From cow sense to data sense: hybrid epistemologies on US dairy farms

Abstract

Data technologies are increasingly being adopted by the dairy industry to manage animal health. While some have hailed the adoption of such technologies into agriculture as transformative, critical data and agriculture scholars have suggested that big data has the potential to displace further the situational knowledge of farmers. Others suggest that these technologies simultaneously catalyze forms of relational agency, but also forms of resistance, where the lived experiences of farmers and cattle can hinder technology's effectiveness. With these critiques in mind, we assess how data technologies are put into practice on dairy farms. Drawing on interviews with producers and key advisers, we found that adoption is not seamless, and often fails to deliver on promises of labor and animal health optimization. The adoption of big data technologies can be confounded by cow and bacteria physiology, by the farm's existing infrastructure, and by the attitudes and knowledge base of farmers. These barriers to data technology adoption have produced hybrid epistemologies around animal health. This involves an uneasy and provisional blending of experiential and analytical methods, objective and subjective reasoning, and an ongoing tempering of the promise of greater optimization with the material realities of dairy farming. These hybrid epistemologies require people with the situational awareness to perform the hidden labor necessary to make the data useful for a given farm site. Despite adoption difficulties, dairy farmers continue to engage with data technologies, but the value of experience endures.