Quantifying changes in the environmental impact of in-crop herbicide use in Saskatchewan, Canada
Bibliographic record
Abstract
Abstract The sustainable management of herbicides is critical to modern agriculture and the environment. This article examines the evolution and environmental implications of herbicide use in Saskatchewan, Canada, agriculture. It quantifies changes in herbicide use and their environmental impacts by analyzing farm-level herbicide use data from 1991 to 1994 and from 2016 to 2019 through the environmental impact quotient. Results confirm significant reductions in both environmental and toxicological impacts of herbicides used, underlining the pivotal shift from tillage-based weed control to herbicide-resistant cropping systems. The environmental impact of the top five herbicides (glufosinate, glyphosate, clethodim, imazamox, and 2,4-D) used from 2016 to 2019 is 65% lower than that for those herbicides (MCPA, 2,4-D, bromoxynil, diclofop-methyl, and trifluralin) used from 1991 to 1994, with a 45% reduction in the active ingredient applied per acre. Despite increased herbicide use due to more crop acres being seeded, the findings highlight a marked improvement in the sustainability of herbicide use, affirming the importance of technological advancements in agriculture. This research contributes valuable insights into long-term trends in herbicide use, offering a practical framework for informed decisions aligning with sustainable agricultural practices as well as reduced biodiversity impacts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".