Scale-dependent relationships between soil organic carbon and urease activity

Many soil properties and processes vary at different spatial scales. As a result, relationships between soil properties often depend on scale. In this paper we show this for two soil properties of biological importance, by means of a nested analysis of covariance. The variables were urease activity (UA) and soil organic carbon (SOC), sampled on an unbalanced nested design at three sites with different land uses (arable, forest and pasture). The objective of this study was to investigate the scale‐dependent relationships of UA and SOC at these three sites to exemplify the phenomenon of scale‐dependency in the covariation of biogeochemical variables. At each site the variables showed different scale dependencies, expressed in their correlations at different scales. At the pasture site, UA and SOC were uncorrelated at all scales in the sampling design (0.2 m, 1 m, 6 m and ≥15 m), and the overall product moment correlation was 0.10. A significant positive scale dependent correlation (0.65) was found at the 1‐m scale for the forested site. The soil properties were not spatially correlated at any of the other scales and the associated product moment correlation for this site was 0.14. Urease activity and soil organic C were found not to be correlated at the shorter scales in the arable site. However, significant positive correlation coefficients of 0.89 and 0.82 were obtained at the longer scales of 6 and ≥15‐m respectively for the arable site. The product moment correlation at this site was 0.65. At both the arable and forest site, we found that correlations at particular scales were stronger than the overall product moment correlation. This approach allowed us to identify significant relationships between urease activity and soil organic carbon and the scales at which these relationships occur and to draw conclusions about the spatial scales, which must be resolved in further studies of these variables in these contrasting environments. This study highlights the pervasive effect of scale in soil biogeochemistry and shows that scale‐dependence must not be disregarded by soil scientists in their investigations of biogeochemical processes.     

Spatial designs and properties of spatial correlation: Effects on covariance estimation

In a spatial regression context, scientists are often interested in a physical interpretation of components of the parametric covariance function. For example, spatial covariance parameter estimates in ecological settings have been interpreted to describe spatial heterogeneity or “patchiness” in a landscape that cannot be explained by measured covariates. In this article, we investigate the influence of the strength of spatial dependence on maximum likelihood (ML) and restricted maximum likelihood (REML) estimates of covariance parameters in an exponential-with-nugget model, and we also examine these influences under different sampling designs—specifically, lattice designs and more realistic random and cluster designs—at differing intensities of sampling (n=144 and 361). We find that neither ML nor REML estimates perform well when the range parameter and/or the nugget-to-sill ratio is large—ML tends to underestimate the autocorrelation function and REML produces highly variable estimates of the autocorrelation function. The best estimates of both the covariance parameters and the autocorrelation function come under the cluster sampling design and large sample sizes. As a motivating example, we consider a spatial model for stream sulfate concentration.     

短距离样点对土壤呼吸空间变异预测精度的影响

不同采样设计会对土壤呼吸空间变异特征的预测精度产生重要影响。本研究选取黄淮海平原北部潮土区1 km×1 km夏玉米样地,在7×7单元规则格网(样点间距167 m)、完全随机(样点平均间距433 m)以及3×3单元规则格网+完全随机(样点平均间距405m)3种布点方式的基础上,保持样本总量(49)不变,以占总样点2%～14%的短距离样点(样点间距4m)随机替换原方案相应样点个数的方法优化布点方式,应用普通克里金法插值,以均方根误差(RMSE)和确定系数(R2)作为验证指标,检验基于3种布点方式设置的短距离样点对土壤呼吸空间变异预测精度的影响。结果表明:研究区土壤呼吸平均速率为2.65μmol·m?2·s?1,空间分布均呈西高东低,表现出中等程度变异。采样设计对土壤呼吸空间分布的预测精度影响显著,基于3种布点方式设置短距离样点可提高预测精度7%～13%。无短距离样点替换时,规则格网+完全随机的布点方式最优,比完全随机布点和规则格网布点的空间插值预测精度分别提高10%和22%;设置短距离样点替换后,在最优布点方式(规则格网+完全随机)中,对土壤呼吸空间变异的预测精度可再提高4%～7%,其中短距离样点个数占样本总量10%对土壤呼吸空间变异预测精度的提高最为明显。研究发现,基于相同的样本数量设置短距离样点可增加区域范围内样点密度,提高土壤呼吸空间变异预测精度及试验结果的可靠性。因此,在黄淮海平原北部潮土区100 hm2尺度的夏玉米样地中,规则格网+完全随机+10%短距离样点的布点方式是预测土壤呼吸空间变异最适宜的采样布点方式。     

