A multivariate spatial model for soil water profiles

Pedotransfer functions are classes of models used to estimate soil water holding characteristics based on commonly measured soil composition data as well as other soil characteristics. These models are important on their own but are particularly useful in modeling agricultural crop yields where only soil composition is known. In this article, an additive, multivariate spatial process model is introduced that offers the flexibility to capture the complex structure typical of the relationship between soil composition and water holding characteristics, thus defining a new form of pedotransfer function. Further, the uncertainty in the soil water characteristics is quantified in a manner to simulate ensembles of soil water profiles. Using this capability, a small study is conducted with the CERES maize crop model to examine the sources of variation in the yields of maize. Here it is shown that the interannual variability of weather is a more significant source of variation in crop yield than the uncertainty in the pedotransfer function for two specific soil textures.     

Neural network ensemble residual kriging application for spatial variability of soil properties

High quality, agricultural nutrient distribution maps are necessary for precision management, but depend on initial soil sample analyses and interpolation techniques. To examine the methodologies for and explore the capability of interpolating soil properties based on neural network ensemble residual kriging, a silage field at Hayes, Northern Ireland, UK, was selected for this study with all samples being split into independent training and validation data sets. The training data set, comprised of five soil properties: soil pH, soil available P, soil available K, soil available Mg and soil available S,was modeled for spatial variability using 1) neural network ensemble residual kriging, 2) neural network ensemble and 3) kriging with their accuracies being estimated by means of the validation data sets. Ordinary kriging of the residuals provided accurate local estimates, while final estimates were produced as a sum of the artificial neural network (ANN) ensemble estimates and the ordinary kriging estimates of the residuals. Compared to kriging and neural network ensemble,the neural network ensemble residual kriging achieved better or similar accuracy for predicting and estimating contour maps. Thus, the results demonstrated that ANN ensemble residual kriging was an efficient alternative to the conventional geo-statistical models that were usually used for interpolation of a data set in the soil science area.     

Comparing sampling patterns for kriging the spatial mean temporal trend

In monitoring the environment one often wishes to detect the temporal trend in a variable that varies across a region. A useful executive summary is then the temporal trend in the spatial mean. In this article, the best linear unbiased predictor of the spatial mean temporal trend and its variance are derived under a universal kriging model. Five different, spatially explicit sampling patterns are compared in terms of this variance. For small spatial ranges, the time-separation pattern is optimal, regardless of the temporal range. For larger spatial ranges, the best pattern depends on the temporal range. If the temporal range is small (less than ca. 1/20 of the spatial range), then the always-revisit pattern is best. For larger temporal ranges (between ca. 1/20 and ca. 1/4 of the spatial range) the serially alternating pattern is best, whereas the time-separation pattern is best for larger temporal ranges. The gain in using the serially alternating pattern or time-separation pattern instead of the always-revisit pattern can be substantial. The potential loss with respect to the always-revisit pattern is only minor for the serially alternating pattern, but can be substantial for the time-separation pattern. Unless one has good knowledge of the spatial and temporal range, the serially alternating pattern is thus a good choice. This work extends that by Urquhart and Kincaid and gives further support to their plea for the serially alternating pattern.     

A comparison of kriging, cubic splines and classification for predicting soil properties from sample information

The clay content of the topsoil in two regions of contrasting physiography was predicted from sample data using four different procedures. The predictors were the means of mapped classes, the usual kriging estimator, a cubic spline interpolator and a kriging estimator within classes using a pooled within-class variogram. The performances of the procedures were evaluated and compared. In the first region, Sandford St Martin on Jurassic sediments where there were some abrupt changes in soil, the classification predicted best within those classes bounded by sharp change. Elsewhere the usual kriging performed somewhat better, and kriging within classes was still more precise. In the second region, Yenne on the alluvial plain of the Rhone where the soil varied gradually, kriging performed better than classification, though a small improvement resulted from combining kriging with classification. Both prediction by class means and kriging attempt to minimize the estimation variance, and their mean prediction variances were close to the theoretical values overall. Spline interpolation is more empirical, and though it followed the abrupt changes better than kriging, it fluctuated excessively elsewhere, and its overall performance was poorer than that of kriging.     

