Soil Organic Carbon Measurements Research Articles

MiPrime: A model for the microbially mediated impacts of organic amendments on measurable soil organic carbon fractions and associated priming effects

Priming effects can influence the efficiency with which organic amendments sequester carbon in the soil. Yet, few soil models currently include priming effects. Those models that do are often based on operationally defined soil pools and implicitly allow only for positive priming effects. This limits the verification of model processes with experimental data and hinders the optimization of our carbon sequestration strategies. To address these shortcomings, we developed MiPrime, which offers a framework for the mechanistic modelling of organic amendment impacts on microbially mediated transformation of carbon fractions that are quantifiable through parsimonious soil extraction methods. MiPrime allows for assessment of organic amendment impacts on soil carbon dynamics, including priming effects, by simulating changes in mineralized, microbial biomass, dissolvable, hot water extractable and insoluble carbon fractions in soil exogenous (i.e. organic amendment-derived) and endogenous (i.e. soil) pools. After calibration of model parameters using Markov Chain Monte Carlo methods to incubation data of three types of isotopically labelled roadside grasses (a fresh grass product, a compost thereof, and a Bokashi-fermented product thereof), MiPrime was able to simulate changes in carbon fractions of the soil with a good degree of accuracy for five compositionally complex organic amendments, namely the three types of roadside grasses, as well as non-isotopically labelled wood chips and water weeds and reeds. Validation of the model results with experimental data demonstrates that changes in total carbon were very well predicted but that there is room for improvement in predicting mineralization rates and changes in dissolvable, hot water extractable and insoluble carbon fractions in the soil endogenous pool. MiPrime thus offers an initial step towards the mechanistic modelling of organic amendment impacts on measurable soil carbon fractions and can operate as a new tool for designing effective carbon sequestration strategies and understanding organic amendment impacts.

Potential Effect of Mangrove Ecosystem on Soil Carbon Sequestration and its Physico-Chemical Properties

The mangrove ecosystem is a natural wetland located in the Red Sea that extends 500 km on the Egyptian western coast. It has great potential to sequestrate soil organic carbon, reduce atmospheric CO2, and enhance soil hydro-physical properties. In this context, this research aims to characterise soil changes induced by mangrove growing. Both modelling and measurements herein were performed on five sampling areas at the Western Strand of the Red Sea (plus one control site - beach without plants). The locations (sites 1 and 2) of mangrove forest were in El-Gouna village with ages of 10 and 5 years, respectively, while (sites 3, 4, and 5) represent mangroves at Hurghada, Abu-Monquar Island, and Safaga, respectively. The mean values of measured soil organic carbon pool (SOCP) at the soil surface (0-90cm) revealed that the lowest values of the SOCP were at Hurghada with 8.81± 0.12 Mgha-1 and the highest values at Abu Monquar island with 59.75 ± 0.15 Mgha-1. At El Gouna 1, 2 and Safaga, the SOCP values were 14.48, 12.86, and 39.98 Mgha-1, respectively. In addition, the SOCP at the control site (beach without plants) was 6.62± 0.25 Mgha-1. Thus, the mangrove ecosystem has a great potential to sequestrate the soil's organic carbon and reduce atmospheric CO2. Soil bulk density (SBD) values varied at El-Gouna1, 2, and Hurghada from 1.63 to 1.75 g/cm³, while the lowest SBD values were observed at Abu-Monquar Island and Safaga with 1.31± 0.02, g/cm³ and 1.53 ± 0.05 g/cm³, respectively. SBD at the control site was 1.75± 0.05 g/cm³, which reflects the higher values. Morphological characteristics reveal that tree height results varied from 110-130 cm, 50-70 cm, 150-200 cm, 200-300 cm, and 200-270 cm for ElGouna1, ElGouna2, Hurghada, Abu-Manqar, and Safaga, respectively. Higher values of tree height, size index, and density are convenient, as are the lower values of SBD at Abu-Monquar and Safaga compared to other site locations. The soil-water HYDRUS-1D model revealed that the soil water storage capacity at Abu-Monquar was higher than the other samples. Thus, the joint use of modelling and measurements enabled deeper insight and a suitable characterisation of soil physicochemical changes induced by mangrove growth.

