Tillage effects on N<sub>2</sub>O emission from soils under corn and soybeans in Eastern Canada
Bibliographic record
Abstract
The ways in which agricultural soils are managed influence the production and emission of nitrous oxide (N2O). A field study was undertaken in 2003, 2004, and 2005 to quantify and evaluate N2O emission from tilled and no-till soils under corn (Zea maysL.) and soybeans (Glycine max L. Merr) in Ontario. Overall, N2O emission was lowest in 2003, the driest and coolest of the 3 yr. In 2004, the significantly larger annual N2O emission from no-till soils and soils under corn was attributed to an episode of very high N2O emission following the application of fertilizer during a period of wet weather. That the N loss by N2O emission occurred only in no-till soils and was large and long-lasting (~4 wk) confirms the strong effect that management has in reducing fertilizer N losses. In 2005, tilled soils had significantly larger N2O emission than no-till soils, most of which was emitted before the end of June. Because the tilled soils were better aerated , nitrification was likely the primary process contributing to the larger emission. Relatively low N2O emission from soybeans suggests biological N fixation does not appear to contribute substantially to the annual N2O emission. Further study of methods to reduce N2O emission in agricultural systems should focus on improving N use efficiency within a particular tillage system rather than looking to differences between tillage systems. Key words: Tillage, corn, soybeans, nitrogen, nitrous oxide, biogenic gas emission, nitrification, denitrification, fertilization
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".