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.     

Why soil erosion models over-predict small soil losses and under-predict large soil losses

Evaluation of various soil erosion models with large data sets have consistently shown that these models tend to over-predict soil erosion for small measured values, and under-predict soil erosion for larger measured values. This trend appears to be consistent regardless of whether the soil erosion value of interest is for individual storms, annual totals, or average annual soil losses, and regardless of whether the model is empirical or physically based. The hypothesis presented herein is that this phenomenon is not necessarily associated with bias in model predictions as a function of treatment, but rather with limitations in representing the random component of the measured data within treatments (i.e., between replicates) with a deterministic model. A simple example is presented, showing how even a `perfect' deterministic soil erosion model exhibits bias relative to small and large measured erosion rates. The concept is further tested and verified on a set of 3007 measured soil erosion data pairs from storms on natural rainfall and run-off plots using the best possible, unbiased, real-world model, i.e., the physical model represented by replicated plots. The results of this study indicate that the commonly observed bias, in erosion prediction models relative to over-prediction of small and under-prediction of large measured erosion rates on individual data points, is normal and expected if the model is accurately predicting erosion rates as a function of environmental conditions, i.e., treatments.     

基于多时相土壤线一致性修正的土壤含水率反演

传统上依赖改进型垂直干旱指数（modified perpendicular dryness index,MPDI）进行土壤水分反演时每个时期的影像反演都需要依赖于地面实测数据进行校准。为降低土壤含水率反演对实测数据的依赖,该研究利用2020—2021年间的哨兵2号卫星数据,分析了近红外与红光波段特征空间中土壤线斜率的变化及其影响因素。并推导了土壤线斜率变化对土壤含水率反演的影响,揭示了MPDI反演土壤含水率时每期都依赖实测数据校准的根本原因,最终提出了一种土壤线一致性修正方法。基于这种修正,该研究构建了一个能够多时相比较的再修正干旱指数（re-modified perpendicular drought index,RPDI）。结果表明,经过统一率定的RPDI与土壤含水率的回归方程在不同时相的影像上均适用,反演结果显示了良好的精度,率定集决定系数R²为0.49,无偏均方根误差为2.88%,验证集决定系数R²为0.54,无偏均方根误差为3.05%,与MPDI每期单独构建回归方程反演相比,RPDI基于统一方程反演与其保持了相近的精度水平,极大减少了在遥感土壤含水率估算中对地面实测数据的依赖,有效提升了遥感技术在土壤水分监测中的应用价值。研究可为光学遥感数据在大范围连续土壤水分反演领域的应用研究提供参考。     

A soil fauna index for assessing soil quality

The characteristics of the faunal community in soil are closely related to soil quality. Using the soil faunal community as an indicator of soil quality is not cost-effective because of the complex taxonomic distinction and identification required, moreover; the power of the assessment is weak in relation to soil function. Recently, a functional method incorporating eco-morphological traits has been proposed, but it depends upon presence/absence data of soil arthropod community exclusively. To overcome the limitation, we designed a novel index using the diversity of the soil faunal community along with its functional traits, and the abundance of its members (Abundance-based Fauna Index, referred as FAI). The FAI method supports both the presence/absence data and its abundance. Using real and simulated data sets with eco-morphological and niche breadth traits, two taxonomic independent functional traits, we tested associations of FAI values with soil quality and found a good relationship. In addition to its usefulness for measuring soil quality, FAI may provide valuable information for linking functional traits of soil fauna and the belowground environmental condition.     

The development of detailed soil maps on the basis of interpolation of data on soil properties

A methodology for creating detailed soil maps on the basis of a dense grid of soil testing points and the numerical interpolation of experimental data on the soil properties is discussed. The study of the soil cover patterns combines regular sampling grids with equal spacing and additional sampling points chosen with due account for the soil cover specificity in particular areas. Soil diagnostics are performed at each of the points, and the diagnostic features of the soils are recorded in the field. In a laboratory, these data are arranged into a database, and a legend to the soil map is created. The necessary and sufficient set of the quantitative soil characteristics is selected, and quantitative criteria of the boundaries between the separate soil polygons are determined on the basis of numerical interpolation. Algorithms to delineate soil polygons on the basis of the selected indices are developed. Separate thematic map layers are produced for each of the selected soil characteristics. An integral soil map for the investigated area is obtained via the superposition of these layers. The thickness and/or the depths of the upper/lower boundaries of the soil layer with definite diagnostic characteristics making it possible to distinguish the given soil from its neighbors are used as the criteria for delineating the boundaries between soil polygons. Special criteria based on the proportions between the thicknesses or depths of several layers can also be applied for this purpose. The creation of a detailed soil map of a plot on the Kamennaya Steppe is discussed as an example of the practical application of this methodology.     

