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

Three-dimensional imaging to estimate in vivo body and carcass chemical composition of growing beef-on-dairy crossbred bulls

The dynamics of cattle body chemical composition during growth and fattening periods determine animal performance and beef carcass quality. The aim of this study was to estimate the empty body (EB) and carcass chemical composition of growing beef-on-dairy crossbred bulls (Brown Swiss breed as dam with Angus, Limousin or Simmental as sire) using three-dimensional (3D) imaging. The 3D images of the cattle's external body shape were recorded in vivo on 48 bulls along growth trajectory (75-520 kg BW and 34-306 kg hot carcass weight [HCW]; set 1) and on 70 bulls at target market slaughter weight, including 18 animals from set 1 (average 517 ± 10 kg BW and 289 ± 10 kg HCW; set 2). The linear, circumference, curve, surface and volume measurements on the 3D body shape were determined. Those predictive variables were used in partial least square regressions, together with the effect of the sire breed whenever significant (P < 0.05), with leave-one-out cross-validation to estimate water, lipid, protein, mineral and energy mass or proportions in the EB and carcass. Mass and proportions were determined directly from postmortem grinding and chemical analyses (set 1) or indirectly using the 11th rib dissection method (set 2). In set 1, bulls' BW and HCW were estimated via 3D imaging, with root mean square error of prediction (RMSEP) of 12 kg and 6 kg, respectively. The EB and carcass chemical component proportions were estimated with RMSEP from 0.2% for EB minerals (observed mean 3.7 ± 0.2%) to 1.8% for EB lipid (11.6 ± 4.2%), close to the RMSEP found for the carcass. In set 2, the RMSEP for estimation via 3D imaging was 9 kg for BW and 6 kg for HCW. The EB energy and protein proportions were estimated, with RMSEP of 0.5 MJ/kg fresh matter (10.1 ± 0.8 MJ/DM) and 0.2% (18.7 ± 0.7%), respectively. Overall, the estimations of chemical component proportions from 3D imaging were slightly less precise for both sets than the mass estimations. The morphological traits from the 3D images appeared to be precise estimators of BW, HCW as well as EB and carcass chemical component masses and proportions.

B-spline polynomials models for analyzing growth patterns of Guzerat young bulls in field performance tests

OBJECTIVE

The aim of this study was to identify suitable polynomial regression for modeling the average growth trajectory and to estimate the relative development of the rib eye area, scrotal circumference, and morphometric measurements of Guzerat young bulls.

METHODS

A total of 45 recently weaned males, aged 325.8±28.0 days and weighing 219.9±38.05 kg, were evaluated. The animals were kept on Brachiaria brizantha pastures, received multiple supplementations, and were managed under uniform conditions for 294 days, with evaluations conducted every 56 days. The average growth trajectory was adjusted using ordinary polynomials, Legendre polynomials, and quadratic B-splines. The coefficient of determination, mean absolute deviation, mean square error, the value of the restricted likelihood function, Akaike information criteria, and consistent Akaike information criteria were applied to assess the quality of the fits. For the study of allometric growth, the power model was applied.

RESULTS

Ordinary polynomial and Legendre polynomial models of the fifth order provided the best fits. B-splines yielded the best fits in comparing models with the same number of parameters. Based on the restricted likelihood function, Akaike's information criterion, and consistent Akaike's information criterion, the B-splines model with six intervals described the growth trajectory of evaluated animals more smoothly and consistently. In the study of allometric growth, the evaluated traits exhibited negative heterogeneity (b<1) relative to the animals' weight (p<0.01), indicating the precocity of Guzerat cattle for weight gain on pasture.

CONCLUSION

Complementary studies of growth trajectory and allometry can help identify when an animal's weight changes and thus assist in decision-making regarding management practices, nutritional requirements, and genetic selection strategies to optimize growth and animal performance.

