A UAV-Based System for Validating a Backward Lagrangian Stochastic Model in a Dairy Cattle Farm

Area of Science:

• Environmental Science
• Agricultural Engineering
• Atmospheric Chemistry

Background:

• Compost-bedded pack barns are significant sources of greenhouse gas emissions.
• Accurate measurement of CO2 fluxes from these barns is crucial for environmental management.
• Existing atmospheric models require validation for livestock environments.

Purpose of the Study:

• To characterize CO2 emissions from a dairy cattle compost-bedded pack barn.
• To validate the bLS (Building Large Eddy Simulation) model using a UAV-based system.
• To assess the performance of a novel multi-sensor platform (MSP) for environmental monitoring.

Main Methods:

• Emission fluxes were calculated using the bLS model (WindTrax).
• CO2 concentrations were measured at 5.0 m and 10.0 m above ground level (ABGL) by a UAV-based system equipped with an NDIR sensor.
• Ground-based sensors provided input data upwind and downwind of the emission source.

Main Results:

• The study validated bLS model concentrations against UAV-measured data at two altitudes.
• Sensitivity analysis showed differences in calculated emission fluxes under stable and unstable atmospheric conditions.
• The UAV-based system demonstrated effectiveness in measuring CO2 concentrations for model validation.

Conclusions:

• This research provides the first validation of bLS model concentrations at multiple altitudes using a UAV-based system for compost-bedded pack barns.
• The findings highlight the potential for UAVs to improve the accuracy of greenhouse gas emission estimations from agricultural sources.
• Accurate emission data is essential for developing effective mitigation strategies in dairy farming.