A Rationale for Atrazine Stewardship in Corn
Bibliographic record
Abstract
In several European nations, including France and Germany, atrazine has been banned because of environmental concerns. However, in Canada, atrazine remains an important component of modern weed control in corn. The objectives of this study were to determine the value of atrazine to corn producers by examining weed control efficacy, yield of corn, adjusted gross return, and the variability associated with PRE and POST herbicides applied alone or in combination with atrazine. A randomized complete-block design experiment was conducted at two locations for 3 yr to evaluate the performance of selected PRE and POST herbicides with and without atrazine. The addition of atrazine to PRE herbicides increased weed control (25%), improved herbicide performance consistency, increased corn yields (8%), increased adjusted gross return (Can$59 ha−1), and reduced risk ($30 ha−1) over sites and years. Although improving weed control, the addition of atrazine to POST herbicides increased the risk of return compared with treatments without atrazine by about $20 ha−1because the increased cost of atrazine was not always offset by higher corn yields. Our results clearly demonstrate a value of atrazine for broadleaf weed control in corn, both in terms of efficacy and economic return. From our findings, we estimated that the economic benefit of atrazine to Ontario, Canada, corn producers to be at least $26.1 million in 2004. Under current economic pressures facing agricultural producers, our findings show that a balance between the environmental effects and the benefits of atrazine to corn producers must be found because no alternative herbicide with equal economic and agronomic attributes is available at this time. To meet this balance, research on further reducing atrazine use rates while maintaining effective weed control in corn and on developing a sustainable stewardship plan is warranted.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".