Divergent responses of soil organic and inorganic carbon to edaphic factors within, but not between, shelterbelts and croplands in dryland of NW China

Divergent responses of soil organic and inorganic carbon to edaphic factors within, but not between, shelterbelts and croplands in dryland of NW China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Divergent responses of soil organic and inorganic carbon to edaphic factors within, but not between, shelterbelts and croplands in dryland of NW China Teng-Fei Yu, Tuo Han, Yidan Yin, Baofeng Li, Haiyang Xi, Wei Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6937799/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Feb, 2026 Read the published version in Plant and Soil → Version 1 posted 6 You are reading this latest preprint version Abstract Background and Aim It remains unclear whether soil carbon fractions—organic (SOC) and inorganic (SIC)—exhibit distinct distribution patterns and responses to edaphic factors for shelterbelts and croplands due to their different management practices. Methods In total, we collected 480 soil samples (30 sites × 4 points × 4 depths) from 30 paired sites of shelterbelts (SF) and croplands (CF) in dryland, NW China. At each site, samples were taken from one SF point and three distance-based CF points (CF_0.25H, CF_0.5H, CF_1H) across four depths: 0–10 cm, 10–30 cm, 30–50 cm, and 50–100 cm. Results There was no significant ( P > 0.05) differences in the content of soil total carbon (STC), SOC, SIC, and SIC:SOC ratio between SF and CF. A significant ( P < 0.01) linear relationship existed between SOC and STC across depths. Both SOC and SOC:STC ratio were deceased significantly ( P < 0.05) with increasing soil depths, but SIC was stabilized. Consistent findings from Pearson’s correction, Random Forest, linear mixed-effects model and structural equation model identified soil nutrients as the primary determinant of SOC, while physicochemical properties were the key driver of SIC. Variance partitioning analysis revealed that edaphic factors explained 78.19% and 70.10% of SOC variation, and 45.10% and 49.70% of SIC variation in SF and CF, respectively, suggests biotic factors (e.g. microorganism) may critically influence SIC accumulation in dryland soils. Conclusion Our study demonstrates contrasting responses of SOC and SIC to edaphic factors within shelterbelts and croplands, yet reveals consistent abiotic drivers governing soil C dynamics in both of them. These findings enhance understanding of soil C sequestration mechanisms and their abiotic controls in dryland agroforestry systems. Soil Carbon Fractions Edaphic Factors Agroforestry Shelterbelts Dryland Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Soil carbon sequestration, including organic (SOC) and inorganic carbon (SIC) for agricultural systems, plays a pivotal role in mitigating climate change by promoting carbon cycling in terrestrial ecosystems (Bossio et al., 2020 ). As the world's largest agricultural country, both of the repeated historical measurements and model simulation showed an increasing trend of SOC content and stock in the topsoil in Chinese croplands (Pan et al., 2010 ; Yan et al., 2011 ; Zhao et al., 2018 ; Ding et al., 2023 ; Zhou et al., 2025 ). Surprisingly, it was being lost in the subsoil (60–100 cm) of China’s upland croplands over the last few decades, that may induced by warming-enhanced decomposition (Zhou et al., 2025 ). In contrast to SOC, an overall decrease in SIC stocks in topsoil (0–30 cm or 0–40 cm) of croplands were observed especially in arid and semi-arid areas (Wu et al., 2009 ; Raza et al., 2020 ; Song et al., 2022 ; Tao et al., 2022 ) and expected persistent in the future (Wiesmeier et al., 2016 ; Li et al., 2024 ) as the consequences of global warming and soil acidification caused by atmospheric acid deposition and agricultural fertilization. It is important to note that those of studies are based on the long time-series comparison (Song et al., 2022 ; Tao et al., 2022 ; Zhou et al., 2025 ), that might be greatly uncertain due to changes in the external environment. Therefore, the horizontal comparison on soil caron between croplands and adjacent shelterbelt or natural vegetation (Zhu et al., 2023 ), may offer more reliable information on soil carbon dynamics under the influence of climate change and human activities. Previous studies in Northeast China's black soil areas showed that the SOC at the top 100 cm and SIC at the top 40 cm in shelterbelts were higher 15.8% and 16.0% than in croplands, respectively (Zhang et al., 2022 ; Zhu et al., 2023 ). At present, some of studies have analyzed the impacts of long-term fertilization on SOC (Halvorson et al., 1999 ; Fan et al., 2005 ) as well as SIC (Wang et al., 2014 ; Raza et al., 2020 ; Chen et al., 2023 ) of croplands in drylands, and find the non-synergistically response of SIC with increase of SOC (Liu et al., 2023a ). However, these studies were conducted separately on SOC or SIC, and without considering the differences in croplands and adjacent shelterbelts, that limited our understanding on the patterns and dynamics of SOC and SIC for oasis in dryland under the scenarios of climate change and human activities. Soil carbon patterns and dynamics were dependent on complex interactions of the various biogeochemical process, of which edaphic factors included nutrient, physical and chemical properties, play the significant role (Wang et al., 2023 ). However, the relative importance of nutrient, physical and chemical properties in determining SOC and SIC between croplands and shelterbelt remains unclear. At global or continental scales, climate, vegetation types, soil properties, and anthropogenic activities dominantly control SOC dynamics (Viscarra Rossel et al., 2019 ; Yang et al., 2023 ). Conversely, at local scales particularly in dryland oasis ecosystems characterized by agroforestry systems, previous studies indicate that soil physical and chemical properties and microorganisms govern SOC sequestration (Pan et al., 2025 ). Similarly, factors regulating SIC exhibit scale-dependent variations (Sharififar et al., 2023 ). Globally, SIC distribution in topsoil is influenced not only by abiotic factors, e.g., edaphic and climatic conditions (Huang et al., 2024 ), but also significantly by biotic factors, including plant/microbial biomass, richness, and diversity (Zeng et al., 2023 ). At local scales, however, agricultural systems show reduced SIC stocks primarily due to nitrogen fertilization-induced acidification and irrigation-driven leaching of Ca²⁺ and Mg²⁺, which limit carbonate formation (Tao et al., 2022 ). Accordingly, we hypothesize that there were: (1) significant difference in soil carbon fractions (SOC and SIC) between croplands and adjacent shelterbelt, and (2) divergent response of soil carbon fractions to edaphic factors owing the different management practice for croplands and shelterbelt. To test these hypotheses, 480 soil samples (30 sites × 4 points × 4 depths) were collected from 30 paired sites encompassed one point in shelterbelts and three distance-based points in croplands at 0–10, 10–30, 30–50, and 50–100 cm depths in the dryland, NW China. All collected soil samples have been measured soil carbon fractions and edaphic factors. We aim to: (i) detect the horizontal variation in soil carbon fractions from shelterbelts to croplands within different distance and the shift in the effect of depths, (ii) verify the relationship between soil carbon fractions and edaphic factors, and (iii) quantify the effects of edaphic factors on soil carbon fractions for croplands and adjacent shelterbelts. 2. Materials and methods 2.1. Study sites and sample collection The study was conducted in the middle reaches of the Heihe River Basin (HRB), located at the Hexi Corridor of NW China (Fig. 1 ). The study area experiences strong, long-term cultivation and thus the dominant soil types consist of non-zonal anthropogenic soils, characterized by the loam of soil texture within 100 cm depths ( Fig. S1 ). Climate in this region was an arid climate characterized by less precipitation and high temperature. The mean annual precipitation (MAP) is 124 mm, and mean annual potential evapotranspiration (PET) is 2190 mm, about 10.2 times of MAP. The mean annual temperature is 7.3 ℃, and high winds and sandstorm are often occurred in this area (Zhang et al., 2016 ). The vegetation was primarily comprised of croplands and adjacent shelterbelts, the height of shelterbelts were significantly differed from that of croplands at the varied distance (Fig. 1 b). Notably, the negative effects of shelterbelts on croplands were observed, i.e. the height of croplands were significantly lower in close to shelterbelts than that of far away from it. Field investigation was carried out in autumn spanning from the end of July to August in 2024. To satisfy the representativeness, 30 paired sites with varied edaphic, hydrological and management practices were selected to sample collection in the middle reaches of HRB. At each site, four sampling points were selected that comprising one point in shelterbelt forests (SF), and three points in adjacent corn fields (CF) at distances of 0.25H (CF_0.25H), 0.5H (CF_0.5H), and 1H (CF_1H) from the shelterbelt edge, where H represents the stand's mean height. At each site, SF were dominated by Gansu poplar ( Populus gansuensis C. Wang & H. L. Yang), while CF supported maize for seed production (Fig. 1 ). At each sampling point, we excavated one soil profiles and obtained approximately 500 g of the freshly mixed soil from each profile at four depths (0–10 cm, 10–30 cm, 30–50 cm and 50–100 cm), to analyze soil carbon fractions (SOC and SIC) and edaphic factors. In total, we collected 480 soil samples (30 sites × 4 points × 4 depths) for this study. 2.2 Soil carbon fractions and edaphic factors measurement After air-drying, these soil samples were subsequently passed through 2 mm sieves in preparation for the analysis of soil carbon fractions and edaphic factors. The content of soil total carbon (STC, g/kg) and total nitrogen (TN, g/kg) were analyzed by a C/N element analyzer (CN802, VELP, Italy). The content of SOC (g/kg) was quantified using the potassium permanganate method with external heating. Subsequently, the content of SIC (g/kg) was calculated by subtracting SOC from STC (Yu et al., 2024 ). Soil nutrient content including total phosphorus (TP, g/kg) and kalium (TK, g/kg), available phosphorus (AP, mg/kg) and kalium (AK, mg/kg) were analyzed using the Olsen’s Method. Soil pH and electrical conductivity (EC, µS/cm) were analyzed using the Multiparameter Analyzer (SevenExcellence S470-BIO, Mettler Toledo, USA). Soil anions and cations including Fluoride ion (F − ), Chloride ion (Cl − ), Nitrate ion (NO 3 − , mg/L), Sulfate ion (SO 4 2− , mg/L), Sodium ion (Na + , mg/L), Kalium ion (K + , mg/L), Calcium ion (Ca 2+ , mg/L), and Magnesium ion (Mg 2+ , mg/L) were analyzed using an ion chromatography system (ICS-2500, Dionex Corporation, USA). Soil moisture content (SMC, %) was measured using the gravimetric method by oven-drying at 105°C for 48 hours. Bulk density (BD, g/cm 3 ) was measured using ring excavation with a constant volume, 100 cm 3 . Soil particle size was analyzed using a laser particle analyzer (Mastersizer 3000, Malvern Company, UK), after which the percentage of Clay (< 2 µm, %), Silt (2 ~ 50 µm, %) and Sand (50 ~ 2000 µm, %) were classified following USDA standard and illustrated using Soil Texture Triangle ( Fig. S1 ). In total, twenty edaphic factors included nutrient (TN, TP, TK, AP, AK), chemical (pH, EC, F − , Cl − , NO 3 − , SO 4 2− , Na + , K + , Ca 2+ , Mg 2+ ) and physical properties (SMC, BD, Clay, Silt, Sand) were measured. 2.3 Statistical analyses The One-way ANOVA was applied to verify the differences in soil carbon fractions and edaphic factors among the different points (SF, CF_0.25H, CF_0.5H and CF_1H), while the differences between two points was tested by t test. Multiple ANOVA analysis was employed to quantify the effects of points, depths, and the interaction among points and depths on soil carbon fractions and edaphic factors. Data were presented as Mean ± SE (Standard Error) ( Table S1 ). Linear regression was utilized to ascertain relationships between SOC and STC at different depths. Pearson’s correlation was utilized to identify relationships between soil carbon fractions (SOC and SIC) and edaphic factors, and plotted using the linkET R package. Random Forest (RF) analysis ( randomForest R package) was employed to determine the relative importance of soil properties for SOC and SIC by evaluating predictor contribution through mean accuracy reduction (Yu et al., 2024 ). Global model significance was assessed via permutation testing (99 iterations, α = 0.05), while the rfPermute R package quantified individual predictor significance by permuting responses to calculate p-values. Linear mixed-effects models (LMMs) was implemented to provide robust effect estimates by accounting for both fixed effects and site-level random effects (Kuznetsova et al., 2017 ). Prior to model construction, all predictor variables were normalized using the Min-Max method (scaling to 0–1 range) to mitigate scaling biases. Structural Equation Modeling (SEM) was employed to gain mechanistic insights into SOC and SIC variation by establishing causal networks among drivers. To simplify the model, metrics significantly contributing to SOC and SIC, as identified by RF analysis, were categorized into nutrient properties, physical properties, and chemical properties to reduce multicollinearity. Maximum likelihood estimation derived path coefficients, while multigroup analysis tested for significant differences between cropland and shelterbelt models. Model fit was assessed using χ², root mean square error of approximation (RMSEA), and comparative fit index (CFI) (Zhao et al., 2019 ). All analyses were implemented in AMOS 22.0 (Amos Development Co., USA). Variance partitioning analysis (VPA) was then performed using the varpart function ( vegan R package) to quantify unique and shared contributions of nutrient, physical, and chemical properties to SOC and SIC variation. 