Dairy manure acidification reduces CH4 emissions over short and long-term
Bibliographic record
Abstract
Acidification with sulphuric acid and cleaning residual manure in tanks are promising practices for reducing methane (CH4), which is a potent greenhouse gas. To date, no data are available on CH4 reductions from acidifying only residual manure (rather than all manure). Moreover, long-term effects of manure acidification (i.e. inoculating ability of previously acidified residual manure in the subsequent storages) are not known. To address these gaps, fresh manure (FM; 150 mL) combined with treated or untreated inoculum (30 mL) were anaerobically incubated at 17°C, 20°C, and 23°C for 116 d. Acidified treatments, regardless of location of acid addition, reduced CH4 production by 81% at 17°C, 78% at 20°C, and 19% at 23°C compared to the control (untreated FM and untreated inoculum). To test long-term acidification effects, FM was inoculated with manure that had been acidified 6-months prior. This created comparable CH4 production to FM with no inoculum and reduced CH4 production by 99% at 17°C and 20°C, and 49% at 23°C compared to the control. Results indicate that residual slurries of acidified manure become poor inoculants in subsequent storage, hence manure acidification has a long-term treatment effect in reducing CH4 production. This could reduce how often acidification is needed in dairy manure tanks and also increasing its cost-effectiveness for farmers.
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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) | 0.001 | 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".