Carbon stock changes and carbon sequestration potential of Flemish cropland soils

Evaluations of soil organic carbon (SOC) stocks are often based on assigning a carbon density to each one of a number of ecosystems or soil classes considered, using data from soil profiles within these categories. A better approach, in which the use of classification methods by which extrapolation of SOC data to larger areas is avoided, can only be used if enough data are available at a sufficiently small scale. Over 190 000 SOC measurements (0–24 cm) have been made in the Flemish cropland (the Northern part of Belgium) in the 1989–2000 period. These SOC data were grouped into 3‐year periods and as means plus standard deviation per (part of) community (polygons). This large dataset was used to calculate SOC stocks and their evolution with time, without data extrapolation. Using a detailed soil map, larger spatial groups of polygons were created based on soil texture and spatial location. Linear regression analysis showed that in the entire study area, SOC stocks had decreased or at best had remained stable. In total, a yearly decrease of 354 kton OC yr^?1 was calculated, which corresponds with a net CO₂ emission of 1238 kton CO₂ yr^?1. Specific regions with a high carbon sequestration potential were identified, based on SOC losses during the 1989–2000 period and the mean 1999 SOC content, compared to the average SOC content of soils in Flanders with a similar soil texture. When restoring the SOC stocks to their 1990 level, we estimated the carbon sequestration potential of the Flemish cropland soils to be some 300 kton CO₂ yr^?1 at best, which corresponds to a 40‐year restoration period. In conclusion, we can say that in regions where agricultural production is very intense, carbon sequestration in the cropland may make only a very modest contribution to a country's effort to reduce greenhouse gas emissions.     

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.     

广西喀斯特移民迁入区土地利用变化对土壤有机碳和全氮储量的影响

土地利用变化是影响土壤碳、氮循环的重要因素,也是研究全球气候变化的热点.本研究基于固定深度法(FD)和等效质量法(ESM),从森林开垦和退耕还林还草两个角度探讨喀斯特移民迁入区土地利用变化对土壤有机碳(SOC)和全氮(TN)储量的影响.结果表明: 原始森林被开垦为草地、桉树林和农田后,SOC和TN储量均显著减少;基于FD方法计算的SOC和TN储量分别损失了47.4%、41.6%,而通过ESM方法计算的SOC和TN的损失率分别为54.8%、49.7%.农田撂荒为草地及种植桉树后,SOC和TN储量显著增加;基于FD方法计算的SOC和TN储量提高了60.5%、49.7%,通过ESM方法计算的SOC和TN分别增加85.5%和70.8%.FD方法忽略了土地利用变化后土壤容重的差异,而森林开垦后会显著增加土壤容重,因此,FD方法高估了SOC和TN储量;农田恢复后土壤容重减小,FD方法则会低估SOC和TN储量的增加.建议相关研究选择ESM方法测算土地利用变化对SOC和TN储量的影响.     

Increase in soil organic carbon stock over the last two decades in China's Jiangsu Province

