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1.
The priming effect (PE) plays a critical role in the control of soil carbon (C) cycling and influences the alteration of soil organic C (SOC) decomposition by fresh C input.However,drivers of PE for the fast and slow SOC pools remain unclear because of the varying results from individual studies.Using meta-analysis in combination with boosted regression tree (BRT) analysis,we evaluated the relative contribution of multiple drivers of PE with substrate and their patterns across each driver gradient.The results showed that the variability of PE was larger for the fast SOC pool than for the slow SOC pool.Based on the BRT analysis,67%and 34%of the variation in PE were explained for the fast and slow SOC pools,respectively.There were seven determinants of PE for the fast SOC pool,with soil total nitrogen (N) content being the most important,followed by,in a descending order,substrate C:N ratio,soil moisture,soil clay content,soil pH,substrate addition rate,and SOC content.The directions of PE were negative when soil total N content and substrate C:N ratio were below 2 g kg~(-1)and 20,respectively,but the directions changed from negative to positive with increasing levels of this two factors.Soils with optimal water content (50%–70%of the water-holding capacity) or moderately low pH (5–6) were prone to producing a greater PE.For the slow SOC pool,soil p H and soil total N content substantially explained the variation in PE.The magnitude of PE was likely to decrease with increasing soil pH for the slow SOC pool.In addition,the magnitude of PE slightly fluctuated with soil N content for the slow SOC pool.Overall,this meta-analysis provided new insights into the distinctive PEs for different SOC pools and indicated knowledge gaps between PE and its regulating factors for the slow SOC pool.  相似文献   

2.
基于环境变量的中国土壤有机碳空间分布特征   总被引：3，自引：0，他引：3  
研究中国土壤有机碳(Soil Organic Carbon,SOC)的空间分布特征对SOC储量估算以及农业生产管理具有重要意义。以全国第二次土壤普查2473个土壤典型剖面的表层(A层)SOC含量为研究对象,探寻地形、气候和植被等环境因素对SOC空间异质性分布的影响;以普通克里格法为对照,利用地理加权回归、地理加权回归克里格、多元线性回归和回归克里格模型建立SOC空间预测模型;并分别绘制了中国SOC的空间分布预测图。结果表明:(1)SOC含量与年均降水量、年均温、归一化植被指数、高程以及地形粗糙指数呈极显著相关关系;(2)平均绝对估计误差、均方根误差、平均相对误差和皮尔逊相关系数等模型验证指标表明地理加权回归的预测精度优于其他模型,可以更好地绘制SOC在大尺度上的空间分布特征;(3)较高SOC含量主要分布在研究区东北部、西南部以及东南部,而西北部SOC含量普遍偏低。本文以期从大尺度上探讨土壤属性与环境变量之间的相关关系,为全国土壤属性的空间制图提供一定的解决方案和思路。  相似文献   

3.
基于GIS的亚热带典型地区土壤有机碳空间分布预测   总被引：19，自引：4，他引：19  
Spatial distribution of organic carbon in soils is difficult to estimate because of inherent spatial variability and insufficient data. A soil-landscape model for a region, based on 151 samples for parent material and topographic factors, was established using a GIS spatial analysis technique and a digital elevation model (DEM) to reveal spatial distribution characteristics of soil organic carbon (SOC). Correlations between organic carbon and topographic factors were analyzed and a regression model was established to predict SOC content. Results for surface soils (0-20 cm) showed that the average SOC content was 12.8 g kg-1, with the SOC content between 6 and 12 g kg-1 occupying the largest area and SOC over 24 g kg-1 the smallest. Also, soils derived from phyllite were the highest in the SOC content and area, while soils developed on purple shale the lowest. Although parent material, elevation, and slope exposure were all significant topographic variables (P < 0.01), slope exposure had the highest correlation to SOC content (r = 0.66). Using a multiple regression model (R2 = 0.611) and DEM (with a 30 m × 30 m grid), spatial distribution of SOC could be forecasted.  相似文献   

