Estimates Of Soil Organic Carbon Stocks Research Articles

Soil organic carbon variability in Australian temperate freshwater wetlands

AbstractGlobally, there is little information on freshwater (non‐tidal) wetland below ground soil organic carbon (SOC) stocks, sequestration rates and, in particular, their within‐wetland variation. This basic information is critical for designing programs to sample SOC stocks and identifying areas that sequester large amounts of carbon so that they can be managed to prevent degradation and carbon loss. Here, focusing on temperate seasonally inundated freshwater wetlands in south‐eastern Australia, we compared SOC stocks and sequestration rates (via radiometric dating) among wetlands, within wetland locations (High water mark, Edge and Middle), and with adjacent terrestrial locations. SOC stocks varied most among wetlands but also varied inconsistently within wetlands. Wetland SOC stocks (20.4 ± 0.1 kg C m−2) were significantly greater than adjacent terrestrial locations (13.3 ± 0.1 kg C m−2). Wetland SOC sequestration rates were similar among all locations (i.e., within a wetland, ranging from 70 g C m−2 yr−1 to 87 g C m−2 yr−1). The relative lack of difference in SOC stocks among wetland locations allows for reduced within‐wetland sampling and subsequently greater replication at the wetland level, at least for temperate ephemeral freshwater wetlands, although these findings need to be confirmed over a range of wetland types. This study also produced the first estimates of temperate freshwater wetland SOC stocks and sequestration rates in Australia, and is among the first studies globally to demonstrate that seasonally inundated wetlands can sequester carbon at significant rates. It therefore provides important justification to support the protection and rehabilitation of temperate freshwater wetlands worldwide.

Effect of altitude and aspect on soil organic carbon and nitrogen stocks in the Himalayan Mawer Forest Range

The soil organic carbon (SOC) and nitrogen (N) stocks in mountainous forests are influenced by the forest diversity, topographic features, and climate change impacts. Role of forest SOC and N stocks in global C cycle has been a subject of great research recently, but the effect of topographic features on their dynamics at the stand level has received less attention especially under temperate conditions. In order to find out how topographic aspect and altitude affect SOC and N budgets, a study was conducted in the Himalayan Mawer Forest Range. We examined SOC and N stocks at two altitude zones (Z1: 1800–2200masl & Z2: 2200–2500masl) under North (N) and South (S) aspects at three soil depths (D1: 0–20cm, D2: 20–40cm and D3: 40–60cm). The SOC stock was found to be decreasing with altitude from 105.9Mgha−1 to 78.3Mgha−1 under N aspect and from 81.6Mgha−1 to 74.0Mgha−1 under S aspect. SOC stock was higher by 16.5% under N aspect as compared to S aspect. The results lead to the conclusion that altitude has a negative effect on SOC stabilization and therefore altitude and aspect effect may be included in SOC stock estimation equations.

Spatially distributed modeling of soil organic carbon across China with improved accuracy

AbstractThere is a need for more detailed spatial information on soil organic carbon (SOC) for the accurate estimation of SOC stock and earth system models. As it is effective to use environmental factors as auxiliary variables to improve the prediction accuracy of spatially distributed modeling, a combined method (HASM_EF) was developed to predict the spatial pattern of SOC across China using high accuracy surface modeling (HASM), artificial neural network (ANN), and principal component analysis (PCA) to introduce land uses, soil types, climatic factors, topographic attributes, and vegetation cover as predictors. The performance of HASM_EF was compared with ordinary kriging (OK), OK, and HASM combined, respectively, with land uses and soil types (OK_LS and HASM_LS), and regression kriging combined with land uses and soil types (RK_LS). Results showed that HASM_EF obtained the lowest prediction errors and the ratio of performance to deviation (RPD) presented the relative improvements of 89.91%, 63.77%, 55.86%, and 42.14%, respectively, compared to the other four methods. Furthermore, HASM_EF generated more details and more realistic spatial information on SOC. The improved performance of HASM_EF can be attributed to the introduction of more environmental factors, to explicit consideration of the multicollinearity of selected factors and the spatial nonstationarity and nonlinearity of relationships between SOC and selected factors, and to the performance of HASM and ANN. This method may play a useful tool in providing more precise spatial information on soil parameters for global modeling across large areas.