Estimates of regional and national topsoil soil organic carbon (SOC) stock change may help evaluating the soil role in mitigation of greenhouse gas (GHG) emissions through carbon (C) sequestration in soils. However, understanding of the exact mitigation role is often constrained by the uncertainty of the stock estimation associated with different methodologies. In this paper, a soil database of topsoil (0–20 cm) SOC measurements of Jiangsu Province, China, obtained from a soil survey in 1982, and from a geological survey in 2004, was used to analyze the variability of topsoil SOC among soil groups and among soil regions, and to estimate the change in SOC stocks that have occurred in the province over the last two decades. The soil survey data was obtained from measurements of 662 690 randomly collected samples, while the geological survey data was from 24 167 samples taken using a 2 km × 2 km grid. Statistical analysis was conducted on SOC values for 1982 and 2004 for different categories of soil groups, soil regions, and administrative municipalities, respectively. Topsoil SOC storage was then calculated and the provincial topsoil SOC stock was estimated for each sampling time. There were remarkable differences in SOC levels between soil groups and soil regions and different municipalities. The grid sampling with the geological survey in 2004 yielded smaller variability of topsoil SOC averages, both with soil groups and with soil spatial distribution than the random sampling method used in 1982. Variation of SOC was greater with soil groups than with soil regions in both sampling times, although it was less variable across soil taxonomic categories than within a spatial category. Little variance of the SOC level with soil groups could be explained by clay content. However, the prevalence of paddy fields in the total cropland area governed the regional and municipal average SOC levels. The average provincial topsoil SOC content increased from 9.45 g kg⁻¹ in 1982 to 10.9 g kg⁻¹ in 2004, and the total provincial topsoil SOC stock was enhanced from 149.0±58.1 Tg C in 1982 to 173.2±51.4 Tg C in 2004, corresponding to a provincial average SOC sequestration rate of 0.16±0.09 t C ha⁻¹ yr⁻¹. The SOC sequestration trend for the last two decades could be, in part, attributed to the enhanced agricultural production, symbolized by the grain yield per hectare. The results of SOC stock changes suggest a significant C sequestration in soils of Jiangsu, China, during 1980–2000, with paddy management playing an important role in regional SOC storage and sequestration capacity.     

Digital mapping of soil organic matter stocks using Random Forest modeling in a semi-arid steppe ecosystem

Spatial prediction of soil organic matter is a global challenge and of particular importance for regions with intensive land use and where availability of soil data is limited. This study evaluated a Digital Soil Mapping (DSM) approach to model the spatial distribution of stocks of soil organic carbon (SOC), total carbon (C_tot), total nitrogen (N_tot) and total sulphur (S_tot) for a data-sparse, semi-arid catchment in Inner Mongolia, Northern China. Random Forest (RF) was used as a new modeling tool for soil properties and Classification and Regression Trees (CART) as an additional method for the analysis of variable importance. At 120 locations soil profiles to 1 m depth were analyzed for soil texture, SOC, C_tot, N_tot, S_tot, bulk density (BD) and pH. On the basis of a digital elevation model, the catchment was divided into pixels of 90 m?×?90 m and for each cell, predictor variables were determined: land use unit, Reference Soil Group (RSG), geological unit and 12 topography-related variables. Prediction maps showed that the highest amounts of SOC, C_tot, N_tot and S_tot stocks are stored under marshland, steppes and mountain meadows. River-like structures of very high elemental stocks in valleys within the steppes are partly responsible for the high amounts of SOC for grasslands (81?C84% of total catchment stocks). Analysis of variable importance showed that land use, RSG and geology are the most important variables influencing SOC storage. Prediction accuracy of the RF modeling and the generated maps was acceptable and explained variances of 42 to 62% and 66 to 75%, respectively. A decline of up to 70% in elemental stocks was calculated after conversion of steppe to arable land confirming the risk of rapid soil degradation if steppes are cultivated. Thus their suitability for agricultural use is limited.     

青海省森林土壤磷储量及其分布格局

磷(P)是地球生态系统中重要的生命元素。全球变化背景下, 科学地探究森林土壤磷储量现状及其影响因子, 对陆地生态系统的稳定以及磷的可持续利用具有重要意义。因此, 该研究利用青海省240个森林标准样地土壤实测数据, 并结合青海省森林资源清查资料估算出了青海省森林土壤磷储量, 揭示了其分布格局, 并讨论了土壤磷储量与环境因子的关系。结果表明: (1)青海省森林土壤磷储量为1.74 Tg, 全省1 m深土壤平均磷密度为4.65 Mg·hm ^-2, 土壤磷密度总体上呈地带性分布。(2)土壤磷密度在中低海拔(2 200-3 000 m)区域随海拔的升高显著减小, 在高海拔(3 300-3 900 m)区域随海拔高度的增加而显著变大。山地灰褐色森林土的磷密度最大且显著大于山地棕色暗针叶林土和山地暗褐土。(3)土壤磷含量随海拔升高显著减小, 山地棕色暗针叶林土各土层磷含量相对较大, 山地暗褐土的磷含量最小, 且土壤磷含量随着土层的加深而减小。(4)海拔、温度、土壤类型以及土壤含水量均对土壤磷含量有直接影响, 且影响较大, 其中海拔和温度是影响土壤磷含量变化的关键因子; 土壤磷密度对土壤容重、土壤磷含量、土壤含水量、海拔、土壤类型的变化响应较为明显, 而土壤容重可能是限制土壤磷密度变化的主导因素。     

