Smart Farming Technologies and Changes to Farm Work: New Insights Into On-Farm Experiences

Nettle, Ruth and Ingram, Julie ORCID: https://orcid.org/0000-0003-0712-4789 (2025) Smart Farming Technologies and Changes to Farm Work: New Insights Into On-Farm Experiences. Technological Forecasting and Social Change, 218. art: 124227. doi:10.1016/j.techfore.2025.124227

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Abstract

The intersect between technologies and the future of work is a key topic for both practitioner and scholarly communities. In this paper, we explore this intersect within agriculture by examining the changes to work for farmers implementing smart farming technologies (SFTs) in the United Kingdom (UK) and Australia. We interviewed 17 farmers across the UK and Australia from horticulture, dairy and mixed farming (arable and livestock) enterprises who were implementing diverse SFTs. Interview questions explored farmers' experiences in implementing SFTs with respect to any changes to work for themselves and their employees. Based on an interdisciplinary conceptual framework we developed from the literature for analysing farm work, we applied qualitative data analysis methods to examine the changes to work. We found the benefits from reduced work-duration were commonly counteracted by time spent in computer set-up and data work, with subsequent negative effects for the cognitive and affective dimensions of workload. The organisation of farm work influenced the type of skills and knowledge required to implement SFTs, with larger and corporate farms outsourcing these requirements to advisers, while smaller-medium sized farms used SFTs to augment their existing knowledge and skills, enabling employers to do more with their own time and enhancing employee engagement in work. We found more similarities than differences in work changes between countries. The interrelationships and feedback loops we have identified between the different aspects of work bring a novel perspective to technological transitions in agriculture and represent an important orientation point for researchers and technology developers to better anticipate work effects from different types of SFTs.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	Robots; Data; Labour; Employment; Augmentation; Upskill
Subjects:	H Social Sciences > H Social Sciences (General) S Agriculture > S Agriculture (General) T Technology > T Technology (General)
Divisions:	Schools and Research Institutes > Countryside and Community Research Institute
Depositing User:	Caitlin Mackenzie
Date Deposited:	09 Jun 2025 11:17
Last Modified:	09 Jun 2025 11:30
URI:	https://eprints.glos.ac.uk/id/eprint/15109

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