Cultural weed management practices shorten the critical weed-free period for soybean grown in the Northern Great Plains
Bibliographic record
Abstract
Abstract Soybean [Glycine max (L.) Merr.] has recently become a popular rotational crop in the Canadian Northern Great Plains where herbicide-resistant (HR) soybean cultivars have been widely adopted. Intense reliance on herbicides has contributed to the development of HR weeds in soybean and other crops. Cultural weed management practices reduce the need for herbicides and lower the selection pressure for HR weed biotypes by improving the competitiveness of the crop. The effects of two row spacings, three target densities, and three cultivars on the critical weed-free period (CWFP) in soybean were evaluated as three separate experiments in southern Manitoba. In the row-spacing experiment, soybean grown in narrow rows shortened the CWFP by up to three soybean developmental stages at site-years with increased weed pressure. In the target density experiment, low-density soybean stands lengthened the CWFP by one soybean developmental stage compared with higher-density soybean stands. The effect of soybean cultivar varied among locations, yet tended to be consistent within location over the 2-yr study, suggesting that competitive ability in these soybean cultivars was linked to edaphic and/or environmental factors. Generally, the cultivar with the shortest days to maturity, which also had the shortest stature, consistently had a longer CWFP. Each of these cultural practices were effective at reducing the need for in-crop herbicide applications.
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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) | 0.000 | 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 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".