Authors: Garry Sotnik*, Idaho State University, Morey Burnham, Idaho State University, Vicken Hillis, Boise State University, Jodi Brandt, Boise State University
Topics: Coupled Human and Natural Systems
Keywords: adaptive capacity, agriculture, cognition, decision-making, knowledge-based, multi-agent, resilience, SOSIEL, vulnerability, water policy.
Session Type: Paper
Start / End Time: 8:00 AM / 9:40 AM
Room: Marriott Ballroom Salon 1, Marriott, Lobby Level
Presentation File: No File Uploaded
The future success of agriculture in the arid and semi-arid western United States hinges on the capacity of farmers and water and agricultural management organizations to adapt to changing water resource constraints. Recent scholarship on agricultural adaptation has called for research to focus on the cross-scalar and spatio-temporal dynamics of adaptation decision-making, with a particular focus on how farmer and organizational adaptation actions interact to produce landscape level outcomes that change farmer resilience, vulnerability, and adaptive capacity (RVAC). To investigate these dynamics, we will describe the development and application of a spatio-temporal social-ecological decision support system (SE DSS) designed to assess the landscape level RVAC outcomes of farmer adaptation to the implementation of a managed aquifer recharge program in southeast Idaho that required them to cut between 4% and 20% of their water use starting in the 2016 irrigation season. The SE DSS couples the knowledge-based cognitive multi-agent Self-Organizing Social & Inductive Evolutionary Learning (SOSIEL) Platform, which simulates cross-generational decision-making in social contexts, with a spatio-temporal model of the agricultural system in southeast Idaho. The knowledge and attributes of the SOSIEL agents representing local farmers is based on focus groups, interviews, and census data. The research serves as a framework for integrating decision-making into RVAC analysis and sets the SE DSS up for participatory research with stakeholders aimed at analyzing how current adaptation decisions may influence future RVAC levels and identifying which adaptation decisions have the highest potential for increasing resilience and adaptive capacity while reducing vulnerability.