3. Results 3.1. Variations of soil carbon fractions and edaphic factors There were no significant differences in STC, SOC, SIC, and SIC:SOC ratio between shelterbelts (SF) and croplands (CF) across distance gradients: CF_0.25H, CF_0.5H, and CF_1H ( P > 0.05, Fig. 2 ), while STC was significant higher in SF than in CF_0.5H and CF_1H ( P < 0.05, Fig. 2 a). However, significant differences were observed in STC, SOC, SIC, and SIC:SOC among depths (Table 1 ), specifically there exhibited significant differences among depths of STC at SF, SOC and SIC:SOC but not for SIC at each of sites ( P < 0.05, Fig. S2 ). The content of STC and SOC was gradually decreased, while SIC and SIC:SOC were increased as the soil depths increase ( Fig. S2 ). Table 1 Statistical characteristics (mean ± SE) of soil carbon fractions, soil total carbon (STC), organic carbon (SOC), inorganic carbon (SIC) and SIC:SOC ratio, with multivariate analysis of variance (MANOVA) results across depth intervals (0–10, 10–30, 30–50, 50–100 cm) between shelterbelts (SF) and croplands (CF) at increasing distance gradients from shelterbelt edges (CF_0.25H, CF_0.5H, CF_1H). MANOVA reports F-values with significance levels: P < 0.05, *P < 0.01, **P < 0.001. Depths (cm) Sites N STC (g kg − 1 ) SOC (g kg − 1 ) SIC (g kg − 1 ) SIC:SOC 0–10 SF 30 23.25 ± 2.05 a 11.88 ± 1.42 a 11.36 ± 1.12 a 1.35 ± 0.21 a CF_0.25H 30 18.92 ± 1.23 a 9.26 ± 0.69 a 9.67 ± 0.94 a 1.20 ± 0.13 a CF_0.5H 30 18.51 ± 1.04 a 8.67 ± 0.58 a 9.84 ± 0.82 a 1.29 ± 0.13 a CF_1H 30 19.04 ± 1.23 a 8.61 ± 0.61 a 10.43 ± 0.93 a 1.35 ± 0.14 a 10–30 SF 30 20.22 ± 1.35 a 7.68 ± 0.65 a 12.54 ± 0.89 a 1.97 ± 0.24 a CF_0.25H 30 18.59 ± 1.15 a 7.67 ± 0.61 a 10.92 ± 0.92 a 1.74 ± 0.21 a CF_0.5H 30 18.46 ± 1.06 a 7.60 ± 0.48 a 10.86 ± 0.92 a 1.58 ± 0.15 a CF_1H 30 18.49 ± 1.12 a 7.84 ± 0.56 a 10.65 ± 0.79 a 1.47 ± 0.10 a 30–50 SF 30 17.85 ± 1.45 a 5.88 ± 0.69 a 11.97 ± 1.03 a 2.84 ± 0.46 a CF_0.25H 30 17.52 ± 1.17 a 5.44 ± 0.64 a 12.08 ± 0.89 a 2.94 ± 0.38 a CF_0.5H 30 17.33 ± 1.25 a 6.20 ± 0.97 a 11.13 ± 1.03 a 2.50 ± 0.31 a CF_1H 30 17.00 ± 1.07 a 5.32 ± 0.52 a 11.68 ± 0.84 a 2.66 ± 0.25 a 50–100 SF 30 17.50 ± 1.36 a 4.63 ± 0.60 a 12.87 ± 1.15 a 3.67 ± 0.42 a CF_0.25H 30 16.51 ± 1.31 a 4.31 ± 0.47 a 12.20 ± 1.14 a 3.53 ± 0.41 a CF_0.5H 30 16.46 ± 1.25 a 4.02 ± 0.41 a 12.44 ± 1.14 a 3.99 ± 0.53 a CF_1H 30 16.06 ± 1.15 a 4.43 ± 0.60 a 11.63 ± 1.02 a 3.65 ± 0.48 a 0-100 SF 120 19.71 ± 1.62 a 7.52 ± 1.02 a 12.19 ± 1.05 a 2.46 ± 0.38 a CF_0.25H 120 17.89 ± 1.21 a 6.67 ± 0.70 a 11.22 ± 0.98 a 2.35 ± 0.35 a CF_0.5H 120 17.69 ± 1.15 a 6.62 ± 0.72 a 11.07 ± 0.99 a 2.34 ± 0.37 a CF_1H 120 17.65 ± 1.15 a 6.55 ± 0.65 a 11.10 ± 0.89 a 2.28 ± 0.33 a MANOVA Sites 480 2.36 1.70 1.19 0.20 Depths 480 5.31 ** 43.60 *** 2.87 * 47.03 *** Sites : Depths 480 0.46 1.24 0.23 0.36 There were significant ( P < 0.01) linearly related between STC and SOC at various sites and depths, and the coefficient of determination ( R 2 ) and ratio of SOC to STC (slope of linear fitting) were decreased as soil depths increase, except for the 30–50 cm at CF_0.25H and CF_0.5H (Fig. 3 ), which suggested that SIC play a dominant role in subsoil. In total, the ratio of SIC to SOC in the top 1 m was ranged from 1.2 to 4.0 with an average of 2.4, where the SIC and SOC content were 11.39 g kg − 1 and 6.84 g kg − 1 , respectively (Table 1 ). Different from soil carbon fractions, significant differences were observed in some of edaphic factors, included SMC at each depth, AK at the depths of 0–10 cm and 10–30 cm, BD and K + at the depths of 0–10 cm. Averaged in the top 1 m, significant differences were found for AK, SMC, BD, Cl − , NO 3 − and K + among points ( P < 0.05, Table S1 and Fig. S3 ). On the whole, except for TK, Clay, Silt, Sand, SO 4 2− and Na + , significant differences were found among soil depths rather than among points. At the same time, strong interactive effect of depths and points on SMC only was presented ( P < 0.05, Table S1 ). 3.2 Associations of soil carbon fractions with edaphic factors Just as assumed, edaphic factors have contrasting effects on SOC and SIC within, but not between SF and CF (Fig. 4 and Fig. 5 ). Pearson’s correction analysis showed that SOC for SF was significantly positive correlated with the majority of edaphic factors including TN, TP, AP, AK, EC, Clay, Silt, Cl − 1 , SO 4 2− , K + , and Mg 2+ , whereas SIC was merely positive associated with TN, SMC, pH, Clay, Silt, SO 4 2− , Na + , and Mg 2+ , and both of negative associated with Sand and insignificantly associated with TK and NO 3 − (Fig. 4 a). However, SOC was significantly positive correlated with TN, TP, TK, AP, AK, SMC, EC, Clay and Silt, not significantly even insignificantly associated with chemical properties. SIC was more positive associated with TN, SMC, Silt and cations including Na + and Mg 2+ especially for CF_0.5H and CF_1H (Fig. 4 c and 4 d), and SOC and SIC associated with edaphic factors were fall in between or transitional for CF_0.25H (Fig. 4 b). Random Forest also demonstrated that soil nutrient including TN, TP and AP has consistent relatively important contribution to the variation of SOC for both of SF and CF, but other edaphic factors has divergent contribution on SOC for SF and CF. Specifically, AK, BD, EC, F − , Mg 2+ and Ca 2+ for SF, BD, Na + , Mg 2+ for CF_0.25H, and Clay, Silt, Sand and EC for CF_0.5H and CF_1H sites (Fig. 5 a). In contrast to SOC, physical and chemical properties including SMC, Silt, Sand and Mg 2+ have consistent relatively important contribution on SIC for both SF and CF, but other edaphic factors have divergent effects on SIC for SF and CF (Fig. 5 b). The Least Absolute Shrinkage and Selection Operator (LASSO) Regression also demonstrated that edaphic factors have divergent effects on SOC and SIC ( Fig. S4 and S5 ). Therefore, the edaphic factors significantly related to SOC including nutrient characteristics (TN, TP and AP), physical (BD, Clay, Sand) and chemical properties (EC, Ca 2+ , and Mg 2+ ), and that of related to SIC including nutrient characteristics (TN, TK and AP), physical (SMC, Silt and Sand) and chemical properties (pH, Mg 2+ and Ca 2+ ), were selected for further identify the determinants of SOC and SIC for SF and CF. 3.3 Determinants of soil organic and inorganic carbon Both linear mixed effects model (LMEM) and structural equation modeling (SEM) consistently demonstrated that soil nutrients significantly influenced SOC, whereas soil physical and chemical properties were critical determinants of SIC (Fig. 6 and Fig. 7 ). The LME model showed soil nutrient, physical and chemical properties accounted for 79.71%, 7.00% and 13.29% of relative effects on SOC for SF, and 80.95%, 17.79% and 1.26% for CF, respectively. In contrast, they accounted for 24.92%, 47.70% and 27.38% of relative effects on SIC for SF, and 17.4%, 64.70% and 17.95% for CF, respectively (Fig. 6 ). Specifically, TN, AP, BD and Mg 2+ have significant contribution to SOC for SF, and TN, TP, Clay, EC for CF, respectively (Fig. 6 a and 6 b). In contrast to SOC, TN, AP, Silt, Sand, Mg 2+ and Ca 2+ have significant contribution to SIC for SF, and TN, TK, AP, SMC, Silt, Sand, Mg 2+ and Ca 2+ for CF, respectively (Fig. 6 c and 6 d). The SEM further indicated that nutrient characteristics had highest direct effects on SOC, while physical properties had highest direct effects on SIC for both of SF and CF, and coefficient of determination ( R 2 ) of the SEM model was higher for SOC than SIC (Fig. 7 ). Overall, variance partitioning analysis (VPA) revealed that edaphic factors explained 78.19% and 70.10% of the variation in SOC for SF and CF, respectively, while 45.10% and 49.70% of SIC. Furthermore, soil nutrient characteristics, physical and chemical properties exhibited distinct explanatory capacities on the variation of SOC and SIC within SF and CF (Fig. 8 ). 4. Discussion 4.1 Comparable soil carbon content between shelterbelt and croplands To date, there has been limited research comparing the SOC and SIC content between croplands and adjacent shelterbelt (Dhillon et al., 2017 ; Mayrinck et al., 2019 ; Zhang et al., 2022 ; Zhu et al., 2023 ; Ji et al., 2025 ), especially in arid and semi-arid areas, NW China. At the black soil areas in northeastern of China, the SOC at the top 1 m and SIC at the top 40 cm in shelterbelt were higher 15.8% and 16.0% than in croplands, respectively (Zhang et al., 2022 ; Zhu et al., 2023 ). Contrary to the previous studies and our first hypothesis, the results demonstrated no significant differences in both of SOC and SIC content between shelterbelt and croplands (Fig. 2 ). This unexpected finding suggests that two land use patterns may exhibit comparable carbon sequestration capacities under current management practices. There were three possible reasons. Firstly, the undifferentiated and insufficient nutrients supply, mainly nitrogen (N) and phosphorus (P), may impose restrictions on SOC accumulation by restraining the growth of crops, shelterbelt and understory herbs. Theoretically, the higher N and P inputs can stimulate the production of crops and soil microbial biomass, thereby increasing soil C stocks (Yu et al., 2017 ; Tang et al., 2023 ), and vice versa (Oldroyd and Leyser, 2020 ). In fact, the soil C:N ratio (SOC:TN) in shelterbelt and croplands was comparable (about 10:1, Table 1 and S1) and far less than the preferred C:N ratio (20–30:1). Considering the SOC content was mainly determined by nutrient in the study areas (Fig. 5 – 8 ), the insufficient N and P supply for shelterbelt and croplands may not satisfy the nutrient requirements for plant and microbes’ growth, resulting in limited plant and microbial residue carbon inputs, and thereby limiting SOC accumulation. Secondly, less SOC content in the study can be attributed to the insufficient mineral conservation, i.e., the limited Clay content (< 10%, Table S1 ) restricted the sorption of organic matter to mineral surfaces and hindered the formation of aggregates (Su et al., 2010 ; Rasmussen et al., 2018 ; Islam et al., 2022 ). In consistent with previous studies in arid areas (Su et al., 2010 ; Li et al., 2020 ), soil carbon content in particular SOC was significantly associated with Clay content (Fig. 4 ). However, it was hard to retain the organic matter and carbonates on soil profile, especially in the presence of flood irrigation in study areas that could lead to SOC and SIC loss through nutrients and ions leaching along water flow. Soil with higher Clay content promotes the accumulation of soil carbon generally via two forms, the first is involved with mineral-associated organic carbon (MAOC), which protect SOC from decomposition via chemical bonds between organic carbon and minerals (Rasmussen et al., 2018 ); and the second is associated with pedogenic inorganic carbon (PIC), which control the accumulation depth of PIC via affecting soil water holding capacity, water penetration and movement (Zamanian et al., 2016 ). Thirdly, the possibly offsetting effects between above-ground biomass accumulation in shelterbelts and enhanced soil carbon stabilization in croplands. For SOC, it mainly produced from crop straw/stover incorporation, although croplands have higher production than shelterbelts, the major crop straw was collected and used for raising livestock or burning cook, resulted in the comparable residue inputs with shelterbelts. Similarly, the long-term fertilization has not reduced SIC in croplands, which might have been neutralized or carried away by brackish groundwater irrigation. The high fluoride (F − ) concentration in soil (0.30–0.35 mg L − 1 , Table S1 ) was consistent with groundwater, that correlates with the prevalence of SO 4 –Cl–Na–Mg and alkaline conditions (He et al., 2013 ). 