Methane production, digestion, ruminal fermentation, nitrogen balance, and milk production of cows fed corn silage- or barley silage-based diets
Bibliographic record
Abstract
This study evaluated the effects of replacing barley silage (BS) with corn silage (CS) in dairy cow diets on enteric CH4 emissions, ruminal fermentation characteristics, digestion, milk production, and N balance. Nine ruminally cannulated lactating cows were used in a replicated 3 × 3 Latin square design (32-d period) and fed (ad libitum) a total mixed ration (TMR; forage:concentrate ratio 60:40; dry matter basis) with the forage portion consisting of either barley silage (0% CS; 0% CS and 54.4% BS in the TMR), a 50:50 mixture of both silages (27% CS; 27.2% CS and 27.2% BS in the TMR), or corn silage (54% CS; 0% BS and 54.4% CS in the TMR). Increasing the CS proportion (i.e., at the expense of BS) also involved increasing the proportion of corn grain (at the expense of barley grain). Intake and digestibility of dry matter and milk production increased linearly as the proportion of CS increased in the diet. Increasing dietary CS proportion decreased linearly the acetate molar proportion and increased linearly that of propionate. Daily CH4 emissions tended to respond quadratically to increasing proportions of CS in the diet (487, 540, and 523 g/d for 0, 27, and 54% CS, respectively). Methane production adjusted for dry matter or gross energy intake declined as the amount of CS increased in the diet; this effect was more pronounced when cows were fed the 54% CS diet than the 27% CS diet. Increasing the CS proportion in the diet improved N utilization, as reflected by decreases in ruminal ammonia concentration and urinary N excretion and higher use of dietary N for milk protein secretion. Total replacement of BS with CS in dairy cow diets offers a strategy to decrease CH4 energy losses and control N losses without negatively affecting milk performance.
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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".