The changes in soil organic carbon stock and quality across a subalpine forest successional series

Soil organic carbon(SOC)affects the function of terrestrial ecosystem and plays a vital role in global carbon cycle.Yet,large uncertainty still existed regarding the changes in SOC stock and quality with forest succession.Here,the stock and quality of SOC at 1-m soil profile were investigated across a subalpine forest series,including shrub,deciduous broad-leaved forest,broadleaf-conifer mixed forest,middle-age coniferous forest and mature coniferous forest,which located at southeast of Tibetan Plateau.The results showed that SOC stock ranged from 9.8 to29.9 kg·m^(-2),and exhibited a hump-shaped response pattern across the forest successional series.The highest and lowest SOC stock was observed in the mixed forest and shrub forest,respectively.The SOC stock had no significant relationships with soil temperature and litter stock,but was positively correlated with wood debris stock.Meanwhile,the average percentages of polysaccharides,lignins,aromatics and aliphatics based on FTIR spectroscopy were 79.89%,0.94%,18.87%and 0.29%,respectively.Furthermore,the percentage of polysaccharides exhibited an increasing pattern across the forest successional series except for the sudden decreasing in the mixed forest,while the proportions of lignins,aromatics and aliphatics exhibited a decreasing pattern across the forest successional series except for the sudden increasing in the mixed forest.Consequently,the humification indices(HIs)were highest in the mixed forest compared to the other four successional stages,which means that the SOC quality in mixed forest was worse than other successional stages.In addition,the SOC stock,recalcitrant fractions and HIs decreased with increasing soil depth,while the polysaccharides exhibited an increasing pattern.These findings demonstrate that the mixed forest had higher SOC stock and worse SOC quality than other successional stages.The high proportion of SOC stock(66%at depth of 20-100 cm)and better SOC quality(lower HIs)indicate that deep soil have tremendous potential to store SOC and needs more attention under global chan ge.

Improving model performance in mapping cropland soil organic matter using time-series remote sensing data

Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effective management policies.As a spatial information prediction technique,digital soil mapping(DSM)has been widely used to spatially map soil information at different scales.However,the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human disturbance.To overcome this limitation,this study systematically assessed a framework of“information extractionfeature selection-model averaging”for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou,China in 2021.The results showed that using the framework of dynamic information extraction,feature selection and model averaging could efficiently improve the accuracy of the final predictions(R^(2):0.48 to 0.53)without having obviously negative impacts on uncertainty.Quantifying the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM,which improved the R^(2)of random forest from 0.44 to 0.48 and the R^(2)of extreme gradient boosting from 0.37to 0.43.Forward recursive feature selection(FRFS)is recommended when there are relatively few environmental covariates(<200),whereas Boruta is recommended when there are many environmental covariates(>500).The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average uncertainty.When the structures of initial prediction models are similar,increasing in the number of averaging models did not have significantly positive effects on the final predictions.Given the advantages of these selected strategies over information extraction,feature selection and model averaging have a great potential for high-accuracy soil mapping at any scales,so this approach can provide more reliable references for soil conservation policy-making.

Persistence of fertilization effects on soil organic carbon in degraded alpine wetlands in the Yellow River source region

In the restoration of degraded wetlands,fertilization can improve the vegetation-soil-microorganisms complex,thereby affecting the organic carbon content.However,it is currently unclear whether these effects are sustainable.This study employed Biolog-Eco surveys to investigate the changes in vegetation characteristics,soil physicochemical properties,and soil microbial functional diversity in degraded alpine wetlands of the source region of the Yellow River at 3 and 15 months after the application of nitrogen,phosphorus,and organic mixed fertilizer.The following results were obtained:The addition of nitrogen fertilizer and organic compost significantly affects the soil organic carbon content in degraded wetlands.Three months after fertilization,nitrogen addition increases soil organic carbon in both lightly and severely degraded wetlands,whereas after 15 months,organic compost enhanced the soil organic carbon level in severely degraded wetlands.Structural equation modeling indicates that fertilization decreases the soil pH and directly or indirectly influences the soil organic carbon levels through variations in the soil water content and the aboveground biomass of vegetation.Three months after fertilization,nitrogen fertilizer showed a direct positive effect on soil organic carbon.However,organic mixed fertilizer indirectly reduced soil organic carbon by increasing biomass and decreasing soil moisture.After 15 months,none of the fertilizers significantly affected the soil organic carbon level.In summary,it can be inferred that the addition of nitrogen fertilizer lacks sustainability in positively influencing the organic carbon content.

