Effects of winter storage conditions and subsequent agitation on gaseous emissions from liquid dairy manure
Bibliographic record
Abstract
An understanding of emissions from liquid manure facilities during winter, spring thaw and agitation is needed to improve national emissions inventories in Canada. In this study, liquid dairy manure was stored in six pilot-scale tanks (1.8 m deep × 6.6 m2 surface area) covered by steady-state chambers that enabled greenhouse gas (GHG) and ammonia (NH3) flux measurement. After 158 d of undisturbed storage, three tanks were agitated for 5 d (8 h per day) consecutively. During storage, methane (CH4) flux was correlated with manure temperature at 30 cm depth (P < 0.05). Nitrous oxide (N2O) fluxes occurred only during spring thaw - at rates comparable with agricultural soil during spring thaw. On a carbon dioxide (CO2) equivalent basis, however, cumulative N2O fluxes were negligible compared with CH4 fluxes. Flux of NH3 was correlated positively with manure temperature near the surface and negatively with the presence of ice or a surface crust (P < 0.01). Agitation did not affect N2O and NH3 fluxes, whereas CO2 and CH4 fluxes increased significantly (P < 0.01) as dissolved gas and bubbles were released. Trapped CH4 released during agitation was estimated to be 6.3 g CH4 m-3 manure, and was depleted in 2 d. Considering the entire storage period, agitated tanks (158 d + 5 d agitation) had 6% higher GHG fluxes due to higher CH4 losses than undisturbed tanks (163 d). This CH4 release is small in context of annual fluxes, but may partially explain discrepancies between predicted and measured winter fluxes.Key words: Manure storage, agitation, greenhouse gas emission, ammonia emission, dissolved gas
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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 | 0.001 | 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".