Growth and Yield Losses of Roundup Ready Soybean as Influenced by Micro-rates of 2,4-D
Bibliographic record
Abstract
Widespread resistance to glyphosate has made weed control very challenging. In response, new approaches to managing resistant biotypes such as the Enlist E3TM have been developed. This technology allows in-crop use of 2,4-D but there is fear associated with unintentional application of the herbicide (e.g. direct application, tank contamination, or spray drift) to sensitive crops. A study was conducted to evaluate Roundup Ready (RR) soybean growth and yield loss as influenced by 2,4-D [six micro rates of 1/5, 1/10, 1/50, 1/100, 1/500 and 1/1000 of the 1,120 g ae ha-1 label recommended dose, and a check with no herbicide applied] applied at V2, R1 and R2 growth stages. In general, RR soybean was more sensitive to 2,4-D at R1 than V2 and R2. The highest 2,4-D rate, 1/5 of the label recommended rate, caused 51% soybean injury symptom, 13 d canopy closure delay, 41.2% plant height reduction, and 68.9% yield loss at R1. Based on effective dose (ED) estimates, 37.7 g ae ha-1 2,4-D caused 5% yield loss (0.23 Mg ha-1) at R1 compared with a 2.5- and 2.0-fold higher dose at V2 and R2, respectively. With respect to number of days to canopy closure, both reproductive stages (R1 and R2) were equally less sensitive to 2,4-D than the vegetative one (V2) as the plants had already achieved maximum growth recorded. On the other hand, ED estimates for plant height have shown that both V2 and R2 were equally more sensitive to 2,4-D than R1. These results clearly indicated that RR soybean growth and yield loss were significantly influenced by the timing of exposure and amount of 2,4-D.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".