Vol. 14 No. 3 (2025)
Special Issue 13th AIEAA Conference

Towards digital farming: exploring technological integration in agricultural practices of a sample of italian livestock farms

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  • Ogochukwu Felicitas Okoye,
  • Selene Righi,
  • Colomba Sermoneta ,
  • Gianluca Brunori,
  • Michele Moretti
Ogochukwu Felicitas Okoye
Department of Agriculture, Food and Environment, University of Pisa, Pisa, Italy
Selene Righi
Department of Agriculture, Food and Environment, University of Pisa, Pisa, Italy
Colomba Sermoneta
National Institute of Statistics (ISTAT), Italy
Gianluca Brunori
Department of Agriculture, Food and Environment, University of Pisa, Pisa, Italy
Michele Moretti
Department of Agriculture, Food and Environment, University of Pisa, Pisa, Italy
DOI: https://doi.org/10.36253/bae-16777

Published 2025-06-03

Keywords

  • Digital agriculture,
  • Livestock farming,
  • technology adoption

How to Cite

Okoye, O. F., Righi, S., Sermoneta , C., Brunori, G., & Moretti, M. (2025). Towards digital farming: exploring technological integration in agricultural practices of a sample of italian livestock farms. Bio-Based and Applied Economics, 14(3), 61–76. https://doi.org/10.36253/bae-16777

Copyright (c) 2025 Ogochukwu Felicitas Okoye, Selene Righi, Colomba Sermoneta , Gianluca Brunori, Michele Moretti

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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Abstract

Despite the rapid rise of digital technologies in agriculture, their application remains more prominent in crop farming than in the livestock sector. Recognizing this gap, our study explores the current state and determinants of digital technology adoption across Italian livestock farms, examining key factors and broader trends in the industry. Using national agricultural census, and national statistical programme data, we applied a logistic regression model to assess the likelihood of adoption of technology. Findings reveal that large ruminant farms, particularly dairy cattle and buffalo, are more likely to integrate digital tools like decision support systems, cloud services, and monitoring devices. In contrast, meat cattle, small ruminants, and pig farms lag. Key determinants include broadband connectivity, ownership structure, education, and age, with additional factors influencing specific technology categories. Our results establish a foundation for future policy and investment, underscoring the need to build digital infrastructure and promote an inclusive model.

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