Original papersA multivariate dynamic linear model for early warnings of diarrhea and pen fouling in slaughter pigs

- Animal and environment data are described with a multivariate dynamic linear.
- Observations repeatedly deviating from model forecasts indicate undesired events.
- Early and accurate warnings of diarrhea and pen fouling were achieved.
- The effect of each variable is assessed by omission and inclusion in the model.

We present a method for providing early, but indiscriminant, predictions of diarrhea and pen fouling in grower/finisher pigs. We collected data on dispensed feed amount, water flow, drinking bouts frequency, temperature at two positions per pen, and section level humidity from 12 pens (6 double pens) over three full growth periods. The separate data series were co-modeled at pen level with time steps of one hour, using a multivariate dynamic linear model. The step-wise forecast errors of the model were unified using Cholesky decomposition. An alarm was raised if the unified error exceeded a set threshold a sufficient number of times, consecutively. Using this method with a 7 day prediction window, we achieved an area under the receiver operating characteristics curve of 0.84. Shorter prediction windows yielded lower performances, but longer prediction windows did not affect the performance.