Residual Herbicides and Cover Crops Interactions for Soybean Weed Control
Bibliographic record
Abstract
Residual herbicides and cover crops are important tools inside an integrated weed management program. The straw produced in crop rotation can interacts with herbicides. The aim of this study was to evaluate the interaction of diclosulam, sulfentrazone, imazethapyr, flumioxazin, s-metolachlor and pyroxasulfone with black oat, cereal rye, common vetch and oilseed radish cover crops and it´s reflection on weed control and soybean production. Were evaluated the biomass production of cover crops and its influence on the soybean population. A phytosociological survey of the weed community was carried out, further evaluating the control provided by the herbicides and its effects on soybean productivity. Diclosulam was the more efficient herbicide tested, reducing both weed density and biomass (68% and 89%, respectively) compared to the fallow, independently of cover crop straw. The best control levels for the population identified were provided by the combination of the herbicides diclosulam with black oat, radish or fallow. We observed that herbicide efficacy in this case was more related with control spectrum than with herbicide-straw interation. This research demonstrates that the integration of cover crops and residual herbicides is efficient in the suppression and control of weeds in the soybean crop in the no-tillage system.
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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".