Diffusion of innovation, internet access, and adoption barriers for precision livestock farming among beef producers





rural connectivity, beef producers' perceptions, technology trialability, adoption complexity, policy recommendations


This study examined the relationship between internet access type and perceptions of Precision Livestock Farming (PLF) Technologies among beef producers in a specific state. Using data collected from an internet-based survey of beef producers (n = 137), this study conducted an exploratory factor analysis to construct variables corresponding to Diffusion of Innovation (DOI) attributes that influence innovation adoption. Findings indicate producers with cable, cellular, and broadband internet access had more favorable perceptions of PLF technologies in terms of barriers to adoption, while those with no internet access or satellite connections reported higher perceived complexity with the use of PLF technologies. Trialability and observability varied across internet types, suggesting hands-on experience and practical demonstrations might be more impactful for certain groups. Beef producers with satellite internet connections were more likely to perceive the need to trial PLF technologies before adoption. This study highlights the importance of internet access in rural areas and its potential impact on the adoption of PLF technologies, offering valuable insights for industry stakeholders and policymakers to promote the adoption of PLF technologies.


Download data is not yet available.


Akinyemi, B. E., Vigors, B., Turner, S. P., Akaichi, F., Benjamin, M., Johnson, A. K., Pairis-Garcia, M., Rozeboom, D. W., Steibel, J. P., Thompson, D. P., Zangaro, C., & Siegford, J. M. (2023). Precision livestock farming: A qualitative exploration of swine industry stakeholders. Frontiers in Animal Science, 4, [1150528]. https://doi.org/10.3389/fanim.2023.1150528

Banhazi, T. M., Lehr, H., Black, J. L., Crabtree, H., Schofield, P., Tscharke, M., & Berckmans, D. (2012). Precision livestock farming: An international review of scientific and commercial aspects. International Journal of Agricultural and Biological Engineering, 5(3), 1–9. http://ijabe.org/index.php/ijabe/article/view/599/498

Berckmans, D. (2017). General introduction to precision livestock farming. Animal Frontiers, 7(1), 6–11. https://doi.org/10.2527/af.2017.0102

Boehlje, M., & Langemeier, M. (2021, March 26). Adoption of precision agriculture technologies. farmdoc daily. https://farmdocdaily.illinois.edu/2021/03/adoption-of-precision-agriculture-technologies.html

Cardinal, R. N., & Aitken, M. R. F. (2005). ANOVA for the behavioural sciences researcher. Taylor & Francis Group. https://doi.org/10.4324/9780203763933

Dziuban, C. D., & Shirkey, E. C. (1974). When is a correlation matrix appropriate for factor analysis? Some decision rules. Psychological Bulletin, 81(6), 358–361. https://doi.org/10.1037/h0036316

Eastwood, C., Klerkx, L., Ayre, M., & Dela Rue, B. (2019). Managing socio-ethical challenges in the development of smart farming: From a fragmented to a comprehensive approach for responsible research and innovation. Journal of Agricultural & Environmental Ethics, 32(5-6), 741–768. https://doi.org/10.1007/s10806-017-9704-5

Fastellini, G., & Schillaci, C. (2020). Chapter 7 - Precision farming and IoT case studies across the world. In A. Catrignanò, G. Buttafuoco, R. Khosla, A. M. Mouazen, D. Moshou, & O. Naud, (Eds.), Agricultural internet of things and decision support for precision smart farming (pp. 331–415). Elsevier Inc. https://doi.org/10.1016/B978-0-12-818373-1.00007-X

Groher, T., Heitkämper, K., & Umstätter, C. (2020). Digital technology adoption in livestock production with a special focus on ruminant farming. Animal, 14(11), 2404–2413. https://doi.org/10.1017/S1751731120001391

Kaler, J., & Ruston, A. (2019). Technology adoption on farms: Using Normalisation Process Theory to understand sheep farmers’ attitudes and behaviours in relation to using precision technology in flock management. Preventive Veterinary Medicine, 170, 104715–104715. https://doi.org/10.1016/j.prevetmed.2019.104715

Klockars, A. J., & Hancock, G. R. (2000). Scheffé’s more powerful f-protected post hoc procedure. Journal of Educational and Behavioral Statistics, 25(1), 13–19. https://doi.org/10.2307/1165310

Lane, D. (2010). Tukey’s honestly significant difference (HSD) test. In N. Salkind, Encyclopedia of research design (pp. 1566-1570). Sage. https://doi.org/10.4135/9781412961288

