Variability Of Soil Organic Carbon Stocks Research Articles

Mapping sub-surface distribution of soil organic carbon stocks in South Africa's arid and semi-arid landscapes: Implications for land management and climate change mitigation

Soil organic carbon (SOC) stocks are critical for land management strategies and climate change mitigation. However, understanding SOC distribution in South Africa's arid and semi-arid regions remains a challenge due to data limitations, and the complex spatial and sub-surface variability in SOC stocks driven by desertification and land degradation. Thus, to support soil and land-use management practices as well as advance climate change mitigation efforts, there is an urgent need to provide more precise SOC stock estimates within South Africa's arid and semi-arid regions. Hence, this study adopted remote-sensing approaches to determine the spatial sub-surface distribution of SOC stocks and the influence of environmental co-variates at four soil depths (i.e., 0-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm). Using two regression-based algorithms, i.e., Extreme Gradient Boosting (XGBoost) and Random Forest (RF), the study found the former (RMSE values ranging from 7.12 t/ha to 29.55 t/ha) to be a superior predictor of SOC in comparison to the latter (RMSE values ranging from 7.36 t/ha to 31.10 t/ha). Nonetheless, both models achieved satisfactory accuracy (R2 ≥ 0.52) for regional-scale SOC predictions at the studied soil depths. Thereafter, using a variable importance analysis, the study demonstrated the influence of climatic variables like rainfall and temperature on SOC stocks at different depths. Furthermore, the study revealed significant spatial variability in SOC stocks, and an increase in SOC stocks with soil depth. Overall, these findings enhance the understanding of SOC dynamics in South Africa's arid and semi-arid landscapes and emphasizes the importance of considering site specific topo-climatic characteristics for sustainable land management and climate change mitigation. Furthermore, the study offers valuable insights into sub-surface SOC distribution, crucial for informing carbon sequestration strategies, guiding land management practices, and informing environmental policies within arid and semi-arid environments.

Changes in the Soil Organic Carbon of Grasslands in the High Andes of Peru after Their Conversion to Croplands and Their Environmental Controls

The high-Andean grasslands of Peru provide a wide range of goods and services, not only locally, but also regionally and globally. However, land-use change and global warming are threatening these ecosystems, of which soil organic carbon (SOC) is a key element affecting their sustainability. In this study, we have analyzed the variation of SOC stocks to a depth of 20 cm in 16 paired cropland and grassland sites located in the Sullccapallcca stream micro-watershed (elevation > 3600 m.a.s.l., Ayacucho, Peru). We have also analyzed the environmental controls on the SOC stocks and their variation with land-use change. We found that the studied high-Andean grasslands store high SOC contents (247 Tn SOC ha−1), whose spatial variability was partially explained by the slope of the terrain (r2 = 0.26, p < 0.05). Despite the higher NDVI, the conversion of these grasslands into croplands decreased the SOC stock by 39 Tn SOC ha−1 on average, a decrease that was more pronounced when the initial SOC content of the grassland was higher (r2 = 0.60, p < 0.05). This study provides the first evidence of the effects of land-use change on the SOC in the region, although the mechanisms involved still need to be investigated.

Factors controlling the spatial heterogeneity of soil organic carbon concentrations and stocks in a boreal forest

