气候和土壤质地对土壤有机碳影响的区域差异研究

The agricultural soil carbon pool plays an important role in mitigating greenhouse gas emission ana unaerstanamg the son orgamc carbon-climate-soil texture relationship is of great significance for estimating cropland soil carbon pool responses to climate change. Using data from 900 soil profiles, obtained from the Second National Soil Survey of China, we investigated the soil organic carbon （SOC） depth distribution in relation to climate and soil texture under various climate regimes of the cold northeast region （NER） and the warmer Huang-Huai-Hai region （HHHR） of China. The results demonstrated that the SOC content was higher in NER than in HHHR. For both regions, the SOC content at all soil depths had significant negative relationships with mean annual temperature （MAT）, but was related to mean annual precipitation （MAP） just at the surface 0-20 cm. The climate effect on SOC content was more pronounced in NER than in HHHR. Regional differences in the effect of soil texture on SOC content were not found. However, the dominant texture factors were different. The effect of sand content on SOC was more pronounced than that of clay content in NER. Conversely, the effect of clay on SOC was more pronounced than sand in HHHR. Climate and soil texture jointly explained the greatest SOC variability of 49.0% （0-20 cm） and 33.5% （20-30 cm） in NER and HHHR, respectively. Moreover, regional differences occurred in the importance of climate vs. soil texture in explaining SOC variability. In NER, the SOC content of the shallow layers （0-30 cm） was mainly determined by climate factor, specifically MAT, but the SOC content of the deeper soil layers （30-100 cm） was more affected by texture factor, specifically sand content. In HHHR, all the SOC variability in all soil layers was predominantly best explained by clay content. Therefore, when temperature was colder, the climate effect became stronger and this trend was restricted by soil depth. The regional differences and soil depth influence underscored the importance of explicitly considering them in modeling long-term soil responses to climate change and predicting potential soil carbon sequestration.     

内蒙古草原碳、氮储量:气候与土壤质地的关系

Understanding the spatial variability of soil carbon(C) storage and its relationship with climate and soil texture is critical for developing regional C models and for predicting the potential impact of climate change on soil C storage. On the basis of soil data from a transect across the Inner Mongolian grasslands, we determined the quantitative relationships of C and nitrogen(N) in bulk soil and particle-size fractions(sand, silt, and clay) with climate and soil texture to evaluate the major factors controlling soil C and N storage and to predict the effect of climate changes on soil C and N storage. The contents of C and N in the bulk soil and the different fractions in the 0–20 and 20–40 cm soil layers were positively correlated with the mean annual precipitation(MAP) and negatively correlated with the mean annual temperature(MAT). The responses of C storage in the soil and particle-size fractions to MAP and MAT were more sensitive in the 0–20 cm than in the 20–40 cm soil layer. Although MAP and MAT were both important factors influencing soil C storage, the models that include only MAP could well explain the variation in soil C storage in the Inner Mongolian grasslands. Because of the high correlation between MAP and MAT in the region, the models including MAT did not significantly enhance the model precision. Moreover, the contribution of the fine fraction(silt and clay) to the variation in soil C storage was rather small because of the very low fine fraction content in the Inner Mongolian grasslands.     

土壤持水特性参数与土壤质地尺度关系的联合多重分形分析

Soil water-retention characteristics at measurement scales are generally different from those at application scales, and there is scale disparity between them and soil physical properties. The relationships between two water-retention parameters, the scaling parameter related to the inverse of the air-entry pressure (αvG, cm-1) and the curve shape factor related to soil pore-size distribution (n) of the van Genuchten water-retention equation, and soil texture (sand, silt, and clay contents) were examined at multiple scales. One hundred twenty-eight undisturbed soil samples were collected from a 640-m transect located in Fuxin, China. Soil water-retention curves were measured and the van Genuchten parameters were obtained by curve fitting. The relationships between the two parameters and soil texture at the observed scale and at multiple scales were evaluated using Pearson correlation and joint multifractal analyses, respectively. The results of Pearson correlation analysis showed that the parameter αvG was significantly correlated with sand, silt, and clay contents at the observed scale. Joint multifractal analyses, however, indicated that the parameter αvG was not correlated with silt and sand contents at multiple scales. The parameter n was positively correlated with clay content at multiple scales. Sand content was significantly correlated with the parameter n at the observed scale but not at multiple scales. Clay contents were strongly correlated to both water-retention parameters because clay content was relatively low in the soil studied, indicating that water retention was dominated by clay content in the field of this study at all scales. These suggested that multiple-scale analyses were necessary to fully grasp the spatial variability of soil water-retention characteristics.     