Mapping soil organic matter in cultivated land based on multi-year composite images on monthly time scales

Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale.We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine(GEE)platform,and reflectance bands and vegetation indices were extracted from these composite images.Then the random forest(RF),support vector machine(SVM)and gradient boosting regression tree(GBRT)models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables.Results showed that firstly,all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM(P<0.05)for the months of January,March,April,October,and November.Secondly,in terms of single-monthly composite variables,the prediction accuracy was relatively poor,with the highest R^(2)value of 0.36 being observed in January.When monthly synthetic environmental variables were grouped in accordance with the four quarters of the year,the first quarter and the fourth quarter showed good performance,and any combination of three quarters was similar in estimation accuracy.The overall best performance was observed when all monthly synthetic variables were incorporated into the models.Thirdly,among the three models compared,the RF model was consistently more accurate than the SVM and GBRT models,achieving an R^(2)value of 0.56.Except for band 12 in December,the importance of the remaining bands did not exhibit significant differences.This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.

Climate and Soil Geochemistry Influence the Soil Organic Carbon Content in Drylands of the Songliao Plain,Northeast China

The understanding of the spatial distribution of soil organic carbon(SOC)and its influencing factors is crucial for comprehending the global carbon cycle.However,the impact of soil geochemical and climatic conditions on SOC remains limited,particularly in dryland farming areas.In this study,we aimed to enhance the understanding of the factors influencing the distribution of SOC in the drylands of the Songliao Plain,Northeast China.A dataset comprising 35,188 measured soil samples was used to map the SOC distribution in the region.Multiple linear regression(MLR)and random forest models(RFM)were employed to assess the importance of driving indicators for SOC.We also carried out partial correlation and path analyses to further investigate the relationship between climate and geochemistry.The SOC content in dryland soils of the Songliao Plain ranged from 0.05%to 11.63%,with a mean value of 1.47%±0.90%.There was a notable increasing trend in SOC content from the southwest to the northeast regions.The results of MLR and RFM revealed that temperature was the most critical factor,demonstrating a significant negative correlation with SOC content.Additionally,iron oxide was the most important soil geochemical indicator affecting SOC variability.Our research further suggested that climate may exert an indirect influence on SOC concentrations through its effect on geochemical properties of soil.These insights highlight the importance of considering both the direct and indirect impact of climate in predicting the SOC under future climate change.

N-fixing tree species promote the chemical stability of soil organic carbon in subtropical plantations through increasing the relative contribution of plant-derived lipids

Biodiversity experiments have shown that soil organic carbon(SOC)is not only a function of plant diversity,but is also closely related to the nitrogen(N)-fixing plants.However,the effect of N-fixing trees on SOC chemical stability is still little known,especially with the compounding effects of tree species diversity.An experimental field manipulation was established in subtropical plantations of southern China to explore the impacts of tree species richness(i.e.,one,two,four and six tree species)and with/without N-fixing trees on SOC chemical stability,as indicated by the ratio of easily oxidized organic carbon to SOC(EOC/SOC).Plant-derived C components in terms of hydrolysable plant lipids and lignin phenols were isolated from soils for evaluating their relative contributions to SOC chemical stability.The results showed that N-fixing tree species rather than tree species richness had a significant effect on EOC/SOC.Hydrolysable plant lipids and lignin phenols were negatively correlated with EOC/SOC,while hydrolysable plant lipids contributed more to EOC/SOC than lignin phenols,especially in the occurrence of N-fixing trees.The presence of N-fixing tree species led to an increase in soil N availability and a decrease in fungal abundance,promoting the selective retention of certain key components of hydrolysable plant lipids,thus enhancing SOC chemical stability.These findings underpin the crucial role of N-fixing trees in shaping SOC chemical stability,and therefore,preferential selection of N-fixing tree species in mixed plantations is an appropriate silvicultural strategy to improve SOC chemical stability in subtropical plantations.

