Reducing the risks of in-crop nitrogen fertilizer applications in spring wheat and canola
Bibliographic record
Abstract
Nitrogen is the most limiting nutrient in crop production on the Canadian prairies. There is great interest in managing it more effectively for environmental and economic reasons. Our objective was to study the effectiveness of using different proportions of recommended nitrogen rates at seeding with the balance at different crop growth stages to minimize the risks of potential yield losses from in-crop nitrogen applications in spring wheat and canola. The field trials with wheat were conducted at three locations from 2003 to 2006 and at two locations for canola from 2004 to 2006. The treatments consisted of applying 100, 67, 50, 33 or 0% of the targeted N rate at seeding using urea in mid-row bands and the balance in-crop at the 1.5, 3.5 or 5.5 leaf stages in spring wheat and at the 5-6 leaf stage, bolting or start of flowering stage in canola using surface dribble band of liquid urea-ammonium nitrate. With spring wheat, applying 33% of the recommended N rate at seeding with the balance in-crop resulted in similar yields to when all the nitrogen was applied at seeding in one study while, in the other, some yield loss was observed at the 3.5 leaf stage. This indicates that a higher proportion, such as 50%, would be more appropriate. With canola, a minimum of 50% of the recommended nitrogen rate was required at seeding and the in-crop application at or before the bolting phase to give yields equivalent to when all fertilizer was applied at seeding. Consequently, applying 50% or more of the recommended N at seeding enhances the opportunity for in-crop applications of nitrogen in spring wheat and canola to better match the soil and climatic conditions. Key words: Canola, wheat, split applications, liquid urea-ammonium nitrate, grain yield, grain protein
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".