Weed control, environmental impact, and net revenue of two-pass weed management strategies in dicamba-resistant soybean
Bibliographic record
Abstract
Six field trials were conducted over a 2-yr period (2014 and 2015) at two locations in southwestern Ontario to compare the level of weed control provided by dicamba applied alone and in combination with dimethenamid-P applied before planting (preplant, PP) in glyphosate- and dicamba-resistant soybean to current industry standards when used in a two-pass weed management program. Crop injury, weed control, soybean seed yield, environmental impact (EI), and profitability were evaluated in this study. No statistically significant injury was documented. Several PP herbicides provided excellent early-season grass and broadleaf weed control, although early-season weed control of those weed species was not acceptable with glyphosate applied alone or in combination with dicamba, dicamba + dimethenamid-P, 2,4-D, or saflufenacil. At 8 wk after application, the sequential application of a PP herbicide followed by glyphosate applied after emergence (POST) provided at least 86% control of the weed species evaluated in this study. Weed interference with no herbicide treatments caused a soybean seed yield loss of 64%. The sequential application of glyphosate had the lowest EI value. The addition of chlorimuron + metribuzin or chlorimuron + imazethapyr did not increase the EI substantially but did improve the level of weed control and reduced weed density and biomass. The inclusion of a PP herbicide in a weed management program has several stewardship benefits and may reduce the selection for herbicide resistant weeds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".