不同尺度下土壤盐分空间变异的指示克里格评价

针对目前黄淮海平原盐渍土改良区存在的土壤盐渍障碍问题,以该区域典型县域禹城市为研究对象,综合运用GIS和非参数地质统计学的指示克立格法,对县级和镇级两个采样尺度下0～20 cm耕层土壤盐分的空间变异性进行了分析,并给出了土壤盐分满足一定条件的概率分布图.结果表明,两个采样尺度下土壤盐分均不符合正态分布且都存在特异值,但...     

基于多元地统计的土壤有机质含量空间格局反演

为了提高土壤有机质含量的空间预测精度,该文采用了一种多元地统计方法来构建遥感定量反演模型。考虑到回归误差在空间上具有一定程度的聚类,该文提出了基于局部变化均值的普通克里金方法,然后用其构建土壤有机质含量遥感定量反演模型。对四川省西南部土壤有机质含量进行空间预测试验,并与普通克里金、普通遥感定量反演、基于回归克里金的遥感定量反演等方法相比较。结果表明:该文提出方法的空间预测结果最优,其原因为该方法通过空间统计来建立采样数据与地表反射率间的联系,充分考虑了数据间的空间相关性,因此可以更精确地获得土壤有机质含量的遥感反演模型;相比基于回归克里金的遥感定量反演方法,基于局部变化均值的普通克里金假设回归误差在局部邻域内的均值也不一定为零,更符合实际情况。该方法为农田养分管理及区域农业的可持续发展提供科学依据。     

UV spectral fingerprinting and analysis of variance-principal component analysis: a useful tool for characterizing sources of variance in plant materials

UV spectral fingerprints, in combination with analysis of variance-principal components analysis (ANOVA-PCA), can differentiate between cultivars and growing conditions (or treatments) and can be used to identify sources of variance. Broccoli samples, composed of two cultivars, were grown under seven different conditions or treatments (four levels of Se-enriched irrigation waters, organic farming, and conventional farming with 100 and 80% irrigation based on crop evaporation and transpiration rate). Freeze-dried powdered samples were extracted with methanol-water (60:40, v/v) and analyzed with no prior separation. Spectral fingerprints were acquired for the UV region (220-380 nm) using a 50-fold dilution of the extract. ANOVA-PCA was used to construct subset matrices that permitted easy verification of the hypothesis that cultivar and treatment contributed to a difference in the chemical expression of the broccoli. The sums of the squares of the same matrices were used to show that cultivar, treatment, and analytical repeatability contributed 30.5, 68.3, and 1.2% of the variance, respectively.     

Integrating soil map information in modelling the spatial variation of continuous soil properties

This paper presents two indicator algorithms that integrate soil map information into modelling the spatial variation of continuous soil properties: these are simple indicator kriging with varying means and the Markov–Bayes algorithm. Both methods are used to evaluate probabilities for copper and cobalt deficiencies in the Borders Region of Scotland. Results are compared with maps obtained by the polygonal method (Thiessen polygons) and an indicator kriging algorithm that does not use soil map information. Accounting for soil map information is shown to improve delineation of the deficient areas, especially where the sampling is sparse. Test locations are classified as deficient or not so as to minimize an expected cost of mis-classification that is derived from local probability distributions of copper or cobalt and functions measuring the cost of overestimating or underestimating metal concentrations. The comparison of classification results with actual copper and cobalt concentrations at test locations shows that the two proposed algorithms can decrease substantially the economic loss attached to misclassification.     

土壤结构的数字图像分析方法与指标

自然地理过程的水、热应力及人为过程的机械应力可以促成土壤结构体的形成,然而土壤结构的分析方法与描述指标仍然较为局限,筛分法仅能提供土壤结构的尺度分布信息,感官评价法受主观影响也较大。本文探讨数字图像处理技术定量土壤结构并提供3个外观几何指标,分别从土壤结构尺度分布、矩形度、棱角性、形状指数4个方面评价黄棕壤块状结构体。结果表明自然地理过程作用下黄棕壤呈块状分布且随结构体尺度的增大,结构体的形状指数及棱角性呈增大的趋势,而矩形度则不断降低。所提供的数字图像分析方法快捷方便,较之筛分法能够提供更多的土壤结构信息,所得土壤结构尺度分布模型更加准确,本方法也可作为实时实地分析耕作过程土壤结构的变化。     

Power analysis based on spatial effects mixed models: A tool for comparing design and analysis strategies in the presence of spatial variability

