Cropland Soil Organic Carbon Research Articles

Estimating spatiotemporal variations of cropland soil organic carbon and its sequestration potential in central China from 1984 to 2019

Assessing spatial and temporal changes in soil organic carbon (SOC) over time is crucial for understanding soil fertility and its impact on the global carbon cycle. This study analyzed the spatiotemporal changes of SOC in a typical agricultural region of the North China Plain from 1984 to 2019 and explored the potential for SOC sequestration. A total of 411 soil samples were collected in 1984, with subsequent sampling in 2009 (411 samples) and 2019 (181 samples). Geostatistical Sequential Gaussian Simulation (SGS) was utilized to quantify the spatiotemporal trends of SOC changes from 1984 to 2019. The SOC sequestration potential (SOCp) was estimated using the Levenberg-Marquardt and Universal Global Optimization algorithms. The results revealed a significant elevation in mean SOC from 5.84 g kg−1 in 1984 to 10.64 g kg−1 in 2019, with an increasing trend observed from the northwest towards the southeast. The SOC increase rate during 2009–2019 (0.48 t/ha yr−1) surpassed that of 1984–2009 (0.32 t/ha yr−1). The fine-textured soil in the north showed a faster increase in SOC compared to coarse-textured soils in the south. Estimated SOCp ranged from 2.81 to 24.34 t C/ha, with a mean of 12.43 t C/ha across the area. Loam clay, silt clay, clay loam and loam in the north exhibited lower SOCp (0–10 t C/ha) compared to sandy loam in the south. The time required to reach SOC saturation (Ts) was high (>20 years) in the south, an area dominated by sandy loam, compared to the north, with a low Ts (0–20 years) covered by fine-textured soil. These findings suggest croplands might be a potential carbon sink to be developed through sustainable and efficient agricultural management in this typical agricultural region of the North China Plain, particularly in the sandy loam of the region’s southern area.

Fungal necromass contribution to carbon sequestration in global croplands: A meta-analysis of driving factors and conservation practices

Fungal necromass carbon (FNC) contributes significantly to the build-up of soil organic carbon (SOC) by supplying abundant recalcitrant polymeric melanin present in the fungal cell wall. However, the influence of a wide range of conservation practices and associated factors on FNC accumulation and contribution to SOC in global croplands remains unexplored. Here, a meta-analysis was performed using 873 observations across three continents, together with structural equation modeling, to evaluate conservation practices and factors responsible for the enhancement of FNC and SOC. FNC content (8.39 g kg−1) of North American soils was highest compared to FNC content of Asian and European soils. The structural equation models showed a significant (p < 0.05) positive influence of microbial biomass carbon (MBC), soil pH, and clay contents on the accumulation of FNC. Soil C/N ratio and climate factors, however, had only minor influences on FNC accumulation. Notably, the main driver of FNC was MBC, which is mainly influenced by the soil total N and geographic factors in the study areas. Typical 5 cropland practices had significant effect size (p < 0.05) on FNC, leading to an increase of 12 % to 26 %, and the FNC content was greatest under straw amendment (26 %). Fungal necromass accumulation efficiency ranged from 23 % to 45 % depending on cropland practices: non- and reduced tillage was the most efficient (45 %), followed by crop coverage (32 %), straw amendment (30 %), and manure application (27 %), while N fertilization had the lowest efficiency (23 %). We conclude that FNC contributes to over a quarter of SOC, highlighting its major role in enhancing C sequestration worldwide. Conservation practices, particularly non-tillage or reduced tillage, are important to enhance C sequestration from FNC in croplands.

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).

Cover crops affect pool specific soil organic carbon in cropland – A meta‐analysis

