Association of myostatin on early calf mortality, growth, and carcass composition traits in crossbred cattle

The objective of this study was to investigate a potential association of an inactive myostatin allele with early calf mortality, and evaluate its effect on growth and carcass traits in a crossbred population. Animals were obtained by mating F1 cows to F1 (Belgian Blue x British Breed) or Charolais sires. Cows were obtained from mating Hereford, Angus, and U.S. Meat Animal Research Center III ( ¼ Hereford, ¼ Angus, ¼ Pinzgauer, and ¼ Red Poll) dams to Hereford, Angus, Tuli, Boran, Brahman, or Belgian Blue sires. Belgian Blue was the source of the inactive myostatin allele. Myostatin genotypes were determined for all animals including those that died before weaning. Early calf mortality was examined in the F2 subpopulation (n = 154), derived from the F1 siresmated to F1 cows from Belgian Blue sires, to evaluate animals with zero, one, or two copies of inactive myostatin allele. An overall 1:2:1 ratio (homozygous active myostatin allele:heterozygous:homozygous inactive myostatin allele) was observed in the population; however, a comparison between calves dying before weaning and those alive at slaughter showed an unequal distribution across genotypes (P \u3c 0.01). Calves with two copies of the inactive allele were more likely (P \u3c 0.01) to die before weaning. Postweaning growth traits were evaluated in the surviving animals (n = 1,370), including birth, weaning, and live weight at slaughter, and postweaning ADG. Carcass composition traits analyzed were hot carcass weight, fat thickness, LM area, marbling score, USDA yield grade, estimated kidney, pelvic, and heart fat, retail product yield and weight, fat yield and weight, bone yield and weight, and percentage of carcasses classified as Choice. Charolais lack the inactive myostatin allele segregating in Belgian Blue; thus, in the population sired by Charolais (n = 645), only animals with zero or one copy of the inactive myostatin allele were evaluated. Animals carrying one copy were heavier at birth and at weaning, and their carcasses were leaner and more muscled. In the population sired by Belgian Blue × British Breed (n = 725), animals with two copies of inactive myostatin allele were heavier at birth, leaner, and had a higher proportion of muscle mass than animals with zero or one copies. Heterozygous animals were heaviest at weaning and had the highest live weight, whereas animals with zero copies had the highest fat content. The use of the inactive myostatin allele is an option to increase retail product yield, but considerations of conditions at calving are important to prevent mortality

Association of myostatin on early calf mortality, growth, and carcass composition traits in crossbred cattle

The objective of this study was to investigate a potential association of an inactive myostatin allele with early calf mortality, and evaluate its effect on growth and carcass traits in a crossbred population. Animals were obtained by mating F1 cows to F1 (Belgian Blue x British Breed) or Charolais sires. Cows were obtained from mating Hereford, Angus, and U.S. Meat Animal Research Center III ( ¼ Hereford, ¼ Angus, ¼ Pinzgauer, and ¼ Red Poll) dams to Hereford, Angus, Tuli, Boran, Brahman, or Belgian Blue sires. Belgian Blue was the source of the inactive myostatin allele. Myostatin genotypes were determined for all animals including those that died before weaning. Early calf mortality was examined in the F2 subpopulation (n = 154), derived from the F1 siresmated to F1 cows from Belgian Blue sires, to evaluate animals with zero, one, or two copies of inactive myostatin allele. An overall 1:2:1 ratio (homozygous active myostatin allele:heterozygous:homozygous inactive myostatin allele) was observed in the population; however, a comparison between calves dying before weaning and those alive at slaughter showed an unequal distribution across genotypes (P \u3c 0.01). Calves with two copies of the inactive allele were more likely (P \u3c 0.01) to die before weaning. Postweaning growth traits were evaluated in the surviving animals (n = 1,370), including birth, weaning, and live weight at slaughter, and postweaning ADG. Carcass composition traits analyzed were hot carcass weight, fat thickness, LM area, marbling score, USDA yield grade, estimated kidney, pelvic, and heart fat, retail product yield and weight, fat yield and weight, bone yield and weight, and percentage of carcasses classified as Choice. Charolais lack the inactive myostatin allele segregating in Belgian Blue; thus, in the population sired by Charolais (n = 645), only animals with zero or one copy of the inactive myostatin allele were evaluated. Animals carrying one copy were heavier at birth and at weaning, and their carcasses were leaner and more muscled. In the population sired by Belgian Blue × British Breed (n = 725), animals with two copies of inactive myostatin allele were heavier at birth, leaner, and had a higher proportion of muscle mass than animals with zero or one copies. Heterozygous animals were heaviest at weaning and had the highest live weight, whereas animals with zero copies had the highest fat content. The use of the inactive myostatin allele is an option to increase retail product yield, but considerations of conditions at calving are important to prevent mortality

