National soil data in EU countries,where do we stand?

At the European scale, soil characteristics are needed to evaluate soil quality, soil health and soil-based ecosystem services in the context of the European Green Deal. While some soil databases exist at the European scale, a much larger wealth of data is present in individual European countries, allowing a more detailed soil assessment. There is thus an urgent and crucial need to combine these data at the European scale. In the frame of a large European Joint Programme on agricultural soils launched by the European Commission, a survey was conducted in the spring of 2020, in the 24 European participating countries to assess the existing soil data sources, focusing on agricultural soils. The survey will become a contribution to the European Soil Observatory, launched in December 2020, which aims to collect metadata of soil databases related to all kind of land uses, including forest and urban soils. Based upon a comprehensive questionnaire, 170 soil databases were identified at local, regional and national scales. Soil parameters were divided into five groups: (1) main soil parameters according to the Global Soil Map specifications; (2) other soil chemical parameters; (3) other physical parameters; (4) other pedological parameters; and (5) soil biological features. A classification based on the environmental zones of Europe was used to distinguish the climatic zones. This survey shows that while most of the main pedological and chemical parameters are included in more than 70% of the country soil databases, water content, contamination with organic pollutants, and biological parameters are the least frequently reported parameters. Such differences will have consequences when developing an EU policy on soil health as proposed under the EU soil strategy for 2023 and using the data to derive soil health indicators. Many differences in the methods used in collecting, preparing, and analysing the soils were found, thus requiring harmonization procedures and more cooperation among countries and with the EU to use the data at the European scale. In addition, choosing harmonized and useful interpretation and threshold values for EU soil indicators may be challenging due to the different methods used and the wide variety of soil land-use and climate combinations influencing possible thresholds. The temporal scale of the soil databases reported is also extremely wide, starting from the '20s of the 20th century.     

Exploring soil databases: a self‐organizing map approach

A soil quality database (SQDB) is a collection of soil samples described by a given set of parameters, allowing farmers, scientists and other stakeholders to make informed decisions about practices, processes and policies for soil use and management. If each parameter is considered as a dimension of the space spanned by the SQDB, extracting information becomes a difficult task when the number of parameters is >3. A widely used approach to explore multidimensional data sets is the self‐organizing map (SOM) method, which is suitable for clustering, visualization and extraction of information from multidimensional data. We applied the SOM method as an exploratory technique to an unlabelled SQDB to extract knowledge – data patterns and data associations – from the data set (the time and location of each sample were unknown). The SQDB used in this study is a set of 1240 unlabelled samples within the Central Valley of Chile, covering ca 7500 km². The predominant soils are Andisols with a large organic matter content (7–12%), small bulk densities (0.6–1.0 g/cm³) and large water‐holding capacity. We identified three patterns: (i) isolated region within the map with close neurons (smooth transitions), (ii) two or more regions with predominantly large or small values and (iii) homogeneous map with small values with an isolated region of large values. These patterns show that the data set represented more than two groups that were not necessarily related. For pH, no important associations with other investigated parameters were observed. Previous studies carried out by the local agricultural research station showed that pH values below 5.5 constrain nutrient uptake. Thus, locations presenting pH<5,5 should be subject to seasonal monitoring to assess management practices that mitigate soil acidity. The component plane for organic matter indicates that ca. 50% of the soil samples had contents <8% related to soil series characteristics and management practices. As the k‐means is initialized by random partitions, the two‐step approach (clustering the map representing the input data) is less sensitive to variations in the input data (subsamples) than is the direct application of k‐means to the input data, but it also reduces the computational cost. The ability of SOMs to visualize multidimensional data sets helps gain an understanding of the data in the exploratory phase, such as the association and integration of physical, chemical and biological parameters.     

Building and testing conceptual and empirical models for predicting soil bulk density

The development of pedotransfer functions offers a potential means of alleviating cost and labour burdens associated with bulk‐density determinations. As a means of incorporating a priori knowledge into the model‐building process, we propose a conceptual model for predicting soil bulk density from other more regularly measured properties. The model considers soil bulk density to be a function of soil mineral packing structures (ρ_m) and soil structure (Δρ). Bulk‐density maxima were found for soils with approximately 80% sand. Bulk densities were also observed to increase with depth, suggesting the influence of over‐burden pressure. Residuals from the ρ_m model, hereby known as Δρ, correlated with organic carbon. All models were trained using Australian soil data, with limits set at bulk densities between 0.7 and 1.8 g cm^?3 and containing organic carbon levels below 12%. Performance of the conceptual model (r² = 0.49) was found to be comparable with a multiple linear regression model (r² = 0.49) and outperformed models developed using an artificial neural network (r² = 0.47) and a regression tree (r² = 0.43). Further development of the conceptual model should allow the inclusion of soil morphological data to improve bulk‐density predictions.     