AbstractCover crops (CC) offer numerous benefits to agroecosystems, particularly in the realm of soil organic carbon (SOC) accrual and loss mitigation. However, uncertainties persist regarding the extent to which CCs, in co‐occurrence with environmental factors, influence SOC responses and associated C pools. We therefore performed a weighted meta‐analysis on the effects of CCs on the mineral‐associated organic carbon (MAOC), the particulate organic carbon (POC) and the microbial biomass carbon (MBC) pool compared to no CC cultivation in arable cropland. Our study summarized global research of comparable management, with a focus on climatic zones representative of Europe, such as arid, temperate and boreal climates. In this meta‐analysis, we included 71 independent studies from 61 articles published between 1990 and June 2023 in several scientific and grey literature databases. Sensitivity analysis was conducted and did not identify any significant publication bias. The results revealed that CCs had an overall statistically significant positive effect on SOC pools, increasing MAOC by 4.8% (95% CI: 0.6%–9.4%, n = 16), POC by 23.2% (95% CI: 13.9%–34.4%, n = 39) and MBC by 20.2% (95% CI: 11.7%–30.7%, n = 30) in the top soil, compared to no CC cultivation. Thereby, CCs feed into the stable as well as the more labile C pools. The effect of CCs on MAOC was dependent on soil clay content and initial SOC concentration, whereas POC was influenced by moderators such as CC peak biomass and experiment duration. For MBC, for example, clay content, crop rotation duration and tillage depth were identified as important drivers. Based on our results on the effects of CCs on SOC pools and significant moderators, we identified several research needs. A pressing need for additional experiments exploring the effects of CCs on SOC pools was found, with a particular focus on MAOC and POC. Further, we emphasize the necessity for conducting European studies spanning the north–south gradient. In conclusion, our results show that CC cultivation is a key strategy to promote C accrual in different SOC pools. Additionally, this meta‐analysis provides new insights into the state of knowledge regarding SOC pool changes influenced by CCs, offering quantitative summary results and shedding light on the sources of heterogeneity affecting these findings.

A high-resolution map of soil organic carbon in cropland of Southern China

An accurate and fine map of soil organic carbon (SOC) plays a vital role in understanding the global carbon cycle and achieving soil carbon sequestration potential. Although global and national maps of SOC are already available with various spatial resolutions, limited sample size, and relative coarse resolution hinder its accuracy and application in local regions. Here, we collected 13,424 soil samples and information of 45 environmental covariates from cropland in Jiangxi Province, Southern China during 2012 and 2013. Then, the optimal covariates were selected by a recursive feature elimination algorithm for mapping SOC. After that, we deployed the random forest (RF) to produce a fine map (30 m) of SOC in the cropland of Jiangxi Province and 100 times bootstrapping was performed to calculate the prediction uncertainty. Finally, we determined the impacts of various covariates on SOC variability using RF and partial least squares structural equation modeling. Our results showed that compared with the predictive model without soil management information, introducing soil management information improved the predictability of SOC with an increase in R2 by 7.35 % (0.73 vs 0.68) and decrease in RMSE by 7.03 % (2.91 vs 3.13 g kg−1). Our results well estimated the uncertainty of the predicted result with a PICP of 0.91 for a 90 % prediction interval. Soil properties and soil management activities make the largest contribution for modelling SOC. Specifically, the total nitrogen content, straw return amount, total potassium content, and multi-resolution valley bottom flatness were found as the most important factors for mapping SOC in the cropland of our study area. Overall, this study deepened our knowledge of the variation of SOC and also emphasizes that incorporating soil management information could help us to achieve more accurate predictions of SOC.

Fine Resolution Mapping of Soil Organic Carbon in Croplands with Feature Selection and Machine Learning in Northeast Plain China

Unsustainable human management has negative effects on cropland soil organic carbon (SOC), causing a decrease in soil health and the emission of greenhouse gas. Due to contiguous fields, large-scale mechanized operations are widely used in the Northeast China Plain, which greatly improves production efficiency while decreasing the soil quality, especially for SOC. Therefore, an up-to-date SOC map is needed to estimate soil health after long-term cultivation to inform better land management. Using Quantile Regression Forest, a total of 396 soil samples from 132 sampling sites at three soil depth intervals and 40 environmental covariates (e.g., Landsat 8 spectral indices, and WorldClim 2 and MODIS products) selected by the Boruta feature selection algorithm were used to map the spatial distribution of SOC in the cropland of the Northeast Plain at a 90 m spatial resolution. The results showed that SOC increased overall from the southern area to the northern area, with an average of 17.34 g kg−1 in the plough layer (PL) and 13.92 g kg−1 in the compacted layer (CL). At the vertical scale, SOC decreased, with depths getting deeper. The average decrease in SOC from PL to CL was 3.41 g kg−1. Climate (i.e., average temperature, daytime and nighttime land surface temperature, and mean temperature of driest quarter) was the dominant controlling factor, followed by position (i.e., oblique geographic coordinate at 105°), and organism (i.e., the average and variance of net primary productivity in the non-crop period). The average uncertainty was 1.04 in the PL and 1.07 in the CL. The high uncertainty appeared in the area with relatively scattered fields, high altitudes, and complex landforms. This study updated the 90 m resolution cropland SOC maps at spatial and vertical scales, which clarifies the influence of mechanized operations and provides a reference for soil conservation policy-making.

