Farm Wealth Implications of Canadian Agricultural Business Risk Management Programs
Bibliographic record
Abstract
This paper examines the effect of Canadian agricultural business risk management (BRM) programs on farm financial performance. Monte Carlo simulation is used to model stochastic prices and production for a representative Alberta cropping operation. Net present value (NPV) analysis is used to evaluate BRM program participation. Participation is modeled for AgriInvest, AgriStability, and AgriInsurance. Adoption of select BMPs is also modeled to examine the impact of BRM programs on incentives to adopt environmental stewardship practices. Results indicate that BRM program participation significantly improves farm financial performance with a corresponding reduction in risk. Much of the benefit from participation comes from subsidization associated with the programs. While recent changes to BRM programs result in reduced support, the impact on representative farm performance is small. BRM program participation reinforces incentives to adopt BMPs that already have positive net benefits (e.g., crop rotation BMPs) and increases the magnitude of disincentives (i.e., net costs) associated with adoption of land use BMPs such as wetland restoration or buffer strips. The results from this analysis raise questions related to both risk management and environmental policy in terms of policy effectiveness, efficiency and compatibility.
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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 | 0.002 | 0.007 |
| 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) | 0.003 | 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".