Can soil organic carbon sequestration and the carbon management index be improved by changing the film mulching methods in the semiarid region?

Plastic film mulching has been widely used to increase maize yield in the semiarid area of China.However, whether long-term plastic film mulching is conducive to agricultural sustainability in this region remains controversial.A field experiment was initiated in 2013 with five different film mulching methods:(i) control method, flat planting without mulching (CK),(ii) flat planting with half film mulching (P),(iii) film mulching on ridges and planting in narrow furrows(S),(iv) full film mulching on double ridges (D), and (v) film mulching on ridges and planting in wide furrows (R).The effects on soil organic carbon (SOC) content, storage, and fractions, and on the carbon management index (CMI)were evaluated after nine consecutive years of plastic film mulching.The results showed that long-term plastic film mulching generally maintained the initial SOC level.Compared with no mulching, plastic film mulching increased the average crop yield, biomass yield, and root biomass by 48.38, 35.06, and 37.32%, respectively, which led to the improvement of SOC sequestration.Specifically, plastic film mulching significantly improved CMI, and increased the SOC content by 13.59%, SOC storage by 7.47%and easily oxidizable organic carbon (EOC) by 13.78%on average,but it reduced the other labile fractions.SOC sequestration and CMI were improved by refining the plastic film mulching methods.The S treatment had the best effect among the four mulching methods, so it can be used as a reasonable film mulching method for sustainable agricultural development in the semiarid area.

Effects of Biochar and Soil Organic Matter Levels on Physicochemical Properties of Mollisol and Soybean's Biomass

Long term tillage in mollisol of Northeast China has led to an inhomogeneous distribution of soil organic matter content.Biochar,a carbon material,changes the soil carbon pool and physical-chemical characteristics after adding to the soil.However,the mechanism remains unclear for the relation between the soil organic matter level and biochar amount.So,the soil physical and chemical properties and soybean growth in a two-year pot experiment were detected at three levels of soil organic matter and three biochar additions(0,1%and 10%).The difference was found in two biochar application rates.The 1%biochar addition had no positive effect on the soil chemical properties based the two-year experiment.However,10%biochar application significantly increased the soil water content(8.0%-39.7%),the total porosity(9.7%-21.3%),pH(0.26-0.84 unit),organic matter content(89.0%-261.2%),and the available potassium content(29.0%-109.1%).The biomass of soybean increased by 19.4%-78.1%after biochar addition,yet,the soil bulk density reduced at the range of 12.6%-26.0%by 10%biochar addition.Only the 100-grain weight was correlated to the interaction of biochar and the native soil organic matter.All the indicators showed that the interaction between biochar and soil organic matter level was weak in mollisol.The effects of biochar on the physical-chemical properties relied on its amount.When biochar is applied to the soil,the amount of biochar should be considered rather than the native soil organic matter level.

Fertilization and Soil Ploughing Practices under Changing Physical Environment Lead to Soil Organic Carbon Dynamics under Conservation Agriculture in Rice-Wheat Cropping System: A Scoping Review

Ploughing and fertilization practices in rice-wheat system have deteriorated the soil carbon (C) pools. Conservation agriculture (CA) based management approaches have proven to enhance C sequestration and reverse the loss of soil-organic-carbon (SOC), which further enhances soil fertility. Different fractions of SOC pools react to the alterations in management practices and indicate changes in SOC dynamics as compared to total C in the soil. Higher SOC levels in soil have been observed in case of reduced/no-till (NT) practices than conventional tillage (CT). However, between CT and zero tillage/NT, total SOC stocks diminished with an increase in soil depth, which demonstrated that the benefits of SOC are more pronounced in the topsoil under NT. Soil aggregation provides physical protection to C associated with different-sized particles, thus, the improvement in soil aggregation through CA is an effective way to mitigate soil C loss. Along with less soil disturbance, residual management, suitable crop rotation, rational application of manures and fertilizers, and integrated nutrient management have been found to be effective in not only improving soil C stock but also enhancing the soil health and productivity. Thus, CA can be considered as a potential method in the build-up of SOC of soil in rice-wheat system.