Major contributions of agricultural management practices to topsoil organic carbon distribution and accumulation in croplands of East China over three decades

Multiple natural and anthropogenic factors jointly drive the spatial distribution of soil organic carbon (SOC) and its dynamics in croplands. Among these factors, agricultural management practices have caused considerable impacts. Previous studies on the driving factors of SOC in croplands have provided significant understanding on this matter. However, whether and how the effects and interplay of these drivers change over time is often unknown, especially agricultural activities. To measure the effects of agricultural management practices on the spatial distribution and temporal change of SOC incorporating the network relationships with other natural drivers at the regional scale, we conducted partial least squares path analysis on the topsoil organic carbon content using two historical soil datasets from cropland samples in East China obtained during the 1980s and the 2010s. Eight indicators and their temporal changes reflecting climate, agricultural management, and edaphic conditions were used to quantify the driving mechanisms of the spatial distribution and temporal change of SOC. The drivers of SOC distribution showed that high SOC was mostly distributed in soils with a relatively low pH and high clay content in warm humid climates. High SOC was associated with the application of N fertilizer, crop residue input, and agricultural machinery in the 1980s and 2010s. The effect of N fertilization on SOC distribution increased from the 1980s to the 2010s, whereas the total edaphic effects significantly decreased from 0.62 to 0.25 (P < 0.01). Regarding the drivers of SOC change over the three decades, the edaphic effects presented the strongest effect (path coefficient of 0.74, P < 0.01), including the negative effects of topsoil acidity and baseline SOC level, as well as the positive effects of total nitrogen (TN) and clay content change. Croplands with lower intensity of management practices in the 1980s generally attained more development in agricultural modernization, which led to a considerable SOC increase. Our research revealed the importance of agricultural management practices on the spatial distribution and temporal change of cropland SOC at the regional scale. The results emphasize the need to measure the changing driving mechanisms of SOC dynamics. The findings indicate that indicators reflecting the effects of agricultural management practices should be included in digital mapping and process-based modeling of soil carbon at different spatiotemporal scales to improve prediction accuracy.

Soil organic carbon changes in China's croplands: A newly estimation based on DNDC model

Soil Organic Carbon (SOC) in cropland represents a significant facet of the terrestrial ecosystem's carbon reservoirs, playing a pivotal role in global climate change mitigation efforts. Within the specific context of China, cropland SOC not only extends its implications beyond environmental impact but also serves as a critical factor in ensuring the stability and security of the nation's food supply. However, there is an ongoing argument about the changes in SOC and their spatial and temporal distribution patterns within China's croplands. In this study, we constructed a new county-level DNDC database for 2020, building upon 2003 research that quantified SOC stock in China's cropland using the DNDC model. Our aim was to assess the SOC storage and temporal changes of China's cropland in 2020 using same methodology to enhance estimation accuracy. The simulation results of the validated DNDC model revealed that the average SOC storage of China's croplands (0–30 cm) in 2020 was 6.02 Pg C, with the Northeast region contributing 23 % (1.37 Pg C). The SOC density in China varied from 18.55 to 152.57 t C ha−1, averaging at 49.65 t C ha−1. In 2020, China's cropland transitioned from a net loss of SOC in 2003 to a carbon sink, with cropland SOC density and SOC storage increased by 18.2 % and 21.6 % respectively. Notably, despite experiencing a loss of SOC compared to 2003, the Northeast region had the highest average SOC density in China. This study highlights that despite the increase in SOC density and storage in China's croplands over the last 17 years, there remains substantial potential for carbon sequestration given the current spatial distribution of SOC density's significant heterogeneity within China. The findings of this study offer data support for China's strategy to achieve food security and carbon neutrality.

