Characterizing Spatial Variability of Soil Textural Fractions and Fractal Parameters Derived from Particle Size Distributions

Soil particle size distribution (PSD) is a fundamental physical property affecting other soil properties. Characterizing spatial variability of soil texture is very important in environmental research. The objectives of this work were: 1) to partition PSD of 75 soil samples, collected from a flat field in the University of Guilan, Iran, into two scaling domains using a piecewise fractal model to evaluate the relationships between fractal dimensions of scaling domains and soil clay, silt, and sand fractions and 2) to assess the potential of fractal parameters as an index used in a geostatistical approach reflecting the spatial variability of soil texture. Features of PSD of soil samples were studied using fractal geometry, and geostatistical techniques were used to characterize the spatial variability of fractal and soil textural parameters. There were two scaling domains for the PSD of soil samples. The fractal dimensions of these two scaling domains (D₁ and D₂) were then used to characterize different ranges of soil particle sizes and their relationships to the soil textural parameters. There was a positive correlation between D₁ and clay content (R² = 0.924), a negative correlation between D₁ and silt content (R² = 0.801), and a negative correlation between D₂ and sand content (R² = 0.913). The geometric mean diameter of soil particles had a negative correlation with D₁ (R² = 0.569) and D₂ (R² = 0.682). Semivariograms of fractal dimensions and soil textural parameters were calculated and the maps of spatial variation of D₁ and D₂ and soil PSD parameters were provided using ordinary kriging. The results showed that there were also spatial correlations between D₁ and D₂ and particle size fractions. According to the semivariogram models and validation parameters, the fractal parameters had powerful spatial structure and could better describe the spatial variability of soil texture.     

Comparison of two models for estimating the soil particle-size distribution curve based on soil textural data

Mathematical models employing textural data are commonly used for the estimation of the particle-size distribution (PSD) curve. In this study, two models were used: (1) the model proposed by Fredlund et al. (2000) (F model), and (2) the model proposed by Skaggs et al. (2001) later modified by Fooladmand and Sepaskhah (2006) (FS model). Comparison of the results obtained by these two models, applied for 30 soils from the south of Iran, showed that the F model was better when the model parameters were determined by setting minimum differences between the measured and estimated PSD curve. Also, three equations based on soil textural data were derived for estimating parameters of the F model, and these equations were used to estimate the PSD curve. The results showed that for 10 independent soils from the south of Iran and 50 soils from the UNSODA database, the FS model was better than the F model based on three new derived equations. Therefore, the FS model can be safely used to estimate the PSD curve with different soil textures; however, the F model is appropriate for representation of the measured PSD curve.     

不同耕作年限水稻土土壤颗粒的体积分形特征研究

应用基于土壤颗粒体积分布确定土壤颗粒大小分布分形维数的方法,对浙江慈溪耕作年限分别为50、100、500、700、1000和2000年的水稻土土壤颗粒体积分形特征进行了研究.结果表明:同基于土壤颗粒质量分布的分形维数相比,基于土壤颗粒体积分布的分形维数计算不需要密度假设,更具有合理性.不同年限水稻土的土壤颗粒大小分布能被体积分形模型显著拟合(p＜0.01).土壤颗粒体积分形维数与黏粒的体积百分含量之间呈显著正相关(p＜0.01),与粉粒和砂粒的体积百分含量之间呈显著负相关(p＜0.01),其中与黏粒含量关系最为密切.耕作年限对水稻土土壤颗粒的体积分形维数和分形模型的拟合度均有一定影响.与50 ～ 500年水稻土相比,700 ～ 2000年水稻土剖面中土壤颗粒体积分形维数的变异性和均值较高,但其拟合决定系数则较低;耕作年限会对水稻土剖面中黏粒的含量和分布产生影响,进而影响到剖面土壤的颗粒体积分形特征;对于同一质地类型的土壤,耕作年限越长,分形维数越高.     

