Fecal Microbial Communities in a Large Representative Cohort of California Dairy Cows
Bibliographic record
Abstract
Improved sequencing and analytical techniques allow for better resolution of microbial communities; however, the agriculture field lacks an updated analysis surveying the fecal microbial populations of dairy cattle in California. This study is a large-scale survey to determine the composition of the bacterial community present in the feces of lactating dairy cattle on commercial dairy operations. For the study, ten dairy farms across northern and central California representing a variety of feeding and management systems were enrolled. The farms represented three typical housing types including five freestall, two drylot and three pasture-based management systems. Fresh feces were collected from 15 randomly selected cows on each farm and analyzed using 16S rRNA gene amplicon sequencing. This study found that housing type, individual farm, and dietary components significantly affected the alpha diversity of the fecal microbiota. While only one Operational Taxonomic Unit (OTU) was common among all the sampled individuals, 15 bacterial families and 27 genera were shared among 95% of samples. The ratio of the families Coriobacteriaceae to Bifidobacteriaceae was significantly different between housing types and farms with pasture fed animals having a higher relative abundance of Coriobacteriaceae. A majority of samples were positive for at least one OTU assigned to Enterobacteriaceae and 31% of samples contained OTUs assigned to Campylobacter. However, the relative abundance of both taxa was < 0.1%. The microbial composition displays individual farm specific signatures, but housing type plays a role. These data provide insights into the composition of the core fecal microbiota of commercial dairy cows in California and will further generate hypotheses for strategies to manipulate the microbiome of cattle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".