Methane production, ruminal fermentation characteristics, nutrient digestibility, nitrogen excretion, and milk production of dairy cows fed conventional or brown midrib corn silage
Bibliographic record
Abstract
The objective of this study was to examine the effect of replacing conventional corn silage (CCS) with brown midrib corn silage (BMCS) in dairy cow diets on enteric CH 4 emission, nutrient intake, digestibility, ruminal fermentation characteristics, milk production, and N excretion. Sixteen rumen-cannulated lactating cows used in a crossover design (35-d periods) were fed (ad libitum) a total mixed ration (forage:concentrate ratio=65:35, dry matter basis) based (59% dry matter) on either CCS or BMCS. Dry matter intake and milk yield increased when cows were fed BMCS instead of CCS. Of the milk components, only milk fat content slightly decreased when cows were fed the BMCS-based diet compared with when fed the CCS-based diet (3.81 vs. 3.92%). Compared with CCS, feeding BMCS to cows increased yields of milk protein and milk fat. Ruminal pH, protozoa numbers, total VFA concentration, and molar proportions of acetate and propionate were similar between cows fed BMCS and those fed CCS. Daily enteric CH 4 emission (g/d) was unaffected by dietary treatments, but CH 4 production expressed as a proportion of gross energy intake or on milk yield basis was lower for cows fed the BMCS-based diet than for cows fed the CCS-based diet. A decline in manure N excretion and a shift in N excretion from urine to feces were observed when BMCS replaced CCS in the diet, suggesting reduced potential of manure N volatilization. Results from this study show that improving fiber quality of corn silage in dairy cow diets through using brown midrib trait cultivar can reduce enteric CH 4 emissions as well as potential emissions of NH 3 and N 2 O from manure. However, CH 4 emissions during manure storage may increase due to excretion of degradable OM when BMCS diet is fed, which merits further investigation.
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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 | 0.001 |
| 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".