Literature DB >> 25959418

Measuring the drinking behaviour of individual pigs housed in group using radio frequency identification (RFID).

J Maselyne1, I Adriaens1, T Huybrechts1, B De Ketelaere1, S Millet2, J Vangeyte3, A Van Nuffel3, W Saeys1.   

Abstract

Changes in the drinking behaviour of pigs may indicate health, welfare or productivity problems. Automated monitoring and analysis of drinking behaviour could allow problems to be detected, thus improving farm productivity. A high frequency radio frequency identification (HF RFID) system was designed to register the drinking behaviour of individual pigs. HF RFID antennas were placed around four nipple drinkers and connected to a reader via a multiplexer. A total of 55 growing-finishing pigs were fitted with radio frequency identification (RFID) ear tags, one in each ear. RFID-based drinking visits were created from the RFID registrations using a bout criterion and a minimum and maximum duration criterion. The HF RFID system was successfully validated by comparing RFID-based visits with visual observations and flow meter measurements based on visit overlap. Sensitivity was at least 92%, specificity 93%, precision 90% and accuracy 93%. RFID-based drinking duration had a high correlation with observed drinking duration (R 2=0.88) and water usage (R 2=0.71). The number of registrations after applying the visit criteria had an even higher correlation with the same two variables (R 2=0.90 and 0.75, respectively). There was also a correlation between number of RFID visits and number of observed visits (R 2=0.84). The system provides good quality information about the drinking behaviour of individual pigs. As health or other problems affect the pigs' drinking behaviour, analysis of the RFID data could allow problems to be detected and signalled to the farmer. This information can help to improve the productivity and economics of the farm as well as the health and welfare of the pigs.

Entities:  

Keywords:  drinking behaviour; group housing; pigs; radio frequency identification; validation

Mesh:

Year:  2015        PMID: 25959418     DOI: 10.1017/S1751731115000774

Source DB:  PubMed          Journal:  Animal        ISSN: 1751-7311            Impact factor:   3.240


  6 in total

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6.  Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs.

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  6 in total

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