In this study, we use barcoded pyrosequencing of the 16S rRNA gene to characterize the fecal microbiota of neonatal calves and identify possible relationships of certain microbiota profiles with health and weight gain. Fecal samples were obtained weekly from 61 calves from birth until weaning (seventh week of the calves' life). Firmicutes was the most prevalent phylum, with a prevalence ranging from 63.84% to 81.90%, followed by Bacteroidetes (8.36% to 23.93%), Proteobacteria (3.72% to 9.75%), Fusobacteria (0.76% to 5.67%), and Actinobacteria (1.02% to 2.35%). Chao1 index gradually increased from the first to the seventh postnatal week. Chao1 index was lower during the third, fourth, and fifth week of life in calves that suffered from pneumonia and were treated with antibiotics. Diarrhea incidence during the first four weeks of the calves' life was also associated with a reduction of microbial diversity during the third week of life. Increased fecal microbial diversity after the second week of life was associated with higher weight gain. Using discriminant analysis we were able to show differences in the microbiota profiles between different weeks of life, between high and low weight gain groups of calves, and between calves affected and not affected with diarrhea during the first four weeks life. The prevalence of Faecalibacterium spp. in the first week of life was associated with weight gain and the incidence of diarrhea, with higher prevalence being associated with higher weight gain and less diarrhea. Representative sequences from Faecalibacterium spp. were closely affiliated to Faecalibacterium prausnitzii. Results presented here provide new information regarding the intestinal microbiota of neonatal calves and its association with health and growth. Fecal microbial diversity was associated with calf age, disease status and growth rates. Results suggesting a possible beneficial effect of Faecalibacterium spp. on health and growth are promising.

The objective of this study was to use pyrosequencing of the 16S rRNA genes to describe the microbial diversity of bovine milk samples derived from clinically unaffected quarters across a range of somatic cell counts (SCC) values or from clinical mastitis, culture negative quarters. The obtained microbiota profiles were used to distinguish healthy, subclinically and clinically affected quarters. Two dairy farms were used for the collection of milk samples. A total of 177 samples were used. Fifty samples derived from healthy, culture negative quarters with a SCC of less than 20,000 cells/ml (group 1); 34 samples derived from healthy, culture negative quarters, with a SCC ranging from 21,000 to 50,000 cells/ml (group 2); 26 samples derived from healthy, culture negative quarters with a SCC greater than 50,000 cells/ml (group 3); 34 samples derived from healthy, culture positive quarters, with a SCC greater than 400,000 (group 4, subclinical); and 33 samples derived from clinical mastitis, culture negative quarters (group 5, clinical). Bacterial DNA was isolated from these samples and the 16S rRNA genes were individually amplified and pyrosequenced. All samples analyzed revealed great microbial diversity. Four bacterial genera were present in every sample obtained from healthy quarters (Faecalibacterium spp., unclassified Lachnospiraceae, Propionibacterium spp. and Aeribacillus spp.). Discriminant analysis models showed that samples derived from healthy quarters were easily discriminated based on their microbiota profiles from samples derived from clinical mastitis, culture negative quarters; that was also the case for samples obtained from different farms. Staphylococcus spp. and Streptococcus spp. were among the most prevalent genera in all groups while a general multivariable linear model revealed that Sphingobacterium and Streptococcus prevalences were associated with increased 10 log SCC. Conversely, Nocardiodes and Paenibacillus were negatively correlated, and a higher percentage of the genera was associated with a lower 10 log SCC.

An algorithm using only computer-based records to guide selective dry-cow therapy was evaluated at a New York State dairy farm via a randomized field trial. DairyComp 305 (Valley Ag Software, Tulare, CA) and Dairy Herd Improvement Association test-day data were used to identify cows as low risk (cows that might not benefit from dry-cow antibiotics) or high risk (cows that will likely benefit). Low-risk cows were those that had all of the following: somatic cell count (SCC) ≤200,000 cells/mL at last test, an average SCC ≤200,000 cells/mL over the last 3 tests, no signs of clinical mastitis at dry-off, and no more than 1 clinical mastitis event in the current lactation. Low-risk cows were randomly assigned to receive intramammary antibiotics and external teat sealant (ABXTS) or external teat sealant only (TS) at dry-off. Using pre-dry-off and postcalving quarter-level culture results, low-risk quarters were assessed for microbiological cure risk and new infection risk. Groups were also assessed for differences in first-test milk yield and linear scores, individual milk weights for the first 30 d, and culling and mastitis events before 30 d in milk. A total of 304 cows and 1,040 quarters in the ABXTS group and 307 cows and 1,058 quarters in the TS group were enrolled. Among cows to be dried, the proportion of cows that met low-risk criteria was 64% (n = 611/953). Of cultures eligible for bacteriological cure analysis (n = 171), 93% of ABXTS cured, whereas 88% of TS cured. Of the non-cures, 95% were contributed by the minor pathogens coagulase-negative staphylococci (n = 19/20). These organisms also accounted for 57.5% of new infections (n = 77/134). We found no statistical differences between treatment groups for new infection risk (TS = 7.3% quarters experiencing new infections; ABXTS = 5.5%), milk production (ABXTS = 40.5 kg; TS = 41.2 kg), linear scores (ABXTS = 2.5; TS = 2.7), culling events (ABXTS, n = 18; TS, n = 15), or clinical mastitis events (ABXTS, n = 9; TS, n = 5). Results suggest that the algorithm used decreased dry-cow antibiotic use by approximately 60% without adversely affecting production or health outcomes.