Coherence as ultrashort pulse train generator

Intense, well-controlled regular light pulse trains start to play a crucial role in many fields of physics. We theoretically demonstrate a very simple and robust technique for generating such periodic ultrashort pulses from a continuous probe wave which propagates in a dispersive thermal gas media

Biological Efficiency Differences Among \u3ci\u3eBos taurus\u3c/i\u3e x \u3ci\u3eBos taurus\u3c/i\u3e and \u3ci\u3eBos indicus\u3c/i\u3e x \u3ci\u3eBos taurus\u3c/i\u3e F\u3csub\u3e1\u3c/sub\u3e-Cross Cows

Matching germplasm to resources through designed crossbreeding programs can contribute to optimum beef production efficiency. This is particularly true in light of the wide diversity of environmental conditions encountered by beef producers in the U.S. This approach requires considerable knowledge about genetic diversity among breeds in components of performance and furthermore how those components interact to influence life-cycle efficiency in the production setting. It was largely this identified need, coupled with the importation of a number of new breeds from continental Europe, that gave impetus for the establishment of the Germplasm Evaluation (GPE) Program. In Cycles I and II of the GPE program, increases in cow output associated with higher breed potential for growth rate and milk production were largely offset by equivalent or greater increases in feed requirements for maintenance and lactation. Additionally, in Cycle III, output of calf weaned per cow in the breeding herd was high for Bos indicus x Bos taurus crosses relative to Bos taurus crosses. More information is needed to evaluate F1 cross of Bos taurus versus Bos indicus x Bos taurus sources of germplasm. Therefore, this study was conducted to: 1) estimate input/output components, and 2) estimate life-cycle efficiency of Cycle III breeds representing these types of F1 cross females

Using simulation models to predict feed intake: Phenotypic and genetic relationships between observed and predicted values in cattle

The objectives of this study were to evaluate the accuracy of the Decision Evaluator for the Cattle Industry (DECI) and the Cornell Value Discovery System (CVDS) in predicting individual DMI and to assess the feasibility of using predicted DMI data in genetic evaluations of cattle. Observed individual animal data on the average daily DMI (OFI), ADG, and carcass measurements were obtained from postweaning records of 504 steers from 52 sires (502 with complete data). The experimental data and daily temperature and wind speed data were used as inputs to predict average daily feed DMI (kg) required (feed required; FR) for maintenance, cold stress, and ADG; maintenance and cold stress; ADG; maintenance and ADG; and maintenance alone, with CVDS (CFRmcg, CFRmc, CFRg, CFRmg, and CFRm, respectively) and DECI (DFRmcg, DFRmc, DFRg, DFRmg, and DFRm, respectively). Genetic parameters were estimated by REML using an animal model with age on test as a covariate and with genotype, age of dam, and year as fixed effects. Regression equations for observed on predicted DMI were OFI = 1.27 (SE = 0.27) + 0.83 (SE = 0.04) × CFRmcg [R2 = 0.44, residual SD (sy.x) = 0.669 kg/d] and OFI = 1.32 (SE = 0.22) + 0.8 (SE = 0.03) × DFRmcg (R2 = 0.53, sy.x = 0.612 kg/d). Heritability of OFI was 0.27 ± 0.12, and heritabilities ranged from 0.33 ± 0.12 to 0.41 ± 0.13 for predicted measures of DMI. Phenotypic and genetic correlations between OFI and CFRmcg, CFRmc, CFRg, CFRmg, CFRm, DFRmcg, DFRmc, DFRg, DFRmg, and DFRm were 0.67, 0.73, 0.41, 0.63, 0.78, 0.73, 0.82, 0.45, 0.77, and 0.86 (P \u3c 0.001 for all phenotypic correlations); and 0.95 ± 0.07, 0.82 ± 0.13, 0.89 ± 0.09, 0.95 ± 0.07, 0.91 ± 0.09, 0.96 ± 0.07, 0.89 ± 0.09, 0.88 ± 0.09, 0.96 ± 0.06, and 0.96 ± 0.07, respectively. Phenotypic and genetic correlations between CFRmcg and DFRmcg, CFRmc and DFRmc, CFRg and DFRg, CFRmg and DFRmg, and CFRm and DFRm were 0.98, 0.94, 0.99, 0.98, and 0.95 (P \u3c 0.001 for all phenotypic correlations), and 0.99 ± 0.004, 0.98 ± 0.017, 0.99 ± 0.004, 0.99 ± 0.005, and 0.97 ± 0.021, respectively. The strong genetic relationships between OFI and CFRmcg, CFRmg, DFRmcg, and DFRmg indicate that these predicted measures of DMI may be used in genetic evaluations and that DM requirements for cold stress may not be needed, thus reducing model complexity. However, high genetic correlations for final weight with OFI, CFRmcg, and DFRmcg suggest that the technology needs to be further evaluated in populations with genetic variance in feed efficiency

