Decision spaces in agricultural risk management: a mental model study of Austrian crop farmers

Hanger-Kopp, S. & Palka, M. (2021). Decision spaces in agricultural risk management: a mental model study of Austrian crop farmers. Environment, Development and Sustainability 10.1007/s10668-021-01693-6.

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Abstract

Drought has become a dominant climate risk both around the world and in Europe, adding to the already challenging task of farming and governing the agricultural sector under climate change. Drought risk management is extremely complex. Apart from irrigation, most drought risk management options have more than one goal and may potentially have negative trade-offs with other risk management objectives. Moreover, government regulations and market mechanisms influence farmers’ decision-making. However, previous studies, both in developed and in developing countries, have predominantly focused on attitudinal and structural influencing factors on farmers’ risk management behavior. In this paper, we comprehensively investigate farmers’ decision spaces with respect to drought risk management. We address two applied research questions: (1) What are farmers’ preferred drought risk management measures? (2) From a farmer’s perspective, what are the dominant factors influencing drought risk management decisions? We find that farmers primarily think of production-based rather than financial measures with respect to drought risk management. At the same time, natural and technical constraints and enabling factors dominate their mental decision space, followed by public and private institutional aspects. This research provides a basis for the design of integrated and holistic drought risk management policy and the drought risk governance needed for sustainable use of land and water resources such as needed to address systemic risks and achieve the Sustainable Development Goals. Moreover, we introduce a novel approach using mental models extracted from interviews to explore cognitive representations of farmers' decision spaces. This approach has the potential to complement mainstream research using standardized surveys and behavioral models to analyze drivers of risk management.

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