Soil organic carbon stock and chemical composition along an altitude gradient in the Lushan Mountain,subtropical China

Soil organic carbon (SOC) stock in mountain ecosystems is highly heterogeneous because of differences in soil, climate, and vegetation with elevation. Little is known about the spatial distribution and chemical composition of SOC along altitude gradients in subtropical mountain regions, and the controlling factors remain unclear. In this study, we investigated the changes in SOC stock and chemical composition along an elevation gradient (219, 405, 780, and 1268 m a.s.l.) on Lushan Mountain, subtropical China. The results suggested that SOC stocks were significantly higher at high altitude sites (1268 m) than at low altitude ones (219, 405, and 780 m), but the lower altitude sites did not differ significantly. SOC stocks correlated positively with mean annual precipitation but negatively with mean annual temperature and litter C/N ratio. The variations in SOC stocks were related mainly to decreasing temperature and increasing precipitation with altitude, which resulted in decreased litter decomposition at high altitude sites. This effect was also demonstrated by the chemical composition of SOC, which showed lower alkyl C and higher O-alkyl C contents at high altitude sites. These results will improve the understanding of soil C dynamics and enhance predictions of the responses of mountain ecosystem to global warming under climate change.     

Contemporary carbon stocks of mineral forest soils in the Swiss Alps

Soil organic carbon (SOC) has been identified as the main globalterrestrial carbon reservoir, but considerable uncertainty remains as toregional SOC variability and the distribution of C between vegetationand soil. We used gridded forest soil data (8–km × 8–km)representative of Swiss forests in terms of climate and forest typedistribution to analyse spatial patterns of mineral SOC stocks alonggradients in the European Alps for the year 1993. At stand level, meanSOC stocks of 98 t C ha^–1 (N = 168,coefficient of variation: 70%) were obtained for the entiremineral soil profile, 76 t C ha^–1 (N =137, CV: 50%) in 0–30 cm topsoil, and 62 t Cha^–1 (N = 156, CV: 46%) in0–20 cm topsoil. Extrapolating to national scale, we calculatedcontemporary SOC stocks of 110 Tg C (entire mineral soil, standarderror: 6 Tg C), 87 Tg C (0–30 cm topsoil, standarderror: 3.5 Tg C) and 70 Tg C (0–20 cm topsoil, standarderror: 2.5 Tg C) for mineral soils of accessible Swiss forests(1.1399 Mha). According to our estimate, the 0–20 cm layers ofmineral forest soils in Switzerland store about half of the Csequestered by forest trees (136 Tg C) and more than five times morethan organic horizons (13.2 Tg C).At stand level, regression analyses on the entire data set yielded nostrong climatic or topographic signature for forest SOC stocks in top(0–20 cm) and entire mineral soils across the Alps, despite thewide range of values of site parameters. Similarly, geostatisticalanalyses revealed no clear spatial trends for SOC in Switzerland at thescale of sampling. Using subsets, biotic, abiotic controls andcategorial variables (forest type, region) explained nearly 60%of the SOC variability in topsoil mineral layers (0–20 cm) forbroadleaf stands (N = 56), but only little of thevariability in needleleaf stands (N = 91,R ² = 0.23 for topsoil layers).Considerable uncertainties remain in assessments of SOC stocks, due tounquantified errors in soil density and rock fraction, lack of data onwithin-site SOC variability and missing or poorly quantifiedenvironmental control parameters. Considering further spatial SOCvariability, replicate pointwise soil sampling at 8–km × 8–kmresolution without organic horizons will thus hardly allow to detectchanges in SOC stocks in strongly heterogeneous mountain landscapes.     