Usability of soil survey soil texture data for soil health indicator scoring

Soil textural information is an important component underlying other soil health indicators. Soil texture analysis is a common procedure, but it can be labor intensive and expensive. Soil texture data typically are available from the Soil Survey Geographic (SSURGO) database, which may be an option for determining soil health texture groups (SHTG). The SSURGO database provides soil texture information in the soil map unit (SMU) name, taxonomic class category (family), and detailed values (≤ 2 mm soil fraction) of percent sand, silt and clay by soil horizon. The objective of this study was to examine the possibility of using SSURGO data for SHTG at the 147-ha Cornell University Willsboro Research Farm in New York state as an alternative for soil texture data determined manually on collected soil core samples. Comparative results revealed that representative values for soil texture from the SSURGO database generally matched measured mean values for all SMUs.     

Uncertainty in the spatial prediction of soil texture: Comparison of regression tree and Random Forest models

Within the southern Ecuadorian Andes, landslides have an impact on landscape development. Landslide risk estimation as well as hydrological modelling requires physical soil data. Statistical models were adapted to predict the spatial distribution of soil texture from terrain parameters. For this purpose, 56 soil profiles were analysed horizon-wise by pipette and laser method. Results by pipette compared to laser method showed the expected shift to higher silt and lower clay contents. Linear regression equations were adapted. The performance of regression tree (RT) and Random Forest (RF) models was compared by hundredfold model runs on random Jackknife partitions. Digital soil maps of sand, silt and clay percentage mean and standard deviation indicate model variability and prediction uncertainty.RF models performed better than RT models. All terrain factors considered in the analysis influenced soil texture of the surface horizon, but altitude a.s.l. was assigned the highest variable importance during model construction. Shallow subsurface flow is considered responsible for increasing sand/clay ratios with increasing altitude, on steep slopes and with overland flow distance to the channel network by removing clay particles downslope. Deeper soil layers are not influenced by this process and therefore, did not show the same texture properties. However, the influence of parent material and landslides on the spatial distribution of soil texture cannot be neglected. Model performance, most probably, could be improved by a bigger dataset.     

平原区土壤质地的反射光谱预测与地统计制图

基于地统计方法的土壤属性制图通常需要大量的采样与实验室测定。本研究提出利用可见光近红外(visible-nearinfrared spectroscopy,VNIR)光谱技术测定替代实验室测定,并与地统计方法相结合预测土壤质地的空间变异。通过建立砂粒(0.02 mm),粉粒(0.002~0.02 mm),黏粒(0.002 mm)含量的VNIR光谱预测模型,将模型预测得到的质地数据和建模点实测质地数据一同用于地统计分析和Kriging插值制图。以江苏北部黄淮平原地区为案例的研究结果表明,砂粒、粉粒、黏粒含量的预测值和实测值的均方根误差(RMSE)分别为8.67%、6.90%3、.51%,平均绝对误差(MAE)分别为6.46%、5.60%、3.05%,显示了较高的预测精度。研究为快速获取平原区土壤质地空间分布提供了新的可能的途径。     

土壤水分特征曲线的分形模拟

Many empirical models have been developed to describe the soil water retention curve （SWRC）. In this study, a fractal model for SWRC was derived with a specially constructed Menger sponge to describe the fractal scaling behavior of soil; relationships were established among the fractal dimension of SWRC, the fractal dimension of soil mass, and soil texture; and the model was used to estimate SWRC with the estimated results being compared to experimental data for verification. The derived fractal model was in a power-law form, similar to the Brooks-Corey and Campbell empirical functions. Experimental data of particle size distribution （PSD）, texture, and soil water retention for 10 soils collected at different places in China were used to estimate the fractal dimension of SWRC and the mass fractal dimension. The fractal dimension of SWRC and the mass fractal dimension were linearly related. Also, both of the fractal dimensions were dependent on soil texture, i.e., clay and sand contents. Expressions were proposed to quantify the relationships. Based on the relationships, four methods were used to determine the fractal dimension of SWRC and the model was applied to estimate soil water content at a wide range of tension values. The estimated results compared well with the measured data having relative errors less than 10% for over 60% of the measurements. Thus, this model, estimating the fractal dimension using soil textural data, offered an alternative for predicting SWRC.     

