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Real-time monitoring of indoor particulate matter concentration in a commercial broiler house

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  10th International Livestock Environment Symposium (ILES X)  .(doi:10.13031/iles.18-132)
Authors:   Alberto Peña Fernández, Theo Demmers, Q. Tong, Ali Youssef, Tomas Norton, Daniel Berckmans
Keywords:   Activity, dust, poultry, ventilation rate, precision livestock farming

Abstract. Measuring indoor particulate matter concentration in poultry houses remains a difficult task. Aerosol analyzers are expensive and require frequent labor intensive maintenance. Precision Livestock Farming technologies support farmers in their daily routine of animal management, through monitoring animals continuously during their life in an automated, non-invasive way, without inducing additional stress in the animals. In this work, a model to relate broiler activity and ventilation rate with indoor particulate matter concentration has been developed.

For a complete growing cycle in a UK commercial broiler farm, broiler activity, using the eYeNamic® system, and ventilation rate, using the climate control system, were measured. Indoor concentration of different particulate matter sizes was continuously monitored using a DustTrak® analyzer. A Multi-Input Single-Output Transfer Function model was developed to monitor the impact of broiler activity and ventilation rate in the indoor particulate matter concentration. This model has an average accuracy of 74%. A Dynamic Linear Regression model was developed to forecast indoor particulate matter concentration levels along every day of the growing cycle in real-time. This model achieved a Mean Relative Prediction Error of 2%. This model has the potential to not only continuously estimate the aerosol concentration in the poultry house by automated monitoring of broiler activity and ventilation rate, but also to be part of a control system to manage aerosol concentration in the farm in real-time.

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