Spatial Distribution Of Soil Organic Carbon Research Articles

Legacy of medieval ridge and furrow cultivation on soil organic carbon distribution and stocks in forests

Land management history can influence soil organic carbon (SOC) stocks over centuries. In this study, the impact of medieval ridge and furrow cropland management on SOC in forests was assessed. Continuous clockwise ploughing in rectangular fields moved topsoil from the outer part of strip-shaped fields towards the centre, thus forming a corrugated microtopography with peripheral furrows and central ridges. This tillage technique led to the burial of former topsoil under the ridges.The effect of this human-created microtopography and the centuries old topsoil burial on forest SOC spatial distribution and stocks was investigated. Five sites with ridge and furrow field strips under deciduous forests on soils of differing texture in Germany were sampled, with three orthogonal transects of the field strips and a defined reference position where neither net soil removal nor accumulation occurred. Reforestation took place between the 17th and 19th century.At 0 to 10cm depth, average SOC content was 28±3gkg−1 at ridges, 37±3gkg−1 at reference positions and 47±5gkg−1 at furrows. SOC stocks were 7±5% lower at ridges and 8±4% higher at furrows than at reference positions. Enhanced C input at furrows through leaf litter accumulation was indicated by higher SOC content in the free light fraction at furrows (10±5gkg−1) than at ridges (6±3gkg−1), higher specific SOC mineralisation (37±4μgCO2-Cg−1 SOC at furrows and 31±3μgCO2-Cg−1 SOC at ridges) and wider C/N ratio at furrows (18±1) compared with ridges (17±1).Buried topsoil under ridges (20 to 33–52cm depth) did not contain significantly less SOC than corresponding samples at furrows and reference positions. However, SOC content was 0.4 to 0.9gkg−1 higher at ridges than at reference positions, indicating long-term preservation of former topsoil SOC by burial under ridges, although enhanced SOC stocks at ridges due to carbon burial could not be significantly confirmed for all sites. It can be concluded that preserved microtopography over centuries and ancient topsoil burial, a legacy of medieval ridge and furrow cultivation, still influences forest SOC spatial distribution and stocks.

Sources of errors and uncertainties in the assessment of forest soil carbon stocks at different scales-review and recommendations.

Spatially explicit knowledge of recent and past soil organic carbon (SOC) stocks in forests will improve our understanding of the effect of human- and non-human-induced changes on forest C fluxes. For SOC accounting, a minimum detectable difference must be defined in order to adequately determine temporal changes and spatial differences in SOC. This requires sufficiently detailed data to predict SOC stocks at appropriate scales within the required accuracy so that only significant changes are accounted for. When designing sampling campaigns, taking into account factors influencing SOC spatial and temporal distribution (such as soil type, topography, climate and vegetation) are needed to optimise sampling depths and numbers of samples, thereby ensuring that samples accurately reflect the distribution of SOC at a site. Furthermore, the appropriate scales related to the research question need to be defined: profile, plot, forests, catchment, national or wider. Scaling up SOC stocks from point sample to landscape unit is challenging, and thus requires reliable baseline data. Knowledge of the associated uncertainties related to SOC measures at each particular scale and how to reduce them is crucial for assessing SOC stocks with the highest possible accuracy at each scale. This review identifies where potential sources of errors and uncertainties related to forest SOC stock estimation occur at five different scales-sample, profile, plot, landscape/regional and European. Recommendations are also provided on how to reduce forest SOC uncertainties and increase efficiency of SOC assessment at each scale.

