Soil Organic Carbon Management Research Articles

Enhanced priming effect in agricultural soils driven by high-quality exogenous organic carbon additions: A meta-analysis.

The addition of exogenous organic carbon (C) to soil can either accelerate or retard the soil organic carbon (SOC) mineralization, i.e., the priming effect (PE), which plays a crucial role in SOC sequestration and thus is significant in the context of global warming. However, the influence of exogenous organic C quality on PE remains poorly understood, potentially limiting our understanding of SOC dynamics. Thus, we conducted a global meta-analysis to reveal the effect of exogenous organic C quality on PE through compiling a data set of 2031 experiment trials. Our results revealed that the addition of organic C significantly enhanced SOC decomposition by 46.23% in agricultural soils. Labile C compounds induced a stronger PE than both intermediate and recalcitrant C compounds. Organic C materials rich in labile C compounds or with low lignin/N ratios exhibited a greater PE than the resistant substrates. Notably, a threshold C/N ratio of 25 was associated with a higher PE in substrates with C/N<25. Given the pronounced PE observed with high-quality organic C addition (characterized by C/N <25, low lignin/N ratio, and easy decomposability), we proposed that "stoichiometric decomposition" might predominate the PE in agricultural soils. Collectively, the current study underscores the significant role of exogenous organic C quality in modulating the PE, emphasizing the need for further research to inform effective SOC management strategies.

Response of Topsoil Organic Carbon in the Forests of Northeast China Under Future Climate Scenarios

Soil organic carbon (SOC) serves as a highly sensitive indicator of climate change and plays a crucial role in terrestrial carbon cycles. Evaluating the impact of regional land use changes on SOC stocks is essential for assessing ecological and environmental effects. In this study, we utilized 157 soil samples and 11 environmental variables—including soil properties, topographic factors, and climatic conditions—to develop boosted regression tree (BRT) and random forest (RF) models to estimate topsoil SOC stocks for the year 2015. We used a 10-fold cross-validation approach, along with four validation metrics, to assess model performance. The BRT model demonstrated superior accuracy, with a higher R2 and Lin’s consistency correlation coefficient and a lower mean absolute error and root mean square error compared to the RF model. The key environmental factors influencing SOC stock variability in the BRT model included mean annual temperature, elevation, mean annual precipitation, the topographic wetness index (TWI), and catchment area. Based on this, we employed the space-for-time substitution approach and BRT model to forecast the spatial distribution of soil organic carbon (SOC) stocks in Northeast China’s forested regions under future climate scenarios for the 2050s and 2090s. Our findings indicate that, compared to the 2015 levels, the forecast indicates that SOC stocks will decrease by 122 Tg carbon and 123 Tg carbon under two different future scenarios, SSP245 and SSP585, respectively, by the 2050s. By the 2090s, these figures are expected to decrease further by 127 Tg C and 126 Tg C, respectively. Throughout both future periods, SOC stocks will predominantly be concentrated in the northwest region. This research highlights the necessity of thoroughly considering climatic factors in future studies of regional SOC stock dynamics. Moreover, the high-resolution maps produced in this study offer a scientific foundation for enhancing the implementation of ecological management practices in the forested regions of Northeast China, fostering environmental improvement and bolstering SOC and soil management strategies in response to future climate change.

Drivers of soil organic carbon recovery under forest restoration: a global meta-analysis

Forest restoration by planting tree seedlings is a crucial strategy to mitigate climate change and restore forest functions. The Intergovernmental Panel on Climate Change (IPCC) targets to remove around 70 Pg carbon (C) from the atmosphere via forest restoration. However, the impact of forest restoration on the recovery of soil organic carbon (SOC) and its driving factors remains unclear. Here, we conducted a global meta-analysis, based on 348 observations from 144 studies across 150 sites, to evaluate the recovery time of SOC and the driving factors of surface SOC recovery during forest restoration. We found that soil clay content and stand age were recognized as the dominant factors regulating SOC recovery during forest restoration. Overall, SOC recovery was lower in boreal and Mediterranean forests than that in tropical, subtropical, and temperate forests, lower in burned areas and mine sites than that in other sites, and lower in soils with 0%–20% clay contents than that in those with higher clay contents, and comparable among soils with different pH values. Across studies, surface SOC in restored forests with stand ages ranging from 1 to 200 years did not fully recover to the levels of reference forests. However, the SOC recovery rate was roughly twice as fast in tree polycultures (~ 10 years to plateau) as that in monocultures (~ 20 years). This global synthesis identifies critical drivers of SOC recovery during forest restoration and provides important insights into SOC management in forests.Graphical

