Long-Term Effects of Feeding Monensin on Methane Production in Lactating Dairy Cows
Bibliographic record
Abstract
The objective of this study was to determine the long-term effects of feeding monensin on methane (CH4) production in lactating dairy cows. Twenty-four lactating Holstein dairy cows (1.46 +/- 0.17 parity; 620 +/- 5.9 kg of live weight; 92.5 +/- 2.62 d in milk) housed in a tie-stall facility were used in the study. The study was conducted as paired comparisons in a completely randomized design with repeated measurements in a color-coded, double-blind experiment. The cows were paired by parity and days in milk and allocated to 1 of 2 treatments: 1) the regular milking cow total mixed ration (TMR) with a forage-to-concentrate ratio of 60:40 (control TMR; placebo premix) vs. a medicated TMR (monensin TMR; regular TMR + 24 mg of Rumensin Premix/kg of dry matter) fed ad libitum. The animals were fed and milked twice daily (feeding at 0830 and 1300 h; milking at 0500 and 1500 h) and CH4 production was measured prior to introducing the treatments and monthly thereafter for 6 mo using an open-circuit indirect calorimetry system. Monensin reduced CH4 production by 7% (expressed as grams per day) and by 9% (expressed as grams per kilogram of body weight), which were sustained for 6 mo (mean, 458.7 vs. 428.7 +/- 7.75 g/d and 0.738 vs. 0.675 +/- 0.0141, control vs. monensin, respectively). Monensin reduced milk fat percentage by 9% (3.90 vs. 3.53 +/- 0.098%, control vs. monensin, respectively) and reduced milk protein by 4% (3.37 vs. 3.23 +/- 0.031%, control vs. monensin, respectively). Monensin did not affect the dry matter intake or milk yield of the cows. These results suggest that medicating a 60:40 forage-to-concentrate TMR with 24 mg of Rumensin Premix/kg of dry matter is a viable strategy for reducing CH4 production in lactating Holstein dairy cows.
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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) | 0.000 | 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) | 0.000 | 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 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".