Business risk management programs and the adoption of beneficial management practices in Canadian crop agriculture
Bibliographic record
Abstract
Abstract Canada's agricultural business risk management (BRM) programs require significant public expenditure, with unclear consequences related to climate change adaptation and mitigation through the adoption of beneficial management practices (BMPs). This study examines the relationship between Canada's current suite of BRM programs and the adoption of practices that mitigate GHG emissions in crop agriculture. We review the impacts of agricultural insurance on climate adaptation and mitigation, identifying impacts on both the intensive and extensive margins of production. We consider five potential program modifications, including: (1) changes in producer insurance premiums in AgriInsurance for the adoption of practices that would decrease the actuarially fair insurance rate if they were properly incorporated in the calculation; (2) dedicated insurance products related to trials of specific BMPs; (3) adjustments to current programs to allow more whole‐farm considerations and intercropping; (4) cross‐compliance measures on AgriInvest tied to environmental education; and (5) reduced insurance coverage for unfavorable environmental practices. While the effects of these potential modifications remain uncertain, they will drive the data collection process necessary to ensure that Canada's BRM programs play an appropriate role in greenhouse‐gas reducing BMP adoption and climate change adaptation and mitigation.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".