Spatial Analysis of Soil Organic Carbon in Zhifanggou Catchment of the Loess Plateau

Soil organic carbon (SOC) reflects soil quality and plays a critical role in soil protection, food safety, and global climate changes. This study involved grid sampling at different depths (6 layers) between 0 and 100 cm in a catchment. A total of 1282 soil samples were collected from 215 plots over 8.27 km². A combination of conventional analytical methods and geostatistical methods were used to analyze the data for spatial variability and soil carbon content patterns. The mean SOC content in the 1282 samples from the study field was 3.08 g·kg⁻¹. The SOC content of each layer decreased with increasing soil depth by a power function relationship. The SOC content of each layer was moderately variable and followed a lognormal distribution. The semi-variograms of the SOC contents of the six different layers were fit with the following models: exponential, spherical, exponential, Gaussian, exponential, and exponential, respectively. A moderate spatial dependence was observed in the 0–10 and 10–20 cm layers, which resulted from stochastic and structural factors. The spatial distribution of SOC content in the four layers between 20 and 100 cm exhibit were mainly restricted by structural factors. Correlations within each layer were observed between 234 and 562 m. A classical Kriging interpolation was used to directly visualize the spatial distribution of SOC in the catchment. The variability in spatial distribution was related to topography, land use type, and human activity. Finally, the vertical distribution of SOC decreased. Our results suggest that the ordinary Kriging interpolation can directly reveal the spatial distribution of SOC and the sample distance about this study is sufficient for interpolation or plotting. More research is needed, however, to clarify the spatial variability on the bigger scale and better understand the factors controlling spatial variability of soil carbon in the Loess Plateau region.     

黄土丘陵区小流域尺度土壤有机碳密度及储量

通过对上黄小流域不同土地利用方式下114个样点的采样分析,结合地统计学原理对小流域不同土层土壤有机碳密度的空间变异程度进行研究。研究表明,除表层土壤有机碳密度的空间变异程度较弱外,其余两层均属于中等强度变异。并呈现东部天然草地分布区与中部带状灌丛林地分布区空间变异程度较强的分布特点。不同土层深度和土地利用方式下土壤有机碳密度存在明显差异,土壤有机碳含量随着土层深度的增加而逐渐减小,有机碳密度则表现为10-30cm最高,30-60cm其次,0-10cm最低。不同土地利用方式下,有机碳密度表现为:天然草地 > 果园 > 灌丛林地 > 河滩、河台地 > 撂荒地 > 人工草地 > 耕地。以土地利用方式为基本单元,对上黄小流域土壤有机碳储量进行估算。结果表明,上黄小流域土壤有机碳总储量为46527.12t,其中,灌丛林地(22052.81t)和天然草地(14573.14t)的储量最高,占总储量的78.72%。     

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.     

Effects of long‐term straw return on soil organic carbon storage and sequestration rate in North China upland crops: A meta‐analysis

