Behavioral responses to environmental and policy changes in agricultural systems
Abstract/Contents
- Abstract
- Designing effective agri-environmental policies requires accurately anticipating grower responses to changing policy, economic, and environmental factors. Current understanding of behavioral response in agricultural systems is limited by models that fail to quantify and propagate uncertainty, fail to probe the implications of assumptions about model structure and underlying data, and fail to include important feedback loops between model components. The data underlying these models further limit understanding when their inadequate resolution or uncertainty specification obscure agricultural system details. We introduce computational research to employ modernized data and modeling in agri-environmental policy analysis. In Chapter 2, we develop Bayesian modeling for unambiguous groundwater depth uncertainty estimation from sparsely sampled groundwater data. The resulting groundwater maps improve spatial bounds and temporal resolution compared to existing depth maps and vastly improve uncertainty quantification. In Chapter 3, we estimate agricultural groundwater irrigation over the growing season at high spatial resolution using (i) new evapotranspiration modeling that offers unprecedented uncertainty quantification and (ii) newly compiled, comprehensive surface water data. We produce crop-specific groundwater irrigation estimates with clear confidence intervals by applying Monte Carlo methods to propagate uncertainty in the model's underlying data. In Chapter 4, we employ large-scale econometrics techniques to elucidate grower adaptation to rapid soil salinization at the field scale, which is the level at which growers adapt to changing conditions. Results show that grower adaptation significantly mitigates damages attributable to soil salinization, indicating underestimation of damages in previous economic assessments that could lead to inaccurate public policy analyses. Chapters 2-4 highlight the value of diverse, highly resolved datasets for environmental policy analysis. The pace of data-driven discovery is limited by diffusion of data sharing and reuse principles in the research community. In Chapter 5, we envision how embracing such data principles would accelerate breakthroughs in water treatment research that are critical to achieve ambitious improvements to our water systems. We recommend practical steps for stakeholders to bridge the gap between existing practices and this envisioned future.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Quay, Amanda |
|---|---|
| Degree supervisor | Mauter, Meagan |
| Thesis advisor | Mauter, Meagan |
| Thesis advisor | Fletcher, Sarah (Sarah Marie) |
| Thesis advisor | Luthy, Richard G |
| Degree committee member | Fletcher, Sarah (Sarah Marie) |
| Degree committee member | Luthy, Richard G |
| Associated with | Stanford University, Civil & Environmental Engineering Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Amanda N. Quay. |
|---|---|
| Note | Submitted to the Civil & Environmental Engineering Department. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/nd070kn5499 |
Access conditions
- Copyright
- © 2022 by Amanda Quay
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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