Uptake and nutrient balance of nitrogen, sulfur, and boron for optimal canola production in eastern Canada
Bibliographic record
Abstract
Abstract Balanced plant nutrition is essential to achieve high yields of canola (Brassica napus L.) and get the best economic return from applied fertilizers. A field study was conducted at nine site‐years across eastern Canada to investigate the effects of nitrogen (N), sulfur (S) and boron (B) fertilization on canola nutrient uptake, nutrient balance, and their relationship to canola yields. The factorial experiment consisted of four N rates of 0 (N0), 50 (N50), 100 (N100), and 150 (N150) kg ha−1, two S rates of 0 (S0) and 20 (S20) kg ha−1, and three B treatments of 0 (B0), 2 kg ha−1 at preplant (B2.0P), and 0.5 kg B ha−1 foliar‐applied at early flowering stage (B0.5F). Each site‐year used the same experimental design and assigned treatments in a randomized complete block design with four replications. Fertilizer S application greatly improved seed yields at six out of nine site‐years, and the highest N use efficiency was in the N150+S20 treatment. Sulfur application generally increased seed S concentration, seed S removal, and plant total S uptake, while B fertilization mainly elevated straw B concentration and content, with minimal effect on seed yields. At the early flowering stage, plant tissue S ranged from 2.2 to 6.6 mg S g−1, but the N : S ratio was over or close to the critical value of 12 in the N150+S0 combination at five site‐years. On average across nine site‐years, canola reached a plateau yield of 3580 kg ha−1 when plants contained 197 kg N ha−1, 33 kg S ha−1 and 200 g B ha−1, with a seed B content of 60 g B ha−1. The critical N, S, and B values identified in this work and their potential for a posteriori nutrient diagnosis of canola should be useful to validate fertilizer requirements for canola production 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".