Development and evaluation of empirical equations to interconvert between twelfth-rib fat and kidney, pelvic, and heart fat respective fat weights and to predict initial conditions of fat deposition models for beef cattle1

Assessment of Equations to Predict Body Weight and Chemical Composition in Growing/Finishing Cattle and Effects of Publication Year, Sex, and Breed Type on the Deviation from Observed Values

Body weight and chemical composition are important aspects of beef cattle nutrition and management; however, existing equations estimating relationships among empty body and carcass chemical components were developed over 40 years ago using different cattle genetics and production systems. The objective of this analysis was to evaluate existing equations in predicting empty body and carcass chemical composition and determine the effect of sex, breed type, and publication year. A dataset was developed from published literature that contained 388 treatment means from 46 studies published between 1970 and 2020. Two equations relating shrunk body weight (SBW) to empty body weight (EBW), and 8 equations relating EBW and hot carcass weight (HCW) were found in the literature and evaluated using the developed dataset. Three sets of equations relating empty body chemical components, 4 sets of equations relating carcass chemical components, and 2 sets of equations relating carcass with empty body chemical components were found in the literature and evaluated using the dataset. Precision and accuracy of the equations were evaluated by simple linear regression of observed on predicted values, mean bias (MB), and concordance correlation coefficient (CCC). Additionally, the fixed effects of publication year, sex, and breed type on the deviation from observed values were evaluated using a general linear model. Both equations relating SBW to EBW and all equations relating EBW to HCW had high precision, but accuracy varied from -3.22 to -0.11% and -9.35 to -3.73% MB, respectively, and all the equations were affected by sex and breed type with 8 out of the 10 equations affected by publication year. For prediction of empty body chemical composition assuming empty body water is known, the 3 sets of equations varied in precision for protein (0.18 to 0.46), but not for fat (0.88 to 0.96) or ash (0.06 to 0.13) based on CCC, although the precision of prediction of protein and ash were poor. Accuracy of the 3 sets of equations varied for predicting empty body fat, protein, and ash with MB of -19.73 to -3.81, 1.67 to 15.91, and -0.16 to 15.75%, respectively. All 3 sets of equations were affected by publication year and breed type for predicting empty body fat, protein, and ash, and by sex for ash. For prediction of carcass chemical components assuming carcass water is known, the precision was similar among the 4 sets of equations for predicting fat (0.92 to 0.95), protein (0.34 to 0.40), and ash (-0.02 to -0.01) based on CCC, although precision was poor for protein and ash, but accuracy varied for prediction of carcass fat, protein and ash with MB of -11.20 to -2.52, 2.72 to 8.92, and -4.66 to 20.12%, respectively. Publication year and breed type affected the prediction of carcass fat and protein, and publication year, sex, and breed type affected the prediction of carcass ash for all 4 sets of equations. The precision of predicting empty body chemical components assuming carcass chemical components are known was high for water (0.96 and 0.98), fat (0.97 and 0.98), protein (0.97 and 0.97), and ash (0.98 and 0.96) and similar between the 2 sets of equations based on CCC. The accuracy of predicting empty body water (-1.68 and -0.33%), fat (6.38 and 2.70%), protein (0.85 and -0.54%), and ash (-0.65 and -4.54%) was moderate to high, but differed between sets of equations for fat and ash. Publication year influenced the prediction of empty body water for both sets of equations and ash for one of the equations, whereas, breed type influenced the prediction of water, protein, and ash, but not fat for both equations. Overall, existing equations may have major limitations to predicting empty body protein and ash unless carcass protein and ash are known. Additionally, all the equations were affected by some combination of publication year, sex, and breed type for one or more chemical components. Thus, a more robust set of equations should be developed to account for sex, breed type, and more recent cattle genetics and management systems.

Effect of rate of weight gain of steers during the stocker phase. IV. Rumen fermentation characteristics and expression of genes involved in substrate utilization for fatty acid synthesis in adipose tissues of growing-finishing beef cattle

