Evaluation of carcass characteristics of Bos indicus and tropically adapted Bos taurus breeds selected for postweaning weight

Residual feed intake and the inclusion of crude glycerin in the diet of feedlot-finished Nellore cattle

Context The beef market faces an increasing demand for an environmentally friendly production system with high-quality final products. The use of strategies that improve system efficiency without detriment on quality are desired for both producers and consumer. Aims This study aimed to evaluate the effects of residual feed intake (RFI) and dietary inclusion of crude glycerin on carcass and meat quality traits of feedlot-finished Nellore cattle. Methods Nellore bulls selected for growth and classified as high (n = 14) and low (n = 14) RFI, with average liveweight of 328 kg and age of 552 days were used. Bulls received a high-concentrate diet for 96 days before slaughter and were randomly allocated to two experimental diets in a 2 × 2 factorial arrangement: RFI class (low or high) and dietary inclusion (CG) or not (WCG) of crude glycerin (20% on dry-matter basis). Data were analysed with the SAS MIXED procedure considering RFI class and diet as fixed effects and selection line as a random effect. Key results There were no significant differences between RFI classes for dry-matter intake during finishing or production traits. Dry-matter intake tended to be reduced by 16% in CG bulls, without alterations in production. Dietary glycerin inclusion tended to increase the protein content in the Longissimus muscle and significantly reduced the fat content. In low-RFI bulls, shear force was higher in unaged beef, and shear force and myofibrillar fragmentation index tended to be higher in meat aged for 14 days. RFI class did not affect Longissimus muscle fatty acid profile, which was highly influenced by dietary glycerin inclusion. Bulls allocated to the CG treatment had a reduction in saturated fatty acids, an increase in odd-chain fatty acids, and a trend towards increased omega-3 fatty acids, which significantly increased the omega-3:omega-6 ratio. Conclusions The use of Nellore bulls classified as low-RFI and crude glycerin inclusion in finishing diets of Nellore cattle do not compromise production, carcass traits or beef properties. Implications This approach increases the efficiency and sustainability of the production process and improves the nutritional characteristics of beef for human consumption.

Genome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattle

Several methods have been used for genome-enabled prediction (or genomic selection) of complex traits, for example, multiple regression models describing a target trait with a linear function of a set of genetic markers. Genomic selection studies have been focused mostly on single-trait analyses. However, most profitability traits are genetically correlated, and an increase in prediction accuracy of genomic breeding values for genetically correlated traits is expected when using multiple-trait models. Thus, this study was carried out to assess the accuracy of genomic prediction for carcass and meat quality traits in Nelore cattle, using single- and multiple-trait approaches. The study considered 15 780, 15 784, 15 742 and 526 records of rib eye area (REA, cm²), back fat thickness (BF, mm), rump fat (RF, mm) and Warner-Bratzler shear force (WBSF, kg), respectively, in Nelore cattle, from the Nelore Brazil Breeding Program. Animals were genotyped with a low-density single nucleotide polymorphism (SNP) panel and subsequently imputed to arrays with 54 and 777 k SNPs. Four Bayesian specifications of genomic regression models, namely, Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression; blending methods, BLUP; and single-step genomic best linear unbiased prediction (ssGBLUP) methods were compared in terms of prediction accuracy using a fivefold cross-validation. Estimates of heritability ranged from 0.20 to 0.35 and from 0.21 to 0.46 for RF and WBSF on single- and multiple-trait analyses, respectively. Prediction accuracies for REA, BF, RF and WBSF were all similar using the different specifications of regression models. In addition, this study has shown the impact of genomic information upon genetic evaluations in beef cattle using the multiple-trait model, which was also advantageous compared to the single-trait model because it accounted for the selection process using multiple traits at the same time. The advantage of multi-trait analyses is attributed to the consideration of correlations and genetic influences between the traits, in addition to the non-random association of alleles.

Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks

The goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumina Bovine HD Bead Chip (HD, 777K from 90 samples) and the GeneSeek Genomic Profiler (GGP Indicus HD, 77K from 485 samples). The quality control for the genotypes was applied on each Chip and comprised removal of SNPs located on non-autosomal chromosomes, with minor allele frequency <5%, deviation from HWE (p < 10^-6 ), and with linkage disequilibrium >0.8. The FImpute program was used for genotype imputation. Pedigree-based analyses indicated that meat tenderness is moderately heritable (0.35), indicating that it can be improved by direct selection. Prediction accuracies were very similar across the Bayesian regression models, ranging from 0.20 (Bayes A) to 0.22 (Bayes B) and 0.14 (Bayes Cπ) to 0.19 (Bayes A) for the additive and dominance effects, respectively. ANN achieved the highest accuracy (0.33) of genomic prediction of genetic merit. Even though deep neural networks are recognized to deliver more accurate predictions, in our study ANN with one single hidden layer, 105 neurons and rectified linear unit (ReLU) activation function was sufficient to increase the prediction of genetic merit for meat tenderness. These results indicate that an ANN with relatively simple architecture can provide superior genomic predictions for meat tenderness in Nellore cattle.

