Distribution Of Organic Carbon Research Articles

Soil Vertical Distribution of Organic Carbon and Sequestration Potential in Ponte de Lima (Alto Minho Region, Northern Portugal)

ABSTRACT Understanding the vertical distributions of organic carbon (OC) is crucial for predicting and simulating the influences of soil units on the terrestrial carbon cycle. The OC in the fine earth fraction was calculated for the soil units Anthrosols, Cambisols, Fluvisols, Leptosols, and Regosols in the municipality of Ponte de Lima, Portugal, at depths of 0–30 cm, 0–100 cm, 0–200 cm, and 0–2590 cm. In the study area, over 40% of the OC is concentrated in the Regosol unit, followed by the Anthrosols with over 23% OC at all depths, and the Leptosols with over 22% OC at all depths. The soil units Cambisols and Fluvisols have a lower representation in the territory, with values below 1.5% and 6.5% respectively at all depths. The obtained results contribute to assessing the potential of the soil units present in the municipality to sequester CO2, promoting the development of carbon inventories and analyzing the distribution of OC through accurate and reliable estimates of current C reserves as an essential tool for analyzing and modeling the effects of different factors involved in the potential of soil OC sequestration.

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

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

The dominant influence of muddy bottom currents on organic-rich fine-grained sedimentary rocks: The lower Congo Basin, west Africa

The Madingo Formation in the Lower Congo Basin (LCB) of West Africa is a fine-grained strata, which is considered to be the most promising source interval during the drift stage. Despite the extensive studies of the internal sedimentary characteristics of the Lower Mading Formation (LMF), muddy bottom currents have not been previously identified in this area. Extensive seismic (2D and 3D data), wire - line measurements and cutting samples in the LCB offer a unique opportunity to study the influence of muddy bottom currents depositional system on organic matter, and related depositional environment of the region and its depositional model of organic rich fine-grained strata. This study aims to unravel the influence of muddy bottom currents on organic matter enrichment within the LMF and build a depositional model. In combination with drilling and thin sections analysis, the M-1 member suggests the initiation and cessation of muddy bottom currents. Utilizing 3D seismic data inversion techniques, geochemical analysis, and sedimentological assessment, we studied the effect of muddy bottom currents on organic matter enrichment from the perspectives of paleoproductivity, preservation conditions, terrigenous input, and total organic carbon (TOC) distribution. Results show that muddy bottom currents have a significant impact on the redistribution of shallow water shelf sediments. They promote the transportation of nutrients and organic matter to deep water shelves, improving paleoproductivity. Most notably, terrigenous input is a key factor affecting organic matter distribution, resulting in dilution effects in deep water shelf bottom current influence areas, but conducive to the preservation of deep organic matter in low-lying areas of shallow water shelf. Muddy bottom currents have a weaker influence on the preservation environment of deep water shelves, but they have a significant effect on redox indicators (eg. U/Th, Ni/Co, V/Cr). Specifically, they reduce the enrichment in particulate elements. We established the depositional models of organic-rich fine-grained sedimentary rocks for the M-1 and M-2 members in LCB. These insights not only provide practical depositional models for marine sources rocks in the LCB, but also improves the general understanding of marine organic matter accumulation in muddy bottom currents environments. Furthermore, they serve as a reference for the study of organic-rich fine-grained sedimentary rocks in similar basins.

Coarse to superfine: can hyperspectral soil organic carbon models predict higher-resolution information?

