Influence of nitrate supplementation on <i>in-vitro</i> methane emission, milk production, ruminal fermentation, and microbial methanotrophs in dairy cows fed at two forage levels
Bibliographic record
Abstract
Abstract Modifying the chemical composition of a diet can be a good strategy for reducing methane emission in the rumen. However, this strategy can have adverse effects on the ruminal microbial flora. The aim of our study was to reduce methane without disturbing ruminal function by stimulating the growth and propagation of methanotrophs. In this study, we randomly divided twenty multiparous Holstein dairy cows into 4 groups in a 2×2 factorial design with two forage levels (40% and 60%) and two nitrate supplementation levels (3.5% and zero). We examined the effect of experimental diets on cow performance, ruminal fermentation, blood metabolites and changes of ruminal microbial flora throughout the experimental period (45-day). Additionally, in vitro methane emission was evaluated. Animals fed diet with 60% forage had greater dry matter intake (DMI) and milk fat content, but lower lactose and milk urea content compared with those fed 40% forage diet. Moreover, nitrate supplementation had no significant effect on DMI and milk yield. Furthermore, the interactions showed that nitrate reduces DMI and milk fat independently of forage levels. Our findings showed that nitrate can increase ammonia concentration, pH, nitrite, and acetate while reducing the total volatile fatty acids concentration, propionate, and butyrate in the rumen. With increasing nitrate, methane emission was considerably decreased possibly due to the stimulated growth of Fibrobacteria , Proteobacteria , type II Methanotrophs, and Methanoperedense nitroreducens , especially with high forage level. Overall, nitrate supplementation could potentially increase methane oxidizing microorganisms without adversely affecting cattle performance.
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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 | 0.000 | 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 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".