Distribution Of Soil Organic Carbon Stocks Research Articles

Impact of city historical management on soil organic carbon stocks in Paris (France)

Assessments of urban soil organic carbon stock (SOCS) are scant because citywide data are scarce. Moreover, we do not know which factors mostly explain SOCS spatial and temporal variations in urban open soils. To fill these knowledge gaps, the present study focused on Paris (France): two periods (1993–2007 and 2008–2016) were compared to better understand SOCS changes at 0–30 cm depth. The effects of anthropic factors (land use, management intensity, cover type, site age and size, city district, and indirectly, resource material used for the site establishment) and soil properties (e.g., soil nutrient contents) on SOCS were studied over the more recent period (2008–2016) on 810 soil samples. The changes in green space management methods between both periods resulted in an additional storage of + 43.5 Gg SOC in Paris open soils in 2008–2016 compared to 1993–2007. During 2008–2016, SOCS was still influenced by city history and particularly by the type of resource material used for landscaping, which has changed since the 1950s. SOCS differed significantly among city districts, which represent a combination of all studied anthropic factors: each district has its own history (i.e., soil resource material, green space age), specific number and size of green spaces, current management methods, and plant cover types. Paris green space SOCS was also influenced by soil nitrogen, sand, and magnesium contents as well as pH. Modifications of local landscape management methods are a main driver of SOCS changes. Moreover, citywide SOCS distribution tends to depend on city history.

Investigating the spatio-temporal variability of soil organic carbon stocks in different ecosystems of China

Soil organic carbon (SOC) significantly influences soil fertility, soil water holding capacity, and plant productivity. In this study, we applied two boosted regression tree (BRT) models to map SOC stocks across China in the 1980s and the 2010s. The models incorporated nine environmental variables (climate, topography, and biology) and 8897 (in the 1980s) and 4534 (in the 2010s) topsoil (0–20 cm) samples. During the two study periods, 20% of the soil samples were randomly selected for model testing, and the remaining samples were used as a training set to construct the models. The verification results showed that incorporating climate environment variables significantly improved the model prediction in both study periods. Mean annual temperature, mean annual precipitation, elevation, and the normalized difference vegetation index were the dominant environmental factors affecting the spatial distribution of SOC stocks. The full-variable model predicted similar spatial distributions of SOC stocks for the 1980s and the 2010s. SOC stocks in China showed an increasing trend over the past 30 years, from 3.9 kg m−2 in the 1980s to 4.6 kg m−2 in the 2010s. In both periods, topsoil SOC stocks were mainly stored in agroecosystems, forests, and grasslands in the 1980s, with values of 9.5, 12.0, and 11.4 Pg C, respectively. Our study provides reliable information on Chain's carbon distribution, which can be used by land managers and the national government to formulate relevant land use and carbon sequestration policies.

Soil organic carbon stocks in an investigated watershed transect linked to ecological restoration practices on the Loess Plateau

AbstractLager scale ecological restoration practices are increasing soil organic carbon (SOC) stocks. Meta‐analysis is by far the most widely applied technique for describing SOC at large spatial scales, but there is a lack of field‐based investigations on a series of individual watersheds. In this study, a watershed transect of six watersheds distributed in a hilly and gully area of the Loess Plateau was investigated in order to achieve sustainable practices. The results indicate that ecological restoration practices significantly increased SOC stocks to varying degrees (p < 0.05). Grasslands were associated with the largest SOC stocks (39.42 ± 22.21 t hm−2), which were larger than the SOC stocks of artificial immature forest (<20 years) (26.58 ± 14.32 t hm−2) in the 50 cm soil profile. However, in the long term, afforestation seems to increase SOC stocks to the level of secondary forest, 81.15 ± 4.69 t hm−2, in a suitable environment. Appropriate land use management is also an effective approach to improving soil C sequestration capacity. SOC stocks were measured as 34.11 ± 7.66 t hm−2 in abandoned terraced farmland, which was similar to the grassland SOC stocks. Soil and plant factors contributed approximately 60% to the variation in the spatial distribution of SOC stocks at 0–50 cm; their contributions increased and diminished with increasing soil depth, respectively. This study indicates naturally restored grassland and engineering practices are suitable on the Loess Plateau by investigating a watershed transect and enhances our understanding of the comprehensive contributions of environmental factors to the spatial distribution of SOC stocks on a large spatial scale.

