The Integration of a Systematic Thinking Science and Cross-Impact Analysis for a Holistic View of Sustainable Agriculture Development: A Case Study in Phraek Nam Daeng Sub-District, Amphawa District, Samut Songkhram Province
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Abstract
Under the dynamics socio-economic and complex environment system, including the risk of climate change, that have affected natural resources security necessary for agriculture system. The Upper Gulf of Thailand is a coastal lowland with the complexity of the diverse ecosystem and human activities, which brings several challenges to local agriculture. Particularly on smallholder farmers that facing the risk of change and pressure toward local sustainable nature-based resources. This study proposes the integration methodology of system thinking, in the form of Causal Loop Diagrams (CLDs) development and the application of cross-impact analysis for a deeper understanding of a holistic view on the inter-linkage variables that lead to foster sustainable agriculture. The study was conducted in Phraek Nam Daeng Sub-district, located at the Upper Gulf of Thailand, with 6 villages as the basic unit of study because it represents a useful scale for the analysis of social-ecological systems. The purpose of study is to conceptualize participatory CLDs to reveal root causes and their interconnected variables, with influencing environmental drivers that affect local agricultural activities in a holistic manner. Further, the cross-impact analysis is adopted to identify the key influencing factors that contribute to prospective agriculture production. The result indicates that: (1) the causal relationships of local agriculture system in long-term perspective; (2) key influencing factors are nature-based capital and social capital to create the necessary condition to enhance sustainable agriculture production. The processes of this study would be applied as learning tools for local farmers and key related agencies to have a deeper understanding toward the system of the inter-linkage variable and key drivers to propose the direction of local agriculture practices. The CLDs developed in this study can be applied to other studies in remote areas that hold more or less similarities in geographical features with the area under this investigation.
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