A First View on the Competencies and Training Needs of Farmers Working with and Researchers Working on Precision Agriculture Technologies

Anastasios Michailidis (), Chrysanthi Charatsari, Thomas Bournaris, Efstratios Loizou, Aikaterini Paltaki, Dimitra Lazaridou and Evagelos D. Lioutas ()
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Anastasios Michailidis: Department of Agricultural Economics, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Chrysanthi Charatsari: Department of Agricultural Economics, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Thomas Bournaris: Department of Agricultural Economics, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Aikaterini Paltaki: Department of Agricultural Economics, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Dimitra Lazaridou: Department of Forestry and Natural Environment Management, Agricultural University of Athens, 36100 Karpenisi, Greece
Evagelos D. Lioutas: Department of Supply Chain Management, International Hellenic University, 60100 Katerini, Greece

Agriculture, 2024, vol. 14, issue 1, 1-12

Abstract: The penetration of precision agriculture technologies in agrifood systems generates the need for efficient upskilling programs targeted at farmers and other actors. A critical first step in this direction is to uncover the training needs of the actors involved in precision agriculture ecosystems. The present study aimed to identify and assess gaps in competencies related to precision agriculture technologies of Greek livestock farmers and researchers specialized in this field. For farmers, we followed a partially mixed research design. To uncover researchers’ training needs, we chose a qualitative-dominant mixed approach. The results revealed that farmers lack competencies concerning the exploitation of precision agriculture technologies. Depending on their area of expertise, researchers have needs associated with predicting how research affects the future of farming and understanding how precision agriculture artifacts interplay with socio-environmental and economic factors. Despite the limited generalizability of the findings, which represent a limitation associated with the reliance of data on two small sample sizes, our results indicate that, beyond technology-related competencies, it is essential to enhance the capacity of producers and researchers to foresight and shape potential (digital) futures.

Keywords: digital agriculture; precision agriculture; competencies; training needs; skills; farmers; researchers; technology; smart farming; digitalization (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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