The effects of feeding 3-nitrooxypropanol on methane emissions and productivity of Holstein cows in mid lactation
Bibliographic record
Abstract
The objective of the current study was to determine the effects of adding 3-nitrooxypropanol to the diet of lactating Holstein cows on methane emissions, rumen fermentation, ruminal microbial profile, and milk production. Twelve ruminally cannulated Holstein cows in midlactation were used in a crossover design study with 28-d periods. Cows were fed a diet containing 38% forage on a dry matter basis with either 2,500 mg/d of 3-nitrooxypropanol (fed as 25 g of 10% 3-nitrooxypropanol on silicon dioxide) or 25 g/d of silicon dioxide (control). After a 21-d diet adaptation period, dry matter intake (DMI) and milk yield were recorded daily. Rumen fluid and digesta were collected on d 22 and 28 for volatile fatty acid analysis and microbial profiling. Enteric methane emissions were measured on d 23 to 27 using the sulfur hexafluoride tracer gas technique. Feeding 3-nitrooxypropanol did not affect DMI; however, methane production was reduced from 17.8 to 7.18 g/kg of DMI. No change in milk or milk component yields was observed, but cows fed 3-nitrooxypropanol gained more body weight than control cows (1.06 vs. 0.39 kg/d). Concentrations of total volatile fatty acids in ruminal fluid were not affected by treatment, but a reduction in acetate proportion and a tendency for an increase in propionate proportion was noted. As such, a reduction in the acetate-to-propionate ratio was observed (2.02 vs. 2.36). Protozoa counts were not affected by treatment; however, a reduction in methanogen copy count number was observed when 3-nitrooxypropanol was fed (0.95 vs. 2.69 × 10(8)/g of rumen digesta). The data showed that feeding 3-nitrooxypropanol to lactating dairy cows at 2,500 mg/d can reduce methane emissions without compromising DMI or milk production.
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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 | 0.001 |
| 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 | 0.001 |
| 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".