Assessing and mapping of soil organic carbon at multiple depths in the semi-arid Trans-Ural steppe zone

The quantification of soil organic carbon (SOC) and its vertical distribution is crucial for understanding carbon dynamics in terrestrial ecosystems. This study aimed to 2.5D digital mapping of SOC content in the Trans-Ural steppe zone (Russia) using a quantile regression forest (QRF) approach. The study utilized a dataset comprising 2495 SOC measurements collected from 1316 locations across three soil depths: 0–20 cm, 20–40 cm, and 40–60 cm. Environmental covariates were incorporated into the SOC modeling process, capturing major soil formation factors, and the uncertainty of the generated maps was estimated. The results revealed that SOC content ranged from 0.59 to 9.05 % in the topsoil, from 0.5 to 6.61 % in the subsurface layer and from 0.06 to 4.64 % in the subsoil. Based on the error metrics, including root mean square error (RMSE), coefficient of determination (R2), and Nash-Sutcliffe efficiency coefficient (NSE), we found a decrease in prediction accuracy with increasing soil depth. Furthermore, climate and vegetation variables, as well as elevation, emerged as key factors influencing the prediction of SOC concentrations at all depths. We also made an attempt to assess the future change of SOC under the influence of climate and anthropogenic impact. We anticipate that climate aridization and plowing will lead to a decline in SOC content in the Trans-Ural steppe region. Our findings contribute to the existing knowledge of SOC dynamics in steppe ecosystems at several depths, supporting informed decision-making for sustainable land use and climate change mitigation strategies.

Accurate Quantification of 0–30 cm Soil Organic Carbon in Croplands over the Continental United States Using Machine Learning

Increases in organic carbon within agricultural soils are widely recognized as a “negative emission” that removes CO2 from the atmosphere. Accurate quantification of soil organic carbon (SOC) to a certain depth in the spatial domain is critical for the effective implementation of improved land management practices in croplands. Currently, there is a lack of understanding regarding what depth strategy should be used to estimate SOC at 0–30 cm when sample datasets come from multiple depths. Furthermore, few studies have examined depth strategies for mapping SOC at the agricultural management level (i.e., field level), opting instead for point-based analysis. Here, three types of approaches with different depth strategies were evaluated for their ability to quantify 0–30 cm SOC content based on soil samples from 0–5 (surface), 5–30 (subsurface), and 0–30 cm (full column). These approaches involved the generalized additive model and machine learning techniques, i.e., artificial neural networks, random forest, and XGBoost. The soil samples used for the model evaluation and selection consisted of the newly collected samples in 2020–2022 and the Rapid Carbon Assessment (RaCA) legacy samples collected in 2010–2011. Environmental covariates corresponding to these SOC measurements were used in model training, including long-term physical climate, short-term weather, topographic and edaphic, and remotely sensed variables. Among the models evaluated in this study, the XGB regression model with a full column depth assignment strategy yielded the best prediction performance for 0–30 cm SOC content, with an r2 (squared Pearson correlation coefficient) of 0.48, an RMSE (root mean square error) of 0.29%, an ME (mean error) of 0.06%, an MAE of 0.25%, and an MEC (modeling efficiency coefficient) of 0.36 at the pixel level and an r2 of 0.64, an RMSE of 0.32%, an ME of −0.20%, an MAE of 0.28%, and an MEC of 0.48 at the field level. This study highlights that machine learning models with a full column depth strategy should be used to quantify 0–30 cm SOC content in agricultural soils over the continental United States (CONUS).

Tracking Land-use Trajectory and Other Potential Drivers to Uncover the Dynamics of Carbon Stocks of Terrestrial Ecosystem in the Songnen Plain

