Response of Underseeded Red Clover (<i>Trifolium pratense</i> L.) to Winter Wheat (<i>Triticum aestivum</i> L.) Herbicides as Affected by Application Timing
Bibliographic record
Abstract
Underseeding red clover in winter wheat is a beneficial agronomic practice. Still, many growers tend to forgo this approach. One reason is that herbicides used on winter wheat may injure underseeded red clover, reducing its biomass and the subsequent benefits it provides. Therefore, the effect of winter wheat herbicides on underseeded red clover needs to be evaluated. The objectives of this research were to assess the crop tolerance of underseeded red clover to ten winter wheat herbicides used in Ontario, Canada and determine if red clover tolerance differed when the herbicides were applied at various winter wheat growth stages. Experiments were conducted in 2009 and 2010 at four different Ontario locations. Each herbicide treatment was either applied at an early, normal or late timing. Overall, red clover was not affected by herbicides applied at the early timing. The likelihood of herbicides causing injury and reducing biomass of underseeded red clover increased when they were applied at the more advanced winter wheat growth stages. If timing is a constraint, the three herbicides bromoxynil/MCPA, tralkoxydim, and fenoxaprop-pethyl are the safest to use on red clover underseeded to winter wheat. The remaining herbicides 2,4-D, dicamba/MCPA/mecoprop, dichlorprop/2,4-D, thifensulfuron/ tribenuron + MCPA, fluroxypyr + MCPA, pyrasulfotole/bromoxynil, and prosulfuron + bromoxynil are more injurious, with the last two being the most harmful. By having identified the least damaging herbicides on underseeded red clover in winter wheat and the optimal timing for herbicide application, growers are more likely to adopt this beneficial agronomic practice, save on fertilizer costs and improve soil quality.
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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".