Characterization of rumen bacterial diversity and fermentation parameters in concentrate fed cattle with and without forage
Bibliographic record
Abstract
AIMS: To determine the effects of the removal of forage in high-concentrate diets on rumen fermentation conditions and rumen bacterial populations using culture-independent methods. METHODS AND RESULTS: Detectable bacteria and fermentation parameters were measured in the solid and liquid fractions of digesta from cattle fed two dietary treatments, high concentrate (HC) and high concentrate without forage (HCNF). Comparison of rumen fermentation conditions showed that duration of time spent below pH 5·2 and rumen osmolality were higher in the HCNF treatment. Simpson's index of 16S PCR-DGGE images showed a greater diversity of dominant species in the HCNF treatment. Real-time qPCR showed populations of Fibrobacter succinogenes (P = 0·01) were lower in HCNF than HC diets. Ruminococcus spp., F. succinogenes and Selenomonas ruminantium were at higher (P ≤ 0·05) concentrations in the solid vs the liquid fraction of digesta regardless of diet. CONCLUSIONS: The detectable bacterial community structure in the rumen is highly diverse. Reducing diet complexity by removing forage increased bacterial diversity despite the associated reduction in ruminal pH being less conducive for fibrolytic bacterial populations. Quantitative PCR showed that removal of forage from the diet resulted in a decline in the density of some, but not all fibrolytic bacterial species examined. SIGNIFICANCE AND IMPACT OF THE STUDY: Molecular techniques such as DGGE and qPCR provide an increased understanding of the impacts of dietary changes on the nature of rumen bacterial populations, and conclusions derived using these techniques may not match those previously derived using traditional laboratory culturing techniques.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".