Temperature as a predictor of fouling and diarrhea in slaughter pigs

TY - JOUR

T1 - Temperature as a predictor of fouling and diarrhea in slaughter pigs

AU - Jensen, Dan Børge

AU - Kristensen, Anders Ringgaard

PY - 2016/1/1

Y1 - 2016/1/1

N2 - The PigIT Project aims at improving welfare and productivity of slaughter pigs by integration of various sensor systems for alarm purposes. Here we present an exploratory analysis to assess the predictive value of temperature sensor data with respect to pen fouling and diarrhea. We recorded the temperature at two locations in 8 pens between November 2013 and December 2014. A single logistic regression model was made to express the probability of either diarrhea or fouling per pen per day, and was reduced via backwards elimination. The predictive performances were evaluated by the area under the receiver operating characteristics curve (AUC). Indiscriminant prediction of either event reached an AUC of 0.80. Similar performances were seen when predicting each of the events on their own using the same model, with AUC values at 0.78 and 0.81 for diarrhea and fouling, respectively. Thus, temperature information seems to provide predictive value in relation to fouling and diarrhea. It would be meaningful to combine this information with other available data by using more advanced models to achieve an optimal predictive power.

AB - The PigIT Project aims at improving welfare and productivity of slaughter pigs by integration of various sensor systems for alarm purposes. Here we present an exploratory analysis to assess the predictive value of temperature sensor data with respect to pen fouling and diarrhea. We recorded the temperature at two locations in 8 pens between November 2013 and December 2014. A single logistic regression model was made to express the probability of either diarrhea or fouling per pen per day, and was reduced via backwards elimination. The predictive performances were evaluated by the area under the receiver operating characteristics curve (AUC). Indiscriminant prediction of either event reached an AUC of 0.80. Similar performances were seen when predicting each of the events on their own using the same model, with AUC values at 0.78 and 0.81 for diarrhea and fouling, respectively. Thus, temperature information seems to provide predictive value in relation to fouling and diarrhea. It would be meaningful to combine this information with other available data by using more advanced models to achieve an optimal predictive power.

U2 - 10.1016/j.livsci.2015.11.007

DO - 10.1016/j.livsci.2015.11.007

M3 - Journal article

SN - 1871-1413

VL - 183

SP - 1

EP - 3

JO - Livestock Science

JF - Livestock Science

ER -