The pool of organic carbon in the soils of Russia (original) (raw)
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Changes in the organic carbon pool of abandoned soils in Russia (1990–2004)
Eurasian Soil Science, 2010
The assessment of the changes in the organic carbon pool in the soils of the Russian Federation that occurred in 1990-2004 was carried out using approximation, soil geoinformation, and simulation approaches. As a result of the changes in the system of land use, after 1990, the organic carbon storages in the 0 to 20 cm thick soil layer could be 196-319 Mt depending on the methodology of the calculation applied and taking into account the abandoned area of 14.8 million ha. As compared to the beginning of the 1990s, the organic matter stock in the former plow layer increased by 1.6-5.8%. The great scatter of the data is mainly related to the incertainty of the estimates of the area of arable soils not used any more in agriculture.
Forest Ecology and Management, 2002
The assessment of carbon balance in forest soils of the Leningrad administrative area (south boreal sub-zone of east European plain) has been carried out using: (1) previous data on carbon pools of forest soils without considering mires area as initial data (organic layer plus 50 cm soil); (2) inventory data on forest stands that has been converted into biomass and data on litter input; (3) meteorological data concerning the mean monthly air temperature and precipitation. The most recent model version of soil organic matter (SOMM) dynamics was applied for a 100-year simulation of carbon dynamics in the 3:22 Â 10 6 ha of forest soils of the Leningrad area considering a constant forest-age structure and climate. The results demonstrate unique carbon dynamics in various soils, and an 8% increase of the total carbon pool of the area's forest soils during the 100-year simulation (from 266 to 286 million tons of carbon). The total carbon input to the soil, in the form of litter carbon, was 8.3 million tons annually, and the carbon emission, in the form of carbon dioxide released from the soils, was 8.1 million tons annually at the end of simulation.
Estimating organic carbon in the soils of Europe for policy support
European Journal of Soil Science, 2005
The estimation of soil carbon content is of pressing concern for soil protection and in mitigation strategies for global warming. This paper describes the methodology developed and the results obtained in a study aimed at estimating organic carbon contents (%) in topsoils across Europe. The information presented in map form provides policy makers with estimates of current topsoil organic carbon contents for developing strategies for soil protection at regional level. Such baseline data is also of importance in global change modelling and may be used to estimate regional differences in soil organic carbon (SOC) stocks and projected changes therein, as required for example under the Kyoto Protocol to UNFCCC, after having taken into account regional differences in bulk density. The study uses a novel approach combining a rule-based system with detailed thematic spatial data layers to arrive at a much-improved result over either method, using advanced methods for spatial data processing. The rule-based system is provided by the pedo-transfer rules, which were developed for use with the European Soil Database. The strong effects of vegetation and land use on SOC have been taken into account in the calculations, and the influence of temperature on organic carbon contents has been considered in the form of a heuristic function. Processing of all thematic data was performed on harmonized spatial data layers in raster format with a 1km x 1km grid spacing. This resolution is regarded as appropriate for planning effective soil protection measures at the European level. The approach is thought to be transferable to other regions of the world that are facing similar questions, provided adequate data are available for these regions. However, there will always be an element of uncertainty in estimating or determining the spatial distribution of organic carbon contents of soils.
Agroznanje, 2023
Spatial assessment of key soil properties is a basic prerequisite for the evidence-based decision making and sustainable use and management of soil. The aim of this work was to estimate the spatial distribution of SOC under different types of land use, by the means of Digital Soil Mapping techniques. A site-specific soil data collection for the Ohrid valley was integrated with continuous and discrete datasets of environmental covariates, serving as predictors. The selected test area outlines the variability of factors influencing the SOC content and spatial distribution. Soil sampling locations were randomly distributed within a predefined mesh with a 1-sq.km spatial resolution and further stratified to outline different types of land use within each mash square. Soil samples were collected from 93 locations at three depths, each 20 cm apart, covering the total area of 10 thousand ha of arable land, forestland, and land under natural vegetation. A set of additional environmental dataset was collected, namely the soil map, land use map, geology map, digital terrain model and its derivatives, satellite images, climate data, as well as a set of indices NDVI, SAVI, BI etc., developed from the remote sensing datasets. Multiple linear regression was used for evaluating the regression pattern between the environmental predictors and the target variable. To estimate spatial variability, several regression tree methods were used. The results obtained using this approach have given a better spatial overview of the most vulnerable areas regarding SOC depletion. Out of 21 locations examined, the content of soil organic carbon in the top layer (0-20 cm.) of forestland was on average 6.81%, 194 Mukaetov and Poposka while at 22 locations examined under grassland, the average content was 4.07%. The arable land, which is under continuous human impact, had the lowest content of SOC of 2.5% under field crops and 2.61% under perennials.
