Soil Organic Carbon Density Research Articles

Evaluation of soil properties and bulk δ15N to assess decadal changes in floodplain denitrification following restoration

Stream and floodplain restoration is a popular billion‐dollar industry in the United States, with many restorations being conducted to satisfy water pollution regulations and nutrient reduction goals. The long‐term efficacy of these restorations is, however, not well studied, and key soil metrics that can be used for performance assessments have not been developed. We evaluated a chronosequence of 12 restoration sites spanning an age range of 0–22 years to assess changes in denitrification rates and associated soil parameters. Restored versus unrestored reaches were compared for denitrification rate and functional gene nosZ, bulk soil δ15N, soil organic carbon (SOC), soil organic matter (SOM), bulk density, and soil moisture. Denitrification, SOM, SOC, and soil moisture were all found to increase with site age at restored sites, with the largest increase for the 10–22 age category. Bulk density decreased with time, with a significant decrease in restored floodplain soils. Bulk soil δ15N was highest immediately after restoration, decreased with restoration age, and was not positively correlated with denitrification. This may reduce its potential as a proxy for denitrification. Overall, this study reveals that selected soil metrics (SOC, SOM, soil moisture, and bulk density) could serve as a valuable proxy for denitrification and could help assess the denitrification effectiveness of floodplain restorations at the decadal time scales. Ideally, the soil metrics should be combined with other short‐term assessment measures, such as those for stream and groundwaters, for a robust performance assessment of restored floodplains.

Dynamic Changes in and Driving Factors of Soil Organic Carbon in China from 2001 to 2020

It remains unclear what changes have occurred in the distribution pattern of and trend in soil organic carbon (SOC) in China against the background of climate and land use change. Clarifying the dynamic changes in SOC and their driving factors in different regions of China is therefore crucial for assessing the global carbon cycle. In this study, we collected and supplemented a large amount of soil organic carbon density (SOCD) data in China from 2001 to 2020 and extracted data on environmental covariates (ECs) for the corresponding years. A random forest model was used to estimate the SOCD at a depth of 0–20 cm and 0–100 cm in China for the years 2001, 2005, 2010, 2015, and 2020, and we explored the trend of SOCD changes and their key driving factors. The results showed the following: (1) Compared with previous studies, the predictive ability of the 0–100 cm depth model was greatly improved; the coefficient of determination (R2) was 0.61 and Lin’s concordance correlation coefficient (LCCC) was =0.76. (2) From 2001 to 2020, China’s soil organic carbon stocks (SOCS) were 38.11, 39.11, 39.88, 40.16, and 41.12 Pg C for the 0–20 cm depth and 110.49, 112.67, 112.80, 113.06, and 114.96 Pg C for the 0–100 cm depth, respectively. (3) The effects of temperature and precipitation on SOCD in China showed obvious regional variability, and land use changes had mainly positive effects on SOCD in all regions of China, which was related to the large-scale implementation of ecological protection and restoration and the policy of returning farmland to forests and grasslands in China. This study provides strong scientific support for addressing climate change and rationalizing the use of land resources.

Including soil spatial neighbor information for digital soil mapping

Digital soil mapping (DSM) is transforming how we understand and manage soil resources, offering high-resolution spatial–temporal soil information essential for addressing environmental challenges. The integration of environmental covariates has advanced soil mapping accuracy, while the potential of neighboring soil sample data has been largely overlooked. This study introduces soil spatial neighbor information (SSNI) as a novel approach to enhance the predictive power of spatial models. Utilizing two open-access datasets from LUCAS Soil and Meuse, our findings showed that incorporating SSNI improved the accuracy of random forest models in mapping soil organic carbon density (reduced %RMSE of 3.1%), cadmium (reduced %RMSE of 3.6%), copper (reduced %RMSE of 5.9%), lead (reduced %RMSE of 11.5%), and zinc (reduced %RMSE of 7.4%). Compared to the inclusion of buffer distance or oblique geographic coordinates for modelling, SSNI also performed better for both LUCAS Soil and Meuse datasets. This study underscores the value of SSNI in improving digital soil maps by capturing the neighboring information. Embracing SSNI could lead to more informed decision-making in soil management and its potential applicability across other disciplines also remains open for exploration in future research endeavors.