松嫩平原土壤有机碳空间分异

Soil organic carbon (SOC) and its relationship with landscape attributes are important for evaluating current regional, continental, and global carbon stores. Data of SOC in surface soils (0–20 cm) of four main soils, Cambisol, Arenosol, Phaeozem, and Chernozem, were collected at 451 locations in Nongan County under maize monoculture in the Song-Nen Plain, Northeast China. The spatial characteristics of soil organic carbon were studied, using geographic information systems (GIS) and geostatistics. Effects of other soil physical and chemical properties, elevation, slope, and soil type on SOC were explored. SOC concentrations followed a normal distribution, with an arithmetic mean of 14.91 g kg-1 . The experimental variogram of SOC was fitted with a spherical model. There were significant correlations between soil organic carbon and bulk density (r =-0.374**), pH (r = 0.549**), total nitrogen (r = 0.781**), extractable phosphorus (r =-0.109*), exchangeable potassium (r = 0.565**), and cation exchange capacity (r = 0.313**). Generally, lower SOC concentrations were significantly associated with high elevation (r =-0.429**). Soil organic carbon was significantly negatively correlated with slope gradient (r =-0.195**). Samples of the Cambisol statistically had the highest SOC concentrations, and samples of the Arenosol had the lowest SOC value.     

Spatial Characteristics of Soil Organic Carbon Storage in China＇s Croplands

The soil organic carbon （SOC） pool is the largest component of terrestrial carbon pools. With the construction of a geographically referenced database taken from the second national general soil survey materials and based on 1546 typical cropland soil profiles, the paddy field and dryland SOC storage among six regions of China were systematically quantified to characterize the spatial pattern of cropland SOC storage in China and to examine the relationship between mean annual temperature, precipitation, soil texture features and SOC content. In all regions, paddy soils had higher SOC storage than dryland soils, and cropland SOC content was the highest in Southwest China. Climate controlled the spatial distribution of SOC in both paddy and dryland soils, with SOC storage increasing with increasing precipitation and decreasing with increasing temperature.     

美国路易斯安那州土壤碳估算的尺度效应——美国STATSGO和SSURGO两大最新土壤数据库的比较研究

Estimation of soil organic carbon (SOC) pools and fluxes bears large uncertainties because SOC stocks vary greatly over geographical space and through time.Although development of the U.S.Soil Survey Geographic Database (SSURGO),currently the most detailed level with a map scale ranging from 1:12 000 to 1:63 360,has involved substantial government funds and coordinated network efforts,very few studies have utilized it for soil carbon assessment at the large landscape scale.The objectives of this study were to 1) compare estimates in soil organic matter among SSURGO,the State Soil Geographic Database (STATSGO),and referenced field measurements at the soil map unit;2) examine the influence of missing data on SOC estimation by SSURGO and STATSGO;3) quantify spatial differences in SOC estimation between SSURGO and STATSGO,specifically for the state of Louisiana;and 4) assess scale effects on soil organic carbon density (SOCD) estimates from a soil map unit to a watershed and a river basin scale.SOC was estimated using soil attributes of SSURGO and STATSGO including soil organic matter (SOM) content,soil layer depth,and bulk density.Paired t-test,correlation,and regression analyses were performed to investigate various relations of SOC and SOM among the datasets.There were positive relations of SOC estimates between SSURGO and STATSGO at the soil map unit (R2=0.56,n=86,t=1.65,P=0.102;depth:30 cm).However,the SOC estimated by STATSGO were 9%,33% and 36% lower for the upper 30-cm,the upper 1-m,and the maximal depth (up to 2.75 m) soils,respectively,than those from SSURGO.The difference tended to increase as the spatial scale changes from the soil map unit to the watershed and river basin scales.Compared with the referenced field measurements,the estimates in SOM by SSURGO showed a closer match than those of STATSGO,indicating that the former was more accurate than the latter in SOC estimation,both in spatial and temporal resolutions.Further applications of SSURGO in SOC estimation for the entire United States could improve the accuracy of soil carbon accounting in regional and national carbon balances.     

