Canadian Agricultural Business Risk Management Programs: Implications for Farm Wealth and Environmental Stewardship
Bibliographic record
Abstract
This paper examines the effects of Canadian agricultural business risk management (BRM) programs on farm financial performance and incentives to adopt environmental stewardship practices (i.e., Beneficial Management Practices [BMPs]). Monte Carlo simulation is used to model stochastic prices and production for a representative Alberta cropping operation. Net present value analysis is used to evaluate BRM program participation. Participation is modeled for Growing Forward and Growing Forward 2 versions of AgriInvest, AgriStability, and AgriInsurance. Adoption of select BMPs is also modeled. 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 changes to BRM programs under Growing Forward 2 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 agri‐environmental policy in terms of policy effectiveness, efficiency, and compatibility. Cet article examine les effets des programmes canadiens de gestion de risques d'entreprises (GRE) sur le rendement financier des exploitations agricoles et les mesures incitatives pour l'adoption de pratiques d'intendance environnementales (comme les pratiques de gestion bénéfiques ou PGB). La simulation Monte Carlo sert à modéliser les prix et la productions stochastiques pour une exploitation agricole albertaine représentative. L'analyse de la valeur actualisée nette sert à évaluer la participation aux programmes GRE. La participation est modélisée pour Cultivons l'avenir et Cultivons l'avenir 2, des versions d'Agri‐investissement, Agri‐stabilité et Agri‐assurance. L'adoption de quelques PGB est aussi modélisée. Les résultats démontrent que la participation aux programmes GRE améliore considérablement le rendement financier avec une réduction correspondante du risque. Une grande part des avantages à la participation proviennent du financement associé aux programmes. La réduction du soutien résultant des modifications aux programmes GRE dans le cadre de Cultivons l'avenir 2 n'a entraîné qu'un impact minime sur le rendement de l'exploitation agricole représentative. La participation aux programmes GRE renforce les mesures incitatives pour l'adoption de PGB qui produisent déjà des avantages positifs nets (comme les PGB de la rotation des cultures) et augmente l'envergure des contre‐incitations (comme le coût net) associées à l'adoption de PGB de l'exploitation des terres comme le rétablissement des zones humides ou tampons. Les résultats de ces analyses soulèvent des questions liées à l'efficacité, l'efficience et à la compatibilité à la fois des politiques de gestion du risque et agroenvironnementales.
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 | 0.002 | 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 | 0.001 | 0.001 |
| 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".