Validating DayCent-CR for cropland soil carbon offset reporting at a national scale

Regenerative soil management practices have been shown to increase soil organic carbon in cropland previously under conventional management, and farmers that adopt regenerative practices could be eligible to participate in carbon offset programs. Due to the high cost of soil sampling at large scales, project developers of agricultural carbon offset programs may employ a hybrid measurement and modeling approach to SOC quantification. While biogeochemical models allow for carbon crediting to occur on larger scales than soil sampling alone would allow, any model used must be unbiased and shown to adequately predict SOC changes, with known uncertainty, across the crops, practice changes, and geographies of interest. The “credit-ready” version of the DayCent ecosystem model, DayCent-CR, was evaluated for performance across 14 combinations of crops and practice categories. Model calibration and validation was performed with a Bayesian Markov chain Monte Carlo approach using k-fold cross validation and 668 SOC stock change measurements from 41 agricultural research sites. Overall model performance met the guidelines established by Climate Action Reserve’s Soil Enrichment Protocol: ≥90% of model prediction intervals covered the measured value, and mean bias in all categories was less than pooled measurement uncertainty. Importantly, posterior distributions of DayCent-CR parameters and variance components enable the calculation of variance, which can then be used to calculate an uncertainty deduction that is applied to overall project credits to ensure conservatism. The calibrated model parameters are therefore valid for use in crediting programs within the domain of the validation dataset.

Integrating microbial community properties, biomass and necromass to predict cropland soil organic carbon

Manipulating microorganisms to increase soil organic carbon (SOC) in croplands remains a challenge. Soil microbes are important drivers of SOC sequestration, especially via their necromass accumulation. However, microbial parameters are rarely used to predict cropland SOC stocks, possibly due to uncertainties regarding the relationships between microbial carbon pools, community properties and SOC. Herein we evaluated the microbial community properties (diversity and network complexity), microbial carbon pools (biomass and necromass carbon) and SOC in 468 cropland soils across northeast China. We found that not only microbial necromass carbon but also microbial community properties (diversity and network complexity) and biomass carbon were correlated with SOC. Microbial biomass carbon and diversity played more important role in predicting SOC for maize, while microbial network complexity was more important for rice. Models to predict SOC performed better when the microbial community and microbial carbon pools were included simultaneously. Taken together our results suggest that microbial carbon pools and community properties influence SOC accumulation in croplands, and management practices that improve these microbial parameters may increase cropland SOC levels.

The AMG model coupled with Rock-Eval® analysis accurately predicts cropland soil organic carbon dynamics in the Tuojiang River Basin, Southwest China

Accurate soil organic carbon models are key to understand the mechanisms governing carbon sequestration in soil and to help develop targeted management strategies to carbon budget. The accuracy and reliability of soil organic carbon (SOC) models remains strongly limited by incorrect initialization of the conceptual kinetic pools and lack of stringent model evaluation using time-series datasets. Notably, due to legacy effects of management and land use change, the traditional spin-up approach for initial allocation of SOC among kinetic pools can bring substantial uncertainties in predicting the evolution of SOC stocks. The AMG model can fulfill these conditions as it is a parsimonious yet accurate SOC model using widely-available input data. In this study, we first evaluated the performance of AMGv2 before and after optimizing the potential mineralization rate (k0) of SOC stock following a leave-one-site-out cross-validation based on 24 long-term field experiments (LTEs) in the Southwest of China. Then, we used Rock-Eval® thermal analysis results as input variables in the PARTYSOC machine learning model to estimate the initial stable SOC fraction (CS/C0) for the 14 LTEs where soil samples were available. The results showed that initializing the CS/C0 ratio using PARTYSOC combined with the optimized k0 further improved the accuracy of model simulations (R2 = 0.87, RMSE = 0.25, d = 0.90). Combining average measured CS/C0 and k0 optimization across all 24 LTEs also improved the model predictive capability by 25% compared to using default parameterization, thus suggesting promising avenue for upscaling model applications at the regional level where only a few measurement data on SOC stability can be available. In conclusion, the new version of the AMG model developed in the Tuojiang River Basin context exhibits excellent performance. This result paves the way for further calibration and validation of the AMG model in a wider set of contexts, with the potential to significantly improve confidence in SOC predictions in croplands over regional scales.

