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Large language models and agricultural extension services

Nature Food volume 4, pages 941–948 (2023)Cite this article

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An Author Correction to this article was published on 30 November 2023

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Abstract

Several factors have traditionally hampered the effectiveness of agricultural extension services, including limited institutional capacity and reach. Here we assess the potential of large language models (LLMs), specifically Generative Pre-trained Transformer (GPT), to transform agricultural extension. We focus on the ability of LLMs to simplify scientific knowledge and provide personalized, location-specific and data-driven agricultural recommendations. We emphasize shortcomings of this technology, informed by real-life testing of GPT to generate technical advice for Nigerian cassava farmers. To ensure a safe and responsible dissemination of LLM functionality across farming worldwide, we propose an idealized LLM design process with human experts in the loop.

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Fig. 1: Idealized LLM design and deployment process for agricultural extension systems with humans in the loop.

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Acknowledgements

This Perspective was made possible by a grant from Templeton World Charity Foundation and CGIAR’s Excellence in Agronomy Initiative and the Digital Innovation Initiative, largely funded by the Bill and Melinda Gates Foundation. The opinions expressed in this publication are those of the author(s) and do not necessarily reflect the views of Templeton World Charity Foundation.

Author information

Authors and Affiliations

  1. CSER, University of Cambridge, Cambridge, UK

    A. Tzachor & C. Richards

  2. School of Sustainability, Reichman University, Herzliya, Israel

    A. Tzachor

  3. International Institute of Tropical Agriculture (IITA), CGIAR, Ibadan, Nigeria

    M. Devare & P. Pypers

  4. Department of Engineering, University of Cambridge, Cambridge, UK

    C. Richards

  5. International Center for Tropical Agriculture (CIAT), CGIAR, Nairobi, Kenya

    A. Ghosh

  6. International Food Policy Research Institute (IFPRI), CGIAR, Washington, DC, USA

    J. Koo

  7. Agstack Project, Linux Foundation, San Francisco, CA, USA

    S. Johal

  8. Digital and Data Innovation Accelerator, CGIAR, Palmira, Colombia

    B. King

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All authors developed the Perspective jointly and contributed to writing the text.

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Correspondence to A. Tzachor.

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Nature Food thanks Pallavi Rajkhowa and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Data 1

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Tzachor, A., Devare, M., Richards, C. et al. Large language models and agricultural extension services. Nat Food 4, 941–948 (2023). https://doi.org/10.1038/s43016-023-00867-x

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