Vol. 14 No. 4 (2025)
Full Research Articles

Agriculture 4.0: Technological adoption, drivers, benefits and challenges in Italy. A descriptive survey

Cosimo Pacciani
Department of Management Engineering, Politecnico di Milano, Via Lambruschini, 4b 20156 - Milano, Italy
Eleonora Catellani
Department of Management Engineering, Politecnico di Milano, Via Lambruschini, 4b 20156 - Milano, Italy
Andrea Bacchetti
Department of Mechanical and Industrial Engineering, University of Brescia, Via Brianze, 38 25123 - Brescia, Italy
Chiara Corbo
Department of Management Engineering, Politecnico di Milano, Via Lambruschini, 4b 20156 - Milano, Italy
Federica Ciccullo
Department of Management Engineering, Politecnico di Milano, Via Lambruschini, 4b 20156 - Milano, Italy
Marco Ardolino
Department of Mechanical and Industrial Engineering, University of Brescia, Via Brianze, 38 25123 - Brescia, Italy

Published 2025-07-14

Keywords

  • agriculture 4.0,
  • smart farming,
  • digital agriculture,
  • survey

How to Cite

Pacciani, C., Catellani, E., Bacchetti, A., Corbo, C., Ciccullo, F., & Ardolino, M. (2025). Agriculture 4.0: Technological adoption, drivers, benefits and challenges in Italy. A descriptive survey. Bio-Based and Applied Economics, 14(4), 101–119. https://doi.org/10.36253/bae-17382

Abstract

This study aims to examine the current state of awareness regarding Agriculture 4.0 (A4.0) among Italian agricultural enterprises and to analyse variations in adoption levels, expressed needs, perceived benefits, challenges and barriers to digitalisation. Drawing on data from a descriptive survey conducted among Italian farms in 2024, this study presents findings from 1,248 respondents. The results indicate varying levels of adoption of A4.0 solutions, with monitoring systems and connected vehicles being the most widely implemented. The primary drivers for A4.0 adoption include farm management, operational control, and the enhancement of production efficiency, all of which are associated with significant perceived benefits. However, challenges such as limited interoperability and skill shortages hinder A4.0 implementation, while financial and structural constraints remain major barriers for farms seeking to transition to A4.0. This study offers valuable insights to inform policymakers, industry stakeholders, and researchers in fostering a more effective and inclusive digital transformation in the Italian agricultural sector.