Introduction: Modeling and mapping of soil organic carbon concentration and distribution at the pedon scale is a current knowledge gap that can be addressed by laboratory-based hyperspectral imaging and chemometric analysis of soil cores. Despite the advancements in soil organic carbon models based on hyperspectral images, it is not clear how these models will perform upon input with images at higher resolutions than those of their training sets. This study aims to measure the generalizability of a soil organic carbon model based on a test set with higher resolution hyperspectral images than that of its training set.Methods: Organic carbon contents were measured at 10 cm intervals on eight soil cores for use as the training set and at 1 cm intervals on a single core for use as the test set. Three regression models, namely, multilayer perceptron, partial least-squares, and support vector regressions, were trained and tested with the median of each hyperspectral image for each of these intervals as the training and test predictors. Permutation importance analysis was performed to explain the models.Results: The results show that although all three models had the same validation R2 of 0.92 for cross-validation on the 10 cm data, multilayer perceptron regression allowed the best generalization with a test R2 of 0.96 compared to the partial least-squares regression (0.81) and support vector regression (0.86). It was demonstrated that the multilayer perceptron model is more robust to soil surface anomalies and that it predicts soil organic carbon on the test set by learning the spectral features related to soil organic matter chromophore activity in the 950–1,150 nm region along with clay mineralogy derived from peaks at 1,400, 1,900, 2,200, 2,250, and 2,350 nm.Conclusions: This study shows that while the regression models based on hyperspectral images perform well at the 10-cm-resolution cross validation, multilayer perceptron regression shows superior generalization and robustness for a higher 1-cm-resolution test set without much loss of prediction power.

The Application of Percolation Theory in Modeling the Vertical Distribution of Soil Organic Carbon in the Changbai Mountains

A power-law formulation rooted in percolation theory has proven effective in depicting the vertical distribution of soil organic carbon (SOC) in temperate forest subsoils. While the model suggests the solute as the primary factor distributing SOC, this may not hold true in the surface soil in which roots contribute significantly to the SOC. This study in the Changbai Mountains Mixed Forests ecoregion (CMMF) evaluates the SOC profiles in three forests to assess the model’s efficacy throughout the soil column. Prediction of the SOC profile based on the regional average values was also assessed using field data. The observed scaling aligned well with predictions in mixed broadleaved and broadleaved Korean pine mixed forests, but disparities emerged in birch forest, possibly due to waterlogging. The predicted SOC levels correlate strongly with the field data and align well with the normalized average SOC levels. The findings suggest that the model remains applicable in the CMMF when considering root-derived carbon. However, the hindrance of solute transport may have a greater impact than roots do. The spatial heterogeneity of the SOC means that a single predicted SOC value at a specific depth may not fit all sites, but the overall agreement highlights the potential of the model for predicting the average or representative SOC profiles, which could further aid in regional-scale carbon stock estimation.

Soil organic carbon fractions and storage potential in Finnish arable soils

AbstractUnderstanding the factors affecting the total amount and distribution of soil organic carbon (OC) across different functional carbon pools is important to better define the future management of soil OC stocks. The interactions between soil management practices, local physicochemical soil properties and climate are essential for determining the OC content of the soil. Nevertheless, how these factors affect the total amount of OC and its distribution across carbon pools, i.e., more labile particulate (POC) and more stable mineral‐associated (MAOC) organic carbon, is only partly known. In this study, we assessed topsoil (0–20 cm) samples from 93 arable farms in the southern half of Finland to determine the total amount of OC, and its distribution in MAOC and POC, along with relevant soil properties (amount of clay and silt, aluminium and iron oxides and pH), climate (precipitation and temperature) and fertilization (mineral versus organic). The fertilization did not affect the total soil carbon content (12–58 g OC kg−1 soil). The share of OC in the MAOC fraction (on average 86% of total OC) was relatively stable across the large range of OC contents and clay contents (2%–68%). We assessed the highest feasible MAOC of the soils with boundary line analyses and their OC saturation state with Hassink's equation (Hassink, 1997). Only soils with the lowest clay content (<10% clay) were assumed to be carbon‐saturated, suggesting that most of the studied soils have a capacity to accrue more MAOC. Simple linear regression showed that clay, aluminium and iron oxides explained 9%, 21% and 22% of the variation in MAOC, respectively. Multiple regression analyses including the amount of clay, clay+silt, aluminium and iron oxides, pH, type of fertilization, precipitation and temperature as explanatory variables explained 33%–53% of the variation in OC and MAOC. In all soils, aluminium oxides were important explanatory variable for MAOC, whereas Fe oxides were significant only in soils with higher clay content (>30%). In soils with a low clay content (<30%), pH had added value in explaining MAOC. Altogether, it seems that various climatic, edaphic and soil management‐related factors are context‐dependently controlling OC and that soil textural information alone is not necessarily an adequate predictor to assess the MAOC saturation state of the soil.