Impact on Soil Organic C and Total Soil N from Cool- and Warm-Season Legumes Used in a Green Manure-Forage Cropping System

Annual forage legumes are important components of livestock production systems in East Texas and the southeastern US. Forage legumes contribute nitrogen (N) to cropping systems through biological N fixation, and their seasonal biomass production can be managed to complement forage grasses. Our research objectives were to evaluate both warm- and cool-season annual forage legumes as green manure for biomass, N content, ability to enhance soil organic carbon (SOC) and soil N, and impact on post season forage grass crops. Nine warm-season forage legumes (WSL) were spring planted and incorporated as green manure in the fall. Forage rye (Secale cereale L.) was planted following the incorporation of WSL treatments. Eight cool-season forage legumes (CSL) were fall planted in previously fallow plots and incorporated as green manure in late spring. Sorghum-sudangrass (Sorghum bicolor x Sorghum bicolor var. sudanense) was planted over all treatments in early summer after forage rye harvest and incorporation of CSL treatments. Sorghum-sudangrass was harvested in June, August and September, and treatments were evaluated for dry matter and N concentration. Soil cores were taken from each plot, split into depths of 0 to 15, 15 to 30 and 30 to 60 cm, and soil C and N were measured using combustion analysis. Nylon mesh bags containing plant samples were buried at 15 cm and used to evaluate decomposition rate of above ground legume biomass, including change in C and N concentrations. Mungbean (Vigna radiata L. [Wilczek]) had the highest shoot biomass yield (6.24 t DM ha-1) and contributed the most total N (167 kg∙ha-1) and total C (3043 kg∙ha-1) of the WSL tested. Decomposition rate of WSL biomass was rapid in the first 10 weeks and very slow afterward. Winter pea (Pisum sativum L. spp. sativum), arrow leaf clover (Trifolium vesiculosum Savi.), and crimson clover (Trifolium incarnatum L.) were the most productive CSL in this trial. Austrian winter pea produced 8.41 t DM ha-1 with a total N yield of 319 kg N ha-1 and total C production of 3835 kg C ha-1. The WSL treatments had only small effects on rye forage yield and N concentration, possibly due to mineralization of N from a large SOC pool already in place. The CSL treatments also had only minimal effects on sorghum-sudangrass forage production. Winter pea, arrow leaf and crimson clover were productive cool season legumes and could be useful as green manure crops. Mungbean and cowpea (Vigna unguiculata [L.] Walp.) were highly productive warm season legumes but may include more production risk in green manure systems due to soil moisture competition.

National Soil Organic Carbon Stocks Inventories under Different Mangrove Forest Types in Gabon

Gabonese’s estuary is an important coastal mangrove setting and soil plays a key role in mangrove carbon storage in mangrove forests. However, the spatial variation in soil organic carbon (SOC) storage remain unclear. To address this gap, determining the SOC spatial variation in Gabonese’s estuarine is essential for better understanding the global carbon cycle. The present study compared soil organic carbon between northern and southern sites in different mangrove forest, Rhizophora racemosa and Avicennia germinans. The results showed that the mean SOC stocks at 1 m depth were 256.28 ± 127.29 MgC ha−1. Among the different regions, SOC in northern zone was significantly (p p < 0.001). The deeper layers contained higher SOC stocks (254.62 ± 128.09 MgC ha−1) than upper layers (55.42 ± 25.37 MgC ha−1). The study highlights that low deforestation rate have led to less CO2 (705.3 Mg CO2e ha−1 - 922.62 Mg CO2e ha−1) emissions than most sediment carbon-rich mangroves in the world. These results highlight the influence of soil texture and mangrove forest types on the mangrove SOC stocks. The first national comparison of soil organic carbon stocks between mangroves and upland tropical forests indicated SOC stocks were two times more in mangroves soils (51.21 ± 45.00 MgC ha−1) than primary (20.33 ± 12.7 MgC ha−1), savanna and cropland (21.71 ± 15.10 MgC ha−1). We find that mangroves in this study emit lower dioxide-carbon equivalent emissions. This study highlights the importance of national inventories of soil organic carbon and can be used as a baseline on the role of mangroves in carbon sequestration and climate change mitigation but the variation in SOC stocks indicates the need for further national data.

