Comparing SSURGO Data with Geospatial Field Measurements to Estimate Soil Texture and Infiltration Rate Classes in Glaciated Soils

The infiltration rate (IR) of water is a key soil property related to hydrological processes, soil health, and ecosystem services. However, detailed measurements of IR in the field and/or laboratory are labor-intensive and expensive to perform. Soil judging in the field provides a rapid and inexpensive method to estimate IR classes based on soil texture, soil organic carbon/matter, and soil structure. The objectives of this study were to classify and compare soil texture and IR for the A horizon across the 147 ha Cornell University Willsboro Research Farm using the Soil Survey Geographic (SSURGO) database and field-based measurements. Soil texture was the dominating factor to explain the general trend of Entisols > Inceptisols > Alfisols with regard to IR in the A horizon. In general, the variability in soil texture observed in field measurements was consistent with the variability reported in the SSURGO database, although the SSURGO representative values for soil texture did not completely match measured mean values for all soil map units. With the exception of one soil map unit, estimates of IR classes utilizing soil judging in the field criteria also were consistent when using either SSURGO or field-based data. Estimating infiltration rate classes for ecosystem services frameworks using geospatial analysis of field and/or SSURGO data can be enhanced with emerging technologies (e.g., sensors) and/or easily measured conventional soil properties.     

Estimating Plant-Available Water in the Ap Horizon Using Geospatial Analysis of Field and SSURGO Data

Soil provides many of the requirements needed for terrestrial plant growth, including an adequate supply of water. Because the proportion of plant roots is usually greatest in the top 10 to 15 cm of soil, the soil moisture content in the Ap horizon is particularly important for plant growth and crop yields. Uncertainties in estimates of plant available water in the Ap horizon (AW_Ap) often arise from variabilities in field, laboratory and geospatial data at a farm scale. The objectives of this study were to quantify and compare AW_Ap estimates across the 147-ha Cornell University Willsboro research farm using four different approaches: a) AW_Ap calculated from values reported in the SSURGO database for available water capacity (AWC) and Ap thickness for the soil map units (SMUs) present on the farm, b) AW_Ap estimated from soil texture data reported for the SSURGO SMUs, c) AW_Ap estimated from soil texture data determined from soil cores taken across the farm that were then averaged within each SMU, and d) spatially interpolating the AW_Ap values predicted from soil cores across the entire farm irrespective of SMU boundaries. Available water in the Ap horizon varied with soil order in the general trend of Alfisols > Inceptisols > Entisols regardless of the estimation approach used. Field measurement-based estimates of AW_Ap were lower, in general than the reported SSURGO values and estimates based on reported texture in the SSURGO database. The higher abundance of coarse fragments in the Ap horizon of the soil cores collected on the farm partially explains the lower field measurement-based AW_Ap estimates. In the SSURGO database, values reported for AWC are frequently obtained from a selected pedon from a “type location” and not from the actual study location. These “type locations” can be located far from study sites and even in different states. Although collecting detailed field data may not always be possible due to the high costs of field and laboratory measurements, it is important to understand the potential benefits and limitations of making field-scale and regional AW estimates using the SSURGO database.     

利用地统计学研究土壤有机质空间分布：评估意大利中部地区土地退化的重要指标

Soil organic matter (SOM) content is one of the main factors to be considered in the evaluation of soil health and fertility. As timing, human and monetary resources often limit the amount of available data, geostatistical techniques provide a valid scientific approach to cope with spatial variability, to interpolate existing data and to predict values at unsampled locations for accurate SOM status survey. Using geostatistical and geographic information system (GIS) approaches, the spatial variability of some physical and chemical soil parameters was investigated under Mediterranean climatic condition in the Abruzzo region of central Italy, where soil erosion processes accelerated by human induced factors are the main causes of soil degradation associated with low SOM content. Experimental semivariograms were established to determine the spatial dependence of the soil variables under investigation. The results of 250 soil sampling point data were interpolated by means of ordinary kriging coupled with a GIS to produce contour maps distribution of soil texture, SOM content related to texture, and C/N ratio. The resulting spatial interpolation of the dataset highlighted a low content of SOM in relation with soil texture in most of the surveyed area (87%) and an optimal C/N ratio for only half of the investigated surface area. Spatial location of degraded area and the assessment of its magnitude can provide decision makers with an accurate support to design appropriate soil conservation strategies and then facilitate a regional planning of agri-environmental measures in the framework of the European Common Agricultural Policy.     