Boreal soils play a crucial role in the global terrestrial carbon cycle, serving as significant carbon reservoirs. However, this region is experiencing rapid climate change which poses a threat to the stability of the soil carbon pool. To understand the carbon balance in the boreal region, it is essential to obtain accurate estimates of soil organic carbon (SOC) stocks. The objective of this study was to investigate factors controlling the spatial heterogeneity of SOC concentrations and stocks in a boreal forested catchment and to quantify spatial variability of SOC concentrations and stocks using a random forest-based regression kriging approach. Soil carbon content, gravel content and bulk density were obtained from the surface soil layer (0–30 cm) of 106 samples across a 390 ha catchment in northwestern Ontario, Canada. Spatial attributes including elevation, wetness index, distance to nearest ridge, relative position index, plan curvature, profile curvature and normalized difference vegetation index were used in the random forest-based regression kriging to estimate spatial heterogeneity of SOC stocks and concentrations across the catchment. Our results showed that shallow soils in well-drained upland areas contained low to moderate SOC concentrations (1.2–16.2%) while depressional wetlands had high concentrations (19.2–54.0%). Topographic variables, including wetness index were the strongest predictors of SOC concentrations, but SOC stocks were very heterogeneous across the study site (12.9–139.0 Mg ha−1) and were not well explained by topographic or vegetation variables. The gravel content was highly variable (0–29.0%), and without accounting for this fraction, SOC stocks were overestimated by 52.5% in this landscape. These results emphasize the substantial catchment scale variability in SOC stocks in boreal landscapes and underscore the significance of considering both landscape-scale drivers and intrinsic soil properties when estimating soil carbon in this region. By understanding these factors, we can better manage and protect the important carbon stores in boreal soils.

Soil Organic Carbon Stock Variation under Different Soil Types and Land Uses in the Sub-Humid Noun Plain, Western Cameroon

Soil Organic Carbon Stock Variation under Different Soil Types and Land Uses in the Sub-Humid Noun Plain, Western Cameroon

Maximizing soil organic carbon stocks under cover cropping: insights from long-term agricultural experiments in North America

Cover crops are widely advocated for increasing soil organic carbon (SOC) levels, thereby benefiting soil health improvement and climate change mitigation. Few regional-scale studies have robustly explored SOC stocks under cover cropping, due to limited long-term experiments. We used the unique experimental data from the North American Project to Evaluate Soil Health Measurements conducted in 2019 to address this issue. This study included 19 agricultural research sites with 36 pairs of cover cropping established between 1896 and 2014. Explanatory variables related to site-specific environmental conditions and management practices were collected to identify and prioritize contributing factors that affect SOC stocks with cover crops, by coupling the Boruta algorithm and structural equation modeling. Overall, cover crops significantly (P < 0.05) improved several indicators of soil health, including greater SOC (concentration: +8%; stock: +7%), total nitrogen (+8%), water-stable aggregates (+15%), and potential carbon mineralization (+34%), on average, compared to no cover crop control. Likewise, on average, cover crops sequestered SOC 3.55 Mg C ha-1 (0–15 cm depth), with a sequestration rate of 0.24 Mg C ha-1 yr-1. In addition, we found climate (Hargreaves climatic moisture deficit) was important in explaining the variation of SOC stocks with cover crops, followed by soil properties (e.g., soil clay content). In terms of management practices, cover crop type had a significant positive (0.36) effect on SOC stocks, with non-legumes showing a greater impact, compared to legumes and mixtures. Crop rotational diversity also had a positive (0.28) effect on SOC accumulation. Our findings suggested that integrating non-legume cover crops into diverse crop rotation is likely to be a promising strategy to maximize SOC stocks with cover crops across North America.

Change in soil organic carbon storage as influenced by forestland and grassland conversion to cropland in Canada

