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Nature Food (2023)
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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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This Perspective was made possible by a grant from Templeton World Charity 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.
CSER, University of Cambridge, Cambridge, UK
A. Tzachor & C. Richards
School of Sustainability, Reichman University, Herzliya, Israel
A. Tzachor
International Institute of Tropical Agriculture (IITA), CGIAR, Ibadan, Nigeria
M. Devare & P. Pypers
Department of Engineering, University of Cambridge, Cambridge, UK
C. Richards
International Center for Tropical Agriculture (CIAT), CGIAR, Nairobi, Kenya
A. Ghosh
International Food Policy Research Institute (IFPRI), CGIAR, Washington, DC, USA
J. Koo
Agstack Project, Linux Foundation, San Francisco, CA, USA
S. Johal
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.
Correspondence to A. Tzachor.
The authors declare no competing interests.
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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Tzachor, A., Devare, M., Richards, C. et al. Large language models and agricultural extension services. Nat Food (2023). https://doi.org/10.1038/s43016-023-00867-x
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DOI: https://doi.org/10.1038/s43016-023-00867-x
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