Effect of reduced herbicide rates on weed control, environmental impact and profitability of corn
Bibliographic record
Abstract
A study was conducted over a 3-yr period (2003, 2004, and 2005) to evaluate the effect of reduced herbicide rates - 20, 40, 60, 80, and 100% of the manufacturer’s recommended rate (MRR) - on weed biomass reduction, environmental impact (EI), yield, and profitability of corn (Zea mays L.) in Ontario. The herbicide rate required to provide 95% biomass reduction of velvetleaf (Abutilon theophrasti Medic.), redroot pigweed (Amaranthus retroflexus L.), common ragweed (Ambrosia artemisiifolia L.), common lambsquarters (Chenopodium album L.), and annual grasses was 92, 30, 41, 28, and 83% of the MRR for isoxaflutole plus atrazine, >200, 119, 23, 23, and 117% of the MRR for dimethenamid plus dicamba/atrazine, 141, 72, 46, 45, and >200% of the MRR for glufosinate plus atrazine, and 81, 29, 18, 24, and 88% of the MRR for nicosulfuron/rimsulfuron plus dicamba/diflufenzopyr, respectively. The herbicide rate required to provide 95% of weed-free corn yield was 61, 22, 130, and 11% of the MRR for isoxaflutole plus atrazine, dimethenamid plus dicamba/atrazine, glufosinate plus atrazine, and nicosulfuron/rimsulfuron plus dicamba/diflufenzopyr, respectively. Nicosulfuron/rimsulfuron plus dicamba/diflufenzopyr had the lowest EI. The results of profitability analysis suggested that the MRR rates do not tend to maximize profit margins. In most cases, there were no significant differences in profit margins for treatments with 40, 60, 80, and 100% of the MRR. Key words: Atrazine, dicamba, diflufenzopyr, dimethenamid, glufosinate, nicosulfuron, rimsulfuron, Zea mays L.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".