Conversion of native grassland and forest to agricultural land results in significant losses of soil organic carbon (SOC). However, the quantity of SOC loss upon conversion of native grassland and forest varies with climate, soil texture and agricultural management practices. In this study, we quantified the change of SOC stock as influenced by native grassland and forest conversion to agricultural soils in Canada through a meta-analysis. We determined both the absolute and relative change of SOC from its native state upon the conversion to agriculture. In more humid and temperate region of Eastern Canada losses of SOC upon the conversion of forest to agricultural land varied with soil textures, on an absolute basis, 18 t C ha−1 for the coarse-textured soil, 43 t C ha−1 for the medium-textured soil, and 65 t C ha−1 for the fine-textured soil from its native vegetation; corresponding, on a relative basis, to 22%, 30% and 32%, respectively. On the Canadian prairies, forestland conversion to cropland resulted in losses of 27 t C ha−1 or 25% for the medium-textured soil whereas there was no change in SOC for the coarse-textured soil. On the Canadian prairies, the conversion of native grassland to agriculture resulted in losses of SOC, on average from 15, 26, and 5 t C ha−1 in coarse-, medium- and fine-textured soils, respectively. However, because of high inherent variation in SOC stocks among the various soil textures, losses of SOC upon grassland conversion, on a relative basis, were highly variable (14–26%), and no statistically significant differences were observed among soil texture classes. The results of this work clearly show that climate and soil texture are main drivers of SOC change after the conversion of native grassland and forest to agriculture and are key factors that must be considered in the development of either national or regional SOC models. The results of this study suggest that more detailed stratification of samples across soil textures and climates improve the accuracy of estimates of SOC loss associated with native grassland and forest conversion to agriculture.

Storage and controlling factors of soil organic carbon in alpine wetlands and meadow across the Tibetan Plateau

AbstractAlpine wetlands and meadows across the Three Rivers Source Region (TRSR) store high soil organic carbon (SOC). However, information on factors affecting SOC storage is scanty. Herein, we investigated SOC storage and explored factors affecting SOC storage, including climate, soil properties and above‐ and belowground biomass, using 50 soil profiles across the TRSR on the Tibetan Plateau. The SOC storage was 491.9 ± 158.5 Tg C and 545.2 ± 160.8 Tg C in the TRSR alpine wetlands and meadow, respectively. The SOC stock was positively correlated with the mean annual precipitation. However, no significant correlation between SOC stock and mean annual temperature was observed, as opposed to the global trend. In addition, SOC stock was positively correlated with both the aboveground biomass (AGB) and belowground biomass (BGB). Soil pH indirectly affected SOC stock, while SOC stock positively correlated with Al and Fe oxyhydroxides. Compared with vegetation biomass and climatic factors, soil properties, including soil pH and Al and Fe oxyhydroxides (Alo and Feo), affected not only SOC stock variation but also affected the impact of vegetation and climatic factors on SOC stock. Climate factors, AGB, BGB, soil pH, Alo and Feo jointly accounted for 59% of SOC stock variation in alpine wetlands and 64% of SOC stock variation in alpine meadow. This study suggests that soil properties are the dominant factors affecting SOC variation in alpine wetlands and meadow on the Tibetan Plateau.

Soil properties affecting soil organic carbon stock of different land use types in two agro-ecological zones of Nigeria

Soil organic carbon (SOC) stock is the carbon proportion that is of biological origin stored with respect to soil depth. It is more prone to loss than soil inorganic carbon. High sequestration of SOC in soil is germane to the improvement of soil quality and mitigating impact of climate change. Thus, this study was carried out to evaluate soil properties affecting SOC stock in the two agro-ecological zones, namely Upland Rainforest (Ado-Ekiti) and southern Guinea Savanna (Kabba) agro-ecological zones of Nigeria, with respect to three selected land use types; arable, oil palm and wetland. Random collection of soil samples was carried out at four varying depths (0-15, 15-30, 30-45, and 45-60 cm) for evaluation of soil properties. The SOC stock regression models for the two agro-ecological zones showed that land use, SOC, pH, SOM (soil organic matter), Ex-K, Ex-Ca, Ex-Mg (exchangeable potassium, calcium, magnesium, respectively), BD (bulk density), and gravel content would predict variation in SOC stock in the two agro-ecological zones with coefficient of determination (R2) values of 0.952 and 0.996 for Kabba and Ado-Ekiti respectively. Principal component analysis identified that in the soil of the southern Guinea Savanna agro-ecological zone, SOC, CEC, EA, Ex-Na, land use, clay content, and soil depth with Eigenvalues &gt; 1 explained 80.58% of sample variance while in the soil of the Upland Rainforest agro-ecological zone, Ex-Ca, SOC, Ex-Na, EA, sand and silt contents with Eigenvalues &gt; 1 explained 78.81% of sample variance as potential determinants of SOC stock.