Distribution of soil carbon in different grassland types of the Qinghai-Tibetan Plateau

Accurate estimate of soil carbon storage is essential to reveal the role of soil in global carbon cycle. However, there is large uncertainty on the estimation of soil organic carbon (SOC) storage in grassland among previous studies, and the study on soil inorganic carbon (SIC) is still lack. We surveyed 153 sites during plant peak growing season and estimated SOC and SIC for temperate desert, temperate steppe, alpine steppe, steppe meadow, alpine meadow and swamp, which covered main grassland in the Qinghai Plateau during 2011 to 2012. The results showed that the vertical and spatial distributions of SOC and SIC varied by grassland types. The SOC amount mainly decreased from southeast to northwest, whereas the SIC amount increased from southeast to northwest. The magnitude of SOC amount in the top 50 cm across grassland types ranked by: swamp > alpine meadow > steppe meadow > temperate steppe > alpine steppe > temperate desert, while the SIC amount showed an opposite order.There was a great deal of variation in proportion of SOC and SIC among different grassland types (from 55.17 to 94.59 for SOC and 5.14 to 44.83 for SIC). The total SOC and SIC storage was 5.78 Pg and 1.37 Pg, respectively, in the top 50 cm of soil in Qinghai Province. The mixed linear model revealed that grassland types was the predominant factor in spatial variations of SOC amount while grassland types and soil pH accounted for those of SIC amount. Our results suggested that the community shift of alpine meadow towards alpine grassland induced by climate warming would decrease carbon sequestration capacity by 6.0 kg C m2.

Soil organic carbon stock explored by geo-spatial modeling at Sagar Island, India

Soil organic carbon (SOC) and organic carbon density (OCD) is an active indicator of soil fertility and productivity, and it fluctuates spatially and temporally. The main objective of the present study is to evaluate the spatial variability of SOC and OCD using geostatistics model as well as field methods at Sagar Island (Sundarban), India. Soil samples at the depths of 0–20 cm were collected from twenty plots at different season to estimate organic carbon concentrations and carbon density in the laboratory. Spatial distributions of SOC and OCD concentrations were estimated using ordinary kriging method. Remote sensing imagery and ground truth helped in identifying different features as well as land use/land cover correctly. Landuse are characterized by coastal water, sand, coastal wetland, salt marsh, agricultural fallow land, sparse vegetation, crop/paddy field, and mixed settlement area. The average value SOC were recorded as 2.80, 1.83, and 2.069 % in post-monsoon, pre-monsoon and monsoon season respectively. Semivariograms models of SOC and OCD in all seasons illustrated strong positive nugget values indicated short scale variability and sampling error. The coefficient of determination (R2) of the SOC model denoted as 0.71, 0.68 and 0.74 and for OCD calculated as 0.78, 0.67 and 0.83 at pre-monsoon, monsoon and post-monsoon season respectively. The results illustrated that mangrove forest portrayed highest amount of SOC concentration in both the pre-monsoon (1.05 kg/m2) and post-monsoon (0.94 kg/m2) season.

Soil organic carbon stocks in permafrost-affected soils in West Greenland

Little is known about soil organic carbon (SOC) stocks in permafrost-affected soils in Greenland. Generally, occurrence and stocks of SOC in permafrost-affected soils of the Arctic were underestimated for many years. Compared to the assumed dimension of the influence of carbon dynamics on climate change this knowledge should be substantially widened. A total of 155 soil samples were used to get a better understanding about SOC stocks, depth function and spatial distribution of SOC in permafrost-affected soils in a characteristic deglaciated valley in West Greenland southeast of Kangerlussuaq. The valley is characterized by a high variability of active layer thickness and pedo-variance mainly caused by topography.The average SOC stock of the Umimmalissuaq valley is 9.9kgm−2 in the upper 30cm and around 30kgm−2 in the first meter, which is remarkably higher than regional predictions with 6–15.9kgm−2 in the first 100cm. To account for spatial heterogeneity landscape units are developed which are most useful for grouping and predicting SOC stocks. The SOC store measured 14.2kgm−2 in the upper 30cm, 11.5kgm−2 on north-facing slopes, and 8.4kgm−2 on south-facing slopes. Little SOC stocks with around 6kgm−2 were found under abrasion fields particularly on hilltops and moraine ridges. Soils on south-facing slopes usually have very low SOC stocks in deeper soil horizons except of organic rich horizons in rarely occurring paleosols. North-facing soils and valley bottom slopes generally have high SOC stocks of around 19kg SOC m−2 in soil horizons with a depth of 30–100cm.In general, the main influencing parameter on SOC stocks is the soil organic matter input from the vegetative cover. The vegetative cover is mainly a result of topographic position and aspect related to the ice margin and katabatic winds. Soil moisture and high active layer may influence SOC stocks positively.