黄土高原小流域土壤有机碳空间变异性研究

Soil organic carbon (SOC) has great impacts on global warming, land degradation and food security. Classic statistical and geostatistical methods were used to characterize and compare the spatial heterogeneity of SOC and related factors, such as topography, soil type and land use, in the Liudaogou watershed on the Loess Plateau of North China. SOC concentrations followed a log-normal distribution with an arithmetic and geometric means of 23.4 and 21.3 g kg^-1, respectively, were moderately variable (CV = 75.9%), and demonstrated a moderate spatial dependence according to the nugget ratio (34.7%). The experimental variogram of SOC was best-fitted by a spherical model, after the spatial outliers had been detected and subsequently eliminated. Lower SOC concentrations were associated with higher elevations. Warp soils and farmland had the highest SOC concentrations, while aeolian sand soil and shrublands had the lowest SOC values. The geostatistical characteristics of SOC for the different soil and land use types were different. These patterns were closely related to the spatial structure of topography, and soil and land use types.     

Land Use and Soil Organic Carbon in China’s Village Landscapes

Village landscapes, which integrate small-scale agriculture with housing, forestry, and a host of other land use practices, cover more than 2 million square kilometers across China. Village lands tend to be managed at very fine spatial scales (≤ 30 m), with managers both adapting their practices to existing variation in soils and terrain (e.g., fertile plains vs. infertile slopes) and also altering soil fertility and even terrain by terracing, irrigation, fertilizing, and other land use practices. Relationships between fine-scale land management patterns and soil organic carbon (SOC) in the top 30 cm of village soils were studied by sampling soils within fine-scale landscape features using a regionally weighted landscape sampling design across five environmentally distinct sites in China. SOC stocks across China’s village regions (5 Pg C in the top 30 cm of 2 × 10 6 km 2 ) represent roughly 4% of the total SOC stocks in global croplands. Although macroclimate varied from temperate to tropical in this study, SOC density did not vary significantly with climate, though it was negatively correlated with regional mean elevation. The highest SOC densities within landscapes were found in agricultural lands, especially paddy, the lowest SOC densities were found in nonproductive lands, and forest lands tended toward moderate SOC densities. Due to the high SOC densities of agricultural lands and their predominance in village landscapes, most village SOC was found in agricultural land, except in the tropical hilly region, where forestry accounted for about 45% of the SOC stocks. A surprisingly large portion of village SOC was associated with built structures and with the disturbed lands surrounding these structures, ranging from 18% in the North China Plain to about 9% in the tropical hilly region. These results confirmed that local land use practices, combined with local and regional variation in terrain, were associated with most of the SOC variation within and across China’s village landscapes and may be an important cause of regional variation in SOC.     

中国土壤氮含量、空间格局及其环境控制

Soil holds the largest nitrogen （N） pool in terrestrial ecosystems, but estimates of soil N stock remain controversial. Storage and spatial distribution of soil N in China were estimated and the relationships between soil N density and environmental factors were explored using data from China＇s Second National Soil Survey and field investigation in northwest China and the Tibetan Plateau. China＇s soil N storage at a depth of one meter was estimated at 7.4 Pg, with an average density of 0.84 kg m^-2. Soil N density appeared to be high in southwest and northeast China and low in the middle areas of the country. Soil N density increased from the arid to semi-arid zone in northern China, and decreased from cold-temperate to tropical zone in the eastern part of the country. An analysis of general linear model suggested that climate and vegetation determined the spatial pattern of soil N density for natural vegetation, which explained 75.4% of the total variance.     