Improvement of spatial estimation for soil organic carbon stocks in Yuksekova plain using Sentinel 2 imagery and gradient descent-boosted regression tree.

Carbon sequestration in earth surface is higher than the atmosphere, and the amount of carbon stored in wetlands is much greater than all other land surfaces. The purpose of this study was to estimate soil organic carbon stocks (SOCS) and investigate spatial distribution pattern of Yuksekova wetlands and surrounding lands in Hakkari province of Turkey using machine learning and remote sensing data. Disturbed and undisturbed soil samples were collected from 10-cm depth in 50 locations differed with land use and land cover. Vegetation, soil, and moisture indices were calculated using Sentinel 2 Multispectral Sensor Instrument (MSI) data. Significant correlations (p≤0.01) were obtained between the indices and SOCS; thus, the remote sensing indices (ARVI 0.43, BI -0.43, GSI -0.39, GNDI 0.44, NDVI 0.44, NDWI 0.38, and SRCI 0.51) were used as covariates in multi-layer perceptron neural network (MLP) and gradient descent-boosted regression tree (GBDT) machine learning models. Mean absolute error, root mean square error, and mean absolute percentage error were 3.94 (Mg C ha -1), 6.64 (Mg C ha-1), and 9.97%, respectively. The simple ratio clay index (SRCI), which represents the soil texture, was the most important factor in the SOCS estimation variance. In addition, the relationship between SRCI and Topsoil Grain Size Index revealed that topsoil clay content is a highly important parameter in spatial variation of SOCS. The spatial SOCS values obtained using the GBDT model and the mean SOCS values of the CORINE land cover classes were significantly different. The land cover has a significant effect on SOC in Yuksekova plain. The mean SOCS for continuously ponded fields was 45.58 Mg C ha-1, which was significantly different from the mean SOCS of arable lands. The mean SOCS in arable lands, with significant areas of natural vegetation, was 50.22 Mg C ha-1 and this amount was significantly higher from the SOCS of other land covers (p<0.01). The wetlands had the highest SOCS (61.46 Mg C ha-1), followed by the lands principally occupied by natural vegetation and used as rangelands around the wetland (50.22 Mg C ha-1). Environmental conditions had significant effect on SOCS in the study area. The use of remote sensing indices instead of using single bands as estimators in the GBDT algorithm minimized radiometric errors, and reliable spatial SOCS information was obtained by using the estimators. Therefore, the spatial estimation of SOCS can be successfully determined with up-to-date machine learning algorithms only using remote sensing predictor variables. Reliable estimation of SOCS in wetlands and surrounding lands can help understand policy and decision makers the importance of wetlands in mitigating the negative impacts of global warming.

Shapley values reveal the drivers of soil organic carbon stock prediction

Abstract. Insights into the controlling factors of soil organic carbon (SOC) stock variation are necessary both for our scientific understanding of the terrestrial carbon balance and to support policies that intend to promote carbon storage in soils to mitigate climate change. In recent years, complex statistical and algorithmic tools from the field of machine learning have become popular for modelling and mapping SOC stocks over large areas. In this paper, we report on the development of a statistical method for interpreting complex models, which we implemented for the study of SOC stock variation. We fitted a random forest machine learning model with 2206 measurements of SOC stocks for the 0–50 cm depth interval from mainland France and used a set of environmental covariates as explanatory variables. We introduce Shapley values, a method from coalitional game theory, and use them to understand how environmental factors influence SOC stock prediction: what is the functional form of the association in the model between SOC stocks and environmental covariates, and how does the covariate importance vary locally from one location to another and between carbon-landscape zones? Results were validated both in light of the existing and well-described soil processes mediating soil carbon storage and with regards to previous studies in the same area. We found that vegetation and topography were overall the most important drivers of SOC stock variation in mainland France but that the set of most important covariates varied greatly among locations and carbon-landscape zones. In two spatial locations with equivalent SOC stocks, there was nearly an opposite pattern in the individual covariate contribution that yielded the prediction – in one case climate variables contributed positively, whereas in the second case climate variables contributed negatively – and this effect was mitigated by land use. We demonstrate that Shapley values are a methodological development that yield useful insights into the importance of factors controlling SOC stock variation in space. This may provide valuable information to understand whether complex empirical models are predicting a property of interest for the right reasons and to formulate hypotheses on the mechanisms driving the carbon sequestration potential of a soil.