The objective of this study was to determine the impact of stocker production systems differing in growth rate on rumen fermentation characteristics and utilization of substrates for fatty acid synthesis in intramuscular (IM), subcutaneous (SC), and perirenal (PR) adipose tissues. Angus steers were assigned to 4 stocker cattle production systems in 2 consecutive years: 1) 1.0 kg/d of 40% CP cottonseed meal–based supplement while grazing dormant native range (CON), 2) ground corn/soybean meal–based supplement while grazing dormant native range fed at 1% of BW (CORN), 3) grazing wheat pasture at a high stocking rate to achieve a low rate of BW gain (LGWP), and 4) grazing wheat pasture at a low stocking rate for a high rate of BW gain (HGWP). Eight ruminally cannulated steers were used to determine rumen fermentation characteristics. Steers were harvested during the stocker phase at similar age (different carcass weight) in Exp. 1 (3 steers/treatment) or at similar carcass weight in Exp. 2 (4 steers/treatment). Adipose tissues were analyzed for mRNA expression of genes involved in glucose (solute carrier family 2, member 4 [GLUT4], glucose-6-phosphate dehydrogenase [G6PDH], phosphofructokinase, muscle [PFKM], and pyruvate kinase 2, muscle [PK2]), lactate (lactate dehydrogenase B [LDHB]), and acetate (acetyl-CoA synthetase, cytosol [ACSS2]) utilization for fatty acid synthesis. The acetate:propionate ratio was least (P < 0.05) for HGWP steers, intermediate for CORN and LGWP steers, and greatest for CON steers. At similar age, LGWP and HGWP steers tended (F-test; P < 0.15) to have greater (P < 0.10) G6PDH and ACSS2 mRNA expression than CON and CORN steers in SC and PR but not IM adipose tissue. Expression of PFKM and PK2 mRNA tended (F-test; P < 0.15) to be greater (P < 0.10) in HGWP than CON and LGWP steers in IM but not SC or PR adipose tissue. At similar HCW, expression of GLUT4 and G6PDH mRNA were greater (P < 0.10) in SC adipose tissue of LGWP and HGWP steers compared with CON and CORN steers but not in IM and PR adipose tissue. Expression of LDHB mRNA was lesser (P < 0.10) in SC adipose tissue but greater (P < 0.10) in PR adipose tissue of LGWP and HGWP steers compared with CON and CORN steers. These results indicate a shift toward glucose utilization in SC adipose tissue but a shift towards lactate utilization in PR adipose tissue. These results suggest that diet and changes in VFA profile can influence substrates utilized for fatty acid synthesis, but diet has a greater effect in SC than IM adipose tissue.

Prediction of Carcass Composition Using Carcass Grading Traits in Hanwoo Steers

The prediction of carcass composition in Hanwoo steers is very important for value-based marketing, and the improvement of prediction accuracy and precision can be achieved through the analyses of independent variables using a prediction equation with a sufficient dataset. The present study was conducted to develop a prediction equation for Hanwoo carcass composition for which data was collected from 7,907 Hanwoo steers raised at a private farm in Gangwon Province, South Korea, and slaughtered in the period between January 2009 and September 2014. Carcass traits such as carcass weight (CWT), back fat thickness (BFT), eye-muscle area (EMA), and marbling score (MAR) were used as independent variables for the development of a prediction equation for carcass composition, such as retail cut weight and percentage (RC, and %RC, respectively), trimmed fat weight and percentage (FAT, and %FAT, respectively), and separated bone weight and percentage (BONE, and %BONE), and its feasibility for practical use was evaluated using the estimated retail yield percentage (ELP) currently used in Korea. The equations were functions of all the variables, and the significance was estimated via stepwise regression analyses. Further, the model equations were verified by means of the residual standard deviation and the coefficient of determination (R²) between the predicted and observed values. As the results of stepwise analyses, CWT was the most important single variable in the equation for RC and FAT, and BFT was the most important variable for the equation of %RC and %FAT. The precision and accuracy of three variable equation consisting CWT, BFT, and EMA were very similar to those of four variable equation that included all for independent variables (CWT, BFT, EMA, and MAR) in RC and FAT, while the three variable equations provided a more accurate prediction for %RC. Consequently, the three-variable equation might be more appropriate for practical use than the four-variable equation based on its easy and cost-effective measurement. However, a relatively high average difference for the ELP in absolute value implies a revision of the official equation may be required, although the current official equation for predicting RC with three variables is still valid.