温带沙漠土壤空间分布的尺度依赖性研究

Spatial variation is a ubiquitous feature of natural ecosystems, especially in arid regions, and is often present at various scales in these regions. To determine the scale dependence of the heterogeneity of soil chemical properties and the dominant scales （factors） for soil heterogeneity in arid regions, the spatial variability of soil resources was investigated in the Gurbantunggut Desert of Central Asia at the scales of 10-3, 10-2, 10-1, 100, 101, 102, 103 and 104 m （from individual plant to population or community to ecosystem）. Soil chemical properties including pH, electrical conductivity （EC）, organic carbon, total nitrogen, available nitrogen, total phosphorus, and available phosphorus were considered in the investigation. At a scale of 10-1 m, which represented the scale of individual plant, significant enrichment of soil resources occurred under shrub canopy and ＂fertile islands＂ formed in the desert ecosystem. Soil EC exhibited the largest heterogeneity at this scale, indicating that individual plants exerted a great influence on soil salinity/alkalinity. Soil nutrients exhibited the greatest heterogeneity at a scale of 102 m, which represented the scale of sand dune/interdune lowlands （between communities）. The main important factors contributing to soil spatial heterogeneity in the Gurbantunggut Desert were individual plants and different topographic characteristics, namely, the appearance of vegetation, especially shrubs or small trees, and existing sand dunes. Soil salinity/alkalinity and soil nutrient status behaved differently in spatial heterogeneity, with an inverse distribution between them at the individual scale.     

气候因子对森林土壤有机碳影响的幅度效应研究

揭示不同幅度上气候因子对土壤有机碳(Soil organic carbon,SOC)影响的主控性变化,是预测未来气候变化对SOC演变趋势影响的基础。本文利用中国西南地区363个森林土壤剖面数据,基于大区、省和地级市3个幅度,研究了气候因子对森林SOC密度的影响随幅度变化的规律及不同幅度下的主控气候因子。结果表明,年均降水量与SOC密度的相关性均随着幅度的减小而减弱,而年均气温与SOC密度的相关性随幅度变化的规律不明显,有较强的区域差异。大区幅度上,SOC密度主要受年均降水量和年均气温的综合作用。省级幅度上,西藏自治区东部主控因子为年均降水量,而四川和云南两省为年均气温。地级市幅度上,各市的主控因子基本与其所属的省一致。气候因子对SOC密度变异的解释能力在大区幅度上约20%,且随着幅度的减小解释能力也逐渐减小。     

Scale effect of climate on soil organic carbon in the Uplands of Northeast China

Purpose  

Climate factors, considered significant factors in regulating soil organic carbon (SOC), are not equally important at all spatial scales. However, the scale which provides the optimal relationship between climate and SOC and how that relationship varies at multiple scales are still unclear. Thus, it is crucial to study the relationship between climate factors and SOC at multiple scales when attempting to accurately predict the SOC pool and evaluate the influence of climate change on global carbon cycling. The objective of this research is to examine the scale effect of climate factors on SOC content in the Uplands of Northeast China.     

GIS空间插值模拟法与土壤类型法估算比较土壤碳储

为研究GIS空间插值模拟与土壤类型法估算土壤有机碳(soil organic carbon,SOC)储量的适用性是否一致,该文以山东省3个典型县为例,通过实地取样,采用GIS空间插值模拟和土壤类型法计算0~20 cm SOC储量以及分析土壤有机碳密度(soil organic C density,SCD)空间分布,比较GIS方法与土壤类型法计算县域尺度C储量的差异,验证GIS空间插值模拟的适用性。结果表明:1)依据GIS空间插值和土壤类型法获得的3个典型县0~20 cm土层SOC储量分别为:平邑3.88、3.93 Tg,莱阳3.54、3.57 Tg,禹城2.78、2.86 Tg;估算的平均SCD为:平邑2.2、2.23 kg/m2,莱阳2.08、2.1 kg/m2,禹城2.74、2.82 kg/m2;2)在满足一定采样量的条件下,两种方法在计算县域尺度上C储量时,结果基本一致,但GIS空间插值模拟与土壤类型法相比,更能突显SCD空间分布特征及空间递变规律,更利于分析不同因素对SCD空间分布的影响。该文可为缺失土壤类型分类或土地变更频率高区域的C储量计算提供依据。     