4.2 Contrasting responses of SOC and SIC to edaphic factors within shelterbelts and croplands, but not between them In consistent with previous studies (Zhao et al., 2018 ; Zhang et al., 2022 ; Lin et al., 2024 ; Zhou et al., 2025 ), our results demonstrated nutrient addition whether through fertilization in croplands or plant residue input into shelterbelt have a positive influence on SOC content (Fig. 5 – 8 ). Numerous in-situ experiments and meta-analyses have revealed that the application of nutrients, such as N and P, enhances SOC accumulation by stimulating plant productivity and microbial activity (Fan et al., 2005 ; Liu et al., 2018 ), and the magnitude of this fertilizer effects varies depending on fertilization types and levels (Liu et al., 2023b ), and initial SOC content (Ling et al., 2025 ). For instance, the long-term N addition has been shown to increase above- and below-ground biomass and litter inputs (Yue et al., 2016 ), thereby boosting carbon sequestration globally especially for agricultural systems (Yang et al., 2025 ). Similarly, P addition under stable N supply promote microbial carbon use efficiency, CUE (Dai et al., 2020 ; Li et al., 2025 ), thereby facilitating the formation of stable carbon compounds (He et al., 2025 ). Compared to the croplands, the shelterbelt acquires nutrients (N, P and K), in addition to atmospheric deposition, mainly through litter and crop roots burning in part of sampling sites, that induced the occurrence of abnormal value particularly in nutrients and chemical properties ( Fig. S3 ). Those of additional crop roots residue C input with enriched nutrition may facilitate the SOC accumulation in shelterbelt and form the same effects as fertilization for croplands (Fig. 2 ). Different from SOC, the determinants of SIC was on the aspect of soil physical and chemical properties (Fig. 5 – 8 ). Following chemical equilibrium equation, i.e., CaCO 3 + H 2 O + CO 2 ≒ 2HCO 3 − + Ca 2+ , the precipitation and dissolution of carbonate is mainly affected by (i) the partial pressure of CO 2 in soil, (ii) soil pH, (iii) soil moisture content and (iv) the contents of Ca 2+ and Mg 2+ . Thus, any slight changes of these controlling factors may have an important effect on the SIC content and storage (Sun et al., 2022 ). Compared to shelterbelt, croplands at different distances have lower TN, pH, K + and Mg 2+ , but higher TP, AP, SMC, BD, SO 4 2+ and Ca 2+ ( Table S1 ), meanwhile SMC, Silt, Sand, pH and Mg 2+ has consistently direct relationships with SIC (Fig. 8 ), indicating that soil physical and chemical processes play an important role in the formation of SIC. Although the factors affecting the variation of SIC are various, the mechanism to explain the SIC variation for agroforestry systems in drylands can be categorized into three groups. At the first, the SIC formation was attribute to the physical processes, namely planted vegetation, shelterbelt and crops, can intercept and deposit fine particles that contain enriched carbonate sources and then causes a massive accumulation of SIC in the topsoil (Table 1 ), which can be proved by the crucial effects of Silt on SIC (Fig. 4 , Figs. 6 and 7 ). However, these processes could not provide a complete explanation for the comparable SIC content between croplands and adjacent shelterbelt (Fig. 2 ). Therefore, the second mechanism, i.e., chemical processes was proposed, namely the increased soil CO 2 concentration via SOC decomposition under moist soil water regime would lead to the production of CO 3 2− and/or HCO 3 − , that combined with the available cations (e.g., Ca 2+ and Mg 2+ ) may cause continuous increase of SIC (Zamanian et al., 2016 ). Our results (Fig. 6 and Fig. 7 ) were aligned with previous studies that emphasized the crucial roles of soil texture, soil water content, Ca 2+ and Mg 2+ in explaining the distribution of SIC (Ferdush and Paul, 2021 ; Tao et al., 2022 ). Thirdly, which is more important, our study implied a pivotal role of biological processes, e.g. microbial-induced carbonate precipitation, MICP (Okyay et al., 2016 ). In determining the SIC content in saline-alkali soil in drylands (Yu et al., 2024 ), considering of edaphic factors can explained only 45.10% and 49.70% of variation in SOC for SF and CF, respectively (Fig. 5 ). The study area are characterized by soils with high pH (> 8.0) and abundant Ca 2+ and Mg 2+ content (> 0.2%) ( Fig. S3 ) that may produce from brackish groundwater irrigation characterized by calcium and magnesium type (He et al., 2013 ). Under the brackish groundwater irrigation, the abundant ionic accompanied with litters and biomass were infiltrate into soil, abiotic and biological processes begin to take effects on the formation of soil carbon (Ball et al., 2023 ; Feng et al., 2023 ). The dissolved lignin and cellulose fractions bind to available Ca²⁺ and Mg²⁺ through ligand exchange, facilitating their incorporation into mineral matrices to form mineral-associated organic carbon (MAOC). This process enhances SOC stabilization through organo-mineral interactions. Subsequently, heterotrophic microbial activity mineralizes soil organic matter (SOM), releasing dissolved CO₂ that reacts with water to form carbonate ions (CO₃²⁻ and HCO₃⁻). These anions combine with divalent cations (predominantly Ca²⁺ and Mg²⁺) to precipitate as pedogenic carbonates (e.g., CaCO₃, MgCO₃), thereby promoting SIC formation. Under alkaline conditions typical of dryland soils, this biogeochemical cascade creates a positive feedback loop where SOC accumulation facilitates SIC sequestration. Although this study demonstrated non-significant differences in soil carbon content between shelterbelts and croplands, several methodological limitations warrant acknowledgment. The 100 cm sampling depth may have overlooked deeper carbon dynamics, particularly for inorganic carbon pools influenced by groundwater fluctuations. Furthermore, single-season sampling captured neither seasonal variations in microbial carbon processing nor groundwater-mediated nutrient fluxes that modulate carbon stabilization. The limited spatial coverage also fails to account for landscape-scale heterogeneity in soil texture and land-use history that drive carbon variability. Crucially, the absence of significant carbon divergence between these distinct ecosystems challenges conventional carbon budgeting assumptions in dryland agroforestry, necessitating: (1) multi-year monitoring to quantify legacy effects and sequestration trajectories, and (2) integrated assessments of subsurface carbon fluxes to reconcile surface stock measurements with regional carbon budgets. 5. Conclusion Contrary to previous studies and our first hypothesis, this work revealed non-significant differences in STC, SOC, SIC, and SIC:SOC ratio between shelterbelts and croplands across distance gradients. Analytical consensus from Pearson's correlation, Random Forest, linear mixed-effects models, and structural equation modeling consistently identified soil nutrient characteristics as primary determinants of SOC, while physicochemical properties governed SIC dynamics. Variance partitioning further demonstrated that edaphic factors explained substantially more variation in SOC (70.10% and 78.19%) than in SIC (45.10% and 49.70%) for both systems. This divergence suggests that beyond abiotic controls, biotic factors particularly microbial-mediated carbonate precipitation, may critically influence SIC accumulation in dryland saline-alkaline soils. Consequently, our findings demonstrate: (1) fundamentally distinct response mechanisms of SOC and SIC to edaphic drivers, and (2) consistent abiotic regulation of carbon sequestration across agroforestry systems despite land-use differences. Declarations Acknowledgments This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB0720401), the Major Research Project on Science and Technology of Gansu Province (Grant No. 23ZDFA018), the Key Technology Projects of Inner Mongolia Autonomous Region (Grant No. 2023JBGS0008), the Outstanding Youth Foundation Project of Gansu Province (Grant No. 24JRRA076), and the Natural Science Foundation of Gansu Province (Grant No. 25JRRG044). References Ball KR, Malik AA, Muscarella C, Blankinship JC (2023) Irrigation alters biogeochemical processes to increase both inorganic and organic carbon in arid-calcic cropland soils. Soil Biol Biochem 187:109189 Bossio DA, Cook-Patton SC, Ellis PW, Fargione J, Sanderman J, Smith P, Wood S, Zomer RJ, von Unger M, Emmer IM, Griscom BW (2020) The role of soil carbon in natural climate solutions. Nat Sustain 3:391–398 Chen B, Feng W, Jing X, Wang Y (2023) Dryland agricultural expansion leads to lower content and higher variability of soil inorganic carbon in topsoil. Agric Ecosyst Environ 356:108620 Dai Z, Liu G, Chen H, Chen C, Wang J, Ai S, Wei D, Li D, Ma B, Tang C, Brookes PC, Xu J (2020) Long-term nutrient inputs shift soil microbial functional profiles of phosphorus cycling in diverse agroecosystems. ISME J 14:757–770 Dhillon GS, Van Rees KCJ, Chang S (2017) Soil organic carbon sequestration by shelterbelt agroforestry systems in Saskatchewan. Can J Soil Sci 97:394–409 Ding W, Chang N, Zhang G, Kang J, Yi X, Zhang J, Zhang J, Wang L, Li H (2023) Soil organic carbon changes in China's croplands: A newly estimation based on DNDC model. Sci Total Environ 905:167107 Fan T, Stewart BA, Yong W, Junjie L, Guangye Z (2005) Long-term fertilization effects on grain yield, water-use efficiency and soil fertility in the dryland of Loess Plateau in China. Agric Ecosyst Environ 106:313–329 Feng W, Jiang J, Lin L, Wang Y (2023) Soil calcium prompts organic carbon accumulation after decadal saline-water irrigation in the Taklamakan desert. J Environ Manage 344:118421 Ferdush J, Paul V (2021) A review on the possible factors influencing soil inorganic carbon under elevated CO2. Catena 204, 105434 Halvorson AD, Reule CA, Follett RF (1999) Nitrogen fertilization effects on soil carbon and nitrogen in a dryland cropping system. Soil Sci Soc Am J 63:912–917 He D, Li G, Luo Z, Wang E (2025) Effects of long-term fertiliser application on cropland soil carbon dynamics mediated by potential shifts in microbial carbon use efficiency. Soil Tillage Res 248:106418 He J, An Y, Zhang F (2013) Geochemical characteristics and fluoride distribution in the groundwater of the Zhangye Basin in Northwestern China. J Geochem Explor 135:22–30 Huang Y, Song X, Wang YP, Canadell JG, Luo Y, Ciais P, Chen A, Hong S, Wang Y, Tao F, Li W, Xu Y, Mirzaeitalarposhti R, Elbasiouny H, Savin I, Shchepashchenko D, Rossel RAV, Goll DS, Chang J, Houlton BZ, Wu H, Yang F, Feng X, Chen Y, Liu Y, Niu S, Zhang GL (2024) Size, distribution, and vulnerability of the global soil inorganic carbon. Science 384:233–239 Islam MR, Singh B, Dijkstra FA (2022) Stabilisation of soil organic matter: interactions between clay and microbes. Biogeochemistry 160:145–158 Ji Q, Jiang H, Xu Z, Zhu M, Zhang S, Wang H, Tang Z, Wang Q, Wang W (2025) Farmland afforestation by poplar shelterbelts increased soil inorganic carbon but showed ambiguous effects on soil organic carbon as revealed by carbon isotopic composition: Inter-fraction and inter-site differences in Northern China. Forests 16:328 Kuznetsova A, Brockhoff PB, Christensen RHB (2017) lmerTest Package: Tests in Linear Mixed Effects Models. J Stat Softw 82:1–26 Li J, Nie M, Pendall E (2020) Soil physico-chemical properties are more important than microbial diversity and enzyme activity in controlling carbon and nitrogen stocks near Sydney. Australia Geoderma 366:114201 Li J, Pei J, Fang C, Li B, Nie M (2024) Drought may exacerbate dryland soil inorganic carbon loss under warming climate conditions. Nat Commun 15:617 Li S, Cui Y, Moorhead DL, Dijkstra FA, Sun L, Xia Z, Gao Y, Ma Q, Yu W (2025) Phosphorus limitation regulates the responses of microbial carbon metabolism to long-term combined additions of nitrogen and phosphorus in a cropland. Soil Biol Biochem 200:109614 Lin Z, Lu X, Xu Y, Sun W, Yu Y, Zhang W, Mishra U, Kuzyakov Y, Wang G, Qin Z (2024) Increased straw return promoted soil organic carbon accumulation in China's croplands over the past 40 years. Sci Total Environ 945:173903 Ling J, Dungait JAJ, Delgado-Baquerizo M, Cui Z, Zhou R, Zhang W, Gao Q, Chen Y, Yue S, Kuzyakov Y, Zhang F, Chen X, Tian J (2025) Soil organic carbon thresholds control fertilizer effects on carbon accrual in croplands worldwide. Nat Commun 16:3009 Liu E, Liu Z, Sun Z, Li J, Gong H, Peng J, Ouyang Z (2023a) Soil inorganic carbon stocks increase non-synergistically with soil organic carbon after ecological restoration practices in drylands. J Environ Manage 348:119070 Liu X, Yang T, Wang Q, Huang F, Li L (2018) Dynamics of soil carbon and nitrogen stocks after afforestation in arid and semi-arid regions: A meta-analysis. Sci Total Environ 618:1658–1664 Liu Y, Li C, Cai G, Sauheitl L, Xiao M, Shibistova O, Ge T, Guggenberger G (2023b) Meta-analysis on the effects of types and levels of N, P, and K fertilization on organic carbon in cropland soils. Geoderma 437:116580 Mayrinck RC, Laroque CP, Amichev BY, Van Rees K (2019) Above- and Below-Ground Carbon Sequestration in Shelterbelt Trees in Canada: A Review. Forests 10:922 Okyay TO, Nguyen HN, Castro SL, Rodrigues DF (2016) CO 2 sequestration by ureolytic microbial consortia through microbially-induced calcite precipitation. Sci Total Environ 572:671–680 Oldroyd GED, Leyser O (2020) A plant's diet, surviving in a variable nutrient environment. Science 368:eaba0196 Pan G, Xu X, Smith P, Pan W, Lal R (2010) An increase in topsoil SOC stock of China's croplands between 1985 and 2006 revealed by soil monitoring. Agric Ecosyst Environ 136:133–138 Pan J, Chen S, He D, Zhou H, Ning K, Ma N, Li K, Liao D, Mi W, Wu Q, Zhang C, Dong Z (2025) Agroforestry increases soil carbon sequestration, especially in arid areas: A global meta-analysis. CATENA 249:108667 Rasmussen C, Heckman K, Wieder WR, Keiluweit M, Lawrence CR, Berhe AA, Blankinship JC, Crow SE, Druhan JL, Pries H, Marin-Spiotta CE, Plante E, Schädel AF, Schimel C, Sierra JP, Thompson CA, Wagai A, R (2018) Beyond clay: towards an improved set of variables for predicting soil organic matter content. Biogeochemistry 137:297–306 Raza S, Miao N, Wang P, Ju X, Chen Z, Zhou J, Kuzyakov Y (2020) Dramatic loss of inorganic carbon by nitrogen-induced soil acidification in Chinese croplands. Glob Chang Biol 26:3738–3751 Sharififar A, Minasny B, Arrouays D, Boulonne L, Chevallier T, van Deventer P, Field DJ, Gomez C, Jang H-J, Jeon S-H, Koch J, McBratney AB, Malone BP, Marchant BP, Martin MP, Monger C, Munera-Echeverri J-L, Padarian J, Pfeiffer M, Richer-de-Forges AC, Saby NPA, Singh K, Song X-D, Zamanian K, Zhang G-L, van Zijl G (2023) Chapter Four - Soil inorganic carbon, the other and equally important soil carbon pool: Distribution, controlling factors, and the impact of climate change. Adv Agron 178:165–231 Song XD, Yang F, Wu HY, Zhang J, Li DC, Liu F, Zhao YG, Yang JL, Ju B, Cai CF, Huang B, Long HY, Lu Y, Sui YY, Wang QB, Wu KN, Zhang FR, Zhang MK, Shi Z, Ma WZ, Xin G, Qi ZP, Chang QR, Ci E, Yuan DG, Zhang YZ, Bai JP, Chen JY, Chen J, Chen YJ, Dong YZ, Han CL, Li L, Liu LM, Pan JJ, Song FP, Sun FJ, Wang DF, Wang TW, Wei XH, Wu HQ, Zhao X, Zhou Q, Zhang GL (2022) Significant loss of soil inorganic carbon at the continental scale. Natl Sci Rev 9, nwab120 Su YZ, Wang XF, Yang R, Lee J (2010) Effects of sandy desertified land rehabilitation on soil carbon sequestration and aggregation in an arid region in China. J Environ Manage 91:2109–2116 Sun Z, Meng F, Zhu B (2022) Influencing factors and partitioning methods of carbonate contribution to CO2 emissions from calcareous soils. Soil Ecol Lett 5:6–20 Tang B, Rocci KS, Lehmann A, Rillig MC (2023) Nitrogen increases soil organic carbon accrual and alters its functionality. Glob Chang Biol 29:1971–1983 Tao J, Raza S, Zhao M, Cui J, Wang P, Sui Y, Zamanian K, Kuzyakov Y, Xu M, Chen Z, Zhou J (2022) Vulnerability and driving factors of soil inorganic carbon stocks in Chinese croplands. Sci Total Environ 825:154087 Viscarra Rossel RA, Lee