Evaluating soil organic carbon stock changes induced by no-tillage based on fixed depth and equivalent soil mass approaches

It is controversial whether no tillage (NT) should be regarded an effective agro-ecosystem management to increase soil organic carbon (SOC). Some studies suggest NT leads to a notable accumulation of SOC stock across the whole sampling profile over the long-term, whereas other studies argue that the effect of duration on SOC stock increase is limited. Similar controversy is common with respect to the role of climatic conditions in SOC changes. In addition, despite the gradual recognition of the importance of equivalent soil mass (ESM) compared to fixed depth (FD) approach, in terms of SOC stock evaluation, the ESM approach mainly focuses on the direct impact of bulk density but ignores the potential indirect effect on SOC concentration. To further understand the SOC sequestration mechanisms under NT, responses of SOC stock after the adoption of NT were investigated based on FD and ESM approaches. The results showed that NT significantly changed the SOC stock distribution in different soil depths according to both FD and ESM, but no significant changes were observed when soil sampling deeper than 50 cm. Notably, the results illustrated that the use of FD overestimated the SOC stock in the soil surface due to the increased bulk density under NT, but underestimated the SOC stock in 30–70 cm soil layers compared to ESM. Consequently, the overestimate of SOC based on FD may not be as high as previously assumed across the sampling depth. Mean annual temperature and mean annual rainfall did not lead to any significant changes in SOC stock of the overall soil profile or different layers, indicating the limited impacts of climatic conditions on carbon sequestration after the adoption of NT. Long-term NT resulted in a significant accumulation in SOC stock in the top 5 cm of the surface soil, but SOC stock changes with time in soil layers deeper than 5 cm was not significant in both FD and ESM approaches. The overall changes in SOC stock as time increased were not significant across the whole profile based on FD, but was weakly significant based on ESM. The results illustrate that using the overall SOC stock change of the whole sampling soil profile deeper than 30 cm masks the beneficial change in SOC stock in the 0–5 cm soil over long-term adoption of NT. The biased interpretation of depth factor is the main reason that fuels the debate over whether long-term NT is beneficial for the accumulation of SOC stock, and hence must be properly considered. Overall, the data illustrate that the depth factor is not only important in terms of assessing total SOC stock changes under NT, but also critical when evaluating the effectiveness of FD and ESM, as well as the impact of experimental duration on SOC increase. The depth factor should thus be comprehensively considered in SOC stock assessment and more field experiments sampling to deeper depth are urgently needed to clarify whether NT is beneficial for SOC sequestration and climate mitigation.

ЗАПАСЫ ОРГАНИЧЕСКОГО УГЛЕРОДА В ЛЕСНЫХ ПОЧВАХ СЕВЕРНОЙ МОНГОЛИИ

Soil organic carbon (SOC) stock and its variation on the regional and large spatial scales are critical for estimating the global SOC inventory and predicting further changes. This study was aimed at estimation of the SOC stock in the boreal forests of Northern Mongolia. The study was carried out in the forested areas of the Bulgan, Selenge and Tuv provinces using a completely randomized design. A total of 900 soil samples from 60 sampling points were col-lected for the laboratory analyses. At each point, a soil profile with a depth of more than 30 cm was laid out, and then soil samples were taken from three soil layers: 0–5, 5–15, and 15–30 cm of each profile. Therefore, the results of the assessment show a high difference of the SOC stock not only between the provinces, but also within each province. The higher SOC stocks were observed in the Selenge (123.5±14.85 t/ha), and lowest in the Tuv (51.23±7.8 t/ha) prov-inces. The estimated SOC stock in the studied regions was 93.77 t/ha on average. We found relatively less SOC stock in the boreal forests of Mongolia compared with the Asian part of Russia including Siberia and the Russian Far East. Such a less SOC stock may be caused by geographical distribution, where the Mongolian forests border the Central Asian dry steppe and frequent water deficit. However, we found that the accumulation of SOC stocks in the boreal forests of Mongolia is largely dependent on the mountain slopes and aspects combined with the distribution of precipitation across the country. Higher amount of SOC stocks were found in north-facing aspects and lower positions with low slopes of the mountains in lower altitude. Consequently, moisture supply basically determines the pattern of the SOC stock distribution in the northern boreal forests of Mongolia. For citation: Tungalag М., Gerelbaatar S., Lobanov A.I. Organic Carbon Stocks in the For-est Soils of Northern Mongolia. Lesnoy Zhurnal [Russian Forestry Journal], 2020, no. 2, pp. 169–176. DOI: 10.37482/0536-1036-2020-2-169-176

