Cultivar and Seeding Rate Effects on the Competitive Ability of Spring Cereals Grown under Organic Production in Northern Canada
Bibliographic record
Abstract
Organically managed production systems often experience greater weed pressure than their conventional counterparts, potentially causing yield losses and increased weed seed build‐up. The use of competitive crop cultivars and the cultural practice of increasing seeding rates may moderate such production constraints. Field trials were conducted at two organically managed locations in Alberta, Canada for 2 yr to determine the effect of competition with tame oat ( Avena sativa L.), cultivar, and crop seeding rate (300 and 600 seeds m −2 ) on the competitive ability and agronomic performance of Canadian spring wheat ( Triticum aestivum L.) and barley ( Hordeum vulgare L.). Cultivars were selected based on their differing heights, tillering capacities, and times to maturity. Simulated weed competition from tame oat reduced grain yield by an average of 27%. Barley cultivars were generally more competitive than wheat cultivars. Height and early maturity were more closely associated with weed suppression and yield maintenance than tillering capacity. The modern semidwarf CDC Go was the highest yielding wheat cultivar, but was a poor weed suppressor. Doubling the seeding rate increased grain yield and weed suppression. This effect was not cultivar specific, which implies that doubling the seeding rate may be a generally effective method of overcoming yield losses and weed seed build‐up associated with increased weed populations under organic production.
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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 | 0.001 | 0.001 |
| 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".