ArticleScientific reports2024
Evaluating responses by ChatGPT to farmers' questions on irrigated lowland rice cultivation in Nigeria.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- The development and evaluation of agricultural question-answering systems based on large language models.Scientific reports · 2026Article
- Generative AI use and advisory performance among agricultural extension agents in Benin.Frontiers in artificial intelligence · 2026Article
- Large language models can help boost food production, but be mindful of their risks.Frontiers in artificial intelligence · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
The limited number of agricultural extension agents (EAs) in sub-Saharan Africa limits farmers' access to extension services. Artificial intelligence (AI) assistants could potentially aid in providing answers to farmers' questions. The objective of this study was to evaluate the ability of an AI chatbot assistant (ChatGPT) to provide quality responses to farmers' questions. We compiled a list of 32 questions related to irrigated rice cultivation from farmers in Kano State, Nigeria. Six EAs from the state were randomly selected to answer these questions. Their answers, along with those of ChatGPT, were assessed by four evaluators in terms of quality and local relevancy. Overall, chatbot responses were rated significantly higher quality than EAs' responses. Chatbot responses received the best score nearly six times as often as the EAs' (40% vs. 7%). The evaluators preferred chatbot responses to EAs in 78% of cases. The topics for which the chatbot responses received poorer scores than those by EAs included planting time, seed rate, and fertilizer application rate and timing. In conclusion, while the chatbot could offer an alternative source for providing agricultural advisory services to farmers, incorporating site-specific input rate-and-timing agronomic practices into AI assistants is critical for their direct use by farmers.
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Registered trials
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