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

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

Whether Wheat–Maize Rotation Influenced Soil Organic Carbon Content in Sushui River Basin

Enhancing soil organic carbon (SOC) content in farmland is crucial for soil quality maintenance and food security. However, the relationship between crop rotation and SOC sequestration remains unclear. We used sample data on SOC, collected in September of every year, from cultivated land for quality monitoring from 2017 to 2021, combined with spatially extracted planting system information, and focused on the effects of wheat–maize crop rotation on SOC in the Sushui River Basin. An analysis of variance (ANOVA) indicated that the SOC content was only significantly different between wheat monoculture and maize monoculture. Among the three cropping systems, wheat–maize rotation did not show absolute superiority. The Geodetector analysis showed that the planting system dominated the spatial distribution of soil organic carbon (p = 0.05), but its explanatory factor was only 5%, and the explanatory power was significantly improved after interaction with other factors. Geographically weighted regression showed that wheat–maize rotation had a trade-off effect with elevation and synergistic effects with rainfall and pH. It displayed a synergistic effect with temperature in the southwest and a trade-off effect in the northeast. The degrees of trade-offs and synergy varied spatially among all interacting factors. We focused on the spatial heterogeneity of soil organic carbon in a small watershed, and the results had scientific significance for the layout of planting systems according to local conditions and the improvement in soil organic carbon levels.

13C evidence for the selective loss of active and inert organic carbon in soil aggregates under rain-induced overland flow erosion

Due to the hierarchical structure of aggregates, raindrops cause changes in the composition, distribution and state of aggregate-wrapped organic carbon (OC) fractions during erosion, greatly affecting soil carbon turnover and sequestration. To explore these regulatory mechanisms, the selective transport of light (LFoc) and heavy (HFoc) fraction OC within aggregates of varying sizes was traced via the 13C/12C carbon isotope ratio (δ13C) under splash and sheet erosion conditions. A “three-zone” variable-slope soil pan was filled with loess soil with a high aggregate concentration to monitor rainfall erosion on a slope. The OC aggregate composition, aggregate stripping and δ13C values of the sediment aggregates of various particle sizes were measured. When the erosion intensity was low, the splash-eroded sediment was mainly enriched in 13C-rich HFoc, and as the rainfall intensity increased, the concentrations of 12C-rich LFoc and HFoc gradually increased. That is, under heavy rainfall, aggregate fragmentation exposed more large fragments rich in younger HFoc and LFoc, and these fragments underwent saltation. There was no obvious correlation between the trends in splash erosion and OC, so runoff transport was an important factor influencing the correlation between δ13C and OC (P < 0.05). Raindrop impact exposed HFoc-rich aggregate fragments of different sizes, e.g., stable mineral-associated OC. Runoff promoted the obvious preferential transport of LFoc and the redistribution of particulate OC (POC) among different sediment particles. Mineral-associated OC and 2–0.05 mm easily decomposable LFoc or POC were preferentially transported, causing OC with the highest and median δ13C values to be preferentially transported. Overall, the transport order of aggregate-exposed OC particles was clay + silt particle-bonded OC, POC, POC-bonded aggregate fragments and silt-bonded aggregate fragments. These results verified the selective loss of aggregate-detached OC fragments during erosion, which led to a change in the OC sequestration and reaggregation potential of OC in the eroded and deposited soil.

