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Drivers for precision livestock technology adoption: A study of factors associated with adoption of electronic identification technology by commercial sheep farmers in England and Wales

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  • Eliana Lima
  • Thomas Hopkins
  • Emma Gurney
  • Orla Shortall
  • Fiona Lovatt
  • Peers Davies
  • George Williamson
  • Jasmeet Kaler

Abstract

The UK is the largest lamb meat producer in Europe. However, the low profitability of sheep farming sector suggests production efficiency could be improved. Although the use of technologies such as Electronic Identification (EID) tools could allow a better use of flock resources, anecdotal evidence suggests they are not widely used. The aim of this study was to assess uptake of EID technology, and explore drivers and barriers of adoption of related tools among English and Welsh farmers. Farm beliefs and management practices associated with adoption of this technology were investigated. A total of 2000 questionnaires were sent, with a response rate of 22%. Among the respondents, 87 had adopted EID tools for recording flock information, 97 intended to adopt it in the future, and 222 neither had adopted it, neither intended to adopt it. Exploratory factor analysis (EFA) and multivariable logistic regression modelling were used to identify farmer beliefs and management practices significantly associated with adoption of EID technology. EFA identified three factors expressing farmer’s beliefs–external pressure and negative feelings, usefulness and practicality. Our results suggest farmer’s beliefs play a significant role in technology uptake. Non-adopters were more likely than adopters to believe that ‘government pressurise farmers to adopt technology’. In contrast, adopters were significantly more likely than non-adopters to see EID as practical and useful (p≤0.05). Farmers with higher information technologies literacy and intending to intensify production in the future were significantly more likely to adopt EID technology (p≤0.05). Importantly, flocks managed with EID tools had significantly lower farmer- reported flock lameness levels (p≤0.05). These findings bring insights on the dynamics of adoption of EID tools. Communicating evidence of the positive effects EID tools on flock performance and strengthening farmer’s capability in use of technology are likely to enhance the uptake of this technology in sheep farms.

Suggested Citation

  • Eliana Lima & Thomas Hopkins & Emma Gurney & Orla Shortall & Fiona Lovatt & Peers Davies & George Williamson & Jasmeet Kaler, 2018. "Drivers for precision livestock technology adoption: A study of factors associated with adoption of electronic identification technology by commercial sheep farmers in England and Wales," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-17, January.
    • Handle: RePEc:plo:pone00:0190489
    DOI: 10.1371/journal.pone.0190489
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    1. Barnes, Andrew P. & Thomson, Steven G. & Ferreira, Joana, 2020. "Disadvantage and economic viability: characterising vulnerabilities and resilience in upland farming systems," Land Use Policy, Elsevier, vol. 96(C).
    2. Grothkopf, Carina & Schulze, Holger, 2021. "Empirische Analyse der Einflussfaktoren auf die Digitalisierung der Milchviehhaltung," 61st Annual Conference, Berlin, Germany, September 22-24, 2021 317061, German Association of Agricultural Economists (GEWISOLA).
    3. Alexandros Theodoridis & Sotiria Vouraki & Emmanuel Morin & Georgia Koutouzidou & Georgios Arsenos, 2022. "Efficiency Analysis and Identification of Best Practices and Innovations in Dairy Sheep Farming," Sustainability, MDPI, vol. 14(21), pages 1-12, October.
    4. Yari Vecchio & Marcello De Rosa & Gregorio Pauselli & Margherita Masi & Felice Adinolfi, 2022. "The leading role of perception: the FACOPA model to comprehend innovation adoption," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 10(1), pages 1-19, December.
    5. Di Liu & Pan Wang, 2023. "WeChat E-Commerce, Social Connections, and Smallholder Agriculture Sales Performance: A Survey of Orange Farmers in Hubei Province, China," Agriculture, MDPI, vol. 13(11), pages 1-14, October.
    6. Paulus, Michael & Pfaff, Sara Anna, 2022. "Factors Affecting the Diffusion of Digital Farming Towards More Resilient Farming Systems - Empirical Evidence from Baden-Württemberg," 62nd Annual Conference, Stuttgart, Germany, September 7-9, 2022 329597, German Association of Agricultural Economists (GEWISOLA).
    7. Sayed Abdul Majid Gilani & Abigail Copiaco & Liza Gernal & Naveed Yasin & Gayatri Nair & Imran Anwar, 2023. "Savior or Distraction for Survival: Examining the Applicability of Machine Learning for Rural Family Farms in the United Arab Emirates," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    8. Shang, Linmei & Heckelei, Thomas & Gerullis, Maria K. & Börner, Jan & Rasch, Sebastian, 2021. "Adoption and diffusion of digital farming technologies - integrating farm-level evidence and system interaction," Agricultural Systems, Elsevier, vol. 190(C).
    9. Piotr Goliński & Patrycja Sobolewska & Barbara Stefańska & Barbara Golińska, 2022. "Virtual Fencing Technology for Cattle Management in the Pasture Feeding System—A Review," Agriculture, MDPI, vol. 13(1), pages 1-14, December.
    10. Barnes, A.P. & Soto, I. & Eory, V. & Beck, B. & Balafoutis, A. & Sánchez, B. & Vangeyte, J. & Fountas, S. & van der Wal, T. & Gómez-Barbero, M., 2019. "Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers," Land Use Policy, Elsevier, vol. 80(C), pages 163-174.
    11. Dongkai Lin & Bingsheng Fu & Kexiao Xie & Wanhe Zheng & Linjie Chang & Jinke Lin, 2023. "Research on the Improvement of Digital Literacy for Moderately Scaled Tea Farmers under the Background of Digital Intelligence Empowerment," Agriculture, MDPI, vol. 13(10), pages 1-26, September.

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