Distribution Of Soil Organic Carbon Research Articles

Soil properties and anthropogenic influences control the distribution of soil organic carbon in grasslands of northern China

AbstractSoil organic carbon (SOC) in grasslands not only plays an important role in carbon cycle but also largely affects soil function and thus determines grasslands' productivity. However, the influence mechanism of different factors on SOC storage in grasslands of northern China remains unclear. Based on an extensive field investigation and multiple statistical approaches, we mapped SOC of the grassland in northern China, and explored their correlations with climatic factors, biological factors, human activity intensity (HAI), and soil physicochemical factors. The effects of these factors on SOC were also analyzed by structural equation model. The results showed that the SOC varied across the regions and was strongly correlated with soil chemical factors (i.e., soil total nitrogen [TN], amorphous Al, and amorphous Fe) and soil physical factors (i.e., clay, silt, and sand). HAI, which was mainly influenced by grazing, had a significant but negative impact on SOC. Different from previous studies, we found that climatic factors such as temperature and precipitation indirectly affected SOC by influencing biological factors such as vegetation coverage, vegetation height, Shannon–Weiner index, and species richness. The soil physicochemical properties and HAI were the dominant influencing factors for controlling SOC distribution in the grasslands of northern China. Our findings will further increase the understanding of the carbon cycle in grassland ecosystems, while providing important scientific references for temperate grassland ecosystem management.

Persistent soil organic carbon deficits from converting primary forests to plantations and secondary forests in subtropical China

Soil organic carbon (SOC) plays a critical role in supporting key ecosystem functions and services for humanity. Despite the widespread conversion of primary forests to plantations and secondary forests, it remains uncertain how it affects the content and distribution of SOC. We evaluated SOC content, its components in two fractions, particulate organic carbon (a mean residence time shorter than decades) and mineral-associated organic carbon (a mean residence time of multiple decades to centuries), and the fine root characteristics (biomass, necromass, and C: N of dead fine roots) across Chinese fir plantations and secondary forests during stand development (5–41 years old) in subtropical China, employing primary forests as a reference. We found that SOC content differed significantly between forest types and soil depths, but not with stand age within plantations or secondary forests. In the topsoil (0–10 cm), the SOC content of primary and secondary forests was 47 % and 25 % higher than that of Chinese fir plantations, respectively. The SOC content in the subsoil (40–60 cm) was approximately 6 ± 1.7 mg C g−1 and did not change significantly with forest types or stand ages. The higher SOC contents in primary forests and secondary forests in topsoil resulted primarily from particulate organic carbon accumulation caused by higher fine root biomass and dead root C: N. The fraction of mineral-associated organic carbon to SOC increased with soil depth and was higher in Chinese fir plantations than in secondary forests, suggesting that more SOC is composed of mineral-associated organic carbon that was associated with a higher amount of fine root necromass. Contrary to our expectations, we found SOC content or its fractions did not increase with 40 years of plantation and secondary forest development. Our results reveal persistent SOC deficits from converting primary forests to plantations and secondary forests and highlight the urgent need to conserve the remaining primary forests and protect secondary forests and plantations from disturbances, allowing them to evolve towards older-aged primary forests.

Characteristics of Soil Organic Carbon Distribution in Different Economic Forests in Gangu County, Gansu Province, China

Characteristics of Soil Organic Carbon Distribution in Different Economic Forests in Gangu County, Gansu Province, China

Change Characteristics of Soil Organic Carbon and Soil Available Nutrients and Their Relationship in the Subalpine Shrub Zone of Qilian Mountains in China