Rubber Plantation Age Affects Soil Aggregate Stability, Organic Carbon, and Nitrogen Distribution in Hainan Island, China

Rubber Plantation Age Affects Soil Aggregate Stability, Organic Carbon, and Nitrogen Distribution in Hainan Island, China

Mapping Soil Properties in the Haihun River Sub-Watershed, Yangtze River Basin, China, by Integrating Machine Learning and Variable Selection.

Mapping soil properties in sub-watersheds is critical for agricultural productivity, land management, and ecological security. Machine learning has been widely applied to digital soil mapping due to a rapidly increasing number of environmental covariates. However, the inclusion of many environmental covariates in machine learning models leads to the problem of multicollinearity, with poorly understood consequences for prediction performance. Here, we explored the effects of variable selection on the prediction performance of two machine learning models for multiple soil properties in the Haihun River sub-watershed, Jiangxi Province, China. Surface soils (0-20 cm) were collected from a total of 180 sample points in 2022. The optimal covariates were selected from 40 environmental covariates using a recursive feature elimination algorithm. Compared to all-variable models, the random forest (RF) and extreme gradient boosting (XGBoost) models with variable selection improved in prediction accuracy. The R2 values of the RF and XGBoost models increased by 0.34 and 0.47 for the soil organic carbon, by 0.67 and 0.62 for the total phosphorus, and by 0.43 and 0.62 for the available phosphorus, respectively. The models with variable selection presented reduced global uncertainty, and the overall uncertainty of the RF model was lower than that of the XGBoost model. The soil properties showed high spatial heterogeneity based on the models with variable selection. Remote sensing covariates (particularly principal component 2) were the major factors controlling the distribution of the soil organic carbon. Human activity covariates (mainly land use) and organism covariates (mainly potential evapotranspiration) played a predominant role in driving the distribution of the soil total and soil available phosphorus, respectively. This study indicates the importance of variable selection for predicting multiple soil properties and mapping their spatial distribution in sub-watersheds.

Vertical distribution of microplastics in an agricultural soil after long-term treatment with sewage sludge and mineral fertiliser

Sewage sludge applications release contaminants to agricultural soils, such as potentially toxic metals and microplastics (MPs). However, factors determining the subsequent mobility of MPs in long-term field conditions are poorly understood. This study aimed to understand the vertical distribution of MPs in soils amended with sewage sludge in comparison to conventional mineral fertiliser for 24 years. The depth-dependent MP mass and number concentrations, plastic types, sizes and shapes were compared with the distribution of organic carbon and metals to provide insights into potentially transport-limiting factors. Polyethylene, polypropylene and polystyrene mass concentrations were screened down to 90 cm depth via pyrolysis-gas chromatography/mass spectrometry. MP number concentrations, additional plastic types, sizes, and shapes were analysed down to 40 cm depth using micro-Fourier transform-infrared imaging. Across all depths, MP numbers were twice and mass concentrations 8 times higher when sewage sludge was applied, with a higher share of textile-related plastics, more fibres and on average larger particles than in soil receiving mineral fertiliser. Transport of MPs beyond the plough layer (0–20 cm) is often assumed negligible, but substantial MP numbers (42 %) and mass (52 %) were detected down to 70 cm in sewage sludge-amended soils. The initial mobilization of MPs was shape- and size-dependent, because the fractions of fragmental-shaped and relatively small MPs increased directly below the plough layer, but not at greater depths. The sharp decline of total MP concentrations between 20 and 40 cm depth resembled that of metals and organic matter suggesting similar transport limitations. We hypothesize that the effect of soil management, such as ploughing, on soil compactness and subsequent transport by bioturbation and via macropores drives vertical MP distribution over long time scales. Risk assessment in soils should therefore account for considerable MP displacement to avoid underestimating soil exposure.

