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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References

  1. Abeni, F., Petrera, F., & Galli, A. (2019). A Survey of Italian Dairy Farmers’ Propensity for Precision Livestock Farming Tools. Animals, 9(5), 202. https://doi.org/10.3390/ani9050202
  2. Alanezi, M. A., Shahriar, M. S., Hasan, M. B., Ahmed, S., Yusuf, A., & Bouchekara, H. R. (2022). Livestock management with unmanned aerial vehicles: A review. IEEE Access, 10, 45001–45028.
  3. Alipio, M., & Villena, M. L. (2023). Intelligent wearable devices and biosensors for monitoring cattle health conditions: A review and classification. Smart Health. Scopus. https://doi.org/10.1016/j.smhl.2022.100369
  4. Alonso, R. S., Sittón-Candanedo, I., García, Ó., Prieto, J., & Rodríguez-González, S. (2020a). An intelligent Edge-IoT platform for monitoring livestock and crops in a dairy farming scenario. Ad Hoc Networks. Scopus. https://doi.org/10.1016/j.adhoc.2019.102047
  5. Alonso, R. S., Sittón-Candanedo, I., García, Ó., Prieto, J., & Rodríguez-González, S. (2020b). An intelligent Edge-IoT platform for monitoring livestock and crops in a dairy farming scenario. Ad Hoc Networks, 98, 102047. https://doi.org/10.1016/j.adhoc.2019.102047
  6. Alshehri, D. M. (2023). Blockchain-assisted internet of things framework in smart livestock farming. Internet of Things (Netherlands). Scopus. https://doi.org/10.1016/j.iot.2023.100739
  7. Altamore, L., Chinnici, P., Bacarella, S., Chironi, S., & Ingrassia, M. (2024). Current Framework of Italian Agriculture and Changes between the 2010 and 2020 Censuses. Agriculture, 14(9), 1603.
  8. An, I. (2024). Meeting the European Union’s digital agriculture requirements.
  9. Baker, D., Jackson, E. L., & Cook, S. (2022). Perspectives of digital agriculture in diverse types of livestock supply chain systems. Making sense of uses and benefits. Frontiers in Veterinary Science. Scopus. https://doi.org/10.3389/fvets.2022.992882
  10. 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. Scopus. https://doi.org/10.3965/j.ijabe.20120503.00?
  11. 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, 80, 163–174. https://doi.org/10.1016/j.landusepol.2018.10.004
  12. Barrett, H., & Rose, D. C. (2022). Perceptions of the Fourth Agricultural Revolution: What’s In, What’s Out, and What Consequences are Anticipated? Sociologia Ruralis, 62(2), 162–189. https://doi.org/10.1111/soru.12324
  13. Birke, F. M., Bae, S., Schober, A., Wolf, S., Gerster-Bentaya, M., & Knierim, A. (2022). AKIS in European countries: Cross analysis of AKIS country.
  14. Bucci, G., Bentivoglio, D., Finco, A., & Belletti, M. (2019). Exploring the impact of innovation adoption in agriculture: How and where Precision Agriculture Technologies can be suitable for the Italian farm system? 275(1), 012004.
  15. Buller, H., Blokhuis, H., Lokhorst, K., Silberberg, M., & Veissier, I. (2020). Animal welfare management in a digital world. Animals. https://doi.org/10.3390/ani10101779
  16. Calcante, A., Tangorra, F. M., Marchesi, G., & Lazzari, M. (2014). A GPS/GSM based birth alarm system for grazing cows. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2013.11.006
  17. Chavan, M., Dhage, S., Gaikwad, U., Deokar, D., Lokhande, A., & Kamble, D. (2024). Digital livestock farming: A review. International Journal of Advanced Biochemistry Research, 8(4S), 469–478. https://doi.org/10.33545/26174693.2024.v8.i4sf.1027
  18. Chen, X., Ou, X., Dong, X., Yang, H., Ubaldo, C., & Yue, X.-G. (2021). Impact of farmer organization forms on agricultural product quality from the perspective of technology adoption. 92–99.
  19. Cox, S. (2002). Information technology: The global key to precision agriculture and sustainability. Computers and Electronics in Agriculture, 36(2–3), 93–111. https://doi.org/10.1016/s0168-1699(02)00095-9
  20. Cui, L., & Wang, W. (2023). Factors Affecting the Adoption of Digital Technology by Farmers in China: A Systematic Literature Review. Sustainability, 15(20), 14824.