Mapping soil organic carbon stocks of northeastern China using expert knowledge and GIS-based methods

The main aim of this paper was to calculate soil organic carbon stock (SOCS) with consideration of the pedogenetic horizons using expert knowledge and GIS-based methods in northeastern China. A novel prediction process was presented and was referred to as model-then-calculate with respect to the variable thicknesses of soil horizons (MCV). The model-then-calculate with fixed-thickness (MCF), soil profile statistics (SPS), pedological professional knowledge-based (PKB) and vegetation type-based (Veg) methods were carried out for comparison. With respect to the similar pedological information, nine common layers from topsoil to bedrock were grouped in the MCV. Validation results suggested that the MCV method generated better performance than the other methods considered. For the comparison of polygon based approaches, the Veg method generated better accuracy than both SPS and PKB, as limited soil data were incorporated. Additional prediction of the pedogenetic horizons within MCV benefitted the regional SOCS estimation and provided information for future soil classification and understanding of soil functions. The intermediate product, that is, horizon thickness maps were fluctuant enough and reflected many details in space. The linear mixed model indicated that mean annual air temperature (MAAT) was the most important predictor for the SOCS simulation. The minimal residual of the linear mixed models was achieved in the vegetation type-based model, whereas the maximal residual was fitted in the soil type-based model. About 95% of SOCS could be found in Argosols, Cambosols and Isohumosols. The largest SOCS was found in the croplands with vegetation of Triticum aestivum L., Sorghum bicolor (L.) Moench, Glycine max (L.) Merr., Zea mays L. and Setaria italica (L.) P. Beauv.

Toward inventory-based estimates of soil organic carbon in forests of the United States.

Soil organic carbon (SOC) is the largest terrestrial carbon (C) sink on Earth; this pool plays a critical role in ecosystem processes and climate change. Given the cost and time required to measure SOC, and particularly changes in SOC, many signatory nations to the United Nations Framework Convention on Climate Change report estimates of SOC stocks and stock changes using default values from the Intergovernmental Panel on Climate Change or country-specific models. In the United States, SOC in forests is monitored by the national forest inventory (NFI) conducted by the Forest Inventory and Analysis (FIA) program within the U.S. Department of Agriculture, Forest Service. The FIA program has been consistently measuring soil attributes as part of the NFI since 2001 and has amassed an extensive inventory of SOC in forest land in the conterminous United States and southeast and southcentral coastal Alaska. That said, the FIA program has been using country-specific predictions of SOC based, in part, upon a model using SOC estimates from the State Soil Geographic (STATSGO) database compiled by the Natural Resources Conservation Service. Estimates obtained from the STATSGO database are averages over large map units and are not expected to provide accurate estimates for specific locations, e.g., NFI plots. To improve the accuracy of SOC estimates in U.S. forests, NFI SOC observations were used for the first time to predict SOC density to a depth of 100cm for all forested NFI plots. Incorporating soil-forming factors along with observations of SOC into a new estimation framework resulted in a 75% (48±0.78Mg/ha) increase in SOC densities nationally. This substantially increases the contribution of the SOC pool, from approximately 44% (17Pg) of the total forest ecosystem C stocks to 56% (28Pg), in the forest C budget of the United States.

Accumulation of soil organic carbon after cropland conversion to short‐rotation willow and poplar

