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For sustainable pork production and maximum pig welfare, all health, welfare and productivity problems in the barn should be detected as early as possible. In this paper, the first results are presented of an automated warning system that is based on measurements of the feeding pattern. The system is able to give real-time alarms for individual fattening pigs. A validated High Frequency Radio Frequency Identification (HF RFID) system was used to measure the feeding pattern of each pig. Using this data, a warning system was developed with time-varying individual control limits using the concept of Synergistic Control. Synergistic Control is the synergy between the models used in Engineering Process Control (EPC) and the control charts used in Statistical Process Control (SPC). During a validation round, 140 pigs were observed closely and individually by observers to identify true alarms, false alarms and problems missed by the warning system. Using this system, 58.3% of the days where a problem was present in individual pigs were detected and 71.0% of the alarms were correct. Severe problems were detected within 1.4 days on average. The SGC approach gave better results than a fixed group limit. Further improvements in sensitivity and precision are still needed, as well as tests to compare the performance to the farmers’ observation. But overall, the system allows better detection and follow-up of individual pigs’ problems, which would in turn increase the pigs’ health, welfare and productivity, improve labour efficiency and help with decision making.
Original languageEnglish
Title of host publicationPapers presented at the 8th European Conference on Precision Livestock Farming
EditorsDaniel Berckmans, Alassane Keita
Number of pages7
Volume2017
Place of PublicationNantes, France
Publication date2017
Publication statusPublished - 2017
Event8th European conference on Precision Livestock Farming 2017 - La Cité, Nantes, France
Duration: 12-Sep-201714-Sep-2017
http://www.ecplf2017.org/
http://www.ecplf2017.org/index.php

ID: 5611735