References

  1. Abbasi, R., Martinez, P., and Ahmad, R. (2022). The Digitization of Agricultural Industry – a Systematic Literature Review on Agriculture 4.0. Smart Agricultural Technology, 2: 100042. https://doi.org/10.1016/j.atech.2022.100042. DOI: https://doi.org/10.1016/j.atech.2022.100042
  2. Addorisio, R., Casolani, N., Maesano, G., Coderoni, S., Perito, M.A., Mattetti, M., Canavari, M., 2025. Barriers and drivers of digital agriculture adoption: Insights from Italian farming stakeholders. https://doi.org/10.18461/ijfsd.v16i1o1 DOI: https://doi.org/10.18461/ijfsd.v16i1o1
  3. Ahmed, B., Shabbir, H., Naqvi, S. R., and Peng, L. (2024). Smart Agriculture: Current State, Opportunities, and Challenges. IEEE Access, 12: 144456-78. https://doi.org/10.1109/ACCESS.2024.3471647. DOI: https://doi.org/10.1109/ACCESS.2024.3471647
  4. Albiero, D., De Paulo, R. L., Felix Junior, J. C., Da Silva Gomes Santos, J., and Melo, R. P. (2020). Agriculture 4.0: A Terminological Introduction. Revista Ciência Agronômica, 51(5). https://doi.org/10.5935/1806-6690.20200083. DOI: https://doi.org/10.5935/1806-6690.20200083
  5. Araújo, S. O., Peres, R. S., Barata, J., Lidon, F., and Ramalho, J. C. (2021). Characterising the Agriculture 4.0 Landscape - Emerging Trends, Challenges and Opportunities. Agronomy, 11(4): 667. https://doi.org/10.3390/agronomy11040667. DOI: https://doi.org/10.3390/agronomy11040667
  6. Assimakopoulos, F., Vassilakis, C., Margaris, D., Kotis, K., and Spiliotopoulos, D. (2024). The Implementation of “Smart” Technologies in the Agricultural Sector: A Review. Information, 15(8): 466. https://doi.org/10.3390/info15080466. DOI: https://doi.org/10.3390/info15080466
  7. Balasundram, S. K., Shamshiri, R. R., Sridhara, S., and Rizan, N. (2023). The Role of Digital Agriculture in Mitigating Climate Change and Ensuring Food Security: An Overview. Sustainability, 15(6): 5325. https://doi.org/10.3390/su15065325. DOI: https://doi.org/10.3390/su15065325
  8. Balyan, S., Jangir, H., Tripathi, S. N., Tripathi, A., Jhang, T., and Pandey, P. (2024). Seeding a Sustainable Future: Navigating the Digital Horizon of Smart Agriculture. Sustainability, 16(2): 475. https://doi.org/10.3390/su16020475. DOI: https://doi.org/10.3390/su16020475
  9. Bongiovanni, R., and Lowenberg-Deboer, J. (2004). Precision Agriculture and Sustainability. Precision Agriculture, 5: 359-387. https://doi.org/10.1023/B:PRAG.0000040806.39604.aa. DOI: https://doi.org/10.1023/B:PRAG.0000040806.39604.aa
  10. Bokusheva, R., and S. Kimura (2016). Cross-Country Comparison of Farm Size Distribution. OECD Food, Agriculture and Fisheries Papers, No. 94, OECD Publishing, Paris. https://doi.org/10.1787/5jlv81sclr35-en DOI: https://doi.org/10.1787/5jlv81sclr35-en
  11. Cambra Baseca, C., Sendra, S., Lloret, J., and Tomas, J. (2019). A Smart Decision System for Digital Farming. Agronomy, 9(5): 216. https://doi.org/10.3390/agronomy9050216. DOI: https://doi.org/10.3390/agronomy9050216
  12. Cisilino, F., Licciardo, F., 2022. Potential and Complexity of Implementing Financial Instruments in the Framework of Rural Development Policies in Italy – The Friuli Venezia Giulia Revolving Fund. Sustainability 14, 16090. https://doi.org/10.3390/su142316090 DOI: https://doi.org/10.3390/su142316090
  13. Da Silveira, F., Da Silva, S. L. C., Machado, F. M., Barbedo, J. G. A., and Amaral, F. G. (2023). Farmers’ Perception of the Barriers That Hinder the Implementation of Agriculture 4.0. Agricultural Systems, 208: 103656. https://doi.org/10.1016/j.agsy.2023.103656. DOI: https://doi.org/10.1016/j.agsy.2023.103656