Impacts of urbanization on soil organic carbon stocks in the northeast coastal agricultural areas of China

Dynamic changes in soil organic carbon pools have significant impacts on regional and global carbon balance. Due to rapid development in urbanized areas, the land use changes dramatically, impacting soil organic carbon (SOC) stocks in topsoil. This study aimed to document the impacts of urbanization on SOC stocks in a rapidly urbanized area from northeastern China. A total of 12 auxiliary variables were as SOC predictors including elevation, slope aspect, slope gradient, topographic wetness index, Landsat TM band3, Landsat TM band4, Landsat TM5, and normalized difference vegetation index. Urban-specific variables including population (POP), gross domestic product (GDP), distance to the socio-economic center, and distance to the roads are also considered. A set of 523 (in 1990) and 847 (in 2015) top soil samples with SOC measurement were collected. Two random forest (RF) models, one with all auxiliary variables except urban-specific variable (MA) and the other with all auxiliary variables (MB) were used to map the spatial distribution of SOC stocks in the two periods. Ten-fold cross-validation was conducted to evaluate the performance of RF models. We find that the full auxiliary variables model had a better performance for the both periods. POP and GDP were key auxiliary variables affecting spatial variability of SOC stocks in 2015. Over a 25-year period, SOC stocks decreased from 2.77 ± 1.09 kg m−2 to 2.16 ± 0.93 kg m−2, resulting in 3.78 Tg SOC loss in this region. Rapid urbanization led to drastic land- use change, which was the main reason for the decrease of SOC stocks. Additionally, urban-specific variables should be used as the main auxiliary variables when predicting SOC stocks in the areas that experience rapid urbanization. We believe that accurate prediction and mapping of SOC stocks will help manage land use and facilitate soil quality assessment so as to increase soil carbon sequestration in the region.

Assessing countrywide soil organic carbon stock using hybrid machine learning modelling and legacy soil data in Cameroon

Countrywide estimates of soil organic carbon stock (SOCS) are useful to set up national strategies for sustainable land use management as well as to enhance the accuracy of global SOCS inventories. We appraised the spatial distribution of SOCS at five depth layers (0–15 cm, 15–30 cm, 30–100 cm, 0–30 cm and 0–100 cm) in Cameroon at 100 m spatial resolution, using a national harmonized legacy soil database (Camsodat 0.1) with 1432 georeferenced soil profiles. We assessed the prediction performances of random forest (RF) and generalized boosted regression (GBR), combined with two hybridization approaches of spatial interpolation of the residuals using ordinary kriging (OK) and inverse distance weighting (IDW). The estimates were compared to two global estimates derived from the Harmonized World Soil Database (HWSD) and SoilGrids250m. The SOCS distribution across the country showed a moderate spatial heterogeneity at all depth layers with coefficients of variation between 35% and 47%, and values ranging from 6 to 108 Mg C ha−1 at 0–15 cm, from 4 to 107 Mg C ha−1 at 15–30 cm, from 10 to 276 Mg C ha−1 at 30–100 cm, from 11 to 210 Mg C ha−1 at 0–30 cm and from 21 to 468 Mg C ha−1 at the 0–100 cm layer. Of the selected environmental covariates, terrain and climate attributes were the most relevant to predict the SOCS spatial distribution at country level. The RF model outperformed the GBR model, with about 10% improvement on prediction performance (R2) for most soil depths. The hybridization further slightly improved performance. However, OK was only slightly better than IDW in the overall assessment. Compared to national estimates, SoilGrids overestimated the SOCS by 15% at 0–30 cm depth, while HWSD underestimated SOCS by 26% at the same depth. Overall, about 5.7 Pg C are stored in the top 1 m of soils in Cameroon, with about 50% of that in the top 30 cm. The national distribution of SOCS is consistent with the pattern of agro-ecological zones. Our assessment provides baseline information for sustainable land management and climate change mitigation, as well as for improving the understanding of the spatial distribution of SOCS in Cameroon.

