Weed Populations, Sweet Corn Yield, and Economics Following Fall Cover Crops
Bibliographic record
Abstract
The effectiveness of cover crops as an alternative weed control strategy should be assessed as the demand for food and fiber grown under sustainable agricultural practices increases. This study assessed the effect of fall cover crops on weed populations in the fall and spring prior to sweet corn planting and during sweet corn growth. The experiment was a split-plot design in a pea cover–cover crop–sweet corn rotation with fall cover crop type as the main plot factor and presence or absence of weeds in the sweet corn as the split-plot factor. The cover crop treatments were a control with no cover crop (no-cover), oat, cereal rye (rye), oilseed radish (OSR), and oilseed radish with rye (OSR+rye). In the fall, at Ridgetown, weed biomass in the OSR treatments was 29 and 59 g m −2 lower than in the no-cover and the cereal treatments, respectively. In the spring, OSR+rye and rye reduced weed biomass, density, and richness below the levels observed in the control at Bothwell. At Ridgetown in the spring, cover crops had no effect on weed populations. During the sweet corn season, weed populations and sweet corn yields were generally unaffected by the cover crops, provided OSR did not set viable seed. All cover crop treatments were as profitable as or more profitable than the no-cover treatment. At Bothwell profit margins were highest for oat at almost Can$600 ha −1 higher than the no-cover treatment. At Ridgetown, compared with the no-cover treatment, OSR and OSR+rye profit margins were between Can$1,250 and Can$1,350 ha −1 and between Can$682 and Can$835 ha −1 , respectively. Therefore, provided that OSR does not set viable seed, the cover crops tested are feasible and profitable options to include in sweet corn production and provide weed-suppression benefits.
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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".