Assessment of the effect of condensed (acacia and quebracho) and hydrolysable (chestnut and valonea) tannins on rumen fermentation and methane production <i>in vitro</i>
Bibliographic record
Abstract
BACKGROUND: Tannins added to animal diets may have a positive effect on energy and protein utilisation in the rumen. The objective of this study was to examine the impact of different sources and concentrations (20, 50, 100, 150 and 200 g kg⁻¹ dry matter (DM)) of condensed (acacia and quebracho) and hydrolysable (chestnut and valonea) tannins on rumen microbial fermentation in vitro. The experiment also included a negative control with no tannins (control) and a positive control with monensin (10 mg L⁻¹). RESULTS: In vitro gas production and total volatile fatty acid (VFA) concentration decreased as tannin concentration increased. Addition of acacia, chestnut or valonea tannins at ≥ 50 g kg⁻¹ or quebracho tannins at ≥ 100 g kg⁻¹ resulted in a decrease (up to 40%) in methane (CH₄) production compared with the control. Valonea tannins were the only tannin source that reduced (-11%) CH₄ production at 50 g kg⁻¹ without affecting VFA concentration. Tannin treatments reduced ammonia (NH₃) and branched-chain VFA concentrations, indicating a reduction in ruminal protein degradation. Monensin reduced CH₄ production (-37%) and NH₃ concentration (-20%) without affecting total VFA concentration. CONCLUSION: Supplying acacia, chestnut or valonea tannins at 50 g kg⁻¹ has the potential to reduce CH₄ production and ruminal protein degradation with minimum detrimental effects on efficiency of ruminal fermentation.
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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".