Evaluating the Competitive Ability of Semileafless Field Pea Cultivars
Bibliographic record
Abstract
The inclusion of competitive crop cultivars in crop rotations is an important integrated weed management (IWM) tool. However, competitiveness is often not considered a priority for breeding or cultivar selection by growers. Field pea (Pisum sativumL.) is often considered a poor competitor with weeds, but it is not known whether competitiveness varies among semileafless cultivars. The objectives of this study were to determine if semileafless field pea cultivars vary in their ability to compete and/or withstand competition, as well as to identify aboveground trait(s) that may be associated with increased competitive ability. Field experiments were conducted in 2012 and 2013 at three locations in western Canada. Fourteen semileafless field pea cultivars were included in the study representing four different market classes. Cultivars were grown either in the presence or absence of model weeds (wheat and canola), and competitive ability of the cultivars was determined based on their ability to withstand competition (AWC) and their ability to compete (AC). Crop yield, weed biomass and weed fecundity varied among sites but not years. Cultivars exhibited inconsistent differences in competitive ability, although cv. Reward consistently exhibited the lowest AC and AWC. None of the traits measured in this study correlated highly with competitive ability. However, the highest-yielding cultivars generally were those that had the highest AC, whereas cultivars that ranked highest for AWC were associated with lower weed fecundity. Ranking the competitive ability of field pea cultivars could be an important IWM tool for growers and agronomists.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".