Prediction of retail product and trimmable fat yields from the four primal cuts in beef cattle using ultrasound or carcass data.

The most widely used system to predict percentage of retail product from the four primal cuts of beef is USDA yield grade. The purpose of this study was to determine whether routine ultrasound measurements and additional rump measurements could be used in place of the carcass measurements used in the USDA yield grade equation to more accurately predict the percentage of saleable product from the four primals. This study used market cattle (n = 466) consisting of Angus bulls, Angus steers, and crossbred steers. Live animal ultrasound measures collected within 7 d of slaughter were 1) scan weight (SCANWT); 2) 12th- to 13th-rib s.c. fat thickness (UFAT); 3) 12th- to 13th-rib LM area (ULMA); 4) s.c. fat thickness over the termination of the biceps femoris in the rump (URFAT; reference point); 5) depth of gluteus medius under the reference point (URDEPTH); and 6) area of gluteus medius anterior to the reference point (URAREA). Traditional carcass measures collected included 1) HCW; 2) 12th-to 13th-rib s.c. fat thickness (CFAT); 3) 12th- to 13th-rib LM area (CLMA); and 4) estimated percentage of kidney, pelvic, and heart fat (CKPH). Right sides of carcasses were fabricated into subprimal cuts, lean trim, fat, and bone. Weights of each component were recorded, and percentage of retail product from the four primals was expressed as a percentage of side weight. A stepwise regression was performed using data from cattle (n = 328) to develop models to predict percentage of retail product from the four primals based on carcass measures or ultrasound measures, and comparisons were made between the models. The most accurate carcass prediction equation included CFAT, CKPH, and CLMA (R2 = 0.308), whereas the most accurate live prediction equation included UFAT, ULMA, SCANWT, and URAREA (R2 = 0.454). When these equations were applied to a validation set of cattle (n = 138), the carcass equation showed R2 = 0.350, whereas the ultrasound data showed R2 = 0.460. Ultrasound measures in the live animal were potentially more accurate predictors of retail product than measures collected on the carcass.

Use of ultrasound to predict body composition changes in steers at 100 and 65 days before slaughter.

Steers from research crossbreeding projects (n = 406) were serially scanned using real-time ultrasound at 35-d intervals from reimplant time until slaughter. Cattle were evaluated for rump fat depth, longissimus muscle area (ULMA), 12th-rib fat thickness (UFAT), and percentage of intramuscular fat (IMF) to determine the ability of ultrasound to predict carcass composition at extended periods before slaughter. Additional background information on the cattle, such as live weight, ADG, breed of sire, breed of dam, implant, and frame score was also used. Carcass data were collected by trained personnel at "chain speed," and samples of the 12th-rib LM were taken for ether extract analysis. Simple correlation coefficients showed positive relationships (P < 0.01) between ultrasound measures taken less than 7 d before slaughter and carcass measures: ULMA and carcass LM area (CLMA, r = 0.66); UFAT and carcass 12th-rib fat thickness (CFAT, r = 0.74); and IMF and carcass numeric marbling score (r = 0.61). The same correlation coefficients for ultrasound measures taken 96 to 105 d before slaughter and carcass values (P < 0.01) were 0.52, 0.58, and 0.63, respectively. Steers were divided into source-verified and nonsource-verified groups based on the level of background information for each individual. Regression equations were developed for the carcass measurements; 46% of the variation could be explained for CLMA and 44% of CFAT at reimplant time, 46% of the variation in quality grade and 42% of the variation in yield grade could be explained. Significant predictors of quality grade were IMF (P < 0.001), natural log of 12th-rib fat thickness (LUFAT, P < 0.001), and ADG (P < 0.01), whereas LUFAT (P < 0.001), ULMA (P < 0.01), live weight (P < 0.001), hip height (P < 0.001), and frame score (P < 0.001) were significant predictors of yield grade. Regressions using ultrasound data taken 61 to 69 d before slaughter showed increasing R2. Live ultrasound measures at reimplant time are a viable tool for making decisions regarding future carcass composition.

Partitioning variances of growth in ultrasound longissimus muscle area measures in Angus bulls and heifers.