依据传统土壤数据应用数字土壤制图法对香港山地地区土壤建立土纲信息

Based on legacy soil data from a soil survey conducted recently in the traditional manner in Hong Kong of China, a digital soil mapping method was applied to produce soil order information for mountain areas of Hong Kong. Two modeling methods (decision tree analysis and linear discriminant analysis) were used, and their applications were compared. Much more eflort was put on selecting soil covariates for modeling. First, analysis of variance (ANOVA) was used to test the variance of terrain attributes between soil orders. Then, a stepwise procedure was used to select soil covariates for linear discriminant analysis, and a backward removing procedure was developed to select soil covariates for tree modeling. At the same time, ANOVA results, as well as our knowledge and experience on soil mapping, were also taken into account for selecting soil covariates for tree modeling. Two linear discriminant models and four tree models were established finally, and their prediction performances were validated using a multiple jackknifing approach. Results showed that the discriminant model built on ANOVA results performed best, followed by the discriminant model built by stepwise, the tree model built by the backward removing procedure, the tree model built according to knowledge and experience on soil mapping, and the tree model built automatically. The results highlighted the importance of selecting soil covariates in modeling for soil mapping, and suggested the usefulness of methods used in this study for selecting soil covariates. The best discriminant model was finally selected to map soil orders for this area, and validation results showed that thus produced soil order map had a high accuracy.     

Multiscale sources of spatial variation in soil. II. A non-Brownian fractal model and its application in soil survey

Stochastic fractals (for example, the fractional Brownian noises) model the often observed power law relation between the variance of a soil property and the length of transect sampled, but fail to account for abrupt changes of the mean (i.e. soil boundaries), for second-order stationarity and for the non self-similarity of variations at different scales that are observed in real data. This paper proposes a non-Brownian, nested model to account for situations where differences of soil have been caused by superimposed, independently acting soil-forming processes having different weights and acting at separate, discrete scales. The model is explained and theoretical examples of the semivariograms and confidence limits that arise from it are given. The model is applied to three sets of experimental data and is found to give a remarkably good fit where lateral mixing of soil has been negligible and soil boundaries are sharp. The implications for efficient mapping in situations where the soil results from a number of superimposed, independent causes are discussed.     

黑土土壤水分反射光谱特征定量分析与预测

选择单一土类黑土作为研究对象, 并准确调配其不同含水量,实验室测定土壤高光谱反射率,利用光谱分析与统计方法,定量描述了不同含水量黑土反射光谱特征,并建立了黑土含水量反射光谱预测模型,结果表明,随土壤含水量的增加,达到一定阈值（300 g kg-1）,反射率存在过饱和现象,但其倒数对数微分可以有效去除饱和问题;土壤反射率倒数对数微分对土壤含水量的响应表现出三个变化阶段,导致1 870 nm波段的倒数对数微分也表现为非线性变化,需要利用分段函数进行土壤含水量的光谱精确速测。     

Automated soil map compilation

Abstract

A soil map is eonventionally prepared by an experieneed surveyor via the following three steps; (1) establishment of taxonomie class, (2) alloeation of sam pie into one of the classes, and (3) delineation of homogeneous areas in terms of mapping unit. These steps involve some degree of arbitrariness; thus soil maps prepared by two surveyors are never identical. The aim of this study is to define a eertain proeedure of soil map eompilation, by introdueing numerical handling of soil data, to obtain a reproducible and easy-to-prepare soil map, with the help of the funetions of the eomputer-based Soil Data Management System (COSMAS).

The authors applied Hayashi's theory of quantification No. 3 to numerical representation of soil profiles based on the pattern of eombination of various soil attributes relevant to soil classification. The following four soH types were recognized in the seattergram plotted using numerical va lues assigned to the soH profiles; Gley Lowland SoH, Gray Lowland Soil, Brown Lowland Soil and Pseudogley SoH. Then, using these numerical values, diseriminant analysis was carried out to classify each profile into one of the above-defined soil types. As a result, 89.7% of the observed profiles were assigned to the same soil types as assigned by a surveyor in the filed. Area delineation for each mapping unit on the basis of soil type assignment and probabHity of membership of a respective soil type group at a sampled si te was automatieally performed by an "AUTOMAP" program whieh was newly developed for COSMAS for graphic representation of soil data. The numerically prepared soi! map showed reasonable agreement with the surveyor's. A wide range of users of soil survey data can prepare various maps using the procedure proposed in this paper.     