Studying the spatial and temporal distribution of soil organic carbon (SOC) content in high-altitude mountainous areas and its correlation with soil nutrients provides a basis for understanding soil carbon stocks and the factors affecting the local carbon cycle. Based on soil samples collected from a semi-sunny slope and semi-shady slope in the subalpine shrub zone of the eastern Qilian Mountains from May to October 2019, we studied the temporal and spatial changes in SOC and soil available nutrients and their relationships. The results showed that SOC content and soil nutrients were greater on the semi-shady slope than on the semi-sunny slope during the growing season and decreased with an increase in soil depth in different slope directions, showing obvious surface aggregation. The soil available nitrogen (SAN) content was consistent with the SOC content and exhibited greater synchronization. SOC was significantly positively correlated with soil available nutrients in the study area during the whole growing season. However, the correlation between SOC and soil nutrients varied among the different soil layers and slope orientations. The SOC content was more obviously correlated with the SAN content in the soil layer at a depth of 30–40 cm (r = 0.67, p < 0.05) on the semi-shady slope. The SOC content was more obviously correlated with soil-available phosphorus (SAP) content in the soil layer at a depth of 30–40 cm (r = 0.57) on the semi-sunny slop. The SOC content was more obviously correlated with the SAP content in the soil layer at a depth of 60–70 cm (r = 0.55) and with the soil-available potassium (SAK) content in the soil layer at a depth of 70–80 cm (r = 0.84) on the semi-sunny slope.

Spatial heterogeneity of soil organic carbon and soil nutrients and their controlling factors in a small watershed in Northeast China

AbstractUnderstanding the spatial distribution of soil organic carbon (SOC) and soil nutrients is crucial for optimizing land management in watersheds. This study explored the spatial distribution of SOC, total nitrogen (TN) and total phosphorus (TP) in topsoil (0–20 cm) at a hilly small watershed site in Northeast China, and identified the controlling factors. The Gaussian mixture model optimally described SOC and TN with the ratio of nugget to sill variance (C0/[C + C0]) indicating moderate spatial dependence for SOC (63.7%) and TN (59.3%). TP was best modelled by the spherical model and demonstrated weak spatial autocorrelation and strong human influence, with a nugget to sill variance of 0.841. Land use significantly affected SOC, TN and TP contents, with the highest values recorded in woodland, followed by farmland and shrubland. Soil properties including bulk density (BD), gravel content (GC), moisture content (MC), soil porosity (SP) and the mean weight diameter (MWD) of water‐stable aggregates significantly correlated with SOC and TN content, but not with TP. Redundancy analysis revealed that soil properties (BD, GC and SP), topography (slope and aspect) and gully erosion contributed 55.7%, 24.7% and 4.3% of the spatial variation in SOC, TN and TP, respectively. Hence, these are the dominant factors shaping the spatial variation of SOC and soil nutrients at the site. These results can aid the further development of optimized land management strategies.

Carbon Sequestration in Remediated Post-Mining Soils: A New Indicator for the Vertical Soil Organic Carbon Variability Evaluation in Remediated Post-Mining Soils

The present study experimentally investigated two different open-cast post-mining areas with different remediation methods for the vertical distribution of sequestered soil organic carbon (SOC). The study has been performed for two soil layers (0–15 cm, and 15–30 cm) for the four areas with different remediation advancement (up to 20 years) at both studied post-mining soils: the limestone post-mining soil remediated with embankment and lignite post-mining soil remediated with sewage sludge. The study revealed that SOC is more stable within soil depths for lignite post-mining soil remediated with sewage sludge in comparison to the limestone post-mining soil remediated with embankment. The lignite post-mining soil remediated with sewage sludge showed a better hydrophobicity, humidity, aromaticity, and C/N ratio according to the 13C NMR. Therefore, in that soil, an increased microbial community has been observed. The study observed a positive correlation between GRSP content with a fungi community within soil depths. For lignite post-mining soil remediated with sewage sludge, the activity of ureases and dehydrogenases was generally lower compared to the post-mining soil remediation with embankment. The investigation found good parameters of Ce and NCER which for both studied areas were negative which indicate for the privilege of the higher capturing of CO2 over its release from the soil into the atmosphere. The study finds no relevant changes in SOC, POXC, TC, and LOI content within soil depth and remediation age. Due to the lack of a possible well-describing indicator of the vertical distribution of SOC stability in post-mining remediation soil, we proposed two different indicators for differentially managed post-mining soil remediations. The model of calculation of vertical SOC variability index can be universally used for different post-mining soils under remediation, however, both proposed calculated indexes are unique for studied soils. The proposed model of an index may be helpful for remediation management, C sequestration prediction, and lowering the carbon footprint of mining activity.

