Seed size and seeding rate effects on canola emergence, development, yield and seed weight
Bibliographic record
Abstract
Harker, K. N., O’Donovan, J. T., Smith, E. G., Johnson, E. N., Peng, G., Willenborg, C. J., Gulden, R. H., Mohr, R., Gill, K. S. and Grenkow, L. A. 2015. Seed size and seeding rate effects on canola emergence, development, yield and seed weight. Can. J. Plant Sci. 95: 1–8. Canola (Brassica napus L.) is the most common dicotyledonous crop in Canada. Here we determine the effect of canola seed size and seeding rate on canola emergence, development, yield and seed weight. In 2013, direct-seeded experiments were conducted at nine western Canada locations. Four canola seed sizes (1000-seed weights ranging from 3.96 to 5.7 g) and one un-sized treatment (4.4 g average) were seeded at two rates (75 and 150 seeds m−2). Higher seeding rates led to higher canola emergence and stubble density at harvest. Higher seeding rates also increased early crop biomass, 1000-seed weights and seed oil content and reduced days to start of flowering and days to crop maturity. Seed size effects on canola emergence, yield or seed quality were not significant. Increasing seed size had a positive linear association with early canola biomass and 1000-seed weights, whereas, both days to flowering and days to the end of flowering had a negative linear association with seed size. Greater biomass from large seeds increases crop competition with weeds and also hastens flowering, shortens the flowering period and reduces the risk that canola will be exposed to high temperatures that can negatively impact flowering and pod development.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".