Spread of marker bacteria from the hides of cattle in a simulated livestock market and at an abattoir.

The spread of microbial contamination on the hides of beef was investigated at two stages in the meat chain: (i) in a simulated livestock market ("the market") using 33 animals, and (ii) in the unloading-to-skinning area of a commercial abattoir using 18 animals. At both stages, harmless bacterial markers (nalidixic acid-resistant Escherichia coli K-12; rifampicin- and nalidixic acid-resistant Pseudomonas fluorescens; and a tetracycline-resistant E. coli) were inoculated on the hides of a small number of selected animals, and their transfer to other animals and the environment was examined. At the market, the initial prevalence of animals positive for the hide markers (9.1% in each phase) introduced in the presale pen, sale ring, and postsale pen changed to 39.4, 15.1, and 54.5%, respectively, by the end of the market process. In addition, widespread contamination of the market environment with the hide markers was observed. At the abattoir, the initial prevalence of animals positive for the hide marker (11.1%) inoculated at unloading increased to 100% (hide before skinning) and 88.8% (skinned carcass). In addition, another marker inoculated on environmental surfaces in lairage pens, races, and stunning box was detected on 83.3% (hide before skinning) and 88.8% (skinned carcass). These results, although obtained with a relatively small number of animals, demonstrate that both the livestock market process and the unloading-to-skinning process at abattoirs can facilitate the extensive spread of microbial contamination on hides not just within, but also between, batches of animals.

Testing white line strength in the dairy cow.

The tensile strength of 576 pieces of white line horn collected over 6 mo from 14 dairy cows restricted to parity 1 or 2 was tested. None of the cows had ever been lame. Seven cows were randomly assigned to receive 20 mg/d biotin supplementation, and 7 were not supplemented. Hoof horn samples were taken from zones 2 and 3 (the more proximal and distal sites of the abaxial white line) of the medial and lateral claws of both hind feet on d 1 and on 5 further occasions over 6 mo. The samples were analyzed at 100% water saturation. Hoof slivers were notched to ensure that tensile strength was measured specifically across the white line region. The tensile stress at failure was measured in MPa and was adjusted for the cross-sectional area of the notch site. Data were analyzed in a multilevel model, which accounted for the repeated measures within cows. All other variables were entered as fixed effects. In the final model, there was considerable variation in strength over time. Tensile strength was significantly higher in medial compared with lateral claws, and zone 2 was significantly stronger than zone 3. Where the white line was visibly damaged the tensile strength was low. Biotin supplementation did not affect the tensile strength of the white line. Results of this study indicate that damage to the white line impairs its tensile strength and that in horn with no visible abnormality the white line is weaker in the lateral hind claw than the medial and in zone 3 compared with zone 2. The biomechanical strength was lowest at zone 3 of the lateral hind claw, which is the most common site of white line disease lameness in cattle.

Somatic cell count distributions during lactation predict clinical mastitis.

This research investigated somatic cell count (SCC) records during lactation, with the purpose of identifying distribution characteristics (mean and measures of variation) that were most closely associated with clinical mastitis. Three separate data sets were used, one containing quarter SCC (n = 1444) and two containing cow SCC (n = 933 and 11,825). Clinical mastitis was defined as a binary outcome, present or absent, for each lactation, and SCC were log (base 10) transformed. A generalized linear mixed model within a Bayesian framework was used for analysis. Parameters were estimated using Markov Chain Monte Carlo with Gibbs sampling. Results from the 3 data sets were similar. Increased maximum and standard deviation log SCC during lactation, rather than increased geometric mean, were the best overall indicators of clinical mastitis. Distributions of SCC were also investigated separately for different mastitis pathogens. Increased maximum log SCC was associated with clinical mastitis caused by all pathogen types. Increased standard deviation log SCC was associated with Staphylococcus aureus, and Streptococcus uberis clinical mastitis and increased coefficient of variation log SCC (standard deviation divided by mean) was associated with Escherichia coli clinical mastitis. Increased geometric mean lactation SCC was associated with an increased risk of Staph. aureus clinical mastitis but a reduced risk of E. coli clinical mastitis. Our results suggest that using measures of variation and maximum cow SCC would enhance the accuracy of predicting clinical mastitis, compared with geometric mean SCC, and therefore improve genetic programs that aim to select for clinical mastitis resistance. The results are also consistent with low SCC increasing susceptibility to some mastitis pathogens.