Rotation length, canola variety and herbicide resistance system affect weed populations and yield
Bibliographic record
Abstract
A 4-year study was initiated in 1997 to provide canola producers with information on the consequences of various rotational intervals with the use of new disease and herbicide-resistant canola varieties. The study was conducted at three locations in Alberta, Canada (Ellerslie, Strathmore, and Warburg). At each location, four canola rotations were established: continuous canola, and canola seeded in 1 of 2, 3, or 4 years. Canola varieties included the conventional varieties ‘AC Excel’ and ‘Quantum’, the glyphosate-resistant variety ‘Quest’, imidazolinone-resistant ‘45A71’, and a glufosinate-resistant hybrid, ‘Invigor 2153’. In the fourth year of the study, when canola was grown in all treatments, weed densities, weed species diversity, and evenness were determined preseeding and before and after in-crop herbicide application. Canola yield was greatest in the northern ecoregions of the Boreal Transition (Warburg) and Aspen Parkland (Ellerslie), and lowest in the Moist Mixed Grassland ecoregion (Strathmore). Weed populations increased and population diversity decreased and became less even where rotations were less diverse; in continuous canola and in the 1-in-4–year rotation. As expected, weed densities increased in poorly competitive, conventional canola varieties compared to herbicide-resistant varieties sprayed with their broader-spectrum herbicides. Where weed densities were high, variety/herbicide system became a critical factor affecting crop yield. Under these conditions, the herbicide-resistant varieties ‘Quest’ and ‘Invigor 2153’ typically outyielded the conventional varieties of ‘AC Excel’ and ‘Quantum’. Canola yield was highest when grown in a 1-in-3– or a 1-in-4–year rotation, although the 1-in-3–year rotation generally had lower weed densities, and allowed high-value canola to be grown more frequently in rotation.
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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 | 0.000 | 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".