Distribution of Soil Organic Carbon in Ox-Drawn and Reduced Tillage System at Wuro Madi, Girei, Adamawa State, Nigeria

Distribution of Soil Organic Carbon in Ox-Drawn and Reduced Tillage System at Wuro Madi, Girei, Adamawa State, Nigeria

Reforestation Increases the Aggregate Organic Carbon Concentration Induced by Soil Microorganisms in a Degraded Red Soil, Subtropical China.

In the process of biological carbon (C) sequestration during reforestation in degraded red soil, due to the decomposition of soil microorganisms, the interaction between soil organic carbon (SOC) and aggregates has an important effect on soil C sequestration. In this study, six common reforestation models and three soil layers were selected in a degraded red soil area of the central subtropical region to determine the composition of soil aggregates and the distribution of SOC in soil aggregates. Based on the results of the soil physicochemical properties and microbial community composition biomass, we assessed the changes in aggregate-associated organic C storage during fluctuations in the stability of the aggregates. After reforestation, the SOC stock increased by 131.28-140.00%. Compared with the three pure forests and broad-leaved mixed forests, coniferous and broad-leaved mixed forests showed the largest proportion of macroaggregates (85.48-89.37%) and higher SOC accumulation. Soil microbial biomass mainly affected the decomposition process of SOC by affecting the stability of the soil aggregates, and the effect of bacteria was more significant. Coniferous and broad-leaved mixed forests can provide more soil microorganisms and C sources than pure forest, thus promoting macroaggregate formation and stability and related organic C storage. This reforestation model has greater C sequestration potential.

Spatial distribution of soil organic carbon and its response to forest growth and soil layer in Cunninghamia lanceolata plantations in mid-subtropical China

To investigate the effects of stand age and soil layer on the temporal and spatial distribution of soil organic carbon (SOC) in Cunninghamia lanceolata plantations, five experimental plots and 15 soil sampling sites were established in five stands of different ages (3, 6, 9, 15, and 25 years) in Huitong, Hunan Province. Tree diameter at breast height, tree height, canopy density and elevation in sample plots, and soil organic carbon concentrations (SOCC), soil enzyme activity, soil pH, and soil bulk density at 0–15, 15–30, 30–45 cm soil layers were measured during stand development. The change in SOC distribution with stand age and soil layer, and soil enzyme activity were investigated using ANOVA, LSD test, and inter-subject effect test and simulation analysis. The results showed that the SOC distribution in Cunninghamia lanceolata plantations had spatiotemporal heterogeneity and surface aggregation. The SOC increased with increasing stand age and decreased with increasing soil depth. Furthermore, SOCC and SOC density (SOCD) were strongly correlated with environmental factors; however, the interaction between forest growth and soil layer reduced the variation of SOC distribution. Forest growth and soil enzyme activity were important factors that affect the temporal and spatial distribution of SOC. Therefore, the management measures to promote the growth of the plantation and improve soil enzyme activity can enhance the SOC storage capacity from the angle of balancing ecological and economic benefits, and contribute to the achievement of the goal of “carbon neutrality”.