Variations in Soil Organic Matter Content in Cultivated and Uncultivated Calcareous Soils from the Mediterranean Island of Malta after 15 Years of Cultivation

The soils of Malta are calcareous and generally undeveloped. Organic matter (OM) in these soils is low and farmers are constantly urged to increase it. The objective of this study was to evaluate any temporal variation in soil OM after 15 years of cultivation, and determine whether soil series, soil depth, and cultivation influence variation. OM was determined in the topsoil and subsoil of 7 agricultural and 4 non-agricultural sites. The sites represented 7 different soil series that are present on the island. In sampling periods 1 (t = 0 years) and 2 (t =15 years), the OM content in the collective (all soil series) bulk (topsoil and subsoil) uncultivated soil was 3.9 % and 3.8 % respectively. This was significantly greater than that of the collective bulk cultivated soil (2.4% and 2.3%). The OM in the collective uncultivated topsoil was 5.4% and 5.2% in periods 1 and 2 and was significantly higher than that of the cultivated topsoil (2.5% in both periods). The OM content in the collective uncultivated subsoil was 2.3% and 2.5% in periods 1 and 2 respectively but only that measured in period 2 was significantly higher than that of the cultivated subsoil (2.2% in both periods). On an individual soil series basis, the OM in the uncultivated topsoils was significantly higher than that of their cultivated counterparts. The differences in the subsoils were not significant. Across the uncultivated soil series, OM was significantly higher in the topsoil than in the subsoil but in the cultivated soil series the differences between topsoil and subsoil were not significant. There was no significant difference in OM between the uncultivated soils of different series, but in the cultivated the OM content was higher in soils that were more mature. After 15 years, no significant change in OM occurred in both the collective cultivated and uncultivated bulk soils, the collective topsoil and subsoil, and in most of the individual series. The OM content of each soil series was also similar to what was reported 60 and 50 years earlier by other researchers.

Deadwood affects the soil organic matter fractions and enzyme activity of soils in altitude gradient of temperate forests

The main objective of our study has been to determine the role of deadwood in the shaping of the amount of soil organic matter fractions in mountain forest soils.For this purpose,a climosequence approach comprising north(N)and south(S)exposure along the altitudinal gradient(600,800,1000 and 1200 m a.s.l.)was set up.By comparing the properties of decomposing deadwood and those of the soils located directly beneath the decaying wood we drew conclusions about the role of deadwood in the shaping of soil organic matter fractions and soil carbon storage in different climate conditions.The basic properties,enzymatic activity and fractions of soil organic matter(SOM)were determined in deadwood and affected directly by the components released from decaying wood.Heavily decomposed deadwood impacts soil organic matter stabilization more strongly than the less decayed deadwood and the light fraction of SOM is more sensitive to deadwood effects than the heavy fraction regardless of the location in the altitude gradient.Increase in SOM mineral-associated fraction C content is more pronounced in soils under the influence of deadwood located in lower locations of warmer exposure.Nutrients released from decaying wood stimulate the enzymatic activity of soils that are within the range of deadwood influence.

Spatial prediction of soil organic carbon in coal mining subsidence areas based on RBF neural network

A quantitative research on the effect of coal mining on the soil organic carbon(SOC)pool at regional scale is beneficial to the scientific management of SOC pools in coal mining areas and the realization of coal low-carbon mining.Moreover,the spatial prediction model of SOC content suitable for coal mining subsidence area is a scientific problem that must be solved.Tak-ing the Changhe River Basin of Jincheng City,Shanxi Province,China,as the study area,this paper proposed a radial basis function neural network model combined with the ordinary kriging method.The model includes topography and vegetation factors,which have large influence on soil properties in mining areas,as input parameters to predict the spatial distribution of SOC in the 0-20 and 2040 cm soil layers of the study area.And comparing the prediction effect with the direct kriging method,the results show that the mean error,the mean absolute error and the root mean square error between the predicted and measured values of SOC content predicted by the radial basis function neural network are lower than those obtained by the direct kriging method.Based on the fitting effect of the predicted and measured values,the R^(2) obtained by the radial basis artificial neural network are 0.81,0.70,respectively,higher than the value of 0.44 and 0.36 obtained by the direct kriging method.Therefore,the model combining the artificial neural network and kriging,and considering environmental factors can improve the prediction accuracy of the SOC content in mining areas.

