BARRIERS AND OPPORTUNITIES OF ARTIFICIAL INTELLIGENCE (AI) ADOPTION IN AGRICULTURAL EXTENSION IN ABUJA, NIGERIA

This study examines the barriers and opportunities for AI adoption in agricultural extension in Abuja by analyzing smallholder farmers' socio-economic characteristics, perceptions of AI opportunities, factors influencing AI adoption, and key barriers and opportunities. Using a multi-stage sampling approach, 300 smallholder farmers were selected. The results show that 66.7% of the farmers are male, with a mean age of 40.2 years and an average farming experience of 12.8 years. Most farmers operate on farms of 1–2 hectares (46.7%), with only 43.3% accessing credit, while 60% maintaining contact with extension agents. Farmers’ perceptions of AI opportunities were positive, with "AI improves access to timely agricultural information" having the highest mean score (3.40), followed by "AI enhances access to market information for farmers" (3.30). The logit regression analysis revealed that educational level (p = 0.000), farm size (p = 0.012), contact with extension agents (p = 0.001), access to credit (p = 0.011), gender (p = 0.040), age (p = 0.015), and farming experience (p = 0.046) significantly influence AI adoption, while marital status (p = 0.498) and cooperative membership (p = 0.279) were nonsignificant. The analysis of barriers ranked inadequate access to AI training (4.50) and unstable electricity supply (4.40) as the most critical barriers. Key opportunities include increased productivity and yield enhancement (4.60) and improved real-time access to advisory services (4.50). The study concludes that addressing barriers while leveraging opportunities will drive AI adoption and recommends promoting AI training and capacity-building programs to enhance farmers' technical skills and digital literacy.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-02-17
DOI
https://doi.org/10.5281/zenodo.14880222
Primary Topic
Species Distribution and Climate Change
Type
article
Field-Weighted Citation Impact
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article

BARRIERS AND OPPORTUNITIES OF ARTIFICIAL INTELLIGENCE (AI) ADOPTION IN AGRICULTURAL EXTENSION IN ABUJA, NIGERIA

Zenodo (CERN European Organization for Nuclear Research)
Species Distribution and Climate Change
article

BARRIERS AND OPPORTUNITIES OF ARTIFICIAL INTELLIGENCE (AI) ADOPTION IN AGRICULTURAL EXTENSION IN ABUJA, NIGERIA

article en

Abstract

This study examines the barriers and opportunities for AI adoption in agricultural extension in Abuja by analyzing smallholder farmers' socio-economic characteristics, perceptions of AI opportunities, factors influencing AI adoption, and key barriers and opportunities. Using a multi-stage sampling approach, 300 smallholder farmers were selected. The results show that 66.7% of the farmers are male, with a mean age of 40.2 years and an average farming experience of 12.8 years. Most farmers operate on farms of 1–2 hectares (46.7%), with only 43.3% accessing credit, while 60% maintaining contact with extension agents. Farmers’ perceptions of AI opportunities were positive, with "AI improves access to timely agricultural information" having the highest mean score (3.40), followed by "AI enhances access to market information for farmers" (3.30). The logit regression analysis revealed that educational level (p = 0.000), farm size (p = 0.012), contact with extension agents (p = 0.001), access to credit (p = 0.011), gender (p = 0.040), age (p = 0.015), and farming experience (p = 0.046) significantly influence AI adoption, while marital status (p = 0.498) and cooperative membership (p = 0.279) were nonsignificant. The analysis of barriers ranked inadequate access to AI training (4.50) and unstable electricity supply (4.40) as the most critical barriers. Key opportunities include increased productivity and yield enhancement (4.60) and improved real-time access to advisory services (4.50). The study concludes that addressing barriers while leveraging opportunities will drive AI adoption and recommends promoting AI training and capacity-building programs to enhance farmers' technical skills and digital literacy.

Zenodo (CERN European Organization for Nuclear Research)
University of Ibadan (NG), University of Northampton (GB), Development Research and Projects Centre (NG), Nigeria Centre for Disease Control (NG), Agricultural & Applied Economics Association (US)
Quality Education
Openalex Percentile: Top 43%
Species Distribution and Climate Change
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