Economics of integrated weed management in herbicide-resistant canola
Bibliographic record
Abstract
Integrated weed management (IWM) decision strategies in herbicide-resistant canola-production systems were assessed for net returns and relative risk. Data from two field experiments conducted during 1998 to 2000 at two locations in Alberta, Canada, were evaluated. A herbicide-based experiment included combinations of herbicide system (glufosinate-, glyphosate-, and imazethapyr-resistant canola varieties), herbicide rate (50 and 100% of recommended dose), and time of weed removal (two-, four-, and six-leaf stages of canola). A seed-based experiment included canola variety (hybrid and open-pollinated), seeding rate (100, 150, and 200 seeds m−2), and time of weed removal (two-, four-, and six-leaf stages of canola). For the herbicide-based experiment, strategies with glyphosate were profitable at Lacombe, but both imazethapyr and glyphosate strategies were profitable at Lethbridge. Weed control at the four-leaf stage was at least as profitable as the two-leaf stage at both sites. For the seed-based experiment, the hybrid was more profitable than the open-pollinated cultivar, seed rates of 100 and 150 seeds m−2were more profitable than 200 seeds m−2, and weed control at the two- and four-leaf stages was more profitable than at the six-leaf stage. When risk of returns and statistical significance was considered, several strategies were included in the risk-efficient set for risk-averse and risk-neutral attitudes at each location. However, the glyphosate-resistant cultivar, the 50% herbicide rate, and weed control at four-leaf stage were more frequent in the risk-efficient IWM strategy set. The open-pollinated cultivar, 200 seeds m−2rate, and weed control at the six-leaf stage were less frequent in the set. The risk-efficient sets of IWM strategies were consistent across a range of canola prices.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".