Spatial prediction of soil organic carbon stocks in Ghana using legacy data

Soil organic carbon (SOC) is a major driver of the multiple functions of soils in the delivery of ecosystem services. While the spatial prediction and mapping of SOC stocks are of key importance for land managers to understand the synergies between SOC management and soil health, a spatially-explicit map of the distribution of SOC stocks in Ghana is non-existent. Therefore, we quantified the spatial distribution of SOC stocks and associated uncertainties to a target depth of 0–30 cm based on regression-kriging modelling to fill this knowledge gap. The mean error (ME) of the predictions is negligible. The mean absolute error (MAE) shows that the model has prediction errors of about 0.48%. The coefficient of determination (R2) shows that the model explains 34% of the variation in model predictions of SOC stocks. The RMSE is 0.63% of the prediction errors. The predicted SOC stocks show significant variation in their spatial distribution throughout the country. Generally, a trend of decreasing SOC stocks from the southwest to the northeast is clearly recognized. SOC stocks are highest in the Semi-Deciduous agro-ecological zone (43.5 Mg C ha−1) and lowest in the Guinea Savannah agro-ecological zone (0.05 Mg C ha−1). About 5.4 Tg of SOC stocks is stored in the top 0–30 cm of the soils in Ghana. This preliminary work at a spatial resolution of 30 arc-seconds (~1 km) has been accomplished within the framework and guidelines of the Global Soil Partnership (GSP). To our knowledge, this is the first time ever in Ghana that a soil property map has been produced along with its uncertainties. Thus, this study represents a significant first step towards revolutionizing future soil property mapping in Ghana. Even though there remains the need to improve on the quality and spatial resolution, the SOC stocks map presented herein is a satisfactory first step to guide future research on soil organic carbon management at both national and global scales.

Short-range-order minerals as powerful factors explaining deep soil organic carbon stock distribution: the case of a coffee agroforestry plantation on Andosols in Costa Rica

Abstract. Soil organic carbon (SOC) constitutes the largest terrestrial C stock, particularly in the Andosols of volcanic areas. Quantitative information on distribution of SOC stocks is needed to construct a baseline for studying temporal changes in SOC. The spatial variation of soil short-range-order minerals such as allophane usually explains the variability of topsoil SOC contents, but SOC data for deeper soil layers are needed. We found that within a 1 km2 Costa Rican basin covered by coffee agroforestry, SOC stocks in the upper 200 cm of soil were highly variable (24 to 72 kg C m−2). Topsoil SOC stocks were not correlated with SOC stocks present in deeper layers. Diffuse-reflectance mid-infrared (MIR) spectroscopy made possible the analysis of a large number of samples (69 soil profiles, i.e. 598 soil samples) for ammonium-oxalate and sodium-pyrophosphate-extractable forms of Al, Fe, and Si, as well as SOC content and bulk density. Using the MIR spectra, we identified two different soil materials, which were identified as allophanic and halloysitic soil material. Allophanic soil occurred on top of the halloysitic soil. The thickness of the allophanic soil material, rich in SRO minerals and related to a young andic A horizon, explained the variability of SOC. This study illustrates that knowledge of topography and pedogenesis is needed to understand and extrapolate the distribution of SOC stocks at landscape scales.

Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China

Forest soil organic carbon (SOC) accounts for a large portion of global soil carbon stocks. Accurately mapping forest SOC stocks is a necessity for quantifying forest carbon cycling and forest soil sustainable management. In this study, we used a boosted regression trees (BRT) model to predict the spatial distribution of SOC stocks during two time periods (1990 and 2015) and calculated their spatiotemporal changes during 25 years in Liaoning Province, China. A total of 367 (1990) and 539 (2015) sampling sites and 9 environmental variables (climate, topography, remote sensing) were used in the BRT model. The ten-fold cross-validation technique was used to evaluate the prediction performance and uncertainty of the BRT model in two periods. It was found that the BRT model could account for 65% and 59% of SOC stocks, respectively for the two periods. MAP and NDVI were the main environmental variables controlling the spatial variability of SOC stocks. Over the 25-year period, the average SOC stocks increased from 5.66 to 6.61 kg m−2. In the whole study area, the SOC stocks were the highest in the northeast, followed by the southwest, and the lowest in the middle of the spatial distribution pattern in the two periods. Our accurate mapping of SOC stocks, their spatial distribution characteristics, influencing factors, and main controlling factors in forest areas will assist soil management and help assess environmental changes in the region.

