On-farm methane measurements during milking correlate with total methane production by individual dairy cows
Bibliographic record
Abstract
The objective of this study was to investigate whether measurement of methane emissions by individual dairy cows during milking could provide a useful technique for monitoring on-farm methane emissions. To quantify methane emissions from individual cows on farm, we developed a novel technique based on sampling air released by eructation during milking. Eructation frequency and methane released per eructation were used to estimate methane emission rate. For 82 cows, methane emission rate during milking increased with daily milk yield (r = 0.71), but varied between individuals with the same milk yield and fed the same diet. For 12 cows, methane emission rate recorded during milking on farm showed a linear relationship (R² = 0.79) with daily methane output by the same cows when housed subsequently in respiration chambers. For 42 cows, the methane emission rate during milking was greater on a feeding regimen designed to produce high methane emissions, and the increase compared with a control regimen was similar to that observed for cows in respiration chambers. It was concluded that, with further validation, on-farm monitoring of methane emission rate during milking could provide a low-cost reliable method to estimate daily methane output by individual dairy cows, which could be used to study variation in methane, to identify cows with low emissions, and to test outcomes of mitigation strategies.
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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) | 0.001 | 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".