Management-intensive grazing impacts on total Escherichia coli, E. coli O157:H7, and antibiotic resistance genes in a riparian stream.

The impacts of management-intensive grazing (MIG) of cattle on concentrations of total Escherichia coli, total suspended solids (TSS), and nitrate-nitrite nitrogen (NO3 + NO2-N), and occurrence of E. coli O157:H7 and selected antibiotic resistance genes (ARGs) in stream water and/or sediments were evaluated. Cattle were grazed for two-week periods in May in each of three years. Overall, grazing increased total E. coli in downstream water by 0.89 log10 MPN/100 mL (p < 0.0001), and downstream total E. coli concentrations were higher than upstream over all sampling intervals. Downstream TSS levels also increased (p ≤ 0.0294) during grazing. In contrast, there was a main effect of treatment for downstream NO3 + NO2-N to be lower than upstream (3.59 versus 3.70 mg/L; p = 0.0323). Overwintering mallard ducks increased total E. coli and TSS concentrations in January and February (p < 0.05). For precipitation events during the 24 h before sampling, each increase of 1.00 cm of rainfall increased total E. coli by 0.49 log10 MPN/100 mL (p = 0.0005). In contrast, there was no association of previous 24 h precipitation volume on TSS (p = 0.1540), and there was a negative linear effect on NO3 + NO2-N (p = 0.0002). E. coli O157:H7 prevalence was low, but the pathogen was detected downstream up to 2½ months after grazing. Examination of ARGs sul1, ermB, blactx-m-32, and intI1 identified the need for additional research to understand the impact of grazing on the ecology of these resistance determinants in pasture-based cattle production. While E. coli remained higher in downstream water compared to upstream, MIG may reduce the magnitude of the downstream E. coli concentrations. Likewise, the MIG strategy may prevent large increases in TSS and NO3 + NO2-N concentrations during heavy rain events. Results indicate that MIG can limit the negative effects of cattle grazing on stream water quality.

Impact of Feeding Syngenta Enogen® Feed Corn Compared to Control Corn in Different Diet Scenarios to Finishing Beef Cattle.

The objective of this pooled statistical analysis was to evaluate Syngenta Enogen® Feed Corn (EFC) versus conventional corn (CON) when fed as either dry-rolled corn (DRC) or high-moisture corn (HMC) for effects on finishing beef cattle performance and carcass characteristics. Corns were evaluated in diets with byproduct inclusion rates of 0, 15, 18, 20, and 30% distiller grains or 25 and 35% Sweet Bran® (a commercial corn gluten feed product). Seven trials (n = 1856) consisting of 200 pen means comparing 26 diet treatments were analyzed using regression in a pooled analysis. When EFC was processed as DRC, the gain efficiency (G:F) improved compared with CON, but the response to feeding EFC decreased from a 4.8% improvement to no improvement compared to CON as distiller grains increased from 0 to 30%, but was significantly improved due to feeding EFC in diets with 0 to 18% distiller grains. Feeding cattle EFC as DRC increased the average daily gain (ADG) and G:F by 4.5% compared with CON corn in diets containing Sweet Bran®. No improvements in animal performance were observed when cattle were fed EFC compared to CON when processed as HMC in any situation. Feeding Enogen® corn improved the gain efficiency of finishing cattle compared with conventional corn when processed as dry-rolled corn and fed in diets with less than 20% distillers or diets that include Sweet Bran®.

The impact of cow size on cow-calf and postweaning progeny performance in the Nebraska Sandhills.

