Quantification Of Soil Organic Carbon Research Articles

Application of predictor variables in spatial quantification of soil organic carbon and total nitrogen using regression kriging in the North Kashmir forest Himalayas

Soil organic carbon (SOC) and total soil nitrogen (TSN) are the critical indices of soil fertility and the biogeochemical cycle. Distribution and variation of SOC and TSN in space are central to climate change and sustainable soil management studies. Little research on spatial prediction of SOC and TSN based on geostatistical techniques employing secondary variables (sampling location) and auxiliary information (topographic factors and type of vegetation) has been conducted globally and under the Himalayas in particular. In this study, 96 soil samples of 0–20 cm depths were taken from the forest area of North Kashmir Himalayas. The effect of topographic factors-elevation, slope, compound topographic index (CTI), stream power index (SPI), sediment transport index (STI), and normalized difference vegetation index (NDVI) on SOC and TSN spatial distribution were studied using regression kriging. Results indicate that regression kriging is a better predictor of SOC and TSN spatial distribution by 97.3% and 96.4% respectively than ordinary kriging with residuals moderately auto-correlated. Semi-variogram test indicated that elevation, slope, and NDVI are major factors driving SOC and TSN spatial variation. The negative correlation of topographic elevation and slope with the spatial distribution of SOC and TSN reveals better stabilization of SOC and TSN at lower degrees of slope and lower altitudes. Our study suggests that regression kriging at greater scale can provide the best estimations in a relatively uniform ecosystem provided there is a significant correlation between auxiliary variables and the SOC and TSN contents with residuals spatially auto-correlated.

Effects of 22-year fertilisation on the soil organic C, N, and theirs fractions under a rice-wheat cropping system

ABSTRACT The quantification of soil organic carbon (SOC) and N fractions under the influence of long-term fertilisation is useful to understand soil organic matter (SOM) stabilization mechanism and to develop optimal management practices. This study was to investigate the responses of different SOC and N fractions to long-term fertilisation and their contributions to the total change in the bulk soil under a rice-wheat cropping system. Compared with soil with no fertilisation, the 22-year fertilisation improved the C and N content of the bulk soil and different fractions with the greatest increase in NPK fertilisers and rice straw treatment. The SOC and N changes of heavy fraction accounted for 92% and 88% of the total SOC increases, while the Fe/Al-bound organic carbon and humic organic carbon in heavy fraction accounted for 43% and 48%, respectively. The SOC and N contents of each fraction were positively correlated with the bulk soil and the others. It is concluded that the NPK fertilisers plus straw returning was efficient to enhance SOC and N accumulation; the chemical binding of Fe and Al in organic-mineral complexes and the further formation of recalcitrant humin might be the potential mechanism for SOM accumulation in the rice-wheat cropping system. Abbreviations: SOM: soil organic matter; SOC: soil organic carbon; STN: soil total nitrogen; POM: particulate organic matter; MOM: mineral associated organic matter; LF: light fraction; HF: heavy fraction; Ca-OC: Ca-bound organic carbon; Fe/Al-OC: Fe- or Al-bound organic carbon; Humin-OC: humic organic carbon

Using Landsat and soil clay content to map soil organic carbon of oxisols and Ultisols near São Paulo, Brazil