  21. D’Agaro, E., Rosa, F., & Akentieva, N. P. (2021). New Technology Tools and Life Cycle Analysis (LCA) Applied to a Sustainable Livestock Production. Eurobiotech Journal. Scopus. https://doi.org/10.2478/ebtj-2021-0022
  22. Eastwood, C. R., Edwards, J. P., & Turner, J. A. (2021). Review: Anticipating alternative trajectories for responsible Agriculture 4.0 innovation in livestock systems. Animal. Scopus. https://doi.org/10.1016/j.animal.2021.100296
  23. Elijah, O., Rahman, T. A., Orikumhi, I., Leow, C. Y., & Hindia, M. N. (2018). An overview of Internet of Things (IoT) and data analytics in agriculture: Benefits and challenges. IEEE Internet of Things Journal, 5(5), 3758–3773.
  24. Finger, R. (2023). Digital innovations for sustainable and resilient agricultural systems. European Review of Agricultural Economics, 50(4), 1277–1309.
  25. Fuentes, S., Gonzalez Viejo, C., Tongson, E., & Dunshea, F. R. (2022). The livestock farming digital transformation: Implementation of new and emerging technologies using artificial intelligence. Animal Health Research Reviews, 23(1), 59–71. https://doi.org/10.1017/S1466252321000177
  26. Gabriel, A., & Gandorfer, M. (2023). Adoption of digital technologies in agriculture—An inventory in a European small-scale farming region. Precision Agriculture. Scopus. https://doi.org/10.1007/s11119-022-09931-1
  27. Goodwin, B. K., Rivieccio, G., De Luca, G., & Capitanio, F. (2024). Computing impulse response functions from a copula-based vector autoregressive model: Evidence from the italian agri-food value chain. Quality & Quantity, 58(2), 1779–1797. https://doi.org/10.1007/s11135-023-01720-w
  28. Grivins, M., & Kilis, E. (2023). Engaging with barriers hampering uptake of digital tools. Italian Review of Agricultural Economics, 78(2), 29–38.
  29. 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
  30. Guntoro, B., Hoang, Q. N., & A’Yun, A. Q. (2019). Dynamic Responses of Livestock Farmers to Smart Farming. IOP Conference Series: Earth and Environmental Science. Scopus. https://doi.org/10.1088/1755-1315/372/1/012042
  31. Hackfort, S. (2021). Patterns of inequalities in digital agriculture: A systematic literature review. Sustainability (Switzerland). https://doi.org/10.3390/su132212345
  32. Kraft, M., Bernhardt, H., Brunsch, R., Büscher, W., Colangelo, E., Graf, H., Marquering, J., Tapken, H., Toppel, K., Westerkamp, C., & Ziron, M. (2022). Can Livestock Farming Benefit from Industry 4.0 Technology? Evidence from Recent Study. Applied Sciences (Switzerland). Scopus. https://doi.org/10.3390/app122412844
  33. MacPherson, J., Voglhuber-Slavinsky, A., Olbrisch, M., Schöbel, P., Dönitz, E., Mouratiadou, I., & Helming, K. (2022). Future agricultural systems and the role of digitalization for achieving sustainability goals. A review. Agronomy for Sustainable Development, 42(4), 70.
  34. Marino, R., Petrera, F., & Abeni, F. (2023). Scientific Productions on Precision Livestock Farming: An Overview of the Evolution and Current State of Research Based on a Bibliometric Analysis. Animals, 13(14), 2280. https://doi.org/10.3390/ani13142280.
