Investigating the effect of farmer land-use decisions on rural landscapes using an agent-based model approach
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Land use and cover change (LUCC) is increasingly recognised as one of the most visible impacts of humans on nature. In rural areas, most of the observed LUCC is associated with agricultural activities. This has traditionally been attributed to the interplay of the socio-economic and political milieu, and the opportunities and constraints arising from the climatic conditions and physical attributes of land. Although there is no doubt that these factors influence farmer decisions, the mosaic of farming systems suggests that farmers do not always behave uniformly, even in areas with comparable socio-economic and environmental conditions. While the multi-facetted and varying nature of farmer decision-making is considered to be established knowledge in rural sociology, it is often neglected in LUCC models that typically describe it as homogeneous and rational in economic terms. This thesis presents an application of mixed-method social survey which aims at improving the representation of the diversity and complexity of farmer decision-making process in LUCC models. Different data collection methods (in-depth, semi-structured interviews, questionnaire) and analyses (thematic analysis, principal components analysis, cluster analysis, choice-based conjoint analysis) were used complementarily to identify the factors that facilitate or constrain farmer participation in environmental management practices (a), to identify the dominant farmer profiles (b) and to assess farmer preferences that influence land use decisions (c). Data collection was conducted in a study area located in the Canton of Aargau, Switzerland, where there is limited knowledge about farmer decision-making drivers and actions. Research findings were used to empirically inform an agent-based model that simulates farmer decisions. Paremeterised storylines were used to explore farmer decisions in alternative futures. An advanced and context-specific representation of human agents in modeling frameworks can make LUCC models valuable tools both for landscape analysis and policy making. In the face of new policy reforms, this thesis contributes to the achievement of this objective, by presenting an approach to explore and organize the heterogeneity of farmer behaviour and to make this usable in agent-based modeling frameworks.