Quantification of soil organic carbon (SOC) is a low-cost and necessary practice to meet increasing agricultural demands. Studies show that remote sensing (RS) is important for SOC prediction and its use has become crucial in agricultural management. In this study, a Multiple Linear Regression (MLR) model was constructed to predict SOC in a site in Piracicaba, São Paulo, Brazil. As predictor variables, we used the optical-satellite data of OLI/Landsat-8 sensor (bands 5 and 7, specifically), clay concentration, and the Normalized Difference Vegetation Index (NDVI). We collected 218 samples at the sampling points in the field to quantify clay and SOC in the laboratory as a calibration procedure. An Exposed Soil Mask (ESM) was created using the method GEOS3 technology, which showed pixels with greater variability of bare soil. The pixels were evaluated with their respective surface reflectance values obtained by the satellite sensor and their respective NDVI index values. We evaluated the model predictive performance based on the adjusted coefficient of determination (R2), the Root Mean-Squared Error (RMSE), and the Ratio of Performance to Interquartile Range (RPIQ) obtained in data validation. The MLR model presented R2 values 0.79 and 0.81 for calibration and validation, respectively. We obtained important RMSE and RPIQ values, 0.14 and 2.32, respectively. The high RPIQ indicated significative sampling distribution around the trendline. After construction, the model was applied to the C spatial distribution using the predictive variables as layers, predominant concentrations of 0.65 to 0.79 g. Kg−1 in 51 (23.4%) soil samples. The analysis presented here offer possibilities for SOC prediction using Geographic Information Systems (GIS) tools.

Quantification of Soil Organic Carbon in Biochar-Amended Soil Using Ground Penetrating Radar (GPR)

The application of biochar amendments to soil has been proposed as a strategy for mitigating global carbon (C) emissions and soil organic carbon (SOC) loss. Biochar can provide additional agronomic benefits to cropping systems, including improved crop yield, soil water holding capacity, seed germination, cation exchange capacity (CEC), and soil pH. To maximize the beneficial effects of biochar amendments towards the inventory, increase, and management of SOC pools, nondestructive analytical methods such as ground penetrating radar (GPR) are needed to identify and quantify belowground C. The use of GPR has been well characterized across geological, archaeological, engineering, and military applications. While GPR has been predominantly utilized to detect relatively large objects such as rocks, tree roots, land mines, and peat soils, the objective of this study was to quantify comparatively smaller, particulate sources of SOC. This research used three materials as C sources: biochar, graphite, and activated C. The C sources were mixed with sand—12 treatments in total—and scanned under three moisture levels: 0%, 10%, and 20% to simulate different soil conditions. GPR attribute analyses and Naïve Bayes predictive models were utilized in lieu of visualization methods because of the minute size of the C particles. Significant correlations between GPR attributes and both C content and moisture levels were detected. The accuracy of two predictive models using a Naïve Bayes classifier for C content was trivial but the accuracy for C structure was 56%. The analyses confirmed the ability of GPR to identify differences in both C content and C structure. Beneficial future applications could focus on applying GPR across more diverse soil conditions.

A comparative study between a new method and other machine learning algorithms for soil organic carbon and total nitrogen prediction using near infrared spectroscopy

Soil organic carbon (SOC) and total nitrogen (TN) are essential elements in agricultural soil and play an important role in many biological and chemical activities for plant growth. The assessment of these parameters in the soil is crucial in agriculture. The problem with traditional chemical analysis methods for SOC and TN is that they are time and resource consuming. In recent years, near-infrared (NIR) spectroscopy has been used as an alternative for SOC and TN determination. Accordingly, in this study, a new approach based on the ensemble learning modelling (ELM) algorithm is used to predict SOC and TN. This approach uses six partial least squares regression (PLS) models with six pre-processing methods as learners for this method. The output of this approach is computed by averaging the predicted values computed by its constituent learners. This algorithm is used to predict the amount of SOC and TN of Moroccan soil collected from four agricultural regions using NIR. The performance of this algorithm is compared with separated regression models, namely PLS, back-propagation neural network (BPNN) with and without variable selection (VS) algorithms. using three metrics, R2, root mean square error (RMSE), and the ratio of performance to deviation (RPD) calculated by a validation dataset. The results show that the ELM outperformed all PLS models and BPNN with and without VS for both elements. Furthermore, BPNN without VS and PLS provided better performance than PBNN with VS for SOC prediction. However, for TN, PLS gave a moderate performance according to other models (R2 = 0.80 and RPD = 2.77). The best predictions were obtained with the E-L model for SOC (R2 = 0.96, RMSE = 1.92, and RPD = 4.87) and TN (R2 = 0.94, RMSE = 0.57, and RPD = 4.91), which classified the model as an excellent one for SOC and TN prediction. The proposed method ELM has the advantage of wider applicability and better performance for TN and SOC quantification by NIR spectroscopy in comparison with separated PLS and BPNN with and without VS.