  35. Michels, M., Fecke, W., Feil, J.-H., Musshoff, O., Pigisch, J., & Krone, S. (2020). Smartphone adoption and use in agriculture: Empirical evidence from Germany. Precision Agriculture, 21(2), 403–425. https://doi.org/10.1007/s11119-019-09675-5
  36. Morrone, S., Dimauro, C., Gambella, F., & Cappai, M. G. (2022). Industry 4.0 and Precision Livestock Farming (PLF): An up to Date Overview across Animal Productions. Sensors, 22(12), 4319. https://doi.org/10.3390/s22124319
  37. Neethirajan, S., & Kemp, B. (2021). Digital Livestock Farming. Sensing and Bio-Sensing Research. Scopus. https://doi.org/10.1016/j.sbsr.2021.100408
  38. Pulina, G., Francesconi, A. H. D., Stefanon, B., Sevi, A., Calamari, L., Lacetera, N., Dell’Orto, V., Pilla, F., Ajmone Marsan, P., Mele, M., Rossi, F., Bertoni, G., Crovetto, G. M., & Ronchi, B. (2017). Sustainable ruminant production to help feed the planet. Italian Journal of Animal Science, 16(1), 140–171. https://doi.org/10.1080/1828051x.2016.1260500
  39. Righi, S., Viganò, E., & Panzone, L. (2023). Consumer concerns over food insecurity drive reduction in the carbon footprint of food consumption. Sustainable Production and Consumption, 39, 451–465. https://doi.org/10.1016/j.spc.2023.05.027
  40. Sozzi, M., Kayad, A., Ferrari, G., Zanchin, A., Grigolato, S., & Marinello, F. (2021). Connectivity in rural areas: A case study on internet connection in the Italian agricultural areas. 466–470.
  41. Subach, T. I., & Shmeleva, Z. N. (2022). Introduction of digital innovations in livestock farming. IOP Conference Series: Earth and Environmental Science, 1112(1), 012079. https://doi.org/10.1088/1755-1315/1112/1/012079
  42. Tey, Y. S., & Brindal, M. (2012). Factors influencing the adoption of precision agricultural technologies: A review for policy implications. Precision Agriculture, 13(6), 713–730. https://doi.org/10.1007/s11119-012-9273-6.
  43. Thomann, B., Würbel, H., Kuntzer, T., Umstätter, C., Wechsler, B., Meylan, M., & Schüpbach-Regula, G. (2023). Development of a data-driven method for assessing health and welfare in the most common livestock species in Switzerland: The Smart Animal Health project. Frontiers in Veterinary Science, 10, 1125806. https://doi.org/10.3389/fvets.2023.1125806.
  44. Trapanese, L., Petrocchi Jasinski, F., Bifulco, G., Pasquino, N., Bernabucci, U., & Salzano, A. (2024). Buffalo welfare: A literature review from 1992 to 2023 with a text mining and topic analysis approach. Italian Journal of Animal Science, 23(1), 570–584. https://doi.org/10.1080/1828051X.2024.2333813
  45. FAO. (2022), The State of Food and Agriculture 2022, FAO. https://doi.org/10.4060/cb9479en
  46. Thornton, P. K., Ericksen, P. J., Herrero, M., & Challinor, A. J. (2014). Climate variability and vulnerability to climate change: A review. Global Change Biology, 20(11), 3313–3328.
  47. Tuyttens, F. A. M., Molento, C. F. M., & Benaissa, S. (2022). Twelve Threats of Precision Livestock Farming (PLF) for Animal Welfare. Frontiers in Veterinary Science. Scopus. https://doi.org/10.3389/fvets.2022.889623
  48. Vaintrub, M. O., Levit, H., Chincarini, M., Fusaro, I., Giammarco, M., & Vignola, G. (2021). Precision livestock farming, automats and new technologies: Possible applications in extensive dairy sheep farming. Animal, 15(3), 100143.
  49. Vecchio, Y., De Rosa, M., Adinolfi, F., Bartoli, L., & Masi, M. (2020). Adoption of precision farming tools: A context-related analysis. Land Use Policy. Scopus. https://doi.org/10.1016/j.landusepol.2020.104481
  50. Weber, R., Braun, J., & Frank, M. (2022). How does the Adoption of Digital Technologies Affect the Social Sustainability of Small-scale Agriculture in South-West Germany? International Journal on Food System Dynamics. Scopus. https://doi.org/10.18461/ijfsd.v13i3.C3
  51. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big Data in Smart Farming – A review. Agricultural Systems, 153, 69–80. https://doi.org/10.1016/j.agsy.2017.01.023
  52. Zhou, Y., Tiemuer, W., & Zhou, L. (2022). Bibliometric analysis of smart livestock from 1998-2022. Procedia Computer Science, 214, 1428–1435. https://doi.org/10.1016/j.procs.2022.11.327