  14. Da Silveira, F., Lermen, F. H., and Amaral, F. G. (2021). An Overview of Agriculture 4.0 Development: Systematic Review of Descriptions, Technologies, Barriers, Advantages, and Disadvantages. Computers and Electronics in Agriculture, 189: 106405. https://doi.org/10.1016/j.compag.2021.106405. DOI: https://doi.org/10.1016/j.compag.2021.106405
  15. Dayıoğlu, M., and Turker, U. (2021). Digital Transformation for Sustainable Future - Agriculture 4.0: A Review. Tarım Bilimleri Dergisi, 27. https://doi.org/10.15832/ankutbd.986431. DOI: https://doi.org/10.15832/ankutbd.986431
  16. Escribà-Gelonch, M., Liang, S., Van Schalkwyk, P., Fisk, I., Van Duc Long, N., and Hessel, V. (2024). Digital Twins in Agriculture: Orchestration and Applications. Journal of Agricultural and Food Chemistry, 72(19): 10737-52. https://doi.org/10.1021/acs.jafc.4c01934. DOI: https://doi.org/10.1021/acs.jafc.4c01934
  17. Fragomeli, R., Annunziata, A., and Punzo, G. (2024). Promoting the Transition towards Agriculture 4.0: A Systematic Literature Review on Drivers and Barriers. Sustainability, 16(6): 2425. https://doi.org/10.3390/su16062425. DOI: https://doi.org/10.3390/su16062425
  18. Gabriel, A., Gandorfer, M., 2023. Adoption of digital technologies in agriculture – an inventory in a European small-scale farming region. Precision Agric 24, 68-91. DOI: https://doi.org/10.1007/s11119-022-09931-1
  19. Gebbers, R., and Adamchuk, V. (2010). Precision Agriculture and Food Security. Science, 327(5967): 828-31. https://doi.org/10.1126/science.1183899. DOI: https://doi.org/10.1126/science.1183899
  20. Giampietri, E., Yu, X. and Trestini, S. (2020). The role of trust and perceived barriers on farmer’s intention to adopt risk management tools. Bio-based and Applied Economics, 9(1): 1-24. https://doi.org/10.13128/bae-8416.
  21. Giorgio, A., Penate Lopez, L.P., Bertoni, D., Cavicchioli, D., Ferrazzi, G., 2024. Enablers to Digitalization in Agriculture: A Case Study from Italian Field Crop Farms in the Po River Valley, with Insights for Policy Targeting. Agriculture 14, 1074. https://doi.org/10.3390/agriculture14071074 DOI: https://doi.org/10.3390/agriculture14071074
  22. Giua, C., Materia V., and Camanzi, L. (2022). Smart Farming Technologies Adoption: Which Factors Play a Role in the Digital Transition?. Technology in Society 68: 101869. https://doi.org/10.1016/j.techsoc.2022.101869 DOI: https://doi.org/10.1016/j.techsoc.2022.101869
  23. Gonzales-Gemio, C., and Sanz-Martín, L. (2025). Socioeconomic Barriers to the Adoption of Carbon Farming in Spain, Italy, Egypt, and Tunisia: An Analysis Based on the Diffusion of Innovations Model. Journal of Cleaner Production 498: 145155. https://doi.org/10.1016/j.jclepro.2025.145155 DOI: https://doi.org/10.1016/j.jclepro.2025.145155
  24. Javaid, M., Haleem, A., Singh, R. P., and Suman, R. (2022). Enhancing Smart Farming through the Applications of Agriculture 4.0 Technologies. International Journal of Intelligent Networks, 3: 150-64. https://doi.org/10.1016/j.ijin.2022.09.004. DOI: https://doi.org/10.1016/j.ijin.2022.09.004
  25. Jin, X. B., Yu, X. H., Wang, X. Y., Bai, Y. T., Su, T. L., and Kong, J. L. (2020). Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System. Sustainability, 12(4): 1433. https://doi.org/10.3390/su12041433. DOI: https://doi.org/10.3390/su12041433
  26. Khanna, M. et al. (2024) ‘Economics of the Adoption of Artificial Intelligence–Based Digital Technologies in Agriculture’, Annual Review of Resource Economics, 16(1), 41-61. https://doi.org/10.1146/annurev-resource-101623-092515. DOI: https://doi.org/10.1146/annurev-resource-101623-092515
  27. Klerkx, L. and Rose, D. (2020) ‘Dealing with the game-changing technologies of Agriculture 4.0: How do we manage diversity and responsibility in food system transition pathways?’, Global Food Security, 24, 100347. Available at: https://doi.org/10.1016/j.gfs.2019.100347. DOI: https://doi.org/10.1016/j.gfs.2019.100347
