Competitive ability of western Canadian spring wheat cultivars in a model weed system
Bibliographic record
Abstract
Economic and social pressures are spurring the study of alternate weed management strategies such as the development of competitive crop cultivars, capable of being used under an integrated management plan. The primary objective of this research was to determine whether western Canadian spring wheat ( Triticum spp.) cultivars differ in their ability to compete against model weeds and whether those differences were expressed when challenged with wild weeds. A total of 71 wheat cultivars were grown in the absence or presence of simulated [cultivated oat ( Avena sativa L.) and oriental mustard ( Brassica juncea L.)] or natural [wild oat ( Avena fatua L.)] weed competition conditions. Significant ( p = 0.01) weed by cultivar interactions involving changes in yield cultivar rank were detected, indicating that the cultivars responded differently to competition. A small minority of cultivars such as Glenlea, CDC Rama, Genesis, AC Taber, AC Vista, Plenty, Napoleon, and BW652 had high-yield potential coupled with yield maintenance under weed pressure. The competitive ability advantage appeared to be associated with plant height or tillers per square meter as well as shorter vernalization requirement combined with photoperiod sensitivity. These outlier cultivar differences could be exploited in breeding new widely adapted varieties for scenarios where reduced herbicide weed control is desired, including situations where herbicide resistance limits chemical options. Cultivars with differing competitive ability under model weed conditions maintained their ranking when challenged by natural weed infestations. This suggests that selecting competitive spring wheat cultivars using a repeatable protocol based on model weeds is realistic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".