PSI-39 Assessment of Rumen Microbiota in Beef Cattle with Different Feed Efficiency Grazing on an Oat Pasture.
Bibliographic record
Abstract
Most cow-calf producers utilize pasture grazing during the summer months to maintain favorable growth at lower costs. Recent studies have demonstrated a relationship between the rumen microbiota and important performance traits such as feed efficiency and methane emissions in cattle. However, this has not been studied in pasture-grazed cattle due to the difficulty of collecting rumen samples and measuring production traits in these systems. The objectives of this study were to characterize variation in rumen microbiota composition and fermentation profiles in cattle grazed on different pastures, and to link this variation to feed efficiency and methane emissions. In this study, rumen fluid samples were taken from 60 heifers being tested for residual feed intake (RFI) in feedlot under a 100% barley silage diet. Similarly, rumen fluid samples were subsequently taken from 8 high-RFI (inefficient) and 8 low-RFI (efficient) heifers while grazing as separate herds on forage oats and tested for methane emissions using an open-path Fourier Transform Infrared (OP-FTIR) spectrometry method. Total DNA was extracted from all rumen fluid samples and quantitative real-time PCR (qPCR) was performed to estimate microbial populations. Volatile fatty acid (VFA) concentrations were assessed by gas chromatography. The qPCR results indicated that inefficient cattle had less rumen protozoa on grazing than when they were in the feedlot (2.5 × 106 vs 8.6 × 106, p-value < 0.05). Regardless of RFI, cattle had more total rumen methanogens during grazing than in feedlot (1.5 × 108 vs 0.6 × 108, p-value < 0.05). The VFA profiles showed that grazed cattle had lower acetate:propionate ratios versus feedlot animals (p-value < 0.05), indicating more efficient microbial activity while grazing. In conclusion, compared to feedlot barley diet with silage, grazing on pasture led to divergent rumen microbial composition and changes in VFA production, which may be associated with cattle feed efficiency and methane emission.
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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 | 0.000 |
| 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) | 0.001 | 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".