土壤质地空间预测方法比较

土壤质地作为成分数据（compositional data）的一种,其空间插值需满足非负、定和、误差最小和无偏估计4个条件。采用成分克里格法（compositional Kriging）和基于对数比转换的普通克里格法对土壤质地各颗粒组成进行空间预测,均方根误差（root mean squared errors,RMSE）和标准化克里格方差（mean squared deviation ratio,MSDR）分别被用来衡量不同方法的预测精度及模型拟合效果。研究结果表明：对数比转换的普通克里格法和成分克里格法能够保证插值结果满足成分数据插值的4个条件;成分克里格法预测的各土壤颗粒组成的RMSE最小,预测精度最高,其黏粒RMSE值相对于非对称对数比转换的普通克里格法提高将近17%;成分克里格法的变异函数拟合效果总体上好于其他两种预测方法,预测结果极差更宽,更能反映土壤质地各颗粒组成与高程、母质和水域分布的关系。     

Geospatial modeling of surface soil texture of agricultural land using fuzzy logic,geostatistics and GIS techniques

ABSTRACT

Soil texture is a key controlling factor of soil properties and its functions include water and nutrient holding capacity, retention of pollutants, root development, soil biodiversity, and biogeochemical cycling. From the geotechnical standpoint, it is interesting to analyze the soil texture in regions due to its relation with the infiltration and runoff processes and, consequently, the effect on erosion processes. The purpose of this study is to present a methodology that provides the soil texture spatial variation by using Fuzzy logic theory and geostatistical technique in Geographic Information System (GIS) platform. A total of 140 soil samples were taken from topsoil (0–30 cm) in the study area located in the north of Guilan Province, the southern coast of Caspian Sea, Northern Iran. The soil textural classes were converted to numerical values (fuzzy values) using the fuzzy logic concept. The fuzzy values were spatially interpolated by ordinary kriging method such that the fitted model on experimental semi-variogram was exponential with moderate structure. The results showed the accuracy of soil texture predictive map was acceptable according to the values of normalized root-mean-square error for train data set (0.182) and test data set (0.179). The knowledge of the spatial variability of soil properties such as the soil texture can be an important tool for land-use planning in order to reduce the potential soil losses during rainy seasons. The results indicated that the integration of fuzzy logic, geostatistics, and GIS can improve the interpolation process.     

中国东北农田土壤质量评价的最小数据集选择

Soil quality assessment provides a tool for agriculture managers and policy makers to gain a better understanding of how various agricultural systems afect soil resources.Soil quality of Hailun County,a typical soybean (Glycine max L.Merill) growing area located in Northeast China,was evaluated using soil quality index(SQI)methods.Each SQI was computed using a minimum data set(MDS) selected using principal components analysis(PCA)as a data reduction technique.Eight MDS indicators were selected from 20 physical and chemical soil measurements.The MDS accounted for 74.9% of the total variance in the total data set(TDS).The SQI values for 88 soil samples were evaluated with linear scoring techniques and various weight methods.The results showed that SQI values correlated well with soybean yield (r=0.658**) when indicators in MDS were weighted by the regression coefcient computed for each yield and index.Stepwise regression between yield and principal components (PCs) indicated that available boron(AvB),available phosphorus (AvP),available potassium (AvK),available iron (AvFe) and texture were the main factors limiting soybean yield.The method used to select an MDS could not only appropriately assess soil quality but also be used as a powerful tool for soil nutrient diagnosis at the regional level.     

Assisting nonsoil specialists to identify soil types for land management: an approach using a soil identification key and toposequence models

Conventional soil survey information is often unclear except to specialists. An approach using soil toposequences and a soil identification key was used to aid the translation of soil survey information into a form suitable for a nonspecialist audience with a case study from Brunei. Soil Taxonomy was used to characterize the major soil types; however, to assist end users, a complementary special‐purpose soil classification system was developed in the form of a soil identification key using plain language terms in English that were also translated into Malay. Easily recognized soil features such as depth, colour and texture were used to categorize soils to match Soil Taxonomy classes. To complement the soil identification key, conceptual soil toposequence models presented the soil distribution patterns in a visual format that local land users understood. Legacy soil survey information along with a widespread distribution of 172 soil sites from 35 traverses in 16 study areas provided a dataset to develop and test soil toposequence models and the soil identification key which both proved reliable and robust. The approach demonstrated in Brunei could be applied to other countries and landscapes.     

