Reduced herbicide rates provide acceptable weed control regardless of corn planting strategy in Ontario field corn
Bibliographic record
Abstract
A study was conducted at three locations in central-southwestern Ontario from 1996 to 1998 to determine if corn (Zea mays L.) productivity and weed control can be maintained when row spacing is narrowed, crop density is increased and herbicide rate is reduced. Post-emergence herbicides [(rimsulfuron + nicosulfuron) plus (dicamba + atrazine)] at four rates (full label rate (1×), 75% full rate (0.75×), 50% full rate (0.5×) and an untreated check) were tested at three corn row-spacings (38, 50, and 75 cm) and two plant densities (75 000 and 90 000 plants ha-1). Herbicide application at the 0.5× rate versus an untreated check still allowed for increased corn yield (8.3 vs. 4.9 t ha-1) and decreased weed dry weight (9.4 vs. 240.4 g m-2), weed plant density (11.0 vs. 52.6 plants m-2) and weed seed density (239 vs. 14 241 seeds m-2). Corn LAI was not affected by decreasing herbicide rate. In this study, increasing corn plant density and decreasing corn row spacing were not factors in reducing herbicide inputs in corn cropping systems in central-southwestern Ontario. Herbicide rate could be reduced by up to 50% while maintaining corn grain yield, weed density, weed dry weight, and the number of weed seeds entering into the soil seed bank. Implementation of these reduced rates will help to increase the economic and environmental sustainability of the Canadian field corn industry. Key words: Agricultural systems, crop yield, seeding rate, sustainability, weed biomass
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".