Soil organic carbon stock on the Majorca Island: Temporal change in agricultural soil over the last 10 years

For quite a long time, soil organic carbon stock (SOCS) has reduced from reconverting forest areas into agricultural land or by inadequate agronomic practices. However in recent decades in Mediterranean areas, abandonment of agricultural areas due to lack of economic profits and to promoting tourism has fostered a change in land use that has impacted soil carbon. In line with this, the Majorca Island (Spain) could be a good study area to evaluate temporary changes in carbon stocks in soils in areas with a Mediterranean climate. The present study analysed the spatial distribution of SOCS and evaluated the temporal changes over 11 years in agricultural soils in relation to the influence of land use and land management’. The global carbon budget (estimated at 30 cm depth) on the Majorca Island was estimated at 31.23 Tg C, and showed wide spatial variability. As expected, SOCS was higher in the forest areas of the Tramuntana Mountain Range (>100 Mg ha−1), while the lowest contents were found in agricultural use (79.3 Mg ha−1), located mainly in the centre and south of this island.The total C stored from 2006 to 2017 increased by 2.62 Tg C (15%) in agricultural areas on the island. We noted a major increase in SOCS in the agricultural zones on mountain slopes (>2-fold higher), associated with abandoned crops in terrace cultivation, but grassland systems also increased. Nonetheless, the present study shows a sharp drop in SOCS in the centre and south of this island. This decrease was more pronounced in annual crops (−14.5%), which could be attributed to intensive soil management and increased irrigated land. Land abandonment has been indicated as the main potential to carbon sequestration in soil, but this potential for carbon sequestration is a finite process. Certain changes in agricultural practices on Majorca, and in rural Mediterranean areas in general, are necessary to avoid carbon loss in cropland soils. Adoption of conservation agriculture practices, e.g. cover crops, crop rotation, organic additions and reduced tillage techniques, can help increase SOC levels in rural Mediterranean areas.

Spatial distribution of soil organic carbon stocks in Masson pine (Pinus massoniana) forests in subtropical China

Masson pine (Pinus massoniana) is a typical reforestation species in subtropical China, which plays a key role in soil and water conservation. Site-specific forest management requires an accurate estimation of the soil organic carbon (SOC) stocks, and information about the spatial distribution of SOC stocks is essential for improving the soil quality and ecosystem productivity. We examined the spatial distribution of SOC stocks using 91 soil samples from Masson pine forests in subtropical China. Ordinary kriging (OK) and inverse distance weighting (IDW) methods were used to compare the spatial patterns of the SOC stocks. A moderate spatial dependence of the SOC stocks suggested that extrinsic and intrinsic factors affected the SOC stocks. Similar spatial distributions but different cross-validation accuracies indicated that OK outperformed IDW. The soil pools at a depth of 0–60 cm were 774.06 Gg and 761.61 Gg as determined by the OK and IDW methods, respectively, which were higher than that measured by the conventional method (CM, 734.22 Gg). This highlights the need to apply different methods when studying the regional SOC pools. On the basis of the comparison of OK, IDW, and CM methods, OK is recommended for determining nonhomogeneous sampling point distributions. The present results enhance our understanding of method selection when studying the spatial distribution of SOC stocks.

