Nitrous Oxide Emissions Respond Differently to No‐Till in a Loam and a Heavy Clay Soil
Bibliographic record
Abstract
The anticipated benefits of increased soil C stocks on net soil‐surface greenhouse gas (GHG) emissions after adoption of soil conservation practices can be offset by increases in soil N 2 O emissions. The objective of this study was to assess the short‐term impacts of no‐till (NT) on soil N 2 O emissions. The study was conducted in eastern Canada in the 3rd, 4th, and 5th yr after initiation of NT and fall moldboard plowing (MP) on heavy clay and gravelly loam soils. Annual emissions of N 2 O were exceptionally high in the heavy clay soil, varying from 12 to 45 kg N 2 O‐N ha −1 during the 3 yr of the study. Such high emissions were probably not associated with fertilizer N inputs but rather with denitrification sustained by the decomposition of large soil organic matter stocks (192 Mg C ha −1 in the top 0.5 m). On average, NT more than doubled N 2 O emissions compared with MP in the heavy clay soil. The influence of plowing on N 2 O flux in the heavy clay soil was probably the result of increased soil porosity that maintained soil aeration and water content at levels restricting denitrification and N 2 O production in the top 0.20 m. In the loam soil, average emissions during the 3 yr were similar in the NT and MP plots. The results of this study indicate that the potential of NT for decreasing net GHG emissions may be limited in fine‐textured soils rich in organic matter that are prone to high water content and reduced aeration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".