Impact of condensed tannin-containing legumes on ruminal fermentation, nutrition, and performance in ruminants: a review
Bibliographic record
Abstract
Legume forages, such as sainfoin (Onobrychis viciifolia Scop.) and birdsfoot trefoil (Lotus corniculatus L.), can increase the forage quality and quantity of Western Canadian pastures, thus increasing producer profitability due to increased gains in grazing ruminants, while also reducing risk of bloat in legume pastures due to the presence of proanthocyanidins. Proanthocyanidins or condensed tannins (CT) are secondary plant polyphenol compounds that have been regarded as anti-nutritional due to their ability to bind protein in feeds, enzymes, and microbial cells, therefore disrupting microbial digestion and slowing ruminal protein and dry matter (DM) digestion. Research has shown that at high concentrations (>50 g·kg−1 DM), CT can disrupt microbial digestion. However, at low dietary inclusion rates (5–10 g·kg−1 DM), they reduce bloat risk, increase ruminal undegradable protein, reduce enteric methane production, and benefit anthelmintic activity. Yet, research gaps still exist regarding grazing persistence and forage yield of novel CT-containing forages and their biological activity due to their vast differences in CT stereochemistry, polymer size, and intermolecular linkages. The objectives of this review are to summarize information regarding the impact of CT on ruminal fermentation, carbohydrate and protein metabolism, and the potential to identify and select for forages that contain CT for ruminant production.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".