Key questions on the use of big data in farming: An activity theory approach

Abstract

Big data represent a pioneering development in the field of agriculture. By producing intuition, intelligence, and insights, these data have the potential to recast conventional process-driven agriculture, plotting the course for a smarter, data-driven farming. However, many open issues about the use of big data in agriculture remain unanswered. In this work, conceptualizing smart agricultural systems as cyber-physical-social systems, and building upon activity theory, we aim at highlighting some key questions that need to be addressed. To our view, big data constitute a tool reciprocally produced by all the actors involved in the agrifood supply chains. The constant flux of this tool and the intricate nature of the interactions among the actors who share it complicate the translation of big data into value. Moreover, farmers’ limited capacity to deal with data complexity, along with their dual role as producers and users of big data, impedes the institutionalization of this tool at the farm level. Although the approach used left us with more questions than answers, we suggest that unraveling the institutional arrangements that govern value co-creation, capturing the motivations of farmers and other actors, and detailing the direct and indirect effects that big data (and the technologies used to generate them) have in farms are important preconditions for setting forth rules that facilitate the extraction and equal exchange of value from big data.