Methane emissions of stored manure from dairy cows fed conventional or brown midrib corn silage
Bibliographic record
Abstract
The objective of this study was to examine the effects of feeding conventional corn silage (CCS) or brown midrib corn silage (BMCS) to dairy cows on CH 4 emissions from stored manure. Eight lactating cows were fed (ad libitum) a total mixed ration (forage:concentrate ratio 65:35; dry matter basis) containing 59% (dry matter basis) of either CCS or BMCS. Feces and urine were collected from each cow and mixed with residual sludge obtained from a manure storage structure. Manure was incubated for 17 wk at 20°C under anaerobic conditions (O 2 -free N 2 ) in 500-mL glass bottles. Methane emissions and changes in chemical composition of the manure were monitored during the incubation period. The total amount of feces and urine excreted was higher for cows fed BMCS than for cows fed CCS [8.6 vs. 6.5 kg/d of volatile solids (VS)]. Manure from cows fed BMCS emitted more CH 4 than manure from cows fed CCS (173 vs. 146 L/kg of VS) throughout the incubation period. Similarly, VS and neutral detergent fiber losses throughout incubation were higher for manure from cows fed BMCS versus cows fed CCS (37.6 vs. 30.6% and 46.2 vs. 31.2%, respectively). Manure NH 3 concentration (79% of total manure N) was not affected by corn silage cultivar. Results of this study show that using a more digestible corn silage cultivar (BMCS vs. CCS) may increase the contribution of manure to CH 4 emissions, and may offset gain achieved by reducing enteric CH 4 emissions.
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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".