Weed Control, Environmental Impact, and Net-Profit of Two-Pass Weed Management Strategies in Dicamba-Resistant Soybean (<i>Glycine max</i>) Using Conventional Tillage
Bibliographic record
Abstract
Traditional two-pass weed management strategies need to be compared with new strategies in glyphosate/dicamba-resistant soybean. Weed control, soybean yield, partial profitability and environmental impact (EI) were evaluated in glyphosate/dicamba-resistant soybean using dicamba applied alone or in a tank-mix with dimethenamid-P applied preemergence (PRE). Trials were conducted at three locations during 2014 and 2015. Several PRE herbicides provided excellent control of broadleaf and grass weeds. Dicamba provided > 91% control of broadleaf weeds, and the addition of dimethenamid-P improved grass control. All weed species at the trial locations were controlled > 94% following a postemergence (POST) application of glyphosate. Weed interference reduced soybean seed yield 33% where no herbicide was applied. A single POST application of glyphosate had the lowest EI. Several treatments improved early-season weed control and reduced early-season weed density and biomass compared to glyphosate and had similar EI values. In this study, there was no benefit to yield or partial profit by including a PRE herbicide for weed management; however, the inclusion of multiple modes-of-action in a herbicide program may reduce the selection for herbicide-resistant weeds.
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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".