Drivers of collective action in agricultural water conservation: Applying the social identity model to Iranian farmers in a wetlands basin
Bibliographic record
Abstract
Wetlands are vital ecosystems underpinning biodiversity, climate regulation, and rural livelihoods, yet they face escalating degradation from drought and anthropogenic pressures, particularly in developing regions. The internationally significant Bakhtegan Wetland in southern Iran exemplifies this crisis. While farmer participation is recognized as critical for sustainable wetland management, the psychosocial drivers of their engagement in collective action remain poorly understood. Addressing this gap, this study applies the Social Identity Model of Collective Action (SIMCA) to investigate the determinants of farmers’ intentions to engage in collective conservation. Data were collected via structured questionnaires from a stratified random sample of 200 farmers in the adjacent Fars Province. Analysis using structural equation modeling (SEM) demonstrated that collective efficacy, negative emotions, and climate-related behaviors are direct predictors of participation intention. Furthermore, social identity functions as a key mediator of these relationships. A key finding is the primacy of climate-related behavior and social identity as direct drivers, whereas the influence of collective efficacy was primarily indirect, acting through the reinforcement of a shared group identity. These findings advance the theoretical integration of social psychology and environmental management by validating the application of SIMCA in a natural resource conservation context. Practically, they suggest that effective policies for community-led wetland restoration should prioritize identity-based interventions, programmes designed to build collective efficacy, and targeted environmental education. This study underscores the necessity of integrating social dynamics into transformative governance strategies for threatened socio-ecological systems. • Applied the Social Identity Model of Collective Action (SIMCA) to wetland conservation. • Collective efficacy and climate behavior emerged as strongest predictors of participation. • Social identity significantly mediated farmers’ intention to engage in conservation. • Findings inform identity-based policies for participatory and transformative wetland governance
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".