Evaluation of a nutrition model in predicting performance of vietnamese cattle

The objective of this study was to evaluate the predictions of dry matter intake (DM) and average daily gain (ADG) of Vietnamese Yellow (Vang) purebred and crossbred (Vang with Red Sindhi or Brahman) bulls fed under Vietnamese conditions using two levels of solution (1 and 2) of the large ruminant nutrition system (LRNS) model. Animal information and feed chemical characterization were obtained from five studies. The initial mean body weight (BW) of the animals was 186, with standard deviation ±33.2 kg. Animals were fed ad libitum commonly available feedstuffs, including cassava powder, corn grain, Napier grass, rice straw and bran, and minerals and vitamins, for 50 to 80 d. Adequacy of the predictions was assessed with the Model Evaluation System using the root of mean square error of prediction (RMSEP), accuracy (Cb), coefficient of determination (r²), and mean bias (MB). When all treatment means were used, both levels of solution predicted DMI similarly with low precision (r² of 0.389 and 0.45 for level 1 and 2, respectively) and medium accuracy (Cb of 0.827 and 0.859, respectively). The LRNS clearly over-predicted the intake of one study. When this study was removed from the comparison, the precision and accuracy considerably increased for the level 1 solution. Metabolisable protein was limiting ADG for more than 68% of the treatment averages. Both levels differed regarding precision and accuracy. While level 1 solution had the least MB compared with level 2 (0.058 and 0.159 kg/d, respectively), the precision was greater for level 2 than level 1 (0.89 and 0.70, respectively). The accuracy (Cb) was similar between level 1 and level 2 (p = 0.8997; 0.977 and 0.871, respectively). The RMSEP indicated that both levels were on average under- or over-predicted by about 190 g/d, suggesting that even though the accuracy (Cb) was greater for level 1 compared to level 2, both levels are likely to wrongly predict ADG by the same amount. Our analyses indicated that the level 1 solution can predict DMI reasonably well for this type of animal, but it was not entirely clear if animals consumed at their voluntary intake and/or if the roughness of the diet decreased DMI. A deficit of ruminally-undegradable protein and/or a lack of microbial protein may have limited the performance of these animals. Based on these evaluations, the LRNS level 1 solution may be an alternative to predict animal performance when, under specific circumstances, the fractional degradation rates of the carbohydrate and protein fractions are not known.

Determination of carcass and body fat compositions of grazing crossbred bulls using body measurements

The objectives of this study were to analyze body measurements of 40 crossbred bulls grazing low quality forage with different supplementation strategies, to estimate interrelationships among those measurements and carcass and body compositions, and to develop systems of equations to predict body fat using body and carcass measurements. Eight animals were slaughtered at the beginning of the experiment, and the remaining animals were slaughtered at 90 or 220 d. The biometric measures (BM) were obtained the day before the slaughter and included hook width, pin width, pelvic girdle length, rump depth, rump height, abdomen width, body length, height at withers, rib depth, girth, and body diagonal length. Other measurements included full, shrunk, and empty BW; internal physical and chemical fats; body volume; body area; carcass weight; 9th- to 11th-rib section weight and composition; fat thickness; subcutaneous fat; intermuscular fat; carcass chemical fat; and empty body physical and chemical fats. The relationships between BM and body components were evaluated, and equations to predict body area, body volume, subcutaneous fat, and carcass and body physical and chemical fat were developed. Biological interpretations of the parameter estimates of equations were similar to those found in the literature such as a ratio of 1 kg of subcutaneous fat to 1.6 kg of intermuscular fat and a deposit of 72 to 76% of body fat in the carcass. The first system used to predict carcass and empty body physical and chemical fat was devised using in vivo information, whereas the second system used BW and the 9th- to 11th-rib fat weight. Our results indicated the combination of BW, carcass traits, and BM was precise and accurate in estimating carcass and body fat composition of backgrounding bulls. The second system had better adequacy statistics [r(2) > 0.92, concordance correlation coefficient (CCC) > 0.957, and root mean square error (RMSE) < 14.4% of the average observed value] compared with the first system. The first system had acceptable adequacy statistics (r(2) > 0.767, CCC > 0.866, and RMSE varying from 15.8 to 22.3% of the average observed value). For both systems, the simultaneous F-test of the linear regression of observed on model-predicted values indicated intercepts were equal to zero, and slopes were equal to 1 (P > 0.246). We concluded that BM can improve the accuracy and precision of the predictions of body composition of grazing animals.

Describing variation in rump P8 fat depth of crossbred cattle from birth to slaughter

A Principal Component Analysis (PCA) was conducted on the matrix of correlations among P8 fat measures at seven different ages for steers and heifers. The P8 fat measures were collected from 1143 steers and heifer calves that were born to Hereford (h) dams inseminated with semen from seven different sire breeds: Angus, Belgian Blue, Hereford, Jersey, Limousin, South Devon and Wagyu, over a 4-year period (1994 to 1997). The first two principal components explained 61 and 57% of the total phenotypic variation in fatness for steers and heifers, respectively. The first component was positively correlated to all measures and was interpreted as a measure of overall fatness. The second component was positively correlated to fatness approximately pre-weaning and negatively correlated thereafter and thus was interpreted as maturity type but could be a function of milk supply. When estimated from a sire model, the heritability estimates were high for the first component (0.59 and 0.67 for steers and heifers, respectively) but low for the second component (0.05 and 0.19). The results demonstrate the value of combining information across multiple measurements to build accuracy, even when relatively crude methods are used.