The objective of the study was to estimate variance components, heritability, and repeatability of ultrasound longissimus muscle area (ULMA) measures. Data included 4,653 serial ULMA measures from 882 purebred Angus bulls and heifers. Animals were born over a 4-yr period from 1998 to 2001. Each year, bulls and heifers were ultrasonically scanned four to eight times, with a 4- to 6-wk interval between scans. Initially, data were subdivided by scan session across years and were analyzed in a multitrait model (MTM). Data pooled across years and scan session were then analyzed using random regression models (RRM) to estimate trends in genetic parameter estimates. Additive direct genetic variance increased with advancing scan session ranging from 8.67 cm4 at the first scan (mean age = 35 wk) to a maximum of 19.48 cm4 at the sixth scan (mean age = 56 wk). Heritability of ULMA increased from 0.35 at first scan to a maximum of 0.48 at the fourth scan (mean age = 50 wk). Additive direct genetic variance and heritability values at about 1 yr of age (fifth scan) were 18.24 cm4 and 0.45, respectively. Estimates from RRM also showed an increase in sigma(a)2 and h2 with age. Trends in sigma(pe)2 estimates, although tending to fluctuate, also increased with age. Additive direct genetic variance at 1 yr of age ranged from 15.8 cm4 to 17.0 cm4 for the different models. Heritability of yearling ULMA measures ranged from 0.40 to 0.42 and repeatabilities ranged from 0.80 to 0.84. For the range of ages used in the current study, both MTM and RRM showed close to maximum heritability values at around 1 yr of age. Therefore, phenotypic differences in yearling ULMA between Angus cattle are better indicators of genetic differences than earlier measurements. Angus breeders could, therefore, use ULMA measures made at around 1 yr of age to select next generation parents.

Prediction of retail product weight and percentage using ultrasound and carcass measurements in beef cattle.

Data from 534 steers representing six sire breed groups were used to develop live animal ultrasound prediction equations for weight and percentage of retail product. Steers were ultrasonically measured for 12th-rib fat thickness (UFAT), rump fat thickness (URPFAT), longissimus muscle area (ULMA), and body wall thickness (UBDWALL) within 5 d before slaughter. Carcass measurements included in USDA yield grade (YG) and quality grade calculations were obtained. Carcasses were fabricated into boneless, totally trimmed retail products. Regression equations to predict weight and percentage of retail product were developed using either live animal or carcass traits as independent variables. Most of the variation in weight of retail product was accounted for by live weight (FWT) and carcass weight with R2 values of 0.66 and 0.69, respectively. Fat measurements accounted for the largest portion of the variation in percentage of retail product when used as single predictors (R2 = 0.54, 0.44, 0.23, and 0.54 for UFAT, URPFAT, UBDWALL, and carcass fat, respectively). Final models (P < 0.10) using live animal variables included FWT, UFAT, ULMA, and URPFAT for retail product weight (R2 = 0.84) and UFAT, URPFAT, ULMA, UBDWALL, and FWT for retail product percentage (R2 = 0.61). Comparatively, equations using YG variables resulted in R2 values of 0.86 and 0.65 for weight and percentage of retail product, respectively. Results indicate that live animal equations using ultrasound measurements are similar in accuracy to carcass measurements for predicting beef carcass composition, and alternative ultrasound measurements of rump fat and body wall thickness enhance the predictive capability of live animal-based equations for retail yield.

Accuracy of predicting weight and percentage of beef carcass retail product using ultrasound and live animal measures.

Five hundred thirty-four steers were evaluated over a 2-yr period to develop and validate prediction equations for estimating carcass composition from live animal ultrasound measurements and to compare these equations with those developed from carcass measurements. Within 5 d before slaughter, steers were ultrasonically measured for 12th-rib fat thickness (UFAT), longissimus area (ULMA), rump fat thickness (URPFAT), and body wall thickness (UBDWALL). Carcasses were fabricated to determine weight (KGRPRD) and percentage (PRPRD) of boneless, totally trimmed retail product. Data from steers born in Year 1 (n = 282) were used to develop prediction equations using stepwise regression. Final models using live animal variables included live weight (FWT), UFAT, ULMA, and URPFAT for KGRPRD (R2 = 0.83) and UFAT, URPFAT, ULMA, FWT, and UBDWALL for PRPRD (R2 = 0.67). Equations developed from USDA yield grade variables resulted in R2 values of 0.87 and 0.68 for KGRPRD and PRPRD, respectively. When these equations were applied to steers born in Year 2 (n = 252), correlations between values predicted from live animal models and actual carcass values were 0.92 for KGRPRD, and ranged from 0.73 to 0.76 for PRPRD. Similar correlations were found for equations developed from carcass measures (r = 0.94 for KGRPRD and 0.81 for PRPRD). Both live animal and carcass equations overestimated (P < 0.01) actual KGRPRD and PRPRD. Regression of actual values on predicted values revealed a similar fit for equations developed from live animal and carcass measures. Results indicate that composition prediction equations developed from live animal and ultrasound measurements can be useful to estimate carcass composition.