Agricultural soil organic carbon stocks in the north-eastern Iberian Peninsula: Drivers and spatial variability

Estimating soil organic carbon (SOC) stocks under agriculture, assessing the importance of their drivers and understanding the spatial distribution of SOC stocks are crucial to predicting possible future SOC stocks scenarios under climate change conditions and to designing appropriate mitigation and adaptation strategies. This study characterized and modelled SOC stocks at two soil depth intervals, topsoil (0–30 cm) and subsoil (30–100 cm), based on both legacy and recent data from 7245 agricultural soil profiles and using environmental drivers (climate, agricultural practices and soil properties) for agricultural soils in Catalonia (NE Spain). Generalized Least Square (GLS) and Geographical Weighted Regression (GWR) were used as modelling approaches to: (i) assess the main SOC stock drivers and their effects on SOC stocks; (ii) analyse spatial variability of SOC stocks and their relationships with the main drivers; and (iii) predict and map SOC stocks at the regional scale. While topsoil variation of SOC stocks depended mainly on climate, soil texture and agricultural variables, subsoil SOC stocks changes depended mainly on soil attributes such us soil texture, clay content, soil type or depth to bedrock. The GWR model revealed that the relationship between SOC stocks and drivers varied spatially. Finally, the study was only able to predict and map topsoil SOC stocks at the regional scale, because controlling factors of SOC stocks at the subsoil level were largely unavailable for digital mapping. According to the resulting map, the mean SOC stock value for Catalan agriculture at the topsoil level was 4.88 ± 0.89 kg/m2 and the total magnitude of the carbon pool in agricultural soils of Catalonia up to 30 cm reached 47.9 Tg. The present study findings are useful for defining carbon sequestration strategies at the regional scale related with agricultural land use changes and agricultural management practices in a context of climate change.

Large, climate-sensitive soil carbon stocks mapped with pedology-informed machine learning in the North Pacific coastal temperate rainforest

Accurate soil organic carbon (SOC) maps are needed to predict the terrestrial SOC feedback to climate change, one of the largest remaining uncertainties in Earth system modeling. Over the last decade, global scale models have produced varied predictions of the size and distribution of SOC stocks, ranging from 1000 to >3000 Pg of C within the top 1 m. Regional assessments may help validate or improve global maps because they can examine landscape controls on SOC stocks and offer a tractable means to retain regionally-specific information, such as soil taxonomy, during database creation and modeling. We compile a new transboundary SOC stock database for coastal watersheds of the North Pacific coastal temperate rainforest, using soil classification data to guide gap-filling and machine learning approaches to explore spatial controls on SOC and predict regional stocks. Precipitation and topographic attributes controlling soil wetness were found to be the dominant controls of SOC, underscoring the dependence of C accumulation on high soil moisture. The random forest model predicted stocks of 4.5 Pg C (to 1 m) for the study region, 22% of which was stored in organic soil layers. Calculated stocks of 228 ± 111 Mg C ha−1 fell within ranges of several past regional studies and indicate 11–33 Pg C may be stored across temperate rainforest soils globally. Predictions compared very favorably to regionalized estimates from two spatially-explicit global products (Pearson’s correlation: ρ = 0.73 versus 0.34). Notably, SoilGrids 250 m was an outlier for estimates of total SOC, predicting 4-fold higher stocks (18 Pg C) and indicating bias in this global product for the soils of the temperate rainforest. In sum our study demonstrates that CTR ecosystems represent a moisture-dependent hotspot for SOC storage at mid-latitudes.