Effects of lipid and starch supplementation as water intake mitigation techniques on performance and efficiency of nursing Holstein calves

Exploring alternative supplementation sources capable of maximizing feed and water efficiency in nursing Holstein calves is often ignored. The goals herein involve investigating the effects of two isoenergetic supplements on a nonmedicated milk replacer diet on total water intake, milk water intake, fresh water intake, feed intake parameters, and performance of Holstein nursing bull calves. Twenty-three animals (body weight [BW] = 94.67 ± 12.07 kg, age = 67 days old) were randomly assigned to one of three treatments for 68 days: control (CON; ad libitum milk replacer, n = 7), carbohydrate supplement (CHO; corn starch on top of ad libitum milk replacer-based diet, n = 8), or lipid supplement (FAT; menhaden fish oil on top of ad libitum milk replacer-based diet, n = 8). The isoenergetic supplementation consisted of 3% menhaden fish oil addition on DM basis for FAT. This was matched energetically with corn starch for the CHO group resulting in a 7% composition in DM basis. All animals were provided free access to mineral mix and 120 g daily dried microbrewer’s spent grains (BG). Data were analyzed with the GLMMIX procedure of SAS in a completely randomized design with the diets as a fixed effect. Dry matter intake (DMI) adjusted by average daily gain (ADG; DMI/ADG) resulted in significantly lower values for supplemented groups with CON = 2.48, CHO = 2.38, and FAT = 2.27 kg/kg _(ADG) (P = 0.033). Energy intake values were lower for CON when analyzing metabolizable energy intake (P < 0.0001), net energy intake for maintenance (P < 0.0001), and net energy intake for gain (P < 0.0001), followed by CHO, and then FAT. Total water intake (P < 0.0001), milk water intake (P < 0.0001), and fresh water intake (P < 0.0001) all resulted in CHO consuming 0.5 L or less water than the other two treatments. Energy requirements as digestible energy (P < 0.0001), metabolizable energy (P < 0.0001), net energy for maintenance (P < 0.0001), and net energy for gain (P < 0.0001) were lower for CHO, followed by CON, and then FAT having the highest requirements. Similar results were observed for residual feed (RFI; P = 0.006) and residual water intakes (RTWI; P = 0.902). Ultimately, no performance differences were detected with regards to BW (CON = 146.71, CHO = 146.25, and FAT = 150.48 kg; P > 0.1). These results indicate that lipid-based and starch-based supplementation can potentially increase feed efficiency and decrease voluntary water intake without adversely affecting performance.

Ultrasonographic measures of body fatness and their relationship with plasma levels and adipose tissue expression of four adipokines in Welsh pony mares

Obesity is responsible for metabolic dysregulations that alter fertility and induce pathologies. The objectives of the present study were to validate a reliable method for the evaluation of body fatness in mares and to associate the body fat estimation data to metabolic changes, including adipokines at the plasma and adipose tissue levels. To reach this purpose, animals were subjected to two extreme breeding conditions to study the variation of morphological, ultrasound, and physiological parameters. Twenty Welsh mares were followed up monthly from April to October before and after animals were moved outdoors to grasslands. Body weight (BW), body length (BL), height at the withers (HW), thoracic perimeter (TP), 5-point body condition score (BCS), and subcutaneous fat thickness (SFT) at the level of the shoulder, the lumbar region, and the rump, measured by ultrasonography, and plasma and adipose tissue metabolic indicators were assessed in parallel. Statistical analysis was performed using a linear mixed-effects model, whereas Pearson tests were used for the analysis of the correlations between the different parameters. Although mean BW did not increase significantly (P = 0.0940), TP (P = 0.0002) and BCS (P < 0.0001) increased during the study period. Ultrasonographic examination of subcutaneous adipose tissue showed an increase in SFT at the level of the shoulder (P < 0.0001), lumbar region (P < 0.0001), and rump (P < 0.0001). Plasma concentrations of nonesterified fatty acids (P < 0.0001), phospholipids (P < 0.0001), and cholesterol (P < 0.0001) increased significantly, whereas triglycerides (P < 0.0001) decreased significantly during the study period. Although both plasma concentrations and adipose tissue expression of leptin (P < 0.0001) and resistin (P < 0.0001) increased significantly, adiponectin (P < 0.0001) significantly decreased and visfatin remained unchanged (P = 0.8401). Expression of adipokine receptors studied showed the opposite pattern compared with their ligand. Ultrasonographic measurements of subcutaneous adipose tissue thickness at the shoulder, lumbar region, and rump are relevant indicators of fatness related with adipokine plasma concentrations and expression of adipokine-related receptors in adipose tissue, and particularly highlight seasonal effects.

