From the attitude towards digitalisation in agriculture to the acceptance of future agricultural technologies

Abstract

As agriculture undergoes a transformative phase propelled by technological innovations, the integration of digital farming tools is becoming increasingly prevalent in animal husbandry and arable farming. In animal husbandry, virtual fences, as a precision livestock farming technology, have emerged as a promising solution for managing livestock. Similarly, the rapid evolution of technology in arable farming continues to redefine the landscape of agricultural practices, with autonomous systems such as fully autonomous hacking robots playing a pivotal role. However, a limited understanding of the social and psychological factors and perceptions of risks and benefits influence farmers’ acceptance of these novel digital farming technologies in Switzerland. This study aimed to provide insights into farmers’ attitudes towards digital agriculture and to help understand the acceptance of digital farming technologies in the future. It sought to explore the drivers of and barriers to the acceptance of digital farming tools among family farm managers. A survey was conducted among 939 Swiss arable and animal farmers, and multiple linear regression models were used to determine robust predictors of attitude and acceptance of virtual fence technology and fully autonomous hacking robots. The results indicate that attitudes towards digital farming technologies depend on farmers’ characteristics, such as age, technology interaction affinity, education level, and digital competence, alongside their financial situation. Acceptance of virtual fences was influenced by farm characteristics (size, workforce), farmers’ perceptions (attitudes towards digital farming), digital competence, and risk–benefit perceptions. In contrast, the acceptance of fully autonomous hacking robots was influenced by farmers’ perceptions, education level, and risk–benefit perceptions. The results emphasise that the acceptance of specific technologies is driven by application-specific reasons and depends on risk–benefit assessments. The findings shed light on decision-making in digital agriculture for small-scale farms, highlighting the need for digital skill development and support for farmers in risk–benefit assessment. Recommendations include peer networks and research settings, such as model farms, to support farmers in adopting digital farming technologies.