Benefits and Risks of Economic vs. Efficacious Approaches to Weed Management in Corn and Soybean
Bibliographic record
Abstract
A 3-yr study was conducted on nine farms across southern Ontario to evaluate the risks and benefits of different approaches to weed management in corn and soybean. Weed control decisions were based on field scouting and recommendations from the Ontario version of HADSS™, the herbicide application decision support system. Treatments were selected to maximize profit (economic threshold approach) or to maximize yield (highest treatment efficacy). Reduced rates of the high efficacy treatment for each field also were included. Weed density before and after treatment, crop yields, weed seed return, and the effect of weed control decisions on weed density 1 yr after treatment were assessed. Crop yield varied among years and farms but was not affected by weed control treatment. Weed control at 28 d after treatment (DAT) was often lower and weed density, biomass, and seed production 70 DAT were often higher with the profit maximization approach compared with the yield maximization approach. However, weed density 1 yr later, after each cooperator had applied a general weed control program, did not vary significantly among the previous year's weed control treatments. Reduced rates of the high efficacy treatments did not lead to increased weed problems the next year, despite lower weed control and increased weed seed production in some years. During the 3 yr of the study, weed control costs with the profit maximization approach were approximately Can$45/ha less than with the yield maximization approach.
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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 | 0.001 | 0.001 |
| 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 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".