Spatial-temporal variations and driving factors of soil organic carbon in forest ecosystems of Northeast China

Forest soil carbon is a major carbon pool of terrestrial ecosystems, and accurate estimation of soil organic carbon (SOC) stocks in forest ecosystems is rather challenging. This study compared the prediction performance of three empirical model approaches namely, regression kriging (RK), multiple stepwise regression (MSR), random forest (RF), and boosted regression trees (BRT) to predict SOC stocks in Northeast China for 1990 and 2015. Furthermore, the spatial variation of SOC stocks and the main controlling environmental factors during the past 25 years were identified. A total of 82 (in 1990) and 157 (in 2015) topsoil (0–20 ​cm) samples with 12 environmental factors (soil property, climate, topography and biology) were selected for model construction. Randomly selected 80% of the soil sample data were used to train the models and the other 20% data for model verification using mean absolute error, root mean square error, coefficient of determination and Lin's consistency correlation coefficient indices. We found BRT model as the best prediction model and it could explain 67% and 60% spatial variation of SOC stocks, in 1990, and 2015, respectively. Predicted maps of all models in both periods showed similar spatial distribution characteristics, with the lower SOC in northeast and higher SOC in southwest. Mean annual temperature and elevation were the key environmental factors influencing the spatial variation of SOC stock in both periods. SOC stocks were mainly stored under Cambosols, Gleyosols and Isohumosols, accounting for 95.6% (1990) and 95.9% (2015). Overall, SOC stocks increased by 471 ​Tg ​C during the past 25 years. Our study found that the BRT model employing common environmental factors was the most robust method for forest topsoil SOC stocks inventories. The spatial resolution of BRT model enabled us to pinpoint in which areas of Northeast China that new forest tree planting would be most effective for enhancing forest C stocks. Overall, our approach is likely to be useful in forestry management and ecological restoration at and beyond the regional scale.

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.

Species Traits and Geomorphic Setting as Drivers of Global Soil Carbon Stocks in Seagrass Meadows

AbstractOur knowledge of the factors that can influence the stock of organic carbon (OC) that is stored in the soil of seagrass meadows is evolving, and several causal effects have been used to explain the variation of stocks observed at local to national scales. To gain a global‐scale appreciation of the drivers that cause variation in soil OC stocks, we compiled data on published species‐specific traits and OC stocks from monospecific and mixed meadows at multiple geomorphological settings. Species identity was recognized as an influential driver of soil OC stocks, despite their large intraspecific variation. The most important seagrass species traits associated with OC stocks were the number of leaves per seagrass shoot, belowground biomass, leaf lifespan, aboveground biomass, leaf lignin, leaf breaking force and leaf OC plus the coastal geomorphology of the area, particularly for lagoon environments. A revised estimate of the global average soil OC stock to 20 cm depth of 15.4 Mg C ha−1 is lower than previously reported. The largest stocks were still recorded in Mediterranean seagrass meadows. Our results specifically identify Posidonia oceanica from the Mediterranean and, more generally, large and persistent species as key in providing climate regulation services, and as priority species for conservation for this specific ecosystem service.

