Optimization of plant spacing and fertilizer dose for high-yielding mustard varieties
Bibliographic record
Abstract
Here is a paradox in canola-type mustard agronomy: breeders keep releasing varieties with higher yield potential, but average on-farm yields in the Maritime provinces haven’t budged in a decade. The disconnect often traces back to planting density and fertiliser calibration that still target older, shorter-statured cultivars. This research optimised plant spacing and fertiliser dose for two high-yielding Brassica juncea varieties (AAC Condor, AAC Summit) at Truro, Nova Scotia, and Guelph, Ontario, during 2023. Five spacings (30×10, 30×15, 30×20, 45×15, 45×20 cm) and three fertiliser rates (80%, 100%, 120% of recommended dose) were arranged in a factorial split-plot design. Seed yield peaked at 30×15 cm with 120% RDF (2.04 t ha⁻¹ for AAC Condor at Truro), 21.3% above the widest spacing at base fertiliser. The spacing–fertiliser interaction was significant (p = 0.003) for yield but not for oil content. AAC Condor outyielded AAC Summit by 6–8% at all combinations. These data support narrowing row spacing to 30 cm and raising the fertiliser rate by 20% as a practical package for new-generation mustard 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".