Vladimir Arkadyevich Romanenkov

The study investigated the role of optimized nutrition and the development of a mixture of cereal grasses in oil-contaminated typical chernozem soil with a high level of pollution. The influence of different forms of nitrogen fertilizers at a moderate level of plant nitrogen supply in the presence of oil pollution was investigated using indicators that characterize the agrochemical and enzymatic properties of the soil, as well as the productivity and qualitative composition of remedial plants. Soil contamination with oil at doses of 5 g oil per g of soil and 7 g oil per g of soil had a significant negative effect on the productivity of remedial plants, manifested in a reduction in biomass and the content of major elements (NPK). It was noted that optimized nutrition with mineral fertilizers increases biomass and improves the qualitative composition of plants growing under oil pollution conditions. It was shown that the most favorable conditions for the growth and development of plants were formed when nitrate forms of nitrogen fertilizers were applied compared to ammonium and ammonium-nitrate forms in equivalent doses. A high dependence of the activity of the studied enzymes (catalase, urease, and phosphatase) on the oil content in the soil was noted. When mineral fertilizers used, the enzymatic activity of typical chernozem increases proportionally to the decrease in oil content in the soil, that indicating an intensification of oil hydrocarbon degradation processes when mineral fertilizers are applied.

The paper demonstrates the importance of creating optimal conditions for mineral nutrition of cereal grasses and microorganisms in oligotrophic oil polluted peat soil under remediation. To establish the effectiveness of using various forms of nitrogen fertilizers in oil pollution, changes in the agrochemical properties and enzymatic activity of the soil, the productivity of cereal grasses and the amount of DNA prokaryotes were investigated. A significant increase in the productivity of plants and the number of bacteria and archaea in oligotrophic peat soil is shown when optimizing nitrogen nutrition, especially pronounced when introducing the ammonium-nitrate form of fertilizer. There is a close relationship between catalase activity in soil and residual oil content in soil.

Soils are the largest terrestrial reservoir of organic carbon, so even small changes in soil carbon stocks can have significant eff ects on the atmosphere and climate. To select effective strategies to mitigate climate change, predictions of how soils will respond to future changes in climate and land use are needed. Achieving meaningful predictions requires a deep understanding of the highly complex, open, multicomponent soil organic matter system. One of the most effective methods for predicting the dynamics of soil organic matter is mathematical modeling. Process-oriented (physically based) models make it possible to present the basic concepts about the mechanisms that determine the behavior of this system in a mathematically formalized form and conduct a quantitative analysis. The uncertainty of the forecasts depends on the level of development of the theory explaining the dynamics of soil organic matter, the models representing it and their experimental support. This review examines the achievements of the last decade in modeling the role of microorganisms in the stabilization of soil organic matter, the concept of soil saturation with organic carbon, temperature control, as well as the development of reactive transport models describing the dynamics of organic carbon in the soil profile, and the representation of the dynamics of soil organic matter in global climate models. Unsolved problems associated with the high variability in the structure of new generation soil organic matter dynamics models are discussed.

We tracked the effect and two-year aftereffect of potassium fertilizers with a single application under beet in rates of 70-240 kg∙ha-1 K2O in six farming trails of the Central Federal District, conducted in the Voronezh and Lipetsk Regions with sugar beet on soils with increased and high soil potassium levels. The contribution of potassium to the yield increase on average over three years was 14-15%. The content of exchangeable potassium during the experiments corresponded to a higher level of potassium supply compared to the available form, by one or two classes. The use of the potassium balance indicator for calculating the rates of fertilizers applied is advisable in years favorable for crop development. Calculation of optimal rates of potassium fertilizers obtained using multi-criteria estimation allows to consider 140 kg∙ha-1 K2O with a possible increase to 210 kg∙ha-1 in years favorable for crop development as the optimal for application to sugar beet in the Voronezh Region, and from 210 to 280 kg∙ha-1 K2O in the Lipetsk Region. A promising approach to determine potassium rates to jointly take into account the indicators of the effect and after-effect of fertilizers in terms of yield growth, economic efficiency, and the possibility of achieving a positive balance. The use of calculations based on only one of the above criteria, as well as on classes or direct assessments of soil potassium supply levels based on standard methods, leads to a significant overestimation or underestimation of the results due to the temporal instability of the determined indicators. Existing methods for calculating rates of potassium fertilizers require both the correction of the proposed classes of potassium availability and the consideration of the emerging potassium balance.

To mitigate the effects of climate change and preserve and enhance soil fertility, considerable attention is being paid to the development of effective land management methods. 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 uses empirical models obtained using digital soil mapping methods. The second direction represents process-based models of the biogeochemical carbon cycle. Each 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 the training data. They demonstrate the superiority of process-based models in predicting the spatial distribution of soil organic carbon stocks in cases where large, high-quality data sets were used. The advantage of biogeochemical models is that they are based on accumulated soil science knowledge of the processes that determine the carbon cycle, making them effective in studying the mechanisms of soil carbon stock dynamics. However, forecasts using these models, especially at regional and global scales, are characterized by high uncertainty. Currently, ensemble modeling (the integration of various artificial intelligence algorithms used in digital soil mapping with process-based models) is proposed to reduce 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 sources of uncertainty in biogeochemical models and the potential use of minimal models to inform the selection of the required set of key processes and mathematical formalisms for their explicit description in models of different spatiotemporal scales. This can reduce the structural uncertainty of nonlinear biogeochemical models.

Abstract The RothC-26.3 model was used to reproduce the 62-year dynamics of soil organic carbon (SOC) in Retisols within a long-term field experiment conducted by the Federal State Budget Research Institution «Federal Research Center for Bast Fiber Crops». Simulation modeling was employed to assess the potential for increasing SOC stocks by 2090 through modifications in agrotechnological practices under two climate scenarios and two adaptive economic scenarios. The feasibility of utilizing alternative fertilizer sources and minimal tillage as supplementary approaches for SOC accumulation was examined. The results indicate that under existing management practices and future climate conditions, SOC stocks in the topsoil (0–20 cm) of the studied soils could increase by up to 34% of the initial content by 2090. Furthermore, the implementation of carbon-saving technologies—including adjustments to crop rotation structure, as well as the rates and sources of organic fertilizer application—could enhance SOC accumulation, leading to a potential increase of up to 82% by 2090. Under different climate scenarios, the maximum sequestration rate was projected to reach up to 11‰ per year. Among the alternative organic fertilizers evaluated, the application of composts and peat mixtures yielded the most favorable outcomes, facilitating an additional SOC accumulation of up to 10.6 tha–1 compared to the use of cattle manure. In contrast, the use of green-manure fallows and minimal tillage cannot be considered an optimal strategy for SOC sequestration, as these practices resulted in lower accumulation relative to cattle manure application. It was demonstrated that the studied soils possess considerable potential for organic carbon accumulation while remaining under agricultural cultivation.