基于土壤物理基本参数的土壤导热率模型

土壤物理基本参数是影响土壤导热率的重要因素,为了获取土壤的颗粒组成、有机质含量与土壤导热率计算模型中参数之间的关系,该文分析了陕西省9个地区的土壤质地对土壤导热率的影响,对不同土壤导热率估算模型的准确性进行评价,并在C?té-Konrad模型和Lu-Ren模型的基础上,建立了基于土壤物理基本参数的改进模型,结果表明:改进的C?té-Konrad模型与改进的Lu-Ren模型可以用来拟合不同质地的土壤导热率,且具有较好的拟合精度,决定系数R2均在0.92以上,相对误差(relative error,Re)均低于9.6%;对于砂粒含量或粉粒含量较高的土壤导热率,改进的C?té-Konrad模型模拟结果的均方根误差(root-mean-square error,RMSE)≤0.1183、R2≥0.9259以及Re≤9.55%,均优于C?té-Konrad模型、Lu-Ren模型和改进Lu-Ren模型;对于砂粒和粉粒含量均较低的土壤导热率,改进Lu-Ren模型模拟结果的RMSE≤0.0815、R2≥0.9326,Re≤8.21%,均明显优于其他3种模型。两种改进的模型分别建立了模型参数与颗粒组成、有机质含量之间的关系,能够更加详细描述土壤物理基本参数与导热率之间的关系,并且针对不同的土壤质地,选取合适的改进模型能够更加准确地计算土壤导热率。     

Determination of soil texture: Comparison of the sedimentation method and the laser‐diffraction analysis

Laser‐diffraction analysis (LDA) is a rapid automated method achieving highly resolved frequency distributions of particle sizes. Recently, LDA has come into use in environmental sciences. However, in the size range of silt and clay deviations from the particle‐size analysis with the standard pipette method, which is regarded as the reference method for soil‐texture classification, have been reported. Therefore, this study concentrates (1) on the verification of systematic relations between both methods using a series of soils of Lower Saxony (Germany) and (2) on the general applicability of the laser‐diffraction method to soil‐texture classification as well as (3) texture‐based estimates of air capacity, available field capacity, and permanent wilting point. The comparison of LDA with the pipette method demonstrated highly significant linear correlations in each of the particle‐size fractions from clay to coarse silt. The slope of regressions ranged from 0.4 with fine silt to 3.1 with clay. If the clay content derived from LDA was applied to texture classification, the resulting textural classes differed from the standard textural classes, except for purely sandy samples with a clay content of <5%. However, the linear‐regression model enabled an approach of the LDA‐based clay content to values produced with the standard pipette method. Using this transformation, a texture classification became practicable in many cases, but, despite of a high significance level between LDA and pipette method, still led to wrong textural classes in several cases. A comparison with regression models from other regions in Europe showed both similarities and discrepancies, even for similar substrates. Hence, the laser‐diffraction analysis cannot be used for the texture classification of soil samples without verification by the standard pipette method.     

基于近红外光谱和正交信号-偏最小二乘法对土壤的分类

不同质地的土壤,由于蓄水能力和土壤颗粒大小的不同使得其光谱特性不同,这为采用近红外光谱技术对土壤质地进行判别分析提供了依据。该研究利用正交信号校正(OSC)方法可以获得与浓度有关的谱图信息这一优势,将其与偏最小二乘方法(PLS)结合,采用近红外光谱技术对不同质地的土壤:砂土、壤土、黏土进行判别分析。结果表明:建模样本的相关系数可达0.965,采用该模型对其余45个样本分别进行了预测,三种土壤预测样本的判别正确率分别为:93.3%,86.6%和86.6%。说明OSC方法可以提取谱图中的微弱的质地信息,实现土壤质地的快速鉴别分析。     