AbstractThe demand for bioenergy has increased the interest in short‐rotation woody crops (SRWCs) in temperate zones. With increased litter input and ceased annual soil cultivation, SRWC plantations may become soil carbon sinks for climate change mitigation. A chronosequence of 26 paired plots was used to study the potential for increasing soil organic carbon (SOC) under SRWC willow and poplar after conversion from cropland (CR) on well‐drained soils. We estimated SOC stocks in SRWC stands and adjacent CR and related the difference to time since conversion, energy crop species, SOC stock of the adjacent CR (proxy for initial SOC of SRWC) and the fine soil percentage (<63 μm) (FS). Soil cores to 40 cm depth were sampled and separated by layers of fixed depths (0–5, 5–10, 10–15, 15–25 and 25–40 cm). Additionally, soils were sampled from soil pits by genetic horizons to 100 cm depth. Comparisons of SOC stocks by equivalent soil masses showed that mean SOC stocks in SRWC were 1.7 times higher than those of CR in the top 5 cm of the soil (P < 0.001). The differences between SRWC and CR remained significant for the plough layer (0–25 cm) by a factor of 1.2 (P = 0.003), while no changes were detectable for the 0–40 cm (P = 0.32), or for the entire 0–100 cm soil layer (P = 0.29). The SOC stock ratio, that is the ratio of SOC stock in SRWC relative to CR, did not change significantly with time since conversion, although there was a tendency to an increase over time for the top 40 cm (P = 0.09). The SOC stock ratio was negatively correlated to SOC in CR and FS percentage, but there was no significant difference between willow and poplar at any depth. Our results suggest that SOC stocks in the plough layer increase after conversion to SRWC.

Assessing fixed depth carbon stocks in soils with varying horizon depths and thicknesses, sampled by horizon

Soil surveys for improving carbon (C) stock estimates frequently involve soil sampling by pre-determined regular depth-intervals, in order to enable more convenient computation of soil organic carbon (SOC) stocks. As a result, soil horizons are often neglected in these surveys, although they represent distinct components of the soil profile. When soil-horizon depths and thicknesses vary considerably within the same site, soil sampling by horizon with accompanying depth measurements may be more suitable. The main objective in this study was to investigate the potential differences in current SOC stocks in different afforested mineral soils, with varying horizon depths and thicknesses, and that were sampled by soil horizon, by using the trapezoidal SOC stock computing approach, and comparing it to the spline approach. An adaptation of the trapezoidal rule computation approach, enabling relatively simple crude estimations of the fixed depth-interval SOC stocks from horizon data, was developed. Estimations of SOC stocks for 18 sites located on three different afforested mineral soils (Gleys, Podzols and Cambisols, aged ≥20years) were done for 0–30cm, 30–60cm and 0–60cm fixed depth-intervals, excluding surface organic layers. The results indicate that the trapezoidal approach is likely to provide cruder estimates of SOC stocks than the spline approach, although no statistically significant differences were observed between the fixed depth-interval SOC stocks (for 0–30cm and 30–60cm) when computed by the two methods. Both methods showed a significant effect of horizon and soil group on SOC stocks. The soil below the 30cm depth was estimated to store over 22% of the total SOC stocks to 60cm depth. Gleys showed significantly greater mineral SOC stocks than Podzols, with differences mainly evident in the upper 30cm, which was observed regardless of the computing methodology used (trapezoidal or spline). The adapted trapezoidal rule computing approach is hoped to facilitate the use of soil-horizon sampling in studies on SOC stocks.

An exponential change decline function to estimate soil organic carbon stocks and their changes from topsoil measurements

Soil organic carbon (SOC) stocks and their changes are important indicators in ecosystem service assessments. Routine soil inventories are often limited to the topsoil, even though a non‐negligible fraction of SOC is known to be stored in deeper horizons. To assess SOC stocks and their changes in the upper metre of the soil profile, vertical extrapolation of topsoil SOC measurements is necessary. The commonly used exponential decline function is not valid, however, for soil types in which subsurface horizons with a larger SOC content (‘anomalies’) occur. Here, we propose an exponential change decline function to account for these profile anomalies. Therefore, we applied the exponential decline function to the difference between the recent (2008–11) and historical (1947–74) SOC contents in the topsoil and compared the results with those derived by the original method. We applied the exponential change decline function to 54 041 agricultural land units (7159 km2) in Flanders (Belgium) and were able to model specific profile characteristics such as spodic horizons, plaggic topsoil and peat substrates. For these particular land units, the exponential decline function underestimated SOC stocks; therefore, it compromised an in‐depth assessment of changes in SOC stocks over time. This study shows that the exponential change decline function is promising for certain soil types and will contribute to the more accurate assessment of ecosystem service indicators. In addition, we emphasize the need for more detailed descriptions of subsoil reference profiles, sampled by pedogenetic horizon rather than by fixed depth interval to optimize calibration of the decline functions.HighlightsWhen recent soil sampling is limited to the topsoil, extrapolation is needed to assess subsoil SOC stocks. We modified the exponential decline function to model SOC‐rich subsurface horizons with the integration of legacy data. An appropriate extrapolation approach is essential for in‐depth SOC assessments. Detailed subsoil data are needed to optimize the calibration of the decline functions.