Research Progress on the Spatiotemporal Dynamics of Soil Organic Carbon in Arable Land based on Space-Ground Integration

This study explores the spatiotemporal dynamics of soil organic carbon in arable land and its management strategies, emphasizing the importance of organic carbon in arable ecosystem health. By integrating space and ground technologies, the study employs a combination of remote sensing and in-situ observations to analyze the impact of different tillage practices, climate change, and soil types on the dynamics of organic carbon. The results indicate that fertilization management, crop rotation and intercropping, and vegetation cover all have significant effects on enhancing soil organic carbon. The study provides a scientific basis for the effective management of soil organic carbon and points out the directions and challenges for future research, promoting the in-depth development of soil ecological studies.

Spatial Variation and Stock Estimation of Soil Organic Carbon in Cropland in the Black Soil Region of Northeast China

Soil organic carbon (SOC) sequestration in cropland is not only instrumental in combating climate change, but it also significantly enhances soil fertility. It is imperative to precisely and accurately quantify the SOC sequestration potential and assess the relative significance of various multiple explanatory factors in a timely manner. We studied 555 soil samples from the cropland topsoil (0–15 cm) across the black soil region in Northeast China between the years 2021 and 2022, and we identified 16 significant impact factors using one-way ANOVA and Pearson correlation coefficient analysis. In addition, the Random Forest (RF) model outperformed the Cubist model in predicting the spatial distribution of SOC contents. The predicted ranges of SOC contents span from 5.24 to 43.93 g/kg, with the average SOC content using the RF model standing at 17.24 g/kg in Northeast China. Stepwise regression and structural equation modeling revealed climate and topography as key factors affecting SOC distribution. The SOC density in the study area varied from 0.51 to 9.11 kg/m2, averaging 3.30 kg/m2, with a total SOC stock of 1226.64 Tg. The SOC sequestration potential in the study area was estimated at 3057.65 Tg by the categorical maximum method, with a remaining sequestration capacity of 1831.01 Tg. The study area has great potential for SOC sequestration. We hope to transform the theoretical value of SOC sequestration potential into actual SOC sequestration capacity by promoting sustainable agriculture and additional strategies. Our findings provide insights into the global soil conditions, SOC storage capacities, and effective SOC management strategies.

Modelling and validating soil carbon dynamics at the long-term plot scale using the rCTOOL R package

We introduce rCTOOL, an open-source R package for carbon (C) turnover modelling, featuring comprehensive documentation and a user-friendly interface. As an enhanced version of the widely used Danish C-TOOL model, rCTOOL maintains minimal input data requirements and reliable performance, while addressing the original model's limitations in openness and documentation. To validate rCTOOL, we analysed topsoil Soil Organic Carbon (SOC) dynamics using data from the long-term Askov straw disposal experiment (1981–2019), which quantifies the impact of varying annual straw C inputs on SOC storage. Our validation shows an unbiased global prediction error of less than 10%, with a mean error of 1.1 Mg C/ha (CI -0.7–3.0 Mg C/ha). The discrepancies between observed and predicted values were primarily due to spatiotemporal variability represented by the block and year in the long-term field experiment. We demonstrate how rCTOOL is a reliable asset for diverse applications in SOC management and research.

