Agroecological transitions aim at developing sustainable farming and food systems, adapted to local contexts. Such transitions require the engagement of local actors and the consideration of their knowledge and reasoning as a whole, which encompasses different natures of knowledge (empirical, scientific, local, generic), related to different dimensions (economic, environmental, technical, social, political), as well as their values and perceived uncertainties. While these transitions are often problematized in relation to technical issues, this article's objective is to start from the way the (...) local actors consider these transitions in order to see what issues are actually involved. In this study, we analyzed the reasoning of diverse farming actors, including farmers and farm advisors. We conducted 30 cognitive mapping interviews, during which the local actors drew cognitive maps to explicit their reasoning concerning their agroecological transition with an open approach. Their reasoning revealed an emphasis on the human & social dimension of the process of agroecological transitions: (i) human and social considerations come first in the transition process, while technical solutions are viewed as secondary, (ii) use and development of human capacities, social interactions and human well-being are crucial to the conduct of agroecological transitions, (iii) human-scale farming appears as a condition for the use of human capacities, human well-being and rural development, (iv) agroecological transitions imply farming advisory transitions, and (v) rural development appears as a condition for agroecological transitions, implying transitions in society as a whole. (shrink)

In this paper we reflect on the effectiveness of cognitive mapping as a method to study farm functioning in its complexity and its diverse forms in the framework of our own experiment with a diverse group of Flemish beef farmers. With a structured direct elicitation method we gathered 30 CMs. We analyzed the content of these maps both qualitatively and quantitatively. The central role of the concept “Income” in most maps indicated a shared concern for economic security. Further, the CMs (...) indicated that farmers dealt with this shared social reality differently, as the relationships included in their maps referred to different functional processes relating to revenue streams, marketing strategies, investment decisions, dependence on production inputs, on-farm resource management, and personal well-being. With a clustering algorithm we grouped farmers based on the relationships in their maps, which allowed us to trace some of the broader patterns within the data, such as the existence of more business- and investment-minded farmers, in contrast to farmers focused on their quality of life, and animal production-oriented in contrast to marketing-oriented farmers. Taking into account farmers’ comments, we find that the applied methods had limited capability to classify farmers based on their perspectives on farming. Still, the system presentations proved useful to study what aspects farmers were working on or towards, and how these aspects may actually fit together as a whole. CMing was therefore mostly effective in exploring farm functioning in its complexity, and less so in exploring farm functioning in its diversity. (shrink)