J, Behrens T, Luo Z, Baldock J, Richards A (2019) Continental-scale soil carbon composition and vulnerability modulated by regional environmental controls. Nat Geosci 12:547–552 Wang S, Xu L, Adhikari K, He N (2023) Soil carbon sequestration potential of cultivated lands and its controlling factors in China. Sci Total Environ 905:167292 Wang XJ, Xu MG, Wang JP, Zhang WJ, Yang XY, Huang SM, Liu H (2014) Fertilization enhancing carbon sequestration as carbonate in arid cropland: assessments of long-term experiments in northern China. Plant Soil 380:89–100 Wiesmeier M, Poeplau C, Sierra CA, Maier H, Fruhauf C, Hubner R, Kuhnel A, Sporlein P, Geuss U, Hangen E, Schilling B, von Lutzow M, Kogel-Knabner I (2016) Projected loss of soil organic carbon in temperate agricultural soils in the 21(st) century: effects of climate change and carbon input trends. Sci Rep 6:32525 Wu H, Guo Z, Gao Q, Peng C (2009) Distribution of soil inorganic carbon storage and its changes due to agricultural land use activity in China. Agric Ecosyst Environ 129:413–421 Yan X, Cai Z, Wang S, Smith P (2011) Direct measurement of soil organic carbon content change in the croplands of China. Glob Change Biol 17:1487–1496 Yang R-M, Huang L-M, Zhang X, Zhu C-M, Xu L (2023) Mapping the distribution, trends, and drivers of soil organic carbon in China from 1982 to 2019. Geoderma 429:116232 Yang X, Ma S, Huang E, Zhang D, Chen G, Zhu J, Ji C, Zhu B, Liu L, Fang J (2025) Nitrogen addition promotes soil carbon accumulation globally. Sci China Life Sci 68:284–293 Yu G, Xiao J, Hu S, Polizzotto ML, Zhao F, McGrath SP, Li H, Ran W, Shen Q (2017) Mineral availability as a key regulator of soil carbon storage. Environ Sci Technol 51:4960–4969 Yu T, Feng Q, Yin Y, Han T, Chen W, Zhu M, Zhao C, Zhao J (2024) Divergent response of upper layer soil organic and inorganic carbon to biotic and abiotic factors in afforestation by aerial seeding in desert. China Catena 246:108383 Yue K, Peng Y, Peng C, Yang W, Peng X, Wu F (2016) Stimulation of terrestrial ecosystem carbon storage by nitrogen addition: a meta-analysis. Sci Rep 6:19895 Zamanian K, Pustovoytov K, Kuzyakov Y (2016) Pedogenic carbonates: Forms and formation processes. Earth Sci Rev 157:1–17 Zeng XM, Bastida F, Plaza C, Zhou GY, Vera A, Liu YR, Delgado-Baquerizo M (2023) The contribution of biotic factors in explaining the global distribution of inorganic carbon in surface soils. Glob Biogeochem Cycles 37, e2023GB007957. Zhang X, Chen S, Yang Y, Wang Q, Wu Y, Zhou Z, Wang H, Wang W (2022) Shelterbelt farmland-afforestation induced SOC accrual with higher temperature stability: Cross-sites 1 m soil profiles analysis in NE China. Sci Total Environ 814:151942 Zhang Y, Zhao W, He J, Zhang K (2016) Energy exchange and evapotranspiration over irrigated seed maize agroecosystems in a desert-oasis region, northwest China. Agric For Meteorol 223:48–59 Zhao W, Zhang R, Cao H, Tan W (2019) Factor contribution to soil organic and inorganic carbon accumulation in the Loess Plateau: Structural equation modeling. Geoderma 352:116–125 Zhao Y, Wang M, Hu S, Zhang X, Ouyang Z, Zhang G, Huang B, Zhao S, Wu J, Xie D, Zhu B, Yu D, Pan X, Xu S (2018) X., S., Economics- and policy-driven organic carbon input enhancement dominates soil organic carbon accumulation in Chinese croplands. Proc. Natl. Acad. Sci. U.S.A. 115, 4045–4050 Zhou Z, Wang C, Li Y, Wang X, He X, Xu M, Cai A (2025) Carbon gain in upper but loss in deeper cropland soils across China over the last four decades. Proc Natl Acad Sci U S A 122, e2422371122 Zhu M, Cheng G, Zhang X, Guo Y, Wu Y, Wang Q, Wang H, Wang W (2023) Shelterbelts increased soil inorganic carbon but decreased nitrate nitrogen, total phosphorus, and bulk density relative to neighbor farmlands depending on tree growth, geoclimate, and soil microbes in the Northeast China Plain. CATENA 231:107344 Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 02 Feb, 2026 Read the published version in Plant and Soil → Version 1 posted Editorial decision: Major revisions 12 Sep, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers invited by journal 23 Jun, 2025 Editor invited by journal 23 Jun, 2025 Editor assigned by journal 23 Jun, 2025 First submitted to journal 20 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6937799","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475477161,"identity":"174f5b58-29c9-4dad-91a4-bba48cbb2f91","order_by":0,"name":"Teng-Fei Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApElEQVRIiWNgGAWjYJCCDx8KGBJI0cDMOHOGAalaZvOQpMXgRv7BZhuDujwG9sMPGH7uIEpLMmNzjgFbMQNPmgFj7xnitLA/zjHgSWxgyAG6sY1YWywMJBIb+N+QooXBwCCxQYJYWyTPPDZs7DFIKGaTeGZwsJcYLXzHEx82/Kioy+PnT3744CcxWhQOQBlsQHwAtzokIN9AlLJRMApGwSgY0QAADn8yEJTY17oAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-9725-8125","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Teng-Fei","middleName":"","lastName":"Yu","suffix":""},{"id":475477162,"identity":"8a747f3e-1649-4e02-ab2a-69f762d4a901","order_by":1,"name":"Tuo Han","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Tuo","middleName":"","lastName":"Han","suffix":""},{"id":475477163,"identity":"1f08f9b2-633f-49b8-a734-a2e19a2f76ec","order_by":2,"name":"Yidan Yin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yidan","middleName":"","lastName":"Yin","suffix":""},{"id":475477164,"identity":"5dba63d1-3a55-452e-81dc-73cd8afad346","order_by":3,"name":"Baofeng Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Baofeng","middleName":"","lastName":"Li","suffix":""},{"id":475477165,"identity":"2f2149fa-e635-4b58-8263-cc26377973a9","order_by":4,"name":"Haiyang Xi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Haiyang","middleName":"","lastName":"Xi","suffix":""},{"id":475477166,"identity":"35ed6581-0b6c-4b73-9950-86f74f9d51c0","order_by":5,"name":"Wei Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Liu","suffix":""},{"id":475477167,"identity":"db39bd9a-8bf1-4030-85d1-158e9435d74c","order_by":6,"name":"Qi Feng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Feng","suffix":""}],"badges":[],"createdAt":"2025-06-20 10:06:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6937799/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6937799/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11104-026-08271-7","type":"published","date":"2026-02-02T15:58:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85431321,"identity":"8df4eeba-d7fc-400e-b5b0-e1d357f655a9","added_by":"auto","created_at":"2025-06-25 18:25:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5833219,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area and sampling design: (a) Location of Heihe River Basin (HRB) within China, showing land-use distribution of oasis and dryland classes; (b) Sampling site distribution across irrigation zones with representative landscape imagery and shelterbelt height variation; (c) Sampling scheme between shelterbelts (SF) and croplands (CF) across distance gradients according to shelterbelt height (H): CF_0.25H, CF_0.5H, and CF_1H.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6937799/v1/e9a10dfc21a7259aeae2a286.png"},{"id":85431320,"identity":"eeb5e408-438f-4cdb-ab89-6de98cd996aa","added_by":"auto","created_at":"2025-06-25 18:25:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":633872,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of (a) soil total carbon (STC), (b) soil organic carbon (SOC), (c) soil inorganic carbon (SIC), and (d) SIC:SOC ratio between shelterbelt forests (SF) and adjacent corn fields (CF) at increasing distance gradientsaccording to shelterbelts height (H): CF_0.25H, CF_0.5H, and CF_1H.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6937799/v1/d5ca5437f338f714248351a1.png"},{"id":85431978,"identity":"f2da382e-a558-4859-869b-f18ca8fd92f0","added_by":"auto","created_at":"2025-06-25 18:41:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":937090,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between soil total carbon (STC) and organic carbon (SOC) across soil depths (0-10, 10-30, 30-50, 50-100 cm) for: (a) shelterbelt forests (SF), and croplands (CF) at increasing distances gradients, (b) CF_0.25H, (c) CF_0.5H, and (d) CF_1H.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6937799/v1/ed577710de624cc072b98a2f.png"},{"id":85430819,"identity":"572d6b9d-52ad-419d-942f-763d20e54321","added_by":"auto","created_at":"2025-06-25 18:17:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2719371,"visible":true,"origin":"","legend":"\u003cp\u003ePearson’s correlation matrices between soil organic carbon (SOC), inorganic carbon (SIC), and edaphic factors across depth intervals (0-100 cm): (a) shelterbelts (SF), and croplands (CF) at increasing distance gradients, (b) CF_0.25H, (c) CF_0.5H, (d) CF_1H.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6937799/v1/5590a91c4dbed0dd3cc335ac.png"},{"id":85431580,"identity":"659a4c9a-5dcf-421f-937b-a06d8d0b50e9","added_by":"auto","created_at":"2025-06-25 18:33:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":425721,"visible":true,"origin":"","legend":"\u003cp\u003eRandom Forest variable importance (%IncMSE) of edaphic factors for predicting (a) soil organic carbon (SOC) and (b) inorganic carbon (SIC) in shelterbelts (SF) and croplands at increasing distance gradients, CF_0.25H, CF_0.5H, CF_1H.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6937799/v1/b0097687af6396836f218830.png"},{"id":85430826,"identity":"8bdc7d2a-5df4-49ee-b2a1-4059c72a5d56","added_by":"auto","created_at":"2025-06-25 18:17:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1428376,"visible":true,"origin":"","legend":"\u003cp\u003eStructural Equation Model (SEM) quantifying direct and indirect effects of edaphic drivers on (a) SOC in shelterbelts (SF), (b) SOC in croplands (CF), (c) SIC in SF, and (d) SIC in CF across the soil profile (0-100 cm). Path coefficients represent standardized effects with ***\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001, **\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01, *\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6937799/v1/ca4d6f4c42d47492fd1d13a1.png"},{"id":85430827,"identity":"45fad366-56ad-46a5-9b48-a71b166f83d9","added_by":"auto","created_at":"2025-06-25 18:17:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":759312,"visible":true,"origin":"","legend":"\u003cp\u003eLinear mixed-effects model coefficients showing standardized effects of edaphic predictors on (a) SOC in shelterbelts (SF), (b) SOC in croplands (CF), (c) SIC in SF, and (d) SIC in CF across 0-100 cm depth.\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6937799/v1/50e064457cf805b6236d7d8a.png"},{"id":85430830,"identity":"18199a3e-7562-42dc-a8d2-a7da1f4b1079","added_by":"auto","created_at":"2025-06-25 18:17:53","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":369280,"visible":true,"origin":"","legend":"\u003cp\u003eVariance partitioning of edaphic drivers for soil carbon fractions: Relative contributions of nutrient, physical, and chemical properties to explained variance in (a) soil organic carbon (SOC) and (b) inorganic carbon (SIC) for shelterbelts (SF) and croplands (CF).\u003c/p\u003e","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6937799/v1/b906ec345c5b6d61870528e1.png"},{"id":102234181,"identity":"5b2fb723-e545-4075-b3d2-53f5c1952c6f","added_by":"auto","created_at":"2026-02-09 16:07:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":19534988,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6937799/v1/c0edca87-eb4f-4022-8ebb-d88735f9111e.pdf"},{"id":85430832,"identity":"0f4c409d-574a-4fac-8579-fd3fa2917d9e","added_by":"auto","created_at":"2025-06-25 18:17:53","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":28286827,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6937799/v1/899c3e6ce27397493477674e.docx"}],"financialInterests":"","formattedTitle":"Divergent responses of soil organic and inorganic carbon to edaphic factors within, but not between, shelterbelts and croplands in dryland of NW China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSoil carbon sequestration, including organic (SOC) and inorganic carbon (SIC) for agricultural systems, plays a pivotal role in mitigating climate change by promoting carbon cycling in terrestrial ecosystems (Bossio et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As the world's largest agricultural country, both of the repeated historical measurements and model simulation showed an increasing trend of SOC content and stock in the topsoil in Chinese croplands (Pan et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Yan et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ding et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Surprisingly, it was being lost in the subsoil (60\u0026ndash;100 cm) of China\u0026rsquo;s upland croplands over the last few decades, that may induced by warming-enhanced decomposition (Zhou et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In contrast to SOC, an overall decrease in SIC stocks in topsoil (0\u0026ndash;30 cm or 0\u0026ndash;40 cm) of croplands were observed especially in arid and semi-arid areas (Wu et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Raza et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tao et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and expected persistent in the future (Wiesmeier et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) as the consequences of global warming and soil acidification caused by atmospheric acid deposition and agricultural fertilization.