Considerations on the use of time-domain reflectometry (TDR) for measuring soil water content

Time-domain reflectometry (TDR) is becoming an increasingly popular technique for measuring soil water content. It is based on measuring the dielectric constant of soil from the propagation velocity of a pulse travelling along an electromagnetic transmission line embedded in the soil. Although various designs of transmission line have been proposed, there have been no attempts to contrast these designs to enable the experimenter to select an appropriate system. A second area which needs consideration is the choice of calibration function, as the use of an empirical calibration with no physical basis persists. In this paper, transmission line design (balanced and unbalanced) is discussed and a simple linear calibration function based on refractive index is developed.     

Model‐based analysis using REML for inference from systematically sampled data on soil

The general linear model encompasses statistical methods such as regression and analysis of variance (anova ) which are commonly used by soil scientists. The standard ordinary least squares (OLS) method for estimating the parameters of the general linear model is a design‐based method that requires that the data have been collected according to an appropriate randomized sample design. Soil data are often obtained by systematic sampling on transects or grids, so OLS methods are not appropriate. Parameters of the general linear model can be estimated from systematically sampled data by model‐based methods. Parameters of a model of the covariance structure of the error are estimated, then used to estimate the remaining parameters of the model with known variance. Residual maximum likelihood (REML) is the best way to estimate the variance parameters since it is unbiased. We present the REML solution to this problem. We then demonstrate how REML can be used to estimate parameters for regression and anova ‐type models using data from two systematic surveys of soil. We compare an efficient, gradient‐based implementation of REML (ASReml) with an implementation that uses simulated annealing. In general the results were very similar; where they differed the error covariance model had a spherical variogram function which can have local optima in its likelihood function. The simulated annealing results were better than the gradient method in this case because simulated annealing is good at escaping local optima.     

喀斯特小流域土壤含水率空间异质性及其影响因素

该文基于网格取样(80 m×80 m),利用地统计学和经典统计学方法,研究了典型喀斯特小流域旱季表层(0~10 cm)土壤含水率(soil moisture content,SMC)的空间变异特征,并分析了其与容重(bulk density,BD)、毛管孔隙度(capillary porosity,CP)、非毛管孔隙度(non-capillary porosity,NCP)、土壤有机碳(soil organic carbon,SOC)、碎石含量(rock fragment content,RC)等土壤理化性质以及坡度(slope gradient,SG)、坡向(slope aspect,SA)、裸岩率(bare rock,BR)等地形因子的关系。结果显示,SMC半方差函数的最优拟合模型为指数模型,变程为381.00 m,块基比为0.382,属于中等程度的空间相关性。普通克里格插值结果显示,SMC呈现出随海拔升高而降低的分布规律。Pearson相关分析表明,除SOC外,其他土壤理化指标均对SMC有显著影响(p0.05);各地形因子中仅SG对SMC有显著影响。协方差分析表明,RC、CP和NCP对SMC的方差解释达到显著水平(p0.05),地形部位(上坡、中坡、下坡、洼地)、土地利用类型(乔木林、灌木林、灌草丛、耕地)及二者的交互作用均未达到显著水平。这说明土壤理化性质是影响SMC的直接因素,地形部位和土地利用类型均通过改变土壤理化性质来影响SMC。该结果有利于辨别喀斯特小流域旱季SMC的主要影响因素,从而为该地区土壤水源涵养功能的提高及流域水文过程研究提供科学依据。     

Some considerations on aggregate sample supports for soil inventory and monitoring

