Detecting abnormalities in pigs’ growth – A dynamic linear model with diurnal growth pattern for identified and unidentified pigs

• Published in: Computers and electronics in agriculture 2018-12, Vol.155, p.180-189
• Main Authors: Stygar, A.H., Kristensen, A.R.
• Format: Article
• Language: English
• Subjects: Abnormalities; Agricultural production; Alarms; Body weight; Correlation analysis; Deterioration; Diurnal pattern; Dynamic linear model; Farmers; Hogs; Monitoring; Outbreaks; Pigs
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2018.10.004

•We have constructed a new tool for frequent growth monitoring in pigs.•The tool is able to produce growth alarms on individual and batch level.•We obtained similar batch alarms for identified and unidentified pigs.•The tool could be used to obtain economic value of frequent growth monitoring. In this paper we described a new tool used for frequent growth monitoring in pig production. The aim of the constructed tool was to alert farmers about deterioration in pigs’ growth and to provide current and historical growth statistics for consecutive batches. The tool was built as a dynamic linear model (DLM) for systems with identified and unidentified pigs. The growth of pigs was described by parameters representing an initial body weight (BW), average daily gain and daily fluctuations in BW of pigs. Moreover, the constructed tool was built to account for increasing variation in BW of pigs over time as well as autocorrelation between BW measurements of observed individuals. The forecast errors obtained from the DLM were standardized and monitored with a tabular cusum. Alarms obtained from the cusum were registered both on batch (identified and unidentified pigs) and individual level (identified pigs). The constructed tool was tested on data gathered between February and September 2016. Altogether, data from 3 batches, 1058 individuals and 146,926 BW observations were collected and analyzed. For all 3 batches, filtered and smoothened growth means were obtained and plotted. Using the model for identified and unidentified pigs, the growth problems were detected on certain dates in Batch 2 and Batch 3. In both identification scenarios, similar batch alarms were obtained. The example on growth alarms for individual pigs (using version of the model for identified pigs) was also provided. The filtered herd mean, obtained from the constructed DLM, was calculated based on all pigs present at the start of fattening. Therefore, the obtained batch BW mean was accounting also for the heaviest individuals sold after exceeding a defined target weight from the observed batches. Hence, the presented tool can be useful in obtaining growth statistics for systems where pigs are sold continuously. Frequent BW information can be useful in informing farmers about unexpected events influencing growth e.g. outbreaks of diseases or management problems. Moreover, the historical information on growth might be valuable in making optimal decisions regarding management. In the future the econ