Greenhouse Gas Mitigation through Dairy Manure Acidification
Bibliographic record
Abstract
Liquid dairy manure storages are sources of methane (CH 4 ), nitrous oxide (N 2 O), and ammonia (NH 3 ) emissions. Both CH 4 and N 2 O are greenhouse gases (GHGs), whereas NH 3 is an indirect source of N 2 O emissions. Manure acidification is a strategy used to reduce NH 3 emissions from swine manure; however, limited research has expanded this strategy to reducing CH 4 and N 2 O emissions by acidifying dairy manure. This study compared control dairy manure (pH 7.4) with two treatments of acidified manure using 70% sulfuric acid (H 2 SO 4 ). These included a medium pH treatment (pH 6.5, 1.4 mL acid L −1 manure) and a low pH treatment (pH 6, 2.4 mL acid L −1 manure). Emissions were measured using replicated mesoscale manure tanks (6.6 m 2 ) enclosed by large steady state chambers. Both CH 4 and N 2 O were continuously measured (June–December 2017) using tunable diode laser trace gas analyzers. Ammonia emissions were measured three times weekly for 24 h using acid traps. On a CO 2 equivalent basis, the medium pH treatment reduced total GHG emissions by 85%, whereas the low pH treatment reduced emissions by 88%, relative to untreated (control) manure. Total CH 4 emissions were reduced by 87 and 89% from medium and low pH tanks, respectively. Ammonia emissions were reduced by 41 and 53% from medium and low pH tanks, respectively. Additional research is necessary to make acidification an accessible option for farmers by optimizing acid dosage. More research is need to describe the manure buffering capacity and emission reductions and ultimately find the best approaches for treating farm‐scale liquid dairy manure tanks. Core Ideas Acidification reduced total CO 2 –eq GHGs from liquid dairy manure by 85 to 88%. Total CH 4 emissions were reduced by 87 to 89% from acidified manure. NH 3 emissions were reduced by 41 to 53% from acidified manure. A range of yearly H 2 SO 4 cost was estimated to be Can$6.55 to $19.6 cow −1 .
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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) | 0.001 | 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 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".