Remote Sensing and Sustainable Management of Soil Organic Carbon in the Sahelian Area, Senegal, West Africa

The mapping of soil organic carbon (SOC) variability was carried out in a Sahelian region of Senegal by testing the effectiveness to include the Sentinel 2 remote sensed data in the characterization of the soil properties. Ordinary kriging (OK) applied under ArcGIS is compared with multiple linear regression (MLR) calibrated under R software. The results showed a slight decrease of the root mean square error ranging from 0.18 with kriging to 0.16 for multiple linear regression. Carbon variability was also detailed at a finer scale with multiple linear regression at the pixel scale from 10 to 20 m. Spectral bands situated in the visible wavelength, NDWI and NDVI were the most discriminating explanatory variables in the spatial modeling of organic carbon by multiple linear regression. Specific locations that require inputs of manure or compost were also geo-localized with multiple linear regression in order to ensure sustainable management of soil organic carbon. The use of remote sensed data also puts into perspective the possibility of spatializing the physical and chemical properties of the soil on larger areas and correcting the lack of information on soil mapping in the Sahelian regions of Africa.

Remote Sensing and Sustainable Management of Soil Organic Carbon in the Sahelian Area, Senegal, West Africa

The mapping of soil organic carbon (SOC) variability was carried out in a Sahelian region of Senegal by testing the effectiveness to include the Sentinel 2 remote sensed data in the characterization of the soil properties. Ordinary kriging (OK) applied under ArcGIS is compared with multiple linear regression (MLR) calibrated under R software. The results showed a slight decrease of the root mean square error ranging from 0.18 with kriging to 0.16 for multiple linear regression. Carbon variability was also detailed at a finer scale with multiple linear regression at the pixel scale from 10 to 20 m. Spectral bands situated in the visible wavelength, NDWI and NDVI were the most discriminating explanatory variables in the spatial modeling of organic carbon by multiple linear regression. Specific locations that require inputs of manure or compost were also geo-localized with multiple linear regression in order to ensure sustainable management of soil organic carbon. The use of remote sensed data also puts into perspective the possibility of spatializing the physical and chemical properties of the soil on larger areas and correcting the lack of information on soil mapping in the Sahelian regions of Africa.

Profile distribution and edaphic controls of soil organic carbon in dominant soil orders of Chitwan, Nepal

Soil profile distribution of soil organic carbon (SOC) in different soil types provides information about the carbon (C) dynamics in terrestrial ecosystems, and is also important for understanding climate feedback mechanisms and for developing a proper farm level SOC management decision. However, there are limited studies on it when we consider soil horizons of dominant soil orders of Nepal, which mostly use a fixed depth approach rather than horizon-based approach while studying profile SOC distribution. We collected soils from master horizons (0 to 100 cm) of three dominant soil orders (Alfisols, Entisols, and Inceptisols) in Chitwan district of Nepal, to understand the controlling factors of SOC accumulation. Dominant soil order regions were identified using a soil map prepared by the National Land Use Planning Project where a pit of 1 m3 was dug for each soil order and replicated four times. The highest SOC concentration (10.1 ± 0.6 g kg−1) was found in Alfisols followed by Entisols (8.8 ± 0.3 g kg−1) and Inceptisols (7.2 ± 8.9 g kg−1). Similarly, the highest SOC stock was found in the soil profile of Alfisols (200.01 ± 15.97 t ha−1) followed by Entisols (124.67 ± 12.20 t ha−1) and Inceptisols (113.27 ± 10.30 t ha−1) horizons. Surface (A) horizons of all three-soil order had significantly higher SOC than sub-surface (B and C) horizons. Regression analysis showed significant variability in SOC to clay content (R2 = 0.45, p < 0.0001), sand (R2 = 0.19, p < 0.001), and total nitrogen (N; R2 = 0.835, p < 0.001). Principal component analysis showed that the controlling edaphic factors differ with the soil types considering SOC change in the whole soil profile. Overall, we found that soil pH, N, clay and sand contents are the major controlling factors that drive the SOC accrual in dominant soil orders of Nepal.Graphical

On the state and tasks of soil organic carbon management

On the state and tasks of soil organic carbon management

Agro-technologies for greenhouse gases mitigation in flooded rice fields for promoting climate smart agriculture