Modeling soil carbon transported by water erosion processes

Long‐term monitoring is needed for direct assessment of soil organic carbon (SOC), soil, and nutrient loss by water erosion on a watershed scale. However, labor and capital requirements preclude implementation of such monitoring at many locations representing principal soils and ecoregions. These considerations warrant the development of diagnostic models to assess erosional SOC loss from more readily obtained data. The same factors affect transport of SOC and mineral soil fraction, suggesting that given the gain or loss of soil minerals, it may be possible to estimate the SOC flux from the data on erosion and deposition. One possible approach to parameterization is the use of the revised universal soil loss equation (RUSLE) to predict soil loss and this multiplied by the per cent of SOC in the near‐surface soil and an enrichment factor to obtain SOC loss. The data obtained from two watersheds in Ohio indicate that a power law relationship between soil loss and SOC loss may be more appropriate. When measured SOC loss from individual events over a 12‐year period was plotted against measured soil loss the data were logarithmically linear (R²=0·75) with a slope (or exponent in the power law) slightly less than would be expected for a RUSLE type model. The stable aggregate size distribution in runoff from a plot scale may be used to estimate the fate of size pools of SOC by comparing size distributions in the runoff plot scale and river watershed scales. Based upon this comparison, a minimum of 73 per cent of material from runoff plots is deposited on the landscape and the most stable carbon pool is lost from watershed soils to aquatic ecosystems and atmospheric carbon dioxide. Implicit in these models is the supposition that water stable soil aggregates and primary particles can be viewed as a tracer for SOC. Copyright © 2000 John Wiley & Sons, Ltd.     

Improved three-dimensional mapping of soil chromium pollution with sparse borehole data: Incorporating multisource auxiliary data into IDW-based interpolation

Deriving an accurate three-dimensional (3D) spatial distribution of heterogeneous soil pollutants at contaminated sites using traditional spatial interpolation methods, such as inverse distance weighting (IDW), is challenging; especially when only limited borehole data are available. This study presents a novel IDW-COV method, where weighting is determined by optimizing relative importance between feature distances of covariates and spatial distances to soil sample locations. The method was tested by mapping the 3D distributions of Cr (VI) and total Cr at a Cr salt production workshop (Site A) and a legacy Cr slag stacking site (Site B). The results were compared with those of IDW, the soil land inference model (SoLIM), and the SoLIM combined with IDW method (SoLIM-IDW). The proposed IDW-COV method returned the highest accuracy, with R² values of 0.83 for Cr (VI) in Site A and 0.75 for total Cr in Site B, compared with 0.65 and 0.57 for IDW, 0.16 and 0.58 for SoLIM, and 0.73 and 0.61 for SoLIM-IDW. This study highlights the importance of considering differences in explanatory power between multidimensional covariate and geographical spatial distance when incorporating multisource auxiliary data into weighted average estimators and provides guidance for mapping 3D distributions of heterogeneous pollutants from sparse soil borehole data .     

基于水稻产量的江苏省稻麦轮作区土壤质量评价

为明确江苏稻麦轮作区土壤质量状况，本研究基于2008～2015年期间江苏省稻麦轮作区10681个测土配方施肥样点的数据，通过相关性和主成分分析筛选了江苏省稻麦轮作区的最小数据集(Minimum data set, MDS)，并对土壤质量进行评价。结果表明：基于水稻产量分析，江苏省稻麦轮作区的最小数据集包括有机质、有效磷、速效钾、有效铁和有效硼。基于最小数据集得出的江苏省土壤质量指数(SQI-MDS)变化范围在0.136～1.000之间（均值0.674），与基于全量数据集得出的土壤质量指数（SQI-TDS）之间呈极显著正相关关系（R2=0.720），这说明最小数据集能够较好的代替全量数据集指标。本研究结果可为江苏省不同地区的稻麦耕地质量恢复和提升及稻麦粮食生产提供科学指导。     

