Sample adequately to estimate variograms of soil properties

The variogram is central in the spatial analysis of soil, yet it is often estimated from few data, and its precision is unknown because confidence limits cannot be determined analytically from a single set of data. Approximate confidence intervals for the variogram of a soil property can be found numerically by simulating a large field of values using a plausible model and then taking many samples from it and computing the observed variogram of each sample. A sampling distribution of the variogram and its percentiles can then be obtained. When this is done for situations typical in soil and environmental surveys it seems that variograms computed on fewer than 50 data are of little value and that at least 100 data are needed. Our experiments suggest that for a normally distributed isotropic variable a variogram computed from a sample of 150 data might often be satisfactory, while one derived from 225 data will usually be reliable.     

Estimating variograms of soil properties by the method-of-moments and maximum likelihood

Variograms of soil properties are usually obtained by estimating the variogram for distinct lag classes by the method‐of‐moments and fitting an appropriate model to the estimates. An alternative is to fit a model by maximum likelihood to data on the assumption that they are a realization of a multivariate Gaussian process. This paper compares the two using both simulation and real data. The method‐of‐moments and maximum likelihood were used to estimate the variograms of data simulated from stationary Gaussian processes. In one example, where the simulated field was sampled at different intensities, maximum likelihood estimation was consistently more efficient than the method‐of‐moments, but this result was not general and the relative performance of the methods depends on the form of the variogram. Where the nugget variance was relatively small and the correlation range of the data was large the method‐of‐moments was at an advantage and likewise in the presence of data from a contaminating distribution. When fields were simulated with positive skew this affected the results of both the method‐of‐moments and maximum likelihood. The two methods were used to estimate variograms from actual metal concentrations in topsoil in the Swiss Jura, and the variograms were used for kriging. Both estimators were susceptible to sampling problems which resulted in over‐ or underestimation of the variance of three of the metals by kriging. For four other metals the results for kriging using the variogram obtained by maximum likelihood were consistently closer to the theoretical expectation than the results for kriging with the variogram obtained by the method‐of‐moments, although the differences between the results using the two approaches were not significantly different from each other or from expectation. Soil scientists should use both procedures in their analysis and compare the results.     

Comparing sampling needs for variograms of soil properties computed by the method of moments and residual maximum likelihood

It has been generally accepted that the method of moments (MoM) variogram, which has been widely applied in soil science, requires about 100 sites at an appropriate interval apart to describe the variation adequately. This sample size is often larger than can be afforded for soil surveys of agricultural fields or contaminated sites. Furthermore, it might be a much larger sample size than is needed where the scale of variation is large. A possible alternative in such situations is the residual maximum likelihood (REML) variogram because fewer data appear to be required. The REML method is parametric and is considered reliable where there is trend in the data because it is based on generalized increments that filter trend out and only the covariance parameters are estimated. Previous research has suggested that fewer data are needed to compute a reliable variogram using a maximum likelihood approach such as REML, however, the results can vary according to the nature of the spatial variation. There remain issues to examine: how many fewer data can be used, how should the sampling sites be distributed over the site of interest, and how do different degrees of spatial variation affect the data requirements? The soil of four field sites of different size, physiography, parent material and soil type was sampled intensively, and MoM and REML variograms were calculated for clay content. The data were then sub-sampled to give different sample sizes and distributions of sites and the variograms were computed again. The model parameters for the sets of variograms for each site were used for cross-validation. Predictions based on REML variograms were generally more accurate than those from MoM variograms with fewer than 100 sampling sites. A sample size of around 50 sites at an appropriate distance apart, possibly determined from variograms of ancillary data, appears adequate to compute REML variograms for kriging soil properties for precision agriculture and contaminated sites.     

