Influence of Glyphosate/Dicamba Application Rate and Timing on the Control of Glyphosate-Resistant Horseweed in Glyphosate/Dicamba-Resistant Soybean
Bibliographic record
Abstract
Abstract Dicamba may be an efficacious option for the control of glyphosate-resistant (GR) horseweed in glyphosate/dicamba-resistant soybean; research is needed to optimize the application rate based on horseweed height at the time of application. The purpose of this study was to determine the effect of glyphosate/dicamba rate and application timing for the control of GR horseweed. Glyphosate/dicamba was applied at three rates (900, 1,350, and 1,800 g ae ha −1 ) at three horseweed application timings (5, 15, and 25 cm) in a factorial design. There was no interaction between glyphosate/dicamba rate and timing for GR horseweed control or soybean yield; however, there was an interaction for GR horseweed density and biomass. At 2 and 4 wk after application (WAA), there was a decrease in GR horseweed control as the height at the time application increased. At 4 WAA, the application of glyphosate/dicamba to GR horseweed that was 5-, 15-, and 25-cm tall provided 87%, 76%, and 62% control, respectively. There was no impact of glyphosate/dicamba application timing on soybean yield. At 2, 4, and 8 WAA, there was an increase in GR horseweed control as the rate of glyphosate/dicamba was increased. At 8 WAA, glyphosate/dicamba applied at 900, 1,350, and 1,800 g ae ha −1 controlled GR horseweed 76%, 87%, and 92%, respectively. Earlier application timings and higher rates of glyphosate/dicamba caused the greatest reduction in GR horseweed density and biomass. Reduced GR horseweed competition resulted in a 100% to 144% increase in soybean yield, but there was no difference in soybean yield among glyphosate/dicamba rates tested.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".