Increased reliance on insecticide applications in<scp>C</scp>anada linked to simplified agricultural landscapes
Bibliographic record
Abstract
Intensification of agriculture and increased insecticide use have been implicated in global losses of farmland biodiversity and ecosystem services. We hypothesized that increased insecticide applications (proportion of area treated with insecticides) in Canada's expansive agricultural landscapes are due, in part, to shifts toward more simplified landscapes. To assess this relationship, we analyzed data from the Canadian Census of Agriculture spanning 20 years including five census periods (1996-2016) and across 225 census units within the four major agricultural regions of Pacific, Prairie, Central, and Atlantic Canada. Generalized mixed effects models were used to evaluate if changes in landscape simplification - defined as the proportion of farmland in crops (cereals, oilseeds, pulses and fruit/vegetables) - alongside other farming and climatic variables, influenced insecticide applications over time. Bayesian spatial-temporal models were further used to estimate the strength of the relationship with landscape simplification over time. We found that landscape simplification increased in 89% and insecticide applications increased in 70% of the Census Division spatial units during the 1996-2016 period. Nationally, significant increases in landscape simplification were observed in the two most agriculturally intensive regions of Prairie (from 55% to 63%) and Central (from 51% to 60%) Canada. For both regions, landscape simplification was a strong and significant predictor of higher insecticide applications, even after accounting for other factors such as climate, farm economics, farm size and land use practices (e.g., area in cash crops and tillage). If current trends continue, we estimated that insecticide applications will increase another 10%-20% by 2036 as a result of landscape simplification alone. To avoid increased reliance on toxic insecticides, agri-environmental policies need to consider that losing diverse natural habitat can increase insect pest pressure and resistance with negative environmental consequences extending beyond the field.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".