Sources of errors and uncertainties in the assessment of forest soil carbon stocks at different scales-review and recommendations.

Spatially explicit knowledge of recent and past soil organic carbon (SOC) stocks in forests will improve our understanding of the effect of human- and non-human-induced changes on forest C fluxes. For SOC accounting, a minimum detectable difference must be defined in order to adequately determine temporal changes and spatial differences in SOC. This requires sufficiently detailed data to predict SOC stocks at appropriate scales within the required accuracy so that only significant changes are accounted for. When designing sampling campaigns, taking into account factors influencing SOC spatial and temporal distribution (such as soil type, topography, climate and vegetation) are needed to optimise sampling depths and numbers of samples, thereby ensuring that samples accurately reflect the distribution of SOC at a site. Furthermore, the appropriate scales related to the research question need to be defined: profile, plot, forests, catchment, national or wider. Scaling up SOC stocks from point sample to landscape unit is challenging, and thus requires reliable baseline data. Knowledge of the associated uncertainties related to SOC measures at each particular scale and how to reduce them is crucial for assessing SOC stocks with the highest possible accuracy at each scale. This review identifies where potential sources of errors and uncertainties related to forest SOC stock estimation occur at five different scales-sample, profile, plot, landscape/regional and European. Recommendations are also provided on how to reduce forest SOC uncertainties and increase efficiency of SOC assessment at each scale.

Soil Organic Carbon Stocks in Terrestrial Ecosystems of China: Revised Estimation on Three-Dimensional Surfaces

The estimation of soil organic carbon (SOC) stock in terrestrial ecosystems of China is of particular importance because it exerts a major influence on worldwide terrestrial carbon (C) storage and global climate change. Map-based estimates of SOC stocks conducted in previous studies have typically been applied on planimetric areas, which led to the underestimation of SOC stock. In the present study, SOC stock in China was estimated using a revised method on three-dimensional (3-D) surfaces, which considered the undulation of the landforms. Data were collected from the 1:4 M China Soil Map and a search work from the Second Soil Survey in China. Results indicated that the SOC stocks were 28.8 Pg C and 88.5 Pg C in soils at depths of 0–20 cm and 0–100 cm, corresponding to significant increases of 5.66% and 5.44%, respectively. Regression analysis revealed that the SOC stock accumulated with the increase of areas on 3-D surfaces. These results provide more reasonable estimates and new references about SOC stocks in terrestrial ecosystems of China. The method of estimation on 3-D surfaces has scientific meaning to promote the development of new approaches to estimate accurate SOC stocks.

Estimating temporal changes in soil carbon stocks at ecoregional scale in Madagascar using remote-sensing

Soil organic carbon (SOC) plays an important role in climate change regulation notably through release of CO2 following land use change such a deforestation, but data on stock change levels are lacking. This study aims to empirically assess SOC stocks change between 1991 and 2011 at the landscape scale using easy-to-access spatially-explicit environmental factors. The study area was located in southeast Madagascar, in a region that exhibits very high rate of deforestation and which is characterized by both humid and dry climates. We estimated SOC stock on 0.1ha plots for 95 different locations in a 43,000ha reference area covering both dry and humid conditions and representing different land cover including natural forest, cropland, pasture and fallows. We used the Random Forest algorithm to find out the environmental factors explaining the spatial distribution of SOC. We then predicted SOC stocks for two soil layers at 30cm and 100cm over a wider area of 395,000ha. By changing the soil and vegetation indices derived from remote sensing images we were able to produce SOC maps for 1991 and 2011. Those estimates and their related uncertainties where combined in a post-processing step to map estimates of significant SOC variations and we finally compared the SOC change map with published deforestation maps. Results show that the geologic variables, precipitation, temperature, and soil-vegetation status were strong predictors of SOC distribution at regional scale. We estimated an average net loss of 10.7% and 5.2% for the 30cm and the 100cm layers respectively for deforested areas in the humid area. Our results also suggest that these losses occur within the first five years following deforestation. No significant variations were observed for the dry region. This study provides new solutions and knowledge for a better integration of soil threats and opportunities in land management policies.

