The Economics of Beneficial Management Practices Adoption on Representative Alberta Crop Farms
Bibliographic record
Abstract
Monte Carlo simulation was used to examine the on-farm economics from adoption of Beneficial Management Practices (BMPs) on five representative Alberta cropping farms. Adoption of shelterbelts, buffer strips, residue management, and the addition of annual and perennial forages, field peas, and oats in crop rotations were included as BMPs that contribute positively to Ecological Goods and Service production from agriculture. Results suggest positive on-farm benefits associated with perennial forage and field pea BMPs. Conversely, BMPs that reduce availability of land for cropping activities, such as shelterbelts and buffer strips, and BMPs that do not increase revenues, such as oats and annual forages in rotation, are costly to producers. The results of this thesis have important policy implications. Policy mechanisms that incorporate positive mechanisms may improve adoption of BMPs that are costly to producers, while extension mechanisms, such as information programs, may improve the adoption of economically feasible BMPs.
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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 | 0.004 |
| 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 | 0.000 |
| 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".