Spatial distribution characteristics and influencing factors of soil organic carbon based on the geographically weighted regression model.

Quantifying the effects of environmental factors on soil organic carbon and spatial distribution is fundamental to soil quality regulation, restoration, and response to climate change. The present study aims to explore the spatial distribution characteristics of the soil organic carbon (SOC) contents in Anhui Province, China, based on national soil data. In addition, we used the geographically weighted regression (GWR) model to quantify the influence degrees of environmental factors on the soil organic carbon density (SOCD). The results showed that the spatial distribution of SOCD in Anhui Province in both 1985 and 2018 was characterized by high in the south and low in the north. The GWR model prediction results of the 0-30cm SOCD showed local coefficients of determination (local R2) ranging from 0.21 to 0.96 and 0.14 to 0.96 in 1985 and 2018, respectively. Therefore, the predicted results were effective in evaluating the overall spatial distribution of the SOCD in Anhui Province. The regression coefficients of the normalized difference vegetation index (NDVI) and air temperature ranged from - 0.39 to 5.67 and - 0.17 to 3.11, respectively, demonstrating their strong controlling effects on the spatiotemporal variations in the 0-30cm SOCD in Anhui Province.

Effect of 67-years of manuring, fertilization and amendments on fractions of soil organic carbon, nutrient dynamics and yield sustainability in an acidic Alfisol

Amending a problem soil in addition to nutrient input is crucial for maintaining a healthy and productive soil in the long run. The study aimed at evaluating the long-term impact of manuring and fertilization with or without liming on different soil attributes and crop yield in an acidic Alfisol. Surface soil samples (0–15 cm) were collected from selected 7 treatments, viz. Control; N; FYM; ½(N+FYM)+PX/2+KY/2; NPK; NPK+L; and P(A-X)K(B-Y)+FYM+L of the Permanent Manurial Trial (PMT) of Birsa Agricultural University, Ranchi, after the harvesting of rainy (kharif) maize (Zea mays L.) at the 67th crop cycle (2023 and 2024) and assessed for various fractions of soil organic carbon (SOC), available nutrients and yield attributes. The experiment was laid out in a randomized block design (RBD) and replicated thrice. The treatments which received manures either alone or in combination with chemical fertilizers recorded significantly higher amount of all the fractions of SOC, available nitrogen and lower bulk density as compare to the unfertilized plot or treatment with inorganic alone. The study also revealed that, addition of lime helped maintaining an optimum pH level, enhanced microbial activities and nutrient availability in the soil leading to a higher crop yield. Therefore, integrated use of manures and fertilizers in addition to lime for a prolonged period would have significant positive impact on soil health and sustaining its productivity.

Random forest regression kriging modeling for soil organic carbon density estimation using multi-source environmental data in central Vietnamese forests

Random forest regression kriging modeling for soil organic carbon density estimation using multi-source environmental data in central Vietnamese forests

Differential responses of soil CO2 dynamics along soil depth to rainfall patterns in the Chinese Loess Plateau

Soil surface carbon dioxide (CO2) efflux not only originates from topsoils, but also significantly involves contributions from deeper soil layers. Soil surface CO2 efflux significantly fluctuated with rainfall patterns in arid and semiarid regions. However, how soil CO2 dynamics respond at different soil depths to varying rainfall patterns remains largely unclear. To address this gap, we continuously monitored soil CO2 concentrations, temperature, and moisture content at 10 cm, 50 cm, and 100 cm depths in situ under cropland and orchards located in the semiarid Loess Plateau over a full year. Rainfall events were meticulously recorded, categorizing them into light (<10 mm), moderate (10 mm–40 mm), and heavy (>40 mm) to discern their impact on soil CO2 dynamics. Specifically, soil CO2 flux was not affected during light rainfall. Moderate and heavy rainfall decreased soil CO2 flux at 0–10 cm by an average of 70% and 83%, respectively. This decrease was associated with reduced gas diffusivity across rainfall patterns. For instance, heavy rainfall reduced gas diffusivity by an average of 83% and 53% at 10 cm and 50 cm soil depths, respectively. Furthermore, soil CO2 concentrations slightly dropped as soil temperature decreased at 10 cm depth during light rainfall. Soil CO2 concentrations at 10 cm and 50 cm depths initially decreased by up to 15% and subsequently increasing by up to 52% during moderate and heavy rainfall. This response was likely influenced by temperature reductions and subsequent rises in moisture content, with a hysteretic response of soil CO2 concentrations to temperature. The rapid increase in soil CO2 concentrations was mainly due to a substantial decrease in gas diffusivity. Notably, heavy rainfall induced a delayed increase in soil moisture content at 50 cm depth and a significant decrease in CO2 concentration by 16% at 100 cm depth. A substantial decrease in soil CO2 concentrations in deep soil layers was primarily related to decreased soil temperature. Additionally, the observed soil CO2 dynamics were partly attributed to biotic factors (microbial biomass carbon and root density) mainly on cropland, but mainly abiotic factors (soil organic carbon and bulk density) under orchards. Overall, these results suggest that reduced gas diffusivity triggered by increased soil moisture content in topsoils and weakened biological processes caused by decreased soil temperature in deep soils typically drive the differential responses of soil CO2 dynamics to rainfall patterns.

