Dairy Manure Total Solid Levels Impact CH<sub>4</sub> Flux and Abundance of Methanogenic Archaeal Communities
Bibliographic record
Abstract
Stored liquid dairy manures are methane (CH4) emission hotspots because of the large amount of slurry volatile solids (VS) converted into CH4 by methanogens under anaerobic conditions. Our research has indicated that a reduction of total solids (TS) of slurries before storage can reduce CH4 emissions. In the current study, methanogen abundance was characterized in tanks with different CH4 emissions. Using mesoscale slurry storage facilities equipped for continuous gaseous emission monitoring, we stored dairy slurries having TS from 9.5 to 0.3% for up to 6 mo. Samples were taken after Day 30 and Day 120 of the storage (20 May–16 Nov. 2010) from the upper and bottom layers of the slurries. Methanogenic communities were studied by targeting the gene encoding the α subunit methyl‐coenzyme M reductase (mcrA), which catalyzes the final step of methanogenesis. Interestingly, mean abundances of methanogens increased by ∼8 and 23% at the top and bottom sections, respectively, as slurry TS decreased from 9.5 to 0.3%. Cumulative CH4 emissions, however, decreased by ∼70% as slurry TS decreased from 9.5 to 0.3%. Nevertheless, compared with Day 30 of storage, mean abundances of methanogens were relatively higher at Day 120 (up to 19%), consistent with an increase in the cumulative CH4 emissions. Polymerase chain reaction denaturing gel electrophoresis analysis indicated a low methanogen diversity, with most bands sequenced closely related to the genus Methanocorpusculum (>95% amino acid sequence similarity), the hydrogenotrophic methanogens. Results suggest that available carbon substrate and not methanogen abundance may be limiting cumulative CH4 emissions at reduced TS levels of dairy slurries. Core Ideas Reduction of total solids levels of stored dairy slurry can reduce cumulative CH4 flux. Abundance and activity of methanogens increase as dairy slurry total solids decrease. Volatile solids‐to‐CH4 conversion rates appear to be higher as dairy slurry total solids decrease. Substrate availability, not methanogen population, limits cumulative CH4 flux.
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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 | 0.001 | 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".