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

Forest soil carbon is a major carbon pool of terrestrial ecosystems,and accurate estimation of soil organic carbon(SOC)stocks in forest ecosystems is rather challenging.This study compared the prediction performance of three empirical model approaches namely,regression kriging(RK),multiple stepwise regression(MSR),random forest(RF),and boosted regression trees(BRT)to predict SOC stocks in Northeast China for 1990 and 2015.Furthermore,the spatial variation of SOC stocks and the main controlling environmental factors during the past 25 years were identified.A total of 82(in 1990)and 157(in 2015)topsoil(0–20 cm)samples with 12 environmental factors(soil property,climate,topography and biology)were selected for model construction.Randomly selected80%of the soil sample data were used to train the models and the other 20%data for model verification using mean absolute error,root mean square error,coefficient of determination and Lin's consistency correlation coefficient indices.We found BRT model as the best prediction model and it could explain 67%and 60%spatial variation of SOC stocks,in 1990,and 2015,respectively.Predicted maps of all models in both periods showed similar spatial distribution characteristics,with the lower SOC in northeast and higher SOC in southwest.Mean annual temperature and elevation were the key environmental factors influencing the spatial variation of SOC stock in both periods.SOC stocks were mainly stored under Cambosols,Gleyosols and Isohumosols,accounting for 95.6%(1990)and 95.9%(2015).Overall,SOC stocks increased by 471 Tg C during the past 25 years.Our study found that the BRT model employing common environmental factors was the most robust method for forest topsoil SOC stocks inventories.The spatial resolution of BRT model enabled us to pinpoint in which areas of Northeast China that new forest tree planting would be most effective for enhancing forest C stocks.Overall,our approach is likely to be useful in forestry management and ecological restoration at and beyond the regional scale.

Long-term light grazing does not change soil organic carbon stability and stock in biocrust layer in the hilly regions of drylands

Livestock grazing is the most extensive land use in global drylands and one of the most extensive stressors of biological soil crusts(biocrusts).Despite widespread concern about the importance of biocrusts for global carbon(C)cycling,little is known about whether and how long-term grazing alters soil organic carbon(SOC)stability and stock in the biocrust layer.To assess the responses of SOC stability and stock in the biocrust layer to grazing,from June to September 2020,we carried out a large scale field survey in the restored grasslands under long-term grazing with different grazing intensities(represented by the number of goat dung per square meter)and in the grasslands strictly excluded from grazing in four regions(Dingbian County,Shenmu City,Guyuan City and Ansai District)along precipitation gradient in the hilly Loess Plateau,China.In total,51 representative grassland sites were identified as the study sampling sites in this study,including 11 sites in Guyuan City,16 sites in Dingbian County,15 sites in Shenmu City and 9 sites in Ansai District.Combined with extensive laboratory analysis and statistical analysis,at each sampling site,we obtained data on biocrust attributes(cover,community structure,biomass and thickness),soil physical-chemical properties(soil porosity and soil carbon-to-nitrogen ratio(C/N ratio)),and environmental factors(mean annual precipitation,mean annual temperature,altitude,plant cover,litter cover,soil particle-size distribution(the ratio of soil clay and silt content to sand content)),SOC stability index(SI)and SOC stock(SOCS)in the biocrust layer,to conduct this study.Our results revealed that grazing did not change total biocrust cover but markedly altered biocrust community structure by reducing plant cover,with a considerable increase in the relative cover of cyanobacteria(23.1%)while a decrease in the relative cover of mosses(42.2%).Soil porosity and soil C/N ratio in the biocrust layer under grazing decreased significantly by 4.1%–7.2%and 7.2%–13.3%,respectively,compared with those under grazing exclusion.The shifted biocrust community structure ultimately resulted in an average reduction of 15.5%in SOCS in the biocrust layer under grazing.However,compared with higher grazing(intensity of more than 10.00 goat dung/m2),light grazing(intensity of 0.00–10.00 goat dung/m2 or approximately 1.20–2.60 goat/(hm2•a))had no adverse effect on SOCS.SOC stability in the biocrust layer remained unchanged under long-term grazing due to the offset between the positive effect of the decreased soil porosity and the negative effect of the decreased soil C/N ratio on the SOC resistance to decomposition.Mean annual precipitation and soil particle-size distribution also regulated SOC stability indirectly by influencing soil porosity through plant cover and biocrust community structure.These findings suggest that proper grazing might not increase the CO_(2) release potential or adversely affect SOCS in the biocrust layer.This research provides some guidance for proper grazing management in the sustainable utilization of grassland resources and C sequestration in biocrusts in the hilly regions of drylands.

