Collaborative agri-environmental governance in the Netherlands: a novel institutional arrangement to bridge social-ecological dynamics
Bibliographic record
Abstract
The theoretical benefits of collaborative landscape-scale approaches to agri-environmental land management have been widely discussed. However, there is little empirical study of the practical governance mechanisms through which such collaborative management may be realized. In 2016, an innovative collaborative agri-environmental scheme was established in the Netherlands. In this scheme, “agricultural collectives”—i.e., groups of farmers organized as certified conservation organizations—are collectively responsible for the implementation of agri-environmental policies at the local level. With a focus on the Dutch model’s multi-level governance dimensions, this article examines how devolving important aspects of decision making on agri-environmental management to the level of a collective body of farmers shapes the implementation of agri-environmental policies on the ground. Based on new empirical data, we highlight the important roles of agricultural collectives in balancing trade-offs between ecological and social targets when setting environmental objectives, coordinating landscape-scale management, and contracting individual farmers. At the same time, the local embeddedness of agricultural collectives and close interpersonal ties can give rise to new governance risks that need to be considered, including goal divergence between the collectives and public bodies, as well as cases of prioritization of social interests over ecological interests in agri-environmental management. As we argue, combining governance through agricultural collectives with a high level of transparency regarding contracting decisions, as well as enhancing the inclusivity of the scheme through new funding opportunities for agri-environmental management, can optimize the benefits of these collectives.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".