Spatial variation of soil organic carbon density in the black soil region of Northeast China under the influence of erosion and deposition

Soil erosion of the Songnen typical Black Soil Region (BSR) in Northeast China where contains higher soil organic carbon is serious, resulting in soil organic carbon spatial redistribution. To study the spatial redistribution pattern of cropland SOC caused by erosion, 115 cropland sample points were selected in the Songnen typical BSR, and soil samples were collected along the entire soil profile. The soil organic carbon density (SOCD) of the soil layer and profile were calculated, and the carbon storage (CS) was calculated for the erosion and deposition areas. The average soil organic carbon density (SOCDav) of the 115 cropland soil profiles (1 m) ranged from 1.5 to 4.5 kg/m2, and the total soil organic carbon density (SOCDt) of the soil profile (1 m) ranged from 10 to 20 kg/m2. The SOCD of each soil layer (20 cm) decreased vertically in a logarithmical manner from the top, and was divided into four groups with thickness through systematic clustering, which were 30–60 cm, 60–100 cm, less than 300 cm and greater than 350 cm. The SOCDt in the area north of the Songhua River and the area west of Nen River (The Greater Khingan Mountains area) ranged from 2.5 to 59.3 kg/m2, with an average is 14.1 kg/m2, and the first sample point group (30–60 cm) was mainly located in this area which had an average slope of 3.2° and an average black soil thickness of 24.6 cm. The SOCDt in the area north of the Songhua River and the area east of the Nen River ranged from 11 to 90 kg/m2, with an average of 28.6 kg/m2, the third and the fourth sample point group (greater than 100 cm) were both mainly located in this area which had an average slope of 2° and an average black soil thickness of 61 cm. The SOCDt in the area south of the Songhua River ranged from 1.6 to 28.7 kg/m2, with an average of 13.8 kg/m2, and the second sample point group (60–100 cm) was mainly located in this area which had an average slope of 2.4° and an average black soil thickness of 58.7 cm. The results showed that soil erosion occurred on steeper slopes, resulting in thinner soil layers, whereas soil deposition occurred on lower grade slopes, resulting in thicker soil layers. Kriging interpolation in ArcGIS showed that the Cs in the erosion and deposition areas of the Songnen typical BSR was 459.7 and 1234.7 Tg, respectively, and the total Cs was 1.6944 Tg (1 Tg = 1012 g).

Mapping soil organic carbon density via geographically weighted regression with smooth terms: A case study in Shanxi Province

Soil, as the largest organic carbon reservoir in terrestrial ecosystems, plays a crucial role in the global carbon cycle. Accurately predicting the spatial distribution of soil organic carbon density (SOCD) is highly important for estimating organic carbon stocks and guiding agricultural production. Due to the complex relationship between the spatial distribution of SOCD and environmental factors, its relationship with a specific region is not exclusively linear or nonlinear; both types of relationships may coexist simultaneously. In this study, a new model, geographically weighted regression with smooth terms (SGWR), was proposed. This model can simultaneously consider both global spatial nonlinear relationships and local spatial nonstationary relationships. It was utilized to predict SOCD in Shanxi Province and was compared to multiple linear regression (MLR), generalized additive models (GAM) and geographically weighted regression (GWR) in terms of spatial prediction performance. The results showed that SGWR was highly competitive in predicting the spatial distribution of SOCD in Shanxi Province. Of factors influencing SOCD in Shanxi Province, elevation and slope were considered local linear variables, while the normalized difference vegetation index (NDVI), gross primary productivity (GPP), annual precipitation (AP), and soil moisture (SM) were considered global nonlinear variables. Among these factors, the NDVI and GPP had the most significant impacts on the distribution of SOCD, while elevation and SM had the least significant impacts on it. The results imply that integrating global linear, local linear, and global nonlinear relationships concurrently, as implemented in SGWR, improves predictive accuracy compared to approaches that solely address either local or global linear relationships based on environmental variable changes in SGWR. The results of this study indicate that the SGWR model is potentially a competitive method for digital soil mapping.

