Simulating ammonia loss from surface applied manure
Bibliographic record
Abstract
The land spreading of manure can result in significant nitrogen (N) losses to the atmosphere through ammonia (NH3) volatilization. It is estimated that agricultural activities, including manure spreading contribute approximately 50% of the total global NH3 emissions. Computer simulation models have been developed in an attempt to predict NH3 losses resulting from manure spreading. Few models have been validated with success and no validation has been completed for conditions found in eastern Canada. The mechanistic model Volt’Air was implemented and tested to estimate NH3 emissions using field-based wind tunnel data from three field locations in eastern Canada. The model was validated using data from 27 wind tunnel trials, which measured NH3 loss over 5-19 d. Sensitivity analysis revealed that Volt’Air was most sensitive to pH. Good overall agreement was observed between measured results and model simulations over 5-19 d (R2 = 0.91, RMSE = 2.9 kg ha-1). Short-term flux simulations (kg ha-1 h-1) within the first 24 h after spreading were underestimated by 29.5% and fluxes on subsequent days were slightly underestimated by 3.7%. Error with Volt’Air appears to be largely confined to the first day following manure application. Volt’Air offers potential for predicting NH3 losses (i.e., 5-19 d) from manure application within eastern Canada. Improving the simulation of short-term pH changes, however, may result in enhanced overall model performance.Key words: Ammonia volatilization, flux, mechanistic model, swine manure, wind tunnels
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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) | 0.001 | 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 | 0.001 | 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".