Enhanced carbon storage in semi-arid soils through termite activity

Termites are keystone species in natural ecosystems and their role in the C cycle is potentially substantial but poorly understood. Large (20–40 m) mounds (heuweltjies) of the harvester termite Microhodotermes viator occupy up to a quarter of the semi-arid west coast region of South Africa but their C storage potential is unknown. This study determined the organic and inorganic C fractions, C stocks, and their correlation with each other, depth, and biogenic features in these mounds. Trenches (30–60 m) were excavated through 3 mounds: Buffels River (m.a.p < 100 mm), Klawer (m.a.p 100–200 mm) and Piketberg (m.a.p 300–400 mm) and grid sampled. Mound soils had significantly higher soil organic carbon (SOC) and inorganic carbon (SIC) than surrounding soils. Total C was strongly correlated (ρ > 0.9; p < 0.001) with SIC in the arid mounds and SOC (ρ > 0.75; p < 0.001) in the higher rainfall mound. There was no consistent relationship between SOC and SIC distributions throughout the mounds, which is likely related to solubility-linked translocations of carbonates. For all mounds, SOC was highest in topsoils with a second clear peak in subsoils (>1 m) that was associated with biogenic features, termite channels and burrows. Subsoils contributed substantially (36–41 %) to the total C stock. Total C stocks for the intermediate rainfall mound (Klawer) were estimated at 14.6 tons per mound, with 1.1 tons SOC. In this region, mounds occupy 27 % of the total area but contribute 44 % of the total SOC stock to a depth of 80 cm. This highlights the disproportionate contribution termite mounds make to carbon stocks of these semi-arid environments and demonstrates the importance of deep (<1 m) soil carbon for C modelling. Termite activity needs to be recognized as a major contributor to C stock variability both laterally and at depth and accounted for in land-use change (CO2-LULUCF) models.

Effects of Soil Microbiological Properties on the Fractional Distribution and Stability of Soil Organic Carbon under Different N Addition Treatments

Soil organic carbon (SOC) fractions are influenced by inputs of nitrogen (N) from globally rising N deposition; however, the mechanisms of how soil microbiological properties are influenced by N deposition and its impact on the fractional distribution and stability of SOC remain unclear. In this study, we assessed the effects on SOC fraction distribution and stability from four aspects of soil microbiological properties: soil microbial biomass (SMB), soil microbial activity, structure diversity, and functional diversity of soil microbial community in a Pinus tabuliformis plantation, which received four N addition levels (0 g N m−2 y−1 (N0), 3 g N m−2 y−1 (N3, low N addition), 6 g N m−2 y−1 (N6, mid-N addition), and 9 g N m−2 y−1 (N9, high N addition)) for 2 years. The N inputs did significantly affect some soil microbiological properties, like SMB, soil phospholipid fatty acid (PLFA), and soil microbial functional diversity. Mid- and high N addition decreased the richness (HPLFA) and evenness (EPLFA) index of the soil microbial community, from 3.24 to 2.91 and 0.93 to 0.87, respectively. In addition, the low N addition promoted the carbon management index (CMI) to 141.35, i.e., higher than the CMIs in the mid- and high-level treatments. The SOC stability also showed significant differences among N addition treatments, and SOC could be the most stable at the mid-N addition level. Regarding the effects of the four soil microbiological attributes on the CMI and stability, SMB and soil respiration positively impacted the CMI, but did not significantly affect the stability. In addition, EPLFA had positive effects, but EBIOLOG had negative effects on CMI and lability. Our findings indicate that soil microbiological properties are essential in SOC fractional distributions and stability. Further identification and study of soil microbial species used to change SOC fractions would help to clarify the detailed mechanisms involved.