Assessing the 3D distribution of soil organic carbon by integrating predictions of water and tillage erosion into a digital soil mapping-approach: a case study for silt loam cropland (Belgium)

Although agricultural intensification has generally increased crop yields, it also resulted in a range of environmental issues. These include increased erosion rates and declined soil organic carbon (SOC) stocks. In order to improve our understanding on how erosion impacts the overall SOC storage capacity of croplands, this study analyses the 3D distribution of SOC as a function of water and tillage erosion in a conventionally ploughed field in the Belgian silt loam region. We present a novel methodological framework to integrate the output of an advanced erosion model as a co-variate within a Digital Soil Mapping (DSM) approach with the objective to create detailed SOC maps. More precisely, we combined (i) the Water and Tillage Erosion Model and Sediment Delivery Model (WaTEM/SEDEM), simulating spatial patterns of soil erosion and sediment deposition due to water and tillage erosions, with (ii) a SOC sampling campaign, resulting in a SOC spatial distribution model that considers both types of erosion. The results show that, as compared to plateaus, SOC stocks are nearly half as large along eroding slopes (i.e. convex slope positions affected by tillage erosion and steep slopes affected by water erosion). Yet, they are up to twice as large in areas characterized by sediment deposition (i.e. concave positions due to tillage erosion and foot slope positions due to water erosion). Our results further show that tillage erosion has a significant influence on the SOC stocks in the top 0.7 m and in particular on the top 0.4 m. The influence of water erosion is less strong but mostly significant along the entire depth profile. Overall, this work demonstrates the relevance of considering different erosion processes when aiming to predict spatial patterns of SOC. Considering the top 1 m and a WaTEM/SEDEM application at a resolution of 10 m our 3D SOC modelling approach obtained a coefficient of determination (R2) of 0.62, a relative root mean square error (RRMSE) of 30.5 % and a relative mean absolute error (RMAE) of 26.0 %. While future work may likely lead to further improvements e.g. a more detailed SOC sampling network along the foot slopes and thalwegs in order to obtain a more spatially detailed prediction of the area characterized by depositions due to water erosion, we demonstrate the great potential of existing erosion and deposition models in doing so.

Iron-bound Organic Carbon Distribution in Freshwater Wetlands with Varying Vegetation and Hydrological Regime

Iron-bound Organic Carbon Distribution in Freshwater Wetlands with Varying Vegetation and Hydrological Regime

Variation of root traits and its influences on soil organic carbon stability in response to altered precipitation in an alpine meadow

Soil organic carbon (SOC) dynamics are strongly controlled by plant roots. Yet, how variation of root traits under precipitation change influences SOC stability remains unclear. As part of a 5-year field experiment manipulating precipitation including 90 % (0.1P), 50 % (0.5P), 30 % (0.7P) decrease, and 50 % increase (1.5P), this study was designed to assess the effects of changing precipitation on root traits and production dynamics by minirhizotron and examine how such influences regulate SOC stability in an alpine meadow on the Qinghai-Tibetan Plateau. We found that root length density (RLD), specific root length (SRL), root branching intensity (RBI), and root residue carbon input (RC input) exhibited no significant response, whereas root turnover (RT), root carbon (C), nitrogen (N) concentrations and C/N ratio were altered by precipitation change with nonlinear trends. Absorptive root RT positively correlated to manipulated precipitation within the interannual precipitation range in topsoil, but it showed no significant change under extreme drought treatment. Alpine meadows can maintain the SOC content and density under varied precipitation. However, it showed significant variation in aggregate stability and organic carbon (OC) distribution in aggregates in topsoil, which were mainly due to the strong direct effects of soil moisture and partly related to RLD and RC input of transport roots. Although subsurface soil aggregate stability and OC associated with aggregates were not modified, our results indicated a risk of SOC stability variation in subsurface soil if absorptive root RT and SRL changed. These findings provide vital information to predict responses of SOC dynamics of alpine meadow to future climate change.

