A multivariate dynamic linear model for early warnings of diarrhea and pen fouling in Slaughter pigs

TY - JOUR

T1 - A multivariate dynamic linear model for early warnings of diarrhea and pen fouling in Slaughter pigs

AU - Jensen, Dan Børge

AU - Toft, Nils

AU - Kristensen, Anders Ringgaard

PY - 2017/4/1

Y1 - 2017/4/1

N2 - 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.

AB - 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.

U2 - 10.1016/j.compag.2016.12.018

DO - 10.1016/j.compag.2016.12.018

M3 - Journal article

SN - 0168-1699

VL - 135

SP - 51

EP - 62

JO - Computers and Electronics in Agriculture

JF - Computers and Electronics in Agriculture

ER -