DETECTIVE has made significant progress in mapping the legal and economic landscape for NGT-derived product traceability. Legal research has assessed current regulatory shortcomings for NGT-derived product traceability and explored how blockchain or probabilistic models can aid traceability, although these do not meet current authorisation requirements. Recommendations for legal framework adaptation are presented. An economic analysis, simulating how prices, quantities, and welfare indicators shift in response to adoption under different regulatory frameworks, shows that NGTs offer benefits in terms of productivity and sustainability while fragmented regulatory framework can act as a barrier. An Austrian case study grounds the work in practical production realities.
DETECTIVE aligns with stakeholder needs through the Systems Mapping Approach and the RRI Roadmap©™. Key stakeholders (e.g. enforcement laboratories and developers), have been engaged to co-define empowerment priorities, laying the foundation for a practical and stakeholder-driven Empowerment Programme. The Community of Practice (CoP), a science-based, multi-actor platform for trust-building and mutual learning, already includes over 40 members without formal promotion. An important and unique EU-wide survey on empowerment needs engages three stakeholder groups: 1) laboratories, 2) agri-food and feed value chain operators, and 3) competent authorities.
DETECTIVE has collected samples from canola, potato, rice, soybean, sugar beet, wheat, cow, goat and pig, representing commercially important plant and animal products of various complexity. In addition to samples derived from targeted mutagenesis, a cisgenic sample (potato) has also been collected. Partners are currently working on these samples to test a range of analytical detection methods for both single and multiple known mutations. In an innovative approach, the project explores machine-learning to analyse patterns to estimate the probability of artificially generated edits.
DETECTIVE develops a knowledge-based detection tool which applies machine-learning to large datasets to detect unknown NGT-derived products by identifying patterns or anomalies across the value chains. The criteria for the corresponding data space have been defined and the data space has been structured around multiple data silos: traits, genetic research, GMO research, NGT research, breeders, seed producers, regions and cultivation data. Seed potato, maize and sugar beet have been selected as proof-of-concept cases. An improved GenEdit database is developed, serving as the most comprehensive and up-to-date repository available. A machine-learning approach is applied to build a knowledge graph that will capture the complexity of the agri-food value chains and serve as a detection strategy targeting NGT-derived products.
DETECTIVE bridges the gap from research and development of detection methods to their implementation in enforcement laboratories. Tightly linked to WP3, a decision support system covering different attributes has been developed to evaluate the analytical methods. The development of standard operating procedures and validation protocols is still pending further progress in WP3.
