Five essays on conservation practice adoption
Date
2025-05
Authors
Du, Zhushan
Major Professor
Advisor
Feng, Hongli
Arbuckle, J.
Crespi, John
Dentzman, Katherine
Hart, Chad
Zhang, Wendong
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
This dissertation consists of five essays examining conservation practices adoption, with focus on farmers’ behavioral factors, environmental uncertainty, and adoption dynamics.
The first essay examines the trade-off between accountability and cost-effectiveness in agricultural carbon payment schemes. Using cover crops as a case study, it compares practice-based and performance-based payment systems, accounting for environmental uncertainty and farmers’ risk and ambiguity aversion. By incorporating Dynamic Land Ecosystem Model simulations, we show that practice-based payments generate about 2.2 times more carbon sequestration and achieve nearly 2.6 times higher enrollment than performance-based schemes when farmers exhibit both risk and ambiguity aversion. Our findings challenge the prevailing focus on performance-based payments in carbon markets.
In the second essay, we extend the first essay by investigating how uncertainty affects farmers’ participation decisions using survey experiments. Our findings reveal that most farmers are risk-averse (72.37%) and uncertainty-averse (over 50%), with transaction costs estimated at $3.3/acre, nearly 20% of expected payoff. The findings emphasize the importance of addressing behavioral responses and transaction cost barriers in designing effective conservation incentives.
The third essay analyzes temporal patterns of adoption and disadoption of conservation practice. We utilize three waves of panel survey data from Iowa farmers to categorize them into distinct behavioral groups: continuous adopters (6.6%), intermittent adopters (28.9%), and continuous non-adopters (64.5%). We demonstrate that both academic research and policy design should shift from focusing largely on “initial adoption” to an integrated emphasis on both “initial and continued adoption” for achieving long-term agricultural sustainability goals.
In the fourth essay, we compare logistic regression models with a Random Forest algorithm to examine factors driving cover crop adoption and disadoption. Our findings show that while traditional models offer interpretability grounded in economic theory, machine learning provides superior predictive power and reveals complex, non-linear relationships among key factors. SHAP analysis identifies adoption scale, past adoption behavior, and environmental factors as primary drivers of disadoption, with farmers having larger previous cover crop acreage and consistent adoption history significantly less likely to disadopt.
The fifth essay examines the bidirectional relationship between Iowa farmers’ climate change beliefs and conservation behavior using a simultaneous equation model with instrumental variables. Our findings reveal an asymmetric relationship: conservation adoption significantly influences climate change beliefs, while climate change beliefs do not significantly impact conservation adoption decisions. This suggests a potential “win-win” strategy where promoting conservation practices through economic incentives not only delivers immediate environmental benefits but also reinforces farmers’ acceptance of climate change over time, potentially increasing support for broader climate mitigation efforts.
Series Number
Journal Issue
Is Version Of
Versions
Series
Academic or Administrative Unit
Type
dissertation