Articles

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.

This study investigates the potential of the WOFOST crop growth simulation model for yield forecasting and crop condition assessment for several key agricultural crops across large extents of Russia over a fifteen-year analysis period (2005–2020) using soil and climatic datasets. Specific emphasis was placed on adapting information from the 1:2,500,000-scale Soil Map (V.M. Fridland edition) to ensure the model's correct performance. Within the study, model parameters for regional hybrids of barley, sunflower, and maize were obtained for several hundred districts within the Russian agricultural belt, including characteristics of phenology, photosynthesis, respiration, and assimilates partitioning. Parameterization was performed using loss functions based on the Brent, Nelder-Mead, and Differential Evolution algorithms to minimize phenology and yield estimation errors. To enable parameterization and independent accuracy assessment, the initial dataset was divided into a training set and a control set in an 80:20 ratio. The regionally parameterized models provided a good match between the error histograms for the training and control sets, with a near-zero systematic bias, while the mean relative absolute deviation of yield estimates ranged from 20% to 26% depending on the crop. Furthermore, it was demonstrated that the model estimates are not only close to the actual yield values for the studied period but also that the model forecast is capable of capturing multi-year trends in the yield dynamics of the studied crops. Finally, the obtained model crop hybrids were used to assess crop state in terms of the deviation of simulated yield from the yield of an optimal season, with the generation of deviation cartogram maps. The results obtained in this study can be used for decision support system of the agricultural sector, including yield forecasting and state assessment of the studied key crops over large areas using simulation modeling, including forecasts made under various climatic scenarios.

The study aimed to perform a systematic study and meta-analysis of national and foreign data (687 data points in total) on the effect of humic preparations (HPs) on crop yields. Specific objectives included evaluating the efficiency range of HPs (yield increase as a percentage of the control) accounting for statistical uncertainty estimated via absolute median deviation, and assessing the influence of HPs characteristics, crop types, and soil properties on this metric. HPs efficiency varied from –59% to +540% (mean 18%) and followed a non-normal distribution. The median efficiency was 12 %, with lower and upper quartiles at 5% and 21%, respectively, and uncertainty of 99 %. Kruskal–Wallis test revealed that soil granulometric composition and HPs enrichment with trace elements did not significantly affect efficiency of HPs. Key influencing factors included: cation salts of humate in HPs (Na salts were the least effective); HPs application method (combined soil and foliar application was most effective); soil acidity and humus content (higher efficiency at pH > 6.6 and humus content < 1%). Plants in the aster family showed the greatest responsiveness to HPs, while legumes and nightshades exhibited significantly lower efficiency. Under nitrogen deficiency, HPs efficiency rose to 22%, and most studies reported increased nitrogen and/or protein content in crop products. These findings suggest that HPs primarily enhance field yields by improving plant nitrogen assimilation. The results indicate that promising future research should evaluate HPs effectiveness against reduced nitrogen fertilizer rates. Given rising fertilizer costs, even modest effects could prove economically viable. Further field studies should optimize HPs use conditions and methods, prioritizing identification of factors driving efficiency uncertainty — preliminarily, application method and soil conditions (pH, humus content, and available nitrogen).

Digital soil mapping (DSM) incorporates the most advanced approaches and methods in the computer analysis and modeling of spatial soil data. Currently, over 90% of all publications in the field of soil mapping are related to DSM. An analysis of scientometric databases and publications shows that the number of publications is growing annually, yet the declared quality of the resulting soil maps remains virtually unchanged. This is due to a number of factors, and the purpose of this article is to identify them through a review of scientific publications. The conducted research has shown that maps created using DSM approaches vary in quality. The accuracy of the models used to create digital soil maps, as declared by the authors, typically ranges from 30–40% to 70–80%. Sixteen potential sources of error in digital soil maps were identified, grouped into errors in the initial data on soil properties, initial data on predictors, errors in model selection, and errors in soils as a modeling object. Some sources of error can be eliminated now or in the near future, but there are errors whose elimination seems impossible at the current stage of scientific and technological development. The greatest potential for significant improvement in the quality of digital soil maps of surface soil horizon properties is present, while the least promising is for maps of soil classification names. The presence of errors in soil maps created using digital soil mapping methods should be disclosed during the creation of any soil map, including masking out areas of the map with the greatest errors.

 

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 decrease in water mobility when the hydrological moisture constant of point of limited availability of water (PLAW) is reached is explained by the presence of film forms of water formed under the influence of the solid phase. It is also known that the existence of such forms of water is limited by the concentration of electrolytes. The aim of the work was to clarify the mechanism of water stabilization by the solid phase of soils in point of limited availability of water, taking into account the colloidal component of soils. The studies were carried out on samples from sod-podzolic and gray forest soils and chernozem. In the work, the value of the PLAW was determined by plotting the secant on the curve of the main hydrophysical characteristic obtained by centrifugation. Scanning electron microscopy, vibrational viscometry, and a technique for extracting gels from soils were also used. During the experiments, it was found that adding 1 n of potassium chloride solution to the soil, compressing the double electric layer on the surface of the particles, does not reduce the value of the PLAW. This suggests that the film forms of water in PLAW are stabilized by soil organomineral gels, since at such concentrations of salts, water films cannot exist in free form. It was also found that an increase in the number of gels in soils by their introduction increases the PLAW of the soil sample, and a decrease in the volume of soil gels leads to a decrease in PLAW. Thus, it is shown that water in case of PLAW is a part of the colloidal component of soils — soil organomineral gels, and the value of PLAW in soils depends on the number and volume of these gels.