Short-term effects of tillage of long-term no-till on nitrous oxide emissions from two contrasting Canadian prairie soils
Bibliographic record
Abstract
The reduction in net CO2 emissions from increased carbon sequestration in soil and slower decomposition of soil organic matter under most long-term no-till (NT) situations can potentially be offset by a concomitant increase in nitrous oxide (N2O) emissions after tillage reversal on long-term NT soils. The objective of this work was to quantify N2O emissions after tillage reversal on two contrasting western Canadian Prairie soils managed under long-term (∼30 yr) NT. We measured one growing season (2010) of soil N2O emissions on a Black Chernozem and Gray Luvisol at Ellerslie and Breton, AB, respectively, following 30 yr of NT and N fertilizer application at two rates (0 and 100 kg N ha−1) subjected to tillage reversal and no disturbance (i.e., continuing NT). Tillage reversal after long-term NT was associated with higher N2O emissions in both soils but was significant only in the Gray Luvisol with 0 kg N ha−1. Long-term N fertilizer applications of 100 kg N ha−1 were associated with higher growing season soil N2O emissions and higher levels of soil N (i.e., a positive, long-term soil N balance) at both sites. Regardless of tillage, the difference in growing season nitrous oxide emissions from the 0 and 100 kg N ha−1 plots on the Gray Luvisol were much greater than the Black Chernozem. A modest increase in N2O emissions upon tillage reversal on a long-term NT soils could translate to a significant increase to agricultural greenhouse gas inventories in the event of large-scale tillage reversal on agricultural land in western Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".