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:

Application of geographically weighted principal components analysis based on soybean yield spatial variation for agro-ecological zoning of the territory

A. Zymaroieva¹*, O. Zhukov², T. Fedonyuk³ and A. Pinkin⁴

¹Zhytomyr National Agroecological University, Faculty of Forestry, Department of Forest Resources Utilization, Stary Blvd. 7, UA10008 Zhytomyr, Ukraine
²Oles Honchar Dnipro National University, Faculty of Biology and Ecology, Department of Zoology and Ecology, pr. Gagarina, 72, UA49010 Dnipro, Ukraine
³Zhytomyr National Agroecological University, Faculty of Forestry, Department of Forest Ecology and Life Safety, Stary Blvd. 7, UA10008 Zhytomyr, Ukraine
⁴Zhytomyr National Agroecological University, Faculty of Engineering and Power Engineering, Department of Electrification, Automation of Production and Engineering Ecology, Stary Blvd. 7, UA10008 Zhytomyr, Ukraine
*Correspondence: nastya.zymaroeva@gmail.com

Abstract:

In this study, the geographically weighted principal components analysis as an alternative method for agro-ecological characterization of the region was provided. The spatial and temporal distribution pattern of soybean yield was analyzed by using spatial statistics technology, which provided a good reference for agricultural development planning. The soybean yield was selected for the present study because it is a comprehensive indicator reflecting the production potential of the regional agroecosystems. The organized data set, which included the average per year yields of soybean in 10 regions (206 administrative districts) of Ukraine, was used for analysis. The regular temporal trend, specific for each district, was previously extracted from the time series data. The principal components analysis of the detrended data allowed to identify four principal components, which altogether can explain 58% of the soybean yield variation. The geographically weighted principal components analysis allowed to reveal that four spatially determined processes were influencing the yield of soybeans and had the oscillatory dynamics of different periodicity. It was hypothesized that the oscillating phenomena were of ecological nature. Geographically weighted principal component analysis revealed spatial units with similar oscillatory component of soybean yield variation. Our study confirmed the hypothesis that within the studied territory there are zones with the specific patterns of the temporal dynamics of soybean yield, which are uniform within each area but qualitatively different between zones. The territorial clusters within which the temporal dynamics of soybean yield is identical can be considered as agro-ecological zones for soybean cultivation.

Key words:

cluster analysis, geographically weighted principal components analysis (GWPCA), productivity, soybean, spatial variability, yield