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  • Author: Alexander Zhukov x
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Alexander Zhukov and Galina Gadorozhnaya


In this research paper, the spatial heterogeneity of mechanical impedance of a typical chernozem was investigated. The distance between experimental points in the mechanical impedance space was explained by means of multidimensional scaling. Spearman’s rank correlation coefficients between dissimilarity indices and gradient separation with different data transformation methods revealed that the use of log-transformed data and Horn-Morisita distance was the most appropriate approach to reflect the relationship between the mechanical impedance of soil and ecological factors. A three dimensional variant of multidimensional scaling procedure was selected as the most appropriate decision. Environmental factors were estimated with the use of phytoindicator scales. Broad, medium and fine-scale components of spatial variation of mechanical impedance of soil were extracted using the principal coordinates of neighbour matrices method (PCNM). In the extracted dimensions, statistically significant phytoindicator scales were found to describe variability from 8 to 33%. Dimension 1 correlated with a thermal climate indicator value, a hygromorphs index, an abundance of steppe species and meadow species. Dimension 2 correlated with a continental climate indicator value, carbonate content in the soil and the soil trophicity index (capacity of the soil for plant nutrition). Dimension 3 correlated with acidity, humidity and cryoclimate indicator values. Variation partitioning results revealed that environmental factors and spatial variables explained 47.8% of the total variation of the dimensions. Purely environmental component explained 18.2% of total variation. The spatial component and spatially structured environmental fractions explained 43.6%. The broad-scale spatial component explained 26.4% of dimensional variation, medium-scale – 6.7% and fine-scale – 5.7%. As a result of regression analysis, the broad-scale spatially structured environmental fractions were found to be connected with variability of moisture and thermal climate indicator values. The medium-scale component was revealed to be connected with variability of moisture, thermal climate, total salt regime and aeration of soil indicator value. The fine-scale component was connected with carbonate content in the soil, acidity and humidity indicator values.