Reclamation during oasification is conducive to the accumulation of the soil organic carbon pool in arid land

Soil organic carbon(SOC)and its stable isotope composition reflect key information about the carbon cycle in ecosystems.Studies of carbon fractions in oasis continuous cotton-cropped fields can elucidate the SOC stability mechanism under the action of the human-land relationship during the oasification of arid land,which is critical for understanding the carbon dynamics of terrestrial ecosystems in arid lands under global climate change.In this study,we investigated the Alar Reclamation Area on the northern edge of the Tarim Basin,Xinjiang Uygur Autonomous Region of China,in 2020.In original desert and oasis farmlands with different reclamation years,including 6,10,18,and 30 a,and different soil depths(0-20,20-40,40-60 cm),we analyzed the variations in SOC,very liable carbon(C_(VL)),liable carbon(C_(L)),less liable carbon(C_(LL)),and non-liable carbon(C_(NL))using the method of spatial series.The differences in the stable carbon isotope ratio(δ^(13)C)and beta(β)values reflecting the organic carbon decomposition rate were also determined during oasification.Through redundancy analysis,we derived and discussed the relationships among SOC,carbon fractions,δ^(13)C,and other soil physicochemical properties,such as the soil water content(SWC),bulk density(BD),pH,total salt(TS),total nitrogen(TN),available phosphorus(AP),and available potassium(AK).The results showed that there were significant differences in SOC and carbon fractions of oasis farmlands with different reclamation years,and the highest SOC was observed at the oasis farmland with 30-a reclamation year.C_(VL),C_(L),C_(LL),and C_(NL) showed significant changes among oasis farmlands with different reclamation years,and C_(VL) had the largest variation range(0.40-4.92 g/kg)and accounted for the largest proportion in the organic carbon pool.The proportion of C_(NL) in the organic carbon pool of the topsoil(0-20 cm)gradually increased.δ^(13)C varied from-25.61‰to-22.58‰,with the topsoil showing the most positive value at the oasis farmland with 10-a reclamation year;while theβvalue was the lowest at the oasis farmland with 6-a reclamation year and then increased significantly.Based on the redundancy analysis results,the soil physicochemical properties,such as TN,AP,AK,and pH,were significantly correlated with C_(L),and TN and AP were positively correlated with C_(VL).However,δ^(13)C was not significantly influenced by soil physicochemical properties.Our analysis advances the understanding of SOC dynamics during oasification,revealing the risk of soil carbon loss and its contribution to terrestrial carbon accumulation in arid lands,which could be useful for the sustainable development of regional carbon resources and ecological protection in arid ecosystem.

Estimation of soil organic matter in the Ogan-Kuqa River Oasis, Northwest China, based on visible and near-infrared spectroscopy and machine learning