Cattle adapted to tropical and subtropical environments: social, nutritional, and carcass quality considerations

Beef production needs to increase from 60 million to 130 million tons by 2050 to feed a growing world population, and 70% of this production increase is expected from beef industries located in subtropical and tropical regions of the world. Bos indicus-influenced cattle predominate in these regions but are often managed using practices developed for Bos taurus breeds reared in temperate climates. Hence, a fundamental step to meet the increasing global demand for beef is to develop specific management for B. indicus-influenced cattle in tropical or subtropical environments. Bos taurus and B. indicus are different subspecies, and diverge in social and biological functions due to selection pressure caused by complex evolutionary and domestication processes. Bos indicus cattle display different social responses compared with B. taurus counterparts, which must be taken into account by management planning as these traits directly impact cattle performance and welfare. In tropical and subtropical regions, warm-season perennial C4 grasses are the dominant forages, and their availability has a significant influence on the productivity of beef cattle systems. The resilience of C4 grasses under adverse conditions is one of their most important characteristics, even though these forages have reduced nutritive value compared with forages from temperate climates. Accordingly, nutritional planning in tropical and subtropical conditions must include management to optimize the quantity and quality of C4 forages. Nutritional requirements of cattle raised within these conditions also require special attention, including inherent metabolic compromises to cope with environmental constraints and altered energy requirements due to body composition and heat tolerance. Nutritional interventions to enhance beef production need to be specifically tailored and validated in B. indicus-influenced cattle. As an example, supplementation programs during gestation or early life to elicit fetal programming or metabolic imprinting effects, respectively, yield discrepant outcomes between subspecies. Bos indicus-influenced cattle produce carcasses with less marbling than B. taurus cattle, despite recent genetic and management advances. This outcome is mostly related to reduced intramuscular adipocyte volume in B. indicus breeds, suggesting a lesser need for energy stored intramuscularly as a mechanism to improve thermotolerance in tropical and subtropical climates.

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.

Genome-wide scan reveals population stratification and footprints of recent selection in Nelore cattle

Background

This study aimed at (1) assessing the genomic stratification of experimental lines of Nelore cattle that have experienced different selection regimes for growth traits, and (2) identifying genomic regions that have undergone recent selection. We used a sample of 763 animals genotyped with the Illumina BovineHD BeadChip, among which 674 animals originated from two lines that are maintained under directional selection for increased yearling body weight and 89 animals from a control line that is maintained under stabilizing selection.

Results

Multidimensional analysis of the genomic dissimilarity matrix and admixture analysis revealed a substantial level of population stratification between the directional selection lines and the stabilizing selection control line. Two of the three tests used to detect selection signatures (F_ST, XP-EHH and iHS) revealed six candidate regions with indications of selection, which strongly indicates truly positive signals. The set of identified candidate genes included several genes with roles that are functionally related to growth metabolism, such as COL14A1, CPT1C, CRH, TBC1D1, and XKR4.

Conclusions

The current study identified genetic stratification that resulted from almost four decades of divergent selection in an experimental Nelore population, and highlighted autosomal genomic regions that present patterns of recent selection. Our findings provide a basis for a better understanding of the metabolic mechanism that underlies the growth traits, which are modified by selection for yearling body weight.

Electronic supplementary material

The online version of this article (10.1186/s12711-018-0381-2) contains supplementary material, which is available to authorized users.

