Weed Management in White Beans with Soil-Applied Grass Herbicides plus Halosulfuron
Bibliographic record
Abstract
There are a limited number of soil-applied herbicides available for broad-spectrum weed control in dry bean production in Ontario, Canada. Four experiments were conducted from 2017 to 2019 in southwestern Ontario to compare the efficacy of six soil-applied grass herbicides [trifluralin (600 g ai ha-1), ethalfluralin (810 g ai ha-1), pendimethalin (1080 g ai ha-1), S-metolachlor (1050 g ai ha-1), dimethenamid-p (544 g ai ha-1) and EPTC (3400 g ai ha-1)] and halosulfuron (35 g ai ha-1) applied alone and in combination, applied preplant incorporated (PPI), on white bean tolerance and yield, and weed control efficacy. There was no white bean injury from the herbicide treatments evaluated. Grass herbicides (trifluralin, ethalfluralin, pendimethalin, S-metolachlor dimethenamid-P and EPTC) controlled velvetleaf 0% - 82%, pigweeds 87% - 99%, common ragweed 0% - 93%, common lambsquarters 81% - 99%, wild mustard 0% - 71%, barnyardgrass 98% - 100% and green foxtail 98% - 99%. Halosulfuron controlled velvetleaf 98%, pigweeds 94%, common ragweed 90% - 94%, common lambsquarters 97%, wild mustard 98% - 100%, barnyardgrass 19% - 24% and green foxtail 20% - 25%. Tankmixes of halosulfuron with soil-applied grass herbicides provided ≥93% control of the weed species evaluated. Reduction in density and biomass generally followed the same trend as visible control with herbicide treatments evaluated. Weed interference reduced white bean seed yield 70%. Seed yield was 53% - 66% of the weed-free control with trifluralin, ethalfluralin, pendimethalin, S-metolachlor and dimethenamid-P, 81% of the weed-free control with EPTC, 58% of the weed-free control with halosulfuron, and 87% - 95% of the weed-free control with halosulfuron tankmixes with the grass herbicides evaluated. Based on these results, halosulfuron in combination with trifluralin, ethalfluralin, pendimethalin, S-metolachlor, dimethenamid-p and EPTC, applied PPI at rates evaluated, can be used to effectively control common annual grass and broadleaf weeds in white beans.
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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) | 0.001 | 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 | 0.001 | 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".