Evaluation of active dried yeast in the diets of feedlot steers-I: Effects on feeding performance traits, the composition of growth, and carcass characteristics1

The use of active dried yeast (ADY) in the diets of feedlot steers may improve feed efficiency, growth performance, and reduce days on feed. Strategic timing of ADY inclusion in the diet may increase feed conversion or aid in the dietary transition from growing to finishing diets. One hundred twenty steers, blocked by weight, were fed four diets for 164 d: grower (70 d), first transition diet (7 d), second transition diet for (7 d), and finisher (80 d) in a GrowSafe System. Four treatment sequences of ADY inclusion were evaluated in a Balaam's design where steers were fed a control diet before and after the grower phase (CC), control before and ADY after the grower phase (CY), ADY before and control after the grower phase (YC), and ADY before and after the grower phase (YY). A random coefficients model was used to evaluate the following variables of interest: feeding performance and growth traits, including biometric measurements and carcass ultrasound measurements, and carcass characteristics. Treatment was a fixed effect and block was a random effect. Treatment did not affect feeding performance or behavior (P ≥ 0.14). The rate of change of biometric measurements were not different (P ≥ 0.16) across treatment groups except for rib girth circumference, which was greater for the YY and CY groups intermediate for the CC group and least for the YC group (0.828 and 0.809 vs. 0.751 vs. 0.666 cm/d, respectively; P < 0.01). Faster growth rates of rib girth circumference resulted in larger final measurements for steers that were finished on ADY (P < 0.01). Ultrasound measurements (backfat, LM area, intra-muscular fat, and rump fat) were not different across treatments (P ≥ 0.15). However, there was a tendency for the YC group to have a slower rate of back fat deposition than other treatment groups (P = 0.09). Steers' final shrunk BWs did not differ (P = 0.61), but shrink percentage was greater for CC than for YY groups (3.7% vs. 2.7%, respectively; P = 0.05). Carcass characteristics were not different across treatments (P ≥ 0.20). Crude fat, CP, ash and moisture analyses of the 9th to 11th rib section were not different across treatments, and there was no difference in adjusted final shrunk BW (P ≥ 0.45). Feeding the ADY strain used in this study to growing and finishing feedlot steers increased rib girth circumference development rate and reduced shrink loss without affecting feeding behavior, feeding performance, or carcass characteristics.

In vivo ultrasound and biometric measurements predict the empty body chemical composition in Nellore cattle

Evaluation of the body chemical composition of beef cattle can only be measured postmortem and those data cannot be used in real production scenarios to adjust nutritional plans. The objective of this study was to develop multiple linear regression equations from in vivo measurements, such as ultrasound parameters [backfat thickness (uBFT, mm), rump fat thickness (uRF, mm), and ribeye area (uLMA, cm2)], shrunk body weight (SBW, kg), age (AG, d), hip height (HH, m), as well as from postmortem measurements (composition of the 9th to 11th rib section) to predict the empty body and carcass chemical composition for Nellore cattle. Thirty-three young bulls were used (339 ± 36.15 kg and 448 ± 17.78 d for initial weight and age, respectively). Empty body chemical composition (protein, fat, water, and ash in kg) was obtained by combining noncarcass and carcass components. Data were analyzed using the PROC REG procedure of SAS software. Mallows' Cp values were close to the ideal value of number of independent variables in the prediction equations plus one. Equations to predict chemical components of both empty body and carcass using in vivo measurements presented higher R2 values than those determined by postmortem measurements. Chemical composition of the empty body using in vivo measurements was predicted with R2 > 0.73. Equations to predict chemical composition of the carcass from in vivo measurements showed R2 lower (R2< 0.68) than observed for empty body, except for the water (R2 = 0.84). The independent variables SBW, uRF, and AG were sufficient to predict the fat, water, energy components of the empty body, whereas for estimation of protein content the uRF, HH, and SBW were satisfactory. For the calculation of the ash, the SBW variable in the equation was sufficient. Chemical compounds from components of the empty body of Nellore cattle can be calculated by the following equations: protein (kg) = 47.92 + 0.18 × SBW - 1.46 × uRF - 30.72 × HH (R2 = 0.94, RMSPE = 1.79); fat (kg) = 11.33 + 0.16 × SBW + 2.09 × uRF - 0.06 × AG (R2 = 0.74, RMSPE = 4.18); water (kg) = - 34.00 + 0.55 × SBW + 0.10 × AG - 2.34 × uRF (R2 = 0.96, RMSPE = 5.47). In conclusion, the coefficients of determination (for determining the chemical composition of the empty body) of the equations derived from in vivo measures were higher than those of the equations obtained from rib section measurements taken postmortem, and better than coefficients of determination of the equations to predict the chemical composition of the carcass.