Spatial distribution of soil organic carbon in the Zoige alpine wetland, northeastern Qinghai–Tibet Plateau

Regional-scale soil organic carbon (SOC) field investigations would significantly improve our understanding of national and global SOC sequestration and carbon (C) cycles. The Zoige wetland is the world's largest high-altitude wetland. It is located in northeastern Qinghai–Tibet Plateau. Although considered sensitive to global climate change, little is known of its wetland SOC stocks and spatial distribution patterns. Accordingly, we investigated SOC stocks and spatial distribution patterns in the uppermost 1m soil layer in Ruoergai and Hongyuan counties in the Zoige wetland. We found that SOC decreased with increasing soil depth, which was highest in the topmost 0.3m soil layer. Total nitrogen and water content were positively correlated to SOC content while bulk density was negatively correlated to SOC content in both wetland and grassland areas. Wetlands had a higher SOC density than grassland. In the uppermost 1m of wetland soil, average SOC density was 69.5kgCm−3, greater by a factor of three compared to mean wetland SOC density in China and greater by a factor of six compared to the national average. SOC stock in the uppermost 1m of wetland soils totaled 514Tg C, of which 50% was stored in the topmost 0.3m. This indicated that the two counties in the Zoige wetland stores from 8.3 to 10.2% of the total SOC in China in the topmost 1m of wetland soils. This vast SOC pool and high SOC density requires sound land use practices to sustain and protect Zoige wetland ecosystem services and C stocks.

Organic carbon and nutrients (N, P) in surface soil horizons in a non-glaciated catchment, SW Spitsbergen

AbstractOrganic carbon, nitrogen, and phosphorus in the soils of the High Arctic play an important role in the context of global warming, biodiversity, and richness of tundra vegetation. The main aim of the present study was to determine the content and spatial distribution of soil organic carbon (SOC), total nitrogen (Ntot), and total phosphorus (Ptot) in the surface horizons of Arctic soils obtained from the lower part of the Fuglebekken catchment in Spitsbergen as an example of a small non-glaciated catchment representing uplifted marine terraces of the Svalbard Archipelago. The obtained results indicate that surface soil horizons in the Fuglebekken catchment show considerable differences in content of SOC, Ntot, and Ptot. This mosaic is related to high variability of soil type, local hydrology, vegetation (type and quantity), and especially location of seabird nesting colony. The highest content of SOC, Ntot, and Ptotoccurs in soil surface horizons obtained from sites fertilized by seabird guano and located along streams flowing from the direction of the seabird colony. The content of SOC, Ntot, and Ptotis strongly negatively correlated with distance from seabird colony indicating a strong influence of the birds on the fertility of the studied soils and indirectly on the accumulation of soil organic matter. The lowest content of SOC, Ntot, and Ptotoccurs in soil surface horizons obtained from the lateral moraine of the Hansbreen glacier and from sites in the close vicinity of the lateral moraine. The content of Ntot, Ptot, and SOC in soil surface horizons are strongly and positively correlated with one another,i.e.the higher the content of nutrients, the higher the content of SOC. The spatial distribution of SOC, Ntot, and Ptotin soils of the Hornsund area in SW Spitsbergen reflects the combined effects of severe climate conditions and periglacial processes. Seabirds play a crucial role in nutrient enrichment in these weakly developed soils.

Comparison of GIS-based interpolation methods for spatial distribution of soil organic carbon (SOC)

The ecological, economical, and agricultural benefits of accurate interpolation of spatial distribution patterns of soil organic carbon (SOC) are well recognized. In the present study, different interpolation techniques in a geographical information system (GIS) environment are analyzed and compared for estimating the spatial variation of SOC at three different soil depths (0–20cm, 20–40cm and 40–100cm) in Medinipur Block, West Bengal, India. Stratified random samples of total 98 soils were collected from different landuse sites including agriculture, scrubland, forest, grassland, and fallow land of the study area. A portable global positioning system (GPS) was used to collect coordinates of each sample site. Five interpolation methods such as inverse distance weighting (IDW), local polynomial interpolation (LPI), radial basis function (RBF), ordinary kriging (OK) and Empirical Bayes kriging (EBK) are used to generate spatial distribution of SOC. SOC is concentrated in forest land and less SOC is observed in bare land. The cross validation is applied to evaluate the accuracy of interpolation methods through coefficient of determination (R2) and root mean square error (RMSE). The results indicate that OK is superior method with the least RMSE and highest R2 value for interpolation of SOC spatial distribution.

