Integration of Conservation Tillage and Herbicides for Sustainable Dry Bean Production
Bibliographic record
Abstract
Development of conservation tillage practices for dry bean has lagged behind that of many other crops. A field study was conducted to determine the effects of various crop residues and herbicide treatments on weed management and dry bean yield within a zero-tillage system. Main plot treatments included wheat stubble, canola stubble, fall-seeded winter rye, fall-seeded spring rye, and a no-cover control. Subplot treatments included various preplant and POST herbicides. Wheat stubble, canola stubble, and winter rye residue provided sufficient ground cover to prevent soil erosion, and they effectively reduced weed density compared with the no-cover control in all years. Fall-seeded spring rye provided only partial soil-erosion protection and reduced weed density in only 1 of 3 yr. Dry bean emergence was 3 to 5 d slower in the crop residue treatments compared with the no-cover control, but crop density was not adversely affected. However, winter rye residue delayed dry bean maturity by 2 to 6 d. Fall-applied granular ethalfluralin followed by POST bentazon/imazethapyr or imazamox provided the most effective weed control. A sole POST imazamox application also provided good weed control when weed densities were reduced by winter rye residue or wheat stubble. Overall, results indicate that with suitable herbicide programs, similar yields were attained when dry bean was seeded directly into crop stubble or cover crop residues compared with the no-cover control. Information gained in this study will be used to encourage greater farmer adoption of conservation tillage practices for dry bean production on the Canadian prairies.Nomenclature: Bentazon, ethalfluralin, imazamox, imazethapyr, canola, Brassica napus L., dry bean, Phaseolus vulgaris L., rye, Secale cereale L., wheat, Triticum aestivum L
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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".