Assessment of body fat composition in crossbred Angus × Nellore using biometric measurements

This study was conducted to assess the body and empty body fat physical and chemical composition through biometric measurements (BM) as well as postmortem measurements taken in 40 F Angus × Nellore bulls and steers. The animals used were 12.5 ± 0.51 mo of age, with an average shrunk BW of 233 ± 23.5 and 238 ± 24.6 kg for bulls and steers, respectively. Animals were fed 60:40 ratio of corn silage to concentrate diets. Eight animals (4 bulls and 4 steers) were slaughtered at the beginning of the trial, and the remaining animals were randomly assigned to a 1 + 2 × 3 factorial arrangement (1 reference group, 2 sexes, and 3 slaughter weights). The remaining animals were slaughtered when the average BW of the group reached 380 ± 19.5 (6 bulls and 5 steers), 440 ± 19.2 (6 bulls and 5 steers), and 500 ± 19.5 kg (5 bulls and 5 steers). Before the slaughter, the animals were led through a squeeze chute in which BM were taken, including hook bone width (HBW), pin bone width, abdomen width (AW), body length (BL), rump height, height at the withers, pelvic girdle length (PGL), rib depth (RD), girth circumference (GC), rump depth, body diagonal length (BDL), and thorax width. Additionally, the following postmortem measurements were obtained: total body surface (TBS), body volume (BV), subcutaneous fat (SF), internal physical fat (InF), intermuscular fat, carcass physical fat (CF), empty body physically separable fat (EBF), carcass chemical fat (CFch), empty body chemical fat (EBFch), fat thickness in the 12th rib, and 9th to 11th rib section fat. The equations were developed using a stepwise procedure to select the variables that should enter into the model. The and root mean square error (RMSE) were used to account for precision and accuracy. The ranges for and RMSE were 0.852 to 0.946 and 0.0625 to 0.103 m, respectively for TBS; 0.942 to 0.998 and 0.004 to 0.022 m, respectively, for BV; 0.767 to 0.967 and 2.70 to 3.24 kg, respectively, for SF; 0.816 to 0.900 and 3.04 to 4.12 kg, respectively, for InF; 0.830 to 0.988 and 3.44 to 8.39 kg, respectively, for CF; 0.861 to 0.998 and 1.51 to 10.98 kg, respectively, for EBF; 0.825 to 0.985 and 5.96 to 8.46 kg, respectively, for CFch; and 0.862 to 0.992 and 5.54 to 12.19 kg, respectively, for EBFch. Our results indicated that BM that could accurately and precisely be used as alternatives to predict different fat depots of F Angus × Nellore bulls and steers are AW, GC, or PGL for CF estimation; HBW and RD for CFch estimation; and body lengths such as BL and BDL for InF and SF estimation, respectively.

Technical note: Estimating body weight and body composition of beef cattle trough digital image analysis

The use of digital images could be a faster and cheaper alternative technique to assess BW, HCW, and body composition of beef cattle. The objective of this study was to develop equations to predict body and carcass weight and body fat content of young bulls using digital images obtained through a Microsoft Kinect device. Thirty-five bulls with an initial BW of 383 (±5.38) kg (20 Black Angus, 390 [±7.48] kg initial BW, and 15 Nellore, 377 [±8.66] kg initial BW) were used. The Kinect sensor, installed on the top of a cattle chute, was used to take infrared light-based depth videos, recorded before the slaughter. For each animal, a quality control was made, running and pausing the video at the moment that the animal was standing with its body and head in line. One frame from recorded videos was selected and used to analyze the following body measurements: chest width, thorax width, abdomen width, body length, dorsal height, and dorsal area. From these body measurements, 23 indexes were generated and tested as potential predictors. The BW and HCW were assessed with a digital scale, whereas empty body fat (EBF) was estimated through ground samples of all tissues. To better understand the relationship among the measurements, the correlations between final BW (488 [±10.4] kg), HCW (287 [±12.5] kg), EBF (14 [±0.610] % empty BW) content, body measurements (taken through digital images), and developed indexes were evaluated. The REG procedure was used to develop the regressions, and the important independent variables were identified using the options STEPWISE and Mallow's Cp in the SELECTION statement. Chest width was the trait most related to weights and the correlations between this measurement and BW and HCW were above 0.85. The analysis of linear regressions between observed and predicted values showed that all models pass through the origin and have a slope of unity (null hypothesis [H]: = 0 and = 1; ≥ 0.993). The models to estimate BW and HCW of Angus and Nellore presented between 0.69 and 0.84 ( < 0.001), whereas from equations to estimate the EBF were lower ( = 0.43-0.45; ≤ 0.006). Index I5 [(chest width) × body length], related to the animal volume, was significant in all models created to estimate BW and HCW, and it explained more than 70% of the variation. This study indicates that digital images taken through a Microsoft Kinect system have the potential to be used as a tool to estimate body and carcass weight of beef cattle.

