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Record W7065930916

Governing Water Quality Limits In Agricultural Watersheds

2019· article· en· W7065930916 on OpenAlexfundno aff

Bibliographic record

VenueScholarWorks -A service of University of Vermont Libraries (University of Vermont) · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaVermont Water Resources and Lake Studies Center, University of VermontNational Science Foundation
KeywordsNonpoint source pollutionAgricultureWater qualityIncentiveAgricultural pollutionCorporate governancePublic goodAgricultural policyFarm waterWater resources
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.010
GPT teacher head0.187
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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