Understanding Farmers' Data Collection Practices on Small-to-Medium Farms for the Design of Future Farm Management Information Systems

DOI

Farm Management Information Systems (FMIS) integrate data from a variety of sources, including sensors, for the purpose of enabling farmers to interpret past activity and predict future performance. FMIS is traditionally designed for and used by large farms, given their capital and need for automation and to scale up. This paper examines the current data collection practices on small and medium farms so that FMIS systems can be better designed to their needs. Our empirical research comprises interviews conducted during 10 farm visits. Our semi-structured interviews incorporated questions about daily activities, points of decision-making, data sharing, and incentives for data collection. We analyzed the interviews by focusing on possible obstacles to adopting expanding digital data collection practices and how expanded data collection might help fulfill farmers' goals and motivations. We found that farmers use their own bespoke data collection techniques instead of or in parallel to more formalized methods and often hold key observations and hypotheses in their heads rather than committing them to any data collection system at all. Key barriers to FMIS adoption include technology skepticism, technical hurdles and lack of support, and self-doubt in technical skills. Based on this empirical work and analysis, we recommend that FMIS systems can best address the needs of small and medium farms by: 1) accounting for the farmers' different approaches to memorizing vs. storing data, 2) integrating rather than trying to replace existing practices, and 3) considering the economic and political motivations driving farm decision-making and practices.