Growth, yield, and yield components of canola as affected by nitrogen, sulfur, and boron application
Bibliographic record
Abstract
Abstract Developing efficient nutrient management regimes is a prerequisite for promoting canola ( Brassica napus L.) as a viable cash crop in eastern Canada. Field experiments were conducted to investigate the growth, yield, and yield components of canola in response to various combinations of preplant and sidedress nitrogen (N) with soil‐applied sulfur (S) and soil and foliar‐applied boron (B). Canola yield and all its yield components were strongly correlated ( r 2 = 0.99) with the amount of N applied, as was the above‐ground biomass at 20% flowering and the leaf area index. Sidedress N was more efficiently utilized by the crop, leading to greater yields than preplant N application. On average, canola yields increased by 9.7 kg ha −1 for preplant N application and by 13.7 kg ha −1 for sidedress N application, for every kg N ha −1 applied, in 6 of the 10 site‐years. Soil‐applied S also increased canola yields by 3–31% in 7 of the 10 site‐years, but had no effect on yield components. While there was no change in yield from soil‐applied B, the foliar B application at early flowering increased yields up to 10%, indicating that canola plants absorb B efficiently through their leaves. In summary, canola yields were improved by fertilization with N (8 of 10 site‐years), S (7 of 10 site‐years) and B (4 of 10 site‐years). Yield gains were also noted with split N‐fertilizer application that involved sidedressing N between the rosette and early flowering stage. Following these fertilizer practices could improve the yield and quality of canola crop grown in rainfed humid regions similar to those in eastern Canada.
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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 | 0.001 | 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) | 0.001 | 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".