Efficacy of Saflufenacil for Dry Bean and Annual Weed Desiccation
Bibliographic record
Abstract
The efficacy of saflufenacil for desiccating dry bean and annual weeds may be influenced by application rate, single or sequential applications, adjuvant selection and rate, and water carrier volume. Five field experiments were conducted from 2021 to 2023 near Exeter and Ridgetown, Ontario, Canada to evaluate the efficacy of saflufenacil applied at two rates, applied once or sequentially, with four adjuvants at various rates, and three water carrier volumes for desiccating dry bean and common weed escapes in Ontario dry bean production. Saflufenacil (25 g ai ha-1) + Merge (1.0 L ha-1) applied with a 200 L ha-1 water carrier volume desiccated dry bean 96, 100, and 100% at 5, 8, and 14 days after application (DAA), respectively; there was no improvement in dry bean desiccation by increasing the rate of Merge to 2.0 L ha-1 or with a sequential application. With saflufenacil (50 g ai ha-1) + Merge (1.0 L ha-1), there was no impact on dry bean desiccation with water carrier volumes of 100, 200, or 300 L ha-1. Dry bean desiccation with saflufenacil (50 g ai ha-1) plus the adjuvants Merge, MSO, AMS, or Merge + AMS was similar. Sequential applications of saflufenacil (25 g ai ha-1) applied twice at a 1-week interval did not improve dry bean desiccation. For weed species desiccation, saflufenacil + Merge desiccated green pigweed (38-100%) and common ragweed (65-96%) most effectively. However, it was less effective on common lambsquarters (0-48%), barnyardgrass (9-24%), and green foxtail (6-14%), with no significant effect of saflufenacil rate, adjuvant selection, adjuvant rate, single vs sequential application, or water carrier volume. This study highlights the potential and limitations of saflufenacil for desiccating dry bean and common weed escapes in Ontario dry bean production.
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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 | 0.000 | 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".