Vera Petrovna Samsonova

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Spatial variability of agrochemical properties of arable soils (case study: Trubchevsk district of the Bryansk region)Moscow University Bulletin. Series 17. Soil science. 2019. 2. p.28-35read more659
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The degree of spatial variation of agrochemically important properties of plowing horizon (pH KCl, hydrolytic acidity, absorption capacity, the degree of saturation of bases, humus content, mobile phosphorus and exchange potassium) is estimated. It is shown that the partition of the set into subsets corresponding to classification units significantly reduces the degree of variation of humus and physico-chemical properties, practically without changing the variability of pH, the content of mobile phosphorus and exchange potassium. Discriminant analysis shows that the arable horizons of sod-medium podzolic, gray forest and dark gray forest soils are satisfactorily classified for a given set of properties. Poorly classified bog-podzolic and gray forest gleyed soils. Light gray forest soils occupy an intermediate position, gravitating toward gray forest soils.
Keywords: spatial variability; agrochemical properties; discriminant analysis
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Scientifi c heritage of the honorary professor E.A. DmitrievMoscow University Bulletin. Series 17. Soil science. 2022. 2. p.3-12read more789
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The article is a review of the scientific problems that the Honored Professor of the Lomonosov Moscow state university Evgeny Anatolyevich Dmitriev. The main area of his scientific interests is the genesis of soil and land cover data. Numerous works carried out under his supervision convincingly demonstrate the influence of sampling methods on the results of determining certain soil properties. As a consequence, this influence also extends to the final conclusions, which can be highly distorted if the method of obtaining them is not taken into account. In an attempt to solve the question “What classifies soil classification?”, he develops the concept of “single soil”, which can be the object of soil classifications, not being directly a soil body, but acting as a kind of standard element of sampling. Theoretical understanding of the influence of heterogeneity of soils and soil cover at all hierarchical levels on the features of its “life” is the subject of the second part of the scientific legacy of E.A. Dmitriev. He puts forward the concept of soil bodies of different dimensions, shows the influence of the dimensional characteristics of these bodies on soil regimes and, in particular, on the water regime, and discusses the need to study soil regimes at different hierarchical levels. To fix the heterogeneity of the soil and soil cover, he developed special devices, with the help of which studies were carried out on soddy-podzolic, gray forest, chestnut, and other soils.
Keywords: spatial heterogeneity of soils and soil cover; statistical methods; soil classification
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Spatial variability of the granulometric composition within arable fi eld on soddy-podzolic soilMoscow University Bulletin. Series 17. Soil science. p.61-67Vera P. Samsonova Yulia. L. Meshalkina Marina I. Kondrashkina Svetlana Ev. Dyadkina Anastasia V. Zotkinaread more613
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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.Keywords: censorship; ordinary kriging; cartogram; loess-like loams; Moscow region; coarse and medium sand; gravel
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The number of repetitions during of the soil organic carbon content monitoring in the forest revisitedMoscow University Bulletin. Series 17. Soil science. 2024. 1. p.16-23read more543
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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.Keywords: carbon stocks; coefficient of variation; estimation of the mean value; contrast of means; significance level; power analyses; sample size; mixed samples
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Mapping of cropland humus content of the Bryansk region using machine learning methodsMoscow University Bulletin. Series 17. Soil science. 2024. 4. p.130-140read more209
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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.Keywords: soil organic matter; digital soil mapping; random forest; cross-validation
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