In this paper we reflect on the effectiveness of cognitive mapping (CMing) 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.

在本文中，我们反思了认知图谱（CMing）作为研究农场功能的方法的有效性，该方法在我们与一群佛兰德牛肉农场主的实验框架内进行了研究，研究了农场功能的复杂性和形式的多样性。通过结构化直接启发方法，我们收集了30个CM。我们定性和定量地分析了这些地图的内容。在大多数地图中，“收入”概念的核心作用表明人们共同关心经济安全。此外，CM还指出，农民对共享社会现实的处理方式有所不同，因为其地图中包含的关系指的是与收入流，营销策略，投资决策，对生产投入的依赖，农场资源管理以及个人福祉。使用聚类算法，我们根据农民在地图中的关系对农民进行了分组，这使我们能够追踪数据中的一些更广泛的模式，例如存在更多具有商业和投资意识的农民，而不是专注于农民的农民。生活质量和以畜牧生产为导向的农民与以市场为导向的农民形成鲜明对比。考虑到农民的意见，我们发现所采用的方法基于农民的农业观点对农民进行分类的能力有限。尽管如此，系统演示仍然被证明对研究农民正在努力或朝着哪些方面以及这些方面如何在整体上实际融合有用。因此，CMing在探索其复杂性的农场功能方面最有效，而在探索其多样性的农场功能方面效果不佳。