Influence of lemongrass and oregano essential oils and their combination on <i>in vitro</i> ruminal fermentation and greenhouse gas emissions in total mixed ration for dairy cows
Bibliographic record
Abstract
Ruminants play a crucial role in the food chain, but are also considered contributors of greenhouse gas (GHG) emissions . Essential oils (EOs) are emerging as natural feed additives in ruminants’ nutrition to enhance animal health, performance and reduce environmental footprint. Among EOs, lemongrass (Cymbopogon winterianus) and oregano (Origanum vulgare) EOs (LEO and OEO) have attracted attention as modulators of ruminal fermentations, but their role needs to be clarified. The experiment was designed using a randomised setup to assess the effects of LEO and OEO on in vitro ruminal fermentation and GHGs, using total mixed ration (TMR) as substrate (incubation time 24h). Experimental treatments included (doses as % of TMR on DM basis): 1) control only TMR (0% EOs) 2) 0.07% LEO 3) 0.07% OEO 4) 0.035% LEO + 0.035% OEO 5) 0.07% LEO + 0.07% OEO. Each treatment was repeated three times in two experimental runs. Only EO combinations reduced total gas (−9%, p=0.001). All EOs decreased CO2 emissions by −5 to −12% with no significant differences between treatments (p<0.001), although anti-methanogenic effects were not observed (p=0.192). Volatile fatty acids were slightly affected only by EOs blend at the highest dose, resulting in a reduction of propionate (−1.3%, p=0.02), an increase in acetate:propionate (+0.16%, p=0.04) and isovalerate (+0.7%, p=0.03). LEO reduced pH (−0.6%, p=0.004), while OEO increased oxidation capacity (+4.2%, p=0.004), but both parameters remained within physiological ranges. Canonical discriminant analysis confirmed distinct EOs effects, highlighting their potential as natural additives for improving ruminal fermentation and mitigating ruminant environmental footprint.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".