Fertigation of wheat and canola in southern Alberta
Bibliographic record
Abstract
An irrigation study in southern Alberta compared spring-banded nitrogen (N) to spring-banded N plus fertigation at three plant growth stages for spring wheat (Triticum aestivum L.) and canola (Brassica napus L.). Yield and quality impacts were quantified when N fertigation was applied to (i) wheat at the early tillering, flag leaf, and anthesis stages and (ii) canola at the four-leaf rosette, bolting, and early flowering stages. For both crops, fertigation could replace some spring-banded N without an effect on yield. However, the results revealed that for canola grown with a large amount of N, applying it all in the spring often generated higher yields than if an equivalent amount of N was delivered at later stages by fertigation. Canola oil concentration declined marginally (about 1%) from no applied N to the high rate of applied N. The application of more than 60 kg N ha−1 and delayed application each increased wheat protein content. Comparing revenues to costs, fertigation did not improve profit margins for canola growers. When growers applied 90 or 120 kg N ha−1 in the spring, fertigation was financially counter-productive. In contrast, the main benefit to wheat growers from fertigation was higher grain protein, especially with N applied at later growth stages. When protein premiums increase during the growing season, fertigation would facilitate growers to obtain higher net returns than they would otherwise.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".