Early seeding improves the sustainability of canola and mustard production on the Canadian semiarid prairie
Bibliographic record
Abstract
Canola and mustard production is increasing in the semiarid prairie and seeding date affects the heat and water stress experienced by those crops. We conduced a 4-yr field study on the effect of fall (November with expected spring germination), early spring (late April) and late spring (late May) seeding on the growth of two cultivars representing two different canola species (B. napus L. ‘Arrow’ and B. rapa L. ‘Sunbeam’) and of two cultivars representing two different mustard species (B. juncea L. Coss. ‘Cultass’ and Sinapis alba L. ‘Pennant’). Generally, all cultivars from the four different species responded similarly to seeding dates. Flowering during a period of less water stress increased grain yields so years with good moisture availability in spring (1999 and 2000) favored earlier seeding (fall and early spring) while a drier spring with moister summers (2001 and 2002) favored spring seeding. Fall seeding resulted in lower plant populations than spring seeding. Early spring seeding was most frequently the highest yielding and, when another seeding date was higher yielding, the yield difference from early spring seeding was relatively small (191 kg ha -1 ). These results, plus the typical Prairie weather pattern of increasing moisture stress from spring into summer, indicate that canola and mustard should be seeded as early in spring as practical. Cutlass was generally the highest yielding cultivar while Sunbeam was the lowest yielding. Key words: Dormant seeding, seeding date, Brassica oilseeds, yield, semiarid prairie
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".