Distribution characteristics of organic carbon (nitrogen) content, cation exchange capacity, and specific surface area in different soil particle sizes

Understanding the distribution of soil organic carbon and nitrogen (OC(N)) content, cation exchange capacity (CEC), and specific surface area (SSA) in different soil particle sizes is crucial for studying soil fertility and properties. In this study, we investigated the distribution characteristics of the OC(N), CECand SSA in different particles of yellow–brown soil under different methods. The result revealed that as the particle size decreased, the soil OC(N), SSA and CEC content gradually increase. The content of OC and ON different soil particles ranged from 1.50–28.16 g·kg−1 to 0.18–3.78 g·kg−1, respectively, and exhibited significant differences between different particles. We observed good linear relationships between OC and ON in different particle sizes of yellow–brown soil under different utilization methods, with correlation coefficients ranging from 0.86 to 0.98, reaching a very significant level (n = 12, p < 0.01). The ranges of SSA and CEC in different particles of the four soils were 0.30–94.70 m2·g−1 and 0.70–62.91 cmol·kg−1, respectively. Additionally, we found logarithmic relationships between SSA (CEC) and the equivalent diameter for the four soils, with correlation coefficients (r2) higher than 0.91. Furthermore, there was an extremely significant linear relationship between CEC and SSA of the four soils, with correlation coefficients (r2) of 0.92–0.97 (n = 12, p < 0.01). These results highlight the close relationship between soil particle size and soil OC(N), SSA, and CEC. The conclusions drawn from this study provide valuable data support and a theoretical basis for further understanding soil properties.

Predicting soil organic carbon in cultivated land across geographical and spatial scales: Integrating Sentinel-2A and laboratory Vis-NIR spectra

Digital mapping of soil organic carbon (SOC) is essential for visualizing the spatial distribution at different regions and scales. However, existing studies using remote sensing are limited by the low spectral resolution of multispectral data for accurate estimation of SOC, and the failure of laboratory visible and near-infrared and shortwave infrared (Vis-NIR) spectroscopy to perform pixel-level mapping. To address these limitations, this study proposes a SOC mapping framework that integrates satellite earth observations and proximal sensing spectral data to reconstruct images having a spectral resolution of 400–2500 nm and a spatial resolution of 10 m, then the clustering probability analysis was applied on the reconstructed images, to develop a high accuracy SOC prediction model and demonstrate its effectiveness across geographical and spatial scales. Specifically, a total of 324 topsoil samples were collected from Baoqing and Suiling counties in northeast China, whose laboratory Vis-NIR reflectance spectra and SOC were measured. Sentinel-2A images of bare soils averaged over three years before and after the samples collection (i.e., 2018 to 2021) were collected. A total of 264 samples from eight fields in Belgium were used to evaluate the performance of the model at the field scale. Then, two models were developed (i) a global regression model with direct prediction using random forest and (ii) a clustering probability model with Gaussian Mixture Model-random forest. Results demonstrated that (1) reconstructed images produced more accurate prediction results than the individual laboratory Vis-NIR spectroscopy and Sentinel-2A models. (2) At the regional scale, clustering probability model achieved better performance in quantification of SOC (coefficient of determination [R2] = 0.78, and root mean square error [RMSE] = 0.78 %) than global regression model (R2 = 0.65, and RMSE = 1.06 %). (3) For field scale, the reconstructed image-based clustering probability SOC prediction model was shown to be effective when using the Belgium data, providing a R2 of 0.67, and RMSE of 0.15 %. (4) Integrating remote sensing multispectral and proximal Vis-NIR information can lead to a smoother and more continuous SOC distribution within the region, and can better show its spatial heterogeneity within the field. This study highlights the advantages of integrating remote sensing with proximal sensing for improving the accuracy of SOC prediction and mapping, enabling applicability of the model across geographical and spatial scales with appreciable accuracy.