Land-use change is an important factor affecting terrestrial carbon balance, and it is crucial to explore the response of terrestrial carbon stocks to land-use change, especially in the Songnen Plain, which faces a fierce conflict between the rapid growth of production activities and ecosystem degradation. In this study, we measured soil organic carbon and vegetation biocarbon stocks in the Songnen Plain based on IPCC-recommended methodologies, and explored the characteristics of carbon stock changes in land-use trajectories, land-use drivers, and specific land-use change scenarios (cropland cultivation, returning cropland to forests, the expansion of land for construction, deforestation, greening, and land degradation). The results showed that soil organic carbon stock in the Songnen Plain decreased by 1.63 × 105 t, and vegetation biocarbon stock increased by 2.10 × 107 t from 2005 to 2020. Human factors and natural factors jointly contributed to the land-use change, but the extent of the role of human factors was greater than that of natural factors. The increase in land-use trajectory led to the decrease in soil organic carbon stock and the increase in vegetation biocarbon stock. There was no difference in the effects of human-induced and natural-induced land-use changes on vegetation biocarbon stocks, but the effects on soil organic carbon stocks were diametrically opposite, increasing by 43.27 t/km2 and decreasing by 182.02 t/km2, respectively. The reclamation of arable land, returning cropland to forests, and greening led to a net increase in terrestrial carbon stocks (+813,291.84 t), whereas land degradation, deforestation, and land-use expansion led to a decrease in terrestrial carbon stocks (−460,710.2 t). The results of this study can provide a reference for the adjustment of land-use structure and the increase in terrestrial carbon stock in the Songnen Plain.

A soil health assessment tool for vegetable cropping systems in tropical soils based on beta-glucosidase, arylsulfatase, and soil organic carbon

Vegetable production is characterized by a strong dependence on external inputs and intense tillage, which can harm soil health (SH) by reducing organic matter levels, soil biota diversity, and overall ecosystem functions. Tools to assess SH affected by organic and conventional vegetable production systems are crucial and still scarce. This study aimed to develop a practical and reliable SH assessment tool for vegetable production systems in tropical soils by integrating Cornell's Comprehensive Assessment of Soil Health (CASH) with the four-quadrant model (4QM). Furthermore, the study evaluated the relationship between management practices and SH status. Arylsulfatase (ARYL) and β-glucosidase (GLU) activities, and soil organic carbon (SOC) were analyzed in 90 soil samples (0 to 10 cm depth) collected from 53 organic and conventional vegetable farms in the Federal District, Brazil. The adoption of practices aimed at improving or maintaining SH was significantly greater on organic farms than on conventionally managed farms, resulting in a SOC content and ARYL activity 27 % and 86 % higher in organic farms than in conventional systems, respectively (P < 0.05). ARYL proved to be more accurate in separating organic and conventional fields than GLU, highlighting its potential for SH evaluation in vegetable production systems. The integration of CASH and the 4QM, along with ARYL, GLU, and SOC measurements, effectively addressed the complexity of both organic and conventional vegetable production systems. This combined approach facilitated differentiation between systems based on varying levels of adoption of best management practices (BMPs). Further research is needed to assess the performance of this approach across different soil types and regions where vegetables are cultivated.

Predicting soil organic carbon content using simulated insitu spectra and moisture correction algorithms in southern Xinjiang, China

Soil organic carbon (SOC) is an essential component of the terrestrial carbon pool, and changes in SOC are critical to ecosystem stability and agricultural production. In-situ field spectroscopy, such as visible and near-infrared (vis-NIR) spectroscopy, has proven to be an ideal tool to achieve rapid and efficient detection of SOC. However, soil moisture interference is one of the main challenges for the measurement of SOC using in-situ spectroscopy. Great progress has been made in using external parameter orthogonalization (EPO), direct standardization (DS) and piecewise direct standardization (PDS) to remove the influence of moisture on using vis-NIR spectroscopy to predict SOC. However, most current studies were conducted based on a certain moisture, which makes it difficult to provide a comprehensive guide for SOC detection using in situ spectroscopy. In this study, 135 surface (0–20 cm) soil samples were collected from the Aksu region of Xinjiang, northwestern China. The soil samples were wetted to a saturated moisture content state after manual removal of salts. The spectra data of 11 different moisture levels during natural air-drying of the wet soil samples were measured to analyze and compare the performance of three algorithms, EPO, DS and PDS, in removing the effect of moisture and improving the accuracy of SOC prediction. The results show that PDS has a relatively poor ability to remove the effect of moisture among the three moisture correction algorithms. When the moisture content of the soil exceeds 48%, EPO, DS and PDS cannot effectively remove the disturbance of moisture. The EPO algorithm was the most effective in removing moisture effects when the soil moisture content (SMC) was 25–48%. Using EPO, the predicted R2 and RPIQ of SOC increased by >0.19 and 0.87, respectively. The DS algorithm was the best method to remove the moisture effect on soil vis-NIR spectra when the SMC was 6–25%, and the R2 and RPIQ predictions of SOC after removing the effect of moisture by DS increased by >0.09 and 0.87, respectively. However, when the moisture content is <6%, the influence of soil moisture on the prediction accuracy of SOC spectra is negligible, and SOC can be predicted with high accuracy using in-situ spectra without the introduction of moisture correction algorithms. An appropriate moisture correction algorithm should be selected to remove moisture interference according to the SMC condition.