Impact of Climate Warming on Paddy Soil Organic Carbon Change in the Sichuan Basin of China

Climate warming can increase soil temperature and lead to soil carbon release, but it can also increase soil organic carbon by increasing primary productivity. Cropland soils are considered to have a huge potential to sequester carbon; however, direct observations for the responses of cropland soil organic carbon to climate warming over broad geographic scales are rarely documented. Paddy soil is one of the important cultivated soils in China. Based on the data of 2217 sampling points obtained during the second national soil survey and the data of 2382 sampling points collected during 2017-2019, this study analyzed the change characteristics of soil organic carbon content of paddy surface soil in Sichuan Basin of China and explored the relationships between the soil organic carbon change of paddy soil and temperature, precipitation, cropland use type, fertilization intensity, and grain yield. The results showed that the content of soil organic carbon of paddy soil changed from 13.33 g·kg-1to 15.96 g·kg-1, with an increase of 2.63 g·kg-1, suggesting that soils in the Sichuan Basin have acted as a carbon sink over past 40 years. The soil organic carbon increment of paddy soil varied with different geomorphic regions and different secondary basins. The increase in SOC content in paddy soil was positively correlated with annual average temperature; negatively correlated with annual average precipitation; and initially increased and then decreased with annual average fertilizer application, annual average increase rate of fertilizer application, annual average grain yield, and annual average grain yield growth rate. The relationship between the increase in SOC content and the annual average temperature growth rate was different under different farmland utilizations, and the increase in the annual average temperature growth rate had significant effects with the increase in SOC content only on paddy-dryland rotation. These results indicate that the paddy soil organic carbon change in Sichuan Basin was co-affected by various factors, but climate warming was an important factor leading to the paddy soil organic carbon change, and its influence was controlled by the water conditions determined by farmland use.

Organic amendment effects on cropland soil organic carbon and its implications: A global synthesis

Increasing soil organic carbon (SOC) content can promote soil health and crop yield. Organic amendment application is a common practice to accelerate soil carbon sequestration in cropland. The effects of different organic amendments (e.g., compost, slurry) on SOC are uncertain. Previous studies have investigated SOC response to environmental factors but not well addressed the effect of organic amendment characteristics (e.g., source, liquid/solid form, pH). Depending on 1,972 comparisons from 424 articles, we undertook a meta-analysis approach to explore organic amendment effects on SOC content and the related influence factors. The results show that organic amendment addition increased SOC by an average of 26.9% (or 5.1 Mg C ha−1). Specifically, organic amendments can enhance SOC over the long term (>20 years) or within 1.0 m depth. Organic-residue inputs increased more SOC in regions with an arid climate, alkaline soils, or no-mineral nitrogen application soils than the humid climate regions, acidic soils, and nitrogen application soils, respectively. Sewage sludge and municipal solid waste as organic amendments led to high SOC. The organic amendments' pH and liquid/solid form caused significant differences in SOC accumulation. Amendments with a relatively low carbon content or rich nitrogen content have great potential to increase SOC storage. Our results illustrate the importance of amendment characteristics when estimating the potential of organic amendment addition to soil carbon accumulation. This study highlights that amendment application might be a sustainable strategy to increase soil carbon storage while promoting organic waste utilization.

Soil organic carbon sequestration potential for croplands in Finland over 2021–2040 under the interactive impacts of climate change and agricultural management

CONTEXTCropland soil organic carbon (SOC) stock can be increased by agricultural management, but is subject to various factors. The extent and rates of SOC sequestration potential, as well as the controlling factors, under different climate and management practices across a region or country are important for policy-makers and land managers, however have been rarely known. OBJECTIVEWe aim to investigate the extent and rates of SOC sequestration potential over 2021–2040 under different scenarios of climate change and Sustainable Soil Management (SSM) practices, and quantify the impacts of climate change and SSM practices on the SOC sequestration potential, for croplands across Finland at a spatial resolution of 1 km. METHODSRothC model is run iteratively to equilibrium to calculate the size of the SOC pools and the annual plant carbon inputs. Then, it is applied to investigate the SOC sequestration potential over 2021–2040 under different scenarios of climate change and SSM practices. Finally, facorial simulation experiments are conducted to quantify the impacts of climate change and SSM practices, alone and in combination, on SOC sequestration potential. RESULTS AND CONCLUSIONUnder the combined impacts of climate change and SSM practices, the SOC sequestration potential during 2021–2040 relative to 2020 will be on average − 0.03, 0.007, 0.05, and 0.13 t C ha−1 yr−1, respectively, with carbon input being business as usual, 5%, 10%, and 20% increase. This is equivalent to an annual change rate of −0.04%, 0.009%, 0.07%, and 0.17%, respectively. Therefore, a 20% increase in C input to soil will not be enough to obtain a 4‰ increase per year over the 20-year period in Finland. Carbon input will promote SOC sequestration potential; however, climate change will reduce it on average by 0.28 t C ha−1 yr−1. Across the cropland in Finland, on average, the relative contributions of C input, temperature, and precipitation to SOC sequestration potential in 2021–2040 will be 56%, 24%, and 20%, respectively, however there is a spatially explicit pattern. The SOC sequestration potential will be relatively high and dominated by C input in west and southwest Finland. By contrast, it will be relatively low and dominated by climate in north and east Finland, and the central part of southern Finland. SIGNIFICANCEOur findings provide the information as to where, how much, and which SSM practices could be applied for enhancing SOC sequestration at a high spatial resolution, which is essential for stakeholders to increase cropland SOC sequestration efficiently.