We investigated methane (CH4) and nitrous oxide (N2O), two important greenhouse gases (GHGs) emissions using the closed chamber method from a flooded rice field in Brahmaputra valley of Assam, northeast part of India. We tried to understand the factors responsible for the emission and identify appropriate agro-technologies for their mitigation. Various factors like water level, drainage management, soil organic carbon management, crop management, fertilizer amendment, cultivar type etc. affect the GHG production and emission from the flooded rice soil. In this study, six treatments were employed, namely, farmer's practice (FP), recommended fertilizer dosage (RDF), direct seeded rice (DSR), intermittent wetting drying (IWD), use of efficient methanotrophs (MTH), and use of ammonium sulfate as a nitrogen source for real-time nitrogen management using leaf color chart, (AS). GHG flux was measured through the static closed chamber technique. Soil temperature, pH, and redox potential (Eh) and other soil physico-chemical and biological properties that have a potential role in GHG emission were also assessed. The lowest CH4 flux was observed in IWD treatment. The highest CH4 but lowest N2O flux was observed in RDF thus portraying a tradeoff relationship among these two GHGs. The highest N2O flux was observed in AS. Changes in Eh strongly altered CH4 and N2O emissions. The CH4 flux for the growing season varied from 62.5 to 86.3 kg ha−1 with an average of 72.4 kg ha−1. The average N2O flux was 0.89 kg ha−1 with values fluctuating between 0.72 – and 1.08 kg ha−1. The findings of this study could assist in understanding the factors affecting the source, production, and sink of these two important GHGs. IWD, along with judicious N-based fertilizer use, could provide significant respite from GHG emissions in rice-based agriculture. These climate-smart strategies not only reduce emissions but also have the potential to improve yield.

The Impact of Raw and Composted Food Waste Anaerobic Digestates on Soil Organic Carbon Management: A Pot Study

PurposeEver increasing food waste production has promoted anaerobic digestion and composting for its proper management, producing a relevant amount of recycled organic waste (OW) for possible agricultural uses. However, little is known regarding soil carbon management using this type of OW.MethodsIn this study, an anaerobic digestate from the wet digestion of food waste (WETD), and one from the dry-batch process (DRYD), along with their respective composts (WETC and DRYC), were utilized in a pot test over two growing cycles (84 + 84 days), with and without mineral nitrogen (N) fertilization, and were compared with a bio-waste compost (BWC) and a chemical reference (Chem). At the end of the two growth cycles (days 84 and 168), the ryegrass dry biomass (DW) and the N uptake were assessed.ResultsThe pot soil was analyzed for soil organic carbon (SOC) and the potassium permanganate (KMnO4) oxidizable fraction (CL) as well as δ13C and Δ13C. At day 84, the SOC (g kg− 1) was the highest in DRYD and DRYC (8.53) > WETD and WETC (7.71) = BWC (7.86) > Chem (6.68), and performed similarly at day 168. At day 84, the carbon management index (CMI) was > 100% in all the organic treatments in comparison with Chem, except for WETD. At day 168, a + 30% CMI was registered in WETD and WETC> BWC> DRYD and DRYC> Chem.ConclusionThis pattern was related to a generally marked δ13C depletion being confirmed by Δ13C, thus indicating the conservation of the carbon form compost, this very likely being related to the preferential lignin accumulation.

Prediction, mapping, and implication for better soil organic carbon management in Ethiopia

AbstractA precise soil organic carbon (SOC) content estimate is crucial soil quality parameter for agricultural produce and ecological safety. Moreover, geospatial modeling of SOC is critical when there are limited laboratory equipment and chemical reagents for soil analysis. This study used geostatistics—ordinary kriging (OK) and inverse distance weighting (IDW)—to map SOC in Libokemkem area, Northwest Ethiopia, for improved SOC management. About 107 soil samples were obtained from the plow layer at a 20‐cm depth and SOC was determined. Statistical Package for Social Sciences version 24.0 was used to generate descriptive statistics, and geostatistical analysis was also performed on the data using ArcGIS platform. The coefficient of determination (R2) and root mean square error (RMSE) derived from the validation of the predicted maps were used to assess the models. The results revealed homogeneity (coefficient of variation &lt; 10%), low (0.12%–1.74%), and optimal (1.74%–4.06%) mean levels of SOC in study area. The OK showed an R2 of 0.74 and an RMSE of 13%, and the IDW revealed an R2 of 0.69 and an RMSE of 14%. The semivariogram results indicate a moderate dependence for SOC with stable, circular, spherical, exponential, and Gaussian models. We conclude that the sustainable monitoring of SOC is significant in enhancing soil quality. However, further study considering all drivers of spatial variability for SOC in the study and other soil sampling approaches improving performance of the prediction models is needed.