The relationship between ultrasound measurements and carcass fat thickness and longissimus muscle area in beef cattle.

Five hundred thirty-four steers were evaluated over a 2-yr period to determine the accuracy of ultrasonic estimates of carcass 12th-rib fat thickness (CFAT) and longissimus muscle area (CLMA). Within 5 d before slaughter, steers were ultrasonically measured for 12th-rib fat thickness (UFAT) and longissimus muscle area (ULMA) using an Aloka 500V real-time ultrasound machine equipped with a 17.2-cm, 3.5-MHz linear transducer. Overall, correlation coefficients between ultrasound and carcass fat and longissimus muscle area were 0.89 and 0.86, respectively. Correlations for UFAT with CFAT were similar between years (0.86 and 0.90), whereas the relationship between ULMA and CLMA was stronger in yr 1 (r = 0.91; n = 282) than in yr 2 (r = 0.79; n = 252). Differences between ultrasonic and carcass measurements were expressed on both an actual (FDIFF and RDIFF) and absolute (FDEV and RDEV) basis. Mean FDIFF and RDIFF indicated that ultrasound underestimated CFAT by 0.06 cm and overestimated CLMA by 0.71 cm2 across both years. Overall mean FDEV and RDEV, which are indications of the average error rate, were 0.16 cm and 3.39 cm2, respectively. Analysis of year effects revealed that FDIFF, FDEV, and RDEV were greater (P < 0.01) in magnitude in yr 1. Further analysis of FDEV indicated that leaner (CFAT < 0.51 cm) cattle were overestimated and that fatter (CFAT > 1.02 cm) cattle were underestimated with ultrasound. Similarly, steers with small CLMA (< 71.0 cm2) were overestimated, and steers with large CLMA (> 90.3 cm2) were underestimated. The thickness of CFAT had an effect (P < 0.05) on the error of UFAT and ULMA measurements, with leaner animals being more accurately evaluated for both traits. Standard errors of prediction (SEP) adjusted for bias of ultrasound measurements were 0.20 cm and 4.49 cm2 for UFAT and ULMA, respectively. Differences in SEP were observed for ULMA, but not UFAT, by year. These results indicate that ultrasound can be an accurate estimator of carcass traits in live cattle when measurements are taken by an experienced, well-trained technician, with only small differences in accuracy between years.

Estimation of genetic parameters for ultrasound-predicted percentage of intramuscular fat in Angus cattle using random regression models.

The present study included 3,358 observations of 675 bulls and heifers from the Iowa State University beef cattle breeding project. Data were collected over a 3-yr period between 1998 and 2000. Each year, cattle were scanned four to six times for ultrasound-predicted percentage of intramuscular fat (UPFAT) and other ultrasound traits, starting at a minimum age of 28 wk. The objective of the current study was to estimate variance components, heritability, and repeatability of UPFAT in young bulls and heifers. Data were subjected to random-regression animal models that included fixed effects of contemporary group, fixed Legendre polynomial of age at measurement, and random regression coefficients on Legendre polynomial of age at measurement for animals' direct genetic and direct permanent environmental effects. Phenotypic and genetic models involving different levels of polynomial fit for the animal component were considered. A model fitting a linear effect of Legendre polynomial of age at a measurement for animal direct genetic and direct permanent environmental effects and a homogeneous error variance described the present data adequately. Heritability of UPFAT ranged from 0.32 at 28 wk of age to a maximum of 0.53 at 63 wk. Repeatability of UPFAT increased from a minimum of 0.60 at ages of 28 to 39 wk to a maximum of 0.80 at ages 61 to 63 wk. Heritability and repeatability of yearling UPFAT were 0.50 and 0.71, respectively. With the exception of minor differences at earlier ages, fitting heterogeneous error variances did not have an effect on genetic parameter estimates for most ages of measurement. The present results showed an optimal heritability and repeatability of UPFAT measures around 52 wk and through at least 63 wk of age. This suggested that differences in UPFAT measures during this period also are good measures of differences in marbling genetic potential of Angus cattle.

Predicting percentage of intramuscular fat using two types of real-time ultrasound equipment.

