Year 2025, Volume 71 Issue 4
| Year : | 2025 |
| Volume : | 71 |
| Issue : | 4 |
| Authors : | Mile MARKOSKI, Tatjana MITKOVA, Ivan MINCHEV,Marija TODOROVSKA, Marko PETEK, Ivana ANGELOVA |
| Title : | SPATIAL DISTRIBUTION OF COPPER (Cu) IN APPLE ORCHARDS IN THE RESEN REGION |
| Abstract : | The soils in apple orchards in the Resen region were examined. The field research was carried out in 2024, during which 26 surface soil samples were taken. In the framework of this master's thesis, an examination of some physical and chemical properties of the soil, as well as the content of the total forms of heavy metals, was carried out. Several soil types are formed in the area covered by the Resen region: regosols, cinnamon forest soils, colluvial soils, and complexes thereof. Based on the obtained values for the mechanical composition, for the most part, the soils in the surface part are sandy clay loam (SCL) at 41%, sandy loam (SL) at 36%, and loamy (L) soils at 23%. The content of total forms of heavy metals in all samples was analysed using atomic emission spectrometry with inductively coupled plasma (AEICP). All statistical analyses were conducted in the R programming environment (version 4.3.2; R Core Team, 2024). The ‘FactoMineR’ and ‘factoextra’ packages were used for principal component analysis (PCA), while ‘cluster’ and ‘ggdendro’ packages were applied for hierarchical cluster analysis (HCA). Prior to PCA, all variables were standardized (z-score normalization) to eliminate the influence of different measurement scales and to ensure comparability among variables. The selection of principal components was based on eigenvalues >1 (Kaiser criterion) and cumulative explained variance. Cluster analysis was performed using Ward’s method and Euclidean distance as the dissimilarity measure to identify similarities among soil parameters. Spatial interpolation of soil properties and total Cu content was performed using the Inverse Distance Weighting (IDW) method in ArcGIS 10.8. The IDW approach was selected because it provides reliable and smooth spatial predictions for datasets with a moderate number of sampling points (n = 26) and without a clearly defined spatial trend or anisotropy. The power parameter (p) was set to 2, which emphasizes the influence of closer points while maintaining interpolation stability. Kriging interpolation was tested but not applied, as the dataset size and semivariogram structure did not justify its use due to the lack of a spatially autocorrelated variogram model. The obtained results allow us to distinguish between possible anthropogenic influences on the presence of heavy metals in the soils of in the Resen region and their lithological origin. |
| For citation : | Markoski, M., Mitkova, T., Minchev, I., Todorovska, M., Petek, M., Angelova, I. (2025). Spatial distribution of copper (Cu) in apple orchards in the Resen region. Agriculture and Forestry, 71 (4): 37-56. https://doi:10.17707/AgricultForest.71.4.03 |
| Keywords : | physical-mechanical properties, characteristics, heavy metals |
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