Soil organic carbon (SOC) is essential for soil fertility and climate change mitigation, and carbon can be sequestered in soil through proper soil management, including straw return. However, results of studies of long‐term straw return on SOC are contradictory and increasing SOC stocks in upland soils is challenging. This study of North China upland agricultural fields quantified the effects of several fertilizer and straw return treatments on SOC storage changes and crop yields, considering different cropping duration periods, soil types, and cropping systems to establish the relationships of SOC sequestration rates with initial SOC stocks and annual straw C inputs. Our meta‐analysis using long‐term field experiments showed that SOC stock responses to straw return were greater than that of mineral fertilizers alone. Black soils with higher initial SOC stocks also had lower SOC stock increases than did soils with lower initial SOC stocks (fluvo‐aquic and loessial soils) following applications of nitrogen‐phosphorous‐potassium (NPK) fertilizer and NPK+S (straw). Soil C stocks under the NPK and NPK+S treatments increased in the more‐than‐20‐year duration period, while significant SOC stock increases in the NP and NP+S treatment groups were limited to the 11‐ to 20‐year period. Annual crop productivity was higher in double‐cropped wheat and maize under all fertilization treatments, including control (no fertilization), than in the single‐crop systems (wheat or maize). Also, the annual soil sequestration rates and annual straw C inputs of the treatments with straw return (NP+S and NPK+S) were significantly positively related. Moreover, initial SOC stocks and SOC sequestration rates of those treatments were highly negatively correlated. Thus, long‐term straw return integrated with mineral fertilization in upland wheat and maize croplands leads to increased crop yields and SOC stocks. However, those effects of straw return are highly dependent on fertilizer management, cropping system, soil type, duration period, and the initial SOC content.     

Controls on Soil Organic Carbon Stocks and Turnover Among North American Ecosystems

Despite efforts to understand the factors that determine soil organic carbon (SOC) stocks in terrestrial ecosystems, there remains little information on how SOC turnover time varies among ecosystems, and how SOC turnover time and C input, via plant production, differentially contribute to regional patterns of SOC stocks. In this study, we determined SOC stocks (gC m⁻²) and used soil radiocarbon measurements to derive mean SOC turnover time (years) for 0–10 cm mineral soil at ten sites across North America that included arctic tundra, northern boreal, northern and southern hardwood, subtropical, and tropical forests, tallgrass and shortgrass prairie, mountain grassland, and desert. SOC turnover time ranged 36-fold among ecosystems, and was much longer for cold tundra and northern boreal forest and dry desert (1277–2151 years) compared to other warmer and wetter habitats (59–353 years). Two measures of C input, net aboveground production (NAP), determined from the literature, and a radiocarbon-derived measure of C flowing to the 0–10 cm mineral pool, I, were positively and SOC turnover time was negatively associated with mean annual evapotranspiration (ET) among ecosystems. The best fit model generated from the independent variables NAP, I, annual mean temperature and precipitation, ET, and clay content revealed that SOC stock was best explained by the single variable I. Overall, these findings indicate the primary role that C input and the secondary role that C stabilization play in determining SOC stocks at large regional spatial scales and highlight the large vulnerability of the global SOC pool to climate change.     

广西主要森林土壤有机碳空间分布及其影响因素

为阐明广西森林土壤有机碳密度分布格局及其主要影响因素, 基于森林资源清查资料和345个调查样地的土壤数据, 估算了广西主要森林土壤有机碳储量, 采用地统计学方法描绘了土壤有机碳密度的空间分布, 并利用主成分分析和逐步回归分析方法分析了影响土壤有机碳密度的主要因子。结果表明: 广西主要森林土壤有机碳储量(0-100 cm)达到1686.88 Tg, 土壤有机碳密度为124.70 Mg·hm^-2, 低于全国森林土壤平均水平。广西主要森林土壤有机碳密度最佳拟合模型为指数模型, 呈中等强度空间相关, Kriging插值显示土壤碳密度高值区在东北区域, 低值区在西北区域, 表现为喀斯特区域低、非喀斯特区域高的特点。广西主要森林土壤碳密度在不同植被类型和土壤类型下表现出一定差异, 其中竹林>落叶阔叶林>暖性针叶林>常绿落叶阔叶林>常绿阔叶林, 黄壤>红壤>赤红壤>石灰土。主成分分析和逐步回归分析结果发现土层深度、经纬度、海拔是影响广西森林土壤有机碳的主要因子, 其中以土层深度影响最大, 主要受岩溶地貌的影响。     

Spatial pattern of soil organic carbon of the main forest soils and its influencing factors in Guangxi,China

