Objective
"Across the world, digitalization has been heralded as the start of a new ""agricultural revolution."" Data collected from digital sensors implanted into soil, satellites, and mobile phones offer the possibility of gathering unprecedented amounts of data. Combined with predictive analytics of artificial intelligence, this data promises critical insights for governments, food producers, and the private sector to address the challenges of food insecurity and climate change. Yet with these new flows of data also come risks. Unequal capacity to collect and extract value from data can exacerbate structural power asymmetries in food systems, with important implications for human rights. To mitigate these risks, it is essential to establish robust data governance, itself no easy task. Agricultural data defy the legal categories that have guided data protection in other sectors. Moreover, most models of agricultural data governance have been developed in the Global North and may not be fit for the Global South, where smallholders make up the majority of food producers. This project, Data Governance for Equitable and Sustainable Digital Food Systems (DIGIFOOD), examines these emerging networks and practices of agricultural data governance through a transnational, comparative, and cross-national study of three countries leading agricultural digitalization and governance in the Global SouthKenya, India, and Colombia. The project breaks new ground by developing an innovative socio-legal and ethnographic approach for analyzing the technical, legal, and cultural dimensions of data governance. It will offer empirical insights into the impact of digitalization on agrarian relations, shedding light on struggles over power and authority within the context of the digital agricultural revolution. By doing so, it aims to make a substantial contribution to the normative evolution of governance standards to enable equitable and sustainable digitization of agriculture and food systems."
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- social sciencessociologygovernance
- natural sciencescomputer and information sciencescomputer securitydata protection
- engineering and technologymechanical engineeringvehicle engineeringaerospace engineeringsatellite technology
- social sciencespolitical sciencespolitical transitionsrevolutions
- agricultural sciencesagriculture, forestry, and fisheriesagriculture
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Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
2311 EZ Leiden
Netherlands