应用基于PLSR的土壤-环境模型预测土壤属性

土壤-环境模型对于正确理解土壤属性与环境因子间的关系,以及进行土壤属性预测与制图均具有重要的意义。研究区位于陕西省长武县内多年退耕还林还草沟壑区域,采集72个土壤表层样本,选择3/4的样本作为建模集,其余1/4的样本作为验证集;环境因子选择容易获取的地形因子和由遥感影像提取的植被因子和湿度因子,建立基于偏最小二乘回归(PLSR)的土壤-环境模型。结果表明:全氮、速效钾、全钾、有机质与环境因子间均有显著相关性;建立的PLSR模型可解释土壤属性的空间变异从23%(全氮)到27%(全钾);与逐步回归方法构建的模型相比,利用PLSR构建的土壤-环境模型可以更好地表征土壤属性与环境变量间的关系,拟合精度和预测精度也相对较高,说明PLSR建立的模型可以更好地应用于相似区域的土壤属性预测。     

On effective linearity of soil process models

There are various circumstances in which it is important to know whether we can treat a model of a soil process as linear with respect to its parameters. In particular this is necessary when we decide how to apply that model so as to generate outputs at different spatial scales. Very few, if any, interesting soil models are strictly linear. However, the assumption of linearity might not be unreasonable if, for the region of interest, the variation of a particular input is fairly limited, or is limited to certain regions of the model’s parameter space, or both. For this reason we propose the concept of effective linearity. We propose that the effective linearity of a model is quantified by the mean square deviation of the model output from a best linear approximation, given some distribution of inputs. This can be computed for a full set of inputs, or for one input with the other inputs assumed to have an independent non‐linear effect. We computed these measures of non‐linearity for a model of ammonia volatilization from the soil. We computed them for regions of different size, given a particular geostatistical model of the spatial covariation of the inputs of interest. This showed that, except for small regions, the model could not be regarded as effectively linear because of the model response to soil pH. As a result the mean square deviation of model predictions relative to a best linear approximation is large by comparison to the analytical variance of the model output.     

On the fractal dimensions of some transient soil properties

Spatial variation of soil properties can be studied by use of the concept of fractals, and the overall roughness of a surface characterized by its fractal dimension. Soil surface strength was recorded by both cone penetrometer and field shear vane, along a series of transects, at 10 cm spacings. In the natural state, such data gave fractal dimensions very close to 2, implying an almost totally random data set, and only the deliberate action of cultivations introduced an order of persistence. Microtopography, measured by a profile meter at 1 cm intervals, had fractal dimensions of 1.7 on two separate sites. Problems are identified specifically in the choice of a suitable model for fitting the semi-variogram, and in the collection of an efficient data set.     

不同治理模式侵蚀劣地土壤物理特性变化的研究

长汀县河田镇在花岗岩山地的“老头松”侵蚀劣地上采取了不同的治理模式 ,取得了成效。林地土壤物理特性得到不同程度改良 ,土壤砂粒和石砾相对减少、分散率降低、团聚度提高 ,水稳性团聚体数量增加 ,结构体破坏率降低 ,土壤孔隙度增大 ,持水量增加 ,渗透性增强。     

On the initialization of soil carbon models and its effects on model predictions for England and Wales

The initial distribution of carbon (C) between model pools can strongly influence predictions of soil C models. Models are often initialized by assuming that C stocks are near steady state, but in many cases this is unrealistic. We explored different ways of initializing the DAYCENT model over the range of soils and climate in England and Wales. We ran the model for the main soil types on arable land and managed grass, identified by taking the top five soil‐land use combinations in each of 87 50‐km grid squares, giving 376 ‘sites’ distributed across the two countries. We compared three initialization methods: (i) using the soil C contents and pool distributions (‘soil microbe C’, ‘slow C’ and ‘passive C’ in DAYCENT) predicted for steady state under the prevailing conditions; (ii) using the steady‐state pool distributions but with the true, initial soil C content; and (iii) by fitting the initial pool distribution to the rates of change in soil C observed in the National Soil Inventory (NSI) of England and Wales during the 1980s and 1990s. The calculated mean net primary production (NPP), and hence C inputs to the soil, was realistic for arable land and permanent grass in England and Wales. The calculated rates of change in soil C were sensitive to the initialization method. Method 1 predicted little change with the actual climate over the NSI survey period and simulated management, but Methods 2 and 3 predicted losses of C. Methods 2 and 3 gave similar trends averaged across soil types and locations, but there were large differences for individual soils and locations. The relationships between losses and the mean soil C content were approximately linear with Methods 2 and 3, but the slopes differed. The predicted losses varied with different climate scenarios applied over the NSI survey period. The predicted differences between climate scenarios were less sensitive to initialization method than to C content. We discuss generic implications for modelling.     

