Effects of surface manure application timing on ammonia volatilization
Bibliographic record
Abstract
The purpose of this study was to evaluate the influence of timing of application on ammonia (NH3) volatilization from surface-applied dairy manure. Field trials were conducted over a 2-yr period (1994 and 1995) at the Agriculture and Agri-Food Canada Research Farm in Nappan, Nova Scotia. A total of eight trials were performed in an attempt to characterize what effect the timing of application (morning vs. late afternoon/early evening) has on NH3 volatilization losses. Ammonia losses were also related to several meteorological parameters. The total percentage of NH3 volatilized in the 10 h following field spreading ranged from as low as < 2% to approximately 66%. Late-day spreading typically resulted in at least a 30% reduction in overall NH3 flux densities, primarily due to substantial reductions in NH3 losses occurring within the first 10 h after manure application. The positive benefits of late-day manure spreading were more pronounced during warm, dry weather conditions. Significant (P < 0.05) positive linear correlations were observed between NH3 volatilization in the 10 h following spreading and solar radiation (r2 = 0.56), wind speed (r2 = 0.53), temperature (r2 = 0.34) and vapour pressure deficit (r2 = 0.53). Results from this study have indicated that late day manure spreading practices can substantially reduce NH3 volatilization. Key words: Slurry, ammonia volatilization, dairy, manure application, timing
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".