Spatial variability among experimental units is known to exist in many designed experiments. Agronomic field trials are a particularly well-known example, but there are others. Historically, spatial variability has been dealt with in one of two ways: either though design, by blocking to account for spatial effects, or though analysis, by nearest neighbor adjustment. More recently, mixed models with spatial covariance structures such as those used in geostatistics have been proposed. These mixed model procedures have tempted some to conclude—to the dismay of many consulting statisticians—that design principles may be bypassed, since spatial covariance models can recover any lost information. Although design principles clearly should not be ignored, spatial procedures do raise questions. Are traditional notions of appropriate design affected? If so, how? How do spatial effects mixed models compare to conventional analysis of variance used in conjunction with blocked designs? This article presents mixed model methods to assess power and precision of proposed designs in the presence of spatial variability and to compare competing design and analysis strategies. The main conclusion is that, if anything, spatial models reinforce the need for sound design principles, particularly the use of incomplete block designs.     

Divergent evolution and the spatial structure of soil landscape variability

The spatial structure of soil variability at the landscape scale was examined on adjacent geomorphic surfaces dating from 80 to 200 ka in eastern North Carolina. The purpose was to determine whether there is evidence at broader scales (distances of 10²–10⁴ m) for the divergent evolution observed in the field at very detailed scales (distances of 10⁰–10² m). The state probability function (SPF), which measures spatial dependence for categorical environmental data along a transect, was applied to soil series mapped at a 1:24,000 scale. The older Talbot Terrace and younger Pamlico Terrace surfaces showed distinctly different patterns of spatial variability. The range of spatial dependence was shorter on the older surface (about 200 vs. 300 m), and the SPF was higher at any given distance, indicating more variability. The SPF for the Pamlico surface also indicates a periodicity related to fluvial dissection of the landscape, which is not readily detectable on the Talbot transect despite its greater degree of dissection. The results confirm earlier field studies which suggest that pedogenesis is marked by divergence, whereby differences in initial conditions or local perturbations persist and increase to produce a more variable soil cover.     

Discrimination of management effects on soil parameters by using principal component analysis: a multivariate analysis case study

One of the major interests in soil analysis is the integrated evaluation of soil properties, which might be indicators of soil quality. Unsupervised methods of multivariate statistics are powerful tools for this integrated assessment and can help soil researchers to extract much more information from their data. A multivariate study was carried out in three farms from Guaíra, State of São Paulo, Brazil. Conventionally managed plots that intensively utilized pesticides and chemical fertilizers were compared with both non-disturbed forest areas and alternatively managed plots. The latter were under ecological farming employing effective microorganisms (EM) integrated with crop residues. Eight soil parameters were determined for each plot. Hierarchical cluster analysis (HCA) was used to verify the similarity among the plots. The multivariate approach of principal component analysis (PCA) allowed us to distinguish the areas as a function of the soil management and determine which are the most important parameters to characterize them. The forest areas presented higher microbial biomass with lower cellulolytics population than at cultivated sites. The alternative plots were characterized by higher microbial biomass and polysaccharide content with lower phosphate solubilizers and cellulolytics microorganisms colony counts than at the conventional areas. The higher observed levels of microbial biomass and polysaccharide content in the alternative areas can be attributed to the effects of the alternative soil amendment. All these effects can be clearer globally visualized with the aid of PCA, through the biplots.     

Biomimetic chemistry as a useful tool for studying reactive metabolites of pesticides

Most organophosphate (OP) pesticides require metabolic activation before attacking the target site, as opposed to chemical nerve agents, such as VX and sarin, which inhibit the enzyme directly. The majority of OP pesticides exhibit weak anticholinesterase activity in vitro compared to their In Vivo activity. Biooxidation is probably the principal route by which these pesticides are activated or detoxified. The oxidized product, usually a short-lived intermediate, may either hit the target directly or hydrolyze rapidly or, following a rearrangement reaction, convert to another species with enhanced reactivity (metaphosphate) or lose its phosphorylation or carbamoylation properties. Biomimetic studies of these processes, using various model systems, have important advantages: in some cases they allow for identifying short-lived intermediates, formed metabolically, and direct monitoring of the systems' properties by NMR. Once identified, they may be synthesized in large amount to investigate their adverse effects, if any. Biomimetic studies allow for monitoring reactions at low temperature seeking transient intermediates and evaluation of activation and detoxification mechanisms as well as mode of action based on chiral isomers. This, in turn, allows for determination of whether certain compounds act directly, on preactivation, or both, and the possible design of safer pesticides. This paper covers over three decades of extensive fundamental and applied research that has been carried out at the Environmental Chemistry and Toxicology Laboratory (ECTL) at the University of California at Berkeley under the supervision of Prof. John E. Casida.     