A remote sensing adapted approach for soil organic carbon prediction based on the spectrally clustered LUCAS soil database

The estimation of the soil organic carbon (SOC) content plays an important role for carbon sequestration in the context of climate change, food security and soil degradation. Reflectance spectroscopy has proven to be a promising technique for SOC quantification in the laboratory and increasingly from air- and spaceborne platforms, where hyperspectral imagery provides great potential for mapping SOC on larger scales with regular updates. When applied on larger scales, soil prediction accuracy decreases due to the inhomogeneity of samples. In this paper, we examined if spectral clustering of the LUCAS EU-wide topsoil database is successful without using other covariates than the spectral database and can improve SOC model performance compared to a reference model that was calibrated on the whole database without clustering. Different clustering methodologies were tested, including a k-means clustering based on principal component analysis or based on spectral feature variables, combined with partial least squares regression (PLSR) models, and a clustering based on a local PLSR approach which builds a different multivariate model for each sample to be predicted. Furthermore, in order to allow for subsequent application to hyperspectral remote sensing data, atmospheric water wavelengths were removed from the analyses. The local PLSR approach achieved best results and was additionally applied to LUCAS spectra resampled to the upcoming hyperspectral EnMAP sensor which led to good results: R2 = 0.66, RMSEP = 5.78 g kg−1 and RPIQ = 1.93. The k-means clustering approach showed slightly better results than the reference model. Overall, our results showed similar performances for SOC prediction models compared to other approaches using PLSR with a larger spectral range and other soil parameters as covariates. This study shows that (i) it is possible to transfer the local PLSR approach onto a wavelengths reduced spectral library and to predict estimations of SOC at low-cost with reasonable accuracy based on large scale soil databases; and (ii) that the local regression approach is a valuable tool for SOC prediction models based solely on spectral data without the use of other soil covariates.

Spatial-Temporal Changes in Soil Organic Carbon and pH in the Liaoning Province of China: A Modeling Analysis Based on Observational Data

Quantification of soil organic carbon (SOC) and pH, and their spatial variations at regional scales, is a foundation to adequately assess agriculture, pollution control, or environmental health and ecosystem functioning, so as to establish better practices for land use and land management. In this study, we used the random forest (RF) model to map the distribution of SOC and pH in the topsoil (0–20 cm) and estimate SOC and pH changes from 1982 to 2012 in Liaoning Province, Northeast China. A total of 10 covariates (elevation, slope gradient, topographic wetness index (TWI), mean annual temperature (MAT), mean annual precipitation (MAP), visible-red band 3 (B3), near-infrared band 4 (B4), short-wave infrared band 5 (B5), normalized difference vegetation index (NDVI), and land-use data) and a set of 806 (in 1982) and 973 (in 2012) soil samples were selected. Cross-validation technology was used to test the performance and uncertainty of the RF model. We found that the prediction R2 of SOC and pH was 0.69 and 0.54 for 1982, and 0.63 and 0.48 for 2012, respectively. Elevation, NDVI, and land use are the main environmental variables affecting the spatial variability of SOC in both periods. Correspondingly, the topographic wetness index and mean annual precipitation were the two most critical environmental variables affecting the spatial variation of pH. The mean SOC and pH decreased from 18.6 to 16.9 kg−1 and 6.9 to 6.6, respectively, over a 30-year period. SOC distribution generated using the RF model showed a decreasing SOC trend from east to west across the city in the two periods. In contrast, the spatial distribution of pH showed an opposite trend in both periods. This study provided important information of spatial variations in SOC and pH to agencies and communities in this region, to evaluate soil quality and make decisions on remediation and prevention of soil acidification and salinization.