A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing

Monitoring soil organic carbon (SOC) typically assumes conducting a labor-intensive soil sampling campaign, followed by laboratory testing, which is both expensive and impractical for generating useful, spatially continuous data products. The present study leverages the power of machine learning (ML) and, in particular, deep neural networks (DNNs) for segmentation, as well as satellite imagery, to estimate the SOC remotely. We propose a new two-stage pipeline for remote SOC estimation, which relies on using a DNN trained to classify land cover to perform feature extraction, while the SOC estimation is performed by a different ML model. The first stage is an image segmentation DNN with the U-Net architecture, which is trained to estimate the land cover for an observed geographical region, based on multi-spectral images taken by the Sentinel-2 satellite constellation. This estimator is subsequently used to extract the latent feature vector for each of the output pixels, by rolling back from the output (dense) layer of the U-Net and accessing the last available convolutional layer of the same dimension as our desired output. The second stage is trained on a set of feature vectors extracted at the coordinates for which manual SOC measurements exist. We tested a variety of ML models and report on their performance. Using the best extremely randomized trees model, we generated a spatially continuous map of SOC estimations for the region of Tuscany, in Italy, with a resolution of 10 m, to share with the researchers as a means of validating the results and to demonstrate the efficiency of the proposed approach, which can can easily be scaled to create a global continuous SOC map.

The importance of accounting method and sampling depth to estimate changes in soil carbon stocks

BackgroundAs interest in the voluntary soil carbon market surges, carbon registries have been developing new soil carbon measurement, reporting, and verification (MRV) protocols. These protocols are inconsistent in their approaches to measuring soil organic carbon (SOC). Two areas of concern include the type of SOC stock accounting method (fixed-depth (FD) vs. equivalent soil mass (ESM)) and sampling depth requirement. Despite evidence that fixed-depth measurements can result in error because of changes in soil bulk density and that sampling to 30 cm neglects a significant portion of the soil profile’s SOC stock, most MRV protocols do not specify which sampling method to use and only require sampling to 30 cm. Using data from UC Davis’s Century Experiment (“Century”) and UW Madison’s Wisconsin Integrated Cropping Systems Trial (WICST), we quantify differences in SOC stock changes estimated by FD and ESM over 20 years, investigate how sampling at-depth (> 30 cm) affects SOC stock change estimates, and estimate how crediting outcomes taking an empirical sampling-only crediting approach differ when stocks are calculated using ESM or FD at different depths.ResultsWe find that FD and ESM estimates of stock change can differ by over 100 percent and that, as expected, much of this difference is associated with changes in bulk density in surface soils (e.g., r = 0.90 for Century maize treatments). This led to substantial differences in crediting outcomes between ESM and FD-based stocks, although many treatments did not receive credits due to declines in SOC stocks over time. While increased variability of soils at depth makes it challenging to accurately quantify stocks across the profile, sampling to 60 cm can capture changes in bulk density, potential SOC redistribution, and a larger proportion of the overall SOC stock.ConclusionsESM accounting and sampling to 60 cm (using multiple depth increments) should be considered best practice when quantifying change in SOC stocks in annual, row crop agroecosystems. For carbon markets, the cost of achieving an accurate estimate of SOC stocks that reflect management impacts on soils at-depth should be reflected in the price of carbon credits.