Simplified Soil Analysis Procedure for Use in Small‐Scale Agriculture

Abstract

Soil analysis for small‐scale farms in developing countries is often inconvenient and prohibitively expensive using currently available procedures, yet the information gained from these soil tests could result in economical and environmental benefits. The pressurized hot water (PHW) extraction coupled with colorimetric or turbidimetric analysis shows promise as a viable alternative based on tests done on a limited range of soils. Before this extraction and analysis can be used in developing countries, testing is needed across the range of soils found in these countries. At Brigham Young University (BYU), 228 soils from different areas of Guatemala and Morocco were analyzed for NO₃‐N, phosphorus (P), and potassium (K) using standard methods (water–CTA, Olsen–molybdic acid, and ammonium acetate–atomic absorption, respectively). Results were correlated to values obtained from the PHW extraction coupled with colorimetric or turbidimetric analytical procedures. The relationships between the values for these tests were good (r² of 0.96, 0.71, and 0.52 for NO₃‐N, P, and K, respectively). Soils from each country were concurrently analyzed for NO₃‐N and P in laboratories in Guatemala and Morocco, and these results were correlated with those from BYU. Positive correlations between BYU values and those from other laboratories were obtained, with the data from the Guatemalan laboratory showing overall closer correlation than the Moroccan laboratory. In an additional study comparing several P extraction methods for Guatemalan soils, relationships between PHW‐extracted P and Olsen‐, Bray I‐, and Mehlich I‐extracted P and measured at BYU (r² of 0.75, 0.67, and 0.46, respectively) indicate that PHW is a promising alternative P extraction for use even with the highly variable soils of Guatemala. Overall, the data support PHW extraction and accompanying analyses as a less expensive alternative to current soil nutrient extraction and analysis procedures for the soils of Morocco and Guatemala.     

Distribution and genesis of Fahlerden (Albeluvisols) in Germany

Fahlerden dominate large areas of the young and old moraine landscapes of N and E Germany. Fahlerden (part of Fahlerden corresponds to Albeluvisols) and their transitional subtypes are supposed to have a higher intensity of clay illuviation than Parabraunerden (Luvisols). Besides this macroscopic feature reflecting periglacial influence, micromorphological features such as lenticular platy microstructure, vesicles, and fragments of clay coatings in Bt streaks document the initiation of Fahlerde genesis, which began in the Late Glacial. A model of Fahlerde genesis chronologically connects sedimentological and periglacial processes, vegetation development, and soil‐forming processes like decalcification, clay illuviation, and humification. The classification criterion of larger differences in clay contents between E and Bt horizons to distinguish Fahlerden from Parabraunerden needs to be reconsidered, because most Fahlerden have developed in stratified parent material in periglacially influenced landscapes. The interpretation of a soil data base listing data of both soil types distributed in Brandenburg demonstrates that the difference in clay contents between E and Bt horizons may even be smaller in Fahlerden than in Parabraunerden.     

红壤地区农业发展区划中土壤信息系统与交互式自组织数据的集成

Element contents of tree rings and soils near tree roots collected from Deodar cedar (Cedrus deodara (Roxb.) G.Don) and Masson pine(Pinus massoniana lamb).were determined to study the relationship between the angular distribution of element contents in tree rings and the environmental information.The chemical composition and properties of soil are very much cmoplicated,which leads to the non-uniform distribution of the element contents in tree rings.The statistical multi-varialbe regression method was used to get the information of the tree-centered distribution of element contents in the environment(soil),(C‘),C‘(Z,θj),from the distribtuion of element contents in tree rings(C),C(Z,θi),which depends on the plane azimuth angle(θi),i.e.,C=C(Z,θi),where Z is the atomic number of the element,with a satisfactory result though this study is only a primary one.     

Proximal sensor-enhanced soil mapping in complex soil-landscape areas of Brazil

Portable X-ray fluorescence (pXRF) spectrometry and magnetic susceptibility (MS) via magnetometer have been increasingly used with terrain variables for digital soil mapping. However, this methodology is still emerging in many countries with tropical soils. The objective of this study was to use proximal soil sensor data associated with terrain variables at varying spatial resolutions to predict soil classes using the Random Forest (RF) algorithm. The study was conducted on a 316-ha area featuring highly variable soil classes and complex soil-landscape relationships in Minas Gerais State, Brazil. The overall accuracy and Kappa index were evaluated using soils that were classified at 118 sites, with 90 being used for modeling and 28 for validation. Digital elevation models (DEMs) were created at 5-, 10-, 20-, and 30-m resolutions using contour lines from two sources. The resulting DEMs were processed to generate 12 terrain variables. Total Fe, Ti, and SiO₂ contents were obtained using pXRF, with MS determined via a magnetometer. Soil class prediction was performed using the RF algorithm. The quality of the soil maps improved when using only the five most important covariates and combining proximal sensor data with terrain variables at different spatial resolutions. The finest spatial resolution did not always provide the most accurate maps. The high soil complexity in the area prevented highly accurate predictions. The most important variables influencing the soil mapping were MS, Fe, and Ti. Proximal sensor data associated with terrain information were successfully used to map Brazilian soils at variable spatial resolutions.     