In the present study, 500 steers were used to develop models for predicting the percentage of intramuscular fat (PIMF) in live beef cattle. Before slaughter, steers were scanned across the 11th and 13th ribs using Aloka 500V (AL-500) and Classic Scanner 200 (CS-200) machines. Four to five images were collected per individual steer using each machine. After slaughter, a cross-sectional slice of the longissimus muscle from the 12th rib facing was used for chemical extraction to determine actual carcass percentage of intramuscular fat (CPIMF). Texture analysis software was used by two interpreters to select a region for determination of image parameters, which included Fourier, gradient, histogram, and co-occurrence parameters. Four prediction models were developed separately for each of AL-500 and CS-200 based on images captured by the respective machines. These included models developed without transformation of CPIMF (Model I), models based on logarithmic transformation of CPIMF (Model II), ridge regression procedure (Model III), and principal component regression procedure (Model IV). Model R2 and root mean square error of AL-500 Models I, II, III, and IV were 0.72, 0.84%; 0.72, 0.85%; 0.69, 0.91%; and 0.71, 0.86%; respectively. The corresponding R2 and root mean square error values of CS-200 Models I, II, III, and IV were 0.68, 0.87%; 0.70, 0.85%; 0.64, 0.94%; and 0.65, 0.91%; respectively. Initially, AL-500 and CS-200 prediction models were validated separately on an independent data set from 71 feedlot steers. The overall mean bias, standard error of prediction, and rank correlation coefficient across the four AL-500 models were 0.42%, 0.84%, and 0.88, respectively. For the four CS-200 models, the corresponding overall mean values were 0.67%, 0.81%, and 0.91, respectively. In a second validation test, only Model II of AL-500 and CS-200 was evaluated separately based on data from 24 feedlot steers. The overall mean bias, absolute difference, and standard error of prediction of AL-500 Model II were 0.71, 0.92, and 0.98%. For CS-200 Model II, the corresponding values were 0.59, 0.97, and 1.03%. Both AL-500 and CS-200 equipment can be used to accurately predict PIMF in live cattle. Further improvement in the accuracy of prediction equations could be achieved through increasing the development data set and the variation in PIMF of cattle used.

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.

Evaluation of carcass, live, and real-time ultrasound measures in feedlot cattle: I. Assessment of sex and breed effects.

Carcass and live-animal measures from 1,029 cattle were collected at the Iowa State University Rhodes and McNay research farms over a 6-yr period. Data were from bull, heifer, and steer progeny of composite, Angus, and Simmental sires mated to three composite lines of dams. The objectives of this study were to estimate genetic parameters for carcass traits, to evaluate effects of sex and breed of sire on growth models (curves), and to suggest a strategy to adjust serially measured data to a constant age end point. Estimation of genetic parameters using a three-trait mixed model showed differences between bulls and steers in estimates of h2 and genetic correlations. Heritability for carcass weight, percentage of retail product, retail product weight, fat thickness, and longissimus muscle area from bull data were .43, .04, .46, .05, and .21, respectively. The corresponding values for steer data were in order of .32, .24, .40, .42, and .07, respectively. Analysis of serially measured fat thickness, longissimus muscle area, body weight, hip height, and ultrasound percentage of intramuscular fat using a repeated measures model showed a limitation in the use of growth models based on pooled data. In further evaluation of regression parameters using a linear mixed model analysis, sex and breed of sire showed an important (P < .05) effect on intercept and slope values. Regression of serially measured traits on age within animal showed a relatively larger R2 (62 to 98%) and a smaller root mean square error (RMSE, .09 to 8.85) as compared with R2 (0 to 58%) and RMSE (.31 to 67.9) values when the same model was used on pooled data. We concluded that regression parameters from a within-animal regression of a serially measured trait on age, averaged by sex and breed, are the best choice in describing growth and adjusting data to a constant age end point.

Evaluation of carcass, live, and real-time ultrasound measures in feedlot cattle: II. Effects of different age end points on the accuracy of predicting the percentage of retail product, retail product weight, and hot carcass weight.

Data from 970 feedlot steers and bulls were used to evaluate effects of different age end points on the accuracy of prediction models for percentage of retail product, retail product weight, and hot carcass weight. Cattle were ultrasonically scanned three to five times for fat thickness, longissimus muscle area, and percentage of intramuscular fat. Live animal measures of body weight and hip height were also taken during some of the scan sessions. Before development of prediction equations, live and ultrasound data were adjusted to four age end points using individual animal regressions. Age end points represented mean age at slaughter (448 d), mean age at the second-to-last scan before slaughter (414 d), mean age at the third-to-last scan before slaughter (382 d), and an age end point of 365 d. Ultrasound and live animal measures accounted for a large proportion of the variation in the dependent variables regardless of the age end point considered. For all three traits, final models based on independent variables adjusted to earlier ages of 365 and 382 d showed better or at least similar model R2 and root mean square errors than those based on independent variables adjusted to a mean slaughter age of 448 d. Validation of the models using independent data from 282 steers resulted in a mean across-age rank correlation coefficient of .78, .88, and .83 between actual and predicted values of the percentage of retail product, hot carcass weight, and retail product weight, respectively. Mean across-age rank correlation of breeding values for the corresponding traits were .92, .89, and .82. The results of this study suggest that live and ultrasound traits measured as early as 365 d could be used to predict end product traits as accurately as similar measures made before slaughter at age 448 d.