Live animal assessments of rump fat and muscle score in Angus cows and steers using 3-dimensional imaging

The objective of this study was to develop a proof of concept for using off-the-shelf Red Green Blue-Depth (RGB-D) Microsoft Kinect cameras to objectively assess P8 rump fat (P8 fat; mm) and muscle score (MS) traits in Angus cows and steers. Data from low and high muscled cattle (156 cows and 79 steers) were collected at multiple locations and time points. The following steps were required for the 3-dimensional (3D) image data and subsequent machine learning techniques to learn the traits: 1) reduce the high dimensionality of the point cloud data by extracting features from the input signals to produce a compact and representative feature vector, 2) perform global optimization of the signatures using machine learning algorithms and a parallel genetic algorithm, and 3) train a sensor model using regression-supervised learning techniques on the ultrasound P8 fat and the classified learning techniques for the assessed MS for each animal in the data set. The correlation of estimating hip height (cm) between visually measured and assessed 3D data from RGB-D cameras on cows and steers was 0.75 and 0.90, respectively. The supervised machine learning and global optimization approach correctly classified MS (mean [SD]) 80 (4.7) and 83% [6.6%] for cows and steers, respectively. Kappa tests of MS were 0.74 and 0.79 in cows and steers, respectively, indicating substantial agreement between visual assessment and the learning approaches of RGB-D camera images. A stratified 10-fold cross-validation for P8 fat did not find any differences in the mean bias ( = 0.62 and = 0.42 for cows and steers, respectively). The root mean square error of P8 fat was 1.54 and 1.00 mm for cows and steers, respectively. Additional data is required to strengthen the capacity of machine learning to estimate measured P8 fat and assessed MS. Data sets for and continental cattle are also required to broaden the use of 3D cameras to assess cattle. The results demonstrate the importance of capturing curvature as a form of representing body shape. A data-driven model from shape to trait has established a proof of concept using optimized machine learning techniques to assess P8 fat and MS in Angus cows and steers.

Using growth and body composition to determine weight at maturity in Nellore cattle

The aim of the present study was to estimate the relationships among water, crude protein (CP), ether extract (EE) and ash in the empty bodyweight (EBW), and the soft tissue and bone and, moreover, to determine an objective method to define weight at maturity in Nellore cattle. A dataset containing carcass and body compositions of 249 animals from 11 experiments was developed. There were 63 bulls, 105 steers, and 81 heifers where all animals were purebred Nellore, aged between zero and 24 months. The contents of water, CP and ash in the EBW were predicted by non-linear regressions, whereas an exponential model was used to predict EE. In addition, the content of CP was predicted on a fat-free dry matter (FFDM) basis, and maturity was defined as the point when no significant accretion of CP in the FFDM was observed. The soft tissue water (STW) was regressed on logistic Gompertz functions, and segmented regression models, whereas the analysis of bone chemical composition in the EBW was conducted using an exponential model. The gender effect was not significant (P > 0.05) for EBW and EE; therefore, this effect was not included in the analysis of FFDM. The exponential model suggests that Nellore cattle reached maturity at ~445 kg and the segmented regression model suggested that maturity was reached at 429 kg. A significant relationship between the concentration of STW and soft tissue EE (STEE) was observed (STEE = 0.920 – 1.147 × STW; r2 = 0.96, mean square error = 1.01), but the soft tissue was not a good predictor of maturity, because it is dependent on the diet. Analysis of bone chemical composition showed that EE, water and ash become constant between 400 and 500 kg of EBW, and that CP in bones was constant at 19.1% of EBW. These data also suggested that bone composition could be a good predictor of maturity; however, with the high variability in our dataset, it was not possible to determine an EBW at which these components became constant with a reliable precision. We concluded that Nellore cattle reach maturity at ~437 kg of EBW and that CP in the FFDM and CP, water and ash in bones are good predictors of maturity, whereas soft tissue composition is not a useful predictor of maturity.