Characterization of the heavy, hydrolysable and non-hydrolysable fractions of soil organic carbon in conventional and no-tillage soils

Quantification of soil organic carbon (SOC) pool fractions under different tillage systems is important in understanding SOC dynamics and storage. Two major pools of SOC that can be isolated are the light (LF) and heavy fractions (HF). Few studies have quantified the effect of tillage systems on the hydrolysable (HYF) and non-hydrolysable fractions (NHF) which comprise the HF. The objective of this study was to evaluate if there were significant changes in the quantity and δ13C of the HF, HYF, and NHF fractions of SOC after six years of no-tillage (NT), on arable soils that were previously under conventional tillage (CT). Our study used 13C natural abundance (rotation of C3 and C4crops) on a calcareous Typic Hapludalf soil in southern Ontario, Canada. The HF (>1.7 g cm−3) was isolated using density fractionation and separated into its HYF and NHF using acid hydrolysis (6 M HCl) for three soil depths: 0–10, 10–20, and 20–30 cm. The HF pool (90–93% of SOC) was significantly greater (P < 0.05) in the NT (28.9) than the CT system (25.5 Mg C ha−1) only for the 0–10 cm depth. The dominant SOC fraction from the HF pool was the NHF (62–65% of SOC) for both tillage systems. However, the HYF (25–30% of SOC) was significantly greater in the NT (9.3) than the CT system (7.0 Mg C ha−1) only for the 0–10 cm depth. Additionally, there was a significantly higher proportion of C4-derived C in the HF only at the 0–10 cm depth of NT soils. Differences in the δ13C of the whole soil and SOC fractions show a preservation of newly derived C in the HF, HYF and NHF of NT soils. We conclude that the adoption of NT systems on arable soils increases the quantity of HF and HYF in the 0–10 cm depth only, but there is no difference in carbon sequestration potential when treatments are compared over the 0–30 cm depth in the short-term.

High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: a case study in a sub-Arctic peatland environment

Abstract. Soil organic carbon (SOC) stored in northern peatlands and permafrost-affected soils are key components in the global carbon cycle. This article quantifies SOC stocks in a sub-Arctic mountainous peatland environment in the discontinuous permafrost zone in Abisko, northern Sweden. Four machine-learning techniques are evaluated for SOC quantification: multiple linear regression, artificial neural networks, support vector machine and random forest. The random forest model performed best and was used to predict SOC for several depth increments at a spatial resolution of 1 m (1×1 m). A high-resolution (1 m) land cover classification generated for this study is the most relevant predictive variable. The landscape mean SOC storage (0–150 cm) is estimated to be 8.3 ± 8.0 kg C m−2 and the SOC stored in the top meter (0–100 cm) to be 7.7 ± 6.2 kg C m−2. The predictive modeling highlights the relative importance of wetland areas and in particular peat plateaus for the landscape's SOC storage. The total SOC was also predicted at reduced spatial resolutions of 2, 10, 30, 100, 250 and 1000 m and shows a significant drop in land cover class detail and a tendency to underestimate the SOC at resolutions &gt; 30 m. This is associated with the occurrence of many small-scale wetlands forming local hot-spots of SOC storage that are omitted at coarse resolutions. Sharp transitions in SOC storage associated with land cover and permafrost distribution are the most challenging methodological aspect. However, in this study, at local, regional and circum-Arctic scales, the main factor limiting robust SOC mapping efforts is the scarcity of soil pedon data from across the entire environmental space. For the Abisko region, past SOC and permafrost dynamics indicate that most of the SOC is barely 2000 years old and very dynamic. Future research needs to investigate the geomorphic response of permafrost degradation and the fate of SOC across all landscape compartments in post-permafrost landscapes.

