Navigating Climate Risk
Abstract
Climate change increases the frequency and intensity of extreme weather events, leading to growing uncertainty in the agricultural sector. This dissertation examines how agricultural entrepreneurs perceive and interpret climate risks and how these perceptions are translated into decision-making on risk management at the farm level. Central to the study is the question of how climate change perceptions develop and how they influence adaptive behavior under conditions of uncertainty. Based on a systematic literature review and qualitative empirical research in the agricultural sector, this dissertation analyzes decision-making using the Value-Belief-Norm (VBN) theory, the Theory of Planned Behavior (TPB), and Contingency Theory (CT). The findings show that climate risk perceptions vary considerably among farmers and evolve over time through personal experiences, economic conditions, and social interactions. Decision-making on risk management is rarely fully rational and instead takes place within a dynamic context characterized by policy uncertainty, financial considerations, market volatility, regulatory constraints, and climatic variability. The dissertation demonstrates that climate risk perceptions and risk management decision-making are mutually influential and highly context-dependent. Effective climate adaptation in agriculture therefore requires measures that align with farmers’ everyday practices and business models. From a policy perspective, the study highlights the importance of stable and predictable policy frameworks, region-specific approaches, and collaboration between farmers, supply chain actors, and public authorities. For small and medium-sized agricultural enterprises, accessible and scalable support is essential to strengthen climate resilience and ensure the continuity of food production.
Fields of Study & Subjects
Climate change impacts on agriculture
Agricultural risk and resilience
Climate Change Communication and Perception
Publication details
- Publication date
- 4 March 2026
- Language
- EN
- Document type
- Thesis