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Impact in Agriculture

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Cybersecurity Resilience for Low-Income Farmers in Developing Nations: An Asset-Dependency Risk Assessment Inspired by Bayesian Networks

Kossi D Bissadu 1 ORCID , Leela Pavani Velagala 1 ORCID , Gahangir Hossain 2 ORCID
1 Information Science Department, College of Information, University of North Texas, Denton 76203, United States
2 Data Science Department, College of Information, University of North Texas, Denton 76203, United States
Journal Impact in Agriculture
Volume 2
Article Number 10
Pages 1–15
Received 26 July 2026
Revised 1 November 2026
Accepted 3 November 2026
Published 6 November 2026
DOI https://doi.org/10.65500/agriculture-2026-010
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Abstract

Low-income farmers in developing nations face cybersecurity risks when adopting digital agriculture. This article presents an asset-based assessment that combines an Asset Added Value Tree with a Bayesian-network-inspired dependency graph and expert-derived influence weights. Individual asset risk scores are propagated through the dependency structure using a deterministic weighted aggregation; the approach does not estimate conventional Bayesian conditional probability tables or perform full probabilistic inference. We applied the method to the “Ferme-Ecole” project in Tsevie, Togo. The case study identified elevated scores for configuration data and data storage, and risks associated with personnel interactions. A comparison with previously published fuzzy cognitive map results for 12 common assets yielded an MAE of 0.1467, an RMSE of 0.2051, a Pearson correlation of 0.5129, and 75% qualitative risk-category agreement. These measures describe agreement between model outputs, not predictive accuracy. The assessment was conducted at one farm with five experts and without observed cybersecurity incidents as ground truth. Accordingly, the results illustrate feasibility in the studied setting but do not establish cost-efficiency, general effectiveness, or transferability to other farms.

Keywords

Agricultural Cybersecurity Cybersecurity Risk Assessment Smart Farming Asset Dependency Analysis Bayesian-Network-Inspired Modeling Smallholder Farmers.

Funding

This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Acknowledgment

The authors thank the leadership and technical team of “Ferme Ecole” of Tsevie for their availability and support.

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