A dataset of soil organic carbon density of typical ecosystems in the arid areas of Northwest China from 2005 to 2020

The soil organic carbon pool is the largest and most active part of the terrestrial ecosystem carbon pool, and plays an important role in the global carbon cycle and global climate change. The arid region of northwest China is located in the hinterland of Eurasia, with extremely fragile ecosystems and extremely sensitive responses to climate change. It is a hot area that many scholars have paid attention to in recent years, and the estimation and spatial distribution characteristics of soil organic carbon storage have increasingly become one of the research focuses in this region. However, insufficient data sharing and the relative lack of measured data are important factors causing the uncertainty of the research results. Based on the long-term soil observation data collected from the ecological field stations of China Ecosystem Research Network (CERN) located in five provinces and regions of northwest China, the soil organic carbon density dataset of typical ecosystems in the arid region of Northwest China was calculated by organic carbon estimation model. This dataset contains the soil organic carbon density data of topsoil (0-20 cm) and soil profile of various long-term observation plots at 11 ecological field stations from 2005 to 2020, in order to provide valuable measured data for the estimation of soil carbon storage and the study of carbon cycle process in the arid area of northwest China.

Hydrology, vegetation, and soil properties as key drivers of soil organic carbon in coastal wetlands: A high-resolution study

Coastal wetlands are important blue carbon ecosystems that play a significant role in the global carbon cycle. However, there is insufficient understanding of the variations in soil organic carbon (SOC) stocks and the mechanisms driving these ecosystems. Here we analyze a comprehensive multi-source dataset of SOC in topsoil (0–20 cm) and subsoil (20–100 cm) across 31 coastal wetlands in China to identify the factors influencing their distribution. Structural equation models (SEMs) reveal that hydrology has the greatest overall effect on SOC in both soil layers, followed by vegetation, soil properties, and climate. Notably, the mechanisms driving SOC density differ between the two layers. In topsoil, vegetation type and productivity directly impact carbon density as primary sources of carbon input, while hydrology, primarily through seawater salinity, exerts the largest indirect influence. Conversely, in subsoil, hydrology has the strongest direct effect on SOC, with seawater salinity also influencing SOC indirectly through soil and vegetation mediation. Soil properties, particularly pH, negatively affect carbon accumulation, while climate influences SOC indirectly via its effects on vegetation and soil, with a diminishing impact at greater depths. Using Random Forest, we generate high-resolution maps (90 m × 90 m) of topsoil and subsoil carbon density (R2 of 0.53 and 0.62, respectively), providing the most detailed spatial distribution of SOC in Chinese coastal wetlands to date. Based on these maps, we estimate that SOC storage to a depth of 1 m in Chinese coastal wetlands totals 74.58 ± 3.85 Tg C, with subsoil carbon storage being 2.5 times greater than that in topsoil. These findings provide important insights into mechanism on driving spatial pattern of blue carbon and effective ways to assess carbon status on a national scale, thus contributing to the advancement of global blue carbon monitoring and management.