Sampling and Data Analysis Optimization for Estimating Soil Organic Carbon Stocks in Agroecosystems

Core Ideas SOC stocks are highly variable at the agroecosystem scale. Optimized sampling and estimation approaches are crucial for low cost SOC assessment. Systematic sampling provided thorough geographic and attribute space coverage. Ordinary kriging outperformed regression kriging, simple mean, and SSURGO approaches. Low‐cost approaches for measuring soil organic C (SOC) stocks are essential for verifying farm management effects on C sequestration in agroecosystems. This study compared sampling and data analysis optimization approaches for estimating SOC stocks of a complex agroecosystem. Soil samples were collected from a 232‐ha area of a working dairy farm with multiple land uses, crop rotations, topographic features, and manure application rates. The SOC stocks were estimated by (i) simple mean, (ii) ordinary kriging, (iii) regression kriging, and (iv) using the USDA Soil Survey Geographic (SSURGO) database. Relationships among sampling schemes, estimation approaches, auxiliary information, and SOC stocks were explored. Slope, elevation, soil type, and land use types displayed a high degree of variation at the study site, yet relationships with SOC stocks were weak or nonsignificant. Systematic sampling provided the best coverage of both geographic and attribute space, allowing reliable variogram estimates and RMSEs that increased little with reduced sample number. Random and stratified random sampling approaches resulted in reduced accuracy. Ordinary kriging had lower RMSEs than either regression kriging or the simple mean, and total SOC stocks estimates fluctuated less when sample sizes were reduced. Using SSURGO overestimated total SOC stocks by up to 5.1% compared with the other approaches, suggesting that this approach may be worthy of evaluation for circumstances with a low budget and low confidence level requirements. Systematic sampling and ordinary kriging can provide an optimal strategy for estimating SOC stocks in agroecosystems with complex topography and land uses.

Spatial assessment of soil organic carbon and physicochemical properties in a horticultural orchard at arid zone of India using geostatistical approaches.

Soil organic carbon (SOC) is a major indicator of long-term sustenance of agricultural production system. Apart from sustaining productivity, SOC plays a crucial role in context of climate change. Keeping in mind these potentials, spatial variation of SOC contents of a fruit orchard comprising several arid fruit plantations located at arid region of India is assessed in this study through geostatistical approaches. For this purpose, surface and subsurface soil samples from 175 locations from a fruit orchard spreading over 14.33ha area were collected along with geographical coordinates. SOC content and soil physicochemical properties of collected soil samples were determined followed by geostatistical analysis for mapping purposes. Average SOC stock density of the orchard was 14.48Mgha(-1) for 0- to 30-cm soil layer ranging from 9.01Mgha(-1) in Carissa carandas to 19.52Mgha(-1) in Prosopis cineraria block. Range of spatial variation of SOC content was found about 100m, while two other soil physicochemical properties, e.g., pH and electrical conductivity (EC) also showed similar spatial trend. This indicated that minimum sampling distance for future SOC mapping programme may be kept lower than 100m for better accuracy. Ordinary kriging technique satisfactorily predicted SOC contents (in percent) at unsampled locations with root-mean-squared residual (RMSR) of 0.35-0.37. Co-kriging approach was found slightly superior (RMSR=0.26-0.28) than ordinary kriging for spatial prediction of SOC contents because of significant correlations of SOC contents with pH and EC. Uncertainty of SOC estimation was also presented in terms of 90% confidence interval. Spatial estimates of SOC stock through ordinary kriging or co-kriging approach were also found with low uncertainty of estimation than non-spatial estimates, e.g., arithmetic averaging approach. Among different fruit block plantations of the orchard, the block with Prosopis cineraria ('khejri') has higher SOC stock density than others.

