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Clarion Congress & Hotel, Cosmos 3B



Evagelos D. Lioutas1a, Chrysanthi Charatsari2, Marcello De Rosa3, Giuseppe La Rocca3

1: Technological Educational Institute of Central Macedonia, Department of Supply Chain Management and Hellenic Open University, School of Social Sciences

2: American Farm School of Thessaloniki, Strategic Project Management Office

3: University of Cassino and Southern Lazio, Department of Economics and Law

a: Corresponding author, evagelos@agro.auth.gr

Topic: It is smarter but is it smart? And, if so, is it farming? Managing the transition from process-driven to data-driven farming

Keywords: data-driven farming, smart farming, transition, data-driven decision-making


The recent boom in cloud computing and Internet of Things, along with the high availability of technologies like wireless sensor networks, automation systems, farmbotics, and unmanned aerial vehicles, led to a high enthusiasm for what researchers, ag-tech companies and policy-makers call smart (or digital) farming. Being based on the collection, analysis and use of real-time data, smart farming emerged as a revolution aimed at supporting farm management by detecting potential risks, predicting future occurrences, uncovering choices, and reducing reliance on heuristics in decision-making. The rise of big data offers farmers the opportunity to access enormous datasets which collect and store data derived from different sources, thus transforming agriculture from process-driven to data-driven. Nevertheless, such a transition is not an evolutionary but a revolutionary shift, associated with significant changes to which the involved actors are required to adapt. In the present set of studies we aim at depicting how Greek farmers and advisors experience the conversion from process- to data-driven farming. Study 1, using a mixed methods research design, and drawing on data from a sample of farmers, revealed that conversion to data-driven farming improves producers’ decision-making performance, but reduces felt work autonomy and creativity, whereas it increases perceived task complexity, work pressure and stress. A follow-up qualitative strand indicated that, during the early stages of transition, farmers are faced with the challenge to reorient their roles and to balance smart technologies and old traditions. In this vein farmers have to play two games at once, since they are trying to marry old farm management practices with data-driven decision making. Our second study, based on data from a sample of advisors, showed that adaptation to a new, data-driven world is not an easy task, since it requires advanced skills, ambidexterity, and the development of a novel problem solving culture. Taken together, the present findings reveal that transition from process- to data-driven farming is an inexorable change, which poses difficult challenges to farmers and advisors, since both of them have to redefine their roles and identities while simultaneously rediscovering the concept of farming.

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