Methods assessment for organic and inorganic carbon quantification in calcareous soils of the Mediterranean region

In calcareous soils, two methods for measuring soil organic carbon (SOC) have been traditionally used: 1) wet oxidation, and 2) dry combustion in which SOC is measured either directly by removing soil inorganic carbon (SIC) from samples through an acid pretreatment, or indirectly by subtracting SIC measured by a calcimeter from total soil C. These methods have well documented experimental limitations that may result in erroneous quantification of C stocks and cycling in calcareous Mediterranean soils. An alternative approach is ramped combustion, which takes advantage of the large difference in the temperatures at which SOC and SIC combust or decompose. Our aim in this study was to test the capability of a continuous, ramped combustion technique combining differential scanning calorimetry and evolved gas analysis to quantify SIC and SOC in calcareous soil samples, and compare this technique with conventional techniques. We also sought to use thermal analysis to test the efficacy of acid fumigation for carbonate removal, and to evaluate the effects of acid fumigation on SOC quality. SIC and SOC concentrations from a set of 23 soil samples differing in carbonate concentrations were well correlated among conventional and ramped combustion methods, indicating that ramped combustion is an effective method to simultaneously quantify SIC and SOC in calcareous soil samples. Ramped combustion was also effective in demonstrating incomplete carbonate removal in several samples, though quantification was made difficult by sample mass issues due to hydrophilicity after fumigation. Ramped combustion also revealed substantial qualitative changes in the thermal lability of residual organic matter, as evidenced by changes in CO2-EGA thermogram shape and peak temperatures. As a result, ramped combustion appears to be a preferable single-sample method for the simultaneous quantification of SOC and SIC in calcareous soil, providing a relatively rapid, cost- and labor-effective method that precludes the use of hazardous reactants such as those used in wet oxidation and acid fumigation.

Spatial variations of soil organic carbon stocks in a coastal hilly area of China

Quantification of soil organic carbon (SOC) stocks and their spatial variations at regional scales is a foundation to adequately assess plant productivity and soil carbon sequestration potentials so as to establish better practices for land use and land management. This study evaluated the spatial variation of SOC stocks from 1982 to 2012 in Wafangdian, Liaoning Province, China. To map SOC stock, we used geographically weighted regression (GWR) and regression kriging (RK) methods and a large set of soil samples, in which nine topographic and remote sensing variables were observed. The GWR approach performed better than the RK approach as the former has smaller absolute mean errors (AME), mean errors (ME), root mean square errors (RMSE) in comparison with observational data. Our results indicated that SOC stocks have an increasing trend in northeast and southwest mountainous areas in our study periods. Land-use changes caused by returning cultivation land to forest promoted SOC accumulation. The total SOC stocks of cultivation land, grasslands and forests within 0–0.2m of soils were estimated to be 5.25 and 5.40Tg in 1982 and 2012, respectively. This study provided important information of spatial variations in SOC stocks to agencies and communities in this region to evaluate soil quality and assess carbon sequestration potentials and carbon credits.

Diffuse Reflectance Spectroscopy (Vis‐Nir‐Swir) as a Promising Tool for Blue Carbon Quantification in Mangrove Soils: A Case of Study in Tropical Semiarid Climatic Conditions

Core Ideas The full‐range DRS spectrum (350–2500 nm) generated the best model of prediction. Further DRS studies are necessary to cope with the variability in the mangrove soils. DRS is an effective and fast method for blue carbon stock quantification in mangroves. The assessment of the soil organic carbon (SOC) stocks in mangrove ecosystems is essential for coastal management activities seeking the mitigation of CO2 emissions. However, the wet chemical analysis conventionally used to quantify SOC may overestimate SOC content due to oxidation of reduced compounds. This work focused on the use of diffuse reflectance spectroscopy (DRS) for predicting SOC in mangrove forest areas. When used properly, DRS may be, in some cases, a more accurate and more efficient method for the determination of SOC in mangrove soils than conventional analytical approaches. Furthermore, variable selection may simplify and improve prediction accuracy, reducing collinearity in the dataset used and allowing better SOC quantification through more interpretable and robust models.