Teores de óleo de linhaça para bovinos confinados: medidas corporais, carcaça e cortes cárneos

RESUMO Os objetivos foram avaliar dietas contendo teores de óleo de linhaça in natura (1,0; 3,8 e 5,2% da MS da dieta) sobre as características da carcaça e cortes cárneos, e correlacionar esses resultados com medidas corporaisin vivo de 14 fêmeas e 15 machos castrados Nelore x Canchim. Os animais foram confinados individualmente recebendo 20% de cana-de-açúcar como volumoso e 80% de concentrado, em um delineamento de blocos ao acaso (teores de óleo x condição sexual). Todos os animais de cada bloco foram abatidos quando o peso corporal médio atingiu 500 kg. Não foram observadas interações entre os fatores estudados. Acrescentar até 5,2% de óleo de linhaçain natura da dieta de bovinos cruzados aumenta o peso dos principais cortes cárneos da carcaça, reduz as perdas no resfriamento e não altera o teor de gordura renal-pélvica-inguinal. Fêmeas e machos castrados Nelore x Canchim produzem quantidades similares de carne do traseiro especial, em peso e rendimento. Juntamente com o peso corporal, a medida do contorno pelviano é a mais indicada para auxiliar na formação de lotes homogêneos para o abate ou experimentação, por apresentar as melhores correlações com as características utilizadas na comercialização brasileira de carne, como peso e rendimento da carcaça.

Evaluation of predictive equations developed to assess body composition of F1 Nellore × Angus bulls and steers

We evaluated and compared empirical equations used for assessing beef cattle body composition, developed in 2010 (M10), 2012 (M12), 2006 (V06) and 1946 (HH46). Forty-eight F1 Nellore × Angus bulls and steers, aged 12.5 ± 0.51 months old, with initial shrunk bodyweight of 233 ± 23.5 kg and 238 ± 24.6 kg, respectively, were used in this experiment. The trial was a randomised factorial arrangement of treatments (two genders and five slaughter weights). The animals were randomly assigned to five slaughter-weight-based groups: baseline, maintenance, and 380, 440 and 500 kg. The diet comprised maize silage and concentrate (60 : 40). After slaughter, the 9th–11th rib section cut was dissected into muscle, fat and bone. The remaining carcass was similarly dissected. Other variables evaluated as partial predictors of body composition included empty bodyweight, dressing percentage, visceral fat percentage, and organ and viscera percentage. The values estimated with predictive equations were compared with observed values. For the physically separable carcass composition, only the M12 equation estimated precisely and accurately the amount of muscle (r2 = 0.98, root-mean-square error (RMSE) = 5.64 kg, concordance correlation coefficient (CCC) = 0.96) and fat (r2 = 0.94, RMSE = 4.91 kg, CCC = 0.96) tissue present in the carcass. The V06 and M10 equations estimated precisely and accurately the amount of carcass chemical components; HH46 could explain only the amount of crude protein (r2 = 0.84, RMSE = 4.71 kg, CCC = 0.90) content in the carcass. The equations used to predict empty body chemical composition failed to estimate correctly the amount of chemical contents present in the empty bodyweight. However, V06 can be used to estimate the crude protein (r2 = 0.91, RMSE = 5.97 kg, CCC = 0.93) content in the empty bodyweight. Furthermore, M10 could be used to estimate ether extract (r2 = 0.94, RMSE = 8.13 kg, CCC = 0.84) content, although this had to be analysed by gender, because such variables (i.e. ether extract) presented a pronounced effect, especially for steers, on total chemical fat.