Modeling the Spatial Distribution of Soil Organic Carbon and Carbon Stocks in the Casanare Flooded Savannas of the Colombian Llanos

Flooded savannas are valuable and extensive ecosystems in South America, but not widely studied. In this study, we quantify the spatial distribution of soil organic carbon (SOC) content and stocks in the Casanare flooded savannas. We sampled 80 sites at two soil-depth intervals (0-10 and 10-30 cm), where SOC values ranged from 0.41% in the surface and 0.23% in the sub-surface of drier soils to over 14.50% and 7.51%, in soils that experienced seasonal flooding. Spatial predictions of SOC were done through two digital soil mapping (DSM) approaches: Expert-Knowledge (EK) and Random-Forest (RF). Although both approaches performed well, EK was slightly superior at predicting SOC. Covariates derived from vegetation cover, topography, and soil properties were identified as key drivers in controlling its distribution. Total SOC stocks were 55.07 Mt with a mean density of 83.1±24.3 t·ha-1 in the first 30 cm of soil, with 12.3% of this located in areas that experience long periods of flooding (semi-seasonal savannas) , which represented only 7.9% of the study area (664,752 ha). Although the study area represents only 15% of the total area of the Casanare department, the intensive pressure of human development could result in the reduction of its SOC stocks and the release of important amounts of greenhouse gases into the atmosphere. At regional level, the impact of a large-scale land use conversions of the flooded Llanos del Orinoco ecosystem area (15 Mha) could transform this area in a future source of important global emissions if correct decisions are not taken regarding the land management of the region.

Remote estimation of soil organic carbon under different land use types in agroecosystems of Eastern China

Soil organic carbon (SOC) plays a key role in soil function, ecosystem services, and the global carbon cycle. Large SOC stocks accumulate in agroecosystems, but the estimates of SOC distribution and magnitude in agroecosystems still have large uncertainties in global products. Based on multiple high-resolution environmental variables (terrain derived from digital elevation model, climate and organism from Landsat 8 OLI) and 369 observed soil samples from 2019 to 2021, two machine learning methods, random forest (RF) and support vector machine (SVM) models, were used to estimate the content and spatial distribution of SOC in agroecosystems of Eastern China. We proposed a hybrid model in which the optimal predictors of SOC were selected for different land use types (cropland, planted forest and grassland), and it aimed to improve the performance of machine learning. Our results showed that (1) the hybrid models performed better than the global models, and the Random forest-Hybrid (RF-Hybrid) model led to the highest prediction accuracy, with a validation R2 of 0.65 and RMSE of 5.76 g kg−1. (2) The SOC content in the agroecosystems of Eastern China among different land use types ranked in the following order: planted forest (19.92 g kg−1) > cropland (17.51 g kg−1) > grassland (17.30 g kg−1). However, in North China, the SOC content in cropland (14.04 g kg−1) was much lower than that in planted forest (17.21 g kg−1) and grassland (17.07 g kg−1), which was caused by excessive mechanized operation. (3) The agroecosystem in Northeast China presented the highest mean SOC content (26.55 g kg−1) due to low temperature. (4) Our estimations (R2 = 0.53, RMSE = 6.74 g kg−1, 30-m resolution) were more detailed and precise than the existing global SOC maps (Soil Grids: R2 = 0.26, RMSE = 12.44 g kg−1, 250-m resolution; HWSD: R2 = 0.06, RMSE = 41.07 g kg−1, 1000-m resolution). The results may improve the accuracy of agroecosystem carbon mapping and contribute to SOC assessments in agricultural ecosystems.

Quantifying soil organic carbon (SOC) density and stock in the Urmodi River watershed of Maharashtra, India: implications for sustainable land management

Soil Organic Carbon (SOC) plays a vital role in the global carbon cycle, affecting soil fertility and agricultural sustainability. Our study focused on areas with low SOC, where increasing its levels could enhance soil health and carbon management. We used an earth science approach to analyze SOC density and stock in different land classes of the Urmodi River watershed in Maharashtra, India. Using GIS, we estimated SOC from soil samples collected up to 20 cm depth, and produced maps of SOC, SOC density (SOCD), and SOC stock. Our results showed that natural vegetation areas, such as closed and open forests, had a higher nutritional status of SOC and SOC stock, indicating the importance of land cover history on soil health. We found the average SOC to be 1.4 g/kg−1, SOCD to be 57.36 kg/m−2, SOC stock to be 3.46 ton/ha−1. We also depicted the relationship between elevation and SOCD using a scatterplot, revealing the distribution of SOC across different elevations. Our study demonstrates the use of modern geoscientific analysis tools to understand the physical and chemical properties of soil, which can be useful in assessing soil health. Overall, our results provide valuable insights into the distribution of SOC and its relationship with other geo-chemical aspects at a regional scale.

