Management strategies to simultaneously reduce ammonia, nitrous oxide and odour emissions from surface-applied swine manure
Bibliographic record
Abstract
Surface-applied swine manure has the potential to generate ammonia (NH3), nitrous oxide (N2O) and odour. Field research was conducted in Prince Edward Island to measure the simultaneous emissions of NH3, N2O and odour following the surface-application of swine manure. Manure was applied to a grain stubble field consisting of a sandy loam soil low in pH (5.6–5.9). The effect of manure type (liquid and solid), application rate [conventional/typical rate (1 ×): 30 000-36 000 L ha-1, double (2 ×): 60 000-72 000 L ha-1 and five times (5 ×): 180 000 L ha-1] and rainfall (8–200 mm) before and after liquid manure application were examined. There was no relationship between odour emissions and manure type, application rate and rainfall before and after spreading, due to high variability. Liquid manure (dry matter (DM = 45 g kg-1) reduced NH3 emissions by 32% compared with solid (DM = 350 g kg-1). Increasing application rates enhanced NH3 emissions; increasing the rate by 2 × and 5 × the typical rate increased losses by 62 and 78%, respectively. Applying manure prior to rainfall reduced NH3 emissions by 37%, compared with application after a rainfall. Ammonia and odour emissions were similarly correlated to atmospheric conditions with increased emissions at higher air and soil temperature, net radiation, vapour pressure deficit and windspeed. Nitrous oxide emissions were low in magnitude and showed no correlation to climatic conditions, suggesting that management strategies to reduce both odour and NH3 did not enhance N2O emissions when applied to a moderately acidic soil with low levels of soil nitrate (< 5 mg N kg-1). Our results indicate that for conditions similar to those in this study, there is no trade-off between NH3 and N2O production and more attention should be placed on controlling and reducing odour and NH3 emissions. Key words: Ammonia, nitrous oxide, odour, swine manure, management strategies
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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 | 0.001 | 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".