Drivers of organic carbon distribution and accumulation in the northern Barents Sea

Sedimentary properties and accumulation rates on the continental shelf and in the deep sea reflect temporal oceanographic, biological and chemical processes occurring in the water column and the sediment surface. We used the radionuclides 210Pb, 226Ra, and 137Cs activities to estimate sedimentation rates during the last century at nine stations in the northern Barents Sea region. Elemental (C, N) and stable isotopic composition (δ13C, δ15N) were also analysed from the nine stations sampled in August 2018, and, for five other stations sampled in August and December 2019, and in March and May 2021. Sediment accumulation rates varied between 130 and 1 410 g m−2 y−1. The < 63 μm normalized total organic carbon (TOC63) and the total nitrogen from the sediment surface varied between 0.90–2.56 % and 0.13–0.33 %, respectively. Ice-free shelf stations had higher TOC63 and possibly fresher organic matter (high δ13C, low δ15N) than ice-covered more northern stations. The opposite trend was observed for total inorganic carbon. We found that these trends in biogeochemical parameters were spatially structured by the winter sea ice concentration and biological production differences, and exhibited a south-north separation of the Polar Front region. The low and stable organic carbon accumulation rate (1.7–13.4 g Corg m-2 y−1; ARtoc) is a function of slow sedimentation rates, and high degradation and residence time in the water column and at the sediment–water interface. Overall, the ARtoc has been stable for the past 100 years, with a slight increase from the early 1970s to the present at the shelf and slope stations. Our results highlight that spatial scales of variability of the studied sedimentary parameters are linked to spatial patterns of important environmental variables (e.g., chlorophyll-a, sea ice concentration) in the region. In contrast, no seasonal differences were observed in the sediment parameters of revisited stations, and the dated sediment geochemical profiles did not exhibit substantial longer-term variation. This means that climate-induced changes in variables that modify the sedimentary geochemistry of the environment may affect benthic community activity and structure before leaving a record in ARtoc.

Numerical modeling of PFAS movement through the vadose zone: Influence of plant water uptake and soil organic carbon distribution

In this study, we investigated the effects of soil organic carbon (SOC) distribution and water uptake by plant roots on PFAS movement in the vadose zone with a deep groundwater table under temperate, humid climate conditions. Two series of numerical simulations were performed with the HYDRUS computer code, representing the leaching of historical PFOS contamination and the infiltration of water contaminated with PFOA, respectively. We considered soil profiles with three distributions of SOC (no SOC, realistic SOC distribution decreasing with depth, and uniform SOC equal to the content measured in topsoil), three root distributions (bare soil, grassland, and forest), and three soil textures (sand, sandy loam, and loam). The SOC distribution had a profound impact on the velocity of PFOS movement. The apparent retardation factor for realistic SOC distribution was twice as large as for the scenario with no SOC and more than three times smaller than for the scenario with uniformly high SOC content. We also showed that the root distribution in soil profoundly impacts the simulations of PFAS migration through soil. Including the root zone significantly slows down the movement of PFAS, primarily due to increased evapotranspiration and reduced downward water flux. Another effect of water uptake by plant roots is an increase of PFAS concentrations in soil water (evapo-concentration). The evapo-concentration and the slowdown of PFAS movement due to root water uptake are more significant in fine-textured soils than in sand.

Storage and Distribution of Organic Carbon and Nutrients in Acidic Soils Developed on Sulfidic Sediments: The Roles of Reactive Iron and Macropores.

In a boreal acidic sulfate-rich subsoil (pH 3-4) developing on sulfidic and organic-rich sediments over the past 70 years, extensive brownish-to-yellowish layers have formed on macropores. Our data reveal that these layers ("macropore surfaces") are strongly enriched in 1 M HCl-extractable reactive iron (2-7% dry weight), largely bound to schwertmannite and 2-line ferrihydrite. These reactive iron phases trap large pools of labile organic matter (OM) and HCl-extractable phosphorus, possibly derived from the cultivated layer. Within soil aggregates, the OM is of a different nature from that on the macropore surfaces but similar to that in the underlying sulfidic sediments (C-horizon). This provides evidence that the sedimentary OM in the bulk subsoil has been largely preserved without significant decomposition and/or fractionation, likely due to physiochemical stabilization by the reactive iron phases that also existed abundantly within the aggregates. These findings not only highlight the important yet underappreciated roles of iron oxyhydroxysulfates in OM/nutrient storage and distribution in acidic sulfate-rich and other similar environments but also suggest that boreal acidic sulfate-rich subsoils and other similar soil systems (existing widely on coastal plains worldwide and being increasingly formed in thawing permafrost) may act as global sinks for OM and nutrients in the short run.

