Durrant A., Markovic M., Matthews D., May D., Leontidis G., Enright J. (2021). How might technology rise to the challenge of data sharing in agri-food? Global Food Security, 01/03/2021, vol. 28, p. 1-8.
https://www.sciencedirect.com/science/article/pii/S2211912421000031
https://www.sciencedirect.com/science/article/pii/S2211912421000031
Titre : | How might technology rise to the challenge of data sharing in agri-food? (2021) |
Auteurs : | A. Durrant ; M. Markovic ; D. Matthews ; D. May ; G. Leontidis ; J. Enright |
Type de document : | Article |
Dans : | Global Food Security (vol. 28, March 2021) |
Article en page(s) : | p. 1-8 |
Langues : | Anglais |
Langues du résumé : | Anglais |
Catégories : |
Catégories principales 06 - AGRICULTURE. FORÊTS. PÊCHES ; 6.1 - Généralités. Situation AgricoleThésaurus IAMM INDUSTRIE ALIMENTAIRE ; SYSTEME AGROALIMENTAIRE ; DONNEE STATISTIQUE ; DIFFUSION DE L'INFORMATION ; DIFFUSION DE LA RECHERCHE ; CHAINE D'APPROVISIONNEMENT ; TECHNOLOGIE |
Mots-clés: | PARTAGE DES DONNEES |
Résumé : | 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. |
Cote : | Réservé lecteur CIHEAM |
URL / DOI : | https://www.sciencedirect.com/science/article/pii/S2211912421000031 |