  28. Kumar Kasera, R., Gour, S., and Acharjee, T. (2024). A Comprehensive Survey on IoT and AI Based Applications in Different Pre-Harvest, during-Harvest and Post-Harvest Activities of Smart Agriculture. Computers and Electronics in Agriculture, 216: 108522. https://doi.org/10.1016/j.compag.2023.108522. DOI: https://doi.org/10.1016/j.compag.2023.108522
  29. Lezoche, M. et al. (2020). Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture. Computers in Industry, 117, 103187. https://doi.org/10.1016/j.compind.2020.103187. DOI: https://doi.org/10.1016/j.compind.2020.103187
  30. Maffezzoli, F., Ardolino, M., and Bacchetti, A. (2022a). The Impact of the 4.0 Paradigm in the Italian Agricultural Sector: A Descriptive Survey. Applied Sciences, 12(18): 9215. https://doi.org/10.3390/app12189215. DOI: https://doi.org/10.3390/app12189215
  31. Maffezzoli, F., Ardolino, M., Bacchetti, A., Perona, M., and Renga, F. (2022b). Agriculture 4.0: A Systematic Literature Review on the Paradigm, Technologies and Benefits. Futures, 142: 102998. https://doi.org/10.1016/j.futures.2022.102998. DOI: https://doi.org/10.1016/j.futures.2022.102998
  32. Meemken, E.-M. et al. (2024). Digital innovations for monitoring sustainability in food systems. Nature Food, 5(8), 656-660. https://doi.org/10.1038/s43016-024-01018-6. DOI: https://doi.org/10.1038/s43016-024-01018-6
  33. Menozzi, D., Fioravanzi, M. and Donati, M. (2015). Farmer’s motivation to adopt sustainable agricultural practices. Bio-based and Applied Economics, 4(2): 125-147 Pages. https://doi.org/10.13128/bae-14776.
  34. Mhlanga, D. and Ndhlovu, E. (2023). Digital Technology Adoption in the Agriculture Sector: Challenges and Complexities in Africa. In: Human Behavior and Emerging Technologies. Edited by Z. Yan, 2023, pp. 1–10. Available at: https://doi.org/10.1155/2023/6951879. DOI: https://doi.org/10.1155/2023/6951879
  35. Mühl, D.D. and De Oliveira, L. (2022). A bibliometric and thematic approach to agriculture 4.0. Heliyon, 8(5), e09369. https://doi.org/10.1016/j.heliyon.2022.e09369. DOI: https://doi.org/10.1016/j.heliyon.2022.e09369
  36. Oliveira, L. F. P., Moreira, A. P., and Silva, M. F. (2021). Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead. Robotics, 10(2): 52. https://doi.org/10.3390/robotics10020052. DOI: https://doi.org/10.3390/robotics10020052
  37. Onyenekwe, C.S. et al. (2023). Heterogeneity of adaptation strategies to climate shocks: Evidence from the Niger Delta region of Nigeria. Bio-based and Applied Economics, 12(1), 17-35. https://doi.org/10.36253/bae-13436. DOI: https://doi.org/10.36253/bae-13436
  38. Papadopoulos, G., Arduini, S., Uyar, H., Psiroukis, V., Kasimati, A., and Fountas, S. (2024). Economic and Environmental Benefits of Digital Agricultural Technologies in Crop Production: A Review. Smart Agricultural Technology, 8, 100441. https://doi.org/10.1016/j.atech.2024.100441 DOI: https://doi.org/10.1016/j.atech.2024.100441
  39. Peladarinos, N., Piromalis, D., Cheimaras, V., Tserepas, E., Munteanu, R. A., and Papageorgas, P. (2023). Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review. Sensors, 23(16), 7128. https://doi.org/10.3390/s23167128 DOI: https://doi.org/10.3390/s23167128
  40. Pierce, F. J., and Nowak, P. (1999). Aspects of Precision Agriculture. Advances in Agronomy, 67, 1-85. https://doi.org/10.1016/S0065-2113(08)60513-1 DOI: https://doi.org/10.1016/S0065-2113(08)60513-1
  41. Polymeni, S., Plastras, S., Skoutas, D. N., Kormentzas, G., and Skianis, C. (2023). The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review. Electronics, 12(12), 2651. https://doi.org/10.3390/electronics12122651 DOI: https://doi.org/10.3390/electronics12122651