4.
As interest in soil organic carbon (SOC) dynamics increases, so do needs for rapid, accurate, and inexpensive methods for quantifying SOC. Objectives were to i) evaluate near infrared reflectance (NIR) spectroscopy potential to determine SOC and soil organic matter (SOM) in soils from across Tennessee, USA; and ii) evaluate potential upper limits of SOC from forest, pasture, no-tillage, and conventional tilled sites. Samples were analyzed via dry-combustion (SOC), Walkley–Black chemical SOM, and NIR. In addition, the sample particle size was classified to give five surface roughness levels to determine effects of particle size on NIR. Partial least squares regression was used to develop a model for predicting SOC as measured by NIR by comparing against SOM and SOC. Both NIR and SOM correlated well (R2 > 0.9) with SOC (combustion). NIR is therefore considered a sufficiently accurate method for quantifying SOC in soils of Tennessee, with pasture and forested systems having the greatest accumulations.Abbreviations SOC, soil organic carbon; NIR, Near Infrared Reflectance Spectroscopy; MTREC, Middle Tennessee Research and Education Center; RECM, Research and Education Center at Milan; PREC, Plateau Research and Education Center; PLS, Partial least squares.  相似文献   

5.
贾豪  严宁珍  程永毅  刘洪斌 《核农学报》2019,33(6):1256-1263
为评价区域农田土壤肥力及优化农业生产管理措施,选取渝东南地区黔江区为研究区域,基于重庆市测土配方施肥的615个表层(0~20 cm)土样数据,运用地统计学和地理信息系统(GIS)相结合的方法分析黔江区土壤有机碳(SOC)的空间分布特征及其影响因素。结果表明,研究区土壤表层SOC含量为13.27 g·kg-1,变异系数为31.44%,具有中等程度的空间变异且空间自相关范围较大。块金效应为45.59%,空间分布受结构性因素和随机性因素的共同影响。研究区SOC分布呈斑块状,总体表现为东高西低。方差分析和回归分析表明,成土母质、土壤类型、土地利用方式及坡度、坡向对SOC的空间分布的影响极显著(P<0.01),土壤质地、海拔高度的影响显著(P<0.05)。随着海拔的增加,土壤中SOC含量也逐渐增加。而随着坡度增加,土壤中SOC含量呈先降低后增加的趋势。本研究结果为渝东南农田SOC管理及农作物合理施肥提供了理论依据。  相似文献   

6.
David J. Brown   《Geoderma》2007,140(4):444-453
Combining global soil-spectral libraries with local calibration samples has the potential to provide improved visible and near-infrared (VNIR, 400–2500 nm) diffuse reflectance spectroscopy (DRS) soil characterization predictions than with either global or local calibrations alone. In this study, a geographically diverse “global” soil-spectral library with 4184 samples was augmented with up to 418 “local” calibration soil samples distributed across a 2nd-order Ugandan watershed to predict the amount of clay-size material (CLAY), soil organic carbon (SOC) and proportion of expansible 2:1 clays (termed “montmorillonite” or MT in the global library). Stochastic gradient boosted regression trees (BRT) were employed for model construction, with a variety of calibration and validation schemes tested. Using the global library combined with 13- and 14-fold cross-validation by local profile for CLAY and SOC, respectively, yielded dambo/upland RMSD values of 89/68 g kg− 1 for CLAY (N = 429/410) and 4.2/2.6 g kg− 1 for SOC (N = 272/105). These results were obtained despite the challenge of combining spectral libraries constructed using different spectroradiometers and laboratory reference measurements (total combustion vs. Walkley–Black, hydrometer vs. pipette). Using only the global library, a VNIR-derived index of MT content was significantly correlated with the square root of X-ray diffraction (XRD) MT peak intensity for local dambo soils (r2 = 0.52, N = 59, p < 0.0001), an acceptable result given the semi-quantitative nature of the reference XRD method. Though VNIR predictions did not approach laboratory precision, for soil-landscape modeling VNIR characterization worked remarkably well for clay mineralogy, was adequate for mapping dambo “depth to 35% clay”, and was insufficiently accurate for SOC mapping.  相似文献   