Towards site-specific management of soil organic carbon: Comparing support vector machine and ordinary kriging approaches based on pedo-geomorphometric factors

Site-specific management of soil organic carbon (SOC) is crucial for cleaner, sustainable agricultural production and climate change mitigation. The Neyshabur plain of northeastern Iran is plagued with spatially variable critically low SOC. Thus, in this study management zones (MZs) were developed for the site-specific management of SOC. Soil samples (0–0.3 m) and digital elevation model-derived geomorphometric variables were collected at 288 locations. Soil pH, clay content, available phosphorus, available potassium, and the vertical distance to channel network significantly (p < 0.05) correlated with SOC. Subsequently, support vector machine (SVM) and ordinary kriging (OK) approaches were used to map the spatial variability and develop MZs based on SOC and its above-listed pedo-geomorphometric covariates. Generally, the coefficient of determination (R2) and root mean square error (RMSE) of OK and SVM models used to interpolate pedo-geomorphometric covariates, were similar. However, the Fuzzy Performance Index (FPI) and Normalized Classification Entropy (NCE) indices suggest that the MZs based on the SVM model (FPI = 0.091, NCE = 0.113) are more accurate compared to those determined using the OK (FPI = 0.135, NCE = 0.164) method. Although the results demonstrate the potential of both OK and SVM approaches for site-specific management of SOC in the Neyshabur plain, the average difference of 0.8 g kg−1 SOC in the SVM approach compared to OK (p < 0.05) was found to be significant, especially along transitional boundaries between MZs. Thus, the SVM-based MZs developed in this study can be used for more accurate site-specific application of organic amendments, mineral fertilizers, and management practices that improve carbon sequestration and sorption of chemical contaminants in the Neyshabur plain of Iran.

Evaluation of Soil Organic Carbon Stability in Different Land Uses in Lithuania

The effective management of soil organic carbon (SOC) is highlighted as one of the strategies and cost-effective options for mitigating climate change, while soil nitrogen (N) often is specified as an essential element for plant growth. This study was conducted to evaluate basic soil physical, chemical, and microbial indicators in three major soil types dominated in Lithuania—Arenosols, Retisols, and Cambisols—under forest land, perennial grassland, and arable land. Furthermore, soil microbial biomass carbon (SMBC) and nitrogen (SMBN), their ratio, and soil microbial respiration (microbial CO2) next to SOC and total N were hypothesized to be important measures for assessing SOC stability under different land uses. Therefore, selected soil indicators were evaluated in the surface 0–10 and 10–20 cm mineral soil layers. The study results showed higher concentrations of SOC, N, SMBC, and SMBN, and soil microbial CO2 in forest land and perennial grasslands than in arable land. The higher SMBC/SOC and SNBN/TN ratios indicated a higher ability to accumulate SOC and N in forest land and grasslands. Higher SOC immobilization in forest land and higher N immobilization in arable land were both specified by the obtained SMBC:SMBN ratio. This study identified forest land followed by grassland as the best land management practice that addresses soil C sequestration through higher C immobilisation. Assessing soil in forest land as a control land use next to the agricultural land could be a reasonable soil management practice to evaluate C sequestration in the region. Additionally, it was assumed that evaluation of the SMBC and SMBN concentrations together with soil physical and chemical indicators allow for a more effective assessment of SOC stability. Taken together, these findings support recommendation to develop grassland (and especially forest land systems) through afforestation or within agroforestry system, without reducing the importance of the agricultural sector.

