Evaluation of Methane Production From Dairy Cow Manure and Vegetable Waste
Bibliographic record
Abstract
Dairy cow manure has high buffering capacity hence a substrate for anaerobic digestion, however the process is not optimised in mono-digestion system due to limited substrate. The aim of the study was to assess the effect of co-digesting animal waste and vegetable waste on methane production. Two systems were applied- batch and continuous anaerobic digestion system to determine effect on methane yield. The experiments were conducted with treatments as: manure alone (M), composite of manure with cabbage (MC), manure with potatoes (MP), manure with cabbage and potatoes (MCP), faecal alone (F), faecal with cabbage (FC), faecal with potatoes (FP) and faecal with cabbage and potatoes (FCP). Rectal grab samples were collected prior to incubation and manure was collected from the pens. All treatments were in replicates. Composite of manure or faecal with cabbage and potatoes produced the highest biogas (FCP: 32.1 mL/g DM, MCP: 29.5 mL/g DM) and methane (FCP: 3.13 mL/g DM, MCP: 2.36 mL/g DM) compared to manure alone or faecal alone (F: 27.0 biogas mL/g DM, M: 26.6 biogas mL/g DM) (F: 1.36 methane mL/g DM, M: 1.18 methane mL/g DM). Co-digesting dairy excreta with cabbage as only vegetable substrate affected anaerobic digestion (FC: 24.8 mL/g DM, MC: 24.9 mL/g DM), since it was the lowest in biogas production compared to all treatments. The anaerobic digestion system had an effect in methane production since continuous anaerobic digestion system produced the highest methane compared to batch anaerobic digestion system in all treatments. The results obtained in this study suggest that composite of manure with both cabbage and potatoes results in the highest biogas and methane production.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".