Temporal changes in ruminal microbiota composition and diversity in dairy cows supplemented with a lactobacilli-based DFM
Bibliographic record
Abstract
Introduction This study aimed to evaluate the impact of lactobacilli-based direct-fed microbial (DFM) supplementation on the composition and diversity of the ruminal microbiota in dairy cows. Understanding how DFM influences microbial populations can inform strategies to enhance animal health and productivity. Methods Over a 16-month period (September 2021 to January 2023), ruminal fluid samples were collected from fifty dairy cows assigned to either a DFM-supplemented group (DFM; n = 25) or an unsupplemented control group (CON; n = 25). Microbial DNA was extracted and subjected to 16S rRNA gene amplification and sequencing. Microbial diversity was assessed using alpha- and beta-diversity metrics ( p < 0.05), and linear discriminant analysis effect size (LEfSe) was employed to identify differentially abundant taxa. Multivariable analyses were used to explore associations with age, average milk production, days in milk (DIM), time, and supplementation. Results The dominant bacterial phyla identified were Bacillota and Bacteroidota, while Methanobacteriaceae was the predominant archaeal family. The DFM group showed significantly higher abundance of genera such as Eubacterium_Q , Atopobium sp. UBA7741 , and Sharpea ( p < 0.05). Conversely, Bacillus_P_294101 and SFMI01 were more abundant in the CON group. Temporal changes in microbial composition were observed, with significant differences in community diversity and structure between groups over time. Discussion These findings demonstrate that lactobacilli-based DFM supplementation can significantly alter the ruminal microbial ecosystem in dairy cows. The observed microbial shifts, including increases in beneficial bacterial taxa, highlight the potential of DFM as a nutritional strategy to modulate rumen function and improve dairy cow performance.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".