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Economics of the Adoption of Artificial Intelligence–Based Digital Technologies in Agriculture

Author

Listed:
  • Madhu Khanna

    (Department of Agricultural and Consumer Economics, University of Illinois Urbana-Champaign, Urbana, Illinois, USA)

  • Shady S. Atallah

    (Department of Agricultural and Consumer Economics, University of Illinois Urbana-Champaign, Urbana, Illinois, USA)

  • Thomas Heckelei

    (Institute for Food and Resource Economics, University of Bonn, Bonn, Germany)

  • Linghui Wu

    (Department of Agricultural and Consumer Economics, University of Illinois Urbana-Champaign, Urbana, Illinois, USA)

  • Hugo Storm

    (Institute for Food and Resource Economics, University of Bonn, Bonn, Germany)

Abstract

Rapid advances and diffusion of artificial intelligence (AI) technologies have the potential to transform agriculture globally by improving measurement, prediction, and site-specific management on the farm, enabling autonomous equipment that is trained to mimic human behavior and developing recommendation systems designed to autonomously achieve various tasks. Here, we discuss the applications of AI-enabled technologies in agriculture, including those that are capable of on-farm reinforcement learning and key attributes that distinguish them from precision technologies currently available. We then describe various ways through which AI-driven technologies are likely to change the decision space for farmers and require changes to the theoretical and empirical economic models that seek to understand the incentives for their adoption. We conclude with a discussion of areas for future research on the economic, environmental, and equity implications of AI-enabled technology adoption for the agricultural sector.

Suggested Citation

  • Madhu Khanna & Shady S. Atallah & Thomas Heckelei & Linghui Wu & Hugo Storm, 2024. "Economics of the Adoption of Artificial Intelligence–Based Digital Technologies in Agriculture," Annual Review of Resource Economics, Annual Reviews, vol. 16(1), pages 41-61, October.
    • Handle: RePEc:anr:reseco:v:16:y:2024:p:41-61
    DOI: 10.1146/annurev-resource-101623-092515
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    More about this item

    Keywords

    precision farming; incentives; economic models; machine learning;
    All these keywords.

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation
    • Q16 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - R&D; Agricultural Technology; Biofuels; Agricultural Extension Services

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