Weed Control, Environmental Impact and Profitability of Pre-Plant Incorporated Herbicides in White Bean
Bibliographic record
Abstract
Three field experiments were conducted over a three-year period (2009 to 2011) to evaluate various preplant incorporated (PPI) herbicides in white bean in Ontario, Canada. There was minimal visible injury in white bean for any of the treatments evaluated. The control of AMARE, AMBEL, CHEAL, SETVI, and SINAR ranged 91%-93%, 13%-21%, 56%-92%, 96%-98%, and 16%-24% with pendimethalin, 93%-99%, 34%-43%, 65%-99%, 95%-100%, and 20%-47% with trifluralin, 66%-77%, 61%-76%, 64%-86%, 94%-99%, and 52%-74% with EPTC, 92%-98%, 27%-48%, 28%-76%, 96%-99%, and 33%-59% with s-metolachlor, 98% - 99%, 50%-72%, 33%-88%, 98%-99%, and 56%-78% with dimethenamid, and 98%-100%, 72%-84%, 97%-100%, 77%-88%, and 98%-100% with imazethapyr, respectively. Weed density and dry weight reduction were similar to visible weed control. Imazethapyr applied PPI at the 75 g ai ha-1 provided yield equivalent to the weed-free check in 2009 and 2011. All other herbicide treatments had significantly lower yield than weed-free check. All herbicide treatments except pendimethalin at 1080 g ai ha-1, EPTC at 4400 g ai ha-1, and imazethapyr at 75 g ai ha-1 had yield equivalent to the weed-free check in 2010. Based on environmental impact (EI) analysis, the herbicide program with the lowest environmental risk was imazethapyr followed by dimethenamid-p, trifluralin, s-metolachlor, EPTC, and then pendimethalin. Economic analysis of herbicide treatments evaluated indicates that imazethapyr had the greatest positive impact on profit margins, followed by dimethenamid-p and EPTC, followed by s-metolachlor and trifluralin and then pendimethalin.
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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 | 0.001 | 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 | 0.001 |
| 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".