Control of volunteer adzuki bean in soybean
Bibliographic record
Abstract
The objective of this research was to evaluate the efficacy of various pre-emergence (PRE) and post-emergence (POST) herbicides for the control of volunteer adzuki bean (Vigna angularis (Willd.) Ohwi & Ohashi) in soybean (Glycine max L.). Trials were conducted at two locations in 2005, 2006, 2007, and 2009. Experiments were arranged in a randomized complete block design with either five PRE or nine POST herbicides. Volunteer adzuki bean interference in soybean resulted in yield loss of up to 25%. Cloransulam-methyl, linuron, metribuzin, flumetsulam, and imazethapyr applied PRE provided up to 6, 24, 14, 8, and 0% control, respectively at 8 weeks after emergence (WAE), while acifluorfen, fomesafen, bentazon, thifensulfuron-methyl, cloransulam-methyl, imazethapyr, and imazethapyr plus bentazon applied POST provided 2, 2, 5, 34, 6, 4, and 12% control, respectively at 8 weeks after application (WAA). Generally, with the aforementioned herbicides, soybean yield was equivalent to the weedy control and soybean grain contamination with adzuki bean seed was consistently above the 1% maximum threshold. Chlorimuron-ethyl and glyphosate applied POST provided up to 84 and 94% visual control at 8 WAA, respectively, decreased adzuki bean density, biomass, and seed production, and generally decreased soybean contamination with adzuki bean below the 1% threshold. The only herbicides evaluated in this study that controlled volunteer adzuki bean in soybean were chlorimuron-ethyl (9 g ai.ha-1) and glyphosate (900 g ai.ha-1) applied POST. All the other PRE and POST herbicides evaluated did not provide adequate control of volunteer adzuki bean in soybean.
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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".