Technical fragmentation still limits scale. Data is generated across production, processing, distribution and retail, yet remains siloed in incompatible formats or proprietary systems; many processes rely on destructive sampling that obscures product-level granularity. The result is duplication, inconsistency and missed opportunities for traceability and AI-enabled analytics. At the core is a lack of common standards. Bayer notes that inconsistent digital product labelling across jurisdictions hampers registration, safe handling and cross-border compliance, reinforcing the need for harmonized data standards to scale innovation responsibly.7 In parallel, Fermata, an agritech firm, highlights that integrating farm systems is time-intensive due to inconsistent vocabularies and metadata – a heavy burden for small and medium-sized providers.8 In Papua New Guinea’s Jiwaka Province, FAO and partners tested RFID and blockchain-based traceability for pigs, building trust in provenance and enabling market access. Researchers emphasize the importance of “data refinery” models to clean, align and structure raw agricultural data for broader use.9 Significant barriers to scaling such models remain. FAO experts with Uppsala University cite transforming “fragmented farm data into usable digital assets” as a top priority, stressing the need for data refinery models that can clean, align and structure raw agricultural data for broader use.10 Interoperability entails more than file formats – it requires promotion, change management and infrastructure investment, including outreach to low-connectivity regions. Clear ownership and accountability are needed to define rules of engagement and ensure consistent practices. Putting this into practice requires capacity building – farmer training on data formats and consent, plus user feedback loops to refine tools. Cooperative networks and extension services are critical for embedding standards in daily operations. With common formats in place, AI and analytics can scale across the value chain, driving precision agriculture, better logistics, and higher quality and productivity.Data standardization protocols4 Interoperability begins with common standards, enabling data to move, connect and deliver value across the entire system. Data and Digital Readiness in Food Systems 8

Data Digital Readiness Food Systems 2025