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Using machine learning techniques to assess the technology adoption readiness levels of livestock producers

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dc.contributor.authorMallinger, K.
dc.contributor.authorCorpaci, L.
dc.contributor.authorGoldenits, G.
dc.contributor.authorNeubauer, T.
dc.contributor.authorTikász, I.E.
dc.contributor.authorBanhazi, T.
dc.date.accessioned2025-08-15T09:57:35Z
dc.date.available2025-08-15T09:57:35Z
dc.date.issued2025
dc.descriptionReceived: March 23rd, 2025 ; Accepted: June 19th, 2025 ; Published: August 5th, 2025 ; Correspondence: kmallinger@sba-research.orgeng
dc.description.abstractTechnology adoption in agriculture, particularly in precision livestock farming (PLF), is often hindered by a range of barriers such as high investment costs, limited infrastructure, and uncertainty regarding the reliability and integration of new systems. Understanding these barriers is crucial for promoting the uptake of innovations that enhance sustainability and productivity. This study investigates technology adoption barriers in precision livestock farming to support sustainable agricultural development. A survey of 266 farms across several European countries and Israel was conducted to assess existing infrastructure and farmers' attitudes toward smart farming technologies. Using machine learning techniques, farmers were grouped into two clusters representing different levels of technological readiness. The study identified the most prominent factors influencing technology adoption, including the presence of smart technologies on-site, market accessibility, cost efficiency, and the ability to manage labor shortages. A Logistic Regression model further demonstrated high predictive accuracy for farmers' technological readiness based on these characteristics. These findings provide valuable insights into the main drivers and barriers of PLF adoption and highlight the relevance of data-driven approaches for requirement analysis and targeted policy interventions. By uncovering critical user traits and adoption barriers, this study offers structured guidance for policymakers, industry stakeholders, and researchers to foster the broader adoption of precision livestock technologies.eng
dc.identifier.citationMallinger, K., Corpaci, L., Goldenits, G., Neubauer, T., Tikász, I. E., & Banhazi, T. (2025). Using machine learning techniques to assess the technology adoption readiness levels of livestock producers. https://doi.org/10.15159/AR.25.074en
dc.identifier.issn2228-4907
dc.identifier.publicationAgronomy Research, 2025, vol. 23, no. 2, pp. 1147–1168eng
dc.identifier.urihttp://hdl.handle.net/10492/10107
dc.identifier.urihttps://doi.org/10.15159/ar.25.074
dc.publisherEstonian University of Life Scienceseng
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)eng
dc.rightsinfo:eu-repo/semantics/openAccesseng
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectcluster analysiseng
dc.subjectmachine learningeng
dc.subjectprecision livestock farmingeng
dc.subjectsurvey designeng
dc.subjecttechnological barrierseng
dc.subjectarticleseng
dc.titleUsing machine learning techniques to assess the technology adoption readiness levels of livestock producerseng
dc.typeArticleeng

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2025, Vol. 23, No. 2