Soil monitoring and inventory require a sampling strategy. One component of this strategy is the support of the basic soil observation: the size and shape of the volume of material that is collected and then analysed to return a single soil datum. Many, but not all, soil sampling schemes use aggregate supports in which material from a set of more than one soil cores, arranged in a given configuration, is aggregated and thoroughly mixed prior to analysis. In this paper, it is shown how the spatial statistics of soil information, collected on an aggregate support, can be computed from the covariance function of the soil variable on a core support (treated as point support). This is done via what is called here the discrete regularization of the core‐support function. It is shown how discrete regularization can be used to compute the variance of soil sample means and to quantify the consistency of estimates made by sampling then re‐sampling a monitoring network, given uncertainty in the precision with which sample sites are relocated. These methods are illustrated using data on soil organic carbon content from a transect in central England. Two aggregate supports, both based on a 20 m 20 m square, are compared with core support. It is shown that both the precision and the consistency of data collected on an aggregate support are better than data on a core support. This has implications for the design of sampling schemes for soil inventory and monitoring.     

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.     

A practical two‐step approach for mixed model‐based kriging,with an application to the prediction of soil organic carbon concentration

Soil scientists often use prediction models to obtain values at unsampled locations. The spatial variation in the soil is best captured by using the empirical best linear unbiased predictor (EBLUP) based on a restricted maximum likelihood (REML) approach that efficiently exploits available data on both mean trends and correlation structures. We proposed a practical two‐step implementation of the REML approach for model‐based kriging, exemplified by predicting soil organic carbon (SOC) concentrations in mineral soils in Estonia from the large‐scale digital soil map information and a previously established prediction model. The prediction model was a linear mixed model with soil type, physical clay content (particle size < 0.01 mm) and A‐horizon thickness as fixed effects and site, transect, plot, year, year‐transect random intercepts and site‐specific random slopes for clay content. We used only the site‐specific intercept EBLUPs for estimating spatial correlation parameters as they described most of the variation in the random effects (86.8%). Fitting an exponential correlation model to these EBLUPs resulted in an estimated range of 10.5 km and the estimated proportion of the variance from the nugget effect was 0.23. The results of a simulation study showed a downwards bias that decreased with sample size. The results were validated through an external dataset, resulting in root mean square errors (RMSE) of 1.06 and 1.07% for the two‐step approach for kriging and the model with only fixed effects (no kriging), respectively. These results indicate that using the two‐step approach for kriging may improve prediction.     

基于RADARSAT-2全极化数据反演周期性地表土壤湿度

土壤湿度是农作物估产和旱情监测的关键参量。目前常用的土壤湿度反演模型都建立在随机粗糙地表条件下,对周期性垄行结构的土壤并不适用。基于RADARSAT-2全极化数据和野外实测数据,分析了交叉极化(vh)后向散射系数对垄行方位角无明显响应;同极化(hh,vv)对方位角的响应为余弦函数,但在方位角为90o±2o位置易出现偏离曲线的异常高亮度值。通过雷达影像上采样点的实测值和Oh模型推算的理论值,拟合得到周期性地表和随机地表之间的误差函数,进而对同极化影像进行纠正。纠正后的同极化比(p)去除了方位角和异常值的影响,交叉极化比(q)受到异常值的影响。通过Oh模型中的p和vh对研究区的地表参数进行反演,17个检验点的验证结果表明,预测的土壤湿度平均相对误差为11.13%,标准差为0.0256cm3/cm3;预测的均方根高度平均相对误差为13%,标准差为0.1315cm。结果与随机粗糙地表土壤湿度和均方根高度的反演精度相当,证明了该模型的有效性。     

