Farmers’ Preferences for Agri-Environmental Incentive Programs, Learning from Experiences in Ontario
Bibliographic record
Abstract
Based on a literature review, an international benchmark, and a case study in Ontario, the dissertation explores farmers' preferences for agri-environmental incentive programs. We use literature in agricultural, environmental and behavioural economics, and social psychology to propose an original analytical framework of farmers' preferences. This framework is tested in Ontario and helps understand farmers' motivations to participate or not to participate in environmental incentive programs. We then review the evolution of agri-environmental programs in Ontario since the 1930s. We develop original frameworks to compare programs fostering either collaboration or competition among farmers and use these frameworks to compare Ontario programs with contrasting programs in other provinces and OECD countries. Based on program design analysis and semi-structured interviews, we identify program characteristics that matter to Ontario farmers. Through a choice experiment, we then test which characteristics matter most in farmers' decisions and segment Ontario farmers into groups of heterogeneous preferences. The dissertation decisively adopts a mixed-methodology approach. Qualitative research is used to design the choice experiment (exploratory sequential design). Open-ended questions in the experiment link farmer choice-profiles with qualitative results, thus confirming, illustrating or nuancing choice experiment findings (convergent design). Finally, the participation of informal field advisors all along the research process allows for a research design targeted on current policy questions and continuous verification of intermediate findings. We find that programs designed to be the most competitive are not necessarily the most cost-efficient because medium and long-term dimensions are not considered. We identify five farmer profiles with different preferences and attitudinal traits. While 'Business Farmers' and 'Busy Farmers' have a strong preference for high incentive levels, other types of farmers either value technical assistance above the monetary incentive, are only willing to participate if their contact point is a fellow farmer, or are largely unwilling to participate. Several farmer profiles would prefer collective approaches. We also find that preferences are endogenous. They evolve as information emerges, applications succeed or fail, engagement takes place, and farmers discuss approaches and their impacts. The concluding chapter presents, on this basis, a series of research-grounded recommendations for agri-environmental policy design in Ontario.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".