7.
耕地土壤有机碳(SOC)是土壤质量的重要指标,也是生态系统健康的重要表征。当前机器学习(Machine Learning, ML)用于SOC数字制图日益热门,但不同算法在高空间分辨率SOC数字制图中的对比研究尚有欠缺。本研究以福建省东北部复杂地形地貌区为例,采用10m空间分辨率Sentinel-2影像数据,选取地形、气候、遥感植被变量为驱动因子,重点分析当前常用的机器学习算法——支持向量机(SupportVector Machine,SVM)、随机森林(RandomForest,RF)在SOC预测中的差异,并与传统普通克里格模型(Ordinary Kriging, OK)进行比较。结果表明:基于地形、遥感植被因子和气候因子构建的RF模型表现最佳(RMSE=2.004,r=0.897),其精度优于OK模型(RMSE=4.571, r=0.623),而SVM模型预测精度相对最低(RMSE=5.190, r=0.431);3种模型预测SOC空间分布趋势总体相似,表现为西高东低、北高南低,其中RF模型呈现的空间分异信息更加精细;最优模型反演得到耕地土壤有机碳平均含量为15.33 g·kg-1; RF模型和SVM模型变量重要性程度表明:高程和降水是影响复杂地貌区SOC空间分布的重要变量,而遥感植被因子重要性程度低于高程。  相似文献   

8.
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.  相似文献   

9.
X. Y. WANG  Y. ZHAO  R. HORN 《土壤圈》2010,20(1):43-54
Depth distribution of soil wettability and its correlations with vegetation type, soil texture, and pH were investigated under various land use (cropland, grassland, and forestland) and soil management systems. Wettability was evaluated by contact angle with the Wilhelmy plate method. Water repellency was likely to be present under permanently vegetated land, but less common on tilled agricultural land. It was mostly prevalent in the topsoil, especially in coarse-textured soils, and decreased in the subsoil. However, the depth dependency of wettability could not be derived from the investigated wide range of soils. The correlation and multiple regression analysis revealed that the wettability in repellent soils was affected more by soil organic carbon (SOC) than by soil texture and pH, whereas in wettable soils, soil texture and pH were more effective than SOC. Furthermore, the quality of SOC seemed to be more important in determining wettability than its quantity, as proofed by stronger hydrophobicity under coniferous than under deciduous forestland. Soil management had a minor effect on wettability if conventional and conservation tillage or different grazing intensities were considered.  相似文献   

10.
Mountainous peatlands are one of the most important terrestrial ecosystems for carbon storage and play an important role in the global carbon cycle. An insight into the carbon cycle of peat swamps located in mountainous regions can be obtained by studying the distribution of soil organic carbon (SOC) and its relationships with environmental factors. This study focused on the development conditions of peat swamps in the Gahai wetlands, located on the Zoigê Plateau, China, with four different altitudinal gradients as experimental sample sites. The distribution of SOC and its relationship with environmental factors were analysed through vegetation surveys and a generalized additive model (GAM). The results show that with increasing altitude, soil temperature decreased while the soil pH and bulk density initially decreased then increased. On the contrary, the topographic wetness index (TWI), SOC content, above-ground biomass and litter count initially increased then decreased. The SOC content of the 0–30 cm soil layer was in the range 226–330 g·kg−1 (coefficient of variation (CV) = 21.4%), and the 30–60 cm layer was 178–257 g·kg−1 (CV = 17.5%) and was significantly correlated (p < .05) with above-ground biomass and litter count. Meanwhile, the SOC content in the 60–90 cm soil layer was in the range 132–167 g·kg−1 (CV = 9.2%) with a significant correlation (p < .05) with soil temperature, pH, bulk density and topographic moisture index. The study showed that the SOC content exhibited more pronounced spatial patterns with increasing altitude, with the peak value in the shallow soil layer appearing in lower elevation areas compared with the deep soil layer. The level of variation changed from medium to low, reflecting the stable mechanism for maintaining SOC within the heterogeneous peat swamp environment.  相似文献   

