The impact of changes in the AgriStability program on crop activities: A farm modeling approach
Bibliographic record
Abstract
Abstract To analyze the production impacts of changes made in 2013 to Canada's AgriStability risk management program, we calibrate a crop allocation model using positive mathematical programming (PMP). Because PMP is not straightforward if farmers are assumed to maximize expected utility (as a risk parameter also needs to be calibrated), we consider possible ways to address this issue but settle on a traditional approach used in the EU's Farm System Simulator. We calibrate farm management models for six different Alberta regions and use it to determine how changes in the AgriStability's payment trigger affect production incentives. Results indicate that, although the initial introduction of the AgriStability program in 2008 might have tilted farmers’ planting decisions toward crops with higher returns and greater risk, changes to this program reduce indemnities and farmers’ expected profits, but do not further alter land‐use decisions. Rather, it is increases in farmers’ aversion to risk that lead to the greatest changes in crop allocation. [EconLit citations: Q14, Q18, C61].
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".