Methane reductions with gypsum and SOP® lagoon additives in liquid manure
Bibliographic record
Abstract
The agriculture industry is an important source of greenhouse gas emissions globally with livestock production being a main contributor. Therefore, there is a need to reduce methane (CH4) emissions from livestock production, including liquid manure storages. Using an additive that decreases methanogenesis is one approach currently being considered. This study tests two additives: SOP® Lagoon (a commercial additive) and a commonly used, local fertilizer, gypsum. The objective was to determine the capability of the two products to reduce CH4 emissions. Tests were done at 24°C in the laboratory with multiple rates of the additives (100, 1,000, 5,000 and 10,000 g/m3). Methane produced by untreated dairy manure (control) was compared to manure with addition of gypsum or SOP® Lagoon over 162 days. Results showed that peak CH4 reduction occurred between 20 and 30 days, then declined. The lowest dose of both additives (100 g/m3) did not significantly reduce CH4 over the duration of the study. Efficacy increased non-linearly with an increasing dose up to 5,000 g/m3. After 30 days, CH4 reduction decreased by 32, 73, 74% for SOP® Lagoon rates 1,000, 5,000, 10,000 g/m3, and 20, 60, and 63% for gypsum. Both SOP® Lagoon and local gypsum showed similar reduction in methane emissions at similar application rates. This is an indication that farmers can confidently use locally sourced gypsum, a low-cost alternative to the commercial additive, without affecting the overall mitigation potential.
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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".