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The Colorado River Basin (CRB) supports the water supply for seven states and forty million people in the Western United States (US) and has been suffering an extensive drought for more than two decades. As climate change continues to reshape water resources distribution in the CRB, its impact can differ in intensity and location, resulting in variations in human adaptation behaviors. The feedback from human systems in response to the environmental changes and the associated uncertainty is critical to water resources management, especially for water-stressed basins. This paper investigates how human adaptation affects water scarcity uncertainty in the CRB and highlights the uncertainties in human behavior modeling. Our focus is on agricultural water consumption, as approximately 80% of the water consumption in the CRB is used in agriculture. We adopted a coupled agent-based and water resources modeling approach for exploring human-water system dynamics, in which an agent is a human behavior model that simulates a farmer’s water consumption decisions. We examined uncertainties at the system, agent, and parameter levels through uncertainty, clustering, and sensitivity analyses. The uncertainty analysis results suggest that the CRB water system may experience 13 to 30 years of water shortage during the 2019–2060 simulation period, depending on the paths of farmers’ adaptation. The clustering analysis identified three decision-making classes: bold, prudent, and forward-looking, and quantified the probabilities of an agent belonging to each class. The sensitivity analysis results indicated agents whose decision making models require further investigation and the parameters with the higher uncertainty reduction potentials. By conducting numerical experiments with the coupled model, this paper presents quantitative and qualitative information about farmers’ adaptation, water scarcity uncertainties, and future research directions for improving human behavior modeling.
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Farming decisions in a complex and uncertain world: Nitrogen management in Midwestern corn agriculture
Excess agricultural nitrogen (N) in the environment is a persistent problem in the United States and other regions of the world, contributing to water and air pollution, as well as to climate change. Efforts to reduce N from agricultural sources largely rely on voluntary efforts by farmers to reduce inputs and improve uptake by crops. However, research has failed to comprehensively depict farmers' N decision-making processes, particularly when engaging with uncertainty. Through analysis of in-depth interviews with US corn (Zea mays L.) growers, this study reveals how farmers experience and process numerous uncertainties associated with N management, such as weather variability, crop and input price volatility, lack of knowledge about biophysical systems, and the possibility of underapplying or overapplying. Farmers used one of two general decision-making management strategies to address these uncertainties: heuristic-based or data-intensive decision-making. Heuristic-based decision-making involves minimizing sources of uncertainty and reliance on heuristics and personal previous experiences, while data-intensive decision-making is the increased use of field- and farm-scale data collection and management, as well as increased management effort within a given growing season.
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- Award ID(s):
- 1832042
- PAR ID:
- 10170319
- Date Published:
- Journal Name:
- Journal of Soil and Water Conservation
- ISSN:
- 0022-4561
- Page Range / eLocation ID:
- jswc.2020.00070
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.2489/jswc.2020.00070