不同种植模式和土壤类型条件下土壤健康的定量评价

Soil health assessment is an important step toward understanding the potential effects of agricultural practices on crop yield, quality and human health. The objectives of this study were to select a minimum data set for soil health evaluation from the physical, chemical and biological properties and environmental pollution characteristics of agricultural soil and to develop a soil health diagnosis model for determining the soil health status under different planting patterns and soil types in Chongming Island of Shanghai, China. The results showed that the majority of the farmland soils in Chongming Island were in poor soil health condition, accounting for 48.9% of the survey samples, followed by the medium healthy soil, accounting for 32.2% of the survey samples and mainly distributed in the central and mid-eastern regions of the island. The indicators of pH, total organic carbon, microbial biomass carbon and Cd exerted less influence on soil health, while the soil salinization and nitrate accumulation under a greenhouse cropping pattern and phosphate fertilizer shortage in the paddy field had limited the development of soil health. Dichlorodiphenyltrichloroethanes, hexachlorocyclohexanes and Hg contributed less to soil health index (SHI) and showed no significant difference among paddy field, greenhouse and open-air vegetable/watermelon fields. The difference of the SHI of the three soil types was significant at P = 0.05. The paddy soil had the highest SHI values, followed by the gray alluvial soil, and the coastal saline soil was in a poor soil health condition, indicating a need to plant some salt-tolerant crops to effectively improve soil quality.     

黄土高原沟壑区土壤抗冲性研究──以黄委会西峰水土保持试验站为例

通过对西峰水保站１９６４－１９８０年径流小区观测资料的统计分析，结果表明，次暴雨侵蚀模数与径流深之间以幂函数相关为最好（ｇ＝ａＨ＾ｂ）。笔以单位面积、单位径流深的冲刷量均值做为描述土壤抗冲性的指标，并对其影响因素进行了分析。     

Transferability of continuous‐ and class‐pedotransfer functions to predict water retention properties of semiarid Syrian soils

Hydraulic properties of soils, particularly water retention, are key for appropriate management of semiarid soils. Very few pedotransfer functions (PTF s) have been developed to predict these properties for soils of Mediterranean regions, where data are particularly scarce. We investigated the transferability of PTF s to semiarid soils. The quality of the prediction was compared to that for soils originating from temperate regions for which most PTF s were developed. We used two soil data sets: one from the Paris basin (French data set, n  = 30) and a Syrian data set (n  = 30). Soil samples were collected in winter when the water content was near field capacity. Composition and water content of the samples were determined at seven water potentials. Continuous‐ and class‐PTF s developed using different predictors were tested using the two data sets and their performance compared to those developed using artificial neural networks (ANN ). The best performance and transferability of the PTF s for both data sets used soil water content at field capacity as predictor after stratification by texture. The quality of prediction was similar to that for ANN ‐PTF s. Continuous‐ and class‐PTF s may be transferable to other countries with performances that vary according to their ability to account for variation in soil composition and structure. Taking into account predictors of composition (particle size distribution, texture, organic carbon content) and structure (bulk density, porosity, field capacity) did not lead to a better performance or the best transferability potential.     

A Rapid Turbidimetric Potassium Test Modified for Use with the Pressurized Hot‐Water Extraction

Abstract

Colorimetric or turbidimetric quantification of soil potassium (K) coupled with pressurized hot‐water (PHW) extraction could provide an inexpensive alternative to standard methods for small‐scale farmers in developing countries. Two of many methods for K analysis, one using sodium tetraphenylborate and the other using sodium cobaltinitrite, were modified for use with PHW extraction and evaluated for the following requirements: readable on the spectrophotometer, minimal equipment requirement, rapid, simple, and comparable in accuracy to proven methods of K analysis. The sodium tetraphenylborate method was unreliable at low K concentrations, did not relate with K extracted using ammonium acetate and analyzed by atomic absorption (AA), required extract filtration and was too expensive in developing countries. Sodium cobaltinitrite was both simple and inexpensive, and results were consistent and reliable. Test results from 38 arid soils from the western United States support the use of sodium cobaltinitrite as an acceptable procedure for K quantification compared to AA analysis (r² of 0.90, p<0.05). When coupled with PHW, K measured using sodium cobaltinitrite was predictably related with ammonium acetate‐AA measured K (r² of 0.67, p<0.05).     