基于三维普通克立格方法的滨海盐土电导率三维空间变异研究

土壤是一个三维自然空间实体,人们关心的不仅是二维平面上的信息,还包括垂直方向的第三维信息,但是目前土壤属性空间变异研究主要集中在水平方向上。即使是以土壤属性三维变异特性描述为目的的研究,也仅仅局限于用一系列水平层来描述土壤不同深度土层的属性变化,没有考虑上下层之间的相互影响[1]。土壤盐分作为滨海盐土区农业生产的一个重要限制性因子,影响着土壤质量和作物产量,严重时导致农用土壤的荒弃[2]。1m深度范围内,滨海盐土的盐分含量随降雨、蒸发、微地形等因素变化明显[3]。因此,研究滨海盐土三维土体盐分的空间变异规律对于海涂土壤改良、滨海盐土区农业的精确管理具有重要意义。通常三维空间的原始数据均是离散的     

Two robust estimators of the cross‐variogram for multivariate geostatistical analysis of soil properties

If we wish to describe the coregionalization of two or more soil properties for estimation by cokriging then we must estimate and model their auto‐ and cross‐variogram(s). The conventional estimates of these variograms, obtained by the method‐of‐moments, are unduly affected by outlying data which inflate the variograms and so also the estimates of the error variance of cokriging predictions. Robust estimators are less affected. Robust estimators of the auto‐variogram and the pseudo cross‐variogram have previously been proposed and used successfully, but the multivariate problem of estimating the cross‐variogram robustly has not yet been tackled. Two robust estimators of the cross‐variogram are proposed. These use covariance estimators with good robustness properties. The robust estimators of the cross‐variogram proved more resistant to outliers than did the method‐of‐moments estimator when applied to simulated fields which were then contaminated. Organic carbon and water content of the soil was measured at 256 sites on a transect and the method‐of‐moments estimator, and the two robust estimators, were used to estimate the auto‐variograms and cross‐variogram from a prediction subset of 156 sites. The data on organic carbon included a few outliers. The method‐of‐moments estimator returned larger values of the auto‐ and cross‐variograms than did either robust estimator. The organic carbon content at the 100 validation sites on the transect was estimated by cokriging from the prediction data plus a set of variograms fitted to the method‐of‐moments estimates and two sets of variograms fitted to the robust estimates. The ratio of the actual squared prediction error to the cokriging estimate of the error variance was computed at each validation site. These results showed that cokriging using variograms obtained by the method‐of‐moments estimator overestimated the error variance of the predictions. By contrast, cokriging with the robustly estimated variograms gave reliable estimates of the error variance of the predictions.     

Effect of forest structure on the spatial variation in soil respiration in a Bornean tropical rainforest

This study was undertaken to identify critical and practical factors explaining spatial variations in soil respiration and to estimate stand-scale soil respiration in an aseasonal tropical rainforest on Borneo Island. To this aim, we conducted soil respiration measurements at 25 points in a 40 m × 40 m subplot of a 4 ha study plot between 2002 and 2006, and examined the spatial variation in soil respiration averaged over the 4 years in relation to soil, root, and forest structural factors. In addition, we examined the spatial representativeness of soil respiration measured in the subplot using a specific scaling procedure. Consequently, we found significant positive correlation between the soil respiration and forest structural parameters such as the mean diameter at breast height (DBH), total basal area, and maximum DBH within 6 m of the measurement points. The most important factor was the mean DBH within 6 m of the measurement points, which had a significant linear relationship with soil respiration. Using the derived linear regression and an inventory dataset, we estimated the 4 ha plot-scale soil respiration. The 4 ha plot-scale estimation (6.0 μmol m⁻² s⁻¹) was nearly identical to the subplot-scale measurements (5.7 μmol m⁻² s⁻¹), which were roughly comparable to the nocturnal CO₂ fluxes calculated using the eddy covariance technique. In addition, we discuss characteristics of the stand-scale soil respiration at this site by comparing with those of other forests reported in previous literature in terms of the soil C balance. Soil respiration at our site was noticeably greater, relative to the incident litterfall amount, than soil respiration in other tropical and temperate forests probably owing to the larger total belowground C allocation by emergent trees. Overall, this study suggests the arrangement of emergent trees with larger DBH and their belowground C allocation could be primary factors controlling spatial variations in soil respiration in the tropical rainforest.     