Underestimation of boreal soil carbon stocks by mathematical soil carbon models linked to soil nutrient status

Abstract. Inaccurate estimate of the largest terrestrial carbon pool, soil organic carbon (SOC) stock, is the major source of uncertainty in simulating feedback of climate warming on ecosystem–atmosphere carbon dioxide exchange by process-based ecosystem and soil carbon models. Although the models need to simplify complex environmental processes of soil carbon sequestration, in a large mosaic of environments a missing key driver could lead to a modeling bias in predictions of SOC stock change.We aimed to evaluate SOC stock estimates of process-based models (Yasso07, Q, and CENTURY soil sub-model v4) against a massive Swedish forest soil inventory data set (3230 samples) organized by a recursive partitioning method into distinct soil groups with underlying SOC stock development linked to physicochemical conditions.For two-thirds of measurements all models predicted accurate SOC stock levels regardless of the detail of input data, e.g., whether they ignored or included soil properties. However, in fertile sites with high N deposition, high cation exchange capacity, or moderately increased soil water content, Yasso07 and Q models underestimated SOC stocks. In comparison to Yasso07 and Q, accounting for the site-specific soil characteristics (e. g. clay content and topsoil mineral N) by CENTURY improved SOC stock estimates for sites with high clay content, but not for sites with high N deposition.Our analysis suggested that the soils with poorly predicted SOC stocks, as characterized by the high nutrient status and well-sorted parent material, indeed have had other predominant drivers of SOC stabilization lacking in the models, presumably the mycorrhizal organic uptake and organo-mineral stabilization processes. Our results imply that the role of soil nutrient status as regulator of organic matter mineralization has to be re-evaluated, since correct SOC stocks are decisive for predicting future SOC change and soil CO2 efflux.

Spatially distributed modelling and mapping of soil organic carbon and total nitrogen stocks in the Eastern Mau Forest Reserve, Kenya

Detailed knowledge about the estimates and spatial patterns of soil organic carbon (SOC) and total nitrogen (TN) stocks is fundamental for sustainable land management and climate change mitigation. This study aimed at: (1) mapping the spatial patterns, and (2) quantifying SOC and TN stocks to 30 cm depth in the Eastern Mau Forest Reserve using field, remote sensing, geographical information systems (GIS), and statistical modelling approaches. This is a critical ecosystem offering essential services, but its sustainability is threatened by deforestation and degradation. Results revealed that elevation, silt content, TN concentration, and Landsat 8 Operational Land Imager band 11 explained 72% of the variability in SOC stocks, while the same factors (except silt content) explained 71% of the variability in TN stocks. The results further showed that soil properties, particularly TN and SOC concentrations, were more important than that other environmental factors in controlling the observed patterns of SOC and TN stocks, respectively. Forests stored the highest amounts of SOC and TN (3.78 Tg C and 0.38 Tg N) followed by croplands (2.46 Tg C and 0.25 Tg N) and grasslands (0.57 Tg C and 0.06 Tg N). Overall, the Eastern Mau Forest Reserve stored approximately 6.81 Tg C and 0.69 Tg N. The highest estimates of SOC and TN stocks (hotspots) occurred on the western and northwestern parts where forests dominated, while the lowest estimates (coldspots) occurred on the eastern side where croplands had been established. Therefore, the hotspots need policies that promote conservation, while the coldspots need those that support accumulation of SOC and TN stocks.

Mapping peat layer properties with multi-coil offset electromagnetic induction and laser scanning elevation data