\u003c/p\u003e \u003cp\u003eIt is important to note that those of studies are based on the long time-series comparison (Song et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tao et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), that might be greatly uncertain due to changes in the external environment. Therefore, the horizontal comparison on soil caron between croplands and adjacent shelterbelt or natural vegetation (Zhu et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), may offer more reliable information on soil carbon dynamics under the influence of climate change and human activities. Previous studies in Northeast China's black soil areas showed that the SOC at the top 100 cm and SIC at the top 40 cm in shelterbelts were higher 15.8% and 16.0% than in croplands, respectively (Zhang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). At present, some of studies have analyzed the impacts of long-term fertilization on SOC (Halvorson et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Fan et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) as well as SIC (Wang et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Raza et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) of croplands in drylands, and find the non-synergistically response of SIC with increase of SOC (Liu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). However, these studies were conducted separately on SOC or SIC, and without considering the differences in croplands and adjacent shelterbelts, that limited our understanding on the patterns and dynamics of SOC and SIC for oasis in dryland under the scenarios of climate change and human activities.\u003c/p\u003e \u003cp\u003eSoil carbon patterns and dynamics were dependent on complex interactions of the various biogeochemical process, of which edaphic factors included nutrient, physical and chemical properties, play the significant role (Wang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the relative importance of nutrient, physical and chemical properties in determining SOC and SIC between croplands and shelterbelt remains unclear. At global or continental scales, climate, vegetation types, soil properties, and anthropogenic activities dominantly control SOC dynamics (Viscarra Rossel et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Conversely, at local scales particularly in dryland oasis ecosystems characterized by agroforestry systems, previous studies indicate that soil physical and chemical properties and microorganisms govern SOC sequestration (Pan et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Similarly, factors regulating SIC exhibit scale-dependent variations (Sharififar et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Globally, SIC distribution in topsoil is influenced not only by abiotic factors, e.g., edaphic and climatic conditions (Huang et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), but also significantly by biotic factors, including plant/microbial biomass, richness, and diversity (Zeng et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). At local scales, however, agricultural systems show reduced SIC stocks primarily due to nitrogen fertilization-induced acidification and irrigation-driven leaching of Ca\u0026sup2;⁺ and Mg\u0026sup2;⁺, which limit carbonate formation (Tao et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccordingly, we hypothesize that there were: (1) significant difference in soil carbon fractions (SOC and SIC) between croplands and adjacent shelterbelt, and (2) divergent response of soil carbon fractions to edaphic factors owing the different management practice for croplands and shelterbelt. To test these hypotheses, 480 soil samples (30 sites \u0026times; 4 points \u0026times; 4 depths) were collected from 30 paired sites encompassed one point in shelterbelts and three distance-based points in croplands at 0\u0026ndash;10, 10\u0026ndash;30, 30\u0026ndash;50, and 50\u0026ndash;100 cm depths in the dryland, NW China. All collected soil samples have been measured soil carbon fractions and edaphic factors. We aim to: (i) detect the horizontal variation in soil carbon fractions from shelterbelts to croplands within different distance and the shift in the effect of depths, (ii) verify the relationship between soil carbon fractions and edaphic factors, and (iii) quantify the effects of edaphic factors on soil carbon fractions for croplands and adjacent shelterbelts.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study sites and sample collection\u003c/h2\u003e \u003cp\u003eThe study was conducted in the middle reaches of the Heihe River Basin (HRB), located at the Hexi Corridor of NW China (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The study area experiences strong, long-term cultivation and thus the dominant soil types consist of non-zonal anthropogenic soils, characterized by the loam of soil texture within 100 cm depths (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Climate in this region was an arid climate characterized by less precipitation and high temperature. The mean annual precipitation (MAP) is 124 mm, and mean annual potential evapotranspiration (PET) is 2190 mm, about 10.2 times of MAP. The mean annual temperature is 7.3 ℃, and high winds and sandstorm are often occurred in this area (Zhang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The vegetation was primarily comprised of croplands and adjacent shelterbelts, the height of shelterbelts were significantly differed from that of croplands at the varied distance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Notably, the negative effects of shelterbelts on croplands were observed, i.e. the height of croplands were significantly lower in close to shelterbelts than that of far away from it.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eField investigation was carried out in autumn spanning from the end of July to August in 2024. To satisfy the representativeness, 30 paired sites with varied edaphic, hydrological and management practices were selected to sample collection in the middle reaches of HRB. At each site, four sampling points were selected that comprising one point in shelterbelt forests (SF), and three points in adjacent corn fields (CF) at distances of 0.25H (CF_0.25H), 0.5H (CF_0.5H), and 1H (CF_1H) from the shelterbelt edge, where H represents the stand's mean height. At each site, SF were dominated by Gansu poplar (\u003cem\u003ePopulus gansuensis\u003c/em\u003e C. Wang \u0026amp; H. L. Yang), while CF supported maize for seed production (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At each sampling point, we excavated one soil profiles and obtained approximately 500 g of the freshly mixed soil from each profile at four depths (0\u0026ndash;10 cm, 10\u0026ndash;30 cm, 30\u0026ndash;50 cm and 50\u0026ndash;100 cm), to analyze soil carbon fractions (SOC and SIC) and edaphic factors. In total, we collected 480 soil samples (30 sites \u0026times; 4 points \u0026times; 4 depths) for this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Soil carbon fractions and edaphic factors measurement\u003c/h2\u003e \u003cp\u003eAfter air-drying, these soil samples were subsequently passed through 2 mm sieves in preparation for the analysis of soil carbon fractions and edaphic factors. The content of soil total carbon (STC, g/kg) and total nitrogen (TN, g/kg) were analyzed by a C/N element analyzer (CN802, VELP, Italy). The content of SOC (g/kg) was quantified using the potassium permanganate method with external heating. Subsequently, the content of SIC (g/kg) was calculated by subtracting SOC from STC (Yu et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Soil nutrient content including total phosphorus (TP, g/kg) and kalium (TK, g/kg), available phosphorus (AP, mg/kg) and kalium (AK, mg/kg) were analyzed using the Olsen\u0026rsquo;s Method. Soil pH and electrical conductivity (EC, \u0026micro;S/cm) were analyzed using the Multiparameter Analyzer (SevenExcellence S470-BIO, Mettler Toledo, USA). Soil anions and cations including Fluoride ion (F\u003csup\u003e\u0026minus;\u003c/sup\u003e), Chloride ion (Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e), Nitrate ion (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, mg/L), Sulfate ion (SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, mg/L), Sodium ion (Na\u003csup\u003e+\u003c/sup\u003e, mg/L), Kalium ion (K\u003csup\u003e+\u003c/sup\u003e, mg/L), Calcium ion (Ca\u003csup\u003e2+\u003c/sup\u003e, mg/L), and Magnesium ion (Mg\u003csup\u003e2+\u003c/sup\u003e, mg/L) were analyzed using an ion chromatography system (ICS-2500, Dionex Corporation, USA). Soil moisture content (SMC, %) was measured using the gravimetric method by oven-drying at 105\u0026deg;C for 48 hours. Bulk density (BD, g/cm\u003csup\u003e3\u003c/sup\u003e) was measured using ring excavation with a constant volume, 100 cm\u003csup\u003e3\u003c/sup\u003e. Soil particle size was analyzed using a laser particle analyzer (Mastersizer 3000, Malvern Company, UK), after which the percentage of Clay (\u0026lt;\u0026thinsp;2 \u0026micro;m, %), Silt (2\u0026thinsp;~\u0026thinsp;50 \u0026micro;m, %) and Sand (50\u0026thinsp;~\u0026thinsp;2000 \u0026micro;m, %) were classified following USDA standard and illustrated using Soil Texture Triangle (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). In total, twenty edaphic factors included nutrient (TN, TP, TK, AP, AK), chemical (pH, EC, F\u003csup\u003e\u0026minus;\u003c/sup\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e, Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e) and physical properties (SMC, BD, Clay, Silt, Sand) were measured.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analyses\u003c/h2\u003e \u003cp\u003eThe One-way ANOVA was applied to verify the differences in soil carbon fractions and edaphic factors among the different points (SF, CF_0.25H, CF_0.5H and CF_1H), while the differences between two points was tested by \u003cem\u003et\u003c/em\u003e test. Multiple ANOVA analysis was employed to quantify the effects of points, depths, and the interaction among points and depths on soil carbon fractions and edaphic factors. Data were presented as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE (Standard Error) (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Linear regression was utilized to ascertain relationships between SOC and STC at different depths. Pearson\u0026rsquo;s correlation was utilized to identify relationships between soil carbon fractions (SOC and SIC) and edaphic factors, and plotted using the \u003cem\u003elinkET\u003c/em\u003e R package.\u003c/p\u003e \u003cp\u003eRandom Forest (RF) analysis (\u003cem\u003erandomForest\u003c/em\u003e R package) was employed to determine the relative importance of soil properties for SOC and SIC by evaluating predictor contribution through mean accuracy reduction (Yu et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Global model significance was assessed via permutation testing (99 iterations, α\u0026thinsp;=\u0026thinsp;0.05), while the \u003cem\u003erfPermute\u003c/em\u003e R package quantified individual predictor significance by permuting responses to calculate p-values. Linear mixed-effects models (LMMs) was implemented to provide robust effect estimates by accounting for both fixed effects and site-level random effects (Kuznetsova et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Prior to model construction, all predictor variables were normalized using the Min-Max method (scaling to 0\u0026ndash;1 range) to mitigate scaling biases.\u003c/p\u003e \u003cp\u003eStructural Equation Modeling (SEM) was employed to gain mechanistic insights into SOC and SIC variation by establishing causal networks among drivers. To simplify the model, metrics significantly contributing to SOC and SIC, as identified by RF analysis, were categorized into nutrient properties, physical properties, and chemical properties to reduce multicollinearity. Maximum likelihood estimation derived path coefficients, while multigroup analysis tested for significant differences between cropland and shelterbelt models. Model fit was assessed using χ\u0026sup2;, root mean square error of approximation (RMSEA), and comparative fit index (CFI) (Zhao et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). All analyses were implemented in AMOS 22.0 (Amos Development Co., USA).\u003c/p\u003e \u003cp\u003eVariance partitioning analysis (VPA) was then performed using the varpart function (\u003cem\u003evegan\u003c/em\u003e R package) to quantify unique and shared contributions of nutrient, physical, and chemical properties to SOC and SIC variation.