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Examining The Adoption and Effectiveness of AI-Enabled Agricultural Extension Services in Enhancing Climate-Smart Farming Practices.
Subject area: Agriculture and Veterinary Sciences · Area of research: AI in Climate-Smart Agriculture
DOI: https://doi.org/10.64388/IREV8I8-1715336
Abstract
Climate change continues to be a major threat to agricultural productivity, especially among the smallholder farmers who are most impacted by the unpredictability of the rains, increasing temperatures, and extreme weather events. Climate-smart farming practices (CSFPs) are one of the solutions which not only raise the productivity but also strengthen the resilience and ensure environmental sustainability. However, the adoption of these practices is still very limited mainly because of the lack of knowledge and access as well as the inadequateness of extension services. Artificial Intelligence (AI)-powered agricultural extension systems have become the most recent development in this field which has led to making services to farmers more effective by being real-time, location-specific, and personalized advisory services that are capable of reaching farmers almost anytime and anywhere. This paper is focused on the use of AI-driven extension services as a means to inspire climate-smart farming practices among smallholder farmers. The study was carried out within the framework of quantitative research with data being collected through the use of structured questionnaires that were administered to 120 farmers and then analyzed by means of descriptive statistics and regression analysis. The results show that, despite a fairly good level of awareness of AI-enabled extension services, the actual use is hampered by digital literacy issues, infrastructural deficiencies, as well as socio-economic factors. Farmers who are regular users of AI-based advisory tools disclose that their decisions have been better supported leading to their increased use of CSFPs and ultimately their greater ability to cope with climate; variability. The regression analysis reveals that there is a positive and statistically significant effect of the use of AI-enabled extension on the adoption of climate-smart practices. The study concludes that AI-driven agricultural extension services can effectively support sustainable farming and climate adaptation, provided that barriers to access and utilization are addressed through policy, infrastructure, and capacity-building initiatives.
Keywords
Artificial Intelligence, Agricultural Extension, Climate-Smart Farming, Adoption, Smallholder Farmers, Decision-Making
References
[1] Aker, J.C., 2011. Dial “A” for agriculture: A review of information and communication technologies for agricultural extension in developing countries. Agricultural Economics, 42(6), pp.631–647.
[2] Anderson, J.R. & Feder, G., 2007. Agricultural extension. In: R. Evenson & P. Pingali, eds. Handbook of Agricultural Economics, Vol. 3. Amsterdam: Elsevier, pp.2343–2378.
[3] Benos, L., Bechar, A. & Bochtis, D., 2021. Machine learning in agriculture: A comprehensive updated review. Sensors, 21(11), pp.1–22.
[4] Billah, M. M., Rahman, M. M., Mahimairaja, S., Lal, A., Srinivasulu, A., & Naidu, R. (2025). Role of agriculture extension and advisory services in supporting climate-smart agricultural systems: machine learning evaluation. The Journal of Agricultural Education and Extension, 1-19.
[5] Creswell, J.W., 2014. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. 4th ed. Thousand Oaks: Sage Publications.
[6] Fabregas, R., Kremer, M. & Schilbach, F., 2019. Realizing the potential of digital development: The case of agricultural advice. Science, 366(6471), pp.1–6.
[7] Food and Agriculture Organization (FAO), 2013. Climate-Smart Agriculture Sourcebook. Rome: FAO.
[8] Kumar, A., Dixit, P. & Singh, R., 2021. AI in agricultural marketing and advisory services: Enhancing farmers’ decision-making. Journal of Rural Studies, 85, pp.205–216.
[9] Mittal, S. & Mehar, M., 2016. Socio-economic factors affecting adoption of modern information and communication technology by farmers in India. The Journal of Agricultural Education and Extension, 22(2), pp.199–212.
[10] Mmbando, G. S. (2025). Harnessing artificial intelligence and remote sensing in climate-smart agriculture: the current strategies needed for enhancing global food security. Cogent Food & Agriculture, 11(1), 2454354.
[11] Munyua, H., 2019. Mobile-enabled market information services for smallholder farmers in Sub-Saharan Africa: A review. Information Technology for Development, 25(3), pp.456–473.
[12] Rose, D.C. & Chilvers, J., 2018. Agriculture 4.0: Broadening responsible innovation in an era of smart farming. Frontiers in Sustainable Food Systems, 2, pp.1–7.
[13] Spanaki, K., Sivarajah, U., Fakhimi, M. & Despoudi, S., 2022. Disruptive technologies in agricultural operations: A systematic review of AI-driven agriculture. Production Planning & Control, 33(16), pp.1–19.
[14] Wolfert, S., Ge, L., Verdouw, C. & Bogaardt, M.J., 2017. Big data in smart farming – A review. Agricultural Systems, 153, pp.69–80.
[15] Zhang, Q., Chen, Y., Wang, S. & Li, Z., 2020. Applications of artificial intelligence in agriculture: A review. Agricultural Engineering International: CIGR Journal, 22(2), pp.1–15.
[16] World Bank, 2019. Harvesting Prosperity: Technology and Productivity Growth in Agriculture. Washington, DC: World Bank.
How to cite this paper
@article{1715336,
author = {Ladejo Yomi},
title = {Examining The Adoption and Effectiveness of AI-Enabled Agricultural Extension Services in Enhancing Climate-Smart Farming Practices.},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {8},
pages = {1115-1123},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715336.pdf},
abstract = {Climate change continues to be a major threat to agricultural productivity, especially among the smallholder farmers who are most impacted by the unpredictability of the rains, increasing temperatures, and extreme weather events. Climate-smart farming practices (CSFPs) are one of the solutions which not only raise the productivity but also strengthen the resilience and ensure environmental sustainability. However, the adoption of these practices is still very limited mainly because of the lack of knowledge and access as well as the inadequateness of extension services. Artificial Intelligence (AI)-powered agricultural extension systems have become the most recent development in this field which has led to making services to farmers more effective by being real-time, location-specific, and personalized advisory services that are capable of reaching farmers almost anytime and anywhere. This paper is focused on the use of AI-driven extension services as a means to inspire climate-smart farming practices among smallholder farmers. The study was carried out within the framework of quantitative research with data being collected through the use of structured questionnaires that were administered to 120 farmers and then analyzed by means of descriptive statistics and regression analysis. The results show that, despite a fairly good level of awareness of AI-enabled extension services, the actual use is hampered by digital literacy issues, infrastructural deficiencies, as well as socio-economic factors. Farmers who are regular users of AI-based advisory tools disclose that their decisions have been better supported leading to their increased use of CSFPs and ultimately their greater ability to cope with climate; variability. The regression analysis reveals that there is a positive and statistically significant effect of the use of AI-enabled extension on the adoption of climate-smart practices. The study concludes that AI-driven agricultural extension services can effectively support sustainable farming and climate adaptation, provided that barriers to access and utilization are addressed through policy, infrastructure, and capacity-building initiatives.},
keywords = {Artificial Intelligence, Agricultural Extension, Climate-Smart Farming, Adoption, Smallholder Farmers, Decision-Making},
month = {February},
doi = {https://doi.org/10.64388/IREV8I8-1715336}
}