Field evaluation of management systems for reduction of N2O emissions from a corn-soybean-wheat rotation
Bibliographic record
Abstract
Nitrous oxide (N2O) is one of the greenhouse gases playing a role in climate change. Agricultural soils are major sources of nitrous oxide. Mitigation strategies for the reduction of N2O emissions are therefore currently being investigated. In this study, N2O fluxes from two management systems applied to a corn-soybean-wheat rotation in Southern Ontario, Canada, were measured from January 2000 to April 2002, using a micrometeorological method. One system, termed the conventional system, employed a conventional till strategy and fertilising as recommended for each crop. The other system, named the best management system, employed a no-till strategy, N fertilization based on soil test level and using a cover crop when possible. Various approaches to calculate the zero displacement values were compared. It was found that using different approaches to calculate the zero plane displacement values caused the eddy diffusivity value to vary. This can be an important source of error when quantifying the exact amount of N2O emitted from soils. Results indicated that cumulative N 2O loss from the 2-year study was 5.37 kg N ha-1 and 3.94 kg N ha-1 for the conventional and best management systems respectively. It was therefore concluded that the best management system holds promise as a means for reducing N2O 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 | 0.001 | 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 | 0.000 | 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".