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Factors Influencing the Adoption of Artificial Intelligence Technologies in Agriculture, Livestock Farming and Aquaculture: A Systematic Literature Review Using PRISMA 2020

Author

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  • Vasileios P. Georgopoulos

    (Department of Fisheries and Aquaculture, School of Agricultural Sciences, University of Patras, 30200 Messolonghi, Greece)

  • Dimitris C. Gkikas

    (Department of Fisheries and Aquaculture, School of Agricultural Sciences, University of Patras, 30200 Messolonghi, Greece)

  • John A. Theodorou

    (Department of Fisheries and Aquaculture, School of Agricultural Sciences, University of Patras, 30200 Messolonghi, Greece)

Abstract

Food production faces significant challenges, mainly due to the increase in the Earth’s population, combined with climate change. This will create extreme pressure on food industries, which will have to respond to the demand while protecting the environment and ensuring high food quality. It is, therefore, imperative to adopt innovative technologies, such as Artificial Intelligence, in order to aid in this cause. To do this, we first need to understand the adoption process that enables the deployment of those technologies. Therefore, this research attempts to identify the factors that encourage and discourage the adoption of Artificial Intelligence technologies by professionals working in the fields of agriculture, livestock farming and aquaculture, by examining the available literature on the subject. This is a systematic literature review that follows the PRISMA 2020 guidelines. The research was conducted on 38 articles selected from a pool of 225 relevant articles, and led to the identification of 20 factors that encourage and 21 factors that discourage the adoption of Artificial Intelligence. The factors that appeared most were of economic nature regarding discouragement (31.5%) and product-related regarding encouragement (28.1%). This research does not aim to quantify the importance of each factor—since more original research becoming available is needed for that—but mainly to construct a list of factors, using spreadsheets, which could then be used to guide further future research towards understanding the adoption mechanism.

Suggested Citation

  • Vasileios P. Georgopoulos & Dimitris C. Gkikas & John A. Theodorou, 2023. "Factors Influencing the Adoption of Artificial Intelligence Technologies in Agriculture, Livestock Farming and Aquaculture: A Systematic Literature Review Using PRISMA 2020," Sustainability, MDPI, vol. 15(23), pages 1-19, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16385-:d:1289849
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    References listed on IDEAS

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    1. Ara, Iffat & Turner, Lydia & Harrison, Matthew Tom & Monjardino, Marta & deVoil, Peter & Rodriguez, Daniel, 2021. "Application, adoption and opportunities for improving decision support systems in irrigated agriculture: A review," Agricultural Water Management, Elsevier, vol. 257(C).
    2. Isik, Murat & Khanna, Madhu, 2002. "Variable-Rate Nitrogen Application Under Uncertainty: Implications For Profitability And Nitrogen Use," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 27(01), pages 1-16, July.
    3. Madhu Khanna, 2001. "Sequential Adoption of Site-Specific Technologies and its Implications for Nitrogen Productivity: A Double Selectivity Model," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(1), pages 35-51.
    4. Feder, Gershon & Just, Richard E & Zilberman, David, 1985. "Adoption of Agricultural Innovations in Developing Countries: A Survey," Economic Development and Cultural Change, University of Chicago Press, vol. 33(2), pages 255-298, January.
    5. Jean‐Paul Chavas & Céline Nauges, 2020. "Uncertainty, Learning, and Technology Adoption in Agriculture," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 42(1), pages 42-53, March.
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