The resistance of centennial soil charcoal to the “Walkley-Black” oxidation

The “Walkley-Black” oxidation with dichromate is a routine protocol for the estimation of soil organic carbon (SOC) content. Dichromate oxidation is also used to quantify black carbon (BC) in soil and sediments based on the larger resistance of BC to dichromate oxidation than uncharred SOC. The aim of this study was (i) to evaluate whether dichromate-based SOC quantification methods discriminate between uncharred SOC and charcoal residues in the topsoil of pre-industrial charcoal kiln sites and (ii) to test the influence of aging on the recovery of charcoal by dichromate oxidation. We selected 40 topsoil samples from 20 pre-industrial charcoal kiln sites of Wallonia, Belgium, and adjacent reference soils unaffected by charcoal production. Samples included soils from forest and cropland, with a diversity of soil types and textural classes. SOC content was estimated by the original Walkley-Black procedure (results were multiplied by the traditional correction factor of 1.32), and by an adapted method that includes external heating of the digestion mixture to overcome incomplete oxidation of uncharred SOC and get rid of the correction factor of 1.32. Results were compared to the amount of total organic carbon (TOC) content, measured by dry combustion, and to the content of charcoal-C in soil, estimated by differential scanning calorimetry. The soil of a currently active charcoal kiln site was also analyzed and used as a reference subject to limited aging. 23.6% of charcoal-C was recovered by the Walkley-Black procedure in the soil of the currently active kiln site against 65% for the soil of pre-industrial kiln sites, which indicates that the resistance of charcoal to dichromate oxidation decreases with aging. The recovery of charcoal increased to 90% after boiling of the digestion mixture, providing evidence that heat catalyzes the oxidation of charcoal. The substantial oxidation of charcoal by dichromate and the variability of recovery according to the degree of alteration of charcoal and conditions of reaction support the idea that the quantification of BC based on its chemical resistance is challenging and can be subject to important biases if calibration is not adapted to the quality of BC of the sample of interest. Because the recovery of BC by the Walkley-Black method is incomplete, the presence of large amounts of BC in soils frequently affected by fire might be a significant cause of underestimation of SOC by the Walkley-Black method in regional and global databases.

Estimating organic carbon content of soil in Papua New Guinea using infrared spectroscopy

Quantification of soil organic carbon (SOC) content is important for sustainable agricultural management and accurate carbon accounting. Infrared (IR) absorbance can be used to estimate SOC content, but the relationship differs between regions due to matrix effects. We developed an IR-based model specific for SOC in Papua New Guinean soils. A total of 437 samples from 0.0–0.3m depth were analysed for SOC using Dumas combustion. IR absorption spectra were collected from the same samples, and a predictive regression model was developed using the 6000–1030cm–1 spectral range. Using a validation set, predicted SOC values resulting from the IR-based model compared well with values from Dumas combustion (R2=0.905; ratio of performance-to-deviation=5.64). Constraining wavelengths to positively correlated regions of the spectra was also explored and showed improved model performance (R2=0.932). Overall, IR analysis provides a robust method for estimating SOC content for a range of Papua New Guinean soils.

Using an inexpensive color sensor for rapid assessment of soil organic carbon

Quantifying soil organic carbon (SOC) is important for soil management, precision agriculture, soil mapping and carbon dynamics research. Inexpensive sensor technologies offer the potential for rapid quantification of SOC in laboratory samples as well as in the field. The objective of this study was to use a commercially-available color sensor to develop SOC prediction models for both dry and moist soils from the Piedmont region of South Carolina. Thirty-one soil samples were analyzed for lightness to darkness, redness to greenness, and yellowness to blueness (CIEL*a*b*) color using a Nix Pro™ color sensor. Soil color was measured under both dry and moist soil conditions and the depth of each soil sample was also recorded. Using L*, a*, b* and soil depth for each sample as initial predictors, regression analyses were conducted to develop SOC prediction models for dry and moist soils. The resulting residual plots, root mean squared errors (RMSE), and coefficients of determination (R2) were used to assess model fits for predicting the SOC content of soil. Cross validation was conducted to determine the efficiency of the predictive models and the mean squared prediction error (MSPE) was calculated. The final models included soil depth, L*, and a* as independent variables (dry soils R2=0.7978 and MSPE=0.0819, moist soils R2=0.7254 and MSPE=0.1536). The results suggest that soil color sensors have potential for rapid SOC determination, and soil depth and color are useful in predicting SOC content in soils.

