The goal of this work was to conduct a systematic review and meta-analysis of domestic and international data (a total of 687 data points) on the effect of humic preparations (HPs) on field yield. The objectives of the study included assessing the range of HP effectiveness (yield increase expressed as a percentage of the control), taking into account statistical uncertainty estimated on the basis of the median absolute deviation, as well as evaluating the influence of the characteristics of HPs, agricultural crops, and soil properties on this parameter. It was established that the effectiveness of HPs varied from –59
The article presents the results of a comprehensive study of the soil cover of the Urup and the Simushir islands, which are part of the Kuril Ridge. The main focus is on analyzing the impact of environmental conditions such as climate, vegetation, topography, and volcanic activity on soil formation and diversity. Test plots with soil pits were laid during the terrain survey, a geobotanical description of plant communities was carried out, and samples for laboratory analysis were taken. It has been revealed that the soil-forming processes on these islands are intermittent and are associated with the periodic burial of soil horizons by pyroclastic material. Based on the morphological descriptions of the profiles of the surveyed territories, individual stratotypes of the soil pyroclastic cover (SPC), reflecting the history of the soil profile formation, were compiled for the plots on the Urup and the Simushir islands. A specific feature of the soils consists in the high organic carbon content in the surface horizons, which varies from 5.1 to 15.1
Considerable attention is now paid to the development of effective land management methods to mitigate the effects of climate change and preserve and increase soil fertility. Reliable forecasts of soil responses to future climate change and anthropogenic impacts are essential for selecting optimal management strategies. Mathematical modeling is a leading method for analyzing and predicting the spatiotemporal variability of soil organic carbon stocks. It is currently being developed in two main directions. The first one uses empirical models obtained by digital soil mapping methods, and the second one represents process-based models of the biogeochemical carbon cycle. Each direction has strengths and weaknesses. Digital soil mapping models are empirical, based only on the analysis of data on soil properties and environmental characteristics, and therefore are limited in explaining the spatial variability of soil carbon stocks. The uncertainty of forecasts based on these models depends on the volume and quality of training data. The prediction of the spatial distribution of soil organic carbon stocks made by them is more correct as compared to process-based models in cases, when large high-quality data sets are used. The advantage of biogeochemical models consists in the fact that they are based on accumulated soil science knowledge of the processes that determine the carbon cycle, and this makes them effective in studying the mechanisms of soil carbon stock dynamics. However, forecasts by these models, at regional and global levels in particular, are characterized by high uncertainty. The ensemble modeling (the integration of various algorithms of artificial intelligence used in digital soil mapping with process-based models) is now proposed to reduce the forecast uncertainty. Effective development of this strategy requires a thorough understanding of the role of key biogeochemical processes in soil organic carbon stock dynamics to improve the conceptual foundations of the models and increase confidence in the predictions. This article discusses the factors of uncertainty in biogeochemical models and the potential use of minimal models to substantiate the selection of the required set of key processes and mathematical formalisms for their explicit description in models of different spatiotemporal scales. This may reduce the structural uncertainty of nonlinear biogeochemical models.
Digital soil mapping (DSM) incorporates the most advanced approaches and methods in the computer analysis and modeling of spatial soil data. Over 90
Information on natural variation in soil bulk density is essential for the analysis of uncertainty in the stocks of organic matter in biogeocenoses. The aim of this study was to assess soil bulk density variation in a secondary birch–spruce forest in Moscow oblast on sod-podzolic soils (Albic Glossic Retisols (Cutanic, Loamic) according to IUSS Working Group WRB, 2022). Five trial plots measuring 50 × 50 m were established within an area of about 2 ha. Each plot included one soil pit and two half-pits, in which soil bulk density was determined using duplicate measurements at 10 cm vertical intervals. Nested analysis of variance was used for data processing, which enabled a quantitative analysis of density variation at different distances and depths. Bulk density in the humus horizons at a depth of 0–10 cm averaged 0.83 g cm–3. It then increased with depth for the transitional A/E horizons. The eluvial horizons were characterized by a bulk density of about 1.50 g cm–3 and occurred at a depth of 30–40 cm. The bulk density of the lower part of the profile varied from 1.65 to 1.85 g cm–3. Both the coefficient of variation and the standard deviation decreased with depth. In the upper part of the profile, the coefficient of variation ranges from 10 to 14
