The impact of COVID-19 on the diffusion of machine learning amongst rural farms: A study of Algeria, Egypt, Morocco, Tunisia and the United Arab Emirates

Gilani, Sayed Abdul Majid and Hashim, Mohamed Ashmel Mohamed and Copacio, Abigail and Sergio, Rommel and Tlemsani, Issam and Tantry, Ansarullah (2025) The impact of COVID-19 on the diffusion of machine learning amongst rural farms: A study of Algeria, Egypt, Morocco, Tunisia and the United Arab Emirates. Sustainable Futures, 10. p. 101291. ISSN 2666-1888

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Abstract

This study investigates the impact of the COVID-19 pandemic on the diffusion of Machine Learning (ML) among rural farms in Algeria, Egypt, Morocco, Tunisia, and the United Arab Emirates (UAE). Despite the proven benefits of ML in agricultural operations, rural farms in the MENA region have lagged in adoption due to barriers such as infrastructure deficits, costs, cultural factors, and limited knowledge/training. Through semi-structured interviews with 50 rural farm owners, this research explores shifts in attitudes towards ML adoption before and after the pandemic. The findings reveal a significant post-pandemic increase in ML awareness, confidence in technology usage, and recognition of ML's benefits, such as operational efficiency and cost reduction. Cultural resistance and knowledge gaps, once major barriers, have diminished, while infrastructure limitations and costs persist. The study introduces an empirically informed Machine Learning Adoption Framework version 3 (MLAFv3), highlighting changes in drivers and barriers pre- and post-COVID-19. It further proposes a practical ML-integrated crop management system to facilitate adoption among rural farmers. The findings contribute to addressing the digital divide in rural MENA agriculture and offer policy and practical recommendations to enhance ML adoption for rural economic resilience and cultural preservation.

Item Type:	Article
Identification Number:	10.1016/j.sftr.2025.101291
Dates:	Date Event 5 September 2025 Accepted 9 September 2025 Published Online
Uncontrolled Keywords:	Machine learning adoption, Rural farms, MENA region, COVID-19 impact, Digital divide, Agricultural innovation
Subjects:	CAH17 - business and management > CAH17-01 - business and management > CAH17-01-02 - business studies
Divisions:	Business School > Management, Business and Marketing
Depositing User:	Gemma Tonks
Date Deposited:	09 Feb 2026 12:45
Last Modified:	09 Feb 2026 12:45
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16848

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