Three-dimensional simulation and spatial characteristics of soil organic carbon based on anisotropy in region

The spatial variability of soil organic carbon (SOC) is one of the reasons leading to uncertainty in the estimation of carbon stocks. Simulation and analysis research of SOC spatial distribution, especially the three-dimensional (3D) spatial distribution characteristics, is of great significance for revealing the soil nutrient and pollutant migration, the precise management of farmland and agricultural sustainable development. The 3D spatial distribution pattern of SOC was estimated using 3D ordinary Kriging with regard to the isotropy (3DOKI), 3D ordinary Kriging and 3D CoKriging coupling Markov with regard to anisotropy (3DOKA and 3DCKMA) in the region. Based on estimation results, the paper revealed 3D distribution pattern of regional SOC content. The mean root mean square error (RMSE), relative improvement (RI), the scatter diagram of SOC simulated and measured value and specific value coverage ratio were used to estimate the accuracy of different approaches, and the mean squared deviation ratio (MSDR) and the 3D variogram were used to evaluate model fitting effect and spatial local uncertainty. The results showed that 3D spatial distribution patterns of SOC content for the 3 kinds of estimation methods were basically the same, and the overall spatial distribution pattern was SOC content of the west and the north was higher, and that of the east and the south was relatively low. SOC contents of 5 layers (0-20, >20-40, >40-60, >60-80 and >80-100 cm) were 11.88±5.76, 10.08±4.89, 8.40±5.49, 7.83±5.89 and 7.17±5.22 g/kg, respectively. With the increase of soil depth, the SOC content of the patch gradually reduced. No matter which kind of method, the spatial distribution of SOC in different soil depth was similar, that was, the distribution characteristics of surface layer of the patch were also the embodiment of the deep soil layer. Compared to 3DOKI, the spatial search strategy and its parameters with regard to anisotropy were able to reduce defect (Bovine and central tendency effect) of Kriging method in a certain extent. The RMSE values for 3DOKI, 3DOKA and 3DCKMA were 3.1645, 2.0523 and 1.6215, respectively, so the RMSE value was the minimum for 3DCKAM, whose RI reached nearly 50%. Specific value coverage ratio for 3DOKI, 3DOKA and 3DCKMA were 33.12%, 57.83% and 76.15%, respectively, which was the largest for 3DCKMA method, whose MSDR value (1.4409) was the most close to 1, and variance was the least. The same position CoKriging method coupling Markov was more accurate, and its model fitting effect was the best, and uncertainty was the smallest, which could better highlight the volatility and reflect the specific value. The related research results of this paper will provide method reference for the regional soil properties, and provide technical guidance for the reasonable and scientific research of the spatial distribution pattern of regional SOC. © 2016, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.

Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem

Soil organic carbon (SOC) plays an important role in soil fertility and carbon sequestration, and a better understanding of the spatial patterns of SOC is essential for soil resource management. In this study, we used boosted regression tree (BRT) and random forest (RF) models to map the distribution of topsoil organic carbon content at the northeastern edge of the Tibetan Plateau in China. A set of 105 soil samples and 12 environmental variables (including topography, climate and vegetation) were analyzed. The performance of the models was evaluated using a 10-fold cross-validation procedure. Maps of the mean values and standard deviations of SOC were generated to illustrate model variability and uncertainty. The results indicate that the BRT and RF models exhibited very similar performance and yielded similar predicted distributions of SOC. The two models explained approximately 70% of the total SOC variability. The BRT and RF models robustly predicted the SOC at low observed SOC values, whereas they underestimated high observed SOC values. This underestimation may have been caused by biased distributions of soil samples in the SOC space. Vegetation-related variables were assigned the highest importance in both models, followed by climate and topography. Both models produced spatial distribution maps of SOC that were closely related to vegetation cover. The SOC content predicted by the BRT model was clearly higher than that of the RF model in areas with greater vegetation cover because the contributions of vegetation-related variables in the two models (65% and 43%, respectively) differed significantly. The predicted SOC content increased from the northwestern to the southeastern part of the study area, average values produced by the BRT and RF models were 27.3gkg−1 and 26.6gkg−1, respectively. We conclude that the BRT and RF methods should be calibrated and compared to obtain the best prediction of SOC spatial distribution in similar regions. In addition, vegetation variables, including those obtained from remote sensing imagery, should be taken as the main environmental indicators and explicitly included when generating SOC maps in Alpine environments.

Three-Dimensional Mapping of Soil Organic Carbon by Combining Kriging Method with Profile Depth Function.

Understanding spatial variation of soil organic carbon (SOC) in three-dimensional direction is helpful for land use management. Due to the effect of profile depths and soil texture on vertical distribution of SOC, the stationary assumption for SOC cannot be met in the vertical direction. Therefore the three-dimensional (3D) ordinary kriging technique cannot be directly used to map the distribution of SOC at a regional scale. The objectives of this study were to map the 3D distribution of SOC at a regional scale by combining kriging method with the profile depth function of SOC (KPDF), and to explore the effects of soil texture and land use type on vertical distribution of SOC in a fluvial plain. A total of 605 samples were collected from 121 soil profiles (0.0 to 1.0 m, 0.20 m increment) in Quzhou County, China and SOC contents were determined for each soil sample. The KPDF method was used to obtain the 3D map of SOC at the county scale. The results showed that the exponential equation well described the vertical distribution of mean values of the SOC contents. The coefficients of determination, root mean squared error and mean prediction error between the measured and the predicted SOC contents were 0.52, 1.82 and -0.24 g kg-1 respectively, suggesting that the KPDF method could be used to produce a 3D map of SOC content. The surface SOC contents were high in the mid-west and south regions, and low values lay in the southeast corner. The SOC contents showed significant positive correlations between the five different depths and the correlations of SOC contents were larger in adjacent layers than in non-adjacent layers. Soil texture and land use type had significant effects on the spatial distribution of SOC. The influence of land use type was more important than that of soil texture in the surface soil, and soil texture played a more important role in influencing the SOC levels for 0.2-0.4 m layer.

Pedometric Mapping of Soil Organic Carbon Loss Using Soil Erosion Maps of Tripura

Importance of soil organic carbon (SOC) in maintaining soil productivity and natural ecosystem has been a major concern throughout the globe. SOC in the humid tropical climate becomes more important in view of undulating hilly terrain in the northeastern region of India. The major concern in such landscape is soil erosion and the necessary conservation practices. In the present study, we discuss the technique of pedometric mapping to link SOC and soil loss. The best-fit semi-variogram model for SOC was found to be exponential model (R 2 = 0.90). The best fit semivariogram models for soil and SOC losses are spherical (R 2 = 0.95) and exponential (R 2 = 0.77) respectively. The spatial distribution of SOC, soil and SOC loss was found to be related with topography and different land-use types and showed moderate spatial dependence. With the help of 196 grid observations, the present study shows a threshold limit of 150 kg ha -1 year -1 SOC loss above which the areas are to be considered as susceptible demanding immediate conservation measures. Pedometric mapping using SOC and soil loss can, thus, be a tool to prioritize areas in humid tropical climate for conservation agriculture.