Evaluating soil maps for prediction of soil water properties

Conventional soil survey stratifies a region into mapping classes and characterizes each by a representative soil profile within it. The efficacy of the procedure for predicting particle-size fractions, bulk density, water retention, and available water capacity (AWC) of the soil at previously unvisited sites on the Plain of Languedoc in southern France is evaluated for three scales of survey (1/10 000, 1/25 000 and 1/100 000) and is compared to that of prediction from stratified random and simple random samples. Data from 85 soil profiles on a random transect were used for evaluation. Classification partitioned the variation of the measured properties, except for AWC, well at the 1/10 000 and 1/25 000 scales, whereas classification at the 1/100 000 scale was less effective. At the 1/10 000 and 1/25 000 scales both classification and stratified random sampling were better for prediction than simple random sampling for the same total sample. On average the representative profiles proved substantially better predictors than the stratified random samples, but in most situations where soil stratification performed well efficiencies of the two predictors were similar. In essence, the more successful the classification was the more difficult it was to improve prediction by selecting representatives instead of sampling randomly within classes. These results confirmed statistically that the soil surveyor can exercise intuition and judgement to classify and select representatives.     

Water retention models for fractal soil structures

A review of water retention functions based upon fractal soil structures is presented. We consider the modelling approach for a fractal fabric, a fractal pore boundary and a fractal pore space, identifying the latter case as one of particular complexity. In each case, the water retention function is derived from the pore volume distribution arising from the structural model in question. We examine published models and highlight problems, namely lack of generality and inconsistency with the assumed fractal structure. The models considered in this paper do not account for the effects of pore connectivity, and as such their validity as a necessary condition for the existence of fractal structure is questionable.     

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.     

A critique of models for freshwater and soil acidification

Four types of models quantifying effects of acid deposition on freshwaters are reviewed. These include Henriksen's empirical model, an adsorption isotherm model, soil-oriented charge balance models (Reuss-Johnson, Birkenes, MAGIC, ILWAS), and the Trickle Down model. Emphasis is on an assessment of critical assumptions; no attempt has been made to run the various models and compare results. The models range from simple to very complex and from empirical to highly process oriented. The various types have all proven useful and there has been a significant convergence concerning key processes. The importance of anion mobility, sulfate adsorption, ion exchange, dissolution of Al bearing minerals and weathering seems to be accepted by most workers. Future model improvement, however, relies to a large extent on further checking against observations.     

Using regression models in design‐based estimation of spatial means of soil properties

The precision of design‐based sampling strategies can be increased by using regression models at the estimation stage. A general regression estimator is given that can be used for a wide variety of models and any well‐defined sampling design. It equals the π estimator plus an adjustment term that accounts for the differences between the π estimators for the spatial means of the auxiliary variables and the true spatial means of these variables. The regression estimator and ratio estimator follow from certain assumptions on the model and the sampling design. These are compared with the π estimator in two case studies. In one study a bivariate field of linearly related variables was simulated and repeatedly sampled by Simple Random Sampling without replacement and sample sizes 10, 25, 50, 100 and 200. For all sample sizes the ratio of the standard error of the simple regression estimator to that of the π estimator was approximately 55%. The bias of the simple regression estimator was negligibly small. The confidence interval estimators were valid for all sample sizes except for n = 10. Also the ratio estimator was approximately unbiased, and the confidence interval estimators were valid for all sample sizes, even for n = 10. This is remarkable because the ratio estimator assumes that the intercept of the regression line is 0 which was incorrect for the simulated field. On the other hand, only approximately 55% of the potential gain was achieved because the model was inappropriate. In a second study the spatial means of the Mean Highest Watertable of map units were estimated by Stratified Simple Random Sampling and the combined (multiple) regression estimator. The NAP elevation, the local elevation, the Easting and the Northing were used as auxiliary variables. For all map units except one the combined (multiple) regression estimator was more precise than the π estimator. The ratio of the standard errors varied from 0.36 to 1.04. The domain for which the regression estimator was less precise than the π estimator showed strong variation between strata. For this domain it was more efficient to group the strata into two groups and to fit simple models for these groups separately.     

