varia nce contributed to nearly 70% of the total variance of observations within an animal. Standard error of animal mean measures showed a 50% reduction wh en the number of images per animal increased to four. Therefore, we conclud ed that increasing the number of images per animal plays a more significant role in reducing the standard error of prediction than taking multiple mea surements within a single image.