Aims
Our objectives were to study the spatial distribution of soil organic carbon (SOC) density and its influencing factors in the main forest ecosystems in Guangxi.
Methods
A total of 345 sample plots were established in Guangxi, and the size of each plot was 50 m × 20 m. Based on the forest resource inventory data and field investigation, the SOC storage of the main forests in Guangxi was estimated. Geostatistics was applied to analyze the spatial pattern of SOC density and the main influencing factors on SOC density were also explored by principal component analysis and stepwise regression.
Important findings
The total SOC storage in the main forests in Guangxi was 1686.88 Tg, and the mean SOC density was 124.70 Mg·hm^-2, which is lower than that of China. The best fitted semivariogram model of SOC density was exponential model, and the spatial autocorrelation was medium. The contour map based on Kriging indicated that northeastern Guangxi had high SOC density and northwestern Guangxi had low SOC density, which corresponded to high SOC density in non-karst region and low SOC density in karst region. The SOC density followed the sequence of bamboo forest > deciduous broadleaf forest > warm coniferous forest > mixed evergreen and deciduous broadleaf forest > evergreen broadleaf forest, and yellow soil > red soil > lateritic red soil > limestone soil. The dominant environment factors affecting SOC density included soil depth, longitude, latitude, and altitude. Soil depth was the most influential factor, which was mainly attributed to the karst landscape.     

A comparative assessment of support vector regression,artificial neural networks,and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape

Soil organic carbon (SOC) is a key indicator of ecosystem health, with a great potential to affect climate change. This study aimed to develop, evaluate, and compare the performance of support vector regression (SVR), artificial neural network (ANN), and random forest (RF) models in predicting and mapping SOC stocks in the Eastern Mau Forest Reserve, Kenya. Auxiliary data, including soil sampling, climatic, topographic, and remotely-sensed data were used for model calibration. The calibrated models were applied to create prediction maps of SOC stocks that were validated using independent testing data. The results showed that the models overestimated SOC stocks. Random forest model with a mean error (ME) of −6.5 Mg C ha⁻¹ had the highest tendency for overestimation, while SVR model with an ME of −4.4 Mg C ha⁻¹ had the lowest tendency. Support vector regression model also had the lowest root mean squared error (RMSE) and the highest R² values (14.9 Mg C ha⁻¹ and 0.6, respectively); hence, it was the best method to predict SOC stocks. Artificial neural network predictions followed closely with RMSE, ME, and R² values of 15.5, −4.7, and 0.6, respectively. The three prediction maps broadly depicted similar spatial patterns of SOC stocks, with an increasing gradient of SOC stocks from east to west. The highest stocks were on the forest-dominated western and north-western parts, while the lowest stocks were on the cropland-dominated eastern part. The most important variable for explaining the observed spatial patterns of SOC stocks was total nitrogen concentration. Based on the close performance of SVR and ANN models, we proposed that both models should be calibrated, and then the best result applied for spatial prediction of target soil properties in other contexts.     

全球和生物群系尺度土壤有机碳分布控制因子及其分异规律

土壤有机碳(SOC)是陆地生态系统碳库的核心组成部分,其动态平衡受气候、土壤、植被、地形及人类活动等的影响,但在不同的空间尺度上,这些影响因素的相对重要性和差异还不明确。为阐明不同尺度和不同土层深度土壤有机碳密度(SOCd,kg/m³土壤)的环境影响因子差异,选用全球113571个土壤剖面SOCd测量数据以及38个环境协变量数据,利用数据挖掘方法,分析了全球尺度和生物群系尺度不同土层深度SOCd的控制因子,并量化了空间自相关对相关结果的影响。研究结果表明:仅空间自相关就能解释全球尺度不同土壤深度13%-20%的SOCd空间变异,但是随土壤深度的增加,空间自相关的解释率降低。在剔除空间自相关的影响后,分析结果表明:全球尺度上,气候因素对SOCd空间变异的解释率最高,但只能解释17%-20%,这种解释率在不同土层之间没有显著差异。在生物群系尺度上,除北方森林地区,气候因素能够解释SOCd空间变异的24%-37%;而在北方森林地区,地形是影响SOCd空间变异的重要因素,对SOCd的解释率为21%-43%。这些结果表明,SOCd的控制因子在不同的尺度上明显不同。无论是在全球尺度上,还是生物群系尺度上,如果不考虑空间自相关,地形的影响会被低估,其他环境因素的影响被严重高估。为了准确计算全球与生物群系尺度上各土层SOCd分布的控制因子及其分异情况,空间自相关必须被考虑。     

