A Bayesian Network Based Monitoring System for Sow Management Contents 1 Introduction 2

Techniques for detecting deviation from expected production level has long been known and applied outside agricultural production. Even though the need for monitoring within agriculture seem obvious, only few examples exists. With recent method-ological improvements this may be changed. As an example the present paper describes a prototype of a system for monitoring pregnancy rate in sow herds. The system is based on the Bayesian network methodology. This approach leads to a monitoring systems that utilises information from matings, heat detections, pregnancy tests and farrowings to present updated estimates of pregnancy rates in the herd as well as probability of a change-point in the level. In addition, the system can be used to study the eeect of number of matings on utilisation of farrowing department. Finally, when information from heat detections and pregnancy rates are included, the eeect of cullings on production level can be studied. The underlying model is described in detail and a simulated scenario is used for illustrating the potential of the system.