The combined effects of supplementing monensin and 3-nitrooxypropanol on methane emissions, growth rate, and feed conversion efficiency in beef cattle fed high-forage and high-grain diets1
Bibliographic record
Abstract
The study objective was to evaluate the combined effects of supplementing monensin (MON) and the methane (CH4) inhibitor 3-nitrooxypropanol (NOP) on enteric CH4 emissions, growth rate, and feed conversion efficiency of backgrounding and finishing beef cattle. Two hundred and forty crossbred steers were used in a 238-d feeding study and fed a backgrounding diet for the first 105 d (backgrounding phase), transition diets for 28 d, followed by a finishing diet for 105 d (finishing phase). Treatments were as follows: 1) control (no additive); 2) MON (monensin supplemented at 33 mg/kg DM; 3) NOP (3-nitrooxypropanol supplemented at 200 mg/kg DM for backgrounding or 125 mg/kg DM for finishing phase); and 4) MONOP (33 mg/kg DM MON supplemented with either 200 mg/kg DM or 125 mg/kg DM NOP). The experiment was a randomized complete block (weight: heavy and light) design with 2 (NOP) × 2 (MON) factorial arrangement of treatments using 24 pens (8 cattle/pen; 6 pens/treatment) at the main feedlot and 8 pens (6 cattle/pen; 2 pens/treatment) at the controlled environment building (CEB) feedlot. Five animals per treatment were moved to chambers for CH4 measurements during both phases. Data were analyzed using a Mixed procedure of SAS with pen as experimental unit (except CH4). Location (Main vs. CEB) had no significant effect and was thus omitted from the final model. Overall, there were few interactions between MON and NOP indicating that the effects of the 2 compounds were independent. When cattle were fed the backgrounding diet, pen DMI was decreased by 7%, whereas gain-to-feed ratio (G:F) was improved by 5% with NOP supplementation (P < 0.01). Similarly, MON improved G:F ratio by 4% (P < 0.01), but without affecting DMI. During the finishing phase, DMI tended (P = 0.06) to decrease by 5% with both MON (5%) and NOP (5%), whereas ADG tended (P = 0.08) to decrease by 3% with MON. Gain-to-feed ratio for finishing cattle was improved with NOP by 3% (P < 0.01); however, no effects were observed with MON. 3-Nitrooxypropanol decreased CH4 yield (g/kg DMI) by 42% and 37% with backgrounding and finishing diets (P ≤ 0.01), respectively, whereas MON did not lower CH4 yield. Overall, these results demonstrate efficacy of NOP in reducing enteric CH4 emissions and subsequently improving feed conversion efficiency in cattle fed high-forage and high-grain diets. Furthermore, effects of NOP did not depend on whether MON was included in the diet.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".