Inclusion of glycerol in forage diets increases methane production in a rumen simulation technique system
Bibliographic record
Abstract
We hypothesised that the inclusion of glycerol in the forage diets of ruminants would increase the proportion of propionate produced and thereby decrease in vitro CH₄ production. This hypothesis was examined in the present study using a semi-continuous fermentation system (rumen simulation technique) fed a brome hay (8·5 g) and maize silage (1·5 g) diet with increasing concentrations (0, 50, 100 and 150 g/kg DM) of glycerol substituted for maize silage. Glycerol linearly increased total volatile fatty acids production (P<0·001). Acetate production was quadratically affected (P=0·023) and propionate and butyrate production was linearly increased (P<0·001). Glycerol linearly increased (P=0·011) DM disappearance from hay and silage. Crude protein disappearance from hay was not affected (P=0·789), but that from silage was linearly increased (P<0·001) with increasing glycerol concentrations. Neutral-detergent fibre (P=0·040) and acid-detergent fibre (P=0·031) disappearance from hay and silage was linearly increased by glycerol. Total gas production tended to increase linearly (P=0·061) and CH₄ concentration in gas was linearly increased (P<0·001) by glycerol, resulting in a linear increase (P<0·001) in mg CH₄/g DM digested. Our hypothesis was rejected as increasing concentrations of glycerol in a forage diet linearly increased CH₄ production in semi-continuous fermenters, despite the increases in the concentrations of propionate. In conclusion, this apparent discrepancy is due to the more reduced state of glycerol when compared with carbohydrates, which implies that there is no net incorporation of electrons when glycerol is metabolised to propionate.
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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) | 0.000 | 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 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".