Relative effects of anaerobically-digested and conventional liquid swine manure, and N fertilizer on crop yield and greenhouse gas emissions
Bibliographic record
Abstract
Anaerobic digestion is a promising technology that could provide an option for managing animal waste with reduced greenhouse gas emissions. A three-year (2006-2008) field experiment was conducted at Star City, Saskatchewan, Canada, to compare the effects of land-applied anaerobically digested swine manure (ADSM), conventionally treated swine manure (CTSM) and N fertilizer on grain yield of barley, applied N use efficiency (ANUE, kg·grain·kg-1 of applied N·ha-1), ammonia (NH3) volatilization and nitrous oxide (N2O) emissions. Treatments included spring and autumn applications of CTSM and ADSM at a 1x rate (10,000 and 7150 L·ha-1, respectively) applied every year, a 3x rate (30,000 and 21,450 L·ha-1, respectively) applied once at the beginning of the experiment, plus a treatment receiving commercial fertilizer (UAN at 60 kg·N·ha-1·yr-1) and a zero-N control. There was a significant grain yield response of barley to applied N in all three years. The ANUE of ADSM or CTSM applied once at the 3x rate were lower than annual applications at the 1x rate (grain yield by 595 kg·ha-1 and NFUE by 6 kg·grain·kg-1 of applied N·ha-1). On average, agronomic performance of ADSM was similar to CTSM. The APNU of N fertilizer was greater than the 3x rate but lower than the 1x rate of ADSM or CTSM. Ammonia loss from ADSM was similar to CTSM, except for much higher loss of NH3-N from CTSM at the 3x rate applied in the autumn (8100 g·N·ha-1) compared to the other treatments (1100 - 2600 g·N·ha-1). The percentage of applied N lost as N2O gas was generally higher for treatments receiving CTSM (4.0%) compared to ADSM (1.4%). In conclusion, the findings suggest that ADSM is equal or slightly better than CTSM in terms of agronomic performance, but has lower environmental impact.
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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 | 0.001 |
| 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".