基于支持向量机的土壤水力学参数预测

为了分析支持向量机在土壤水力学参数预测方面的效果，应用支持向量机构建用于预测土壤水力学参数的土壤传递函数，以土壤粒径分布、容重、有机质含量等土壤理化性质为输入项，分别预测土壤饱和导水率、饱和含水率、残余含水率，以及van Genuchten公式参数的对数形式。结果表明预测值和实测值不存在显著性差异，用支持向量机预测土壤水力学参数是可行的。不同输入项处理的预测分析表明，输入项为粒径分布、粒径分布和容重、粒径分布和有机质含量3种情况的预测效果差异不明显，而输入项为粒径分布、容重和有机质含量时预测效果优于前3种情况。支持向量机在预测土壤水力学参数方面的效果要优于多元线性逐步回归模型，而与BP神经网络模型相比不具有明显好的预测效果。     

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.     

Historical aspects of soil classification in Japan

Historical perspective of soil classification in Japan from Max Fesca's soil classification in 1882 to the “Unified Soil Classification System of Japan (2002)” was outlined, aiming at reviewing the progress in soil classification. The evolution can be divided into the following five aspects: 1) Max Fesca's soil texture survey and soil classification from the agro-geological point of view under the influence of the German school; 2) Introduction of the concept of pedology into the classification under the influence of the Russian school led by Dokchaev; 3) Brief history of the classification of Andosols which has exerted a considerable influence on soil classification worldwide; 4) Soil classifications developed through the implementation of national soil survey projects to independently evaluate land suitability for the cultivation of paddy rice, upland crops, and for forest establishment; 5) Attempts to develop a comprehensive soil classification system in order to unify soil classification systems for the above-mentioned land uses from 1963 to the present.     

气候和土壤质地对土壤有机碳影响的区域差异研究

The agricultural soil carbon pool plays an important role in mitigating greenhouse gas emission ana unaerstanamg the son orgamc carbon-climate-soil texture relationship is of great significance for estimating cropland soil carbon pool responses to climate change. Using data from 900 soil profiles, obtained from the Second National Soil Survey of China, we investigated the soil organic carbon （SOC） depth distribution in relation to climate and soil texture under various climate regimes of the cold northeast region （NER） and the warmer Huang-Huai-Hai region （HHHR） of China. The results demonstrated that the SOC content was higher in NER than in HHHR. For both regions, the SOC content at all soil depths had significant negative relationships with mean annual temperature （MAT）, but was related to mean annual precipitation （MAP） just at the surface 0-20 cm. The climate effect on SOC content was more pronounced in NER than in HHHR. Regional differences in the effect of soil texture on SOC content were not found. However, the dominant texture factors were different. The effect of sand content on SOC was more pronounced than that of clay content in NER. Conversely, the effect of clay on SOC was more pronounced than sand in HHHR. Climate and soil texture jointly explained the greatest SOC variability of 49.0% （0-20 cm） and 33.5% （20-30 cm） in NER and HHHR, respectively. Moreover, regional differences occurred in the importance of climate vs. soil texture in explaining SOC variability. In NER, the SOC content of the shallow layers （0-30 cm） was mainly determined by climate factor, specifically MAT, but the SOC content of the deeper soil layers （30-100 cm） was more affected by texture factor, specifically sand content. In HHHR, all the SOC variability in all soil layers was predominantly best explained by clay content. Therefore, when temperature was colder, the climate effect became stronger and this trend was restricted by soil depth. The regional differences and soil depth influence underscored the importance of explicitly considering them in modeling long-term soil responses to climate change and predicting potential soil carbon sequestration.     

A simple soil organic carbon level metric beyond the organic carbon-to-clay ratio