Spatial and Seasonal Variations of Soil Carbon and Nitrogen Content and Stock in a Tidal Salt Marsh with Tamarix chinensis, China

To investigate the spatial and seasonal variations of soil organic carbon (SOC) and total nitrogen (TN) contents and stocks in tidal salt marsh soils, 15 cores to a depth of 40 cm were collected in five sampling sites along a sampling belt during three seasons. Our results showed that higher SOC and TN contents occurred in the surface soils in three sampling seasons. Spatial distributions of SOC and TN showed moderate variability. The C/N ratios were higher in the summer and autumn than in spring. The soil organic carbon density (SOCD) and soil total nitrogen density (TND) ranked in the following order: autumn > spring > summer. And the SOCD values positively correlated with the distances from the tidal creek in summer, while this correlation was negative in autumn and spring. The soil properties, such as the soil moisture, salinity, C/N ratio and C/P ratio, significantly correlated with the SOC and TN contents and stocks. The water and salinity regulation and the alternation of ratios of ecological stoichiometry should be considered to strengthen carbon and nitrogen sequestration in coastal wetlands.

Analysing soil organic C gradients in a smallholder farming village of East Zimbabwe

We set out to map the soil organic carbon (SOC) content, as an indicator of soil fertility, at the village scale, and to relate the SOC content to farm scale management and landscape scale characteristics. Topsoil samples were taken at 100 random locations in the Murewa smallholder farming area in Zimbabwe and analysed for organic carbon. Using digital soil mapping techniques and Landsat TM images we could explain 50% of the observed SOC variance. The average SOC content was estimated to be 1.5%, although the sandy cropping area had a much lower average of 0.8% and the red clays and valleys had higher average of 1.8%. The SOC variability could not be linked to farm management. No fertility gradients were observed, mostly due to a strong dominance of clay content on the spatial distribution of SOC. Clay content was able to explain 57% of the SOC variance, while farm area and labour size, typically used for farmer typology, were able to explain only an additional minor part of the SOC variance. This strong landscape scale effect needs to be included in future village-scale studies. We conclude that digital soil mapping of soil fertility gradients at the village scale has several scale issues that need to be addressed if the envisioned global digital soil map is to be relevant for smallholder farmers in sub-Saharan Africa.

Priority selection rating of sampling density and interpolation method for detecting the spatial variability of soil organic carbon in China

Soil sampling density and spatial interpolation method both have effects on interpreting the spatial variability of regional soil organic carbon (SOC). However, there are few comparisons of the effects between the two factors. Based on three soil sampling designs in Yujiang County, Jiangxi Province of China, the SOC spatial distributions in a specific area, imposed on three grid sampling densities of 2 × 2 km (G2×2), 1 × 1 km (G1×1), and 0.5 × 0.5 km (G0.5×0.5), were predicted via two interpolation methods: Ordinary Kriging (OK) and Kriging combined with land use information (LUK). Prediction accuracies from OK and LUK at three sampling densities were compared on the basis of 65 validation samples in the area. The results demonstrated that the correlation coefficients (r) between the measured and predicted values of validation locations obtained from OK (r = 0.212, 0.491 and 0.512) and LUK (r = 0.602, 0.776 and 0.875) increased with decreased grid size, and the root mean square errors (RMSE) from OK (RMSE = 6.79, 5.33, and 5.19 g kg−1) and LUK (RMSE = 4.74, 3.60, and 3.14 g kg−1) all decreased as expected with the sampling density increasing from G2×2 to G0.5×0.5. The rs from LUK were all higher and RMSEs were all lower than those from OK at three densities, respectively. More interesting, the prediction accuracy of LUK from G2×2 was not only lower than that of OK at same density, but also lower than those of OK at G1×1 and G0.5×0.5. This indicates that LUK can use several times fewer soil samples than OK to predict SOC spatial variability with same accuracy. The conclusion is that the efficient interpolation method not only makes sense to obtain high-precision SOC distribution information, but also can save lots of sampling points and research costs. Therefore, research on efficient interpolation method is a key step and should be paid more attention than increasing sampling points for revealing SOC spatial variability in the hilly red soil region of China, even in the regions with similar complex terrain.