The Impact of Agriculture on Soil Texture Due to Wind Erosion

Wind erosion produces textural changes on topsoil of semiarid and arid environments; however, the selection of particles on different textured soils is unclear. Our objectives were to evaluate textural changes induced by wind erosion on cultivated soils of different granulometry and to asses if textural changes produced by wind erosion are linked to aggregation of granulometric particles into different sizes of aggregates formed in contrasting textured soils. Considering this, we studied the particle size distribution (PSD) with full dispersion (PSD_F) of 14 cultivated (CULT) and uncultivated (UNCULT) paired soils and, on selected sites, the PSD with minimum dispersion (PSD_MIN) and the quotient PSD_MIN/F. Results showed that the content of silt plus clay was lower in CULT than in UNCULT in most of the sites. The highest removal of silt was produced in soils with low sand and high silt content; meanwhile, the highest removal of clay was observed in soils with medium sand content. According to PSD_MIN, particles of 250–2,000 μm predominated in the sandy soil, in the loamy soil particles between 50 and 250 μm and in the silty loam soil particles between 2 and 50 μm. For clay sized particles, PSD_MIN/F was lower than 1 in all soils and managements, but this quotient was higher in CULT compared with UNCULT only in the loamy soil. This means a decrease of clay accumulation in aggregates of larger sizes produced by agriculture, which indicates an increase in the risk of removal of these particles by wind in loamy soils. Copyright © 2014 John Wiley & Sons, Ltd.     

Estimation of soil texture from permanent wilting point measured with a chilled‐mirror dewpoint technique

Soil texture is an important factor governing a range of physical properties and processes in soil. The clay and fine fractions of soil are particularly important in controlling soil water retention, hydraulic properties, water flow and transport. Modern soil texture analysis techniques (x‐ray attenuation, laser diffraction and particle counting) are very laborious with expensive instrumentation. Chilled‐mirror dewpoint potentiameters allows for the rapid measurement of the permanent wilting point (PWP) of soil. As the PWP is strongly dictated by soil texture, we tested the applicability of PWP measured by a dewpoint potentiameter in predicting the clay, silt and sand content of humid tropical soils. The clay, silt, and sand content, organic matter and PWP were determined for 21 soils. Three regression models were developed to estimate the fine fractions and validated using independent soil data. While the first model showed reasonable accuracy (RMSE 16.4%; MAE 13.5%) in estimating the clay, incorporating the organic matter into the equation improved the predictions of the second model (RMSE 17.3%; MAE 10.9%). When used on all soil data, the accuracy of the third model in predicting the fine fraction was poor (RMSE 31.9%; MAE 24.5%). However, for soils with silt content greater than 30%, the model prediction was quite accurate (RMSE 7–12%; MAE 7–9%). The models were used to estimate the sand content and soil textures of soils, which proved relatively accurate. The dewpoint potentiometer can serve a dual purpose of rapidly estimating the PWP and the clay, fine fraction, and soil texture of soils in a cost efficient way.     

崩岗不同土层土壤水力学特性差异性分析

为研究崩岗不同土层土壤水力学特性的差异性,采用离心法测定不同土层土壤水分特征曲线,筛选出适合的土壤水分特征曲线拟合模型,结合统计模型,推求土壤的当量孔径分布、比水容量、非饱和导水率和扩散率,分析崩岗不同土层土壤水力学参数的变化规律。结果表明,崩岗土层从红土层到砂土层的变化过程中,土壤质地由黏土向砂土变化;Fredlund&Xing模型对崩岗土壤土水特征曲线拟合效果最好;参数θs、α、n随着质地变黏重逐渐减小;随着土层深度的增加,土壤的持水性能降低;土壤比水容量、非饱和导水率和扩散率受土壤质地和基质吸力的共同影响。在低吸力阶段,3个指标随基质吸力变化比较平缓,砂土层土壤比水容量和非饱和导水率最大,扩散率最小;而在高吸力阶段,砂土层土壤的这些指标降低较快,且低于其他土层,各层土壤间导水率和扩散率差异随着基质吸力的增加而增大。     

