1. Temperature as a predictor of fouling and diarrhea in slaughter pigs
FACULTY OF HEALTH AND MEDICAL SCIENCES Department of large animal sciences
UNIVERSITY OF COPENHAGEN
Temperature as a predictor of fouling and diarrhea in slaughter pigs
Dan Børge Jensen, Nils Toft1, Anders Ringgaard Kristensen
Abstract We assess the predictive value of pen level temperature with respect to pen fouling and diarrhea. We recorded the temperature at 2 pen locations for 16 groups of 18 pigs. Logistic regression models were made to express the probability of fouling and diarrhea per day. Furthermore, fitting the models was attempted with data averaged over 10, 15, 30 and 60 minutes. Both conditions were consistently detected at better than random chance. We conclude that temperature contains significant predictive value, but is not enough on its own.
Background
This study was done as part of the PigIT project, which aims at improving welfare and production of slaughter pigs by integration of sensors for alarm purposes.
Temperature, when too high or when changing rapidly, is known to cause stress and discomfort in pigs, increasing the risk of disease outbreaks and undesired behavior.
Thus an exploratory analysis was done to estimate the utility of temperature in predicting diarrhea and pen fouling.
The ambition of the PigIT Project
In the PigIT project, we collect data on live weight, water and feed consumption, humidity and temperature in pig herds. Through modeling, these are used to predict major health and welfare problems, such as diarrhea an pen fouling.
Findings
The probability of diarrhea and pen fouling were found to be described by the logistic regression models shown below. The best models were based on temperature data averaged over periods of 10 minutes, yielding MCC values at 56.0 and 55.7 for fouling and diarrhea, respectively.
Both events were consistently predicted with better than random performance, but not accurately enough in practice. Thus temperature holds information applicable to predicting these events, but not enough to be used alone.
Predictive performance
The best performances of the reduced logistic regression models, describing the probability of pen fouling and diarrhea. Both events were predicted up to one day before the event, with a performance consistently better than random chance.
Methods
Temperature was recorded continuously at two locations (by the water nipple and corridor) in each pen for 16 groups of growing pigs. The two series per pen were averaged over 10, 15, 30 and 60 minutes, and summarized by day. The summary statistics were: highest temperature, lowest temperature, greatest rate of temperature increase and greatest rate of temperature decrease.
For each of the averaging periods, a logistic regression model was made based on the 2x4 summary statistics and reduced via backwards elimination. Performances, when predicting events up to 1 day in advance, were evaluated with Matthews Correlations Coefficient (MCC). a) b)
Temperature monitoring in the pens a) The temperature in each pen is continuously monitored using two separate thermometers – one near the drinking nipple (blue rectangle) and one near the corridor of the section (red rectangle). b) Temperatures measured at the drinking nipple (blue) were generally a few degrees higher than temperatures near the corridor (red)
Perspectives
Integration of temperature with other sensor data – e.g. using Dynamic Linear Models and Bayesian Networks
Timeliness – how early can we predict problems? And how early should we, in order to keep the predictions useful?
1National Veterinary Institute, Technical University of Denmark, Bülowsvej 27, 1870 Frederiksberg C, Denmark
Acknowledgement: This research was carried out with support from The Danish Council for Strategic Research (The PigIT Project, Grant number )