Simplifying the evaporation method for quantifying soil hydraulic properties

Since about 25 years, we have measured the unsaturated hydraulic conductivity function and water retention curve with the evaporation method of more than 1500 mineral and organic soils samples. From this data base, 104 representative samples of varying texture and dry bulk density were selected and the temporal dynamics of the basic measured values (mass or water loss, respectively, and tension change over time) was analyzed. With the exception of sand, water loss per time interval was constant in all other mineral and organic soils during the measuring time in the tension range between 0 and about 60 kPa. In sands, the nonlinear water loss over time by evaporation can be described by a quadratic function with high accuracy (r² > 0.99). For all other soils, a linear function is sufficient (r² > 0.99). The use of evaporation functions enables extending weighing intervals. This reduces costs for the measuring equipment and increases the effectiveness of the method while maintaining the same quality of unsaturated hydraulic conductivity and water retention functions. It was confirmed that measuring with two tensiometers is sufficient for accurate hydraulic conductivity and water retention function. Reducing evaporation by screening the sample surface helps to decrease hydraulic gradients and keeps tension distributions approximately linear with depth. This is recommended in particular for clayey soils.     

土壤颗粒分布参数模型对黄土性土壤的适应性研究

土壤颗粒组成是土壤最基本的物理性质之一,其分布曲线可用来估算土壤的水力学性质,然而对于土壤颗粒分布曲线的完整表达需要借助于参数模型,对于不同类型的土壤,参数模型的拟合效果不尽相同.为了选择能够较好描述黄土性土壤颗粒分布状况的参数模型,该文采用了3个指标--相关系数(R),均方根误差(RMSE)和Akaike信息准则(AIC)值,对3类共10个参数模型(单参数模型2个,二参数模型6个,三参数模型2个)在黄土性土壤上的适应性进行了评价(共828个土壤颗粒分析资料).结果表明:简化的三参数Fredlund模型对黄土性土壤颗粒分布的拟合效果最好,且受质地影响较小,二参数Weibull模型次之,单参数的Jaky模型效果最差.三参数Fredlund模型是估算黄土性土壤颗粒组成的最适宜的模型.     

红壤丘陵区不同植被恢复模式土壤理化性质相关分析

南方红壤丘陵区土壤侵蚀严重,成为我国生态系统退化地带之一,为了探讨植被恢复模式与土壤理化性质改良关系,合理利用丘陵区土地,本文研究了草本、果树、果树+草、果树+草+农作物和果树+农作物等不同植被恢复模式对土壤物理及化学性质的影响,并运用灰色关联分析方法对不同恢复模式的土壤理化性质进行相关分析。研究结果表明:不同植被恢复模式土壤理化性质影响差异显著,百喜草全园覆盖的草本模式改良土壤理化性质的综合效应最佳,然后依次是果树+农作物、果树+草+农作物、果树+草。横坡套种土壤改良效应明显优于纵坡套种,全区覆盖处理优于带状覆盖处理。南方丘陵区侵蚀劣地植被恢复,可以优先考虑恢复百喜草的草本模式和果树+农作物模式,尤其是,果树+横坡套种农作物模式适宜在南方丘陵区大力推广。     

A system for measuring soil physical properties in the field

An automated soil sampler and a system for measuring the physical properties of soil cores in the field were developed for the study of soil compaction and tillage effects on soil physical properties. The system measures the bulk density, air-filled porosity, intrinsic air permeability, and moisture content on 47.6 mm diameter soil cores as well as measuring cone penetration resistance in the field. Results of a laboratory calibration study illustrated that the system measures the soil properties with acceptable accuracy. A main benefit of the system was the fact that it could measure all of the above properties on ten core samples down to a depth of 0.5 m, typically within 15 min.