Online Measurement of Soil Organic Carbon as Correlated with Wheat Normalised Difference Vegetation Index in a Vertisol Field

This study explores the potential of visible and near infrared (vis-NIR) spectroscopy for online measurement of soil organic carbon (SOC). It also attempts to explore correlations and similarities between the spatial distribution of SOC and normalized differential vegetation index (NDVI) of a wheat crop. The online measurement was carried out in a clay vertisol field covering 10 ha of area in Karacabey, Bursa, Turkey. Kappa statistics were carried out between different SOC and NDVI data to investigate potential similarities. Calibration model of SOC in full cross-validationresulted in a good accuracy (R 2 = 0.75, root mean squares error of prediction (RMSEP) = 0.17%, and ratio of prediction deviation (RPD) = 1.81). The validation of the calibration model using laboratory spectra provided comparatively better prediction accuracy (R 2 = 0.70, RMSEP = 0.15%, and RPD = 1.78), as compared to the online measured spectra (R 2 = 0.60, RMSEP = 0.20%, and RPD = 1.41). Although visual similarity was clear, low similarity indicated by a low Kappa value of 0.259 was observed between the online vis-NIR predicted full-point (based on all points measured in the field, e.g., 6486 points) map of SOC and NDVI map.

Spatial Distribution of Soil Organic Carbon and Total Nitrogen Based on GIS and Geostatistics in a Small Watershed in a Hilly Area of Northern China

The spatial variability of soil organic carbon (SOC) and total nitrogen (STN) levels is important in both global carbon-nitrogen cycle and climate change research. There has been little research on the spatial distribution of SOC and STN at the watershed scale based on geographic information systems (GIS) and geostatistics. Ninety-seven soil samples taken at depths of 0–20 cm were collected during October 2010 and 2011 from the Matiyu small watershed (4.2 km2) of a hilly area in Shandong Province, northern China. The impacts of different land use types, elevation, vegetation coverage and other factors on SOC and STN spatial distributions were examined using GIS and a geostatistical method, regression-kriging. The results show that the concentration variations of SOC and STN in the Matiyu small watershed were moderate variation based on the mean, median, minimum and maximum, and the coefficients of variation (CV). Residual values of SOC and STN had moderate spatial autocorrelations, and the Nugget/Sill were 0.2% and 0.1%, respectively. Distribution maps of regression-kriging revealed that both SOC and STN concentrations in the Matiyu watershed decreased from southeast to northwest. This result was similar to the watershed DEM trend and significantly correlated with land use type, elevation and aspect. SOC and STN predictions with the regression-kriging method were more accurate than those obtained using ordinary kriging. This research indicates that geostatistical characteristics of SOC and STN concentrations in the watershed were closely related to both land-use type and spatial topographic structure and that regression-kriging is suitable for investigating the spatial distributions of SOC and STN in the complex topography of the watershed.

Spatial and Temporal Variability of Soil Organic Carbon in the Corn Belt of Northeastern China, 1980s–2005: A Case Study in Four Counties

Estimates of changes and possible causes of regional soil organic carbon (SOC) are critical for evaluation of potential responses of terrestrial biosphere to global changes. A total of 382 soil samples, collected in the 1980s from four counties in the Corn Belt of northeastern China, and of 1,514 samples collected in 2005 from the same area, were examined for SOC concentrations. Spatial and temporal SOC concentrations were evaluated after the 25-year interval using geostatistics and kriging interpolation method. Results indicated that the average topsoil SOC concentrations of the study area increased from 1.24% in the 1980s to 1.47% in 2005. Spatial distributions of SOC in the two separate evaluations showed greater SOC concentrations in the middle part of the study area and lower SOC concentrations in the remaining areas. From the 1980s to 2005, SOC concentrations in the central part increased, whereas those in the northeastern and southwestern parts decreased. The increasing trend of SOC concentrations might be attributed to the land-use and land cover changes, crop productivity increase, and agricultural management.