Modelling the spatiotemporal dynamics of cropland soil organic carbon by integrating process-based models differing in structures with machine learning

Modelling the spatiotemporal dynamics of cropland soil organic carbon by integrating process-based models differing in structures with machine learning

Profile soil organic and inorganic carbon sequestration in maize cropland after long-term straw return

Promoting cropland carbon (C) sequestration through straw return has always been the focus of numerous studies. However, there is still a lack of comprehensive understanding of the straw return effects on soil organic carbon (SOC) and soil inorganic carbon (SIC) sequestration. Therefore, the present study aims to investigate the effects of long-term straw return on SOC and SIC sequestration across the 0–100 cm soil profile in the maize planting cropland in Northeastern China. The results showed an increasing trend in SOC contents in the 0–100 cm soil profile following long-term straw return, while significant decreases in SIC contents were observed in the surface (0–20 cm) and subsoil (20–60 cm) layers, respectively. In addition, the SOC stock increased significantly in the subsoil layer following long-term straw return, by an average value of 44%, which is higher than those observed in other soil layers. On the other hand, the SIC stock in the subsoil layer increased by an average value of 24% and decreased in the surface and under-subsoil layers by average values of 53% and 33%, respectively. Moreover, the exchangeable calcium contents were positively correlated with SOC and SIC stock, demonstrating the soil calcium contributes to SOC and SIC sequestration. The present study highlighted the importance of the subsoil layer for effective straw return strategies in cropland to promote SOC and SIC sequestration in croplands.

Incorporating agricultural practices in digital mapping improves prediction of cropland soil organic carbon content: The case of the Tuojiang River Basin

Accurate mapping of soil organic carbon (SOC) in cropland is essential for improving soil management in agriculture and assessing the potential of different strategies aiming at climate change mitigation. Cropland management practices have large impacts on agricultural soils, but have rarely been considered in previous SOC mapping work. In this study, cropland management practices including carbon input (CI), length of cultivation (LC), and irrigation (Irri) were incorporated as agricultural management covariates and integrated with natural variables to predict the spatial distribution of SOC using the Extreme Gradient Boosting (XGBoost) model. Additionally, we evaluated the performance of incorporating agricultural management practice variables in the prediction of cropland topsoil SOC. A case study was carried out in a traditional agricultural area in the Tuojiang River Basin, China. We found that CI was the most important environmental covariate for predicting cropland SOC. Adding cropland management practices to natural variables improved prediction accuracy, with the coefficient of determination (R2), the root mean squared error (RMSE) and Lin's Concordance Correlation Coefficient (LCCC) improving by 16.67%, 17.75% and 5.62%, respectively. Our results highlight the effectiveness of incorporating agricultural management practice information into SOC prediction models. We conclude that the construction of spatio-temporal database of agricultural management practices derived from inventories is a research priority to improve the reliability of SOC model prediction.

