Optimal dose of 3-nitrooxypropanol for decreasing enteric methane emissions from beef cattle fed high-forage and high-grain diets
Bibliographic record
Abstract
The objective of the present study was to determine the dose response of the methane (CH4) inhibitor 3-nitrooxypropanol (NOP) on enteric CH4 production and dry matter intake (DMI) for beef cattle fed a high-forage or high-grain diet. Fifteen crossbred yearling steers were used in two consecutive studies (high-forage backgrounding, high-grain finishing), each designed as an incomplete block with two 28-day periods with a 7-day washout in between and treatments corresponding to six doses of NOP (0 (Control), 50, 75, 100, 150, 200 mg/kg DM). The NOP was provided in the ration daily with the dose increased gradually over the first 10 days of each period. No treatment effects were observed on overall DMI or DMI of cattle when they were in the chambers either for the high-forage (P = 0.54) or high-grain (P = 0.26) diet. With the high-forage diet, NOP supplementation lowered total CH4 emissions (g/day) (P = 0.05), with the response at 200 mg NOP/kg DM different from Control (P < 0.05). Similarly, CH4 emissions corrected for DMI (g/kg DMI) and as a percentage of gross energy intake were linearly reduced in the high-forage diet with supplemental NOP (P < 0.01) and responses observed at 100, 150 and 200 mg NOP/kg DM differed from Control (P < 0.05). For the high-grain diet, total CH4 emissions decreased with incremental increases in the concentration of NOP supplemented (P = 0.04) and responses observed at 150 and 200 mg/kg DM differed from Control. Similarly, linear responses were observed with CH4 emissions corrected for DMI (P = 0.04) and gross energy intake (P = 0.02), with 100–200 mg NOP/kg DM differing from Control. Overall, results from the present study demonstrated that for beef cattle fed high-forage and high-grain diets, supplementation of 100–200 mg NOP/kg DM lowered enteric CH4 emissions without inducing any negative effects on DMI.
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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) | 0.001 | 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".