冲积平原区土壤碳密度估算及其空间分布

冲积平原区通常具有复杂的剖面质地层次排列,为了准确估算冲积平原区土壤碳密度的空间分布特征,该文在华北冲积平原区的河北曲周县选取了121个土壤剖面,测定了各土层有机碳含量,构建了基于负指数函数的土壤有机碳垂向分布模型,结合地统计学方法绘制了该县土壤碳密度的空间分布图。结果表明,土壤有机碳含量随深度增加呈逐渐递减的趋势,各土层有机碳含量均属于中等变异程度。0~20和20~40 cm土壤有机碳空间连续性较好,它们的空间相关距离分别为14和3 km,而下层(40 cm)土壤有机碳均表现为纯块金效应结构。土壤有机碳垂向分布模型可以很好地描述剖面土壤有机碳含量的变化特征,且预测与实测的土壤有机碳含量的均方根误差仅为0.70 kg/m3,决定系数达到了0.95。曲周县土壤有机碳密度的空间分布总体表现为西北高东南低的趋势。其空间分布主要受土壤类型和质地的影响,其中潮土和盐化潮土的碳密度明显高于褐土化潮土,质地较细的土壤(轻壤、中壤和粘土)碳密度明显高于质地较粗的土壤(砂土和砂壤)。该研究为冲积平原区土壤碳密度的估算提供了一种新的方法。     

结合多等级代表性采样和模糊隶属度的区域土壤制图方法

High-resolution and detailed regional soil spatial distribution information is increasingly needed for ecological modeling and land resource management. For areas with no point data, regional soil mapping includes two steps: soil sampling and soil mapping. Because sampling over a large area is costly, efficient sampling strategies are required. A multi-grade representative sampling strategy, which designs a small number of representative samples with different representative grades to depict soil spatial variations at different scales,could be a potentially efficient sampling strategy for regional soil mapping. Additionally, a suitable soil mapping approach is needed to map regional soil variations based on a small number of samples. In this study, the multi-grade representative sampling strategy was applied and a fuzzy membership-weighted soil mapping approach was developed to map soil sand percentage and soil organic carbon(SOC) at 0–20 and 20–40 cm depths in a study area of 5 900 km2 in Anhui Province of China. First, geographical sub-areas were delineated using a parent lithology data layer. Next, fuzzy c-means clustering was applied to two climate and four terrain variables in each stratum. The clustering results(environmental cluster chains) were used to locate representative samples. Evaluations based on an independent validation sample set showed that the addition of samples with lower representativeness generally led to a decrease of root mean square error(RMSE). The declining rates of RMSE with the addition of samples slowed down for 20–40 cm depth, but fluctuated for 0–20 cm depth. The predicted SOC maps based on the representative samples exhibited higher accuracy, especially for soil depth 20–40 cm, as compared to those based on legacy soil data. Multi-grade representative sampling could be an effective sampling strategy at a regional scale. This sampling strategy, combined with the fuzzy membership-based mapping approach, could be an optional effective framework for regional soil property mapping. A more detailed and accurate soil parent material map and the addition of environmental variables representing human activities would improve mapping accuracy.     

近30 a玛纳斯县北部土壤有机碳储量变化

研究玛纳斯县北部土壤有机碳时空变异特征,可以为当地土壤肥力管理提供理论依据。本文采用地统计学和GIS相结合的方法,研究了玛纳斯县北部地区1980-2011年间土壤有机碳的时空变异特征。研究结果表明:研究区32a来1m深土体土壤有机碳密度和储量呈现增加的趋势,分别较1980年二次土壤普查时增加1.81kg/m2和7.7×106kg;2011年0～20、>20～60和>60～100cm土壤有机碳质量分数平均值为5.74、4.44和2.17g/kg;0～20cm和>20～60cm土壤有机碳含量符合正态分布特征,相应土壤有机碳变异函数理论模型分别符合指数和球状模型;0～20cm土壤有机碳和>20～60cm土壤有机碳均具有中等程度的空间变异性,土壤有机碳的空间分布受土壤母质、地形等结构因素和耕作、施肥等随机因素的共同影响并呈现出南部和东北部高,中部地区偏低的分布特征;>60～100cm土壤有机碳呈现出南部高北部低的空间分布特征。本文获取了玛纳斯县北部地区土壤有机碳时空变异特征,该结果对研究区域土壤肥力管理具有重要意义。     