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Variations of soil carbon fractions and edaphic factors\u003c/h2\u003e \u003cp\u003eThere were no significant differences in STC, SOC, SIC, and SIC:SOC ratio between shelterbelts (SF) and croplands (CF) across distance gradients: CF_0.25H, CF_0.5H, and CF_1H (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), while STC was significant higher in SF than in CF_0.5H and CF_1H (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). However, significant differences were observed in STC, SOC, SIC, and SIC:SOC among depths (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), specifically there exhibited significant differences among depths of STC at SF, SOC and SIC:SOC but not for SIC at each of sites (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cb\u003eFig. S2\u003c/b\u003e). The content of STC and SOC was gradually decreased, while SIC and SIC:SOC were increased as the soil depths increase (\u003cb\u003eFig. S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical characteristics (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE) of soil carbon fractions, soil total carbon (STC), organic carbon (SOC), inorganic carbon (SIC) and SIC:SOC ratio, with multivariate analysis of variance (MANOVA) results across depth intervals (0\u0026ndash;10, 10\u0026ndash;30, 30\u0026ndash;50, 50\u0026ndash;100 cm) between shelterbelts (SF) and croplands (CF) at increasing distance gradients from shelterbelt edges (CF_0.25H, CF_0.5H, CF_1H). MANOVA reports F-values with significance levels: P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepths (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTC (g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSOC (g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSIC (g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSIC:SOC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.25\u0026thinsp;\u0026plusmn;\u0026thinsp;2.05\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCF_0.25H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCF_0.5H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCF_1H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e10\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCF_0.25H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCF_0.5H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCF_1H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e30\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCF_0.25H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCF_0.5H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCF_1H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCF_0.25H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCF_0.5H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCF_1H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCF_0.25H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCF_0.5H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCF_1H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMANOVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.31\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e43.60\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.87\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e47.03\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSites : Depths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere were significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) linearly related between STC and SOC at various sites and depths, and the coefficient of determination (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) and ratio of SOC to STC (slope of linear fitting) were decreased as soil depths increase, except for the 30\u0026ndash;50 cm at CF_0.25H and CF_0.5H (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which suggested that SIC play a dominant role in subsoil. In total, the ratio of SIC to SOC in the top 1 m was ranged from 1.2 to 4.0 with an average of 2.4, where the SIC and SOC content were 11.39 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 6.84 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDifferent from soil carbon fractions, significant differences were observed in some of edaphic factors, included SMC at each depth, AK at the depths of 0\u0026ndash;10 cm and 10\u0026ndash;30 cm, BD and K\u003csup\u003e+\u003c/sup\u003e at the depths of 0\u0026ndash;10 cm. Averaged in the top 1 m, significant differences were found for AK, SMC, BD, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and K\u003csup\u003e+\u003c/sup\u003e among points (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Fig. S3\u003c/b\u003e). On the whole, except for TK, Clay, Silt, Sand, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e and Na\u003csup\u003e+\u003c/sup\u003e, significant differences were found among soil depths rather than among points. At the same time, strong interactive effect of depths and points on SMC only was presented (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Associations of soil carbon fractions with edaphic factors\u003c/h2\u003e \u003cp\u003eJust as assumed, edaphic factors have contrasting effects on SOC and SIC within, but not between SF and CF (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Pearson\u0026rsquo;s correction analysis showed that SOC for SF was significantly positive correlated with the majority of edaphic factors including TN, TP, AP, AK, EC, Clay, Silt, Cl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e, and Mg\u003csup\u003e2+\u003c/sup\u003e, whereas SIC was merely positive associated with TN, SMC, pH, Clay, Silt, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, and Mg\u003csup\u003e2+\u003c/sup\u003e, and both of negative associated with Sand and insignificantly associated with TK and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). However, SOC was significantly positive correlated with TN, TP, TK, AP, AK, SMC, EC, Clay and Silt, not significantly even insignificantly associated with chemical properties. SIC was more positive associated with TN, SMC, Silt and cations including Na\u003csup\u003e+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e especially for CF_0.5H and CF_1H (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed), and SOC and SIC associated with edaphic factors were fall in between or transitional for CF_0.25H (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRandom Forest also demonstrated that soil nutrient including TN, TP and AP has consistent relatively important contribution to the variation of SOC for both of SF and CF, but other edaphic factors has divergent contribution on SOC for SF and CF. Specifically, AK, BD, EC, F\u003csup\u003e\u0026minus;\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e and Ca\u003csup\u003e2+\u003c/sup\u003e for SF, BD, Na\u003csup\u003e+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e for CF_0.25H, and Clay, Silt, Sand and EC for CF_0.5H and CF_1H sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). In contrast to SOC, physical and chemical properties including SMC, Silt, Sand and Mg\u003csup\u003e2+\u003c/sup\u003e have consistent relatively important contribution on SIC for both SF and CF, but other edaphic factors have divergent effects on SIC for SF and CF (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The Least Absolute Shrinkage and Selection Operator (LASSO) Regression also demonstrated that edaphic factors have divergent effects on SOC and SIC (\u003cb\u003eFig. S4 and S5\u003c/b\u003e). Therefore, the edaphic factors significantly related to SOC including nutrient characteristics (TN, TP and AP), physical (BD, Clay, Sand) and chemical properties (EC, Ca\u003csup\u003e2+\u003c/sup\u003e, and Mg\u003csup\u003e2+\u003c/sup\u003e), and that of related to SIC including nutrient characteristics (TN, TK and AP), physical (SMC, Silt and Sand) and chemical properties (pH, Mg\u003csup\u003e2+\u003c/sup\u003e and Ca\u003csup\u003e2+\u003c/sup\u003e), were selected for further identify the determinants of SOC and SIC for SF and CF.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Determinants of soil organic and inorganic carbon\u003c/h2\u003e \u003cp\u003eBoth linear mixed effects model (LMEM) and structural equation modeling (SEM) consistently demonstrated that soil nutrients significantly influenced SOC, whereas soil physical and chemical properties were critical determinants of SIC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The LME model showed soil nutrient, physical and chemical properties accounted for 79.71%, 7.00% and 13.29% of relative effects on SOC for SF, and 80.95%, 17.79% and 1.26% for CF, respectively. In contrast, they accounted for 24.92%, 47.70% and 27.38% of relative effects on SIC for SF, and 17.4%, 64.70% and 17.95% for CF, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Specifically, TN, AP, BD and Mg\u003csup\u003e2+\u003c/sup\u003e have significant contribution to SOC for SF, and TN, TP, Clay, EC for CF, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). In contrast to SOC, TN, AP, Silt, Sand, Mg\u003csup\u003e2+\u003c/sup\u003e and Ca\u003csup\u003e2+\u003c/sup\u003e have significant contribution to SIC for SF, and TN, TK, AP, SMC, Silt, Sand, Mg\u003csup\u003e2+\u003c/sup\u003e and Ca\u003csup\u003e2+\u003c/sup\u003e for CF, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). The SEM further indicated that nutrient characteristics had highest direct effects on SOC, while physical properties had highest direct effects on SIC for both of SF and CF, and coefficient of determination (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) of the SEM model was higher for SOC than SIC (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Overall, variance partitioning analysis (VPA) revealed that edaphic factors explained 78.19% and 70.10% of the variation in SOC for SF and CF, respectively, while 45.10% and 49.70% of SIC. Furthermore, soil nutrient characteristics, physical and chemical properties exhibited distinct explanatory capacities on the variation of SOC and SIC within SF and CF (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Comparable soil carbon content between shelterbelt and croplands\u003c/h2\u003e \u003cp\u003eTo date, there has been limited research comparing the SOC and SIC content between croplands and adjacent shelterbelt (Dhillon et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mayrinck et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ji et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), especially in arid and semi-arid areas, NW China. At the black soil areas in northeastern of China, the SOC at the top 1 m and SIC at the top 40 cm in shelterbelt were higher 15.8% and 16.0% than in croplands, respectively (Zhang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Contrary to the previous studies and our first hypothesis, the results demonstrated no significant differences in both of SOC and SIC content between shelterbelt and croplands (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This unexpected finding suggests that two land use patterns may exhibit comparable carbon sequestration capacities under current management practices. There were three possible reasons.\u003c/p\u003e \u003cp\u003eFirstly, the undifferentiated and insufficient nutrients supply, mainly nitrogen (N) and phosphorus (P), may impose restrictions on SOC accumulation by restraining the growth of crops, shelterbelt and understory herbs. Theoretically, the higher N and P inputs can stimulate the production of crops and soil microbial biomass, thereby increasing soil C stocks (Yu et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and vice versa (Oldroyd and Leyser, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In fact, the soil C:N ratio (SOC:TN) in shelterbelt and croplands was comparable (about 10:1, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and S1) and far less than the preferred C:N ratio (20\u0026ndash;30:1). Considering the SOC content was mainly determined by nutrient in the study areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), the insufficient N and P supply for shelterbelt and croplands may not satisfy the nutrient requirements for plant and microbes\u0026rsquo; growth, resulting in limited plant and microbial residue carbon inputs, and thereby limiting SOC accumulation.\u003c/p\u003e \u003cp\u003eSecondly, less SOC content in the study can be attributed to the insufficient mineral conservation, i.e., the limited Clay content (\u0026lt;\u0026thinsp;10%, \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e) restricted the sorption of organic matter to mineral surfaces and hindered the formation of aggregates (Su et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rasmussen et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In consistent with previous studies in arid areas (Su et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), soil carbon content in particular SOC was significantly associated with Clay content (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, it was hard to retain the organic matter and carbonates on soil profile, especially in the presence of flood irrigation in study areas that could lead to SOC and SIC loss through nutrients and ions leaching along water flow. Soil with higher Clay content promotes the accumulation of soil carbon generally via two forms, the first is involved with mineral-associated organic carbon (MAOC), which protect SOC from decomposition via chemical bonds between organic carbon and minerals (Rasmussen et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); and the second is associated with pedogenic inorganic carbon (PIC), which control the accumulation depth of PIC via affecting soil water holding capacity, water penetration and movement (Zamanian et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThirdly, the possibly offsetting effects between above-ground biomass accumulation in shelterbelts and enhanced soil carbon stabilization in croplands. For SOC, it mainly produced from crop straw/stover incorporation, although croplands have higher production than shelterbelts, the major crop straw was collected and used for raising livestock or burning cook, resulted in the comparable residue inputs with shelterbelts. Similarly, the long-term fertilization has not reduced SIC in croplands, which might have been neutralized or carried away by brackish groundwater irrigation. The high fluoride (F\u003csup\u003e\u0026minus;\u003c/sup\u003e) concentration in soil (0.30\u0026ndash;0.35 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e) was consistent with groundwater, that correlates with the prevalence of SO\u003csub\u003e4\u003c/sub\u003e\u0026ndash;Cl\u0026ndash;Na\u0026ndash;Mg and alkaline conditions (He et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.2 Contrasting responses of SOC and SIC to edaphic factors within shelterbelts and croplands, but not between them\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn consistent with previous studies (Zhao et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lin et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), our results demonstrated nutrient addition whether through fertilization in croplands or plant residue input into shelterbelt have a positive influence on SOC content (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Numerous in-situ experiments and meta-analyses have revealed that the application of nutrients, such as N and P, enhances SOC accumulation by stimulating plant productivity and microbial activity (Fan et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and the magnitude of this fertilizer effects varies depending on fertilization types and levels (Liu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e), and initial SOC content (Ling et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For