基于最小数据集的青藏公路沿线土壤质量评价

为明确青藏公路沿线土地土壤质量的基本特征,通过采集沿线3种不同土地利用类型(农地、草地和沙地)的土壤样品,采用主成分分析法(PCA,Principal Component Analysis)筛选最小数据集(MDS,Minimum Data Set)指标,并构建土壤质量指数(SQI,Soil Quality Index)定量评价其土壤质量。结果表明:(1)相较于农地和草地,沙地的土壤肥力较为贫瘠,其土壤有机质、全氮和有效磷的土壤肥力等级均为6级。9个土壤指标均为中低度敏感指标,沙地土壤黏粒和粉粒含量具有更强的空间分异性。(2)沿线土壤质量评价最小数据集由全钾、全磷、有机质、黏粒和粉粒组成,最小数据集能够较好地替代全数据集进行土壤质量评价(p<0.01)。(3)沿线农地的土壤质量指数SQI(0.535±0.043)高于草地SQI(0.499±0.044)和沙地SQI(0.449±0.066)。该研究发现沿线农地土壤质量为中等水平,沿线草地和沿线沙地土壤质量处于较低水平,土壤有机质是影响青藏公沿线土壤质量的主要因素。     

基于小区实测数据的不同类型土壤可蚀性因子计算

[目的]对不同类型土壤可蚀性因子进行计算,为土壤侵蚀定量评价、水土流失动态监测提供科学依据。[方法]通过收集全国范围野外径流小区实测资料,采用一定的数据遴选标准,利用小区法计算我国主要土壤的K值;同时考虑实测值评价Wischmeier和EPIC K值估算公式在我国的适用性。[结果]我国主要土壤的K值范围分布在0.0008～0.0705 (t·hm~2·h)/(hm~2·MJ·mm)之间。黑土、黄绵土和褐土的K值较大,红壤和紫色土因子值较小。全国范围内,公式估算得到的K值与实测值存在较大误差;修订后的Wischmeier公式估算东北黑土区K值精度相对较高。[结论]基于全国实测站点数据和文献数据,计算出修正到标准小区的K值,K值空间变化幅度较大,大体呈现出从北向南减小的趋势。为获得比较准确的K值,建立相关经验公式需进一步加强监测点管理和长序列观测数据的积累。     

Digital soil mapping in Germany—a review

Digital soil mapping as a tool to generate spatial soil information provides solutions for the growing demand for high‐resolution soil maps worldwide. Even in highly developed countries like Germany, digital soil mapping becomes essential due to the decreasing, time‐consuming, and expensive field surveys which are no longer affordable by the soil surveys of the individual federal states. This article summarizes the present state of soil survey in Germany in terms of digitally available soil data, applied digital soil mapping, and research in the broader field of pedometrics and discusses future perspectives. Based on the geomorphologic conditions in Germany, relief is a major driving force in soil genesis. This is expressed by the digital–soil mapping research which highlights the great importance of digital terrain attributes in combination with information on parent material in soil prediction. An example of digital soil mapping using classification trees in Thuringia is given as an introduction in digital soil‐class mapping based on correlations to environmental covariates within the scope of the German classification system.     

International Comparison of Analytical Methods of Determining the Soil Organic Matter Content of Lithuanian Eutric Albeluvisols

Abstract

Several soil organic matter (SOM) methodologies have been employed to analyze a suite of subsampled soils, and their results have been correlated. This will permit future comparison of the large archive of SOM databases, which widely exist in Lithuania and other Central and Eastern European countries, with those of other international countries. Samples were collected (n=92) from topsoil and subsoil horizons of Eutric Glassoboralfs (Eutric Albeluvisols) at five long‐term monitoring sites (three sites with 8 years' duration and two sites with 20 years' duration) containing a total of 46 experimental field plots. Each soil sample was subsampled and SOM determined by several analytical approaches (namely, dry combustion, Walkley–Black, Tyurin photometric, Tyurin titrimetric, and loss‐on‐ignition methods). Correlation coefficients between multiple sets of results varied between r=0.831 and r=0.965 (n=92, P<0.001). Based on the strength and significance of these relationships, we propose that simple linear regression equations can be confidently employed to recalculate SOM data among various analytical methodologies and thus help resolve the issue of international data comparison.     

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.