Integrating Landscape Pattern Metrics to Map Spatial Distribution of Farmland Soil Organic Carbon on Lower Liaohe Plain of Northeast China

Accurate digital mapping of farmland soil organic carbon (SOC) contributes to sustainable agricultural development and climate change mitigation. Farmland landscape pattern has changed greatly under anthropogenic influence, which should be considered an environmental variable to characterize the impact of human activities on SOC. In this study, we verified the feasibility of integrating landscape patterns in SOC prediction on Lower Liaohe Plain. Specifically, ten variables (climate, topographic, and landscape pattern variables) were selected for prediction with Random Forest (RF) and Support Vector Machines (SVMs). The effectiveness of landscape metrics was verified by establishing different variable combinations: (1) natural variables, and (2) natural and landscape pattern variables. The results confirmed that landscape variables improved mapping accuracy compared with natural variables. R2 of RF and SVM increased by 20.63% and 20.75%, respectively. RF performed better than SVM with smaller prediction error. Ranking of importance of variables showed that temperature and precipitation were the most important variables. The Aggregation Index (AI) contributed more than elevation, becoming the most important landscape variable. The Mean Contiguity Index (CONTIG-MN) and Landscape Contagion Index (CONTAG) also contributed more than other topographic variables. We conclude that landscape patterns can improve mapping accuracy and support SOC sequestration by optimizing farmland landscape management policies.

Topography and canopy cover influence soil organic carbon composition and distribution across a forested hillslope in the discontinuous permafrost zone

AbstractTopography and canopy cover influence ground temperature in warming permafrost landscapes, yet soil temperature heterogeneity introduced by mesotopographic slope positions, microtopographic differences in vegetation cover, and the subsequent impact of contrasting temperature conditions on soil organic carbon (SOC) dynamics are understudied. Buffering of permafrost‐affected soils against warming air temperatures in boreal forests can reflect surface soil characteristics (e.g., thickness of organic material) as well as the degree and type of canopy cover (e.g., open cover vs. closed cover). Both landscape and soil properties interact to determine meso‐ and microscale heterogeneity of ground warming. We sampled a hillslope catena transect in a discontinuous permafrost zone near Fairbanks, Alaska, to test the small‐scale (1 to 3 m) impacts of slope position and cover type on soil organic matter composition. Mineral active layer samples were collected from backslope, low backslope, and footslope positions at depths spanning 19 to 60 cm. We examined soil mineralogical composition, soil moisture, total carbon and nitrogen content, and organic mat thickness in conjunction with an assessment of SOC composition using Fourier‐transform ion cyclotron resonance mass spectrometry (FT‐ICR‐MS). Soils in the footslope position had a higher relative contribution of lignin‐like compounds, whereas backslope soils had more aliphatic and condensed aromatic compounds as determined using FT‐ICR‐MS. The effect of open versus closed tree canopy cover varied with the slope position. On the backslope, we found higher oxidation of molecules under open cover than closed cover, indicating an effect of warmer soil temperature on decomposition. Little to no effect of the canopy was observed in soils at the footslope position, which we attributed, in part, to the strong impact of soil moisture content in SOC dynamics in the water‐gathering footslope position. The thin organic mat under open cover on the backslope position may have contributed to differences in soil temperature and thus SOC oxidation under open and closed canopies. Here, the thinner organic mat did not appear to buffer the underlying soil against warm season air temperatures and thus increased SOC decomposition as indicated by the higher oxidation of SOC molecules and a lower contribution of simple molecules under open cover than the closed canopy sites. Our findings suggest that the role of canopy cover in SOC dynamics varies as a function of landscape position and soil properties, namely, organic mat thickness and soil moisture. Condition‐specific heterogeneity of SOC composition under open and closed canopy cover highlights the protective effect of canopy cover for soils on backslope positions.

