Mapping of air temperature and relative humidity in Compost-Bedded Pack Barn systems using a network of easily obtainable recording sensors
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Abstract
Real-time mapping of the thermal microenvironment in dairy cattle facilities enables the identification of areas and times with thermal comfort issues for the animals. To achieve this, sensor networks are necessary to characterize the entire area occupied by the animals and act in a targeted manner. However, most producers and researchers in this field cannot implement this approach, as monitoring systems typically involve expensive and challenging-to-operate components. In this context, the aim of this study was to develop and validate a network recording sensors (NRS) of air temperature (tair) and relative humidity (RH) using easily obtainable electronic components and apply this NRS for mapping these variables in a Compost-Bedded Pack Barn (CBP) system. The developed NRS was calibrated in meteorological shelters (in comparison with commercial loggers) and used for monitoring a CBP system in the Zona da Mata region, MG, Brazil. Data of tair and RH were collected during winter and summer, grouped by period (dawn, morning, afternoon, and night) and analysed using geostatistics. From the results, a strong spatial dependence was identified, and maps were generated. The spatial distribution maps indicated that the most challenging conditions occurred during the summer, with tair being thermally uncomfortable during the mornings and afternoons (tair ≥ 24.0 °C). During this season, RH was low in the afternoons (RH ≤ 68.0%), indicating potential for evaporative cooling. The developed NRS has shown potential for field-level application, capable of integration with heating systems and Internet of Things (where possible) for reliable monitoring and control of the production environment, with reduced costs.
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confinement systems, dairy cattle, embedded system, environmental monitoring, geostatistics, thermal microenvironment
Language
English
Citation
11th European Conference on Precision Livestock Farming, p. 1681-1688.
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