11.
The effects of fire and the conversion to vineyard on soil organic carbon (SOC), and soil aggregate size distribution and stability were studied in a forest of Iran. For this purpose, topsoil was sampled in an unburned area, a portion of the forest burned three years earlier, and a vineyard, all three contiguous and showing similar topographic features. In the burned forest, soil was sampled in areas undergone high, moderate, or low severity. Air‐dried soil samples were sieved to obtain four aggregate size classes, which were subsequently wet sieved. Soil aggregate distribution index, mean weight diameter, geometric mean diameter, and aggregate stability index were determined on both dry and wet specimens. No significant differences in SOC between burned and unburned forest were found, most probably because of the supply of charred biomass to soil, while in the vineyard thirty years of cultivation had removed half of initial SOC. Both severe fire and cultivation had decreased the stability of aggregates and the relative amount of the biggest ones (8 to 2‐cm diameter). However, aggregate stability was significantly lower in the vineyard than in the burned forest, which points out to a stronger impact of prolonged cultivation than a single fire, although severe. Cultivation and severe fire had decreased the proportion of C in macroaggregates, to the advantage of meso (1 to 0.25 mm) and micro (<0.25 mm) aggregates. A hierarchical cluster analysis of all investigated properties and indices demonstrated that cultivation and highly severe fire both were causes of soil degradation. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

12.
基于不同地表曲面模型预测土壤有机碳含量   总被引：1，自引：0，他引：1  
Local terrain attributes,which are derived directly from the digital elevation model,have been widely applied in digital soil mapping.This study aimed to evaluate the mapping accuracy of soil organic carbon (SOC) concentration in 2 zones of the Heihe River in China,by combining prediction methods with local terrain attributes derived from different polynomial models.The prediction accuracy was used as a benchmark for those who may be more concerned with how accurately the variability of soil properties is modeled in practice,rather than how morphometric variables and their geomorphologic interpretations are understood and calculated.In this study,2 neighborhood types (square and circular) and 6 representative algorithms (Evans-Young,Horn,Zevenbergen-Thorne,Shary,Shi,and Florinsky algorithms) were applied.In general,35 combinations of first-and second-order derivatives were produced as candidate predictors for soil mapping using two mapping methods (i.e.,kriging with an external drift and geographically weighted regression).The results showed that appropriate local terrain attribute algorithms could better capture the spatial variation of SOC concentration in a region where soil properties are strongly influenced by the topography.Among the different combinations of first-and second-order derivatives used,there was a best combination with a more accurate estimate.For different prediction methods,the relative improvement in the two zones varied between 0.30％ and 9.68％.The SOC maps resulting from the higher-order algorithms (Zevenbergen-Thorne and Florinsky) yielded less interpolation errors.Therefore,it was concluded that the performance of predictive methods,which incorporated auxiliary variables,could be improved by attempting different terrain analysis algorithms.  相似文献   

13.
The dynamics of soil organic carbon (SOC) pools determine potential carbon sequestration and soil nutrient improvement. This study investigated the characteristics of SOC pools in five types of cultivated topsoils (0–15 cm) in subtropical China using laboratory incubation experiments under aerobic conditions. The sizes and turnover rates of the active, slow and resistant C pools were simulated using a first‐order kinetic model. The relative influence of soil environmental properties on the dynamics of different SOC pools was evaluated by applying principal component analysis (PCA) and aggregated boosted trees (ABTs) analysis. The results show that there were significantly greater sizes of different SOC pools and lower turnover rates of slow C pool in two types of paddy soils than in upland soils. Land use exerted the most significant influence on the sizes of all SOC pools, followed by clay content and soil pH. The soil C/N ratio and pH were the major determinants for turnover rates of the active and slow C pools, followed by clay content which had more impact on the turnover rates of the active C pool than the slow C pool. It is concluded that soil type exerts a significant impact on the dynamics of SOC.  相似文献   

14.
Monitoring of soil organic carbon (SOC) and pH is needed to manage soil protection and tackle possible degradation in support of, i.e, the upcoming European Soil Framework Directive. Harmonized monitoring procedures and protocols produced under the auspices of the International Organization for Standardization (ISO) and the European Committee for Standardization (CEN) will be recommended. The uncertainty contributions of sampling, sample pretreatment, and analysis in the monitoring of soil pH and organic carbon in agricultural parcels using these harmonized monitoring procedures have been studied.

A within-laboratory comparison between the different analytical methods and sample pretreatments was made on 451 soil samples for SOC and 150 samples for soil acidity. Thereafter, a field study was performed to evaluate the contribution of the sampling method. Finally, an interlaboratory trial (including sampling) was organized to assess the overall monitoring uncertainty.