  42. Pradel, M., De Fays, M., and Seguineau, C. (2022). Comparative Life Cycle Assessment of Intra-Row and Inter-Row Weeding Practices Using Autonomous Robot Systems in French Vineyards. Science of The Total Environment, 838, 156441. https://doi.org/10.1016/j.scitotenv.2022.156441 DOI: https://doi.org/10.1016/j.scitotenv.2022.156441
  43. Rose, D. C., Wheeler, R., Winter, M., Lobley M., Chivers, C. (2021). Agriculture 4.0: Making it work for people, production, and the planet. Land Use Policy, 100. https://doi.org/10.1016/j.landusepol.2020.104933 DOI: https://doi.org/10.1016/j.landusepol.2020.104933
  44. Rotz, S., Gravely, E., Mosby, I., Duncan, E., Finnis, E., Horgan, M., LeBlanc, J., Martin, R., Tait Neufeld, H., Nixon, A., Pant, L., Shall, V., Fraser, E. (2019). Automated pastures and the digital divide: How agricultural technologies are shaping labour and rural communities. Journal of Rural Studies, 68, 112-122. https://doi.org/10.1016/j.jrurstud.2019.01.023 DOI: https://doi.org/10.1016/j.jrurstud.2019.01.023
  45. Sharma, V., Tripathi, A. K., and Mittal, H. (2022). Technological Revolutions in Smart Farming: Current Trends, Challenges and Future Directions. Computers and Electronics in Agriculture, 201, 107217. https://doi.org/10.1016/j.compag.2022.107217 DOI: https://doi.org/10.1016/j.compag.2022.107217
  46. Sott, M. K., Furstenau, L. B., Kipper, L. M., Giraldo, F. D., Lopez-Robles, J. R., Cobo, M. J., Zahid, A., Abbasi, Q. H., and Imran, M. A. (2020). Precision Techniques and Agriculture 4.0 Technologies to Promote Sustainability in the Coffee Sector: State of the Art, Challenges and Future Trends. IEEE Access, 8, 149854–67. https://doi.org/10.1109/ACCESS.2020.3016325 DOI: https://doi.org/10.1109/ACCESS.2020.3016325
  47. Sozzi, M., Ahmed, K., Ferrari, G., Zanchin, A., Grigolato, S., and Marinello, F. (2021). Connectivity in rural areas: A case study on internet connection in the Italian agricultural areas. IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Trento-Bolzano, Italy, 466-470. https://doi.org/10.1109/MetroAgriFor52389.2021.9628665 DOI: https://doi.org/10.1109/MetroAgriFor52389.2021.9628665
  48. Sponchioni, G., Vezzoni, M., Bacchetti, A., Pavesi, M., and Renga, F. (2019). The 4.0 revolution in agriculture: A multi-perspective definition. XXIV Summer School “Francesco Turco” – Indust Syst Eng, 1, 143–149.
  49. van Selm, M., and Jankowski, N. W. (2006). Conducting Online Surveys. Quality and Quantity, 40, 435-456. https://doi.org/10.1007/s11135-005-8081-8 DOI: https://doi.org/10.1007/s11135-005-8081-8
  50. Yousaf, A., Kayvanfar, V., Mazzoni, A., and Elomri, A. (2023). Artificial Intelligence-Based Decision Support Systems in Smart Agriculture: Bibliometric Analysis for Operational Insights and Future Directions. Frontiers in Sustainable Food Systems, 6, 1053921. https://doi.org/10.3389/fsufs.2022.1053921 DOI: https://doi.org/10.3389/fsufs.2022.1053921
  51. Zhai, Z., Martínez, J. F., Beltran, V., and Martínez, N. L. (2020). Decision Support Systems for Agriculture 4.0: Survey and Challenges. Computers and Electronics in Agriculture, 170, 105256. https://doi.org/10.1016/j.compag.2020.105256 DOI: https://doi.org/10.1016/j.compag.2020.105256
  52. Zul Azlan, Z., Hazim, Z. F., Junaini, S. N., Bolhassan, N. A., Wahi, R., and Arip, M. A. (2024). Harvesting a Sustainable Future: An Overview of Smart Agriculture’s Role in Social, Economic, and Environmental Sustainability. Journal of Cleaner Production, 434, 140338. https://doi.org/10.1016/j.jclepro.2023.140338 DOI: https://doi.org/10.1016/j.jclepro.2023.140338