Weed management in azuki bean with preplant incorporated herbicides
Bibliographic record
Abstract
Abstract There are only two soil‐applied herbicides, pendimethalin and imazethapyr, registered for weed management in azuki bean production in Ontario, Canada. Three experiments were completed in Ontario over three consecutive years (2017, 2018, and 2019) to develop weed management programs in azuki bean with various preplant incorporated (PPI) herbicides. There was up to 6% azuki bean injury with the herbicide treatments evaluated. Pendimethalin + S‐metolachlor controlled redroot pigweed, common lambsquarters, barnyard grass, and green foxtail at ≥99% but provided poor control of common ragweed and wild mustard. The two‐way mixtures of pendimethalin + imazethapyr and pendimethalin + halosulfuron, and the three‐way mixtures of pendimethalin + S‐metolachlor + imazethapyr and pendimethalin + S‐metolachlor + halosulfuron controlled redroot pigweed, common lambsquarters, wild mustard, barnyard grass, and green foxtail at ≥99% and common ragweed at >73%. Common ragweed control was consistently higher with tankmixes that contained halosulfuron. Metribuzin controlled redroot pigweed, common ragweed, common lambsquarters, wild mustard, barnyard grass, and green foxtail at >93%, 88%, 99%, 97%, 68%, and 50%, respectively. Weed density and aboveground biomass reductions were similar to those of weed control. Weed presence reduced azuki bean yield by 83%. Reduced weed presence with the herbicide treatments assessed resulted in azuki bean yield that was similar to that of the weed‐free control with the exception of pendimethalin, halosulfuron, and metribuzin (low rate); weed interference with the aforementioned herbicides decreased azuki bean yield by 50%, 56%, and 44%, respectively. Based on these results, pendimethalin, S‐metolachlor, halosulfuron, and imazethapyr applied PPI, applied alone, or in a two‐way or three‐way tankmixture can be used safely for the control of common annual grass and broadleaf weeds in Ontario, Canada.
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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 | 0.000 | 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 | 0.001 |
| 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".