Spatial distribution of soil organic carbon content in the agricultural land uses: Case study at the territory of the Rahoveci municipality, Kosovo

Due to the soil formation factors and different geographic areas of Dukagjini Plain, particularly in Rahovec municipality, the variation of soil organic carbon is high. Soil organic carbon (SOC) has a crucial role in the determination of the physical, chemical and biological behaviour of the soil. The most common land use types of this area are vineyards, table grapes, horticulture such as peppers and cabbage, and arable lands such as maize, winter wheat, alfalfa, and meadows. Considering the lack of soil information data in Kosovo, it is necessary to have soil information about this territory. The main objectives of the present study are, therefore: i) to investigate and determine the concentration of the soil organic matter (SOM), SOC, nitrogen (N) and soil pH-H2O, using laboratory analysis, and ii) to show the spatial distribution of SOC, SOM, N and pH using the Kriging and inverse weighting interpolation methods. Spatial variability of soil chemical parameters such as SOM, SOC, N, and pH are important to be interpolated to view the changeable soil properties by kriging and inverse distance weighting method and to generate the continuous sample for site-specific management. Disturbed soil samples were collected from the top soil 0-30 cm and 30-60 cm depth, to determine selected soil chemical parameters, during June-July 2019. A large number of soil samples were collected, 2087 in the first horizon and 2065 in the second. The average of SOC of the first horizon was 0.91%, which variates from 0.07 to 4.06%, while in the second horizon was 0.0 to 2.84%, the average content of N in the first horizon was 0.09%, which variate from 0.01 to 0.60%, while in the second horizon was 0.0 to 0.39%, meanwhile, the average of soil pH-H2O in the first horizon was 7.67, which variates from 4.25 to 9.35, while in the second horizon was 7.79, which variate from 3.25 to 9.30.

Spatial Distribution of Soil Organic Carbon in Relation to Land Use, Based on the Weighted Overlay Technique in the High Andean Ecosystem of Puno—Peru

Soil organic carbon (SOC) is a crucial component of the planet and is essential for agriculture development. Our region is known for its livestock and agricultural activities. Hence, understanding the spatial distribution of SOC is crucial for sustainable land management of soils in the mountain ecosystems in the Andes. The methodology consisted of obtaining 53 soil samples from depths of 0 to 105 cm, which were analyzed to obtain SOC (Kg/m2) and organic matter (OM) (%). Ordinary kriging, a geostatistical method, was used to determine SOC. Pearson’s statistical method was applied to determine the association between SOC and precipitation, temperature, altitude, and organic matter and gave acceptable correlations of 0.38, −0.32, 0.40, and 0.59. These results were used to apply weighting criteria for climatological and environmental variables. The weighted overlay tool was used for modeling and mapping the spatial variability of SOC. The estimated spatial distribution of SOC in the micro-watershed reveals an increasing trend from south to north, specifically within the 0–20 cm depth profile. The study confirmed through the spatial analysis that regions with intensive agriculture have low reserves (&lt;3 Kg/m2) of SOC, and areas without agricultural activity but with grazing have average resources of 3 Kg/m2 to 5 Kg/m2 of SOC. Finally, in the upper micro-watersheds where there is no agricultural activity, the reserves are high (5 Kg/m2 to 6.8 Kg/m2). Accordingly, we can promote sustainable and responsible land use practices that support long-term productivity, environmental protection, and societal well-being by prioritizing efficient land utilization, soil conservation, biodiversity conservation, land restoration, and informed land use planning in the high Andean ecosystem of Puno–Peru.