Investigation of management practices to optimize cereal rye cover crop-based weed mitigation in Canadian sweet corn production
Bibliographic record
Abstract
Abstract Fall-sown cereal rye has gained popularity as a cover crop in vegetable production due to its weed-suppressive capabilities. However, previous research has shown that replacing preemergence and/or postemergence herbicide applications with roller-crimped rye has variable success at controlling weeds and maintaining vegetable cash crop yields. The objective of this research was to determine whether roller-crimped rye can provide season-long weed control and maintain sweet corn yield. Two rye cultivars (early vs. standard maturity) were compared at three seeding rates (150, 300, and 600 seeds m−2) for their effect on weed control and sweet corn yield. The trial was conducted at three locations: Harrow, ON, and St. Jean-sur-Richelieu, QC, from 2019 to 2021; and Agassiz, BC, in 2019 and 2021. Results suggest that although the early-maturing cultivar allowed for earlier roller crimping in some locations, it was inferior at weed control and resulted in lower sweet corn yield than local standard cultivars. The average rye biomass was lower than the current literature recommendations, and the resulting level of weed control was not high enough to prevent sweet corn yield loss in cover crop treatments. Weed control provided by roller-crimped rye peaked between crimping and 8 wk after crimping and was highest in the standard cultivars sown at 300 and 600 seeds m−2. Preliminary testing of supplemental postemergence weed control showed evidence for sweet corn yields comparable to the weed-free no-cover crop check. However, more research is needed. Overall, with the cultivars and seeding rates tested, roller-crimped rye is not a suitable stand-alone weed control option in sweet corn production. Given the benefits of cover crops, further research should evaluate its potential as a component of an integrated weed management program.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".