Topography and land use impact erosion and soil organic carbon burial over decadal timescales

To provide a more accurate assessment of soil organic carbon (SOC) stocks in agroecosystems, sampling strategies should be designed in a way that accounts for topographic variability and soil redistribution via erosion and deposition. While the importance of topography for influencing soil properties is long established, recent advances in digital elevation models (DEMs) permit more rapid assessment of topographic attributes. We determined primary terrain attributes from a 3 m DEM to guide sampling in cultivated fields and nearby grasslands in Southeastern Minnesota, USA. Soil samples were analyzed to quantify SOC stocks and activity of 137Cs, a radioisotope employed to track soil movement over decadal timescales. In addition to soil depth, digital terrain attribute values were important for developing predictive models in both cropland and grassland sites but contributed more to predictions in cropland soils. Profile and planform curvature were significant terms in the regression model for both cropland and grassland soils. Additionally, percent slope was also significant in models for cropland soils. Overall, regression models were able to explain 90% and 73% of variability in SOC stocks observed in grassland and cropland sites, respectively. Soil profile 137Cs inventories were correlated with SOC stocks in both land cover types and show that soil movement can explain SOC variability in upland soils. Soil erosion or deposition rates (based on 137Cs inventories) ranged from 34.8 (erosion) to −70.1 (deposition) t ha−1 yr−1 in cropland sites and from 11.7 to −17.2 t ha−1 yr−1in grassland sites. Results from this study showed that soil redistribution can result in burial of eroded OC-rich surface soil in depositional locations characterized by low slopes and concave curvature in agricultural fields.

Influences of slope aspects on soil properties of Anatolian black pine forests in the semiarid region of Turkey

This study assessed the influences of slope aspect on soil physicochemical properties (soil quality indicators) on contiguous south-north facing slopes and hill plain (ridge crest) of the mid-mountain in Central Anatolia, having the same climate, vegetation and parent material. Anatolian black pine (Pinus nigra subsp. Pallasiana var. Pallasiana (Arnold)) was the dominant, and Cedar (Cedrus libani A. Richard), oak (Quercus sp.) were the co-dominant species in all aspects, whereas juniper (Juniperus sp.) was only recorded in north-facing forests. Some of the soil properties were determined on a grid with a 50 m sampling distance on the topsoil (0-15 cm depth). Accordingly, a total of 150 samples were taken from the three adjacent aspects. The data was analyzed using one-way ANOVA statistical methods. The investigated soil variables were soil organic matter (SOM), soil organic carbon (SOC), total nitrogen (TN), bulk density (BD), texture, lime (CaCO3), and pH. The showed that differences between SOC, BD and soil texture were statistically significant at 0.05 levels. Topographic aspect induced microclimatic differences were found to be important factors for the significant variations in SOC stocks. The resulting analyses showed no significant variation (p &lt; 0.05) across slope aspects for SOM, TN, lime, and pH. The differences may be attributed to topographic aspect induced microclimatic differences, which cause differences in the biotic soil component and organic matter trend and affect soil fertility. These results suggest that the slope aspect affects the soils of mountain forests through their direct influence on radiation, evaporation, biological activity, and soil moisture content.

Soil organic carbon and fine particle stocks along a volcanic chrono- and elevation-sequence on the Galápagos archipelago/Ecuador