Estimation of Soil Organic Carbon Density on the Qinghai–Tibet Plateau Using a Machine Learning Model Driven by Multisource Remote Sensing

Estimation of Soil Organic Carbon Density on the Qinghai–Tibet Plateau Using a Machine Learning Model Driven by Multisource Remote Sensing

Changes in Soil Organic Carbon Density and Its Response to Climate Change and Human Activities Before and After the Grain for Green Project on the Loess Plateau

Accurately assessing the changes in soil organic carbon storage （SOCS） before and after the Grain for Green Project （GFG） in the Loess Plateau （LP） and exploring the relationship between its spatial and temporal distribution and the influencing factors were important references for the development of regional recycling as well as the formulation of ecological protection policies. Based on the data of climate, human activities, and SOCD in the surface （0-20 cm） and deep （0-100 cm） soil before and after GFG in the LP from 2001 to 2020, we investigated the changes in SOCD at different spatial and temporal scales by using the methods of trend analysis, the kriging method, and variance partitioning analysis. The results showed that： ① Before and after the GFG, the surface SOCS of the whole region increased by 8 338.7×104 t； the deep SOCS increased by 1 160.02×104 t. ② In each bioclimatic subregion, the whole-region average SOCD of Ⅰ （Semi-Humid Forest Region）, Ⅱ （Semi-Humid Semi-Arid Forest and Grassland Region）, and Ⅲ （Semi-Arid Typical Grassland Region） showed a significant increasing trend, with a decreasing trend in Ⅳ （arid semi-arid desert grassland area） and Ⅴ （arid desert area）. ③ The average surface SOCS increase in different ecosystems was ranked as follows： cropland &gt; grassland &gt; woodland &gt; shrubs &gt; bare land and sparse vegetation. The deep soil increase was ranked as follows： grassland &gt; cropland &gt; woodland &gt; shrubs &gt; bare land and sparse vegetation. ④ Climate factors were the most important driving factors for changes in SOCD； the annual average temperature and precipitation were significantly positively correlated with changes in SOCD. The results of the study could provide data support for regional ecological management and land use policy formulation to promote high quality development of the ecological environment in the LP.

The impact of different grazing intensity and management measures on soil organic carbon density in Zhangye grassland

Studying the spatial and temporal changes of grassland soil organic carbon (SOC) is helpful in promote the management of regional ecosystem carbon sinks. Grazing is one of the main ways of rational utilization of grassland. Different grazing intensities will affect the change of SOC density. Under different grazing intensity and management measures in Zhangye grassland, this study uses the parameter localized CENTURY model to simulate the temporal and spatial variations of SOC density from 1970 to 2022. The results showed that long-term light grazing reduced the average SOC by 195.114 g·m−2 and 1.91%. Moderate and severe grazing, respectively, for a long time made the total SOC density loss of 5.21% and 17.69%. In a short period, mild and moderate grazing reduced total SOC first and then increased it. Under light grazing, total SOC density appeared higher relative changes in the southeast, and lower in the northwest and central. There was no significant difference in the relative changes of total SOC between steppe and desert grasslands under light grazing. The decrease range of steppe was gradually greater than that in desert grassland. Since different management measures were implemented in some sampling sites in 2017, we divided the study period into two periods, 1970–2016 and 2017–2022. The implementation of degraded grassland improvement, fallow grazing, and rotational grazing would increase the total SOC density and mild SOC density, rotational grazing > degraded grassland improvement > rest grazing. Rotational grazing and the improvement of degraded grassland increased the density of active and inert SOC, while resting grazing decreased the density of SOC.

Effects of fertile islands on soil organic carbon density and labile organic carbon distribution in elm (Ulmus macrocarpa Hance)-dominated sparse wood grasslands in southeastern Inner Mongolia, China

In semi-arid savanna grasslands, the sparse distribution of trees often forms what are commonly known as fertile islands. However, the effect of these trees on soil organic carbon (SOC), microbial biomass carbon (MBC), and dissolved organic carbon (DOC), remains poorly understood. In this study, the spatial distribution patterns of SOC, MBC, and DOC were investigated across soil profiles (0–5, 5–10, 10–30, 30–50, and 50–100 cm) at three distances from the tree trunk in an elm-dominated savanna ecosystem in Northeast China. Our results suggest that SOC, MBC, and DOC were significantly influenced by both the distance from the tree and the soil depth. The research provides evidence that elm trees can form fertile island effects, thereby enhancing the contents of SOC and labile organic carbon fractions.

