Data challenges and metrological approaches in the agrifood supply chain: Data accuracy, data availability, and data privacy issues.

The agrifood supply chain is undergoing a profound digital transformation driven by the adoption of advanced sensing technologies, data analytics, and interconnected information systems. While these innovations create unprecedented opportunities to improve food safety, traceability, authenticity, and sustainability, they also introduce major challenges related to data accuracy, data availability, and data privacy and ethical governance. In this evolving landscape, metrology provides the scientific framework needed to ensure that digital data are reliable, comparable, and fit for decision-making. This review examines the principal data challenges affecting digital agrifood systems and discusses how metrological principles-including traceability, calibration, validation, uncertainty evaluation, and standardised measurement protocols-can be extended beyond traditional laboratory measurements to emerging digital technologies such as IoT, remote sensing, blockchain, Digital Twins, and artificial intelligence. By integrating these principles with FAIR data practices, digital agrifood ecosystems can improve data interoperability, transparency, and long-term reusability while supporting trustworthy data sharing and responsible governance. The review also highlights how research infrastructures such as METROFOOD-RI and governance initiatives such as DRG4FOOD provide complementary examples of implementing these principles in practice by combining metrological rigor with ethical and privacy-aware data governance. Overall, this review proposes a metrological perspective for addressing emerging data challenges and demonstrates how the integration of measurement science, digital technologies, and responsible governance can support trustworthy digital agrifood ecosystems, evidence-based decision-making, and more sustainable and resilient food systems.