Soil is a precious and non-renewable resource that is under increasing pressure and the development of indicators to monitor its state is pivotal. Soil organic carbon (SOC) is important for key physical, chemical and biological soil properties and thus a central indicator of soil quality and soil health. The content of SOC is driven by many abiotic factors, such as texture and climate, and is therefore strongly site-specific, which complicates, for example, the search for appropriate threshold values to differentiate healthy from less healthy soils. The SOC:clay ratio has been introduced as a normalized SOC level metric to indicate soils' structural condition, with classes ranging from degraded (<1:13) to very good (>1:8). This study applied the ratio to 2958 topsoils (0–30 cm) in the German Agricultural Soil Inventory and showed that it is not a suitable SOC level metric since strongly biased, misleading and partly insensitive to SOC changes. The proportion of soils with SOC levels classified as degraded increased exponentially with clay content, indicating the indicator's overly strong clay dependence. Thus, 94% of all Chernozems, which are known to have elevated SOC contents and a favourable soil structure, were found to have either degraded (61%) or moderate (33%) normalized SOC levels. The ratio between actual and expected SOC (SOC:SOC_exp) is proposed as an easy-to-use alternative where expected SOC is derived from a regression between SOC and clay content. This ratio allows a simple but unbiased estimate of the clay-normalized SOC level. The quartiles of this ratio were used to derive threshold values to divide the dataset into the classes degraded, moderate, good and very good. These classes were clearly linked to bulk volume (inverse of bulk density) as an important structural parameter, which was not the case for classes based on the SOC:clay ratio. Therefore, SOC:SOC_exp and its temporal dynamic are proposed for limited areas such as regions, states or pedoclimatic zones, for example, in a soil health monitoring context; further testing is, however, recommended.     

土壤质地对玉米不同生理指标水分有效性的影响

为确定土壤质地对玉米不同生理指标水分有效性的影响,该文利用3种土壤（重壤土、中壤土和砂壤土）的盆栽控水试验和1种土壤（重壤土）的田间小区控水试验,研究了玉米不同生理指标随相对土壤含水率（土壤含水率占田间持水率的比）的动态变化。结果表明：3种土壤中各生理指标相对值在相对土壤含水率降低到土壤水分阈值之前保持不变,低于此阈值时随相对含水率的进一步降低而线性降低,且均可用分段函数来拟合（R2=0.824～0.999）。土壤水分有效性大小排序为：砂壤土＞中壤土＞重壤土,而且瞬时生理指标的土壤水分阈值低于日变化和整个试验阶段的累积指标。因此土壤质地和不同生理指标的时间尺度都会影响玉米生理指标对土壤水分有效性的响应。     

Assessment of cocoa input needs using soil types and soil spectral analysis

In this study, diffuse reflectance spectroscopy (DRS) approach was examined for making input recommendations in the smallholder cocoa farms of Papua New Guinea (PNG). Soil samples were collected from four provinces of PNG. Soil samples from four different depths (0–10, 10–30, 30–60 and 60–90 cm) of 32 profiles in each of these site were used to create a database of soil chemical and physical properties. Spectral reflectance values at 1 nm interval covering visible to shortwave‐infrared (350–2,500 nm) were collected for each of these soil samples to develop partial least squares regression models. Soil textural fractions, soil organic carbon contents and available N were well predicted by the DRS approach with R² values larger than 0.75. Moderate to poor estimation efficiencies were observed for remaining parameters. Nevertheless, the estimated soil attributes and their corresponding measured soil parameters were used as inputs to an input recommendation model of soil diagnosis to create input recommendation for a targeted cocoa yield of 1,000 kg dry cocoa beans ha^‐1 Resulting input recommendations were similar for both of these input sources (measured and DRS‐estimated) suggesting that the DRS approach may provide an easy way to create input recommendations.     

分形模型在利用颗粒分布数据评价土壤持水性质中的应用

Soil water retention characteristics are the key information required in hydrological modeling.Frac-tal models provide a practical alternative for indirectly estimating soil water retention characteristics from particle-size distribution data.Predictive capabilities of three fractal models,i.c.,Tylcr-Wheatcraft model,Rieu-Sposito model,and Brooks-Corey model,were fully evaluated in this work using experimental data from an international database and literature.Particle-size distribution data were firstly interpolated into 20 classes using a van Genuchten-type equation.Fractal dimensions of the tortuous pore wall and the pore surface were then calculated from the detailed particle-size distribution and incorporated as a parameter in fractal water retention models.Comparisons between measured and model-estimated water retention cha-racteristics indicated that these three models were applicable to relatively different soil textures and pressure head ranges.Tyler-Whcatcraft and Brooks-Corey models led to reasonable agreements for both coarse-and medium-textured soils,while the latter showed applicability to a broader texture range.In contrast,Rieu-Sposito model was more suitable for fine-textured soils.Fractal models produced a better estimation of water contents at low pressure heads than at high pressure heads.     