Optimizing beef production system efficiency requires an understanding of genetic potential suitable for a given production environment. Therefore, the objective of this retrospective analysis was to determine the influence of cow body weight (BW) adjusted to a common body condition score (BCS) of 5 at weaning-influenced cow-calf performance and postweaning steer and heifer progeny performance. Data were collected at the Gudmundsen Sandhills Laboratory, Whitman, NE, on crossbred, mature cows (n = 1,607) from 2005 to 2017. Cow BCS at calving, prebreeding, and weaning were positively associated (P < 0.01) with greater cow BW. Increasing cow BW was positively associated (P < 0.01) with the percentage of cows that conceived during a 45-d breeding season. For every additional 100-kg increase in cow BW, calf BW increased (P < 0.01) at birth by 2.70 kg and adjusted 205-d weaning BW by 14.76 kg. Calf preweaning average daily gain (ADG) increased (P < 0.01) 0.06 kg/d for every additional 100-kg increase in cow BW. Heifer progeny BW increased (P < 0.01) postweaning with every additional 100-kg increase in dam BW. Dam BW did not influence (P ≥ 0.11) heifer puberty status prior to breeding, overall pregnancy rates, or the percentage of heifers calving in the first 21 d of the calving season. Steer initial feedlot BW increased by 7.20 kg, reimplant BW increased by 10.47 kg, and final BW increased by 10.29 kg (P ≤ 0.01) for every additional 100-kg increase in dam BW. However, steer feedlot ADG was not influenced (P > 0.67) by dam BW. Hot carcass weights of steers were increased (P = 0.01) by 6.48 kg with every additional 100-kg increase in cow BW. In a hypothetical model using the regression coefficients from this study, regardless of pricing method, cow-calf producers maximize the highest amount of profit by selecting smaller cows. Overall, larger-sized cows within this herd and production system of the current study had increased reproductive performance and offspring BW; however, total production output and economic returns would be potentially greater when utilizing smaller-sized cows.

Comparison of molecular breeding values based on within- and across-breed training in beef cattle.

BACKGROUND: Although the efficacy of genomic predictors based on within-breed training looks promising, it is necessary to develop and evaluate across-breed predictors for the technology to be fully applied in the beef industry. The efficacies of genomic predictors trained in one breed and utilized to predict genetic merit in differing breeds based on simulation studies have been reported, as have the efficacies of predictors trained using data from multiple breeds to predict the genetic merit of purebreds. However, comparable studies using beef cattle field data have not been reported. METHODS: Molecular breeding values for weaning and yearling weight were derived and evaluated using a database containing BovineSNP50 genotypes for 7294 animals from 13 breeds in the training set and 2277 animals from seven breeds (Angus, Red Angus, Hereford, Charolais, Gelbvieh, Limousin, and Simmental) in the evaluation set. Six single-breed and four across-breed genomic predictors were trained using pooled data from purebred animals. Molecular breeding values were evaluated using field data, including genotypes for 2227 animals and phenotypic records of animals born in 2008 or later. Accuracies of molecular breeding values were estimated based on the genetic correlation between the molecular breeding value and trait phenotype. RESULTS: With one exception, the estimated genetic correlations of within-breed molecular breeding values with trait phenotype were greater than 0.28 when evaluated in the breed used for training. Most estimated genetic correlations for the across-breed trained molecular breeding values were moderate (> 0.30). When molecular breeding values were evaluated in breeds that were not in the training set, estimated genetic correlations clustered around zero. CONCLUSIONS: Even for closely related breeds, within- or across-breed trained molecular breeding values have limited prediction accuracy for breeds that were not in the training set. For breeds in the training set, across- and within-breed trained molecular breeding values had similar accuracies. The benefit of adding data from other breeds to a within-breed training population is the ability to produce molecular breeding values that are more robust across breeds and these can be utilized until enough training data has been accumulated to allow for a within-breed training set.

Sparse inverse of covariance matrix of QTL effects with incomplete marker data.

Gametic models for fitting breeding values at QTL as random effects in outbred populations have become popular because they require few assumptions about the number and distribution of QTL alleles segregating. The covariance matrix of the gametic effects has an inverse that is sparse and can be constructed rapidly by a simple algorithm, provided that all individuals have marker data, but not otherwise. An equivalent model, in which the joint distribution of QTL breeding values and marker genotypes is considered, was shown to generate a covariance matrix with a sparse inverse that can be constructed rapidly with a simple algorithm. This result makes more feasible including QTL as random effects in analyses of large pedigrees for QTL detection and marker assisted selection. Such analyses often use algorithms that rely upon sparseness of the mixed model equations and require the inverse of the covariance matrix, but not the covariance matrix itself. With the proposed model, each individual has two random effects for each possible unordered marker genotype for that individual. Therefore, individuals with marker data have two random effects, just as with the gametic model. To keep the notation and the derivation simple, the method is derived under the assumptions of a single linked marker and that the pedigree does not contain loops. The algorithm could be applied, as an approximate method, to pedigrees that contain loops.