The Effects of Individual Variables, Farming System Characteristics and Perceived Barriers on Actual Use of Smart Farming Technologies: Evidence from the Piedmont Region, Northwestern Italy

Federica Caffaro,Eugenio Cavallo

doi:10.3390/agriculture9050111

Federica Caffaro, Eugenio Cavallo

Open Access

https://doi.org/10.3390/agriculture9050111

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Abstract

Smart Farming Technologies (SFTs) have a real potential to deliver more productive and sustainable agricultural production. However, limited empirical research is available on the role played by objective and subjective factors in the adoption of such disruptive innovations, especially in the Italian context. This study investigated the role of education, farm size, being a sole farmer, and perceived barriers in affecting the use of SFTs in a sample of Italian farmers from the Piedmont region (North-West Italy). Three hundred and ten farming operators were questioned via a paper-and-pencil questionnaire. The analyses showed that low levels of education and working on-farm alone were positively associated with perceived economic barriers, which in turn were negatively associated with the adoption of SFTs. Farm size had a positive direct effect on SFT adoption. The results pointed out the need for targeted policies and training interventions to encourage the use of SFTs.

Highlights

Agriculture is the oldest sector of the economy and after a period of consolidation of technologies since 1990–2000, it has experienced a spectacular evolution under the so-called Precision Agriculture (PA)
Smart Farming goes beyond the response of PA technologies to the field variability through the rapid advancement in the last decade of a combination of internet technologies and future-oriented technologies such as the internet of things (IoT), cloud computing and data analytics techniques, ability to store and process data, and to return information for decision making in real-time [1,2,3]: smart farming has a strong relation, but it is not limited, to the concepts of PA
As regards the investigated Smart Farming Technologies (SFTs), 37.1% of the participants focused on SFT Type 1, whereas 62.9% chose SFT Type 2

Summary

Introduction

Agriculture is the oldest sector of the economy and after a period of consolidation of technologies since 1990–2000, it has experienced a spectacular evolution under the so-called Precision Agriculture (PA). Smart Farming goes beyond the response of PA technologies to the field variability through the rapid advancement in the last decade of a combination of internet technologies and future-oriented technologies such as the internet of things (IoT), cloud computing and data analytics techniques, ability to store and process data, and to return information for decision making in real-time [1,2,3]: smart farming has a strong relation, but it is not limited, to the concepts of PA These technologies, together with synthetic biology, neurotechnologies, nanomaterials, advance energy and storage technologies are expected to disrupt and greatly affect economies and societies over the future (10–15 years) unfolding their effects on the entire agricultural value chain [2,4,5]. Smart Farming Technologies (SFTs) are recognized to have a real potential to deliver several advantages to agriculture: a more productive, profitable and sustainable agricultural production, based on a more precise and resource-efficient approach able to reduce the ecological footprint of this sector, safer and better quality products and enhanced consumers’ products acceptance through traceability and new direct forms of interaction among farmers, traders, processors, retailers

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