Emissions of Nitrous Oxide and Carbon Dioxide
Bibliographic record
Abstract
Innovative management practices are required to increase the efficiency of N fertilizer usage and to reduce nitrous oxide (N 2 O) and carbon dioxide (CO 2 ) emissions from agricultural soils. The objectives of this study were to evaluate the feasibility of using conservation tillage and N fertilizer placement depth to reduce N 2 O and CO 2 emissions associated with corn ( Zea mays L.) production on clay loam soils in Eastern Canada. A 3‐yr field study was established on a wheat ( Triticum aestivum L.)‐corn–soybean [Glycine max (L.) Merr.] rotation with each phase of the rotation present every year. Investigations were focused on the corn phase of the rotation. The tillage treatments following winter wheat included fall moldboard plow tillage (15 cm depth), fall zone‐tillage (21 cm width, 15 cm depth), and no‐tillage. The N placement treatments were “shallow” placement of sidedress N (2‐cm depth) and “deep” placement of sidedress N (10‐cm depth). Nitrous oxide emissions were measured 53 times and CO 2 emissions were measured 43 times over three growing seasons using field‐based sampling chambers. There was a significant tillage and N placement interaction on N 2 O emissions. Averaged over all three tillage systems and site‐years, N 2 O emissions from shallow N placement (2.83 kg N ha −1 yr −1 ) were 26% lower than deep N placement (3.83 kg N ha −1 yr −1 ). The N 2 O emissions were similar among the tillage treatments when N was placed in the soil at a shallow depth. However, when N was placed deeper in the soil (10 cm), the 3‐yr average N 2 O emissions from zone‐tillage (2.98 kg N ha −1 yr −1 ) were 20% lower than from no‐tillage (3.71 kg N ha −1 yr −1 ) and 38% lower than those from moldboard plow tillage (4.81 kg N ha −1 yr −1 ). Tillage type and N placement depth did not affect CO 2 emissions (overall average = 5.80 Mg C ha −1 yr −1 ). Hence, zone‐tillage and shallow N placement depth reduced N 2 O emissions without affecting CO 2 emissions.
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".