Peatlands store large amounts of soil organic carbon (SOC). Depending on their present condition, they act as a source or sink of carbon dioxide. Therefore, peatlands are highly relevant for climate change investigations and there is considerable interest to assess spatial heterogeneity of peat soil properties in order to assess the total amount of stored carbon. However, reliable information about peat properties remains difficult to obtain at the field scale. A potential way to acquire this information is the indirect mapping of easily recordable physical variables that correlate with peat properties, such as the apparent electrical conductivity (ECa). In this study, we aim to explore the potential of multi-coil offset electromagnetic induction (EMI) measurements to provide spatial estimates of SOC content, bulk density, and SOC stock for a highly variable and disturbed peatland relict (~35ha) with a remaining peat layer thickness of less than 1m. EMI measurements comprised six integral depths that varied from 0–0.25 to 0–1.80m. In combination with ancillary laser-scanning elevation data, a multiple linear regression model was calibrated to reference data from 34 soil cores that were used to calculate integral properties of the upper 0.25, 0.5, and 1m layer, as well as for the total peat layer. Leave-one-out cross-validation for the different depth ranges resulted in a root mean square error of prediction (RMSEP) between 1.36 and 5.16% for SOC content, between 0.108 and 0.183gcm−3 for bulk density, and between 3.56 and 9.73kgm−2 for SOC stocks, which corresponds to roughly 15%, 10%, and 20% of the total field variability, respectively. The selection of explanatory variables in the regression models showed that the EMI data were important for accurate model predictions, while the topography-based variables mainly acted as noise suppressors. The accuracy of the SOC content estimates roughly equalled the quality of SOC content predictions obtained in previous field applications of the visible-near infrared technique (vis-NIR). The spatial variation of the predicted peat layer properties showed similarities to the former land use distribution. Overall, it was concluded that EMI measurements offer a useful alternative to the established vis-NIR method for SOC content mapping in carbon-rich soils.

Scaling impacts on environmental controls and spatial heterogeneity of soil organic carbon stocks

Abstract. The spatial heterogeneity of land surfaces affects energy, moisture, and greenhouse gas exchanges with the atmosphere. However, representing the heterogeneity of terrestrial hydrological and biogeochemical processes in Earth system models (ESMs) remains a critical scientific challenge. We report the impact of spatial scaling on environmental controls, spatial structure, and statistical properties of soil organic carbon (SOC) stocks across the US state of Alaska. We used soil profile observations and environmental factors such as topography, climate, land cover types, and surficial geology to predict the SOC stocks at a 50 m spatial scale. These spatially heterogeneous estimates provide a data set with reasonable fidelity to the observations at a sufficiently high resolution to examine the environmental controls on the spatial structure of SOC stocks. We upscaled both the predicted SOC stocks and environmental variables from finer to coarser spatial scales (s = 100, 200, and 500 m and 1, 2, 5, and 10 km) and generated various statistical properties of SOC stock estimates. We found different environmental factors to be statistically significant predictors at different spatial scales. Only elevation, temperature, potential evapotranspiration, and scrub land cover types were significant predictors at all scales. The strengths of control (the median value of geographically weighted regression coefficients) of these four environmental variables on SOC stocks decreased with increasing scale and were accurately represented using mathematical functions (R2 = 0.83–0.97). The spatial structure of SOC stocks across Alaska changed with spatial scale. Although the variance (sill) and unstructured variability (nugget) of the calculated variograms of SOC stocks decreased exponentially with scale, the correlation length (range) remained relatively constant across scale. The variance of predicted SOC stocks decreased with spatial scale over the range of 50 m to ~ 500 m, and remained constant beyond this scale. The fitted exponential function accounted for 98 % of variability in the variance of SOC stocks. We found moderately accurate linear relationships between mean and higher-order moments of predicted SOC stocks (R2 ∼ 0.55–0.63). Current ESMs operate at coarse spatial scales (50–100 km), and are therefore unable to represent environmental controllers and spatial heterogeneity of high-latitude SOC stocks consistent with observations. We conclude that improved understanding of the scaling behavior of environmental controls and statistical properties of SOC stocks could improve ESM land model benchmarking and perhaps allow representation of spatial heterogeneity of biogeochemistry at scales finer than those currently resolved by ESMs.