Multiscale sources of spatial variation in soil. I. The application of fractal concepts to nested levels of soil variation

Soil variation has often been considered to be composed of‘functional’ or ‘systematic’ variation that can be explained, and random variation (‘noise’) that is unresolved. The distinction between systematic variation and noise is entirely scale dependent because increasing the scale of observation almost always reveals structure in the noise. The white noise concept of a normally distributed random function must be replaced to take into account the nested, autocorrelated and scale-dependent nature of unresolved variations. Fractals are a means of studying these phenomena. The Hausdorff-Besicovitch dimension D is introduced as a measure of the relative balance between long- and short-range sources of variation; D can be estimated from the slope of a double logarithmic plot of the semivariogram. The family of Brownian linear fractals is introduced as the model of ideal stochastic fractals. Data from published and unpublished soil studies are examined and compared with other environmental data and simulated fractional Brownian series. The soil data are fractals because increasing the scale of observation continues to reveal more and more detail. But soil does not vary exactly as a Brownian fractal because its variation is controlled by many independent processes that can cause abrupt transitions or local second order stationarity. Estimates of D values show that soil data usually have a much higher proportion of short-range variation than landform or ground water surfaces. The practical implication is that interpolation of soil property values based on observations from single 10 cm auger observations will be unsatisfactory and that some method of bulking or block kriging should be used whenever longrange variations need to be mapped.     

Measuring soil disturbance effects and assessing soil restoration success by examining distributions of soil properties

Successful restoration of an ecosystem following disturbance is typically assessed according to similarity between the restored site and a relatively undisturbed reference area. While most comparisons use the average or mean parameter to represent measured properties, other aspects of the distribution, including the variance of the properties may assist in a more robust assessment of site recovery. Our purpose was to compare soil properties in different ages of reclaimed soils with those in reference areas by incorporating the potentially different distributions according to areas. On two sampling dates, in consecutive years, we examined soil properties on a chronosequence of reclaimed natural gas pipelines spanning recovery ages of <1–54 years, obtaining data on soil moisture, organic carbon, nitrogen, electrical conductivity, pH, and microbial abundance. To make the comparisons, we analyzed our data with a Bayesian hierarchical linear mixed model and obtained posterior predictive distributions for the soil properties. This allowed us to probabilistically quantify the extent to which a soil property from a reclaimed treatment was similar to that from an undisturbed reference. We found that the posterior predictive variance of most soil properties was particularly sensitive to disturbance and reclamation, especially, within the first few years of recovery. Response of this variance to disturbance, reclamation, and recovery was not necessarily accompanied by a shift in the posterior predictive mean value of the property. Patterns for all soil properties changed over time, with posterior predictive distributions of soil properties generally becoming more similar to those of the undisturbed reference sites as recovery time increased. We suspect these trends in altered variability coincide with the degree of spatial heterogeneity in soil properties that results following disturbance and reclamation, which is also coupled to patterns of vegetation recovery.     

The rice soils of Cambodia. II. Statistical discrimination of soil properties by the Cambodian Agronomic Soil Classification system (CASC)

Abstract. A novel agronomic system of soil classification for managing rice soils in Cambodia (CASC) was developed recently. It integrates local knowledge about the soil with its conceptual and taxonomic understanding by soil scientists. Using quantitative soil survey data we evaluated the agronomic efficacy of the classification system. Although the CASC is based on simple field criteria it explained 25 to 44% of the variation in soil prop-erties relevant for agronomic decision making. It failed, however, to differentiate soil types based on pH (6% of variances explained). Despite its simplicity it performed as well or better than the most widely used soil map in Cambodia (Crocker, 1962), and unlike this small-scale soil map it allows classification on a field-specific basis. The average values of soil properties were often significantly different between groups. Organic carbon, clay content and plant available magnesium and calcium contributed most to the discrimination of CASC soil groups. The predictive accuracy when allocating new samples on the basis of quantitative survey data to soil groups of the CASC was 50% to 100%, except for soil groups Kein Svay (0%) and Kampong Siem (20%). The CASC is valuable for managing Cambodian rice soils and may also be used for up-scaling and mapping of soil information.     

土壤环境质量数据库设计与实现

本文在研究和分析现有国内外土壤数据库建设方法及其应用现状的基础上,参考国内外土壤数据库设计与管理的相关标准和行业规范,探讨了土壤环境质量数据库的编码设计、结构设计及数据组织与存储方式,并以江苏省土壤环境质量数据为案例建立了示范性的土壤环境质量数据库管理系统。     

Testing the public–private soil data and information sharing model for sustainable soil management outcomes

Soil data form the basis of soil information systems across the globe. Soil information needs, and the questions posed by users, are likely to evolve in response to advances in technology in this era of Big Data. This poses a challenge to the pedological community which is already experiencing a decline in soil knowledge and expertise. With a decrease in soil data collection by governments, it is timely to reconsider how and what soil information should be provided to future users. A public–private partnership is advocated to deliver timely and accessible soil information to users. Two public–private provisioning programs are presented, and advantages and considerations for sharing soil data and information amongst industry, government, research organizations, service providers and land managers for these are discussed. Interoperable, open‐source and agreed soil community standards are used to present soil data and information through spatial web portals with tools for interpretation of soil data for public and private beneficiaries.     