Genetic parameters for carcass traits estimated from Angus field records.

The American Angus Association has sponsored a carcass evaluation since 1974. The carcass data collected as a part of this program are used by the association to conduct a biannual sire evaluation for carcass merit. This paper presents age-adjustment factors and genetic parameter estimates for carcass traits to be used in the Angus carcass genetic evaluation program. Because of the large range in slaughter ages, age classes were defined as all those animals slaughtered at an age of < or = 480 d and those with a slaughter age > 480 d. Linear and quadratic partial regressions on slaughter age for hot carcass weight (HCW), USDA marbling score (MS), 12th rib longissimus muscle area (LMA), and 12th-rib fat thickness (FT) were estimated within sex and age class. Quadratic age regressions were not significant, nor was the linear age regression coefficient for FT in steers in the > 480-d age class. Heritability estimates for age-constant HCW, MS, LMA, and FT were .31, .26, .32, and .26, respectively. The estimated genetic correlation (rg) between HCW and LMA was .47. The estimated rg between HCW and FT was .38 and between MS and FT was -.13. The linear genetic trends for CWT and LMA were significantly positive at .414 kg/yr and .075 cm2/yr, respectively. The genetic trends for FT and MS were very small but significantly negative at -.004 cm/yr and -.003 units/yr, respectively.

Predicting percentage of retail yield from carcass measurements, the yield grading equation, and closely trimmed, boxed beef weights.

Over the past 3 yr, 100 carcasses (64 steers, 24 bulls, and 12 heifers) were fabricated into closely trimmed (6 mm maximum fat cover), boxed beef and further evaluated for percentage of retail yield at the Iowa State University Meat Laboratory. Hot carcass weight ranged from 235 to 399 kg with a least squares mean (LSM) and standard error across all sex classes of 318 +/- 3 kg. Additionally, fat cover ranged from .30 to 1.78 cm with an average of .91 +/- .05 cm. The LSM for longissimus muscle area (LMA) across all sex classes was 81.6 +/- 1.0 cm2. Bulls had significantly less subcutaneous fat (P less than .01) and greater LMA (P less than .01) than did either steers or heifers. Retail yield from the boxed chuck, expressed as a percentage of cold carcass weight, was 19.2 for bulls and 14.8 for steers. This difference was due primarily to a reduction of intermuscular fat. Similarly, bulls had a greater yield (P less than .01) of the boxed round than did steers. When cattle of differing frame sizes were compared, only percentage of retail yield of the boxed round was significant (P less than .01): large-framed cattle yielded 14.3 +/- .2%, compared with 12.8 +/- .2% for the small-framed cattle. When all possible regression analyses were run, sex class differences accounted for 25.7% of the variation in retail yield. The current USDA retail yield equation accounted for only 37.2% of the variation. Percentage of closely trimmed, boneless round had an R2-value of .57.(ABSTRACT TRUNCATED AT 250 WORDS)

Optimal beef cattle diets formulated by nonlinear programming.

A beef diet model based on National Research Council recommendations is significantly nonlinear for feed ingredients, daily gain and weight of cattle. Solving a diet model has been difficult, but advances in nonlinear programming now allow solutions that are quick and easy. This study developed a nonlinear programming method for optimally planning a feeding program by choosing feeds, daily gains and selling weight. Two types of diets are important for this purpose:optimal-return diets and least-cost-gain diets. For a given weight of cattle, an optimal-return diet chooses feeds and daily gain to maximize returns above feed costs. A least-cost-gain diet chooses feeds and daily gain to minimize feed plus yardage costs per kilogram of gain. In an optimal feeding program, a sequence of optimal-return diets is fed to increasing weights of cattle. Feed costs plus yardage per kilogram of gain rise to equal the actual selling price at the optimal selling weight, and the cattle are sold. Cattle feeders and researchers with access to a microcomputer can maximize net returns from a feeding program.