Spatiotemporal variations and driving factors of farmland soil organic carbon in various landforms of a complex topography

Estimating the spatiotemporal variations and driving factors of farmland soil organic carbon density (SOCD) is of great significance for enhancing soil carbon sequestration capacity. Herein, a large region of complex topography was targeted, which includes hill–mountain, valley–basin, and plain–platform. Based on the massive amounts of sampling data (57,254 measured values) and a large-scale soil map of 1:10,000 (371,976 polygonal patches), the gravity center migration model and gray correlation model were used to quantify the spatiotemporal variations and driving factors of farmland SOCD. The results indicated that the farmland soils in the study area had dual functions of carbon source and sink during 1982–2018, of which 45.50 % and 54.50 % were identified as carbon source and sink, respectively. Specifically, the SOCD for the entire study area, its hill–mountain, and valley–basin increased from 2.79 kg m−2, 2.97 kg m−2, and 3.06 kg m−2 to 2.87 kg m−2, 3.06 kg m−2, and 3.14 kg m−2, respectively, with 0.08 kg m−2 carbon sequestrations and a northeast migration direction for the SOCD gravity center (angle: 21.94°, 23.56°, and 18.82°; distance: 1.56 km, 2.73 km, and 3.20 km). There was a smaller increase of 0.07 kg m−2 in SOCD for the plain–platform from 2.38 kg m−2 (1982) to 2.45 kg m−2 (2018), and the SOCD gravity center migrated to the southwest with an angle of −172.46° and a distance of 1.84 km. Thus, the spatiotemporal variations of farmland SOCD in various landforms varied greatly. Over the past 36 years, SOCD variations were driven by a combination of intrinsic soil factors and external factors such as human disturbance. However, the driving effects of these factors on the landforms of hill–mountain, valley–basin, and plain–platform were quite different in size and order. Therefore, we suggest that topography must be considered when formulating policies to improve soil carbon sequestration, and priority should be given to landform-specific SOCD variation and the factors contributing to them.

Spatial–Temporal Variations in Soil Organic Carbon and Driving Factors in Guangdong, China (2009–2023)

Spatial–temporal variation in soil organic carbon is an important factor for national targets to mitigate climate change and land degradation impacts. In this research, we took Guangdong Province of China as the study area, evaluated the spatial–temporal distributions of soil organic carbon using data from three China Geochemical Baseline projects (conducted in 2009, 2016, and 2023, respectively), and quantified the main driving factors of spatial–temporal variations in soil organic carbon using the random forest algorithm, further predicting the density and inventories of soil organic carbon. The results demonstrate that the mean value of SOC in Guangdong in 2009 was 0.81%; in 2016 it was 1.13%; and in 2023 it was 1.02%. The inventories of soil organic carbon (0–30 cm) in Guangdong Province were 0.61 Pg in 2009, 0.74 Pg in 2016, and 0.62 Pg in 2023. Soil in Guangdong acted as a carbon sink from 2009 to 2023 as a whole, and the most important driving force behind spatial–temporal variations in soil organic carbon was temperature, followed by precipitation and vegetation cover.

Conservation tillage enhances the sequestration and iron-mediated stabilization of aggregate-associated organic carbon in Mollisols

Conservation tillage practices, which involve minimal or no soil disturbance and crop residue retention, are known to help preserve soil organic carbon (SOC). However, the mechanisms underlying the minerals-mediated chemical and physical stabilization of SOC remain unclear. Here, a long-term field experiment was initiated in 2012 to investigate the effects of tillage managements on the contents and chemical composition of SOC density fractions, iron oxides transformation and aggregate stability in Mollisols. We utilized three treatments: conventional tillage (CT) without crop residue, reduced tillage (RT) and no tillage (NT) with straw mulching. Compared to CT, RT and NT significantly increased SOC content by 13.6 % and 17.9 % in the 0–20 cm soil layer due to an increase in the aromatic compound contents. Furthermore, NT and RT increased the mean weight diameter by 17.7 % and 10.7 %, respectively, indicating increased aggregate stability compared to CT. Additionally, the contents of amorphous iron oxides (Feo) and complex iron oxides (Fep) increased under NT and RT by 10.6–14.4 % and 12.7–41.1 %, respectively, in bulk soil and silt + clay fractions within macroaggregates (>0.25 mm). The contents of Feo and Fep were strongly positively correlated with aggregate stability (p < 0.001, r2 = 0.64), and promote the physical protection of SOC. Both NT and RT enhanced the aromatic-C content and aromatic-C/aliphatic-C ratio by 13.6 %–24.6 % and 16.5 %–38.9 % in macroaggregates compared with CT. Moreover, the aromatic-C/aliphatic-C ratio increased with increasing Feo plus Fep contents (p < 0.001, r2 = 0.73), which led to an increased in the recalcitrant-C proportion, and this shift was benefit for the accumulation of mineral-associated OC. These results indicate that long-term conservation tillage can augment the accessibility of Fe for binding C, possibly by forming organo-Fe complexes, which subsequently improve soil aggregation, and thus promoted the chemical stability and long-term sequestration of SOC in Mollisols of Northeast China.

