Effects of Feeding Garlic and Juniper Berry Essential Oils on Milk Fatty Acid Composition of Dairy Cows
Bibliographic record
Abstract
Essential oils (EOs) from plant extracts have been reported to have an antibacterial activity against gram-negative and gram-positive bacteria. Several of the gram-positive bacteria are involved in ruminal biohydrogenation of fatty acids (FAs), thus suggesting that feeding EOs could lower biohydrogenation of FA because of a decrease in the number of bacteria involved in that process. As a result, milk FA profiles are expected to be modified. In addition, monensin was approved as an antibiotic to be fed in dairy cattle, and it was reported that dairy cows supplemented with monensin produced milk containing higher concentration of 18:1 t10 and 18:1 t11. The objective of this study was to evaluate the effects of two EOs (garlic and juniper berry oils) and monensin on FA profiles of milk fat. Four ruminally fistulated Holstein dairy cows were used in a 4 × 4 Latin square experiment. Cows were fed for ad libitum intake a total mixed ration without supplementation (control), or supplemented with monensin (330 mg/head per day), garlic oil (5 g/head per day), or juniper berry oil (2 g/head per day). The FA composition of saturated, monounsaturated, or polyunsaturated was not affected by supplementation of EO and monensin. However, proportion of conjugated linoleic acid trans 10, cis 12 (CLA t10, c12) was higher (P < 0.05) for cows fed EO or monensin than for control cows. Supplementation of monensin increased (P < 0.05) the proportion of total trans FA compared with the control. These results indicate that supplementation of the dairy cow diet with garlic or juniper berry EO or monensin had the potential to increase the proportion of CLA t10, c12 in milk fat with minimal overall effects on FA of milk fat. The results also confirm the increase of 18:1 t10 in milk fat by feeding monensin to dairy cows.
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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".