Response of Dicamba-Resistant Soybean to Glyphosate/Dicamba Application Rate and Timing
Bibliographic record
Abstract
Three field experiments were conducted during 2016 and 2017 in southwestern Ontario, Canada to evaluate the effect of glyphosate/dicamba (2:1 ratio) applied at the V2/V3 or V4/V5 growth stage on dicamba-resistant (DR) soybean injury, growth response and yield. At 1 DAA, glyphosate/dicamba at 450, 900, 1350, 1800 and 3600 g·ae·ha-1 caused 0, 0.3, 1.8, 3.3 and 5.8% growth response (leaf droop) when applied at the V2/V3 growth stage and 0.3%, 1.3%, 1.5%, 2.3% and 4.5% growth response when applied at V4/V5 growth stage in DR soybean, respectively. The growth response was similar at 3 DAA. This response was transient with no growth response observed at 7 DAA, 2 WAB, 4 WAB, 8 WAB, and late Sept. (R8 growth stage). Glyphosate/dicamba at 450, 900, 1350, 1800 and 3600 g·ae·ha-1 caused 0.0, 1.4%, 3.6%, 5.7% and 10.7% injury (speckled chlorosis and necrosis and leaf distortion) when applied at the V2/V3 growth stage and 0.5%, 0.7%, 2.0%, 3.7% and 6.9% injury when applied at the V4/V5 growth stage in DR soybean, respectively. The injury observed was transient with no injury observed at 4 and 8 WAB and late Sept. (R8). There was no impact of glyphosate/dicamba at various rates evaluated on maturity and seed yield except at 1350 g·ae·ha-1 (V2/V3) and 3600 g·ae·ha-1 (V4/V5) which caused a 6% reduction in yield of DR soybean yield compared to the weed-free control. Based on these results, glyphosate/dicamba at the labelled rates can be safely applied at the V2/V3 and V4/V5 growth stage in DR soybean. However, care is needed to avoid spray overlaps as the 2× rate can result in significant crop injury and yield reduction in DR soybean.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".