A meta-analysis of 3-nitrooxypropanol effects on methane production and yield in beef cattle
Bibliographic record
Abstract
Abstract Beef cattle are a major source of enteric methane (CH 4 ) emissions, a potent greenhouse gas (GHG). The feed additive 3-nitrooxypropanol (3-NOP) has been shown to reduce CH 4 emissions by inhibiting methyl-coenzyme M reductase, an enzyme critical to methanogenesis in archaea. This study aimed to quantify the effects of 3-NOP on CH 4 production (g/d) and yield (g/kg DM intake; DMI) in beef cattle and to evaluate how diet composition influences the mitigation response. A systematic literature review identified 17 peer-reviewed in vivo studies, yielding 45 treatment means. Treatment effects were expressed as absolute and relative mean differences versus control groups. Predictor variables included 3-NOP dose, 3-NOP dose 2 , DMI, dietary concentration of NDF, CP, starch, fat, and organic matter (OM), roughage proportion, BW, and dietary inclusion of monensin (yes/no). Four types of models were developed, all including the intercept and 3-NOP dose as fixed predictors, differing as follows: (model 1) optional inclusion of 3-NOP dose 2 when P < 0.10; (model 2) model 1 plus pre-inclusion of NDF concentration; (model 3) pre-inclusion of NDF concentration plus additional predictors (pairwise r ≤ 0.5) that significantly improved model accuracy ( P < 0.10); and (model 4) additional predictors selected under the same criteria as model 3, without pre-inclusion of NDF concentration. For models 3 and 4, a maximum of 5 predictors were considered and evaluated using leave-one-out cross-validation. Across studies, 3-NOP doses ranged from 32 to 338 mg/kg of DM. On average, 3-NOP reduced CH 4 production by 49.9 ± 28.61 g/d (36.2 ± 24.42%) and CH 4 yield by 5.3 ± 3.61 g/kg DMI (33.2 ± 25.54%). The best models were selected based on biological interpretability, statistical significance, and predictive accuracy (as measured by RMSE) and included 3-NOP dose, dietary NDF concentration, DMI, and BW as significant predictors (the latter two only for absolute CH 4 production). Mitigation efficacy increased with higher DMI and declined with increasing NDF concentration and BW. Absolute reductions of 53.1 g/d and 5.88 g/kg of DMI, and relative reductions of 37.6% in CH 4 production and 35.0% in CH 4 yield were predicted when moderators were at their mean value (3-NOP dose = 134.4 mg/kg of DM; NDF concentration = 32.8% of DM; DMI of 8.6 kg/d). These results support the effectiveness of 3-NOP in mitigating enteric CH 4 emission in beef cattle and provide quantitative models to be used in assessment tools and GHG inventory methodology. Implications The feed additive 3-nitrooxypropanol effectively reduces enteric methane emissions in beef cattle. This meta-analysis found average reductions of 36.2% in methane production and 33.2% in methane yield. Efficacy depended on diet composition; declining with increasing NDF concentration for both methane production (g/d) and yield (g/kg of DM intake; DMI). Greater DMI increased absolute methane production reduction but did not influence absolute methane yield reduction or relative reduction of both methane production and yield. These results support the targeted use of 3-nitrooxypropanol as a mitigation strategy and provide empirical models to inform greenhouse gas inventories and carbon accounting.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.047 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".