National soil organic carbon estimates can improve global estimates

Global inventories of spatial and vertical distribution of soil organic carbon (SOC) stocks are being used in national and global initiatives targeted to mitigate climate change and land degradation impacts. Yet, national level high-resolution estimates of SOC stocks can be useful for improving the accuracy of global SOC inventories. We estimated spatially resolved SOC stocks of surface 0–30 cm and subsurface 30–100 cm layers at a spatial resolution of 30 m in tropical Island, Sri Lanka using a legacy harmonized soil database of 122 soil profiles. The national estimates were compared with two global estimates derived from WISE30sec and SoilGrids250m. The tropical Island (land area = 64,610 km2) occupying 0.03% of global land area showed a considerable heterogeneity in SOC stocks ranging from 2.0–342.5 Mg ha−1 and 2.7–391.7 Mg ha−1 in the surface and subsurface soil layers, respectively. We found, elevation, precipitation and slope angle as main environmental controllers of the spatial distribution of SOC stocks under tropical climate. Incorporating the pedogenic information (derived from soil series level legacy map, soil orders and suborders) with environmental controllers resulted in better regression models of predicting surface (R2 = 0.61) and subsurface (R2 = 0.81) SOC stocks. Geographically weighted regression kriging derived maps of SOC stocks revealed that 0–100 cm soil layer of the tropical Island stored 500 Tg C contributing for 0.04% of the global SOC stocks. The validation results of our estimates showed low Mean Estimation Error (MEE: surface −1.6 and subsurface −1.6 Mg ha−1) and Root Mean Square Error (RMSE: surface 29.5 and subsurface 24.9 Mg ha−1) indicating a low bias and satisfactory predictions. The relative improvement of the prediction accuracy of the SOC stocks of our geospatial estimates in the 0–30 cm layer in comparison to SoilGrids250m and WISE30sec data derived SOC stocks were 51.7% and 35.2%, respectively. The SOC stocks predictions of the 30–100 cm soil layer showed even better relative improvement compared to SoilGrids250m (78.4%) and WISE30sec (57.4%) SOC estimates. Compared to estimates of total SOC stocks resulted in this study, WISE30sec data derived SOC stock maps showed 30% over estimation of the C stock in surface 0–30 cm (332 Tg C) and 41% overestimation in 30–100 cm layer (343 Tg C). The over estimation of total SOC stocks by the SoilGrids250 SOC stocks map for the surface 0–30 cm layer was 122% (567 Tg C) and for the 30–100 cm layer it was 209% (750 Tg C). We conclude that the fusion of legacy soil information of SOC stocks with appropriate environmental covariates and pedogenic information derived from legacy area-class soil maps at national level can produce more accurate inventories of spatial and vertical distribution of SOC stocks. These national inventories have a great potential of upgrading global inventories of SOC stocks.

High organic inputs explain shallow and deep SOC storage in a long-term agroforestry system – combining experimental and modeling approaches

Abstract. Agroforestry is an increasingly popular farming system enabling agricultural diversification and providing several ecosystem services. In agroforestry systems, soil organic carbon (SOC) stocks are generally increased, but it is difficult to disentangle the different factors responsible for this storage. Organic carbon (OC) inputs to the soil may be larger, but SOC decomposition rates may be modified owing to microclimate, physical protection, or priming effect from roots, especially at depth. We used an 18-year-old silvoarable system associating hybrid walnut trees (Juglans regia × nigra) and durum wheat (Triticum turgidum L. subsp. durum) and an adjacent agricultural control plot to quantify all OC inputs to the soil – leaf litter, tree fine root senescence, crop residues, and tree row herbaceous vegetation – and measured SOC stocks down to 2 m of depth at varying distances from the trees. We then proposed a model that simulates SOC dynamics in agroforestry accounting for both the whole soil profile and the lateral spatial heterogeneity. The model was calibrated to the control plot only. Measured OC inputs to soil were increased by about 40 % (+ 1.11 t C ha−1 yr−1) down to 2 m of depth in the agroforestry plot compared to the control, resulting in an additional SOC stock of 6.3 t C ha−1 down to 1 m of depth. However, most of the SOC storage occurred in the first 30 cm of soil and in the tree rows. The model was strongly validated, properly describing the measured SOC stocks and distribution with depth in agroforestry tree rows and alleys. It showed that the increased inputs of fresh biomass to soil explained the observed additional SOC storage in the agroforestry plot. Moreover, only a priming effect variant of the model was able to capture the depth distribution of SOC stocks, suggesting the priming effect as a possible mechanism driving deep SOC dynamics. This result questions the potential of soils to store large amounts of carbon, especially at depth. Deep-rooted trees modify OC inputs to soil, a process that deserves further study given its potential effects on SOC dynamics.

Influence of altitude on soil organic carbon stocks in land use systems of Uttarakhand State: Geospatial approach

The Soil Organic Carbon (SOC) stock of soil plays an important role in mitigating the effect of climate change. The soils capacity to the atmospheric carbon depends upon climate, land use and altitude of area. The present study was conducted to estimate the SOC stocks (0–25 cm soil) along the altitudinal gradient viz., low mountain ( 2500m) in associated forest, agricultural, plantations and grass land/open scrub land uses in Uttarakhand state using geospatial technique. Altitudinal zone had observed to have strong influence on the distribution of SOC stock in the state. Study confirms that upper mountain areas hold SOC stocks ranging from 51to 160 t C ha−1. This can be attributed to the fact that whole of the studied area in upper mountains falls under forests and grasslands/open scrub. In general, forest and grassland/open scrub had recorded SOC stock >51 t C ha−1 (72.7% of forest area and 77.7% area under grassland/open scrub) as compared to agriculture (18.6% of agriculture land).

