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Analyzing the Relationship between Agricultural AI Adoption and Government-Subsidized Insurance

Author

Listed:
  • Chad Patrick Osorio

    (Law Group, Wageningen University and Research, 6706 KN Wageningen, The Netherlands
    Environmental Economics and Natural Resources (ENR) Group, Wageningen University and Research, 6706 KN Wageningen, The Netherlands
    School of Environmental Science and Management, University of the Philippines Los Baños, Los Baños 4031, Philippines)

  • Francesca Leucci

    (Law Group, Wageningen University and Research, 6706 KN Wageningen, The Netherlands)

  • Donatella Porrini

    (Department of Cultural Heritage, Università del Salento, 73100 Lecce, Italy
    National Biodiversity Future Center (NBFC), 90133 Palermo, Italy)

Abstract

Due to the increased unpredictability and severity of weather patterns caused by climate change, traditional farming practices and risk management strategies are becoming increasingly inadequate. In this paper, we explore the literature to understand the potential of artificial intelligence (AI) in mitigating climate-related agricultural risks and the pivotal role that public institutions play in encouraging farmers to adopt such technologies. We propose a framework to integrate AI into government-subsidized insurance structures, focusing on reduced premiums through government intervention. We argue that AI’s potential to reduce the uncertainty and severity of climate-induced damages could lower the overall risk profile of insured farmers, thereby justifying lower premiums in the long run. We further discuss the implications of such policies on insurance markets, agricultural sustainability, and global food security. Our initial exploration contributes to the literature by addressing a relatively underexplored intersection of two critical fields—agricultural insurance and artificial intelligence—suggesting directions for future research.

Suggested Citation

  • Chad Patrick Osorio & Francesca Leucci & Donatella Porrini, 2024. "Analyzing the Relationship between Agricultural AI Adoption and Government-Subsidized Insurance," Agriculture, MDPI, vol. 14(10), pages 1-20, October.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:10:p:1804-:d:1497979
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    References listed on IDEAS

    as
    1. Just, Richard E., 2003. "Risk research in agricultural economics: opportunities and challenges for the next twenty-five years," Agricultural Systems, Elsevier, vol. 75(2-3), pages 123-159.
    2. Carter, Michael R. & Cheng, Lan & Sarris, Alexandros, 2016. "Where and how index insurance can boost the adoption of improved agricultural technologies," Journal of Development Economics, Elsevier, vol. 118(C), pages 59-71.
    3. Keith H Coble & Ashok K Mishra & Shannon Ferrell & Terry Griffin, 2018. "Big Data in Agriculture: A Challenge for the Future," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 40(1), pages 79-96.
    4. Reimund Schwarze & Oleksandr Sushchenko, 2022. "Climate Insurance for Agriculture in Europe: On the Merits of Smart Contracts and Distributed Ledger Technologies," JRFM, MDPI, vol. 15(5), pages 1-16, May.
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