Topographic correction of visible near‐infrared reflectance spectra for horizon‐scale soil organic carbon mapping

AbstractUnderstanding soil organic carbon (SOC) response to global change has been hindered by an inability to map SOC at horizon scales relevant to coupled hydrologic and biogeochemical processes. Standard SOC measurements rely on homogenized samples taken from distinct depth intervals. Such sampling prevents an examination of fine‐scale SOC distribution within a soil horizon. Visible near‐infrared hyperspectral imaging (HSI) has been applied to intact monoliths and split cores surfaces to overcome this limitation. However, the roughness of these surfaces can influence HSI spectra by scattering reflected light in different directions posing challenges to fine‐scale SOC mapping. Here, we examine the influence of prescribed surface orientation on reflected spectra, develop a method for correcting topographic effects, and calibrate a partial least squares regression (PLSR) model for SOC prediction. Two empirical models that account for surface slope, aspect, and wavelength and two theoretical models that account for the geometry of the spectrometer were compared using 681 homogenized soil samples from across the United States that were packed into sample wells and presented to the spectrometer at 91 orientations. The empirical approach outperformed the more complex geometric models in correcting spectra taken at non‐flat configurations. Topographically corrected spectra reduced bias and error in SOC predicted by PLSR, particularly at slope angles greater than 30°. Our approach clears the way for investigating the spatial distributions of multiple soil properties on rough intact soil samples.

Measuring and modelling the impact of outdoor pigs on soil carbon and nutrient dynamics under a changing climate and different management scenarios

AbstractA mixed agricultural system that integrates livestock and cropping is essential to organic, agroecological, and regenerative farming. The demand for improved welfare systems has made the practice of outdoor rearing of pigs very popular; it currently makes up 40% of the UK pig industry and has also been integrated into arable rotations. Besides the benefits of outdoor production systems, they also potentially pose environmental risks to farmlands, such as accumulation of nitrogen and phosphorus in the soil, soil erosion and compaction and carbon loss. Despite this, the impact of outdoor pigs and arable crop rotations on soil health has been under‐researched relative to other livestock species. This study was conducted at the University of Leeds Research Farm from 2018 to 2020 using a combined experimental and modelling approach to understand the impact of outdoor pigs on soil carbon and nutrient dynamics. The physio‐chemical properties of arable soil were measured prior to the introduction of the pigs and after introducing the pigs at the end of first and second years, consecutively. There was assessment of control sites (without pigs, mowing once a year) and pig pens (pigs in a rotation with arable crops). The soil was sampled at two different depths, 0–10 cm and 10–20 cm. It was observed that measured soil organic carbon (SOC) stocks in the soil depths of 0–10 cm and 10–20 cm layer were decreased by 7% and 3%, respectively, in the pig pens from 2019 to 2020, and total available nitrogen and phosphorus were significantly higher in pig pens than the control sites. Hence, at a depth between 0 and 20 cm, the average total available nitrogen was 2.51 and 2.68 mg kg−1 in the control sites and 21.76 and 20.45 mg kg−1 in the pig pens in 2019 and 2020, respectively. The average total available phosphorus at 0–20 cm was 26.54 and 37.02 mg kg−1 in control sites and 48.15 and 63.58 mg kg−1 in pig pens during 2019 and 2020, respectively. A process‐based model (DayCent) was used to simulate soil carbon and nitrogen dynamics in the arable rotation with outdoor pigs and showed SOC stock losses of – 0.09 ± 0.23 T C ha−1 year−1 using the future climate CMIP5 RCP 8.5 scenario for 2020 to 2048. To reduce this loss, we modelled the impact of changing the management of the pig rotation and found that the loss of SOC stock could be decreased by shortening the period of pig retention in the field, growing grass in the field, and leguminous crops in the crop rotation.

Effects of aridification on soil total carbon pools in China's drylands.

Drylands are important carbon pools and are highly vulnerable to climate change, particularly in the context of increasing aridity. However, there has been limited research on the effects of aridification on soil total carbon including soil organic carbon and soil inorganic carbon, which hinders comprehensive understanding and projection of soil carbon dynamics in drylands. To determine the response of soil total carbon to aridification, and to understand how aridification drives soil total carbon variation along the aridity gradient through different ecosystem attributes, we measured soil organic carbon, inorganic carbon and total carbon across a ~4000 km aridity gradient in the drylands of northern China. Distribution patterns of organic carbon, inorganic carbon, and total carbon at different sites along the aridity gradient were analyzed. Results showed that soil organic carbon and inorganic carbon had a complementary relationship, that is, an increase in soil inorganic carbon positively compensated for the decrease in organic carbon in semiarid to hyperarid regions. Soil total carbon exhibited a nonlinear change with increasing aridity, and the effect of aridity on total carbon shifted from negative to positive at an aridity level of 0.71. In less arid regions, aridification leads to a decrease in total carbon, mainly through a decrease in organic carbon, whereas in more arid regions, aridification promotes an increase in inorganic carbon and thus an increase in total carbon. Our study highlights the importance of soil inorganic carbon to total carbon and the different effects of aridity on soil carbon pools in drylands. Soil total carbon needs to be considered when developing measures to conserve the terrestrial carbon sink.

