Agronomic and environmental factors influence weed composition and canola competitiveness in southern Manitoba
Bibliographic record
Abstract
Canola yield in Manitoba has reached a plateau in recent years. The causes for this, as related to agronomic, environmental, weed interference and canola competitiveness factors, were identified using observational data from 31 canola fields in southern Manitoba in a 2-yr on-farm research study. Agronomic and environmental factors contributing to weed density and composition were determined with multivariate canonical correspondence analysis. Agronomic and environmental factors most influential on absolute and relative canola biomass were determined with multiple regression analysis. Most weeds were adaptable across a broad range of crop environments; however, some functional groups of weeds were either positively or negatively favored by specific environmental or agronomic conditions. Absolute canola biomass prior to bolting was greater as soil growing degree days (GDD) increased and canola was dense and seeded early. Lower weed density, increased soil GDD, and reduced surface soil moisture were significant factors contributing to higher relative canola biomass. Results from this study indicate that seeding canola early and at a rate sufficient to achieve a dense crop stand can increase canola competitiveness and reduce weed interference. Key words: Agronomy, canola, competitiveness, environment, weeds
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".