Estimating effective soil depth at regional scales: Legacy maps versus environmental covariates

Soil depth reflects the quantity and ecosystem service functions of soil resources. However, there is no universal standard to measure soil depth at present, and digital soil mapping approaches for predicting soil depth at the regional scale remain immature. Using observation of soil profile morphology, we compared the soil depth nomenclatures from the World Reference Base for Soil Resources, Chinese Soil Taxonomy, and Soil Taxonomy. For this study, shallow soils were defined as those with an effective soil depth < 100 cm. Based on legacy data and field soil survey, the spatial distribution of shallow soils in Xinjiang, China, and the main controlling environmental factors were explored. Results showed that shallow soils in Xinjiang are mainly distributed in high altitude regions such as the Tian Mountains. At the regional scale, significant correlations were observed between soil depth and climate factors, as well as between soil depth and vegetation fractional coverage. Contrary to previous conclusions at small spatial scales, terrain attributes could not explain soil depth variation at the regional scale. This study addressed knowledge gaps on soil depth prediction at regional scales while elucidating climate‐vegetation‐soil coevolution.     

省域土壤有机碳空间分布的主控因子——土壤类型与土地利用比较

基于河北省第二次全国土壤普查数据,运用方差分析和回归分析对比了河北省土壤类型和一级土地利用类型对0～20 cm深土壤有机碳空间分布的影响,探讨了省域土壤有机碳空间分布的主控因子。研究结果表明,土壤类型和土地利用是河北省表层土壤有机碳密度空间分布的重要影响因子。其中土壤类型对土壤有机碳密度空间分布的影响与土壤分类级别相关,土壤分类级别越低,对土壤有机碳密度空间变异的反映能力越大。与土壤类型相比,土地利用对表层土壤有机碳密度空间分异的解释能力要大于土类,但小于亚类和土属。为此,在省域尺度对土壤有机碳密度进行区域预测和估算时应将土地利用和土壤类型结合起来作为土壤有机碳空间分布的主控因子,优先考虑土地利用后,在相同土地利用类型内再尽量以低级土壤分类进行空间预测或估算。     

Effect of sampling density on regional soil organic carbon estimation for cultivated soils

An extensive knowledge of how sampling density affects soil organic C (SOC) estimation at regional scale is imperative to reduce uncertainty to a meaningful confidence level and aid in the development of sampling schemes that are both rational and economical. Using kriging prediction, this paper examined the effect of sampling density on regional SOC‐concentration estimations in cultivated topsoils at six scales in a 990 km² area of Yucheng County, a typical region in the N China Plain. Except the original data set (n = 394), five other sampling densities were recalculated using grids of 8 km × 8 km (n = 28), 8 km × 4 km (n = 44), 4 km × 4 km (n = 82), 4 km × 2 km (n = 142), and 2 km × 2 km (n = 257), respectively. Experimental SOC semivariances and kriging interpolations at six sampling density scales were calculated and modeled to estimate regional SOC variability. Accuracy of the effects of the five sampling densities on regional SOC estimations was assessed using the indices of mean error (ME) and root mean square error (RMSE) with 100 independent validation samples. By comparison with the kriged grid map derived from the 394 samples data set, the relative error (RE,%) was spatially calculated to highlight the spatial variability of prediction errors at five sampling‐density scales due to the intrinsic limitations of ME and RMSE in accuracy assessment. The results indicated that sampling density significantly affected the estimation of regional SOC concentration. Particularly when the sampling density was < 4 km × 4 km, the large spatial variation of SOC was concealed. Semivariance analysis indicated that different sampling density had significant effect on reasonable detection of the dominant factors which influenced SOC spatial variation. Greater sampling density could more exactly reveal regional SOC variation caused by human management. The prediction accuracy for regional SOC estimation increased with the increasing of sampling density. The critical areas with larger RE values should be intensified in the future sampling scheme, and the areas of lower RE values should be decreased relatively. A specific sampling scheme should be considered in accordance with the demand to the estimation accuracy of regional SOC stock at a certain confidence level. Our results will facilitate a better understanding of the effect of sampling density on regional SOC estimation for future sampling schemes by providing meaningful confidence levels.