Governing Water Quality Limits In Agricultural Watersheds
Bibliographic record
Abstract
The diffuse runoff of agricultural nutrients, also called agricultural nonpoint source pollution (NPS), is a widespread threat to freshwater resources. Despite decades of research into the processes of eutrophication and agricultural nutrient management, social, economic, and political barriers have slowed progress towards improving water quality. A critical challenge to managing agricultural NPS pollution is motivating landowners to act against their individual farm production incentives in response to distant ecological impacts. The complexity of governing the social-ecological system requires improved understanding of how policy shapes farmer behavior to improve the state of water quality. This dissertation contributes both theoretically and empirically to NPS pollution governance by examining the impacts of water quality policy design on farmer nutrient management decision making and behavior. In the first study, I theoretically contextualize the issue of agricultural NPS pollution in the broader discussion of environmental public goods dilemmas to suggest that an increased focus on the link between policy and behavior can improve sustainable resource management. I propose two empirical approaches to study the policy-behavior link in environmental public goods dilemmas: 1) explicit incorporation of social psychological and behavioral variables and 2) utilization of actor mental models, or perceptions of the world that guide decision making, to identify behavioral drivers and outcomes. In the second and third studies, I then use these approaches to examine how water quality policies for agricultural NPS collectively change farmer behavior to reduce nutrient emissions. The second chapter uses a quantitative, survey-based approach to examine the relationship between mandatory policy design and behavior change in New Zealand. I find that a shift to mandatory policy is not immediately associated with increased adoption of nutrient management practices, but the mandatory policy design is important for potential future behavior change and long-term policy support. In the third study, I combine qualitative methodology with network analysis of qualitative data to examine a spectrum of agricultural NPS pollution policies in Vermont, USA and Taupo and Rotorua, New Zealand. I use farmer mental models to examine behavior change within each of the regions, the perceived drivers of behavior change and perceived outcomes of the policy. In this study, farmers across all three regions cite mandatory water policy as a key behavioral driver, but in each region, policy design interacts with the social-ecological context to produce distinct patterns of behaviors and perceived outcomes. Taken together, this dissertation demonstrates that agricultural NPS pollution policy design must consider the interactions between policy and other social-ecological behavioral drivers in order to achieve long term water quality improvements.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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 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".