Predicting efficiency of use of metabolizable energy to net energy for gain and maintenance of Nellore cattle

Twenty-six comparative slaughter studies were used (n = 752 animals) and coded within each experiment by gender (431 bulls, 204 steers, and 117 heifers) and breed (447 Nellore and 305 Bos indicus and Bos taurus crossbreds) to develop equations to predict the efficiency of use of ME to NE for growth (kg) and ME to NE for maintenance (km). The retained energy (RE) was regressed on ME intake (MEI) available for gain using orthogonal regression to obtain the kg within each experiment. The estimated kg was regressed on RE as protein (REp) according to the following equation: kg = a/(b + REp). Gender and breed effects were not tested because of limited number of experiments. The km was estimated as the intercept of the following equation: HP = β0 × e((β1 × MEI)), in which HP is heat production, β0 and β1 are coefficients, and e is the natural logarithm. The ME for maintenance (MEm) was computed assuming MEI equals to HP at maintenance. The km was obtained using the stepwise procedure of a multiple regression including ADG, empty body gain (EBG), empty BW (EBW), EBW(0.75), kg, and energy content in the EBW. A random coefficient model, assuming a random variation for study effects, was used to test breed and gender effects to identify the best model to estimate km. The overall equation to predict kg was 0.327 (±0.142)/[0.539 (±0.317) + REp], with an R(2) of 0.963. The equation to predict km was 0.513 (±0.024) + 0.173 (±0.061) × kg + a × EBG, R(2) = 0.92, in which a = 0.100 (±0.021) for B. indicus or a = 0.073 (±0.021) for crossbreds. Our results indicated that B. indicus were more efficient to use ME for maintenance. We concluded that km can be predicted from kg and EBG and that B. indicus × B. taurus crossbreds can affect km. Furthermore, kg can be predicted from REp and neither gender nor crossbreeding (B. indicus × B. taurus) affected kg. Because our database consisted of Nellore and B. indicus and B. taurus crossbreds, it is necessary to further evaluate differences between B. taurus and B. indicus regarding the kg.

Predicting carcass and body fat composition using biometric measurements of grazing beef cattle

This study was conducted to develop equations to predict carcass and body fat compositions using biometric measures (BM) and body postmortem measurements and to determine the relationships between BM and carcass fat and empty body fat compositions of 44 crossbred bulls under tropical grazing conditions. The bulls were serially slaughtered in 4 groups at approximately 0 d (n = 4), 84 d (n = 4), 168 d (n = 8), 235 d (n = 8), and 310 d (n = 20) of growth. The day before each slaughter, bulls were weighed, and BM were taken, including hook bone width, pin bone width, abdomen width, body length, rump height, height at withers, pelvic girdle length, rib depth, girth circumference, rump depth, body diagonal length, and thorax width. Others measurements included were total body surface (TBS), body volume (BV), subcutaneous fat (SF), internal fat (InF), intermuscular fat, carcass physical fat (CFp), empty body physical fat (EBFp), carcass chemical fat (CFch), empty body chemical fat (EBFch), fat thickness in the 12th rib (FT), and 9th- to 11th-rib section fat (HHF). The stepwise procedure was used to select the variables included in the model. The r(2) and the root-mean-square error (RMSE) were used to account for precision and variability. Our results indicated that lower rates of fat deposition can be attributed to young cattle and low concentration of dietary energy under grazing conditions. The BM improved estimates of TBS (r(2) = 0.999) and BV (r(2) = 0.997). The adequacy evaluation of the models developed to predict TBS and BV using theoretical equations indicated precision, but lower and intermediate accuracy (bias correction = 0.138 and 0.79), respectively, were observed. The data indicated that BM in association with shrunk BW (SBW) were precise in accounting for variability of SF (r(2) = 0.967 and RMSE = 0.94 kg), InF (r(2) = 0.984 and RMSE = 1.26 kg), CFp (r(2) = 0.981 and RMSE = 2.98 kg), EBFp (r(2) = 0.985 and RMSE = 3.99 kg), CFch (r(2) = 0.940 and RMSE = 2.34 kg), and EBFch (r(2) = 0.934 and RMSE = 3.91 kg). Results also suggested that approximately 70% of body fat was deposited as CFp and 30% as InF. Furthermore, the development of an equation using HHF as a predictor, in combination with SBW, was a better predictor of CFp and EBFp than using HHF by itself. We concluded that the prediction of physical and chemical CF and EBF composition of grazing cattle can be improved using BM as a predictor.

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.