Comparison of three simple field methods for ammonia volatilization from manure
Bibliographic record
Abstract
Land application of swine manure can result in a loss of nitrogen (N) through ammonia (NH3) volatilization. Accurate NH3 loss assessments are essential to support the development of regional and global emission factors and identify best management strategies for reducing losses. The objectives of this study were to simultaneously assess the reliability and applicability of three simple NH3 flux measurement techniques and their effectiveness and usefulness for the measurement of emissions from surface-applied swine manure. Ammonia losses from two chamber systems were compared: (i) static and (ii) wind tunnel chamber, as well as the (iii) micrometeorological theoretical profile shape (TPS) method. A series of field tests was performed at the Agriculture and Agri-Food Canada (AAFC) Research Farm in Harrington, Prince Edward Island (PE) on a sandy loam, stubble field that was surface applied with 100 kg NH4-N ha-1 of liquid swine manure. After application the three NH3 emission techniques were established and measurements were performed over a 5-d period. Ammonia losses from the wind tunnels were comparable with those from the TPS method. The static chambers were found to underestimate NH3 emissions (by ~95–99%), compared with both the wind tunnel and TPS methods. It is therefore recommended that either the TPS or wind tunnel approaches be used to more accurately quantify NH3 losses for field systems. Key words: Ammonia volatilization, flux measurements, swine manure, micrometeorology, static chambers, wind tunnel
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".