Methodological comparison for quantitative analysis of fossil and recently derived carbon in mine soils with high content of aliphatic kerogen

In mine soil, quantification of soil organic carbon (OC) derived recently from biomass decomposition is complicated by the presence of fossil (geogenic) C derived from coal, oil shale, or similar material in the overburden. The only reliable method for such measurement is 14C analysis (i.e. radiocarbon dating) using instrumentation such as accelerator mass spectrometry, which is too expensive for routine laboratory analysis. We tested two previously used and two new methods for recent C quantification and compared them with 14C AMS radiocarbon dating as a reference using a set of soil samples (n=14) from Sokolov, Czech Republic: (i) 13C isotope ratio composition, (ii) cross polarization magic angle spinning 13C nuclear magnetic resonance (CPMAS 13C NMR) spectroscopy, (iii) near infrared spectroscopy (NIRS) coupled with partial least squares regression and (iv) Rock–Eval pyrolysis. Conventional methods for OC determination (dry combustion, wet dichromate oxidation, loss-on-ignition) were also compared to quantify any bias connected with their use. All the methods provided acceptable recent carbon estimates in the presence of mostly aliphatic fossil C from kerogen. However, the most accurate predictions were obtained with two approaches using Rock–Eval pyrolysis parameters as predictors, namely (i) S2 curve components and (ii) oxygen index (OI). The S2 curve approach is based on the lower thermal stability of recent vs. fossil organic matter. The OI approach corresponded well with 13C NMR spectra, which showed that samples rich in recent C were richer in carboxyl C and O-alkyl C. These two methods showed the greatest potential as routine methods for recent C quantification.

Comparison of Three Methods for Measurement of Soil Organic Carbon

Quantification of soil organic carbon (SOC) is an important element in the assessment of the carbon sequestration potential of soils in tree-based intercropping (TBI) systems. The organic carbon (OC) concentrations of soils in TBI systems often differ from those in conventional agricultural systems due to the additional C inputs from litter fall and roots. However, the presence of soil inorganic carbon (SIC) can confound the measurements of SOC. This study compared three methods of measuring SOC: (i) measurement of the total soil C (TC) in one subsample and, after treatment in a muffle furnace (575 °C) for 24 h, measurement of SIC in another subsample; (ii) SOC measured after fumigation with 12 M hydrochloric acid (HCl) to remove SIC; and (iii) SOC measured after digestion with 0.73 M H2SO3 to remove SIC. The TC, SOC, and SIC concentrations were determined by combustion. A correction factor was applied to express SIC and SOC concentrations on an original, untreated soil basis. Measurement of SOC by the muffle furnace method resulted in the greatest SOC concentrations for Populus spp. (hybrid poplar) for samples from two of the three depths (0–10 and 20–40 cm). Measurement of SOC by the HCl fumigation and H2SO3 digestion methods were highly correlated, suggesting complete removal of SIC with minimal oxidation of SOC. These results have implications for the method of measuring SOC in calcareous soils under coniferous and deciduous tree species to a depth of 40 cm.

Digital soil mapping of soil organic carbon stocks under different land use and land cover types in montane ecosystems, Eastern Himalayas