Accuracy of genomic breeding values for meat tenderness in Polled Nellore cattle

Zebu () cattle, mostly of the Nellore breed, comprise more than 80% of the beef cattle in Brazil, given their tolerance of the tropical climate and high resistance to ectoparasites. Despite their advantages for production in tropical environments, zebu cattle tend to produce tougher meat than Bos taurus breeds. Traditional genetic selection to improve meat tenderness is constrained by the difficulty and cost of phenotypic evaluation for meat quality. Therefore, genomic selection may be the best strategy to improve meat quality traits. This study was performed to compare the accuracies of different Bayesian regression models in predicting molecular breeding values for meat tenderness in Polled Nellore cattle. The data set was composed of Warner-Bratzler shear force (WBSF) of longissimus muscle from 205, 141, and 81 animals slaughtered in 2005, 2010, and 2012, respectively, which were selected and mated so as to create extreme segregation for WBSF. The animals were genotyped with either the Illumina BovineHD (HD; 777,000 from 90 samples) chip or the GeneSeek Genomic Profiler (GGP Indicus HD; 77,000 from 337 samples). The quality controls of SNP were Hard-Weinberg Proportion -value ≥ 0.1%, minor allele frequency > 1%, and call rate > 90%. The FImpute program was used for imputation from the GGP Indicus HD chip to the HD chip. The effect of each SNP was estimated using ridge regression, least absolute shrinkage and selection operator (LASSO), Bayes A, Bayes B, and Bayes Cπ methods. Different numbers of SNP were used, with 1, 2, 3, 4, 5, 7, 10, 20, 40, 60, 80, or 100% of the markers preselected based on their significance test (-value from genomewide association studies [GWAS]) or randomly sampled. The prediction accuracy was assessed by the correlation between genomic breeding value and the observed WBSF phenotype, using a leave-one-out cross-validation methodology. The prediction accuracies using all markers were all very similar for all models, ranging from 0.22 (Bayes Cπ) to 0.25 (Bayes B). When preselecting SNP based on GWAS results, the highest correlation (0.27) between WBSF and the genomic breeding value was achieved using the Bayesian LASSO model with 15,030 (3%) markers. Although this study used relatively few animals, the design of the segregating population ensured wide genetic variability for meat tenderness, which was important to achieve acceptable accuracy of genomic prediction. Although all models showed similar levels of prediction accuracy, some small advantages were observed with the Bayes B approach when higher numbers of markers were preselected based on their -values resulting from a GWAS analysis.

Residual feed intake, carcass traits and meat quality in Nellore cattle

A total of 127 Nellore bulls classified as low and high residual feed intake (RFI), from three selection lines, were evaluated in five experiments aiming to identify associations between RFI, carcass, and meat quality traits. Feedlot performance, carcass traits, and meat quality of Longissimus muscle were evaluated. A mixed model including the fixed effects of RFI class, selection line, interaction between RFI class and selection line, and age at slaughter as a covariate, and the random effects of diet within experiment and experiment was used, with means compared by the t-test. Feed intake average difference was 0.962kg/day; low RFI animals consumed 0.479kg/day less feed and high RFI animals consumed 0.483kg/day more feed than expected. No differences between RFI classes were detected for most of studied variables, demonstrating the absence of unfavorable associations between RFI and carcass and meat quality traits. Low RFI Nellore animals use feed efficiently and produce good quality carcasses and meat.

Performance of beef Guzerat and Guzerat-cross bulls during the feedlot, and carcass traits of Guzerat-cross groups

Two experiments were conducted to evaluate performance and carcass traits of Guzerat-based beef bulls. In experiment 1, Guzerat; F1 Guzerat × Holstein (‘Guzholstein’); F1 Guzerat × Nellore (‘Guzonell’); and 1/2 Simmental + 1/4 Guzerat + 1/4 Nellore (Three-Cross) bulls (n = 12 each group) were kept in a feedlot for 84 days, receiving sorghum silage and concentrate supplement. ‘Guzholstein’ bulls had greater average daily gain (ADG; 1.7 kg/day) compared with ‘Guzonell’ (1.4 kg/day), but similar to Three-Cross and Guzerat (1.6 and 1.5 kg/day respectively). ‘Guzonell’ bulls gained less bodyweight (BW; 85.8 kg); however, the gain : feed ratio did not differ among groups. Dry matter intake (DMI) was less for Guzerat (11.8 kg) compared with other groups (12.4, 12.4, and 12.6 kg for ‘Guzholstein’, ‘Guzonell’, and Three-Cross respectively); DMI, as a percentage of BW, was lesser for Three-Cross bulls (2.5%) compared with other groups (2.7%, 2.8%, and 2.6% for Guzerat, ‘Guzholstein’, and ‘Guzonell’ respectively). In experiment 2, 18 bulls from experiment 1 were randomly selected from ‘Guzonell’, ‘Guzholstein’ and Three-Cross groups (n = 6, each breed group), transferred to a state-inspected slaughterhouse, and humanely slaughtered. Three-Cross bulls had greater carcass weight gain (80 kg), greater striploin yield (2.39%), and lesser leg yield (1.86%). ‘Guzholstein’ bulls had lesser dressing-out percentage and greater liver yield (51.6 and 1.28% respectively), whereas ‘Guzonell’ bulls had greater rump yield (1.47%). Forequarter yield, hindquarter weight and yield, eye round, flank steak, rump skirt, topside, rump cap, head, heart, lungs and trachea, spleen, tail, and tongue yield did not differ among groups. Despite the limited number of animals used in these experiments, we can conclude that all breed groups have potential for meat production because carcass traits meet the Brazilian beef industry standards, and ‘Guzholstein’ bulls could be an option for producers to diversify revenues.

