Nitrate supplementation at two forage levels in dairy cows feeding: milk production and composition, fatty acid profiles, blood metabolites, ruminal fermentation, and hydrogen sink
Bibliographic record
Abstract
Abstract Nitrate may reduce the ruminal methane emission by competing methanogenesis to achieve more hydrogen. For this purpose, twenty Holstein lactating cows were examined using a 2×2 factorial design in 4 groups for 60 days with two forage levels (40% and 60%) and supplemental nitrate 0% (F40 and F60) and 3.5% (F40N and F60N) of diet dry matter (DM). Then, the effect of nitrate and forage levels on cow performance, ruminal fermentation, methane emission, and metabolic hydrogen sink were evaluated. The nitrate supplementation did not significantly affect milk yield and ECM/DMI, while milk urea nitrogen was increased. Lowest quantity of milk vitamins (A and E) was observed in nitrate groups. The nitrate supplementation increased c9-C18:1, unsaturated fatty acids, and n-6/n-3 contents of the milk. Blood parameters were affected by nitrate supplementation. Blood met-Hb concentration was increased, while blood glucose was decreased in nitrate groups. High forage and nitrate fed animals (F60N) had higher ruminal acetate and lower propionate concentration, and higher acetate+butyrate to propionate ratio than other groups. Nitrite and NH 3 -N concentrations were higher in the rumen of nitrate fed animals. Nitrate supplementation inhibited gas volume and methane emission without affecting volatile fatty acids at 12 and 24 h of incubation. The H 2 balance, H 2 production and consumption, and recovery percentage were significantly lower in F60N group. In conclusion, nitrate supplementation can be employed as an alternative strategy for improving ruminal fermentation, milk quality and methane inhibition.
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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) | 0.001 | 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 | 0.001 | 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".