Preliminary Results in Innovative Solutions for Soil Carbon Estimation: Integrating Remote Sensing, Machine Learning, and Proximal Sensing Spectroscopy

This paper explores the application and advantages of remote sensing, machine learning, and mid-infrared spectroscopy (MIR) as a popular proximal sensing spectroscopy tool in the estimation of soil organic carbon (SOC). It underscores the practical implications and benefits of the integrated approach combining machine learning, remote sensing, and proximal sensing for SOC estimation and prediction across a range of applications, including comprehensive soil health mapping and carbon credit assessment. These advanced technologies offer a promising pathway, reducing costs and resource utilization while improving the precision of SOC estimation. We conducted a comparative analysis between MIR-predicted SOC values and laboratory-measured SOC values using 36 soil samples. The results demonstrate a strong fit (R² = 0.83), underscoring the potential of this integrated approach. While acknowledging that our analysis is based on a limited sample size, these initial findings offer promise and serve as a foundation for future research. We will be providing updates when we obtain more data. Furthermore, this paper explores the potential for commercialising these technologies in Australia, with the aim of helping farmers harness the advantages of carbon markets. Based on our study’s findings, coupled with insights from the existing literature, we suggest that adopting this integrated SOC measurement approach could significantly benefit local economies, enhance farmers’ ability to monitor changes in soil health, and promote sustainable agricultural practices. These outcomes align with global climate change mitigation efforts. Furthermore, our study’s approach, supported by other research, offers a potential template for regions worldwide seeking similar solutions.

Spatial patterns of soil stoichiometry and their responses to land use in a desertified area: A case study of China's Horqin Sandy Land

AbstractEcological stoichiometry facilitates the understanding of biogeochemical cycles by studying the balance among multiple elements in ecosystems. However, the spatial patterns of soil stoichiometry and their responses to land use in desertified land are unclear. In this study, we selected 525 sample points to collect topsoil (0–20 cm) samples throughout China's Horqin Sandy Land and measured soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP). We then calculated stoichiometric ratios (C:N:P). SOC (8.7 g kg−1), TN (0.9 g kg−1), and TP (0.3 g kg−1) were lower in this region than in Chinese and global terrestrial ecosystems due to aeolian desertification. SOC, TN, C:P, and N:P were significantly (p &lt; 0.05) higher in grassland, followed by woodland, cropland, and sandy land. This sequence may result from the large number of dead roots that participate in the SOC turnover in grassland. TP was significantly higher in cropland due to fertilization. C:N did not differ significantly (p &gt; 0.05) among grassland, woodland, and cropland because of the strong coupling relationship between SOC and TN. The values of SOC, TN, TP, C:P, and N:P in the Horqin Sandy Land were high in the north (dominated by mountain woodland and grassland) and were low in the south (dominated by desertified land). SOC, TN, and TP contents and C:N:P ratios were mainly affected by mean annual temperature (29.6%), land use (11.5%), and bulk density (11.4%) through their influence on soil microbial metabolism, litter and nutrient input, and soil structure, respectively. The results provide information on the overall changes in soil stoichiometry in the Horqin Sandy Land and its responses to land‐use changes, and can therefore guide ecological restoration in semiarid regions affected by desertification in the modern context of climate change.

Soil carbon sequestration potential of cultivated lands and its controlling factors in China

