Optimizing performance and welfare of fattening pigs using High Radio Frequency Identification (HF RFID) and synergistic control on individual level

    Project Details


    Main research question/goal
    How can we use an individual fattening pig’s eating and drinking pattern to automatically detect problems as soon as they occur? Pig farms have to be highly efficient to stay profitable. The pig farmer needs to closely monitor every individual pig to prevent and minimize diseases and behavioural problems, because these result in diminished growth and economic loss. One of the best indicators for the productivity, health and welfare of fattening pigs is their eating and drinking pattern, because this is the first to change when problems occur. Automatic registration of these patterns for individual pigs housed in groups and a system to interpret these data can provide the pig farmer with crucial information about the animals and lead to earlier intervention.

    Research approach
    In this project, RFID (radio-frequency identification) sensors are developed and validated to monitor the eating and drinking pattern of individual fattening pigs housed in groups. During several fattening periods the trend, the normal variation and the abnormal variation (caused by health-, welfare- and performance problems) of these patterns is examined. Using the concept of Synergistic Control, these data are analyzed: every pig is considered as its own reference and excessively large deviations from this reference (abnormal variation) are detected. The sensor system and the Synergistic Control are merged into an Early Warning System. We validate the Early Warning System during several fattening periods and evaluate its ability to detect problems with individual pigs.

    Taken together, the RFID sensor system and the Synergistic Control form a new tool, the Early Warning System. This notifies the pig farmer when abnormal variation occurs in the eating or drinking pattern of an individual pig. This variation could be caused by welfare-, health- or performance problems. The visually-based monitoring currently used on pig farms can be supplemented by a technical tool that allows accurate and detailed monitoring of each pig and allows the farmer to execute more directed actions.

    Funding provider(s)
    IWT - Instituut voor de aanmoediging door wetenschap en technologie in Vlaanderen

    External partner(s)
    ASE - Aarhus School of Engineering
    Georg-August Universität Göttingen
    ISMB - Istituto Superiore Mario Boella
    KULeuven - Dept. Biosystemen
    Effective start/end date1/09/1131/08/15