Using simulation models to predict feed intake: Phenotypic and genetic relationships between observed and predicted values in cattle

The objectives of this study were to evaluate the accuracy of the Decision Evaluator for the Cattle Industry (DECI) and the Cornell Value Discovery System (CVDS) in predicting individual DMI and to assess the feasibility of using predicted DMI data in genetic evaluations of cattle. Observed individual animal data on the average daily DMI (OFI), ADG, and carcass measurements were obtained from postweaning records of 504 steers from 52 sires (502 with complete data). The experimental data and daily temperature and wind speed data were used as inputs to predict average daily feed DMI (kg) required (feed required; FR) for maintenance, cold stress, and ADG; maintenance and cold stress; ADG; maintenance and ADG; and maintenance alone, with CVDS (CFRmcg, CFRmc, CFRg, CFRmg, and CFRm, respectively) and DECI (DFRmcg, DFRmc, DFRg, DFRmg, and DFRm, respectively). Genetic parameters were estimated by REML using an animal model with age on test as a covariate and with genotype, age of dam, and year as fixed effects. Regression equations for observed on predicted DMI were OFI = 1.27 (SE = 0.27) + 0.83 (SE = 0.04) × CFRmcg [R2 = 0.44, residual SD (sy.x) = 0.669 kg/d] and OFI = 1.32 (SE = 0.22) + 0.8 (SE = 0.03) × DFRmcg (R2 = 0.53, sy.x = 0.612 kg/d). Heritability of OFI was 0.27 ± 0.12, and heritabilities ranged from 0.33 ± 0.12 to 0.41 ± 0.13 for predicted measures of DMI. Phenotypic and genetic correlations between OFI and CFRmcg, CFRmc, CFRg, CFRmg, CFRm, DFRmcg, DFRmc, DFRg, DFRmg, and DFRm were 0.67, 0.73, 0.41, 0.63, 0.78, 0.73, 0.82, 0.45, 0.77, and 0.86 (P \u3c 0.001 for all phenotypic correlations); and 0.95 ± 0.07, 0.82 ± 0.13, 0.89 ± 0.09, 0.95 ± 0.07, 0.91 ± 0.09, 0.96 ± 0.07, 0.89 ± 0.09, 0.88 ± 0.09, 0.96 ± 0.06, and 0.96 ± 0.07, respectively. Phenotypic and genetic correlations between CFRmcg and DFRmcg, CFRmc and DFRmc, CFRg and DFRg, CFRmg and DFRmg, and CFRm and DFRm were 0.98, 0.94, 0.99, 0.98, and 0.95 (P \u3c 0.001 for all phenotypic correlations), and 0.99 ± 0.004, 0.98 ± 0.017, 0.99 ± 0.004, 0.99 ± 0.005, and 0.97 ± 0.021, respectively. The strong genetic relationships between OFI and CFRmcg, CFRmg, DFRmcg, and DFRmg indicate that these predicted measures of DMI may be used in genetic evaluations and that DM requirements for cold stress may not be needed, thus reducing model complexity. However, high genetic correlations for final weight with OFI, CFRmcg, and DFRmcg suggest that the technology needs to be further evaluated in populations with genetic variance in feed efficiency