油菜机械直播作业厢面地表粗糙度测量与分析

油菜机械直播后种床整理质量是影响油菜成苗率的关键要素。针对目前油菜机械直播种床厢面粗糙度测量和数据处理方法不能完全反映地表真实情况的现实问题,该文提出一种基于激光雷达扫描技术的区域地表粗糙度现场测量和量化方法,通过测量装置可快速获取油菜直播厢面幅宽内的地表高程三维数据。基于油菜机械直播作业特性,对采集的作业厢面高程数据进行去倾斜趋势和去边坡趋势处理,降低了厢面倾斜和边坡特征对粗糙度计算的影响。对不同空间采样间隔和不同采样角度截面数据的粗糙度统计结果表明:170 mm采样间隔下的平均均方根高度和均方根高度平均误差均高于5 mm采样间隔下的计算结果;在垂直机组前进方向0°、45°和90°三个方向上地表截面高程数据的均方根高度最大差值和相关长度最大差值分别为7.69 mm和25.14 mm,且带有种沟等结构化特征的油菜种床厢面存在明显的各向异性。以不同大小区域的滑动取样窗口进行局部粗糙度量化的统计结果表明:当窗口宽度为厢面幅宽和0.5倍时,窗口长度大小不低于1.2 m可使均方根高度的标准差稳定在0.27 mm以内,而通过对每个取样窗口进行单独去倾斜趋势处理可消除地表局部倾斜对粗糙度计算的影响。研究结果可为油菜机械直播作业厢面粗糙度测量和量化方法提供参考依据。     

Analysing repeated measurements in soil monitoring and experimentation

Field monitoring, leaching studies, and experimentation in soil biology are often now being done non‐destructively using fixed installations so that measurements are made repeatedly on the same units. The resulting data for each unit (suction cup, lysimeter, incubation chamber) constitute a time series in which there may be autocorrelation. The usual methods of statistical analysis, such as the analysis of variance, must be modified or replaced by more suitable ones to take account of the possible correlation. This paper describes the split‐plot design of such experiments, shows how to assess the variance–covariance matrix of residuals for uniformity by the Greenhouse–Geisser statistic, and shows how to use this statistic to adjust the degrees of freedom in a formal test of significance. It also describes more recent methods. Ante‐dependence analysis identifies the extent of the temporal correlation in the data and provides approximate significance tests for the treatments. Alternatively, the paper also shows how the traditional analysis of variance may be replaced by a restricted maximum likelihood analysis which gives Wald statistics. The techniques are illustrated with data on CO₂ evolved from soil incubated for 75 days in closed chambers, during which time the gas was measured on 24 occasions to give time series for three replicates of each combination of two soils (limed and unlimed) and three types of ryegrass amendment. An ante‐dependence structure (extending to ninth order) weakened the usual significance test within the subunit stratum. The Wald statistics showed that there was, nevertheless, a strong interaction between the treatments and time.     

坡面植物生长期内对~7Be的截留吸收及影响因素分析

对坡面植物在生长期内7Be含量的动态变化进行研究,发现随着生长植物在生长期内7Be含量增加,单位面积上植物截留吸收7Be量增加;研究区坡面7Be的植被截留吸收率为3.35%~8.44%,说明植被覆盖对土壤中7Be含量有重要的影响。同时还发现,不同采样间隔期7Be日均增量不定,在整个采样期内变异性大,变异系数为0.69,但从春季到夏季的前4个采样间隔期变异性相对较小;不同采样间隔期7Be日均增量和日均降雨量呈正比,相关系数为0.64。     

Spatial scaling of soil salinity indices along a temporal coastal reclamation area transect in China using wavelet analysis

High spatial variability of soil salinity in coastal reclamation regions makes it difficult to obtain accurate scale-dependent information. The objectives of this study were to describe the spatial patterns of saline-sodic soil properties (using soil pH, electrical conductivity (EC_1:5) and sodium ion content (SIC) as indicators) and to gain knowledge of the scaling relationships between those variables. The soil pH, EC_1:5 and SIC data were measured at intervals of 285 m along a 13,965-m temporal transect in a coastal region of China. The spatial variability of soil pH was weak but it was strong for soil EC_1:5 and SIC at the measurement scale. There was a significant positive correlation between soil EC_1:5 and SIC, while correlations between soil pH and either EC_1:5 or SIC were weak and negative. For each saline-sodic soil parameter, the variability changed with the decomposition scales. The high-variance area at the larger scales (≥570 m) occupied less than 10% of the total area in the local wavelet spectrum, which meant that the spatial variations of the salinity indicators were insignificant at these scales. For local wavelet coherency, at a scale of 1500–2800 m and a sampling distance of 0–4500 m, the covariance was statistically significant between any two of the saline-sodic soil parameters.     

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.     