土壤颗粒的分形特征及其应用

本文应用土壤颗粒的质量分布原理来描述土壤颗粒的分形特征。通过对10种土壤颗粒的机械组成进行分析,分别计算出它们的分形维数（D=2.489～2.896）,并分析了其与土壤质地之间的关系。同时对土壤颗粒分形维数与所对应土壤的幂函数型水分特征曲线的拟合分形维数进行比较分析,建立了二者之间的相关关系。结果表明:分形维数的大小反映了土壤质地中粘粒、粉粒和砂粒含量的变化,随粘粒含量的增多分形维数增大,随砂粒含量的增多分形维数减小;同时土壤颗粒分形维数与所对应的水分特征曲线的拟合分形维数呈现出良好的一致性,因而对所研究的土壤而言,可应用土壤颗粒的质量分形维数结合幂函数模型来估算土壤水分特性曲线。     

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.     

基于激光衍射的土壤粒径测定法的评价与校正

为比较与评价激光法与吸管法测定土壤粒径分布的准确性,该文采用激光法与吸管法测定了23组来自中国13个不同省份或自治区土壤样本的粒径分布,将激光法与传统吸管法的测定结果进行比较,并在此基础上对激光法测定参数进行了修正。结果表明:1)与吸管法相比,激光衍射法低估土壤样品中的黏粒含量,其相对误差为36.33%;高估粉粒含量,其相对误差为36.51%;2)对吸管法与激光衍射法的实测结果进行线性关系分析表明,其中黏粒与粉粒的线性关系较好,决定系数分别为0.91,0.90;3)经过模型转换后,基于激光衍射法的土壤粒径分布结果的相对误差明显降低:黏、粉的相对误差分别降低至16.25%、12.83%,说明激光衍射法可以用于大规模不同类型的土壤粒径分析。该研究可为土壤系统化和规范化的对比研究以及建立基于激光衍射技术的土壤质地划分标准提供依据。     

农田土壤颗粒尺寸分布分维及颗粒体积分数的空间变异性

盐渍土膜下滴灌过程中的土壤理化性质存在空间变异性,对灌溉管理有一定影响。该文根据3个不同尺度下土壤采样及颗粒尺寸分布（PSD）测定结果,探讨了土壤PSD分维和颗粒体积分数之间的定量关系,分析了各尺度和综合尺度下土壤PSD分维和颗粒体积分数的经典统计特征和地质统计特征,根据颗粒体积分数的半方差函数计算了不同尺度颗粒体积分数的分形维,并做出了等值线图。研究表明,土壤PSD分维与黏粒的体积分数呈正相关关系,黏粒体积分数增大引起PSD分维增加的原因在于细颗粒的比表面积比粗颗粒的大;各尺度土壤PSD分维及颗粒体积分数均无强变异特征;不同尺度下土壤PSD分形维的半方差函数中块金和基台值均非常低;50 m尺度土壤PSD分维和黏粒体积分数的等值线图具有类似的集中度和走势,而砂粒体积分数和粉粒体积分数则具有相反的集中度和走势。该研究说明土壤PSD分维和颗粒体积分数都有一定的尺度依赖性。     

激光法与湿筛－吸管法测定土壤颗粒组成的转换及质地确定

湿筛-吸管法是测定土壤颗粒组成(PSD)的传统方法,而激光法则是新兴的土壤颗粒测定方法,为了明确二者测定数据间的转换关系,应用两种方法分别测定了中国6个主要土纲的265个土壤样品。结果表明,激光法测定的土壤黏粒含量明显地小于湿筛-吸管法测定的数据,激光法测定的土壤粉粒含量明显地大于湿筛-吸管法测定的数据,而对于土壤砂粒含量的测定结果二者互有高低。两种方法测定的黏粒、粉粒和砂粒间均分别具有很好的相关性,甚至按照美国的7级分类标准,每个粒度级别在两种方法间均具有很好的相关性。按照激光法和吸管法测定数据间的转换关系式得出了用激光法测定数据的砂土、壤土和黏土质地划分界限,从而能够应用激光法测定的数据直接进行质地划分,这对于推动激光法在土壤学中的进一步应用和推动土壤科学的发展均具有重要意义。