Understanding soil organic carbon (SOC) stocks and carbon sequestration potential in cultivated lands can have significant benefit for mitigating climate change and emission reduction. However, there is currently a lack of spatially explicit information on this topic in China, and our understanding of the factors that influence both saturated SOC level (SOCS) and soil organic carbon density (SOCD) remains limited. This study predicted SOCS and SOCD of cultivated lands across mainland China based on point SOC measurements, and mapped its spatial distribution using environmental variables as predictors. Based on the differentiation between SOCS and SOCD, the soil organic carbon sequestration potentials (SOCP) of cultivated land were calculated. Boosted regression trees (BRT), random forest (RF), and support vector machine (SVM) were evaluated as prediction models, and the RF model presented the best performance in predicting SOCS and SOCD based on 10-fold cross-validation. A total of 991 topsoil (0–20 cm) SOC measurements and 12 environmental variables explaining topography, climate, organism, soil properties, and human activity were used as predictors in the model. Both SOCS and SOCD suggested higher SOC levels in northeast China and lower levels in central China. The cultivated lands in China had the potential to sequester about 2.13 ± 0.96 kg m−2 (3.25 Pg) SOC in the top 20 cm soil depth. Northeastern China had the largest SOCP followed by Northern China, and Southwestern China had the lowest SOCP. The primary environmental variables that affected the spatial variation of SOCS were mean annual temperature, followed by clay content and normalized difference vegetation index (NDVI). The assessment and mapping of SOCP in China's cultivated lands holds significance importance as it can provide valuable insights to policymakers and researchers about SOCP, and aid in formulating climate change mitigation strategies.

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.

Comparison of methods for determining organic carbon content of urban soils in Central Ohio

Reliable methods are key to quantify organic carbon in urban soils. Samples from across the Ohio State University campus were divided into two datasets (a) pH ≤ 7.5 and (b) pH ≥ 7.8. Organic carbon was assessed using multiple methods, including heated acid dichromate digestion (OCDichromate), loss-on-ignition (LOI) at 400 °C and 550 °C, a dry combustion method (OCTC), and an acid pre-treatment (OCacid). Mean organic C, in g kg−1, were OCTC (39.8), LOI550 (39.8), LOI400 (33.2), OCDichromate (31.8) for soils with pH ≤ 7.5. LOI400 and OCDichromate were more accurate than OCTC and LOI550. Mean organic C, in g kg−1, were LOI550 (23.6) > OCacid (20.2) ≥ LOI400 (17.9), OCDichromate (16.0) for soils with pH ≥ 7.8. Similar to pH ≤ 7.5 soils, LOI400 and OCDichromate methods were more accurate measures of OC. The two loss-on-ignition methods and the dichromate method were strongly correlated with each other (r = 0.91) however the two dry combustion methods were weakly correlated with the others (0.45 ≥ r ≤ 0.67). The dichromate oxidation proved to have the lowest variance of the methods tested (29.7 g2 kg−2). Paired t-tests found no significant differences between the loss-on-ignition 400° and the acid-pretreatment methods, and no significant differences between the dichromate and inorganic subtraction method (p < 0.05). Dichromate oxidation method should be implemented to measure soil organic carbon in urban soils that have carbonates present, especially dolomite. The dichromate oxidation method was found to be simple, rapid, and reliably determines SOC without the need to remove SIC.

Carbon loss and emissions within a permafrost collapse chronosequence

Permafrost carbon release is potentially the largest terrestrial feedback contributing to climate change. However, the changes in carbon release caused by the abrupt thawing of permafrost and their controlling factors remain largely unknown. Here, we measured soil organic carbon (SOC), total nitrogen (TN) concentrations, and carbon dioxide (CO2) and methane (CH4) emission rates among seven permafrost collapse features over 3 years in the northern Qinghai-Tibetan Plateau (QTP). The results showed soil carbon and nitrogen loss caused by permafrost collapse ranged from − 12% to 28% and − 1% to 38%, respectively. We found there was a nonlinear relationship between soil carbon loss and permafrost collapse chronosequence. Permafrost collapse significantly reduced ecosystem respiration (Reco) and weakened carbon sinks. The net ecosystem exchange (NEE) decreased from − 2.59 to − 0.21 µmol CO2 m−2 s−1. The Reco and NEE values showed no significant changes over time after the initial permafrost collapse. In contrast, the CH4 fluxes increased over time following permafrost collapse, and the CH4 fluxes significantly increased 2 to 10 times in the exposed area compared with that in the control area. Soil temperature, moisture, and nutrient availability exerted the most controls over the carbon emission during permafrost collapse. This study provides the patterns of carbon loss and emissions in different permafrost collapse landscapes, which will provide deep insights and reliable data for future prediction of the abrupt thawing of permafrost-carbon feedback.