Reference Area Method for Mapping Soil Organic Carbon Content at Regional Scale
Procedia Earth and Planetary Science, 2014
Soil organic carbon, the major component of soil organic matter, is very important in all soil processes. The decline in soil organic carbon (SOC) is recognized as one of the eight soil threats identified in the European Union Thematic Strategy for Soil Protection (COM (2006)231 final) (EC, 2006). The research challenge in this paper is to develop aggregation techniques which can 'catch' the variability at local level and being able to transfer this knowledge to larger scales. The latest developments included the research in upscaling modelling techniques which allow transferring the data values from local to regional scale. Digital Soil Mapping (DSM) and in particular regression kriging has been selected as the most appropriate modelling approach for the study. It has been successfully implemented at regional level and a soil organic carbon map was produced for the selected region (Slavkovsky Forest, 2400 km 2 , Czech Republic) by transferring knowledge from Lysina Critical Zone Observatory (0.40 km 2 ). The Lysina CZO is situated in western Bohemia, the Czech Republic. Using digital soil mapping (DSM), demonstrated that it is possible to upscale the processes that take place at Critical Zone Observatories (CZOs) towards larger regions. According to the prediction results, the soil organic carbon values in the Slavkovsky Forest are ranging between 0 % -35.11%. The results of the linear regression procedure is promising, however, the statistical indicators are relatively low (R 2 =0.31) in the first step of the two stage model. The results of the study encourage applying similar approach at a wider scale. However, the ground data availability is still the key component to have more robust geostatistical models. The model and its outputs can be improved by using more ground data and high resolution environmental covariates.
Predictors for digital mapping of forest soil organic carbon stocks in different types of landscape
Soil and Water Research, 2022
Forest soils have a high potential to store carbon and thus mitigate climate change. The information on spatial distribution of soil organic carbon (SOC) stocks is thus very important. This study aims to analyse the importance of environmental predictors for forest SOC stock prediction at the regional and national scale in the Czech Republic. A big database of forest soil data for more than 7 000 sites was compiled from several surveys. SOC stocks were calculated from SOC content and bulk density for the topsoil mineral layer 0–30 cm. Spatial prediction models were developed separately for individual natural forest areas and for four subsets with different altitude range, using random forest method. The importance of environmental predictors in the models strongly differs between regions and altitudes. At lower altitudes, forest edaphic series and soil classes are strong predictors, while at higher altitudes the predictors related to topography become more important. The importance ...
Analysis of the Spatiotemporal Distribution of Soil Organic Carbon
Feedbacks between atmospheric and terrestrial carbon stocks remain unclear. Soil carbon stores are affected by a complex interaction among several biophysical and hydroclimatic processes, including the dynamics of soil moisture, insolation, and temperature. Studying the spatiotemporal distribution of soil organic carbon (SOC) and its dynamic changes is necessary for building a soil carbon pool inventory, as well as predicting the potential for soil carbon sequestration (Dorji et al., 2014). Many environmental controls on soil organic carbon have been analysed at site-specific plot-scales, while long term temporal studies of SOC dynamics are less common. The continued uncertainty of carbon cycle feedbacks, and the complexity of interactions of controls on, and transport of, soil carbon at regional scales, justifies further investigation. This study investigates the spatiotemporal relationships between surface SOC and a number of environmental variables across a catchment. The findings of this research will contribute to overall understanding of SOC distribution and controls for large regional scales. The catchment for this study is located in the Upper Hunter Valley region of New South Wales, Australia. The study focuses on the Krui River catchment, having an area of 562 km 2. Soil samples were obtained across the catchment using a 1 km grid scale. Cores with a depth of 220 mm were obtained from 59 grazing sites in 2006 and from 52 grazing sites in 2014, with 41 of the sites common to both 2006 and 2014. At each sampling location, aboveground biomass (AGB), soil moisture, and soil temperature was sampled within a 0.25m 2 quadrat. Land use at each sample site was classified as either cropping or grazing. For a comparison of the temporal variability in SOC concentrations, average SOC from the two sampling dates were compared using Student's t-test. To determine which variables were the most important, principle component analysis was performed for topographic (elevation, slope, aspect, plan curvature, profile curvature, TWI), soil (pH, Electroconductivity, clay %) and vegetation (sampled vegetation biomass, remotely sensed vegetation biomass) variables for the sample sites. SOC, elevation, Normalised Difference Vegetation Index (NDVI) and plan curvature were found to be the most important variables for the first 2 principle components. Linear regression and heteroscedasticity tests were applied to the strongest correlations between SOC and the other variables for both sampling periods. The results of this study show that soil carbon was spatially and temporally stable over medium time scales (8+ years), with the variables of SOC, elevation and NDVI having strong, positive correlations with each other for both sampling periods. Strong, positive Pearson's r correlations were observed between SOC and NDVI, a surrogate for Aboveground Net Primary Production (ANPP), for both sampling periods. Thus regions of higher net primary production corresponding with higher concentrations of SOC. Grazing intensity, represented in this study by sampled AGB, did not affect SOC. Topography strongly influences vegetation via its control on such climate variables as precipitation and temperature. Elevation was found to explain much of the variability in NDVI, and hence SOC, although slope and aspect also had weak to moderate correlations with NDVI. The relationship between SOC and aspect was weak. This study demonstrates that the variables of elevation and NDVI can be used to digitally map the spatiotemporal distribution of SOC across large (~500 km 2) catchments of elevations ranging from ~300 to ~1100 m. However, long term seasonal climate variability may affect the predictive ability of SOC using these variables. The spatial and long term temporal stability of catchment SOC demonstrated here has major implications for soil carbon sequestration. SOC across the catchment appears to be at equilibrium, with minimal variation observed after 8 years of continuous grazing. Carbon sequestration methods would therefore require major changes in grazing land-use to achieve observable increases in soil carbon.
Prediction of Soil Organic Carbon across Different Land-use Patterns
Soil Science Society of America Journal - SSSAJ, 2005
Mathematical modeling has widely been used to predict soil organic ingful in creating a real picture of spatial distribution carbon (SOC). However, there are characteristics of the models such as over simplification, ignorance of complex nonlinear interactions of SOC. Attempts have been made to estimate global etc., which limit their use in accurately assessing the distribution of the SOC using the pedon database and extrapolating them C across the landscapes. Artificial neural network (ANN) modeling to soil units of the world soil map (Bohn, 1976, 1982; approach that provides a tool to solve complex problems related to Batjes, 1996; Buringh, 1984; Kimble et al., 1990). The larger data sets was therefore used here to predict SOC contents pedon database of the USDA Soil Conservation Service across different land use patterns in a study conducted in Sri Lanka. and related organizations has been used to estimate the Selection of variables was made using a priori knowledge of the regional distribution of organic C in the USA (Kern, relationships between the variables. Thus, soils of the sites were sam-1994). However, previous studies indicated that there pled and analyzed for organic C by internal heat of dilution (Ci) and are uncertainties associated with such SOC estimates external heat of dilution (Ce), and the results were presented as grams and often related to variations in soil map scales and per kilogram (g kg Ϫ1). In addition, some landscape attributes and environmental parameters of the sites were also collected. The pre-series. As a whole the uncertainties associated with meadictive performance of ANN was compared with multi-linear regres-suring and detecting changes in soil C pools remain sion (MLR) models. The best ANN model predicted the measured high, both at individual sites and extrapolating site-level Ci content with R 2 of 0.92. However, comparison of the two types of data to regional, national, or global scales (Vance, 2003). models indicated less bias and high accuracy of the ANN compared Accurate and precise approaches yet to be available for with MLR in predicting Ci, but the reverse for Ce. In order to better assessing the effect of management practices and land predict Ce, it is recommended to use other architectures of neural use change on the soil C for the purpose of incorporation networks and training algorithms for improving predictive accuracy. of this important pool into future C accounting systems. The predictive capability of the ANN developed with easily available The Kyoto Protocol, for instance, limits reporting of C climatic and terrain data are of importance in predicting SOC with sequestration activities to "measurable and verifiable" minimum cost, labor, and time. pools (Vance, 2003). Mathematical modeling has been used to predict soil C evolution (Jenkinson and Rayner, 1977; Parton et al.