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Article

Harnessing digital technologies for food security in developing countries: A focus on conflict-affected regions (literature review)

Szilvia Veress Juhaszne, Zoltan Rajnai
Abstract

Food insecurity has become an increasingly urgent global challenge, particularly in conflict-affected developing countries where armed violence, climate-related shocks, institutional fragility, and market disruptions undermine the resilience of food systems and threaten sustainable development. The aim of this study was to examine how digital technologies can contribute to strengthening food security governance in conflict-affected regions, with particular emphasis on Sub-Saharan Africa, and to develop an integrated framework supporting monitoring and decision-making processes. The research adopted a qualitative approach based on an integrative literature review, comparative case analysis, and conceptual framework development. The findings demonstrated that digital technologies can significantly enhance the monitoring and management of food security by improving information availability, supply chain transparency, risk assessment, and early warning capabilities. Mobile-based advisory systems facilitate communication with vulnerable populations and support agricultural decision-making. Remote sensing and GIS technologies enable the continuous observation of environmental and agricultural conditions, while blockchain-based solutions can improve transparency and accountability within food supply chains. Furthermore, predictive analytics and artificial intelligence offer new opportunities for anticipating food security risks and supporting evidence-based humanitarian interventions. Based on these findings, the study proposed an integrated digital food security monitoring framework that combines household-level monitoring, environmental observation, supply chain transparency, and predictive decision-support functions within a unified socio-technical system. An illustrative pilot implementation scenario was also presented to demonstrate the practical applicability of the proposed framework. The results may support policymakers, humanitarian organisations, development agencies, and food security practitioners in designing more effective, data-driven, and resilience-oriented monitoring systems for conflict-affected environments

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Received 02.02.2026

Revised 22.04.2026

Accepted 12.06.2026

Published 30.06.2026

https://doi.org/10.63341/esbur/1.2026.160
Retrieved from Vol. 17, No. 1, 2026
Pages 160-171

Suggested citation

Veress Juhaszne, S., & Rajnai, Z. (2026). Harnessing digital technologies for food security in developing countries: A focus on conflict-affected regions (literature review). Ecological Safety and Balanced Use of Resources, 17(1), 160-171. https://doi.org/10.63341/esbur/1.2026.160

