Short communication: Antimethanogenic effects of 3-nitrooxypropanol depend on supplementation dose, dietary fiber content, and cattle type
Bibliographic record
Abstract
3-Nitrooxypropanol (NOP) is a promising methane (CH 4 ) inhibitor. Recent studies have shown major reductions in CH 4 emissions from beef and dairy cattle when using NOP but with large variation in response. The objective of this study was to quantitatively evaluate the factors that explain heterogeneity in response to NOP using meta-analytical approaches. Data from 11 experiments and 38 treatment means were used. Factors considered were cattle type (dairy or beef), measurement technique (GreenFeed technique, C-Lock Inc., Rapid City, SD; sulfur hexafluoride tracer technique; and respiration chamber technique), dry matter (DM) intake, body weight, NOP dose, roughage proportion, dietary crude protein content, and dietary neutral detergent fiber (NDF) content. The mean difference (MD) in CH 4 production (g/d) and CH 4 yield (g/kg of DM intake) was calculated by subtracting the mean of CH 4 emission for the control group from that of the NOP-supplemented group. Forest plots of standardized MD indicated variable effect sizes of NOP across studies. Compared with beef cattle, dairy cattle had a much larger feed intake (22.3 ± 4.13 vs. 7.3 ± 0.97 kg of DM/d; mean ± standard deviation) and CH 4 production (351 ± 94.1 vs. 124 ± 44.8 g/d). Therefore, in further analyses across dairy and beef cattle studies, MD was expressed as a proportion (%) of observed control mean. The final mixed-effect model for relative MD in CH 4 production included cattle type, NOP dose, and NDF content. When adjusted for NOP dose and NDF content, the CH 4 -mitigating effect of NOP was less in beef cattle (−22.2 ± 3.33%) than in dairy cattle (−39.0 ± 5.40%). An increase of 10 mg/kg of DM in NOP dose from its mean (123 mg/kg of DM) enhanced the NOP effect on CH 4 production decline by 2.56 ± 0.550%. However, a greater dietary NDF content impaired the NOP effect on CH 4 production by 1.64 ± 0.330% per 10 g/kg DM increase in NDF content from its mean (331 g of NDF/kg of DM). The factors included in the final mixed-effect model for CH 4 yield were −17.1 ± 4.23% (beef cattle) and −38.8 ± 5.49% (dairy cattle), −2.48 ± 0.734% per 10 mg/kg DM increase in NOP dose from its mean, and 1.52 ± 0.406% per 10 g/kg DM increase in NDF content from its mean. In conclusion, the present meta-analysis indicates that a greater NOP dose enhances the NOP effect on CH 4 emission, whereas an increased dietary fiber content decreases its effect. 3-Nitrooxypropanol has stronger antimethanogenic effects in dairy cattle than in beef cattle.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".