Chemical composition and cooking properties of beef forequarter muscles of Mexican cattle from different genotypes

Beef forequarter muscles biceps brachii (BRB), brachialis (BRA), complexus (COM), splenius (SPL), infraspinatus (INF), teres major (TER), rhomboideus (RHO), subscapularis (SUB), supraspinatus (SUP), triceps brachii long-head (TRB) and triceps brachii lateral-head (TRI) were obtained from Mexican beef carcasses originated from Bos indicus (Bi, n=10) or Bos taurus (Bt, n=10) young bulls. Muscles were analyzed for WBSF, cooking loss, and moisture and fat contents. INF had the lowest WBSF (37.23N) and cooking loss (31.78%) of all. RHO exhibited the lowest moisture (72.62%) and highest fat content (5.23%) of all. Bi genotype exhibited higher WBSF (53.78, 48.69N), higher cook loss (36.27, 34.32%), higher fat content (2.93, 2.35%), and lower moisture content (73.70, 75.07%) than Bt. Further research is needed to estimate the actual consumer acceptance of beef forequarter muscles and their marketing potential as individual cuts in the Mexican market.

Chemical composition of whole body and carcass of Bos indicus and tropically adapted Bos taurus breeds

Relationships between the chemical composition of the 9th- to 11th-rib section and the chemical composition of the carcass and empty body were evaluated for Bos indicus (108 Nellore and 36 Guzerah; GuS) and tropically adapted Bos taurus (56 Caracu; CaS) bulls, averaging 20 to 24 mo of age at slaughter. Nellore cattle were represented by 56 animals from the selected herd (NeS) and 52 animals from the control herd (NeC). The CaS and GuS bulls were from selected herds. Selected herds were based on 20 yr of selection for postweaning BW. Carcass composition was obtained after grinding, homogenizing, sampling, and analyzing soft tissue and bones. Similarly, empty body composition was obtained after grinding, homogenizing, sampling, analyzing, and combining blood, hide, head + feet, viscera, and carcass. Bulls were separated into 2 groups. Group 1 was composed of 36 NeS, 36 NeC, 36 CaS, and 36 GuS bulls and had water, ether extract (EE), protein, and ash chemically determined in the 9th- to 11th-rib section and in the carcass. Group 2 was composed of 20 NeS, 16 NeC, and 20 CaS bulls and water, EE, protein, and ash were determined in the 9th- to 11th-rib section, carcass, and empty body. Linear regressions were developed between the carcass and the 9th- to 11th-rib section compositions for group 1 and between carcass and empty body compositions for group 2. The 9th- to 11th-rib section percentages of water (RWt) and EE (RF) predicted the percentages of carcass water (CWt) and carcass fat (CF) with high precision: CWt, % = 29.0806 + 0.4873 × RWt, % (r(2) = 0.813, SE = 1.06) and CF, % = 10.4037 + 0.5179 × RF, % (r(2) = 0.863, SE = 1.26), respectively. Linear regressions between percentage of CWt and CF and empty body water (EBWt) and empty body fat (EBF) were also predicted with high precision: EBWt, % = -9.6821 + 1.1626 × CWt, % (r(2) = 0.878, SE = 1.43) and EBF, % = 0.3739 + 1.0386 × CF, % (r(2) = 0.982, SE = 0.65), respectively. Chemical composition of the 9th- to 11th-rib section precisely estimated carcass percentages of water and EE. These regressions can accurately predict carcass and empty body compositions for Nellore, Guzerah, and Caracu breeds.

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 carcass quality traits of crossbred cattle

In order to investigate variation in carcass quality traits, during a four-year period, mature Hereford cows (637) were mated to 97 sires from seven breeds (Jersey, Wagyu, Angus, Hereford, South Devon, Limousin and Belgian Blue), resulting in 1144 calves. Carcass production traits (carcass weight = HCWt, fat depth = P8, eye muscle area = EMA, intramuscular fat = IMF) were obtained from these cattle that constitute the Australia's Southern Crossbreeding Project. Data were analysed using multi-variate sire model containing fixed effects of sex, sire breed, slaughter age nested within sexes. Random effects were sire, dam, management (location-year-post-weaning groups) and environmental effects. HCWt of South Devon, Belgian Blue, Limousin and unexpectedly, Angus were the heaviest on the average. Hereford calves were intermediate and Jersey and Wagyu were lighter on the average than others. Carcasses of the Belgian Blue and Limousin had low P8 and IMF, carcasses of Hereford and South Devon were intermediate and Angus, Jersey and Wagyu had high P8 and IMF. Management group effects were greatest especially for EMA and IMF. The sire variation was about 6, 6, 4 and 2% of total variation for HCWt, P8, EMA and IMF. Heritability ranged from 0.20 to 0.37 (carcass weight). The genetic correlation between the two fat depots was not as high (0.18) as expected. Results from this study suggest that strategies to increase genetic potential for HCWt would increase the genetic potential for EMA but may reduce marbling and tend to slightly increase P8. All phenotypic correlations were positive, although not large.