Läpple, D., & Kelley, H. (2013). Understanding the uptake of organic farming: Accounting for heterogeneities among Irish farmers. Ecological Economics, 88, 11–19. https://doi.org/10.1016/j.ecolecon.2012.12.025

Makinde, A., Islam, M. M., Wood, K. M., Conlin, E., Williams, M., & Scott, S. D. (2022). Investigating perceptions, adoption, and use of digital technologies in the Canadian beef industry. Computers and Electronics in Agriculture, 198, 1-12. https://doi.org/10.1016/j.compag.2022.107095

Miller, R. L. (2015). Rogers’ Innovation Diffusion Theory (1962, 1995). In M. N. Al-Suqri & A. S. Al-Aufi (Eds.), Information seeking behavior and technology adoption: Theories and trends (pp. 261–274). IGI Global. https://doi.org/10.4018/978-1-4666-8156-9.ch016

Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222. https://doi.org/10.1287/isre.2.3.192

Neethirajan, S. (2017). Recent advances in wearable sensors for animal health management. Sensing and Bio-Sensing Research, 12, 15–29. https://doi.org/10.1016/j.sbsr.2016.11.004

Pillai, K. C. S., & Sudjana (1975). Exact robustness studies of tests of two multivariate hypotheses based on four criteria and their distribution problems under violations. The Annals of Statistics, 3(3) 617 – 636. https://doi.org/10.1214/aos/1176343126

Pruitt, J. R., Gillespie, J. M., Nehring, R. F., & Qushim, B. (2012). Adoption of technology, management practices, and production systems by U.S. beef cow-calf producers. Journal of Agricultural and Applied Economics, 44(2), 203-222. https://doi.org/10.1017/S1074070800000274

Ramirez, A. (2013). The influence of social networks on agricultural technology adoption. Procedia - Social and Behavioral Sciences, 79, 101–116. https://doi.org/10.1016/j.sbspro.2013.05.059

Rogers, E. M. (1962) Diffusion of innovations. Free Press.

Rose, D. C., Sutherland, W. J., Parker, C., Lobley, M., Winter, M., Morris, C., Twining, S., Ffloukes, C., Amano, T., & Dicks, L. V. (2016). Decision support tools for agriculture: Towards effective design and delivery. Agricultural Systems, 149, 165–174. https://doi.org/10.1016/j.agsy.2016.09.009

Tarrant, M., Manfredo, M. J., Bayley, P. B., & Hess, R. (1993). Effects of recall bias and nonresponse bias on self‐report estimates of angling participation. North American Journal of Fisheries Management, 13(2), 217–222. https://doi.org/10.1577/1548-8675(1993)013%3C0217:EORBAN%3E2.3.CO;2

Tobias, S., & Carlson, J. E. (1969). Brief report: bartlett’s test of sphericity and chance findings in factor analysis. Multivariate Behavioral Research, 4(3), 375–377. https://doi.org/10.1207/s15327906mbr0403_8

Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, EM-29(1), 28–45. https://doi.org/10.1109/TEM.1982.6447463

Upton, G., & Cook, I. (2001). Introducing Statistics (2nd ed). Oxford University Press.

Venkatesh, V., Thong, J., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328-376. https://doi.org/10.17705/1jais.00428

Vesely, S., & Klöckner, C. A. (2020). Social desirability in environmental psychology research: Three meta-analyses. Frontiers in Psychology, 11, 1395–1395. https://doi.org/10.3389/fpsyg.2020.01395

Vogels, E. A. (2021, August 19). Some digital divides persist between rural, urban and suburban America. Pew Research Center. https://policycommons.net/artifacts/1808201/some-digital-divides-persist-between-rural-urban-and-suburban-america/2543052

Warne, R (2014). A primer on multivariate analysis of variance (MANOVA) for behavioral scientists. Practical Assessment, Research & Evaluation, 19(17). https://doi.org/10.7275/sm63-7h70

Watkins, M. W. (2018). Exploratory factor analysis: A guide to best practice. Journal of Black Psychology, 44(3), 219–246. https://doi.org/10.1177/0095798418771807




How to Cite

Greig, J., Cavasos, K., Boyer, C., & Schexnayder, S. (2023). Diffusion of innovation, internet access, and adoption barriers for precision livestock farming among beef producers. Advancements in Agricultural Development, 4(3), 103–116. https://doi.org/10.37433/aad.v4i3.329