How might technology rise to the challenge of data sharing in agri-food?

Durrant, Aiden; Markovic, Milan; Matthews, David; May, David; Leontidis, Georgios; Enright, Jessica

doi:10.1016/j.gfs.2021.100493

How might technology rise to the challenge of data sharing in agri-food?

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Durrant, Aiden ORCID: https://orcid.org/0000-0002-8375-4523, Markovic, Milan, Matthews, David, May, David, Leontidis, Georgios and Enright, Jessica (2021) How might technology rise to the challenge of data sharing in agri-food? Global Food Security, 28. ISSN 2211-9124

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Abstract

Data sharing is often hindered by a number of real word challenges caused by a mixture of technological and social factors. To date, the agri-food sector significantly lags behind other sectors in overcoming these challenges. However, the benefits of data sharing are too great to be ignored as they have a potential to address many historical failings such as issues related to food safety, traceability and transparency, and must be carefully considered as the sector is undergoing a widespread digitalisation. In this article, we explore the potential of different technologies in addressing the challenges presented by data sharing in the agri-food sector, and how the use of these technologies in the narrative of a Data Trust may address many of these obstacles. We argue the importance of utilising semantic web technologies, distributed ledger technologies, machine learning, and privacy preserving technologies to enable future transformative data sharing infrastructures in the agri-food sector. The utilisation of holistic statistical analysis of the shared data is also discussed, vital in supporting many of the sectors optimisation and sustainability goals.

Item Type:	Article
Additional Information:	Publisher Copyright: © 2021 Elsevier B.V.
Uncontrolled Keywords:	agri-food supply chains,ai technologies,data sharing,data trusts,food science,ecology,safety, risk, reliability and quality,safety research ,/dk/atira/pure/subjectarea/asjc/1100/1106
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and AI
Related URLs:	https://www.scopus.com/pages/publication... https://eprints.gla.ac.uk/232309/1/23230...
Depositing User:	LivePure Connector
Date Deposited:	10 Jul 2026 08:09
Last Modified:	10 Jul 2026 08:14
URI:	https://ueaeprints.uea.ac.uk/id/eprint/103811
DOI:	10.1016/j.gfs.2021.100493

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