Quantification of soil organic carbon (SOC) stocks is quite useful for accurate monitoring of C sequestration. However, there are still substantial gaps in our knowledge of SOC stocks in many parts of the world, including the Himalayas. We investigated the total SOC stocks and its spatial distribution under different land use and land cover (LULC) types in montane ecosystems of Bhutan. 186 Soil profiles were described and sampled by genetic horizons at sites located using conditioned Latin hypercube sampling. SOC concentrations at the standard depths designated for the GlobalSoilMap.Net were estimated with an equal-area spline profile function. SOC concentrations at these depth intervals were digitally mapped to a fine resolution matrix of 90m grid using regression kriging. We found significant influence of LULC categories on SOC concentration, SOC density, SOC stocks and their spatial distributions, although this influence decreased with increasing soil depth. The estimated mean SOC density in the top 1m were highest in fir forest soils (41.4kgm−2) and lowest in paddy land (12.0kgm−2). Allowing for LULC relative areas, mixed conifer forest had the highest SOC stocks in the upper meter (12.4Mt) with orchards the lowest (0.1Mt). The total SOC stocks for the whole study area for the 0–5, 5–15, 15–30, 30–60 and 60–100cm depths were 2.6, 5.0, 6.5, 7.5 and 5.4Mt, respectively. The overall SOC stock of the study area for the upper meter was approximately 27.1Mt. The combined forests accounted for more than 77.5% of the total SOC stocks of the study area. This and the relative SOC densities indicate that the conversion of even a fraction of forests to other LULC types could lead to substantial loss of SOC stocks. This loss of SOC stock is even greater when the decrease in aboveground biomass is also taken into consideration. However, appropriate management of the agricultural lands could increase their sequestration of atmospheric CO2.

A Comparison of Feature-Based MLR and PLS Regression Techniques for the Prediction of Three Soil Constituents in a Degraded South African Ecosystem

The accurate assessment of selected soil constituents can provide valuable indicators to identify and monitor land changes coupled with degradation which are frequent phenomena in semiarid regions. Two approaches for the quantification of soil organic carbon, iron oxides, and clay content based on field and laboratory spectroscopy of natural surfaces are tested. (1) A physical approach which is based on spectral absorption feature analysis is applied. For every soil constituent, a set of diagnostic spectral features is selected and linked with chemical reference data by multiple linear regression (MLR) techniques. (2) Partial least squares regression (PLS) as an exclusively statistical multivariate method is applied for comparison. Regression models are developed based on extensive ground reference data of 163 sampled sites collected in the Thicket Biome, South Africa, where land changes are observed due to intensive overgrazing. The approaches are assessed upon their prediction performance and significance in regard to a future quantification of soil constituents over large areas using imaging spectroscopy.

Forest topsoil organic carbon content in Southwest Bohemiaregion

The aim of this study was to evaluate organic carbon content (SOC) in the surface layers of forest soils in the two Natural Forest Regions situated in Southwest Bohemia, namely Z&amp;aacute;padočesk&amp;aacute; pahorkatina (NFR 6) and Česk&amp;yacute; les (NFR 11). The study is based upon on two consecutive soil sampling campaigns during autumn 2003 and 2004. While the sampling of 2003 was inadequate to estimate bulk density, the consecutive campaign used a defined sample volume to permit an estimation of bulk density and quantification of soil organic carbon (SOC) for soil organic layers and the upper mineral horizon. The total sampling depth was 30 cm including both organic and mineral layer. SOC of organic horizon was on average 1.99 kg&amp;nbsp;C/m&lt;sup&gt;2&lt;/sup&gt;. It differed by stand site type ranging from 0.70&amp;nbsp;to 3.04 kg&amp;nbsp;C/m&lt;sup&gt;2&lt;/sup&gt;. The organic layer SOC was smallest under beech (1.03 kg&amp;nbsp;C/m&lt;sup&gt;2&lt;/sup&gt;), whereas it was higher under pine (2.19 kg&amp;nbsp;C/m&lt;sup&gt;2&lt;/sup&gt;) and spruce &lt;br /&gt;(2.09 kg&amp;nbsp;C/m&lt;sup&gt;2&lt;/sup&gt;). SOC in the mineral layer was in average 7.28 kg&amp;nbsp;C/m&lt;sup&gt;2&lt;/sup&gt;. SOC differed significantly by the major tree species and reached 10.6; 5.67 and 7.5 kg&amp;nbsp;C/m&lt;sup&gt;2&lt;/sup&gt; for beech, pine and spruce sites, respectively. The average SOC for the total soil layer (0&amp;ndash;30 cm) reached 9.33 kg&amp;nbsp;C/m&lt;sup&gt;2&lt;/sup&gt;. The methodological aspects of regional estimation of SOC and the potential of utilization of the national forest inventory program are also discussed.