Efficacy of Saflufenacil for Control of Glyphosate-Resistant Horseweed (<i>Conyza canadensis</i>) as Affected by Height, Density, and Time of Day
Bibliographic record
Abstract
Control of glyphosate-resistant (GR) horseweed in soybean with glyphosate (900 g ai ha−1) plus saflufenacil (25 gaiha−1) has been variable. The objective of this research was to determine the effect of GR horseweed height, density, and time of day (TOD) at application on saflufenacil plus glyphosate efficacy in soybean. All experiments were completed six times during a 2 yr period (2014, 2015) in fields previously confirmed with GR horseweed. Applications from 0900 to 2100 hours provided optimal control of GR horseweed 8 WAA. Soybean yield paralleled GR horseweed control with the highest yield of 3000kgha−1at 1500 hours, and the lowest yield of 2400kgha−1at 0600 hours. The height and density of GR horseweed at application had minimal effect on saflufenacil efficacy. Saflufenacil provided>99% control of GR horseweed when applied to small plants and low densities; however, control decreased to 95% when>25 cm tall, and to 96% in densities>800 plants m−2at 6 WAA due to some plant regrowth. TOD of application had a greater influence on GR horseweed control with saflufenacil than height or density. To optimize control of GR horseweed, saflufenacil should be applied during daytime hours to small plants at low densities. Optimizing GR horseweed control minimizes weed seed return and weed interference.
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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".