Environmental Effects on the Relative Competitive Ability of Canola and Small-Grain Cereals in a Direct-Seeded System
Bibliographic record
Abstract
Growing crops that exhibit a high level of competition with weeds increases opportunities to practice integrated weed management and reduce herbicide inputs. The recent development and market dominance of hybrid canola cultivars provides an opportunity to reassess the relative competitive ability of canola cultivars with small-grain cereals. Direct-seeded (no-till) experiments were conducted at five western Canada locations from 2006 to 2008 to compare the competitive ability of canola cultivars vs. small-grain cereals. The relative competitive ability of the species and cultivars was determined by assessing monocot and dicot weed biomass at different times throughout the growing season as well as oat (simulated weed) seed production. Under most conditions, but especially under warm and relatively dry environments, barley cultivars had the greatest relative competitive ability. Rye and triticale were also highly competitive species under most environmental conditions. Canada Prairie Spring Red wheat and Canada Western Red Spring wheat cultivars usually were the least competitive cereal crops, but there were exceptions in some environments. Canola hybrids were more competitive than open-pollinated canola cultivars. More importantly, under cool, low growing degree day conditions, canola hybrids were as competitive as barley, especially with dicot weeds. Under most conditions, hybrid canola growers on the Canadian Prairies are well advised to avoid the additional selection pressure inherent with a second in-crop herbicide application. Combining competitive cultivars of any species with optimal agronomic practices that facilitate crop health will enhance cropping system sustainability and allow growers to extend the life of their valuable herbicide tools.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".