TY - CONF

T1 - Reducing alarms and prioritising interventions in pig production by simultaneous monitoring of water consumption in multiple pens

AU - Dominiak, Katarina Nielsen

AU - Hindsborg, Jeff

AU - Pedersen, L. J.

AU - Kristensen, Anders Ringgaard

N1 - Conference code: 8

PY - 2017

Y1 - 2017

N2 - Spatial modelling of water consumption in growing pigs can be a useful tool foridentifying high risk pens or sections in early detection of diseases and variousbehavioural problems.In this study a multivariate dynamic linear model (DLM) is developed based on data from simultaneous monitoring of water consumption across multiple pens in two separate herds. The two herds consist of a commercial finisher herd (Herd A) and a research farm with weaners (Herd B).Parameters in the model can be defined individually at herd, section or pen level. This spatial distinction allows early warnings to be generated at pen level or merged at section or herd level to reduce the number of alarms. Information on which specific pens or sections are of higher risk of stress or diseases is communicated to the farmer and target work effort to pens at risk.For Herd A, all model parameters defined at section level resulted in the best fit (MSE =13.85 litres2/hour). For Herd B, parameters defined at both pen and section level resulted in the best fit (MSE = 1.47 litres2/hour).For both Herd A and Herd B, preliminary results support the spatial approach bygenerating a reduced number of alarms when comparing section levels to pen levels.This study is a part of an on-going project aiming to improve welfare and productivity in growing pigs using advanced ICT methods.

AB - Spatial modelling of water consumption in growing pigs can be a useful tool foridentifying high risk pens or sections in early detection of diseases and variousbehavioural problems.In this study a multivariate dynamic linear model (DLM) is developed based on data from simultaneous monitoring of water consumption across multiple pens in two separate herds. The two herds consist of a commercial finisher herd (Herd A) and a research farm with weaners (Herd B).Parameters in the model can be defined individually at herd, section or pen level. This spatial distinction allows early warnings to be generated at pen level or merged at section or herd level to reduce the number of alarms. Information on which specific pens or sections are of higher risk of stress or diseases is communicated to the farmer and target work effort to pens at risk.For Herd A, all model parameters defined at section level resulted in the best fit (MSE =13.85 litres2/hour). For Herd B, parameters defined at both pen and section level resulted in the best fit (MSE = 1.47 litres2/hour).For both Herd A and Herd B, preliminary results support the spatial approach bygenerating a reduced number of alarms when comparing section levels to pen levels.This study is a part of an on-going project aiming to improve welfare and productivity in growing pigs using advanced ICT methods.

M3 - Paper

T2 - European Conference on Precision Livestock Farming

Y2 - 12 September 2017 through 14 September 2017

ER -