Tillage and Straw Management Practices Influences Soil Nutrient Distribution: A Case Study from North-Eastern Romania

Tillage practices govern crop quality and quantity through soil nutrient availability and crop root systems. A deeper knowledge of the impact of conservation tillage on soil chemical characteristics (such as pH, soil organic carbon, macro and micronutrient storage and distribution) is required for both the promotion of agricultural sustainability and environmental preservation. This study assesses the changes in soil features and properties in the context of a long-field experiment with different tillage systems and straw management practices. Research findings revealed that compared with conventional tillage (CT) conservative tillage with partial straw retention (MT) and no-tillage with straw mulching (NT) substantially boosted the organic carbon (OC) (by 6–19%), total nitrogen (TN) (by 2–12%), and available potassium content (AK) (by 2–5%), in 0–30 cm soil depth. However, the stratification trend was observed for available macro and micronutrient content (Zn, Fe, Mn) in both conservative management practices. The concentration of Cu indicates a constant pattern through a 0–30 cm soil profile with a higher concentration under MT (1.41 mg kg−1) compared to NT (1.10 mg kg−1). In particular, the results failed to establish if conservation tillage can increase the total phosphorus (TP) and potassium content (TK), where only in surface 0–10 cm an increase was observed. This research also suggested that the X-ray fluorescence analysis (XRF) of total micronutrient content (Zn, Cu, Fe, Mn) is minimal or unpredictable with no substantial differences between the tillage systems and straw return management practices. These findings suggest that conservation tillage in north-eastern Romania might be optimal to maintain soil quality status and sustain high yields.

Quantifying aboveground biomass, soil organic carbon and erosion with a detailed crop map and PESERA model in the Yangtze River Basin

AbstractSoil erosion represents a primary threat to soil systems with adverse implications for ecosystem services, crop production, potable water and carbon storage. While numerous studies have quantified the spatial distribution of aboveground Biomass (AGB), soil erosion and soil organic carbon (SOC) in the Yangtze River Basin (YRB), limited attention has been given to assessing the contributions of different land use types and especially crop types to AGB, soil erosion and SOC. In most studies, cropland is taken as a land use class, while detailed crop types and rotation patterns, and their effect on soil erosion and SOC, vary significantly. In this study, we used the Metronamica model to generate a detailed crop rotation and distribution map across the YRB and subsequently employed the Pan‐European Soil Erosion Risk Assessment (PESERA) model to simulate the spatial distribution of AGB, soil erosion and SOC on a monthly basis. PESERA model simulations indicate an average soil erosion rate across the entire YRB of 7.7 ton/ha/yr, with erosion hotspots concentrated in the Sichuan Basin and the central‐southern regions. The southwestern region and western Sichuan show elevated levels of AGB and SOC, while the eastern plains display lower levels. Erosion rates are lowest in areas designated as artificial land, pasture and grassland, whereas croplands and fruit tree plantations experience the highest erosion rates. In terms of crop types, the highest erosion rates and lowest AGB are observed under fallow and potato cultivation, while the lowest erosion rates and highest AGB are found in rice‐wheat rotation fields. To the best of our knowledge, this is the first study taking detailed crop types and patterns into account while evaluating their effect at a relatively large scale (i.e., YRB). These findings can help to develop sustainable soil management and (cropping) conservation strategies.