Soil carbon stocks and changes after oil palm introduction in the Brazilian Amazon

As oil palm has been considered one of the most favorable oilseeds for biodiesel production in Brazil, it is important to understand how cultivation of this perennial crop will affect the dynamics of soil organic carbon (SOC) in the long term. The aim of this study was to evaluate the changes in soil C stocks after the conversion of forest and pasture into oil palm production in the Amazon Region. Soil samples were collected in March 2008 and September 2009 in five areas: native forest (NARF), pasture cultivated for 55 years (PAST), and oil palm cultivated for 4 (OP‐4), 8 (OP‐8) and 25 years (OP‐25), respectively. Soils were sampled in March 2008 to evaluate the spatial variability of SOC and nitrogen (N) contents in relation to the spacing between trees. In September 2009, soils were sampled to evaluate the soil C stocks in the avenues (inter rows) and frond piles, and to compare the total C stocks with natural forest and pasture system. Soil C contents were 22–38% higher in the area nearest the oil palm base (0.6 m) than the average across the inter row (0–4.5 m from the tree), indicating that the increment in soil organic matter (SOM) must have been largely derived from root material. The soil C stocks under palm frond piles were 9–26% higher than in the inter rows, due to inputs of SOM by pruned palm fronds. The soil carbon stocks in oil palm areas, after adjustments for differences in bulk density and clay content across treatments, were 35–46% lower than pasture soil C stocks, but were 0–18% higher than the native forest soil C content. The results found here may be used to improve the life cycle assessment of biodiesel derived from palm oil.     

Soil carbon stocks,deforestation and land‐cover changes in the Western Ghats biodiversity hotspot (India)

Habitat loss and soil organic carbon (SOC) stock variations linked to land‐cover change were estimated over two decades in the most densely populated biodiversity hotspot in the world, in order to assess the possible influence of conservation practices on the protection of SOC. For a study area of 88 484 km², 70% of which lie inside the Western Ghats Biodiversity Hotspot (WGBH), land‐cover maps for two dates (1977, 1999) were built from various data sources including remote sensing images and ecological forest maps. SOC stocks were calculated from climatic parameters, altitude, physiography, rock type, soil type and land‐cover, with a modelling approach used in predictive learning and based on Multiple Additive Regression Tree. The model was trained on 361 soil profiles data, and applied to estimate SOC stocks from predictor variables using a Geographical Information System (GIS). Comparison of 1977 and 1999 land‐cover maps showed 628 km² of dense forests habitat loss (6%), corresponding to an annual deforestation rate of 0.44%. This was found consistent with other studies carried out in other parts of the WGBH, but not with FAO figures showing an increase in forest area. This could be explained by the different forest definitions used, based on ecological classification in the former, and on percentage tree cover in the latter. Unexpectedly, our results showed that despite ongoing deforestation, overall SOC stock was maintained (～0.43 Pg). But a closer examination of spatial differences showed that soil carbon losses in deforested areas were compensated by sequestration elsewhere, mainly in recent plantations and newly irrigated croplands. This suggests that more carbon sequestration in soils could be achieved in the future through appropriate wasteland management. It is also expected that increasing concerns about biodiversity loss will favour more conservation and reinforce the already prevailing protective measures, thus further maintaining C stocks.