Interactive effects of crop types and nitrogen sources on N2O emissions in a cool humid climate
Bibliographic record
Abstract
Abstract Aims Perennial forages in rotation with annual crops can improve agricultural resilience by increasing soil organic carbon. However, how nitrogen (N) sources interact with rotation diversity to influence soil nitrous oxide (N2O) emissions is not well understood. Methods During three snow-free seasons, N2O emissions, crop yields, and ancillary variables were measured at three experimental sites with contrasting soil textures (silty clay and sandy loam) in eastern Canada. Using a split-plot design, we compared a corn (Zea mays L.)-soybean (Glycine max [L.] Merr.)-corn rotation and a mixed perennial grass sward receiving N via: i) mineral fertilizer (MIN), ii) liquid dairy manure (LDM), and iii) inclusion of alfalfa (Medicago sativa L.) to the perennial forages with no additional N (LEG). Results When summed across sites over all three years, cumulative N2O emissions were greater for LDM than MIN in annual crops (8.75 ± 1.63 and 5.15 ± 0.96 kg N2O-N ha–1, respectively), but not in perennial grasses (2.95 ± 0.55 and 3.76 ± 0.70 kg N2O-N ha–1, respectively). When comparing N sources within each crop type over the three years, MIN generated greater yields than LDM in annual and perennial crops, but lower yield-scaled N2O emissions than LDM in annual crops only. During forages post-seeding years, area- and yield-scaled N2O emissions induced by LDM and LEG were lower than MIN. Conclusion Our results suggest that for a cool humid climate using LDM or LEG in perennial forages and MIN on annual crops can reduce overall N2O emissions, while generating similar or lower yield-scaled emissions.
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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 | 0.000 | 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".