A field compaction study showed that the system was capable of detecting the effects of a 15.2 tonne axle load on the soil down to a depth of 0.4 m, 1 year after compaction.     

Assessment the effect of homogenized soil on soil hydraulic properties and soil water transport

Soil hydraulic properties play a crucial role in simulating water flow and contaminant transport. Soil hydraulic properties are commonly measured using homogenized soil samples. However, soil structure has a significant effect on the soil ability to retain and to conduct water, particularly in aggregated soils. In order to determine the effect of soil homogenization on soil hydraulic properties and soil water transport, undisturbed soil samples were carefully collected. Five different soil structures were identified: Angular-blocky, Crumble, Angular-blocky (different soil texture), Granular, and subangular-blocky. The soil hydraulic properties were determined for undisturbed and homogenized soil samples for each soil structure. The soil hydraulic properties were used to model soil water transport using HYDRUS-1D.The homogenized soil samples showed a significant increase in wide pores (wCP) and a decrease in narrow pores (nCP). The wCP increased by 95.6, 141.2, 391.6, 3.9, 261.3%, and nCP decreased by 69.5, 10.5, 33.8, 72.7, and 39.3% for homogenized soil samples compared to undisturbed soil samples. The soil water retention curves exhibited a significant decrease in water holding capacity for homogenized soil samples compared with the undisturbed soil samples. The homogenized soil samples showed also a decrease in soil hydraulic conductivity. The simulated results showed that water movement and distribution were affected by soil homogenizing. Moreover, soil homogenizing affected soil hydraulic properties and soil water transport. However, field studies are being needed to find the effect of these differences on water, chemical, and pollutant transport under several scenarios.     

Human impacts on soil properties and their implications for the sensitivity of soil systems in Scotland

Human activities have had pronounced impacts on soil properties. Conifer afforestation in the uplands has caused significant decreases in soil pH and in the quality and turnover of organic matter. Acid deposition has increased soil acidity by a similar amount to conifer afforestation but has been shown to affect soils at greater depths. Acid deposition has also increased the mobility of trace metals in the soil and therefore increased metal concentrations in drainage waters. Applications of sewage sludge to the soil have been shown to increase metal concentrations, although most of the Scottish soils affected have high trace metal binding capacities. Intensification of arable cultivation in the lowlands has reduced organic matter concentrations, structural stability and soil workability, and has had effects on soil erodibility. Human trampling, while highly localised, affects sensitive mountain soils in popular areas, leading to loss of surface organic horizons, and therefore, carbon storage. The future impacts of human activities on the soil may be exacerbated by changing climate, and the need to monitor and predict these will not diminish.     

土壤调节剂对土壤物理性质的改善

Effects of non-ionic polyacrylamide(PAM),anionic polyacrylamide(PHP),cationic polyacrylamide(PCAM),non-ionic polyvinylalcohol(PVA),anionic hydrolyzed polyacrylonitrile(HPAN)and polyethleneoxide(PEO)on the physical properties of three different soil stpes were studied.content of water-stable aggregates larger than 0.25mm increased to varying extents for different soils and soil conditioners,Among the six kinds of condiftioners,non-ionic polyacrylamide(PAM) was the most effective for red soil while polyethyleneoxide(PEO)the least effective for Chao soil,red soil and yellow-brown soil.Water-stable aggregates with the molecular weight of PEO within a certain range.Only evaporation rate of Chao soil decreased after aplication of PAM and HPAN to Chao soil and red soil.