Anthropogenic and climate impacts on carbon stocks of grassland ecosystems in Inner Mongolia and adjacent region

Up to date, most studies reported that degradation is worsened in the grassland ecosystems of Inner Mongolia and adjacent regions as a result of intensified grazing. This seems to be scientific when considering the total forage or total above-ground biomass as a degradation indicator, but it does not hold true in terms of soil organic carbon density (SOCD). In this study, we quantified the changes of grassland ecosystem carbon stock in Inner Mongolia and adjacent regions from the 1980s to 2000s and identified the major drivers influencing these variations, using the National Grassland Resource Inventory and Soil Survey Dataset in 1980s and the Inventory data during 2002 to 2009 covering 624 sampling plots concerned vegetal traits and edaphic properties across the study region. The result indicated that the above-, below-ground and total vegetation biomass declined from the 1980s to 2000s by ∼ 10 %. However, total forage production increased by 6.72 % when considering livestock intake. SOCD remained stable despite a 67 % increase in grazing intensity. A generalized linear model (GLIM) analysis suggested that an increase in grazing intensity from the 1980s to 2000s could only explain 1.04 % of the total biomass change, while changes in precipitation and temperature explained 17.7 % (p < 0.05) of total vegetation biomass (TVB) change. Meanwhile, SOCD change during 1980s - 2000s could be explained 10.08 % by the soil texture (p < 0.05) and <1.6 % by changes in climate and livestock. This implies that the impacts of climate change on grassland biomass are more significant than those of grazing utilization, and SOCD was resistant to both climate change and intensified grazing. Overall, intensified grazing did not result in significant negative impacts on the grassland carbon stocks in the study region during the 1980s and 2000s. The grassland ecosystems possess a mechanism to adjust their root-shoot ratio, enabling them to maintain resilience against grazing utilization.

Effects of ant mounds construction on the carbon cycle in a drained peatland, Northeast China

Acting as ecosystem engineers, ant activities are significantly crucial in altering carbon (C) cycles by modifying the energy flows and nutrient cycles in soils. However, how ant mound constructions reshape soil organic carbon (SOC) pool heterogeneity and stability remains elusive. In this study, we compared differences in soil properties, vegetation biomass, greenhouse gas (GHG) emission, and molecular composition of SOC between ant mounds and nearby soils from a drained peatland, intending to decipher factors that govern carbon dynamics during ant excavates. Results indicated that ant mound construction simultaneously caused a decrease in SOC contents and an increase in nutrient level (total nitrogen and phosphorus), dissolved organic carbon (DOC), and its proportion to SOC (DOC/SOC) dramatically. The proportions of pyrolytic products derived from lignin, phenol, and aromatics decreased significantly from 31.70 % in nearby soils to 27.43 % in ant mounds. However, no significant difference in iron-bound organic carbon (OC-Fe) was found between ant mounds and nearby soils (p > 0.05). Ant mound building greatly accelerated SOC mineralization, with average CO2 emission fluxes 1–2 times higher than that in nearby soils. Notably, CH4 emissions from ant mounds was negative, meaning ant mounds acted as CH4 sinks. Meanwhile, net CH4 flux emission into the atmosphere from nearby soils were positive. Both plant biomass and SOC density (SOCd) in ant mounds were considerably lower than those observed in nearby soils. The structural equation model indicated that soil nutrient enrichment in ant mounds indirectly stimulated SOC decomposition by stimulating microbial abundance. Still, ant mounds construction had direct positive effects on labile carbon components accumulation and posed negative effects on resistant SOC compounds. Moreover, redundancy analysis and variation partitioning analysis confirmed that SOCd decline in ant mounds was primarily attributed to reduced soil moisture and elevated microbial abundance. The conceptual model implied that ants could effectively strengthen SOC content heterogeneity at the microscales, characterized by lower SOC storage and stability, and higher CO2 emission than those in nearby soils.