Placement and nitrogen source effects on N<sub>2</sub>O emissions for canola production in Manitoba
Bibliographic record
Abstract
Abstract This study examined fertilizer nitrous oxide (N 2 O) emissions and canola ( Brassica napus L.) grain yield for two granular urea sources (conventional and a dual urease and nitrification inhibitor [SuperU]) and three at‐planting placement methods (broadcast‐incorporated, shallow banded, and deep banded) on commercial fields in Manitoba. Nitrogen (N) rates were 100% and 70% of what was recommended based on soil test and target yield, with the 100% treatments having N 2 O emissions monitored. SuperU fertilizer consistently reduced N 2 O emissions (area‐, N‐applied‐, and yield‐based) compared to urea. Significant reductions in N 2 O emissions with SuperU occurred in four of six site‐years and coincided with delayed nitrification. Compared to broadcast‐incorporated, subsurface banding of conventional urea reduced N 2 O emissions in drier site‐years but increased emissions when rainfall was higher (especially in shallow banded urea). Across all six site‐years, shallow banding of urea resulted in significantly higher emission intensities than all other treatments. Nitrogen placement did not affect the emission reduction benefit of SuperU. N source or placement did not greatly affect canola yield within either 100% or 70% N rates. Fertilizer N recovery efficiency was also not greatly impacted by either N source or placement. The results demonstrate that dual inhibited granular urea fairly consistently reduces N 2 O emissions in canola production in southern Manitoba, whereas nitrogen placement had variable effects depending on growing season rainfall. However, with little agronomic benefit, the added cost of enhanced efficiency fertilizers must be overcome for widespread adoption and to achieve greenhouse gas reduction targets for the Canadian agriculture sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".