Prediction of spatial soil organic carbon distribution using Sentinel-2A and field inventory data in Sariska Tiger Reserve

Dynamic and vigorous top soil is the source for healthy flora, fauna, and humans, and soil organic matters are the underpinning for healthy and productive soils. Organic components in the soil play significant role in stimulating soil productivity processes and vegetation development. This article deals with the scientific demand for estimating soil organic carbon (SOC) in forest using geospatial techniques. We assessed distribution of SOC using field and satellite data in Sariska Tiger Reserve located in the Aravalli Hill Range, India. This study utilized the visible and near-infrared reflectance data of Sentinel-2A satellite. Three predictor variables namely Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Renormalized Difference Vegetation Index were derived to examine the relationship between soil and SOC and to identify the biophysical characteristic of soil. Relationship between SOC (ground and predicted) and leaf area index (LAI) measured through satellite data was examined through regression analysis. Coefficient of correlation (R 2) was found to be 0.95 (p value < 0.05) for predicted SOC and satellite measured LAI. Thus, LAI can effectively be used for extracting SOC using remote sensing data. Soil organic carbon stock map generated through Kriging model for Landsat 8 OLI data demonstrated variation in spatial SOC stocks distribution. The model with 89% accuracy has proved to be an effective tool for predicting spatial distribution of SOC stocks in the study area. Thus, optical remote sensing data have immense potential for predicting SOC at larger scale.

The sequestration and turnover of soil organic carbon in subtropical leucaena-grass pastures

Abstract The sequestration of soil organic carbon (SOC) in agricultural systems has the potential to partially mitigate climate change by removing carbon dioxide (CO2) from the atmosphere. Furthermore, incorporating forage legumes into a grazing system may aid in C storage, as mature unfertilized pasture soils are typically nitrogen (N) limited. Leucaena leucocephala, a leguminous shrub, is often incorporated into grazing systems to provide a source of protein for cattle production. Additionally, leucaena improves the N fertility of grazing lands. Despite this, there is limited research on the C and N dynamics beneath leucaena-grass pastures, particularly at depth in the soil profile (1 m). We assessed the potential of leucaena to sequester SOC by estimating the origin (C3 or C4), quantity and vertical distribution of SOC stocks. A chronosequence of seasonally grazed leucaena stands (0, 9, 22, 34, 40 years) were sampled for organic C and natural δ13C abundance. Pasture type affected SOC stocks within the upper 0.2 m (grass C signature. Hence, SOC sequestration primarily occurred due to N accretion by leucaena (36 kg N ha−1 year−1) contributing to the increased C4 grass productivity, humus formation and slowed SOC decomposition. Therefore, there is a need to maintain soil fertility in grazed pastures to ensure SOC stocks are accreted and conserved.

Spatial distribution of soil organic carbon stock in Moso bamboo forests in subtropical China

Moso bamboo (Phyllostachys heterocycla (Carr.) Mitford cv. Pubescens) is an important timber substitute in China. Site specific stand management requires an accurate estimate of soil organic carbon (SOC) stock for maintaining stand productivity and understanding global carbon cycling. This study compared ordinary kriging (OK) and inverse distance weighting (IDW) approaches to study the spatial distribution of SOC stock within 0–60 cm using 111 soil samples in Moso bamboo forests in subtropical China. Similar spatial patterns but different spatial distribution ranges of SOC stock from OK and IDW highlighted the necessity to apply different approaches to obtain accurate and consistent results of SOC stock distribution. Different spatial patterns of SOC stock suggested the use of different fertilization treatments in Moso bamboo forests across the study area. SOC pool within 0–60 cm was 6.46 and 6.22 Tg for OK and IDW; results which were lower than that of conventional approach (CA, 7.41 Tg). CA is not recommended unless coordinates of the sampling locations are missing and the spatial patterns of SOC stock are not required. OK is recommended for the uneven distribution of sampling locations. Our results can improve methodology selection for investigating spatial distribution of SOC stock in Moso bamboo forests.