Development and use of a database of hydraulic properties of European soils

Many environmental studies on the protection of European soil and water resources make use of soil water simulation models. A major obstacle to the wider application of these models is the lack of easily accessible and representative soil hydraulic properties. In order to overcome this apparent lack of data, a project was initiated to bring together the available hydraulic data which resided within different institutions in Europe into one central database. This information was then used to derive a set of pedotransfer functions applicable to studies at a European scale. These pedotransfer functions predict the hydraulic properties from parameters collected during soil surveys and can be a good alternative for costly and time-consuming direct measurement of these properties. A total of 20 institutions from 12 European countries collaborated in establishing the database of draulic operties of uropean oils (HYPRES). This database has a flexible relational structure capable of holding a wide diversity of both soil pedological and hydraulic data. As these data were contributed by 20 different institutions it was necessary to standardise both the particle-size and the hydraulic data. A novel similarity interpolation procedure was successfully used to achieve standardization of particle-sizes according to the FAO clay, silt and sand particle-size ranges. Standardization of hydraulic data was achieved by fitting the Mualem-van Genuchten model parameters to the individual θ(h) and K(h) hydraulic properties stored in HYPRES. The HYPRES database contains information on a total of 5521 soil horizons (including replicates). Of these, 4030 horizons had sufficient data to be used in the derivation of pedotransfer functions. Information on both water retention and hydraulic conductivity was available for 1136 horizons whereas 2894 horizons had only information on water retention. Each soil horizon was allocated to one of 11 possible soil textural/pedological classes derived from the six FAO texture classes (five mineral and one organic) and the two pedological classes (topsoil and subsoil) recognised within the 1:1 000 000 scale Soil Geographical Data Base of Europe. Next, both class and continuous pedotransfer functions were developed. By using the class pedotransfer functions in combination with the 1:1 000 000 scale Soil Map of Europe, the spatial distribution of soil water availability within Europe was derived.     

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.     

土壤润湿性受土壤特性及土地使用的影响

Depth distribution of soil wettability and its correlations with vegetation type, soil texture, and pH were investigated under various land use (cropland, grassland, and forestland) and soil management systems. Wettability was evaluated by contact angle with the Wilhelmy plate method. Water repellency was likely to be present under permanently vegetated land, but less common on tilled agricultural land. It was mostly prevalent in the topsoil, especially in coarse-textured soils, and decreased in the subsoil. However, the depth dependency of wettability could not be derived from the investigated wide range of soils. The correlation and multiple regression analysis revealed that the wettability in repellent soils was affected more by soil organic carbon (SOC) than by soil texture and pH, whereas in wettable soils, soil texture and pH were more effective than SOC. Furthermore, the quality of SOC seemed to be more important in determining wettability than its quantity, as proofed by stronger hydrophobicity under coniferous than under deciduous forestland. Soil management had a minor effect on wettability if conventional and conservation tillage or different grazing intensities were considered.     

A classification scheme for earthworm populations (Lumbricidae) in cultivated agricultural soils in Brandenburg,Germany

Earthworm activity is observed at long‐term monitoring sites as an indicator of soil function to assess changes resulting from environmental and management conditions. In order to assess changes, characteristic values of earthworm populations under different site conditions have to be known. Therefore, a classification scheme for site‐specific earthworm populations was developed for soil in agricultural use, taking interactions between earthworm populations and soil properties into account. Characteristics of sites grouped by means of a cluster analysis after principal‐component analysis served as a basis for the derivation of the classification scheme. Soil variables found to characterize site differences with respect to earthworm populations were the texture of the topsoil, the texture of the subsoil, and the soil organic‐matter (SOM) content. The textural classes of the topsoil were divided into five groups comprising sandy soils (Ss), silty sand soils (Su), slightly loamy sand soils (Sl2), medium to strongly loamy sand soils (Sl3/Sl4), and loam and clay soils. Soil organic matter was divided into grades of equal size in a range from <1%, 1%–2% up to >6%. The variables “earthworm abundance” and “earthworm species” were selected to represent earthworm populations and were divided into six groups ranging from very low to extremely high. Defined groups of earthworm populations showed a clear structure in relation to soil textural groups and the content of SOM. From this distribution, a classification scheme was derived as basis for prognostic values of site‐specific earthworm populations, thus enabling the interpretation of changes over time. For some soil textural groups, selected variables appeared to enable the derivations of expected earthworm densities and species composition outside the range of the given database, but for some soil textural groups, broader databases will be needed to specify these derivations.