Exploring the data divide through a social practice lens: A qualitative study of UK cattle farmers

• Published in: Preventive veterinary medicine 2023-11, Vol.220, p.106030-106030, Article 106030
• Main Authors: Doidge, C., Palczynski, L., Zhou, X., Bearth, A., van Schaik, G., Kaler, J.
• Format: Article
• Language: English
• Subjects: Beef farmers; Dairy farmers; Focus group; Qualitative; Social practice theory; Technology; Thematic analysis; Youngstock data
• ISSN: 0167-5877; 1873-1716
• DOI: 10.1016/j.prevetmed.2023.106030

Appropriate management decisions are key for sustainable and profitable beef and dairy farming. Data-driven technologies aim to provide information which can improve farmers’ decision-making practices. However, data-driven technologies have resulted in the emergence of a “data divide”, in which there is a gap between the generation and use of data. Our study aims to further understand the data divide by drawing on social practice theory to recognise the emergence, linkages, and reproduction of youngstock data practices on cattle farms in the UK. Eight focus groups with fifteen beef and nineteen dairy farmers were completed. The topics of discussion included data use, technology use, disease management in youngstock, and future goals for their farm. The transcribed data were analysed using reflexive thematic analysis with a social practice lens. Social practice theory uses practices as the unit of analysis, rather than focusing on individual behaviours. Practices are formed of three elements: meaning (e.g., beliefs), materials (e.g., objects), and competencies (e.g., skills) and are connected in time and space. We conceptualised the data divide as a disconnection of data collection practices and data use and interpretation practices. Consequently, we were able to generate five themes that represent these breaks in connection.Our findings suggest that a data divide exists because of meanings that de-stabilise practices, tensions in farmers’ competencies to perform practices, spatial and temporal disconnects, and lack of forms of feedback on data practices. The data preparation practice, where farmers had to merge different data sources or type up handwritten data, had negative meanings attached to it and was therefore sometimes not performed. Farmers tended to associate data and technology practices with larger dairy farms, which could restrict beef and small-scale dairy farms from performing these practices. Some farmers suggested that they lacked the skills to use technologies and struggled to transform their data into meaningful outputs. Data preparation and data use and interpretation practices were often tied to an office space because of the required infrastructure, but farmers preferred to spend time outdoors and with their animals. There appeared to be no normalisation of what data should be collected or what data should be analysed, which made it difficult for farmers to benchmark their progress. Some farmers did not have access to discussion groups