Methane emissions from beef cattle: Effects of fumaric acid, essential oil, and canola oil1
Bibliographic record
Abstract
The objective of this study was to identify feed additives that reduce enteric methane emissions from cattle. We measured methane emissions, total tract digestibility (using chromic oxide), and ruminal fermentation (4 h after feeding) in growing beef cattle fed a diet supplemented with various additives. The experiment was designed as a replicated 4 x 4 Latin square with 21-d periods and was conducted using 16 Angus heifers (initial BW of 260 +/- 32 kg). Treatments were: control (no additive), fumaric acid (175 g/d) with sodium bicarbonate (75 g/d), essential oil and spice extract (1 g/d), or canola oil (4.6% of DMI). The basal diet consisted of 75% whole-crop barley silage, 19% steam-rolled barley, and 6% supplement (DM basis). Four large chambers (2 animals fed the same diet per chamber) were equipped to measure methane emissions for 3 d each period. Adding canola oil to the diet decreased (P = 0.009) total daily methane emissions by 32% and tended (P = 0.09) to decrease methane emissions as a percentage of gross energy intake by 21%. However, much of the reduction in methane emissions was due to decreased (P < 0.05) feed intake and lower (P < 0.05) total tract digestibility of DM and fiber. Digestibility of all nutrients was also lowered (P < 0.05) by feeding essential oil, but there were no effects on ruminal fermentation or methane emissions. In contrast, adding fumaric acid to the diet increased total VFA concentration (P = 0.03), increased propionate proportions (P = 0.01), and decreased the acetate:propionate ratio (P = 0.002), but there was no measurable effect on methane emissions. The study demonstrates that canola oil can be used to reduce methane losses from cattle, but animal performance may be compromised due to lower feed intake and decreased fiber digestibility. Essential oils had no effect on methane emissions, whereas fumaric acid caused potentially beneficial changes in ruminal fermentation but no measurable reductions in methane emissions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".