Automated monitoring of feeding and drinking patterns in growing-finishing pigs: towards a warning system for performance, health and welfare problems in individual pigs

A pig farmer aims to maximize profit whilst maintaining the health and welfare status of the animals at optimal level. This requires adequate follow-up of each individual animal to spot and treat upcoming or present health, welfare and productivity problems. Currently, this follow-up is done through visual observation of the animals. However, with the economically-driven trends towards larger farms and larger groups of pigs in one pen, this visual observation is becoming too time-consuming and difficult. Therefore, in this PhD thesis, an automated warning system for performance and welfare monitoring of individual fattening pigs is developed to support the pig farmer’s activities. The aim is to signal upcoming performance, health and welfare problems to the pig farmer. Changes in the daily feeding and drinking patterns of an individual pig are used to detect these problems. A High Frequent (HF) Radio Frequency Identification (RFID) system attached to a standard feeding and drinking system used in group-housing allowed to measure this behaviour automatically. Each pig was equipped with a passive RFID tag with a unique number in its ear. These measurement systems were validated thoroughly through comparison with observations. Irregular intervals occurred between the registrations of a feeding or drinking pig. Range measurements of the RFID system revealed that changes in tag position and orientation relative to the antenna during feeding and drinking most likely caused this phenomenon. From registrations of the RFID tag at the feeder or drinker; number, duration and timing of feeding and drinking visits can be derived. However, feeding and drinking visits first had to be constructed from the raw RFID registrations. Visit criteria were found to be the optimal method to do this. Then, several variables of the feeding and drinking pattern were extracted from the RFID data and compared to the observations. Correlations between observed and RFID based feeding or drinking variables were found. This was especially the case for the duration of feeding and drinking, which was highly correlated to both the duration of the RFID based visits and the raw number of RFID registrations. In addition, also water usage could be estimated by the RFID drinking system. Four warning systems were designed to monitor changes in the feeding patterns of the individual pigs. The number of registrations per pig and the average gap between RFID based feeding visits were chosen as variables to be used in the detection algorithms. For each variable, fixed limits that were constant for all the pigs during the entire fattening period were compared to Synergistic Control (SGC) limits that were individual and time-varying. The concept of Synergistic Control allows differentiating normal variation in the pigs’ feeding and drinking behaviour, such as age-effects, from abnormal variation pointing towards problems. Because every fattening pig acts as its own reference and the limits are pig-specific when using SGC, possible problems can be signalled on an individual level. Abnormal points detected by each of the warning systems were signalled daily in the form of an ‘alert’ for each pig that crossed its individual threshold value. An extensive validation of the warning systems was performed on a group of pigs that was closely monitored on a daily basis to determine the number of true and false alerts and the number of missed problems. The best performing warning system was with Synergistic Control limits on the number of RFID registrations and the use of historical data to initialize the warning system. This led to a sensitivity of 66 %, specificity of 98 %, accuracy of 97 % and precision of 67 % for all health, welfare and productivity problems spotted by the observers. The average time till the first false alert was 82 days for individual pigs and severe problems were detected after 1.1 day on average. Further research is required to quantify the added value of an automated warning system for the farmer at different levels (e.g. health, performance, efficiency, labour, costs, welfare, sound use of antibiotics). The alerts of the warning systems were now compared to problems detected by observers. However, there is no information available yet on which problems a pig farmer would detect (and when) and more importantly, which problems are most important to detect (e.g. because they require treatment, are difficult to detect visually). Future research should also focus on increasing the performance of the warning system in terms of sensitivity and precision. The optimal variables for problem detection should still be determined, as well as the best combination of variables and warning systems. Such combinations could also include automatic monitoring of the drinking behaviour.status: publishe