Farmer preference for marginal land use and their impact on soil quality

Changes in forest land use into dry lands have become a concern of the global community because of environmental damage. The research objectives were to determine land use priority for agricultural development and its impact on soil quality. The research was conducted in Baras District, Pasangkayu Regency, West Sulawesi. The socio-economic survey was conducted using a focus group discussion technique. Stratified soil surveys were carried out on priority land uses. Analysis of the physicochemical properties of soil was carried out at the Soil Science Laboratory, Faculty of Agriculture, University of Tadulako. Support for the development of farmer institutions, ease of product marketing, financial benefits for farming, and the availability of quality seeds are the dominant factors influencing people’s preference for the priority of developing oil palm farming. Respectively, Soil quality indexes in oil palm blocks aged 3 years, 6 years, and 24 years were 0.5584 (moderate), 0.3072 (slightly poor), and 0.4362 (moderate). Soil quality decreased early in plant growth up to 6 years of age, but improved with effective soil organic carbon management up to 24 years of age.

Carbon Storage in Cropland Soils: Insights from Iowa, United States

The restoration of soil organic matter (SOM, as measured by soil organic carbon (SOC)) within the world’s agricultural soils is imperative to sustaining crop production and restoring other ecosystem services. We compiled long-term studies on the effect of management practices on SOC from Iowa, USA—an agricultural region with relatively high-quality soil data—to highlight constraints on detecting changes in SOC and inform research needed to improve SOC measurement and management. We found that strip-tillage and no-tillage increased SOC by 0.25–0.43 Mg C ha−1 yr−1 compared to losses of 0.24 to 0.46 Mg C ha−1 yr−1 with more intensive tillage methods. The conversion of cropland to perennial grassland increased SOC by 0.21–0.74 Mg C ha−1 yr−1. However, diversifying crop rotations with extended rotations, and supplementing synthetic fertilizer with animal manure, had highly variable and inconsistent effects on SOC. The improved prediction of changes in SOC requires: the use of methods that can identify and disentangle multiple sources of variability; looking beyond total SOC and toward systematic collection of data on more responsive and functionally relevant fractions; whole-profile SOC monitoring; monitoring SOC in long-term studies on the effect of multiple conservation practices used in combination; and deeper collaboration between field soil scientists and modelers.

Predicting spatiotemporal soil organic carbon responses to management using EPIC-IIASA meta-models

The management of Soil Organic Carbon (SOC) is a critical component of both nature-based solutions for climate change mitigation and global food security. Agriculture has contributed substantially to a reduction in global SOC through cultivation, thus there has been renewed focus on management practices which minimize SOC losses and increase SOC gain as pathways towards maintaining healthy soils and reducing net greenhouse gas emissions. Mechanistic models are frequently used to aid in identifying these pathways due to their scalability and cost-effectiveness. Yet, they are often computationally costly and rely on input data that are often only available at coarse spatial resolutions. Herein, we build statistical meta-models of a multifactorial crop model in order to both (a) obtain a simplified model response and (b) explore the biophysical determinants of SOC responses to management and the geospatial heterogeneity of SOC dynamics across Europe. Using 5600 unique simulations of crop growth from the gridded Environmental Policy Integrated Climate-based Gridded Agricultural Model (EPIC-IIASA GAM) covering 86,000 simulation units across Europe, we build multiple polynomial regression ensemble meta-models for unique combinations of climate and soil across Europe in order to predict SOC responses to varying management intensities. We find that our biophysically-explicit meta models are highly accurate (R2 = 0.97) representations of the full mechanistic model and can be used in lieu of the full EPIC-IIASA GAM model for the estimation of SOC responses to cropland management. Model stratification by means of climate and soil clustering improved the performance of the meta-models compared to the full EU-scale model. In regional and local validations of the meta-model predictions, we find that the meta-models largely capture broad SOC dynamics such as the linear nature of SOC responses to residue application, yet they often underestimate the magnitude of SOC responses to management. Furthermore, we find notable differences between the results from the biophysically-specific models throughout Europe, which point to spatially-distinct SOC responses to management choices such as nitrogen fertilizer application rates and residue retention that illustrate the potential for these models to be used for future management applications. While more accurate input data, calibration, and validation will be needed to accurately predict SOC change, we demonstrate the use of our meta-models for biophysical cluster and field study scale analyses of broad SOC dynamics with basically zero fine-tuning of the models needed. This work provides a framework for simplifying large-scale agricultural models and identifies the opportunities for using these meta-models for assessing SOC responses to management at a variety of scales.

