Mixing Public and Private Agri-Environment Schemes: Effects on Farmers Participation in Quebec, Canada
Bibliographic record
Abstract
Incentive-based mechanisms, such as payments for ecosystem services (PES) are increasingly being employed to encourage adoption of biodiversity conservation practices in agriculture. Farmers’ participation in a PES depends – amongst other factors – on their interactions with previous programs and schemes. This research analyses how the institutional characteristics and interactions of incentive-based mechanisms shape the type of farmers’ participation and the achievement of desired socio-ecological outcomes. This research focusses on the institutional frameworks of two programs in the Province of Quebec, Canada: the ‘Prime-Vert’ Program (public agri-environment scheme) and the ‘Alternative Land Use Services’ (ALUS) initiative (a privately-funded “PES” scheme). The institutional prescriptions of these two programs were examined and compared through the lenses of the Institutional Analysis and Development framework. We reveal the impact of the institutional framework on farmers’ participation by assessing the degree of farmers’ engagement in the implementation and management of schemes. Our results showed a strong dependence of the private PES on the public scheme, rendering both programs ultimately managed under the remit of the provincial government. While the complementarity of both programs diversifies sources of funding for farmers, the presence of rigid rules governing these incentives tend to treat farmers as passive beneficiaries of a network of centralized subsidies which they have little control over. This compromises farmers’ autonomy as the rigidity of rules impedes any attempt to achieve active participation in the design and implementation of agri-environmental practices.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".