The Galápagos archipelago consists of a chain of volcanic islands of different ages. We used a chronosequence and altitudinal sequence approach to investigate the influence of soil age (1.5 to 1070 ka) and climate (arid to very humid; 47–866 m a.s.l.) on the stocks of soil organic carbon (SOC) and stocks of silt- and clay-sized particles.We determined SOC stocks of the entire developed profiles. The youngest soil of the chronosequence had the lowest SOC stock (7.7 kg m−2), and a first maximum of 21.6 kg m−2 was observed in an allophanic Andosol (at 4.3 ka age), followed by somewhat lower SOC stocks in older soils and a second maximum in the oldest and particularly deeply developed soil (25.6 kg m−2). In the highly weathered soils, the share of subsoil SOC stocks (>20 cm) was 50% or more. Climatic differences induced similar variability in SOC stocks (0.8 to 20.3 kg m−2) as did soil age. The SOC stocks along the chronosequence were likely influenced by several factors with partly cancelling effects, i.e. increasing solum thickness with soil age and increasing proportions of fine fractions, but decreasing specific surface area related with changes in the mineralogical composition of the soils from allophanes to increasingly crystalline Fe oxides.Stocks of silt+clay-sized particles varied from 9.6 to 1724 kg m−2 across all soil profiles studied. Along the chronosequence, the clay particle formation rate decreased logarithmically from >90 g m−2 a−1 at the youngest to <0.2 g m−2 a−1 at the oldest sites. Soil age (under humid climate) and changes in moisture regime showed similarly strong effects on the stocks of fine fractions. An important finding of general relevance was the logarithmical correlation of stocks of fine particle fractions and SOC stocks, showing that with increasing stocks of silt- and clay-sized particles in older soils, SOC binding relative to fine particle stocks became less, likely due to altered mineralogical composition and microstructure.

Defining Quantitative Targets for Topsoil Organic Carbon Stock Increase in European Croplands: Case Studies With Exogenous Organic Matter Inputs

The EU Mission Board for Soil Health and Food proposed a series of quantitative targets for European soils to become healthier. Among them, current soil organic carbon (SOC) concentration losses in croplands (0.5% yr−1 on average at 20 cm depth) should be reversed to an increase of 0.1–0.4% yr−1 by 2030. Quantitative targets are used by policy makers to incentivize the implementation of agricultural practices that increase SOC stocks. However, there are different approaches to calculate them. In this paper, we analyzed the effect of exogenous organic matter (EOM) inputs on the evolution of SOC stocks, with a particular focus on the new European targets and the different approaches to calculate them. First, we illustrated through two case-study experiments the different targets set when the SOC stock increase is calculated considering as reference: 1) the SOC stock level at the onset of the experiment and 2) the SOC stock trend in a baseline, i.e., a control treatment without EOM addition. Then, we used 11 long-term experiments (LTEs) with EOM addition in European croplands to estimate the amount of carbon (C) input needed to reach the 0.1 and 0.4% SOC stock increase targets proposed by the Mission Board for Soil Health and Food, calculated with two different approaches. We found that, to reach a 0.1 and 0.4% increase target relative to the onset of the experiment, 2.51 and 2.61 Mg C ha−1 yr−1 of additional C input were necessary, respectively. Reaching a 0.1 and 0.4% increase target relative to the baseline required 1.38 and 1.77 Mg C ha−1 yr−1 of additional input, respectively. Depending on the calculation method used, the estimated amounts of additional C input required to reach each quantitative target were significantly different from each other. Furthermore, the quality of C input as represented by the C retention rate of the additional organic material (EOM and crop residue), had a significant effect on the variation of SOC stocks. Our work highlights the necessity to take into consideration the additional C input required to increase SOC stocks, especially for soils with decreasing SOC stocks, when targets are set independently of the baseline.

Spatial prediction of soil organic carbon stocks in an arid rangeland using machine learning algorithms.