Regional spatial variability of soil organic carbon in 0–5 m depth and its dominant factors

Soil organic carbon (SOC) stored in deeper soils is becoming increasingly significant in carbon management and carbon cycle especially in the face of climate and land use alterations. However, spatial variability of deep SOC at a regional scale and its related dominant factors are rarely investigated. To ascertain the vertical distribution and regional spatial variations in the SOC content of deep soil, 1648 soil samples were collected from 67 sites across the Chinese Loess Plateau (CLP) at a depth of 500 cm. Results showed that the mean SOC content of three land use types were recorded as follows: cropland (2.93 ± 0.29 g kg−1) > forest (2.56 ± 0.24 g kg−1) > grassland (2.39 ± 0.29 g kg−1), and the vertical distribution of soil profile from 0 to 500 cm was similar. Furthermore, a significant alteration in SOC content was observed in the soil layer of 0–180 cm among the three land use types (p < 0.05). Spatial distribution maps of SOC content indicated that there was a central region of the northern CLP with low SOC content, which corresponds to the eolian sand area in Ordos. Both the spatial variations and influencing factors of SOC content were found to be depth-dependent, but soil texture and temperature were identified as the dominant factors affecting the 0–500 cm soil profile of the CLP. Clarifying this information is helpful for understanding content level and variation of deep SOC, which can provide a reference for carbon sequestration in deep soil and developing sustainable SOC management for the CLP and other related ecological regions.

Soil organic carbon sequestration in agricultural long-term field experiments as derived from particulate and mineral-associated organic matter

Soil organic matter (SOM) is indispensable for soil health and, in the context of climate change, is considered a significant CO2 sink. Improving agricultural management to increase long-term soil organic carbon (SOC) stocks for mitigating climate change requires tools that estimate short and long-cycling SOM pools. In this study, we analyzed changes in fast-cycling particulate organic matter (POM) and slow-cycling mineral-associated organic matter (MAOM) induced by common management practices, i.e., fertilization and crop rotation in topsoils from 25 Central European long-term field experiments. When relating MAOM-C contents to recent MAOM-C saturation levels, estimated sequestration potentials were only met in coarse-textured soils under appropriate agricultural management or fine-textured soils under extreme organic fertilization. Soil texture, organic fertilization, and below-ground OC inputs through root exudates and root biomass were decisive for estimating MAOM-C, allowing for calibration of a mixed-effects model (Nakagawa’s: marginal R2m = 0.6, conditional R2c = 0.89). While the models containing soil texture and organic fertilization parameters can be validated and generalized (R2 = 0.43), the below-ground OC input predictor substantially decreases the generalizability of the validated models (R2 = 0.14). According to quantile regression models, we estimate the average difference in MAOM-C concentration between well-managed and control site (without organic fertilization) topsoils to 4.1 mg g−1 soil. In dependence on the soil bulk density, this amounts to 1.38 – 1.84 t ha−1 MAOM-C stocks or 5.06 – 10.1 t ha−1 CO2-equivalents. POM-C was difficult to predict (R2 = 0.28), presumably due to strong POM dynamics. The POM-C / MAOM-C ratio can inform on the effects of agricultural practices in before/after management change comparisons. Under increasing SOC concentration, an increasing POM-C / MAOM-C ratio indicates that the effects of organic fertilization do not transfer to real effects on long-term SOC sequestration. Because MAOM-C depends on soil texture, this ratio is also a covariate of soil texture, limiting it for comparisons between sites with different textures. However, our data indicate that agricultural long-term field experiment soils constantly approximate MAOM-C saturation when the POM-C/MAOM-C ratio is >0.35. This ratio might be used as a management goal to prevent organic over-fertilization and N loss, especially on coarse-textured soils. Thereby, the POM-C / MAOM-C ratio can help to optimize SOC management and sequestration on agricultural soils and support climate change mitigation strategies in Central Europe.