Remotely piloted aircraft for monitoring greenhouse gases in dairy production systems

J.V. Aguiar¹, P.F.P. Ferraz²*, G.A.S. Ferraz², J.C. Ferreira², D. Cecchin³, A. Mattia⁴, L. Conti⁴ and G. Rossi⁴

¹Federal University of Lavras (UFLA), Department of Animal Science, Faculty of Animal Science and Veterinary Medicine, BR 37200–900 Lavras, Brazil
²Federal University of Lavras (UFLA), Department of Agricultural Engineering, BR37200–900 Lavras, Brazil
³Department of Agricultural and Environmental Engineering, Fluminense Federal University (UFF), BR 24210–240 Niteroi, Brazil
⁴Department of Agriculture, Food, Environment and Forestry, University of Florence, IT 50145 Florence, Italy
*Correspondence: patricia.ponciano@ufla.br

Abstract:

The monitoring of greenhouse gas (GHG) emissions in dairy cattle facilities is essential for understanding and mitigating the environmental impact of livestock farming. Among the main gases emitted in dairy production systems, methane (CH₄) and carbon dioxide (CO₂) play significant roles in global warming. The objective of this research was to evaluate the spatial variability of CH₄ (ppm) and CO₂ (ppm) concentrations, as well as environmental variables (dry bulb temperature, t_db, °C, and relative humidity, RH, %), in a compost barn dairy production system. For gas concentration monitoring, an electrochemical sensor was used for CH₄ and a non–dispersive infrared (NDIR) sensor for CO₂. For the environmental variables, a Hobo® MX2301A datalogger was used, and both pieces of equipment were attached to a remotely piloted aircraft (RPA), the DJI Matrice 350. Measurements were carried out over three days, with flights conducted over the facility’s roof. The data obtained were analysed using geostatistics to characterise spatial variability of the GHG. A strong spatial dependence was observed in gas concentrations and environmental variables. The highest concentrations of CH₄ (129–134.4 ppm) and CO₂ (434–479 ppm) were recorded on the first day. T_db ranged between 24.2 °C and 32 °C, while RH fluctuated between 38.8% and 68%. The use of RPA proved to be an efficient tool for GHG monitoring, allowing the identification of spatial distribution patterns. This technology provides a novel approach to measuring GHG emissions, addressing the environmental challenges of the agricultural sector.

Key words:

carbon dioxide, compost barn, dairy cattle, drone, methane, spatial variability

Airflow profile study of a compost dairy barn using a low-cost 3D-printed anemometer network

F.A. Obando Vega¹*, A.P. Montoya Rios¹, F.A. Damasceno², J.A. Osorio Saraz¹ and J.A. Costa Do Nascimento²

¹Universidad Nacional de Colombia sede Medellín, Facultad de Ciencias Agrarias, Departamento de Ingeniería Agrícola y Alimentos, CO050034 Medellín, Colombia
²Federal University of Lavras, Department of Engineering, BR37200-000 Lavras, Minas Gerais, Brazil
*Correspondence: faobando@unal.edu.co

Abstract:

Mechanical ventilation is commonly used for environmental thermal regulation inside closed-field agricultural production systems. Analyzing the air distribution inside these facilities and the correct operation of the fans can be a challenging. This could be determined using cost prohibitive techniques as particle image velocimetry or deploying large wind sensors networks on-site. To avoid this limitation without a lack of measurement accuracy, this research was focused on developing and test a low-cost anemometer network based in low cost propeller’s anemometers, built using fused 3D-printed and open-hardware platforms. Four propeller anemometers with three to six blades were simulated using the 6-DOF method of ANSYS computer fluid dynamics software. Similar results were obtained for all the simulated models with minor differences. Anemometers were tested in an open circuit wind tunnel before to be evaluated in two open compost dairy barn building using high-volume low-speed and low-volume high-speed fans. Data were analyzed by employing contour maps, descriptive statistics and correlation. The results show that the anemometer network determines the fan’s wind profile for wind speeds over 0.7 m s^-1 and it was possible to determine the facilities spots with ventilation problems. The proposed anemometer network and methodology are a good alternative to analyze the operating conditions of the tested agricultural facilities and optimize its performance.

Key words: