Strategies and tactics for herbicide use reduction in field crops in Canada: A review
Bibliographic record
Abstract
There are many economic and health reasons for reducing pesticide use in Canada. Herbicide use on field crops is by far the most common pesticide use in Canada. This paper is a review of four topics related to herbicide use reduction on field crops in Canada: (1) broad strategies and (2) specific tactics for herbicide use reduction; (3) factors affecting adoption; and 4) research approaches for improving the implementation of herbicide use reduction. Numerous tactics exist to use herbicides more efficiently and herbicides can sometimes be replaced by non-chemical weed control methods. Many of these tactics and methods have been investigated and demonstrated for use on field crops in Canada. However, herbicide use reduction is fundamentally dependent upon preventative strategies designed to create robust cropping systems that maintain low weed densities. Diverse crop rotation forms the basis of preventative strategies as it inherently varies cropping system conditions to avoid weed adaptation. There is evidence that residual weed densities resulting from herbicide use reduction are manageable within competitive cropping systems. A great deal of research has been done on herbicide use reduction on field crops in Canada, and most projects report definite possibilities for herbicide use reduction in field crop production in Canada. Synthesizing and extending this information and customizing it for use on individual farms remain challenges. Collaboration between researchers and farmers can help to build successful strategies for herbicide use reduction which reflect the context of modern farming, the will of farmers and the culture of technology adoption among farmers. Key words: Pesticide use reduction, low-input agriculture, integrated pest management, integrated weed management
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".