Visible and near-infrared(vis-NIR)spectroscopy technique allows for fast and efficient determination of soil organic matter(SOM).However,a prior requirement for the vis-NIR spectroscopy technique to predict SOM is the effective removal of redundant information.Therefore,this study aims to select three wavelength selection strategies for obtaining the spectral response characteristics of SOM.The SOM content and spectral information of 110 soil samples from the Ogan-Kuqa River Oasis were measured under laboratory conditions in July 2017.Pearson correlation analysis was introduced to preselect spectral wavelengths from the preprocessed spectra that passed the 0.01 level significance test.The successive projection algorithm(SPA),competitive adaptive reweighted sampling(CARS),and Boruta algorithm were used to detect the optimal variables from the preselected wavelengths.Finally,partial least squares regression(PLSR)and random forest(RF)models combined with the optimal wavelengths were applied to develop a quantitative estimation model of the SOM content.The results demonstrate that the optimal variables selected were mainly located near the range of spectral absorption features(i.e.,1400.0,1900.0,and 2200.0 nm),and the CARS and Boruta algorithm also selected a few visible wavelengths located in the range of 480.0–510.0 nm.Both models can achieve a more satisfactory prediction of the SOM content,and the RF model had better accuracy than the PLSR model.The SOM content prediction model established by Boruta algorithm combined with the RF model performed best with 23 variables and the model achieved the coefficient of determination(R2)of 0.78 and the residual prediction deviation(RPD)of 2.38.The Boruta algorithm effectively removed redundant information and optimized the optimal wavelengths to improve the prediction accuracy of the estimated SOM content.Therefore,combining vis-NIR spectroscopy with machine learning to estimate SOM content is an important method to improve the accuracy of SOM prediction in arid land.

Distribution Characteristics of Soil Organic Carbon in Degraded Forest Land in the Sandstorm Area of Jingbian County,Shaanxi Province

[Objectives]To explore the distribution characteristics of soil organic carbon in degraded forest land in the sandstorm area of Jingbian County,Shaanxi Province.[Methods]The distribution characteristics and abundance of 0-20 cm shallow soil organic carbon in 5 towns in the sandstorm area in the north of Jingbian County were studied by field sampling and indoor detection.[Results]The average soil organic carbon contents in Hongdunjie Town,Haizetan Town,Huanghaojie Town,Ningtiaoliang Town and Dongkeng Town were 2.93,3.21,2.53,2.54 and 4.08 g/kg,respectively,which were all lower than the national background value(31.00 g/kg).The coefficients of variation of soil organic carbon content in Hongdunjie Town,Huanghaojie Town and Dongkeng Town were 59.04%,35.97%and 47.55%,respectively,with higher coefficients of variation and larger differences in spatial distribution.The organic carbon content of Haizetan Town and Dongkeng Town was above the abundance,accounting for 70%and 50%,which were relatively rich,while the soil organic carbon content of Hongdunjie was relatively scarce.The average content of soil organic carbon in the sandstorm area was 3.03 g/kg,which was also lower than the national background value.The coefficient of variation was 46.53%,showing high coefficient of variation and large difference in spatial distribution.In addition,20.41%of the average content of soil organic carbon in the sandstorm area was in the deficient level,and 79.59%were in the medium or above level.[Conclusions]The study of distribution characteristics of soil organic carbon in degraded forest land in the sandstorm area of Jingbian County will better serve the precise management of soil resources.

Soil Organic Carbon Stock and Soil Quality under Four Major Agroecosystems in the Eastern Flank of Mount Bambouto (West-Cameroon)

Assessing soil organic carbon stock (SOCS) and soil quality (SQ) helps design better agricultural practices to improve environmental sustainability and productivity. The purpose of the study is to assess SOCS and soil quality SQ in the main agroecosystems (AES) of the eastern flank of Mount Bambouto (West, Cameroon). Using multiple statistics tests and principal component analysis (PCA), SOCS and Soil Quality Index (SQI) were computed for each AES. SOCS and SQI were computed based on soil chemical properties and analysis of variance. Topsoil samples (0 - 30 cm) were collected in a different AES and analyzed in the laboratory. The four AES identified and selected are cultivated land (CL), forest areas (FA), mixed areas (MA), and bush areas (BA). Further, multiple comparison tests were used to compare soils from different AES. PCA was used to select the most appropriate indicators that control SOCS and SQ. Several soil properties showed high to very high coefficient of variation within the AES. Organic matter (OM) was significantly high in FA. SOCS and SQ differ significantly (p = 0.000) between the AES. The study further indicates that the main variables controlling SQ within the eastern flank of Mount Bambouto are OM, pHw, N, C/N, and CEC. While the main soil parameters controlling SOCS are OM, OC, BD, C/N, S, and pHKCl.