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DocumentOther documentReport of the third meeting of the European and Eurasian Soil Laboratory Network (EUROSOLAN) - EUROSOLAN-III/21/Report 2021
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MeetingMeeting documentReport of the second meeting of the Eurasian and European Soil Laboratory Network (EUROSOLAN). EUROSOLAN-II/20/Report
Virtual meeting, 30 September – 2 October 2020
2020Also available in:
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MeetingMeeting documentReport of the first meeting of the European and Eurasian Soil Laboratory Network (EUROSOLAN)
Chişinău, Moldova, 2-5 October 2019
2019Also available in:
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Book (series)Technical studyRisk Assessment of Food Allergens. Part 2: Review and establish threshold levels in foods for the priority allergens 2022
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No results found.Knowledge of thresholds constitutes a critical requirement to assessing the risk from allergens, as they are a characteristic of the hazard that allergens present to the food-allergic population. FAO and WHO reconvened the Ad hoc Joint FAO/WHO Expert Consultation on Risk Assessment of Food Allergens for a second meeting to provide scientific advice on review and establish threshold levels in foods for the priority allergens. The expert committee concurred that the benchmark dose/probabilistic hazard assessment approach aligned most closely with the requests of the Codex Committees. After extensive discussion, the expert committee reached a consensus on reference doses (RfD) for priority allergenic foods, meeting the criterion for HBGV that they should reflect a range of exposure without appreciable health risk. -
Book (stand-alone)Technical bookGlobal Forest Resources Assessment 2025 2025FAO completed its first assessment of the world’s forest resources in 1948. Since then, the Global Forest Resources Assessment (FRA) has evolved into a comprehensive evaluation of forest resources and their condition, management and uses, covering all the thematic elements of sustainable forest management. This, the latest of these assessments, examines the status of, and trends in, forest resources over the period 1990–2025, drawing on the efforts of hundreds of experts worldwide. The results of FRA 2025 are available in several formats, including this report, an interactive story on key findings and an online database at https://fra-data.fao.org.
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Book (stand-alone)Technical bookArtificial intelligence for food safety
A literature synthesis, real-world applications and regulatory frameworks
2025Also available in:
No results found.Artificial Intelligence (AI) is increasingly applied in food safety management, offering new capabilities in data analysis, predictive modelling, and risk-based decision-making. A review of the literature identifies three primary areas of application: scientific advice, inspection and border control, and operational activities of food safety competent authorities. Five country examples with the real-world use cases illustrate diverse uses of AI tools, including pathogen detection, import sampling prioritization, and language models for regulatory data processing. Regulatory frameworks, as well as voluntary governance, addressing AI in the public sector are emerging worldwide. National and international initiatives often highlight the importance of data governance, transparency, ethical considerations, and human oversight. Challenges such as biased data, explainability, and data governance gaps appear across different contexts, along with potential risks from deploying AI systems prematurely. Access to high-quality, interoperable data and collaboration among stakeholders can support effective integration of AI technologies. AI readiness often depends on understanding specific problems to be addressed, current capacities, and the quality of available data. Human oversight and continuous evaluation contribute to maintaining trust in AI systems. Collaborative efforts involving academia, the private sector, and international organizations help build shared knowledge and resources for AI development in food safety. Overall, AI presents opportunities to enhance resilience, efficiency, and responsiveness in food safety systems. Careful consideration of governance, data management, and multi-stakeholder cooperation can shape AI’s contribution to achieving sustainable and equitable outcomes in agrifood systems.