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 (CH 4 ) 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 CH 4 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/m 3 ). 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 CH 4 reduction occurred between 20 and 30 days, then declined. The lowest dose of both additives (100 g/m 3 ) did not significantly reduce CH 4 over the duration of the study. Efficacy increased non-linearly with an increasing dose up to 5,000 g/m 3 . After 30 days, CH 4 reduction decreased by 32, 73, 74% for SOP ® Lagoon rates 1,000, 5,000, 10,000 g/m 3 , 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 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".