Yulia Lvovna Meshalkina

The spatial variability of the content of granulometric fraction d > 0,25 mm in the arable layer of soddy-podzolic cultivated soil on an area of 18 hectares was studied. The soil formed in the loess-like loam, underlain by moraine deposits. Samples were taken from the upper (0–10 cm) and lower parts of the arable layer (10–20 cm) following a random-stratified sampling scheme. The total number of samples was 350 cores. The average fraction content was about 22%, the coefficient of variation was 39–41%, the distributions were approximated by a logarithmically normal distribution. The Spearman correlation coefficient between values at different depths equaled to 0,87. The spatial distribution cartograms were constructed by the ordinary kriging method using a spherical variogram model. It is shown that preliminary censoring of high sample values (quantile 0,95) gives better results when constructing a cartogram than removing a linear spatial trend and logarithm of the original data. Spatial structures with reduced and increased values were found on the site, the average linear sizes of which was about 100 m.  Presumably, they were associated with the heterogeneity of soil-forming material.

Using the example of data from the article by E.A. Dmitriev et al., the estimation of the required number of soil samples to assess the SOC content in the forest biogeocenosis during monitoring studies is considered. Primary data on SOC content were obtained in the spruce forest at 166 points in layers 0–10, 10–20, and 20–30 cm after removal of the litter. The sampling was carried out at the nodes of a regular grid of equilateral triangles with 1 m side within a regular hexagon with a side of 7 m. The SOC content was determined by the Tyurin method. The original article presents statistics for three zones — near-stem, under-crown and inter-crown space. Spatial variation in all zones and at all depths is high, the coefficients of variation are about 50%. It is shown that the number of replicates required for estimating the average SOC content at a 95% confidence level in the 0–10 cm layer is hundreds of samples and decreases to tens of samples in the 20–30 cm layer. Since the number of repetitions for testing hypotheses about the equality of means depends not only on the confidence level, but also on the power of the criterion used, the required number of repetitions increases several times. Sampling with samples taken from the entire vertical layer of 0–30 cm and forming mixed samples from them reduces the number of required repetitions, however, careful observance of sample preparation, including primary mixing of samples, is required.

The FAO methodology within the Global Soil Nutrient and Nutrient Budget Maps (GSNmap) project was tested for the first time for mapping humus content with a spatial resolution of 250 meters per pixel in soils of the Russian Federation at the regional scale, using the Bryansk Region as an example. The map was created in the R soft ware environment using data from Agrochemical Service and remote sensing, global databases and soil maps. The centroids of the sites from which the composite samples were taken by Agrochemical Service were selected as sampling points. The set of predictors available under the FAO project was expanded by additional data, including soil maps and maps of soil-forming rocks. The importance of the predictors was assessed using the Boruta algorithm, which is usually used as an initial stage for a random forest. The model was created using the caret package with the quantile regression forest method. The modeling efficiency coefficient (MEC) was 55%, the coefficient of determination (R2) was 0.57. The map reflects current information that can be used to monitor the dynamics of organic matter content in the soil and assess the state of the arable soils in the Bryansk region.

Microbial production and CO2 emission in Albic Retisol (Loamic) and Albic Retisol (Aric, Loamic) of Lomonosov Moscow State University carbon polygon «Chashnikovo»≫ in the Moscow region were studied during the peak of vegetation activity. The object represents monitoring sites in two natural ecosystems (secondary spruce forest and mixed-grass meadow) and in two agroecosystems (perennial grasses and bare fallow). The soils differed in their stocks
of organic matter carbon (Corg), microbial carbon (Cmic), and potentially mineralizable carbon (Cpm). The highest values of microbial indicators and carbon content were observed in the upper 30 cm with a maximum in the upper 10 cm. Corg stocks in the 0–30 cm layer were 1.1–1.3 times higher in agricultural soils (72.7–75.6 t ha-1) compared to natural ones (59.4–65.0 tha-1). The contribution of Cmic stocks to Corg stocks in meadow soils and agricultural soils under perennial grasses (1.8 and 1.4%) was higher than in forest soils and soils under bare fallow. Сpm stocks in natural soils (1.34 and 0.97 tha-1 for a layer of 0–10 cm) were 1.3–2.3 times greater than in agricultural soils, but the intensity of mineralization of organic matter in agricultural soils was 1.4–2.9 times lower than in soils of natural ecosystems. For ecosystems with similar vegetation, the influence of Сpm stocks on the amount of microbial CO2 production was shown; its proportional increase was noted. Maximum potential microbial CO2 production (4.8 gCm-2day-1 for the 0–10 cm layer and 10.5 gCm-2day-1 for the 0–30 cm layer), Cmic stocks (0.50 tha-1), as well as CO2 emission (11.09±0.29 gCm-2day-1) were characteristic of the soils of a dry meadow. In other ecosystems, CO2 production by microorganisms was lower by approximately 2 times. Since CO2 emission, in addition to microbial respiration, is also caused by the respiration of plant roots, its minimum values were observed in agricultural soils under bare fallow (5.01±1.43 gCm-2・day-1). CO2 emission from forest soils and agricultural soils under perennial grasses was statistically not significantly lower compared to the meadow.

Approximately one third of the scientific publications dedicated to digital soil mapping includes morphometric variables as predictors for soil cover models. For each specific territory with its own set of specific landforms, a unique set of significant morphometric variables is needed to describe the features of such a complex system as soil cover, selected directly with the research tasks. Thus, the aim of this research is to find relationships between soils taxa as vell as its morphological features determining soil diagnostic at different taxonomic levels, and morphometric variables in the case study for the farmlands soil cover on the left bank of the Don River in the Voronezh region, Liskinsky district. Soil cover consists not only of bleached, leached, and typical chernozems, but also chernozem-like meadow soil. Methods of parametric and nonparametric statistics confirm that the most important morphometric variables influencing all analyzed soil properties with diagnostic importance for determining soil allocations are  catchment area, vertical curvature and minimum curvature. The largest number of correlations between morphometric variables and soil properties occurs at maximum level of model generalization in the range of 17 m, 51 m, 102 m, 306 m.