instance, the long-term N addition has been shown to increase above- and below-ground biomass and litter inputs (Yue et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), thereby boosting carbon sequestration globally especially for agricultural systems (Yang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Similarly, P addition under stable N supply promote microbial carbon use efficiency, CUE (Dai et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), thereby facilitating the formation of stable carbon compounds (He et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Compared to the croplands, the shelterbelt acquires nutrients (N, P and K), in addition to atmospheric deposition, mainly through litter and crop roots burning in part of sampling sites, that induced the occurrence of abnormal value particularly in nutrients and chemical properties (\u003cb\u003eFig. S3\u003c/b\u003e). Those of additional crop roots residue C input with enriched nutrition may facilitate the SOC accumulation in shelterbelt and form the same effects as fertilization for croplands (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDifferent from SOC, the determinants of SIC was on the aspect of soil physical and chemical properties (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Following chemical equilibrium equation, i.e., CaCO\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;H\u003csub\u003e2\u003c/sub\u003eO\u0026thinsp;+\u0026thinsp;CO\u003csub\u003e2\u003c/sub\u003e ≒ 2HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e + Ca\u003csup\u003e2+\u003c/sup\u003e, the precipitation and dissolution of carbonate is mainly affected by (i) the partial pressure of CO\u003csub\u003e2\u003c/sub\u003e in soil, (ii) soil pH, (iii) soil moisture content and (iv) the contents of Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e. Thus, any slight changes of these controlling factors may have an important effect on the SIC content and storage (Sun et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Compared to shelterbelt, croplands at different distances have lower TN, pH, K\u003csup\u003e+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e, but higher TP, AP, SMC, BD, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2+\u003c/sup\u003e and Ca\u003csup\u003e2+\u003c/sup\u003e (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e), meanwhile SMC, Silt, Sand, pH and Mg\u003csup\u003e2+\u003c/sup\u003e has consistently direct relationships with SIC (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), indicating that soil physical and chemical processes play an important role in the formation of SIC.\u003c/p\u003e \u003cp\u003eAlthough the factors affecting the variation of SIC are various, the mechanism to explain the SIC variation for agroforestry systems in drylands can be categorized into three groups. At the first, the SIC formation was attribute to the physical processes, namely planted vegetation, shelterbelt and crops, can intercept and deposit fine particles that contain enriched carbonate sources and then causes a massive accumulation of SIC in the topsoil (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which can be proved by the crucial effects of Silt on SIC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). However, these processes could not provide a complete explanation for the comparable SIC content between croplands and adjacent shelterbelt (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Therefore, the second mechanism, i.e., chemical processes was proposed, namely the increased soil CO\u003csub\u003e2\u003c/sub\u003e concentration via SOC decomposition under moist soil water regime would lead to the production of CO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e and/or HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, that combined with the available cations (e.g., Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e) may cause continuous increase of SIC (Zamanian et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our results (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) were aligned with previous studies that emphasized the crucial roles of soil texture, soil water content, Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e in explaining the distribution of SIC (Ferdush and Paul, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tao et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThirdly, which is more important, our study implied a pivotal role of biological processes, e.g. microbial-induced carbonate precipitation, MICP (Okyay et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In determining the SIC content in saline-alkali soil in drylands (Yu et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), considering of edaphic factors can explained only 45.10% and 49.70% of variation in SOC for SF and CF, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The study area are characterized by soils with high pH (\u0026gt;\u0026thinsp;8.0) and abundant Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e content (\u0026gt;\u0026thinsp;0.2%) (\u003cb\u003eFig. S3\u003c/b\u003e) that may produce from brackish groundwater irrigation characterized by calcium and magnesium type (He et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Under the brackish groundwater irrigation, the abundant ionic accompanied with litters and biomass were infiltrate into soil, abiotic and biological processes begin to take effects on the formation of soil carbon (Ball et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Feng et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The dissolved lignin and cellulose fractions bind to available Ca\u0026sup2;⁺ and Mg\u0026sup2;⁺ through ligand exchange, facilitating their incorporation into mineral matrices to form mineral-associated organic carbon (MAOC). This process enhances SOC stabilization through organo-mineral interactions. Subsequently, heterotrophic microbial activity mineralizes soil organic matter (SOM), releasing dissolved CO₂ that reacts with water to form carbonate ions (CO₃\u0026sup2;⁻ and HCO₃⁻). These anions combine with divalent cations (predominantly Ca\u0026sup2;⁺ and Mg\u0026sup2;⁺) to precipitate as pedogenic carbonates (e.g., CaCO₃, MgCO₃), thereby promoting SIC formation. Under alkaline conditions typical of dryland soils, this biogeochemical cascade creates a positive feedback loop where SOC accumulation facilitates SIC sequestration.\u003c/p\u003e \u003cp\u003eAlthough this study demonstrated non-significant differences in soil carbon content between shelterbelts and croplands, several methodological limitations warrant acknowledgment. The 100 cm sampling depth may have overlooked deeper carbon dynamics, particularly for inorganic carbon pools influenced by groundwater fluctuations. Furthermore, single-season sampling captured neither seasonal variations in microbial carbon processing nor groundwater-mediated nutrient fluxes that modulate carbon stabilization. The limited spatial coverage also fails to account for landscape-scale heterogeneity in soil texture and land-use history that drive carbon variability. Crucially, the absence of significant carbon divergence between these distinct ecosystems challenges conventional carbon budgeting assumptions in dryland agroforestry, necessitating: (1) multi-year monitoring to quantify legacy effects and sequestration trajectories, and (2) integrated assessments of subsurface carbon fluxes to reconcile surface stock measurements with regional carbon budgets.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eContrary to previous studies and our first hypothesis, this work revealed non-significant differences in STC, SOC, SIC, and SIC:SOC ratio between shelterbelts and croplands across distance gradients. Analytical consensus from Pearson's correlation, Random Forest, linear mixed-effects models, and structural equation modeling consistently identified soil nutrient characteristics as primary determinants of SOC, while physicochemical properties governed SIC dynamics. Variance partitioning further demonstrated that edaphic factors explained substantially more variation in SOC (70.10% and 78.19%) than in SIC (45.10% and 49.70%) for both systems. This divergence suggests that beyond abiotic controls, biotic factors particularly microbial-mediated carbonate precipitation, may critically influence SIC accumulation in dryland saline-alkaline soils. Consequently, our findings demonstrate: (1) fundamentally distinct response mechanisms of SOC and SIC to edaphic drivers, and (2) consistent abiotic regulation of carbon sequestration across agroforestry systems despite land-use differences.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB0720401), the Major Research Project on Science and Technology of Gansu Province (Grant No. 23ZDFA018), the Key Technology Projects of Inner Mongolia Autonomous Region (Grant No. 2023JBGS0008), the Outstanding Youth Foundation Project of Gansu Province (Grant No. 24JRRA076), and the Natural Science Foundation of Gansu Province (Grant No. 25JRRG044).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBall KR, Malik AA, Muscarella C, Blankinship JC (2023) Irrigation alters biogeochemical processes to increase both inorganic and organic carbon in arid-calcic cropland soils. Soil Biol Biochem 187:109189\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBossio DA, Cook-Patton SC, Ellis PW, Fargione J, Sanderman J, Smith P, Wood S, Zomer RJ, von Unger M, Emmer IM, Griscom BW (2020) The role of soil carbon in natural climate solutions. Nat Sustain 3:391\u0026ndash;398\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen B, Feng W, Jing X, Wang Y (2023) Dryland agricultural expansion leads to lower content and higher variability of soil inorganic carbon in topsoil. Agric Ecosyst Environ 356:108620\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai Z, Liu G, Chen H, Chen C, Wang J, Ai S, Wei D, Li D, Ma B, Tang C, Brookes PC, Xu J (2020) Long-term nutrient inputs shift soil microbial functional profiles of phosphorus cycling in diverse agroecosystems. ISME J 14:757\u0026ndash;770\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhillon GS, Van Rees KCJ, Chang S (2017) Soil organic carbon sequestration by shelterbelt agroforestry systems in Saskatchewan. Can J Soil Sci 97:394\u0026ndash;409\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing W, Chang N, Zhang G, Kang J, Yi X, Zhang J, Zhang J, Wang L, Li H (2023) Soil organic carbon changes in China's croplands: A newly estimation based on DNDC model. Sci Total Environ 905:167107\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan T, Stewart BA, Yong W, Junjie L, Guangye Z (2005) Long-term fertilization effects on grain yield, water-use efficiency and soil fertility in the dryland of Loess Plateau in China. Agric Ecosyst Environ 106:313\u0026ndash;329\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng W, Jiang J, Lin L, Wang Y (2023) Soil calcium prompts organic carbon accumulation after decadal saline-water irrigation in the Taklamakan desert. J Environ Manage 344:118421\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerdush J, Paul V (2021) A review on the possible factors influencing soil inorganic carbon under elevated CO2. Catena 204, 105434\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHalvorson AD, Reule CA, Follett RF (1999) Nitrogen fertilization effects on soil carbon and nitrogen in a dryland cropping system. Soil Sci Soc Am J 63:912\u0026ndash;917\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe D, Li G, Luo Z, Wang E (2025) Effects of long-term fertiliser application on cropland soil carbon dynamics mediated by potential shifts in microbial carbon use efficiency. Soil Tillage Res 248:106418\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe J, An Y, Zhang F (2013) Geochemical characteristics and fluoride distribution in the groundwater of the Zhangye Basin in Northwestern China. J Geochem Explor 135:22\u0026ndash;30\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang Y, Song X, Wang YP, Canadell JG, Luo Y, Ciais P, Chen A, Hong S, Wang Y, Tao F, Li W, Xu Y, Mirzaeitalarposhti R, Elbasiouny H, Savin I, Shchepashchenko D, Rossel RAV, Goll DS, Chang J, Houlton BZ, Wu H, Yang F, Feng X, Chen Y, Liu Y, Niu S, Zhang GL (2024) Size, distribution, and vulnerability of the global soil inorganic carbon. Science 384:233\u0026ndash;239\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIslam MR, Singh B, Dijkstra FA (2022) Stabilisation of soil organic matter: interactions between clay and microbes. Biogeochemistry 160:145\u0026ndash;158\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJi Q, Jiang H, Xu Z, Zhu M, Zhang S, Wang H, Tang Z, Wang Q, Wang W (2025) Farmland afforestation by poplar shelterbelts increased soil inorganic carbon but showed ambiguous effects on soil organic carbon as revealed by carbon isotopic composition: Inter-fraction and inter-site differences in Northern China. Forests 16:328\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuznetsova A, Brockhoff PB, Christensen RHB (2017) lmerTest Package: Tests in Linear Mixed Effects Models. J Stat Softw 82:1\u0026ndash;26\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Nie M, Pendall E (2020) Soil physico-chemical properties are more important than microbial diversity and enzyme activity in controlling carbon and nitrogen stocks near Sydney. Australia Geoderma 366:114201\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Pei J, Fang C, Li B, Nie M (2024) Drought may exacerbate dryland soil inorganic carbon loss under warming climate conditions. Nat Commun 15:617\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi S, Cui Y, Moorhead DL, Dijkstra FA, Sun L, Xia Z, Gao Y, Ma Q, Yu W (2025) Phosphorus limitation regulates the responses of microbial carbon metabolism to long-term combined additions of nitrogen and phosphorus in a cropland. Soil Biol Biochem 200:109614\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin Z, Lu X, Xu Y, Sun W, Yu Y, Zhang W, Mishra U, Kuzyakov Y, Wang G, Qin Z (2024) Increased straw return promoted soil organic carbon accumulation in China's croplands over the past 40 years. Sci Total Environ 945:173903\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLing J, Dungait JAJ, Delgado-Baquerizo M, Cui Z, Zhou R, Zhang W, Gao Q, Chen Y, Yue S, Kuzyakov Y, Zhang F, Chen X, Tian J (2025) Soil organic carbon thresholds control fertilizer effects on carbon accrual in croplands worldwide. Nat Commun 16:3009\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu E, Liu Z, Sun Z, Li J, Gong H, Peng J, Ouyang Z (2023a) Soil inorganic carbon stocks increase non-synergistically with soil organic carbon after ecological restoration practices in drylands. J Environ Manage 348:119070\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, Yang T, Wang Q, Huang F, Li L (2018) Dynamics of soil carbon and nitrogen stocks after afforestation in arid and semi-arid regions: A meta-analysis. Sci Total Environ 618:1658\u0026ndash;1664\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Li C, Cai G, Sauheitl L, Xiao M, Shibistova O, Ge T, Guggenberger G (2023b) Meta-analysis on the effects of types and levels of N, P, and K fertilization on organic carbon in cropland soils. Geoderma 437:116580\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMayrinck RC, Laroque CP, Amichev BY, Van Rees K (2019) Above- and Below-Ground Carbon Sequestration in Shelterbelt Trees in Canada: A Review. Forests 10:922\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkyay TO, Nguyen HN, Castro SL, Rodrigues DF (2016) CO\u003csub\u003e2\u003c/sub\u003e sequestration by ureolytic microbial consortia through microbially-induced calcite precipitation. Sci Total Environ 572:671\u0026ndash;680\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOldroyd GED, Leyser O (2020) A plant's diet, surviving in a variable nutrient environment. Science 368:eaba0196\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan G, Xu X, Smith P, Pan W, Lal R (2010) An increase in topsoil SOC stock of China's croplands between 1985 and 2006 revealed by soil monitoring. Agric Ecosyst Environ 136:133\u0026ndash;138\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan J, Chen S, He D, Zhou H, Ning K, Ma N, Li K, Liao D, Mi W, Wu Q, Zhang C, Dong Z (2025) Agroforestry increases soil carbon sequestration, especially in arid areas: A global meta-analysis. CATENA 249:108667\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRasmussen C, Heckman K, Wieder WR, Keiluweit M, Lawrence CR, Berhe AA, Blankinship JC, Crow SE, Druhan JL, Pries H, Marin-Spiotta CE, Plante E, Sch\u0026auml;del AF, Schimel C, Sierra JP, Thompson CA, Wagai A, R (2018) Beyond clay: towards an improved set of variables for predicting soil organic matter content. Biogeochemistry 137:297\u0026ndash;306\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaza S, Miao N, Wang P, Ju X, Chen Z, Zhou J, Kuzyakov Y (2020) Dramatic loss of inorganic carbon by nitrogen-induced soil acidification in Chinese croplands. Glob Chang Biol 26:3738\u0026ndash;3751\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharififar A, Minasny B, Arrouays D, Boulonne L, Chevallier T, van Deventer P, Field DJ, Gomez C, Jang H-J, Jeon S-H, Koch J, McBratney AB, Malone BP, Marchant BP, Martin MP, Monger C, Munera-Echeverri J-L, Padarian J, Pfeiffer M, Richer-de-Forges AC, Saby NPA, Singh K, Song X-D, Zamanian K, Zhang G-L, van Zijl G (2023) Chapter Four - Soil inorganic carbon, the other and equally important soil carbon pool: Distribution, controlling factors, and the impact of climate change. Adv Agron 178:165\u0026ndash;231\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong XD, Yang F, Wu HY, Zhang J, Li DC, Liu F, Zhao YG, Yang JL, Ju B, Cai CF, Huang B, Long HY, Lu Y, Sui YY, Wang QB, Wu KN, Zhang FR, Zhang MK, Shi Z, Ma WZ, Xin G, Qi ZP, Chang QR, Ci E, Yuan DG, Zhang YZ, Bai JP, Chen JY, Chen J, Chen YJ, Dong YZ, Han CL, Li L, Liu LM, Pan JJ, Song FP, Sun FJ, Wang DF, Wang TW, Wei XH, Wu HQ, Zhao X, Zhou Q, Zhang GL (2022) Significant loss of soil inorganic carbon at the continental scale. Natl Sci Rev 9, nwab120\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu YZ, Wang XF, Yang R, Lee J (2010) Effects of sandy desertified land rehabilitation on soil carbon sequestration and aggregation in an arid region in China. J Environ Manage 91:2109\u0026ndash;2116\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun Z, Meng F, Zhu B (2022) Influencing factors and partitioning methods of carbonate contribution to CO2 emissions from calcareous soils. Soil Ecol Lett 5:6\u0026ndash;20\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang B, Rocci KS, Lehmann A, Rillig MC (2023) Nitrogen increases soil organic carbon accrual and alters its functionality. Glob Chang Biol 29:1971\u0026ndash;1983\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTao J, Raza S, Zhao M, Cui J, Wang P, Sui Y, Zamanian K, Kuzyakov Y, Xu M, Chen Z, Zhou J (2022) Vulnerability and driving factors of soil inorganic carbon stocks in Chinese croplands. Sci Total Environ 825:154087\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eViscarra Rossel RA, Lee J, Behrens T, Luo Z, Baldock J, Richards A (2019) Continental-scale soil carbon composition and vulnerability modulated by regional environmental controls. Nat Geosci 12:547\u0026ndash;552\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang S, Xu L, Adhikari K, He N (2023) Soil carbon sequestration potential of cultivated lands and its controlling factors in China. Sci Total Environ 905:167292\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang XJ, Xu MG, Wang JP, Zhang WJ, Yang XY, Huang SM, Liu H (2014) Fertilization enhancing carbon sequestration as carbonate in arid cropland: assessments of long-term experiments in northern China. Plant Soil 380:89\u0026ndash;100\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWiesmeier M, Poeplau C, Sierra CA, Maier H, Fruhauf C, Hubner R, Kuhnel A, Sporlein P, Geuss U, Hangen E, Schilling B, von Lutzow M, Kogel-Knabner I (2016) Projected loss of soil organic carbon in temperate agricultural soils in the 21(st) century: effects of climate change and carbon input trends. Sci Rep 6:32525\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu H, Guo Z, Gao Q, Peng C (2009) Distribution of soil inorganic carbon storage and its changes due to agricultural land use activity in China. Agric Ecosyst Environ 129:413\u0026ndash;421\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan X, Cai Z, Wang S, Smith P (2011) Direct measurement of soil organic carbon content change in the croplands of China. Glob Change Biol 17:1487\u0026ndash;1496\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang R-M, Huang L-M, Zhang X, Zhu C-M, Xu L (2023) Mapping the distribution, trends, and drivers of soil organic carbon in China from 1982 to 2019. Geoderma 429:116232\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang X, Ma S, Huang E, Zhang D, Chen G, Zhu J, Ji C, Zhu B, Liu L, Fang J (2025) Nitrogen addition promotes soil carbon accumulation globally. Sci China Life Sci 68:284\u0026ndash;293\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu G, Xiao J, Hu S, Polizzotto ML, Zhao F, McGrath SP, Li H, Ran W, Shen Q (2017) Mineral availability as a key regulator of soil carbon storage. Environ Sci Technol 51:4960\u0026ndash;4969\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu T, Feng Q, Yin Y, Han T, Chen W, Zhu M, Zhao C, Zhao J (2024) Divergent response of upper layer soil organic and inorganic carbon to biotic and abiotic factors in afforestation by aerial seeding in desert. China Catena 246:108383\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYue K, Peng Y, Peng C, Yang W, Peng X, Wu F (2016) Stimulation of terrestrial ecosystem carbon storage by nitrogen addition: a meta-analysis. Sci Rep 6:19895\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZamanian K, Pustovoytov K, Kuzyakov Y (2016) Pedogenic carbonates: Forms and formation processes. Earth Sci Rev 157:1\u0026ndash;17\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng XM, Bastida F, Plaza C, Zhou GY, Vera A, Liu YR, Delgado-Baquerizo M (2023) The contribution of biotic factors in explaining the global distribution of inorganic carbon in surface soils. Glob Biogeochem Cycles 37, e2023GB007957.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Chen S, Yang Y, Wang Q, Wu Y, Zhou Z, Wang H, Wang W (2022) Shelterbelt farmland-afforestation induced SOC accrual with higher temperature stability: Cross-sites 1 m soil profiles analysis in NE China. Sci Total Environ 814:151942\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Zhao W, He J, Zhang K (2016) Energy exchange and evapotranspiration over irrigated seed maize agroecosystems in a desert-oasis region, northwest China. Agric For Meteorol 223:48\u0026ndash;59\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao W, Zhang R, Cao H, Tan W (2019) Factor contribution to soil organic and inorganic carbon accumulation in the Loess Plateau: Structural equation modeling. Geoderma 352:116\u0026ndash;125\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Wang M, Hu S, Zhang X, Ouyang Z, Zhang G, Huang B, Zhao S, Wu J, Xie D, Zhu B, Yu D, Pan X, Xu S (2018) X., S., Economics- and policy-driven organic carbon input enhancement dominates soil organic carbon accumulation in Chinese croplands. Proc. Natl. Acad. Sci. U.S.A. 115, 4045\u0026ndash;4050\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Z, Wang C, Li Y, Wang X, He X, Xu M, Cai A (2025) Carbon gain in upper but loss in deeper cropland soils across China over the last four decades. Proc Natl Acad Sci U S A 122, e2422371122\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu M, Cheng G, Zhang X, Guo Y, Wu Y, Wang Q, Wang H, Wang W (2023) Shelterbelts increased soil inorganic carbon but decreased nitrate nitrogen, total phosphorus, and bulk density relative to neighbor farmlands depending on tree growth, geoclimate, and soil microbes in the Northeast China Plain. CATENA 231:107344\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Soil Carbon Fractions, Edaphic Factors, Agroforestry, Shelterbelts, Dryland","lastPublishedDoi":"10.21203/rs.3.rs-6937799/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6937799/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Aim\u003c/h2\u003e \u003cp\u003eIt remains unclear whether soil carbon fractions\u0026mdash;organic (SOC) and inorganic (SIC)\u0026mdash;exhibit distinct distribution patterns and responses to edaphic factors for shelterbelts and croplands due to their different management practices.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn total, we collected 480 soil samples (30 sites \u0026times; 4 points \u0026times; 4 depths) from 30 paired sites of shelterbelts (SF) and croplands (CF) in dryland, NW China. At each site, samples were taken from one SF point and three distance-based CF points (CF_0.25H, CF_0.5H, CF_1H) across four depths: 0\u0026ndash;10 cm, 10\u0026ndash;30 cm, 30\u0026ndash;50 cm, and 50\u0026ndash;100 cm.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThere was no significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) differences in the content of soil total carbon (STC), SOC, SIC, and SIC:SOC ratio between SF and CF. A significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) linear relationship existed between SOC and STC across depths. Both SOC and SOC:STC ratio were deceased significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with increasing soil depths, but SIC was stabilized. Consistent findings from Pearson\u0026rsquo;s correction, Random Forest, linear mixed-effects model and structural equation model identified soil nutrients as the primary determinant of SOC, while physicochemical properties were the key driver of SIC. Variance partitioning analysis revealed that edaphic factors explained 78.19% and 70.10% of SOC variation, and 45.10% and 49.70% of SIC variation in SF and CF, respectively, suggests biotic factors (e.g. microorganism) may critically influence SIC accumulation in dryland soils.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study demonstrates contrasting responses of SOC and SIC to edaphic factors within shelterbelts and croplands, yet reveals consistent abiotic drivers governing soil C dynamics in both of them. These findings enhance understanding of soil C sequestration mechanisms and their abiotic controls in dryland agroforestry systems.\u003c/p\u003e","manuscriptTitle":"Divergent responses of soil organic and inorganic carbon to edaphic factors within, but not between, shelterbelts and croplands in dryland of NW China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 18:17:48","doi":"10.21203/rs.3.rs-6937799/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2025-09-12T07:53:14+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-06-26T05:42:35+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-24T03:21:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Plant and Soil","date":"2025-06-23T09:23:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-23T08:46:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant and Soil","date":"2025-06-20T06:05:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"26c1f877-9bd8-4665-bc61-03529a760ab4","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:04:02+00:00","versionOfRecord":{"articleIdentity":"rs-6937799","link":"https://doi.org/10.1007/s11104-026-08271-7","journal":{"identity":"plant-and-soil","isVorOnly":false,"title":"Plant and Soil"},"publishedOn":"2026-02-02 15:58:46","publishedOnDateReadable":"February 2nd, 2026"},"versionCreatedAt":"2025-06-25 18:17:48","video":"","vorDoi":"10.1007/s11104-026-08271-7","vorDoiUrl":"https://doi.org/10.1007/s11104-026-08271-7","workflowStages":[]},"version":"v1","identity":"rs-6937799","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6937799","identity":"rs-6937799","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}