“White paper for food chains actors for using agrobiodiversity, listing consumer expectations and aversions” is intended to guide innovation and research in the development of food chains valuing agrobiodiversity, with a special attention to neglected and underutilised crops (NUCs). It is addressed to the DIVINFOOD consortium, and to all value chain actors, development services, policy-makers, researchers and consumer-citizens concerned by the use of agrobiodiversity in food chains. It presents the methodology and the results of i) an online survey implemented in the 7 European countries of the DIVINFOOD project, in which citizen-consumers have been invited to judge different options to use cultivated biodiversity in food chains; ii) focus groups in specific regions in which a focus has been done on NUCs. Results are synthesised through recommendations for DIVINFOOD members, and for all concerned actors.
“Methodology to elaborate a consumption white paper” is intended to guide the implication of consumers, considered as citizens, in the decisions relative to the development of food chains. It is addressed to the DIVINFOOD consortium, and to all value chain actors, development services, policy-makers, researchers or citizen associations seeking methods to make consumer-citizens decision-makers in their innovation, policy or research activities concerning food chains. The document first reviews inspiring approaches for involving citizens in developing recommendations on potentially complex topics, used in the agriculture and food sector. Second, it describes the methodology developed in DIVINFOOD to elaborate a white paper, which can be replicated or adapted in other contexts.
“Framework for LL facilitation and data production” is intended for all Living Labs and all DIVINFOOD project partners. This document provides a framework to situate LLs’ definition and contribution to the overall aim of the DIVINFOOD project. It orients LL coordinators throughout the setting up and development of living lab interactions, experiments and data collection. It also suggests tools to support LL facilitation and interactions at local level.