Ammonia volatilization following application of pig slurry increases with slurry interception by grass foliage
Bibliographic record
Abstract
Efficient liquid manure application systems that minimize ammonia volatilization are required for use on perennial forage grasses. Ammonia volatilization was monitored using wind tunnels for 10 d after three pig slurry applications using four boom-mounted applicators: a broadcast splash-plate system, a trailing-shoe system and a drag-hose system with and without previous soil aeration. Average losses of 32, 20 and 15% of the total ammonia-N (TAN) applied to plots were observed for the splash-plate, the trailing-shoe and the drag-hose systems, respectively. The grass canopy intercepted, on average, 14% of pig slurry TAN using the splash-plate system compared with 4% for the trailing-shoe and 5% for the drag-hose systems. Reductions in canopy interception explained 58% of differences in volatilization rates among the application systems. On two of three application dates, NH3 volatilization was lower using the drag-hose than the trailing-shoe system. This was attributed to the sealing of the soil surface by the passage of the trailing-shoe, which decreased slurry infiltration rate. Ammonia volatilization was not reduced by soil aeration prior to slurry application using a drag-hose system likely because aeration did not increase slurry infiltration into the soil. Producers could be advised to reduce volatilization losses by using a simple drag-hose system to avoid application of slurry to leaf canopies. Key words: Ammonia volatilization, liquid hog manure, pig slurry, application method, ammonium
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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 | 0.000 | 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 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".