Introduction
Climate change has intensified water scarcity and unpredictability, putting pressure on traditional irrigation systems. In Taiwan, local farmers face complex challenges in managing water resources efficiently under climate stress. This study introduces the concept of semantic governance, which integrates data-driven and community-based decision-making. By applying a situational grounded model, the research explores how local coordination and adaptive management can strengthen agricultural resilience in dynamic environmental conditions.
Semantic Governance Framework
Semantic governance emphasizes the integration of shared meanings, local knowledge, and digital systems to coordinate resource use. In the context of irrigation, it focuses on harmonizing communication between farmers, policymakers, and water agencies. The framework enables a common understanding of climate data, soil conditions, and irrigation needs, fostering collective responses. This approach helps bridge scientific insights and traditional wisdom for sustainable water governance.
Climate Stress and Agricultural Systems
Taiwan’s agriculture faces increasing challenges from erratic rainfall, droughts, and extreme weather events. Climate stress directly affects irrigation scheduling, crop yield, and water distribution. Understanding these stressors through semantic mapping allows communities to anticipate risks and adapt. The research highlights how integrating climate modeling with social data can enhance farmers’ capacity to cope with water-related uncertainties.
Situational Grounded Model Development
The situational grounded model used in this study captures the interactions between local actors and environmental variables. It employs qualitative and quantitative insights to build a flexible governance structure adaptable to real-time conditions. By analyzing farmer narratives, institutional reports, and sensor data, the model reveals how social dynamics influence water coordination and collective decision-making in agricultural irrigation.
Local Irrigation Coordination Mechanisms
Effective irrigation coordination requires collaboration among local farmers, irrigation associations, and government bodies. The study identifies how semantic tools—such as shared data platforms and context-aware communication—enhance these networks. Coordination mechanisms built on mutual understanding and information exchange can reduce conflict and optimize water distribution, particularly during drought or flood conditions.
Policy Implications and Future Research
Findings from this study offer valuable insights for designing climate-adaptive water governance policies. Encouraging participatory irrigation management and integrating semantic technologies can significantly improve resilience. Future research should focus on cross-regional comparisons, technological scalability, and policy frameworks that strengthen community-driven water resource management under increasing climate stress.
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