References

  1. Abraha, D.T. (2025). Blockchain-based solution for addressing refugee management in the Global South: Transparent and accessible resource sharing in humanitarian organizations. Frontiers in Human Dynamics, 7, article number 1391163. doi: 10.3389/fhumd.2024.1391163.
  2. Alemu, M.G., & Zimale, F.A. (2025). Integration of remote sensing and machine learning algorithm for agricultural drought early warning over Genale Dawa river basin, Ethiopia. Environmental Monitoring and Assessment, 197, article number 243. doi: 10.1007/s10661-025-13708-0.
  3. Bellemare, M.F. (2015). Rising food prices, food price volatility, and social unrest. American Journal of Agricultural Economics, 97(1), 1-21. doi: 10.1093/ajae/aau038.
  4. Besenyő, J., & Sólyomfi, A.H. (2024). Mali: Safe heaven to terrorist? In J. Besenyő, L. Issaev & A. Korotayev (Eds.), Terrorism and political contention. Perspectives on development in the Middle East and North Africa (MENA) Region (pp. 153-167). Cham: Springer. doi: 10.1007/978-3-031-53429-4_8.
  5. Brück, T., d’Errico, M., & Pietrelli, R. (2019). The effects of violent conflict on household resilience and food security. World Development, 119, 203-223. doi: 10.1016/j.worlddev.2018.01.002.
  6. Burke, M., Driscoll, A., Lobell, D.B., & Ermon, S. (2016). Using satellite imagery and machine learning to predict poverty. Science, 353(6301), 790-794. doi: 10.1126/science.aaf7894.
  7. Devidal, P. (2024). Lost in digital translation? The humanitarian principles in the digital age. International Review of the Red Cross, 106(925), 120-154. doi: 10.1017/S181638312400008.
  8. Diepeveen, S., Bryant, J., & Wasuge, M. (2025). Outsourcing accountability: Extractive data practice and inequities of power in humanitarian third-party monitoring. Big Data & Society, 12(1). doi: 10.1177/20539517251328250.
  9. Faith, B., Roberts, T., & Hernandez, K. (2022). Risks, accountability and technology: Thematic working paper. Brighton: Institute of Development Studies. doi: 10.19088/BASIC.2022.003.
  10. FAO. (2023). The state of food security and nutrition in the world 2023. Retrieved from https://doi.org/10.4060/cc3017en.
  11. Funk, C., et al. (2019). Recognizing the famine early warning systems network: Over 30 years of early warning science advances and partnerships promoting global food security. Bulletin of the American Meteorological Society, 100(6), 1011-1027. doi: 10.1175/BAMS-D-17-0233.1.
  12. GNAFC. (2023). Global report on food crises 2023. Retrieved from https://www.fsinplatform.org/report/global-report-food-crises-2023.
  13. Heiss, N., Meier, J., Gessner, U., & Kuenzer, C. (2025). A review: Potential of Earth observation (EO) for mapping small-scale agriculture and cropping systems in West Africa. Land, 14(1), article number 171. doi: 10.3390/land14010171.
  14. Idris, I. (2019). Benefits and risks of big data analytics in fragile and conflict affected states. Brighton: Institute of Development Studies.
  15. Jain, M., Mondal, P., DeFries, R.S., Small, C., & Galford, G.L. (2016). Mapping cropping intensity of smallholder farms: A comparison of methods using multiple sensors. Remote Sensing of Environment, 134, 210-223. doi: 10.1016/j.rse.2013.02.029.
  16. Juhász, P.G., & Szeremley, C. (2024). Work of a local NGO VETO, in contrast with the international organisations in the Eastern Congo. Journal of Central and Eastern European African Studies, 3(3), 18-39. doi: 10.59569/jceeas.2023.3.3.221.
  17. Juhaszné, S.V. (2024). Food terrorism as a real threat. In T.A. Kovács & I. Fürstner (Eds.), Critical infrastructure protection: Advanced technologies for crisis prevention and response (pp. 177-188). Dordrecht: Springer. doi: 10.1007/978-94-024-2308-2_12.
  18. Justino, P. (2012). War and poverty. IDS Working Papers, 2012, 21-29. doi: 10.1111/j.2040-0209.2012.00391.x.
  19. Kamilaris, A., & Prenafeta-Boldú, F.X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70-90. doi: 10.1016/j.compag.2018.02.016.
  20. Kovács, G., & Spens, K.M. (2007). Humanitarian logistics in disaster relief operations. International Journal of Physical Distribution & Logistics Management, 37(2), 99-114. doi: 10.1108/09600030710734820.
  21. Kreutzer, T., Orbinski, J., Appel, L., An, A., Marston, J., Boone, E., & Vinck, P. (2025). Ethical implications related to processing of personal data and artificial intelligence in humanitarian crises: A scoping review. BMC Medical Ethics, 26, article number 49. doi: 10.1186/s12910-025-01189-2.
  22. Lundberg, S.M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In NIPS’17: Proceedings of the 31st international conference on neural information processing systems (pp. 4768-4777). Long Beach: Neural Information Processing Systems Foundation.
  23. Martin-Shields, C.P., & Stojetz, W. (2019). Food security and conflict: Empirical challenges and future opportunities. World Development, 119, 150-164. doi: 10.1016/j.worlddev.2018.07.011.
  24. Masinde, B.K., Gevaert, C.M., Nagenborg, M.H., & Zevenbergen, J.A. (2023). Group-privacy threats for geodata in the humanitarian context. ISPRS International Journal of Geo-Information, 12(10), article number 393. doi: 10.3390/ijgi12100393.
  25. Moisa, M.B., Roba, Z.R., Purohit, S., Deribew, K.T., & Gemeda., D.O. (2025). Evaluating the impact of land use and land cover change on soil moisture variability using GIS and remote sensing technology in southwestern Ethiopia. Environmental Monitoring and Assessment, 197, article number 824. doi: 10.1007/s10661-025-14301-1.
  26. Moomen, A.W., Yevugah, L.L., Boakye, L., Osei, J.D., & Muthoni, F. (2024). Review of applications of remote sensing towards sustainable agriculture in the Northern Savannah Regions of Ghana. Agriculture, 14(4), article number 546. doi: 10.3390/agriculture14040546.
  27. Mubonderi, N., Manyevere, A., & Mashamaite, C.V. (2025). Optical remote sensing for monitoring soil erosion in sub-Saharan grassland biomes: A systematic review. Environmental Monitoring and Assessment, 197, article number 976. doi: 10.1007/s10661-025-14426-3.
  28. Mustapha, M., & Zineddine, M. (2024). An evaluative technique for drought impact on variation in agricultural LULC using remote sensing and machine learning. Environmental Monitoring and Assessment, 196, article number 515. doi: 10.1007/s10661-024-12677-0.
  29. Paillé, P., Besse, J., Toole, H., Politi, C., Viswanathan, S., Namirembe, E., & Ohrvik-Stott, J. (2024). Emerging technologies in the humanitarian sector. Retrieved from https://www.rand.org.
  30. Rajnai, Z., & Fregan, B. (2016). Critical infrastructures protection (legislation). Technical Scientific Publications, 5, 349-352. doi: 10.33895/mtk-2016.05.78.
  31. Raleigh, C., Linke, A., Hegre, H., & Karlsen, J. (2010). Introducing ACLED: Armed conflict location and event dataset. Journal of Peace Research, 47(5), 651-660. doi: 10.1177/0022343310378914.
  32. Serraj, R., & Pingali, P. (2018). Agriculture and food systems to 2050: Global trends, challenges and opportunities. Singapore: World Scientific. doi: 10.1142/11212.
  33. Sundarakani, B., & Ghouse, A. (2024). A systematic literature review and bibliometric analysis of blockchain technology for food security. Foods, 13(22), article number 3607. doi: 10.3390/foods13223607.
  34. von Grebmer, K., et al. (2023). 2023 Global Hunger Index: The power of youth in shaping food systems. Retrieved from https://www.globalhungerindex.org/pdf/en/2023.pdf.
  35. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big data in smart farming – a review. Agricultural Systems, 153, 69-80. doi: 10.1016/j.agsy.2017.01.023.

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