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Developing an indicator of price anomalies as an early warning tool: A compound growth approach









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    Article
    Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools 2024
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    To enhance the resilience of food systems to food safety risks, it is vitally important for national authorities and international organizations to be able to identify early signals of emerging food safety risks and to provide early warning in a timely manner. This review provides an overview of existing and experimental applications of artificial intelligence (AI), big data, and internet of things tools and methods as part of early warning and emerging risk identification in the food safety domain. There is an ongoing rapid development of systems fed by numerous, real-time, and diverse data with the aim of early warning and identification of emerging food safety risks. The suitability of big data and AI to support such systems is illustrated by two cases in which climate change drives the emergence of risks, namely, harmful algal blooms affecting seafood and fungal growth and mycotoxin formation in crops. Automation and machine learning are crucial for the development of future real-time food safety risk early warning systems. Although these developments and tools increase the feasibility and effectiveness of prospective early warning and emerging risk identification, their implementation may prove challenging, particularly for low- and middle-income countries due to low connectivity and data availability. It is advocated to overcome these challenges by improving the capability and capacity of national authorities, as well as by enhancing their collaboration with the private sector and international organizations.
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    Book (stand-alone)
    Early warning tools and systems for emerging issues in food safety
    Technical background
    2023
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    Early warning (EW) systems have a critical role in the reduction of risks from various hazards. The capability and capacity to identify early signals and emerging food safety risks, and to provide on-time EW that would allow for the mitigation of related upcoming risks have therefore become vital for national and international authorities and organizations dealing with food safety. The developments in early warning systems show a shift from reactive towards proactive systems. With the rapid development of modern systems fed by numerous, real-time and diverse data, as well as the advancements achieved in artificial intelligence and machine learning techniques, increasingly tested and validated digital methods and models have become available for food safety early warning and analysis. This technical background report enhances the awareness of the available evidence-based innovative digital tools and provides technical background information to support their use for proactive food safety early warning.
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    Document
    FCC-EMPRES Information Sheets No. 1: FAO Advanced Tools and Technologies for Locust Monitoring and Early Warning 2016
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    MONITORING, providing EARLY WARNING, and acting promptly to control outbreaks of locust and other migratory pests are crucial to ensure that timely and appropriate action can be taken when an emergency arises. The Desert Locust Information Service (DLIS) at FAO Headquarters continuously monitors global weather, ecological conditions and the locust situation. Geo-referenced data in the field are collected and analysed on a daily basis. This analysis, carried out by the national control locust cen tres and by FAO DLIS, relies on a range of computer-based software tools which were developed by FAO and several partners for early warning purposes. The handheld device called eLocust is a striking example of these tools. These components constitute the oldest migratory pest monitoring and early warning system in the world.

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