Temporal Changes in Faecal Microbiota Composition and Diversity in Dairy Cows Supplemented with a Lactobacillus-Based Direct-Fed Microbial
Bibliographic record
Abstract
The rumen microbiota of dairy cows plays a crucial role in fermenting fibrous material, essential for nutrient extraction and overall productivity, detoxification of anti-nutritional toxic compounds, synthesis of vital nutrients, and is essential for optimal animal health. This study investigated the impact of Lentilactobacillus-, Lactocaseibacillus-, and Lacticaseibacillus-based direct-fed microbial (DFM) supplementation on dairy cows’ faecal microbial composition and diversity. The study was carried out on a commercial dairy farm using 50 Holstein-Friesian cows randomly assigned into control (CON) and treatment (TRT) groups. Faecal samples were collected directly from the rectum every two months from September 2021 to January 2023. The bacterial 16S rRNA gene and fungal ITS-1 regions were amplified, sequenced, and analysed. Microbial diversity was assessed through alpha- and beta-diversity metrics. Linear discriminant analysis effect size (LEfSe) was performed to identify which taxa were driving the changes seen in the microbiota over time and treatment. Bacteroidaceae were the most prevalent bacterial family, followed by Lachnospiraceae and Muribaculaceae in both CON and TRT cows. Ascomycota, Basidiomycota, and Mucoromycota were the dominant three fungal phyla in the faeces of both CON and TRT cows. Bacterial genera Fructilactobacillus was abundant in the CON and Absicoccus in the TRT groups. Fungal taxa Chaetothryriales_incertae_sedis and Pseudomentella were absent in the faeces of TRT cows. Significant temporal and specific taxonomic differences were observed between the CON and TRT groups. The study’s findings underscore the dynamic nature of microbial communities and the importance of targeted dietary interventions. Further research is necessary to elucidate these microbial shifts, long-term impacts, and functional implications, aiming to optimise ruminant nutrition and enhance 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 | 0.000 |
| 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".