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

Madhu Khanna (), Shady S. Atallah, Thomas Heckelei, Linghui Wu and Hugo Storm
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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

Annual Review of Resource Economics, 2024, vol. 16, issue 1, 41-61

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.

Keywords: precision farming; incentives; economic models; machine learning (search for similar items in EconPapers)
JEL-codes: O13 O33 Q12 Q16 Q55 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1146/annurev-resource-101623-092515

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