Disaggregation of legacy soil data using area to point kriging for mapping soil organic carbon at the regional scale

Legacy data in the form of soil maps, which often have typical property measurements associated with each polygon, can be an important source of information for digital soil mapping (DSM). Methods of disaggregating such information and using it for quantitative estimation of soil properties by methods such as regression kriging (RK) are needed. Several disaggregation processes have been investigated; preferred methods include those which include consideration of scorpan factors and those which are mass preserving (pycnophylactic) making transitions between different scales of investigation more theoretically sound. Area to point kriging (AtoP kriging) is pycnophylactic and here we investigate its merits for disaggregating legacy data from soil polygon maps. Area to point regression kriging (AtoP RK) which incorporates ancillary data into the disaggregation process was also applied. The AtoP kriging and AtoP RK approaches do not involve collection of new soil measurements and are compared with disaggregation by simple rasterization. Of the disaggregation methods investigated, AtoP RK gave the most accurate predictions of soil organic carbon (SOC) concentrations (smaller mean absolute errors (MAEs) of cross-validation) for disaggregation of soil polygon data across the whole of Northern Ireland.Legacy soil polygon data disaggregated by AtoP kriging and simple rasterization were used in a RK framework for estimating soil organic carbon (SOC) concentrations across the whole of Northern Ireland, using soil sample data from the Tellus survey of Northern Ireland and with other covariates (altitude and airborne radiometric potassium). This allowed direct comparison with previous analysis of the Tellus survey data. Incorporating the legacy data, whether from simple rasterization of the polygons or AtoP kriging, substantially reduced the MAEs of RK compared with previous analyses of the Tellus data. However, using legacy data disaggregated by AtoP kriging in RK resulted in a greater reduction in MAEs. A jack-knife procedure was also performed to determine a suitable number of additional soil samples that would need to be collected for RK of SOC for the whole of Northern Ireland depending on the availability of ancillary data. We recommend i) if only legacy soil polygon map data are available, they should be disaggregated using AtoP kriging, ii) if ancillary data are also available legacy data should be disaggregated using AtoP RK and iii) if new soil measurements are available in addition to ancillary and legacy soil map data, the legacy soil map data should be first disaggregated using AtoP kriging and these data used along with ancillary data as the fixed effects for RK of the new soil measurements.     

Labelling the salt content in foods: a useful tool in reducing sodium intake in Finland

OBJECTIVE: To estimate the impact of choosing food products labelled either as low or high in salt on salt intake in the Finnish adult population. SETTING AND SUBJECTS: The National FINDIET 2002 survey with 48-hour recalls from 2007 subjects aged 25-64 years. Sodium intake was calculated based on the Fineli food composition database including the sodium content of natural and processed foods as well as the salt content of recipes. The distribution of salt intake was calculated in different ways: the present situation; assuming that all breads, cheeses, processed meat and fish, breakfast cereals and fat spreads consumed would be either 'lightly salted' or 'heavily salted' based on the current labelling practice; and, in addition, assuming that all foods would be prepared with 50% less or more salt. RESULTS: Excluding underreporters, the mean salt intake would be reduced by 1.8 g in men and by 1.0 g in women if the entire population were to choose lightly salted products and further by 2.5 and 1.8 g, respectively, if also salt used in cooking were halved. Choosing heavily salted products would increase salt intake by 2.1 g in men and by 1.4 g in women. In the worst scenarios, salt intake would be further increased by 2.3 g in men and by 1.6 g in women. CONCLUSIONS: These calculations show that the potential impact of labelling and giving consumers the possibility to choose products with less salt is of public health importance. In addition, strategies to reduce the salt content of all food groups are needed.     

A kriging approach to the analysis of climate model experiments

A climate model is a computer implementation of a mathematical model for the physical, chemical and biological processes underlying the climate. An immediate use of a climate model is in performing climate model experiments, where uncertain input quantities, such as greenhouse gas and aerosol concentrations, are systematically varied to gain insight into their effects on the climate system. Climate models are computationally intensive, allowing only small experiments. We present a multidimensional kriging method to predict climate model variables at new inputs, based on the experimental data available. The method is particularly suitable for situations in which the climate model data sets share a common pattern across the input space, such as surface temperatures that are lower at the Poles, higher at the Equator, and increasing over time. The results demonstrate the potential of our kriging method as an exploratory tool in climate science.