土壤养分类型与玉米氮磷最适施肥比例

研究证明,土壤养分耗损后至少存在平衡与不平衡土壤养分类型,两种土壤养分类型种植玉米,均以氮磷配合施用效果最佳,且存在特定施肥比例,平衡类氮磷施用比例以1∶1较佳,不平衡类氮磷施用比例为1∶0.667。     

化学工业污水灌溉对土壤中砷分布的影响——以开封市化肥河为例

对开封市郊区化肥河沿岸及全郊区土壤进行典型采样调查 ,研究了化肥厂含砷废水灌溉对土壤中砷含量的影响。结果表明 :(1 )不同灌溉方式下砷在土壤中的含量变化规律是重污灌区水浇地和菜地高于污灌混灌区耕地和菜地 ;(2 )土壤中砷含量沿化肥河以排污口为界向下游逐渐增加 ,垂直于河流流向因距河远近、不同土地利用方式也表现出明显的规律性变化特征。     

成都平原区土壤速效磷时空变化特征

根据1982年173个样点和2002年117个样点的分析数据，利用地统计学方法和GIS技术对成都平原耕层（0～20cm）土壤速效磷含量的时空变异特征进行了研究。结果表明，成都平原耕层土壤样点间速效磷平均含量由1982年的7.1mg／kg增至2002年的14.3mg／kg，平均增加了103.40％。20年间由于受区域间农业管理措施、土地利用方式和土壤颗粒组成差异的影响，占平原耕地面积75.86％的土壤速效磷含量增加，且区域增加幅度呈由东向西逐渐降低的趋势。土壤速效磷含量与土壤颗粒组成的相关性分析表明，1982年土壤速效磷含量与土壤砂粒（2～0.02mm）之间呈极显著的负相关，与土壤粉粒（0.02～0.002mm）之间相关未达到显著水平，与土壤粘粒（〈0.002mm）之间呈显著正相关；2002年土壤速效磷含量与土壤砂粒之间呈显著的负相关，与土壤粉粒之间呈极显著正相关，与土壤粘粒之间相关未达到显著水平。     

Short‐range spatial variability of metal contents in soil on a one hectare agricultural plot

Abstract

As the soil varies spatially, monitoring of temporal changes might be biased by spatial variation. Hence, there is a need to develop tools to identify spatial structures and to optimize sampling designs. This study documents the short‐range variability of lead (Pb), copper (Cu), and chromium (Cr) in a 1‐ha agricultural plot. The aims of this work were to identify the spatial structures of these metals, to discuss their origin, and to optimize sampling design for monitoring the long‐term changes of these characteristics. We used geostatistics and sampling simulations on a set of 65 individual samples. Results demonstrate that small changes in metal contents might be hidden by spatial variation if the sampling design for monitoring is not appropriate and that former land‐use history and old management practices may have long‐term significant effects on the distribution of some metals.     

石灰岩山地侧柏纯林非生长季土壤氮矿化研究

为探明石灰岩山地侧柏纯林非生长季土壤氮矿化能力,采用PVC管原位培养法对济南南部山区石灰岩山地人工恢复不同年限(5,10,25年)的侧柏纯林土壤的氮矿化能力进行研究。结果表明:3种不同恢复年限侧柏纯林NO3--N含量先升高后降低然后再有所升高,NH4+-N含量变化没有明显的规律性。其净硝化速率和净氮矿化速率均在11月份达到最大值,分别为(0.07±0.04)mg/(kg.d),(0.11±0.01)mg/(kg.d),(0.15±0.02)mg/(kg.d)和(0.15±0.04)mg/(kg.d),(0.29±0.02)mg/(kg.d),(0.33±0.03)mg/(kg.d)。石灰岩山地侧柏纯林土壤的净氮矿化速率与土壤湿度表现为二次函数关系,但随湿度增加,土壤净氮矿化能力降低。土壤净氮矿化与土壤pH值呈显著负相关(r=-0.452,p=0.018),与土壤有机碳含量、微生物量碳含量和C/N比均呈正相关关系,但相关性不显著(r=0.076,p=0.707;r=0.374,p=0.055;r=0.337,p=0.086)。非生长季石灰岩山地侧柏纯林土壤有较强的氮转化能力,气温变化所导致的土壤环境因素对氮矿化有显著影响。