The results indicate that the influence of different sample pretreatments (e.g., milling) in combination with different analytical methods (elemental combustion versus chemical oxidation) are the main contributions to the observed uncertainty in the monitoring of SOC. For the monitoring of soil acidity, a similar observation was made, showing that differences in the practical implementation of the analytical method (e.g., mechanical shaking) are the main contributions to the monitoring uncertainty. The monitoring uncertainties derived from an interlaboratory trial (including sampling) amounted to ±20% (95% confidence interval, CI) for SOC and ±0.3 pH units (95% CI) for soil acidity on an agricultural parcel.  相似文献   

15.
东北黑土漫岗区春耕期土壤水分空间变异及地形影响   总被引：1，自引：0，他引：1  
土壤水分存在强空间变异特征,在多重尺度上受地形、土壤、土地利用、植被等因素综合影响,是农业生产和耕作的关键要素。为了揭示东北黑土漫岗区春耕期农田土壤水分空间变异特征及分析地形因子对其影响,以赵光农场为研究对象,利用Sentinel-1数据反演的土壤水分和DEM数据,采用半方差函数、集成推进树算法(ABT)等方法分析了春耕期土壤水分的空间变异及地形因子(坡度、坡向、坡位、高程、地形湿度指数)对土壤水分空间异质性的相对影响,并系统分析了土壤水分在不同坡位、坡度和坡向的变化特征。结果表明:研究区2018年4月24日处于春耕时期黑土漫岗区的土壤质量含水量分布在25%～37%; 地块内部变异系数为5.81%,相邻地块间变异系数为4.16%; 针对整个农场尺度土壤水分空间变异的有效变程为3 000 m,地块尺度上有效变程为300 m。土壤水分分布与地形湿度指数呈显著正相关,与坡度、坡向、高程、坡位呈显著负相关; 坡位、坡度、坡向是影响土壤水分空间变异的主控因子,其累计相对解释率超过了70%,其中坡位占36.28%。研究结果有助于了解东北黑土漫岗区春耕期农田土壤水分空间分异规律及影响机制,对黑土漫岗区土壤水分管理、春耕春播期农机科学调度、保障粮食安全具有重要意义。  相似文献   

16.
[目的] 研究土壤有机碳（SOC）在小型丘陵山地集水区的分异规律及其影响因素,为土壤资源的可持续利用以及保护南水北调水源地提供科学依据。[方法] 基于数字高程、Landsat 8 OLI影像和2016—2018年实测土壤有机碳等数据,运用相关分析、主成分分析法等研究湖北省十堰市犟河流域表层土壤有机碳含量的时空变化,厘清其影响因子和主导因素。[结果] 犟河流域SOC含量整体呈条带状分布的格局,由东北向西南逐渐增加,呈中等强度变异。夏秋两季SOC处于流失状态,而冬季SOC含量明显增加。不同质地SOC的平均含量：石灰性冲积土 > 简育高活性淋溶土 > 不饱和雏形土。不同覆盖下SOC平均含量：农田 > 园地 > 混交林 > 针叶林 > 灌木。土壤SOC含量呈现随地表曲率绝对值增大而增大,随比值植被指数（RVI）和归一化植被指数（NDVI）增加而增加的趋势。[结论] 地形因子（地表曲率）是影响犟河流域土壤有机碳的主导因子,植被因子（NDVI和RVI）是次要因子。改变局部小地貌、增加林种、改善水肥管理等措施均可以提高流域土壤有机碳含量。  相似文献   