Relationship between carbon pool changes and environmental changes in arid and semi-arid steppe—A two decades study in Inner Mongolia, China

As the arid and semi-arid grassland with the most extensive distribution area in northern China, the carbon stored in Inner Mongolia (IM) grassland is highly susceptible to environmental changes. With the global warming and drastic climate changes, exploring the relationship between carbon pool changes and environmental changes and their spatiotemporal heterogeneity is necessary. This study estimates the carbon pool distribution of IM grassland during 2003–2020 by combining the measured below ground biomass (BGB) dataset, measured soil organic carbon (SOC) dataset, multi-source satellite remote sensing data products, and random forest regression modeling method. It also discusses the variation trend of BGB/SOC and its correlation with critical environmental factors, vegetation condition factors and drought index. The results show that the BGB/SOC in IM grassland was stable during 2003–2020, with a weak upward trend. The correlation analysis reveals that high temperature and drought environment were unfavorable for developing vegetation roots and would lead to a decrease in BGB. Furthermore, temperature rise, soil moisture decrease, and drought adversely effected grassland biomass and SOC in areas with low altitude, high SOC density, suitable temperature and humidity. However, in areas with relatively poor natural environments and relatively low SOC content, SOC was not significantly affected by environmental deterioration and even showed an accumulation trend. These conclusions provide directions for SOC treatment and protection. In areas where SOC is abundant, it is important to reduce carbon loss caused by environmental changes. However, in areas with poor SOC, due to the high carbon storage potential of grasslands, carbon storage can be improved through scientifically managing grazing and protecting vulnerable grasslands.

Enhancing rhizosphere soil water retention in wheat through colonization with endophytic fungus Serendipita indica

Effect of S. indica colonization of wheat (Triticum aestivum) on water retention and physical quality of the rhizosphere soil was investigated in a greenhouse study. Colonized and uncolonized seedlings of four wheat cultivars (i.e., Roshan, Ghods, Pishtaz, and Kavir) were grown in rhizoboxes. Plants were harvested and intact samples were taken from the rhizosphere to measure soil organic carbon (SOC), hot-water soluble carbohydrates (HWSC), and soil water retention curve (SWRC). Water contents at field capacity (θFC) and permanent wilting point (θPWP), and plant available water (PAW) were obtained from the measured SWRC data. The integral energy (EI(PAW)) and integral water storage (WI(PAW)), two alternative indices of soil water availability and physical quality, were computed by integration of SWRCs over the PAW range with respect to water content and matric potential, respectively. Low EI(PAW) and high WI(PAW) indicate improved soil physical quality and better soil water availability to plant. The results showed that the SOC storage and HWSC of the wheat rhizosphere were approximately 146 and 83% greater than those of unplanted soils, respectively. Moreover, compared with the uncolonized counterparts, S. indica colonization significantly increased the SOC and HWSC by 76 and 134%, respectively. The presence of plant roots and S. indica colonization improved soil water retention by increasing SOC and HWSC and subsequently enhancing portion of structural porosity (i.e., pore space between the soil aggregates in the size range of 0.5–500 μm, mainly includingmacropores and mesopores). Despite having the same PAW, colonized soil samples had lower EI(PAW) (≈ 133 J kg−1) compared to uncolonized counterparts (≈ 153 J kg−1). This finding implies that plant roots take up water from the colonized wheat-planted soils easier than from the uncolonized counterparts. Compared to uncolonized samples, an increase of 50% in S1 (Dexter's index of soil physical quality) of the colonized Kavir cultivar [with the lowest root extension, SOC (i.e., 0.97 g kg−1), and HWSC (i.e., 0.059 g kg−1)] showed that S. indica can improve soil physical quality due to greater structural pores. Among the four wheat cultivars, the rhizosphere of Roshan (≈ 1.60 g kg−1) and Ghods (1.61 g kg−1) had the highest SOC values. Higher HWSC (i.e., 0.138 g kg−1) and more extensive root distribution for Roshan, especially upon co-cultivation with S. indica, led to greater contribution of structural pores in water retention in the rhizosphere soil when compared with three other cultivars. To conclude, S. indica colonization not only in the presence of plant roots and by increasing SOC and HWSC improves the physical quality of the rhizosphere soil but also the fungus by itself enhances soil physical quality due to enhancing portion of structural porosity involving in water transition and storage.