Conference Agenda

AGROVOC, developed by the Food and Agriculture Organization (FAO), is a comprehensive multilingual thesaurus that standardizes terminology across agriculture, forestry, fisheries, food security, and related domains. As artificial intelligence (AI) and machine learning increasingly underpin decision-support systems in agriculture, the role of structured, interoperable vocabularies becomes critical. AGROVOC provides semantic consistency and facilitates linked data integration, enabling AI-based applications to process, interpret, and reason over heterogeneous datasets with improved accuracy. This paper explores AGROVOC’s architecture, its alignment with Semantic Web standards, and its integration into knowledge graphs and ontologies. Furthermore, we analyze its significance for enhancing interoperability among global agricultural information systems and its impact on the development of intelligent solutions for sustainable farming. By bridging linguistic and conceptual gaps, AGROVOC emerges as a foundational resource for advancing data-driven innovation in agriculture.

For long-term space-flight human exploration and the colonization of planets a constant and self-sustaining food supply is needed. Insects are valuable candidates for integrated food and waste management in Bioregenerative Life Support Systems. However, studies on terrestrial circular food systems have revealed knowledge gaps on insect physiology and species interactions under space-like stressors such as microgravity (μG) and radiation, and the information remains scattered among the species, without a clear understanding of the effects for specific taxa.

A systematic and critical analysis of the studies on the effects of μG on the biology of edible insects (Acheta domesticus, Tenebrio molitor, and Apis mellifera) under real μG conditions was performed. Results indicate that the μG effects are species-specific and generally the development and behaviour are not strongly affected. The embryonic developmental and metamorphic processes seem to be able to be completed in space and the reproduction and completion of life cycle for some species are possible. Negative effects from μG occurred in the immune system and in physiology of some species. Based on these data, studies on the effects of μG on edible insects on the complete life cycle and the qualitative and nutritional properties of the related products are ongoing.

This review examines how the Technology Acceptance Model (TAM) has been applied to identify common and context-specific factors influencing drone adoption in civilian contexts, particularly in agriculture and forestry. A Scopus search (2017–2024) using combined queries (e.g., “technology acceptance” AND “UAV” or “drone”) yielded 163 publications, from which 42 TAM-based studies were selected. For each, we identified the TAM constructs and additional variables measured, comparing their usage across contexts.

The studies span diverse samples—farmers, consumers, students, healthcare workers, and public-sector stakeholders—across countries in Asia, Europe, North America, Africa, and South America. The most frequently examined factor was behavioural intention to use (24 studies), followed by perceived usefulness (15), perceived ease of use (14), and attitude (12). Other commonly included constructs were acceptance/adoption (10), personal innovativeness (8), perceived risk (8), trust (7), social influence (7), perceived benefits (7), speed expectancy (6), perceived privacy risk (6), facilitating conditions (6), and privacy concerns (5).

These findings confirm the centrality of TAM’s core constructs while highlighting the relevance of contextual variables. The review underscores the importance of extending TAM to better capture the diverse factors shaping drone adoption in civilian sectors.