Targeting Bacteria and Methanogens To Understand the Role of Residual Slurry as an Inoculant in Stored Liquid Dairy Manure
Bibliographic record
Abstract
ABSTRACT Microbial communities in residual slurry left after removal of stored liquid dairy manure have been presumed to increase methane emission during new storage, but these microbes have not been studied. While actual manure storage tanks are filled gradually, pilot- and farm-scale studies on methane emissions from such systems often use a batch approach. In this study, six pilot-scale outdoor storage tanks with (10% and 20%) and without residual slurry were filled (gradually or in batch) with fresh dairy manure, and methane and methanogenic and bacterial communities were studied during 120 days of storage. Regardless of filling type, increased residual slurry levels resulted in higher abundance of methanogens and bacteria after 65 days of storage. However, stronger correlation between methanogen abundance and methane flux was observed in gradually filled tanks. Despite some variations in the diversity of methanogens or bacteria with the presence of residual slurry, core phylotypes were not impacted. In all samples, the phylum Firmicutes predominated (∼57 to 70%) bacteria: >90% were members of Clostridia . Methanocorpusculum dominated (∼57 to 88%) archaeal phylotypes, while Methanosarcina gradually increased with storage time. During peak flux of methane, Methanosarcina was the major player in methane production. The results suggest that increased levels of residual slurry have little impact on the dominant methanogenic or bacterial phylotypes, but large population sizes of these organisms may result in increased methane flux during the initial phases of storage. IMPORTANCE Methane is the major greenhouse gas emitted from stored liquid dairy manure. Residual slurry left after removal of stored manure from tanks has been implicated in increasing methane emissions in new storages, and well-adapted microbial communities in it are the drivers of the increase. Linking methane flux to the abundance, diversity, and activity of microbial communities in stored slurries with different levels of residual slurry can help to improve the mitigation strategy. Mesoscale and lab-scale studies conducted so far on methane flux from manure storage systems used batch-filled tanks, while the actual condition in many farms involves gradual filling. Hence, this study provides important information toward determining levels of residual slurry that result in significant reduction of well-adapted microbial communities prior to storage, thereby reducing methane emissions from manure storage tanks filled under farm conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".