17.
县域土壤质量数字制图方法比较   总被引：2，自引：1，他引：2  
土壤质量研究几乎涵盖土壤研究的所有领域,土壤质量制图理论与方法是土壤质量研究的一项重要研究内容。该研究以北京市密云县为研究区,基于土壤质量评价最小数据集和指数和法计算的土壤质量指数,探究了在地学模型支持下区域土壤质量数字制图方法。研究设计了5种区域土壤质量数字制图方法,并比较了不同方法的空间数字制图精度。结果显示,目前广泛使用的基于参评指标空间插值结果的土壤质量数字制图方法精度最低、工序较繁琐,且无法反映研究区景观高度异质的特点;而基于计算后的土壤质量指数（soil quality index,SQI）,借助于地统计学方法的土壤质量数字制图方法相对比较科学合理,其中又以基于计算后的SQI和回归克里格法预测效果最好,均方根误差最小,仅为0.01897,相对于基于参评指标空间插值结果的土壤质量数字制图方法,精度相对提高率最大,达到50%以上。综合考虑空间制图精度、工序的繁简程度,在该研究设计的5种方法中基于计算的SQI和回归克里格法最佳,该法避免了地统计插值在景观高度异质区的应用局限性,预测结果与实际最为相符。  相似文献   

18.
Soil pH affects food production, pollution control and ecosystem services. Mapping soil pH levels, therefore, provides policymakers with crucial information for developing sustainable soil use and management policies. In this study, we used the LUCAS 2015 TOPSOIL data to map soil pH at a European level. We used random forest kriging (RFK) to build a predictive model of spatial variability of soil pH, as well as random forest (RF) without co-kriging and boosted regression trees (BRT) modelling techniques. Model accuracy was evaluated using a ten-fold cross-validation procedure. While we found that all methods accurately predicted soil pH, the accuracy of the RFK method was best with regression performance metrics of: R2 = 0.81 for pH (H2O) and pH (CaCl2); RMSE = 0.59 for pH (H2O) and RMSE = 0.61 in pH (CaCl2); MAE = 0.41 for pH (H2O) and MAE = 0.43 in pH (CaCl2). Dominant explanatory variables in the RF and BRT modelling were topography and remote sensing variables, respectively. The generated maps broadly depicted similar spatial patterns of soil pH, with an increasing gradient of soil pH from north to south Europe, with the highest values mainly concentrated along the Mediterranean coast. The mapping could provide spatial reference for soil pH assessment and dynamic monitoring.  相似文献   

19.
Data scarcity often prevents the estimate of regional (or national) scale soil organic carbon (SOC) stock and its spatial distribution. This study attempts to overcome the data limitations by combining two existing Irish soil databases [SoilC and national soil database (NSD)] at the national scale, to create an improved estimate of the national SOC stock. Representative regression models between the near‐surface SOC concentration and those of deeper depths, and between SOC concentration and bulk density (BD) were developed based on the SoilC database. These regression models were then applied to the NSD derived SOC concentration map, resulting in an improved SOC stock and spatial distribution map for the top 10 cm, 30 cm and 50 cm depths. Western Ireland, particularly coastal areas, was found to have higher SOC densities than eastern Ireland, corresponding to the spatial distribution of peatland. We estimated the national SOC stock at 383 ± 38 Tg for the near‐surface of 0–10 cm depth; 1016 ± 118 Tg for 0–30 cm depth; and 1474 ± 181 Tg for 0–50 cm depth.  相似文献   

20.
Abstract

Soil aggregate-size distribution and soil aggregate stability are used to characterize soil structure. Quantifying the changes of structural stability of soil is an important element in assessing soil and crop management practices. A 5-year tillage experiment consisting of no till (NT), moldboard plow (MP) and ridge tillage (RT), was used to study soil water-stable aggregate size distribution, aggregate stability and aggregate-associated soil organic carbon (SOC) at four soil depths (0–5, 5–10, 10–20 and 20–30 cm) of a clay loam soil in northeast China. Nonlinear fractal dimension (Dm) was used to characterize soil aggregate stability. No tillage led to a significantly greater aggregation for >1 mm aggregate and significant SOC changes in this fraction at 0–5 cm depth. There were significant positive relationships between SOC and >1 mm aggregate, SOC in each aggregate fraction, but there was no relationship between soil aggregate parameters (the proportion of soil aggregates, aggregate-associated SOC and soil stability) and soil bulk density. After 5 years, there was no difference in Dm of soil aggregate size distribution among tillage treatments, which suggested that Dm could not be used as an indicator to assess short-term effects of tillage practices on soil aggregation. In the short term, > 1 mm soil aggregate was a better indicator to characterize the impacts of tillage practices on quality of a Chinese Mollisol, particularly in the near-surface layer of the soil.  相似文献   

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