Global distribution of soil organic carbon – Part 1: Masses and frequency distributions of SOC stocks for the tropics, permafrost regions, wetlands, and the world

Abstract. The global soil organic carbon (SOC) mass is relevant for the carbon cycle budget and thus atmospheric carbon concentrations. We review current estimates of SOC stocks and mass (stock × area) in wetlands, permafrost and tropical regions and the world in the upper 1 m of soil. The Harmonized World Soil Database (HWSD) v.1.2 provides one of the most recent and coherent global data sets of SOC, giving a total mass of 2476 Pg when using the original values for bulk density. Adjusting the HWSD's bulk density (BD) of soil high in organic carbon results in a mass of 1230 Pg, and additionally setting the BD of Histosols to 0.1 g cm−3 (typical of peat soils), results in a mass of 1062 Pg. The uncertainty in BD of Histosols alone introduces a range of −56 to +180 Pg C into the estimate of global SOC mass in the top 1 m, larger than estimates of global soil respiration. We report the spatial distribution of SOC stocks per 0.5 arcminutes; the areal masses of SOC; and the quantiles of SOC stocks by continents, wetland types, and permafrost types. Depending on the definition of "wetland", wetland soils contain between 82 and 158 Pg SOC. With more detailed estimates for permafrost from the Northern Circumpolar Soil Carbon Database (496 Pg SOC) and tropical peatland carbon incorporated, global soils contain 1325 Pg SOC in the upper 1 m, including 421 Pg in tropical soils, whereof 40 Pg occurs in tropical wetlands. Global SOC amounts to just under 3000 Pg when estimates for deeper soil layers are included. Variability in estimates is due to variation in definitions of soil units, differences in soil property databases, scarcity of information about soil carbon at depths > 1 m in peatlands, and variation in definitions of "peatland".

Benchmark values for forest soil carbon stocks in Europe: Results from a large scale forest soil survey

Soil organic carbon (SOC) stocks in forest floors and in mineral and peat forest soils were estimated at the European scale. The assessment was based on measured C concentration, bulk density, coarse fragments and effective soil depth data originating from 4914 plots in 22 EU countries belonging to the UN/ECE ICP Forests 16×16km Level I network. Plots were sampled and analysed according to harmonized methods during the 2nd European Forest Soil Condition Survey. Using continuous carbon density depth functions, we estimated SOC stocks to 30-cm and 1-m depth, and stratified these stocks according to 22 WRB Reference Soil Groups (RSGs) and 8 humus forms to provide European scale benchmark values. Average SOC stocks amounted to 22.1tCha−1 in forest floors, 108tCha−1 in mineral soils and 578tCha−1 in peat soils, to 1m depth. Relative to 1-m stocks, the vertical SOC distribution confirmed global patterns reported for forest soils: ~50% of SOC was stored in the upper 20cm, and ~55–65% in the upper 30cm of soil. Assuming 163Mha of European forest cover and by using various scaling up procedures, we estimated total stocks at 3.50–3.94Gt C in forest floors and 21.4–22.7Gt C in mineral and peat soils down to 1-m, which is ~40% more than commonly published. The most useful predictors and stratifiers for C stocks were humus form and tree species for the forest floor, RSG for mineral soils and parent material for peat soils.This dataset will be further explored, predominantly for validation of soil C models, resampling and comparison with legacy and future forest SOC inventories.

Carbon Sequestration in the Soils under Horticulture Land Use in Haryana State

A study was undertaken in Haryana to estimate Soil organic carbon stock under different orchards available there under horticulture land uses which covered 47,036 ha area in Haryana. Maximum organic carbon stock was observed under Mango (36.24 t ha-1) followed by Nebu (35.85 t ha-1), Ber (30.37 t ha-1), Guava (29.45 t ha-1), Kinnu (25.11 t ha-1), Aonla (24.67 t ha-1), Malta (24.20 t ha-1) and the least was under the combination of Aonla + Anar (14.97 t ha-1). When SOC stock under different horticulture species was tested by one - way ANOVA, it was found that SOC stock under different species was significantly different (Variance ratio, F = 4.086; p = < 0.05). SOC stock under Mango was significantly different from the SOC stock under all other species except Nebu, Malta and Grapes. Mitigation potential indicates that soils under Mango and Nebu can sequester nearly two and half times more SOC as compared to that in Aonla + Anar orchard, while the soils under Ber and Guava and Kinnu can sequester nearly double the amount of SOC stock in comparison to Aonla+ Anar orchard. Over all, in Haryana the soils under horticulture land use have 1.37 million tones of SOC stock which is equivalent to 5.05 million tons of CO2.