芦芽山亚高山草甸、云杉林土壤有机碳、全氮含量的小尺度空间异质性研究

PDF HTML阅读 XML下载 导出引用 引用提醒 芦芽山亚高山草甸、云杉林土壤有机碳、全氮含量的小尺度空间异质性 DOI: 10.5846/stxb201210231466 作者: 作者单位: 山西农业大学林学院,山西农业大学林学院,山西农业大学林学院,山西农业大学林学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金资助项目（30970480）；山西省自然基金资助项目（2010021027-4，2010021028-6）；山西农业大学博士科研启动资助项目；山西农业大学“学术骨干”资助项目 Spatial heterogeneity of soil organic carbon and total nitrogen at small scale in subalpine meadow and Picea meyeri forest in Luya Mountain Author: Affiliation: Forestry College of Shanxi Agriculture University,Taigu,Forestry College of Shanxi Agriculture University,Taigu,Forestry College of Shanxi Agriculture University,Taigu,Forestry College of Shanxi Agriculture University,Taigu Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:分析比较了山西芦芽山不同海拔处分布的亚高山草甸（样地A，海拔2756.3 m；样地B，海拔2542.3 m）和云杉林（样地C，海拔2656.8 m；样地D，海拔2387.2 m）土壤有机碳和全氮的小尺度空间异质性特征。结果表明：相同植被类型下海拔较高的样地有机碳含量较高（A：49.84 g/kg，B：38.33 g/kg，C：47.06 g/kg，D：40.67 g/kg），而较低海拔的样地土壤有机碳含量的异质性较高；除样地A以外的其他3个样地均表现为高度空间依赖性。亚高山草甸土壤全氮含量的异质性远远高于云杉纯林，四个样地中均表现出强的空间自相关性。亚高山草甸样地土壤有机碳和全氮含量均在较大尺度上空间自相关，云杉纯林样地则表现为较小尺度的空间自相关变异。 Abstract:The patterns and controls of soil organic carbon(SOC) and total nitrogen are critical for our understanding of recycling of nutrients. Spatial heterogeneity causes uneven soil resource distribution. Quantification of the spatial variability is essential for evaluating attributes at unsampled locations. This study was conducted in Luya Mountain, Shanxi Province, China. Four sample plots (30m×30m) were placed, two subalpine meadow plots (Plot A, 2756.3 m; Plot B, 2542.3 m) and two Picea meyeri forest plots (Plot C, 2656.8 m;Plot D, 2387.2 m), The spatial heterogeneity of soil organic carbon(SOC) and total nitrogen(TN) was analyzed based on theory and methodology of spatial pattern analysis in geostatistics.. Soil samples (n=119) were collected from each of the plots in the summer of 2010. We calculated the isotropic semivariograms of SOC and TN. Then spherical models were used to test the semi-variances of SOC and TN for spatial dependence. The experiment results indicated that the SOC of four plots were 49.84 g/kg (A), 38.33 g/kg (B), 47.06 g/kg (C), and 40.67 g/kg (D), respectively. The SOC contents appeared to be higher in the plots with higher elevation. Rather, the spatially variance showed more intensively in the plots with lower altitude. In contrast to the random variation of SOC in plot A, there were high spatial dependences in the spatial distribution of SOC in the others. The spatial heterogeneity of TN in Picea meyeri forest plots were higher than those in subalpine meadow plots,and spatial autocorrelation were high in all the plots. The ranges of spatial autocorrelation variation (the distances within which parameters are spatially dependent) for SOC and TN were larger for subalpine meadow plots, but smaller for Picea meyeri forest plots. 参考文献 相似文献 引证文献

Evolution of soil organic carbon in an olive orchard under arid climate

Soil organic carbon (SOC) variation was followed in an olive orchard covering 134ha during six years. Spatial and temporal variation models were adapted and developed in order to detect heterogeneity of SOC spatial distribution and mineralization rate. These parameters allow adaptation of cultural practices, i.e. organic fertilization, to field reality. Indeed, the detection of spatial variability of SOC identified zones needing urgent organic fertilization. Moreover, the variability of SOC mineralization rate facilitated the choice of the organic fertilizer type. The spatial variability and temporal variation of SOC were assessed and modeled. At the orchard level SOC shown an important spatial variation (CV=28%) following the spherical distribution model. The developed models for temporal variation was calibrated and validated and gave acceptable results (R2=0.79). These kinds of models can be used as decision support systems for fertilization and soil fertility management and are necessary for impact of this management forecasting and long-term strategy implementation at orchard level.