Assessing the role of machine learning (ML) models concerning environmental predictors on spatial variation of soil organic carbon stocks (SOCS) in arid rangelands is very necessary. This study was conducted to explore the variability of surface SOCS in rangeland in the west of Iran using ML approaches. A number of 33 environmental predictors derived from Sentinel-2B and DEM were used. The optimal soil sampling (n = 80) position was determined by Latin hypercube sampling (cLHS) method. Robust and popular random Forest (RF), cubist (CB) along with random forest-ordinary kriging (RF-OK), and cubist-ordinary kriging (CB-OK) hybrid ML models were applied to the prediction of SOCS. Ten-fold CV was implemented for modeling performance and uncertainty map. According to data analysis, the maximum, minimum, and average values of SOCS are 44.50, 10.50, and 20.50 (ton. ha-1) at the surface depth (0-30cm), respectively. In general, normalized and standardized height covariates had a higher effect related to other predictors. On the other hand, two remote sensing (RS) indices, including salinity ratio (salinity) and GNDVI index, had a better impact on SOCS variability. The external validation of model performance indicated that RF-OK with (R2 = 0.75, RMSE = 6.33 ton. ha-1) with the high and low uncertainty range (3.33-9.50 ton. ha-1) was the outperformed ML model in compare with other models as RF (R2 = 0.65, RMSE = 7.38 ton. ha-1), CB-OK (R2 = 0.56, RMSE = 9.22 ton. ha-1), and CB (R2 = 0.33, RMSE = 10.42 ton. ha-1). In general, the hybrid models improved the accuracy of RF and CB with increased 0.11 until 0.23 of R2, and 1.05 to 1.2 (ton. ha-1) decreased RMSE of model's prediction. Hence, we conclude that the topographic attributes (especially normalized and standardized height) were the most critical factors in controlling surface SOCS in arid rangelands when combining with robust RF ML model, and optimized soil sampling methods like RF-cLHS can prepare acceptable soil properties maps.

The magnitude and causes of edge effects on soil organic carbon stocks within and across urban to rural forest patches

Landscape fragmentation has created large areas of forest edge. Understanding how soil organic carbon (SOC) stocks within forest edges respond to fragmentation is essential to assess carbon budgets; however, the causes and magnitude of edge effects on SOC stocks have been poorly characterized. Here, we quantify the edge effects on SOC stocks within and across urban to rural forest patches from three fragmented urban forests to a large patch of rural forest. The SOC stocks within 20 m of the rural forest edge (1.86kgCm−2) is on average 80% lower than the interiors of rural forest (10.47kgCm−2). We found that biotic factors, including annual litterfall mass (R2 > 0.94), peak leaf area index (R2 > 0.92), and fine-root mass density (R2 > 0.77), explained the spatial variation in SOC stocks within the rural forest. In urban forests, human activities at forest edges led to contrasting edge effects on SOC stocks, for instance, the SOC stocks at the east edges (4.74 kgC m−2) were 56% greater than at the west edges (3.03 kgC m−2) explained by the adjacent land uses (e.g., paved roads vs. non-paved soils) and in-situ litterfall management. We also found significant differences in summer soil temperature (ΔTS > 2.8 °C) and soil moisture (ΔVWC > 0.05 m3 m−3) between the east and west forest edges. Our results reveal that the factors responsible for the edge effects on SOC stocks in rural forests are biotic factors, while heterogeneous human activities at the local scale lead to complex edge effects on urban forest SOC stocks.

The effectiveness of digital soil mapping with temporal variables in modeling soil organic carbon changes

The effectiveness of using changes in environmental conditions to explain the spatiotemporal variability in soil organic carbon (SOC) with digital soil mapping (DSM) requires investigation. In this study, temporal variables representing temporal patterns of climate, vegetation, and land cover factors were explored. Models to predict SOC stocks were developed using a random forest algorithm and data from China during two periods (the 1980s and 2010s). We forecasted and hindcasted the developed models and assessed their temporal projections against temporally independent data. Models were developed for both periods using different sets of variables (with/without temporal variables), and their temporal projections were compared. The important temporal variables were identified by applying the recursive feature elimination algorithm. The results showed that the performances of temporal projections for the 1980s and 2010s were improved by approximately 17% and 47%, respectively, when temporal variables were included in the models. Spatially, the maps of changes in SOC stocks derived from the models that included temporal variables presented stronger associations with temporal changes in climate, vegetation, and land cover than those derived from the models that did not include temporal variables. This work highlights that variation in SOC stocks can be linked to temporal patterns of environmental factors. The findings also provide evidence that the application of temporal patterns of environmental factors to DSM models can be useful for the large-scale prediction of changes in SOC.