Cropland carbon stocks driven by soil characteristics, rainfall and elevation

Soil organic carbon (SOC) can influence atmospheric CO2 concentration and then the extent to which the climate emergency is mitigated globally. It follows the elucidation of the driving factors of cropland SOC stocks, which is fundamental to reducing soil carbon loss and promoting soil carbon sequestration. Here, we examined the influence of 16 environmental variables on SOC stocks and sequestration based on three machine learning soil mapping methods, i.e. multiple linear regression (MLR), random forest (RF) and extreme gradient boosting (XGBOOST), with 2875 observed soil samples from cropland topsoil across Hunan Province, China in 2010. We employed a structural equation model (SEM) to extricate the driving mechanisms of environmental variables on SOC stocks at the regional scale. Our results show that XGBOOST had the most reliable performance in predicting SOC stocks, explaining 66 % of the total SOC stock variation. Croplands with high SOC stocks were distributed in low-altitude and water-sufficient areas. The partial dependence of SOC on precipitation showed a trend of increasing and then slowly decreasing. In addition, the grid-based SEM results clearly presented the direct and indirect routes of environmental variables' impacts on cropland SOC stocks. Soil properties regulated by elevation, were the most influential natural factor on SOC stocks. Precipitation and elevation drove SOC stocks through direct and indirect effects respectively. Our SEM combined with machine learning approach can provide an effective explanation of the driving mechanism for SOC accumulation. We expect our proposed modelling approach can be applied to other regions and offer new insights, as a reference for mitigating cropland soil carbon loss under climate emergency conditions.

Characteristics of Soil Organic Carbon in Croplands and Affecting Factors in Hubei Province

SOC storage (SOCS) plays a vital role in global climate change. Understanding the spatial pattern and features of soil organic carbon (SOC) and its influencing factors is important for increasing SOC fixation. However, few studies exist on the organic carbon reserves of farmland on a regional scale. This study revealed the SOCD and SOCS values and distribution using Hubei Province as a sampling region. The results demonstrated that the spatial distributions of farmland system carbon storage and density were uneven, and the spatial heterogeneity was related to geography, cultivated area, and soil type. The SOCD ranged from 0.559 to 10.613 kg/m2, with an average of 3.3710 ± 0.0337 kg/m2, and the soil carbon reserve of the farmland system was ~17.81 Tg. The SOCD varied with topography and soil type: in mountainous cultivated land, it was generally higher than that in hilly land and in the plains. However, the plain cultivated areas contained the highest carbon reserves. Within the farmland system, paddy soil, the dominant soil type, exhibited higher SOCD and larger SOC storage capacity. Soil types with the same physicochemical properties exhibited different organic carbon storage capacity in different geomorphic and regional environments. Specifically, paddy soil was found to have higher SOCD and SOCS than the other soil types, and its soil carbon storage capacity was high; the SOC reserves of wheat-rice tillage were the largest among the main tillage methods. Boosting the soil carbon sink requires fundamental improvement in soil properties by improving soil texture, using conservation tillage to increase soil organic matter, and reducing unnecessary human interference.

Effects of land clearing for agriculture on soil organic carbon stocks in drylands: A meta-analysis.

Agricultural activities have been expanding globally with the pressure to provide food security to the earth's growing population. These agricultural activities have profoundly impacted soil organic carbon (SOC) stocks in global drylands. However, the effects of clearing natural ecosystems for cropland (CNEC) on SOC are uncertain. To improve our understanding of carbon emissions and sequestration under different land uses, it is necessary to characterize the response patterns of SOC stocks to different types of CNEC. We conducted a meta-analysis with mixed-effect model based on 873 paired observations of SOC in croplands and adjacent natural ecosystems from 159 individual studies in global drylands. Our results indicate that CNEC significantly (p < .05) affects SOC stocks, resulting from a combination of natural land clearing, cropland management practices (fertilizer application, crop species, cultivation duration) and the significant negative effects of initial SOC stocks. Increases in SOC stocks (in 1m depth) were found in croplands which previously natural land (deserts and shrublands) had low SOC stocks, and the increases were 278.86% (95% confidence interval, 196.43%-361.29%) and 45.38% (26.53%-62.23%), respectively. In contrast, SOC stocks (in 1m depth) decreased by 24.11% (18.38%-29.85%) and 10.70% (1.80%-19.59%) in clearing forests and grasslands for cropland, respectively. We also established the general response curves of SOC stocks change to increasing cultivation duration, which is crucial for accurately estimating regional carbon dynamics following CNEC. SOC stocks increased significantly (p < .05) with high long-term fertilizer consumption in cleared grasslands with low initial SOC stocks (about 27.2Mg ha-1 ). The results derived from our meta-analysis could be used for refining the estimation of dryland carbon dynamics and developing SOC sequestration strategies to achieve the removal of CO2 from the atmosphere.