Test of a combined soil moisture/soil heat simulation model on a bare field soil in Southern Turkey

In order to simulate soil suction and soil temperature, a field study was carried out in the experimental farm of the Agricultural Faculty, University of Cukurova (Adana/TURKEY), for a period of two years. During this period the soil was bare. Data collected regularly include soil suction and soil temperature. Meteorological data were obtained from the Adana Climatological Station. Data on soil suction and soil temperature were used to calibrate the simulation model, proposed by Huwe and van der Ploeg (1988). It was found that the model, after having been calibrated, performed quite well. A fair agreement between measured and simulated soil temperatures was obtained. However, the calculated and measured soil suction values were found to be considerably different. The simulation model used in this study appears to be applicable for the conditions of the Cukurova Region in Southern Turkey.     

Field-scale spatial variability of soil calcium in a semi-arid region: Implications for soil erosion and site-specific management

Excess calcium(Ca) in soils of semi-arid and arid regions has negative effects on soil structure and chemical properties, which limits the crop root growth as well as the availability of soil water and nutrients. Quantifying the spatial variability of soil Ca contents may reveal factors influencing soil erosion and provide a basis for site-specific soil and crop management in semi-arid regions. This study sought to assess the spatial variability of soil Ca in relation to topography, hydraulic attributes, and soil types for precision soil and crop management in a 194-ha production field in the Southern High Plains of Texas,USA. Soils at four depth increments(0–2, 0–15, 15–30, and 30–60 cm) were sampled at 232 points in the spring of 2017. The Ca content of each sample was determined with a DP-6000 Delta Premium portable X-ray fluorescence(PXRF) spectrometer. Elevation data was obtained using a real-time kinematic GPS receiver with centimeter-level accuracy. A digital elevation model(DEM) was derived from the elevation data, and topographic and hydraulic attributes were generated from this DEM. A generalized least-squares model was then developed to assess the relationship between soil Ca contents of the four layers and the topographic and hydraulic attributes. Results showed that topographic attributes, especially slope and elevation, had a significant effect on soil Ca content at different depths(P 0.01). In addition, hydraulic attributes, especially flow length and sediment transport index(STI), had a significant effect on the spatial distribution of soil Ca. Spatial variability of soil Ca and its relationships with topographic and hydraulic attributes and soil types indicated that surface soil loss may occur due to water or wind erosion, especially on susceptible soils with high slopes. Therefore, this study suggests that the application of PXRF in assessing soil Ca content can potentially facilitate a new method for soil erosion evaluation in semi-arid lands. The results of this study provide valuable information for site-specific soil conservation and crop management.     

Influence of soil moisture on sorption and degradation of hexazinone and simazine in soil.

Sorption and degradation rates of hexazinone and simazine on soil were determined in a sandy loam soil incubated, during 44 days, at 25 degrees C with moisture contents ranging from 4% to 18%. Herbicide levels in soil solution were also measured, after extraction of this solution by a centrifugation method. All experiments were conducted with treated soil in plastic columns, and the results showed that this method is suitable for the simultaneous study of pesticide sorption and degradation in soil at different environmental conditions. In general, sorption of both herbicides was higher for aged herbicide residues compared to recently applied herbicides, and soil subjected to drying and rewetting cycles had the highest sorption values. K(f) values ranged from 0.5 to 1.2 for simazine and from 0.2 to 0.4 for hexazinone. Degradation rates increased with soil moisture content for both herbicides, and drying-rewetting of soil yielded degradation rates slower than that obtained at 10% soil moisture content. Hexazinone concentration in soil solution decreased with incubation time faster than simazine.     

Evaluating the impact of soil management on soil loss in olive orchards

Abstract. The effects of soil management on soil losses from olive plantations in southern Spain were evaluated using the Revised Universal Soil Loss Equation (RUSLE), a review of published experiments, and preliminary results of an on-going field trial. Experimental data were used to parameterize the RUSLE for olive orchards under various soil management regimes. The predictions agreed qualitatively with the data available, and the model provided a simple way to assess the effects of soil management on erosion. Our results showed that no-tillage caused the greatest soil loss, while cover crops showed the least. Tillage and planting following contours proved only partially effective and did not reduce soil erosion as much as protective crops. One scenario studied suggests that, on slight to moderate slopes, land transformed from row crops to olive orchards may remain below the maximum tolerable soil erosion limit, if a cover crop is included between the trees. A scenario for marginal olive orchards located on steep slopes suggests that effective erosion control could only be achieved with a cover crop system that would have the side-effect of reducing the yield of rain-fed olives. Quantifying the effects of soil management on soil erosion in olive orchards is uncertain because very few experimental results are available. Further research that monitors soil loss in carefully selected long-term experiments at different scales and follows the changes in key soil parameters is urgently required to develop effective erosion control policies.