Effects of cinnamon leaf, oregano and sweet orange essential oils on fermentation and aerobic stability of barley silage
Bibliographic record
Abstract
BACKGROUND: Silage additives are marketed with the primary aim of improving the fermentation and/or aerobic stability of silage. The objective of this study was to evaluate the impact of three different essential oils (EOs; cinnamon leaf (CIN), oregano (ORE) and sweet orange (SO)) on the fermentation characteristics and stability of barley silage. Chopped whole-plant barley (Hordem vulgare L.) forage was ensiled either untreated (0 mg kg⁻¹ dry matter (DM)) or treated with CIN, ORE or SO (37.5, 75 and 120 mg kg⁻¹ DM). RESULTS: Moulds were not detected in any treatments, including the control, after 7 days of air exposure. All EOs at a concentration of 120 mg kg⁻¹ silage DM decreased (P = 0.001) yeast populations in comparison with the control during air exposure. Net gas, methane and ammonia concentrations in vitro did not differ among treatments. Changes in volatile fatty acid concentrations were small, and in situ data showed no changes in DM and neutral detergent fibre digestion rates for CIN, ORE or SO at concentrations up to 120 mg kg⁻¹ DM. CONCLUSION: The findings from this study show that a concentration of 120 mg EO kg⁻¹ DM decreased yeast counts during aerobic stability tests. However, all EO treatments had minimal effects on data from in vitro and in situ incubations.
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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".