Properties and classification of volcanic ash soils around Lake Kuwanuma on the eastern footslope of Mt. Funagata in Miyagi prefecture,northeastern Japan

One of the developments in recognition of soil properties and different soils is from morphological to analytical. With an incorporation of analytical data, transitional soils can be systematically named using a key-out order as well as typical soils. Our major objective was to evaluate soils with multiple forming processes such as andosolization, podzolization, etc., using soil analytical data, selecting a small area having different soil names based on soil morphological properties. According to a local soil map, Ando soils, Brown forest soils (Dark) and Wet and Dry podzols soils are adjacently distributed around Lake Kuwanuma on the eastern footslope of Mt. Funagata in Miyagi prefecture, northeastern Japan. We studied the morphological, chemical and mineralogical properties of these soils and classified them according to the recent Comprehensive Soil Classification System of Japan (CSCSJ), United States Department of Agriculture Soil Taxonomy (ST) and the World Reference Base for Soil Resources (WRB). The elevation of Lake Kuwanuma is 780 m above sea level and a plateau is located next to a steep slope on the western side of the lake. Three pedons were sampled from the northern side of Lake Kuwanuma, and 3 additional pedons were sampled from the plateau. The average difference in elevation between these two groups of pedons was 229 m. All 6 pedons were classified as Andosols in CSCSJ, Udands in ST and Andosols in WRB. Thus, andosolization was the dominant soil-forming process throughout the study area. The major modification of Andisols in the study area was caused by forest vegetation. Of the 6 pedons sampled, three were classified as Fulvudands in ST and had the Fulvic prefix qualifier in WRB. Furthermore, weak podzolization was suggested on the basis of soil profile observations. One pedon on the plateau had a Bs horizon, which satisfied the spodic horizon requirements of ST. Thus, weak podzolization, especially on the plateau, was another accessory characteristic in the present study area. The nearby distribution of Podzols soils and Ando soils in the local soil map may be explained by differences in temperature, leaching intensity and other factors. A podzolic subgroup of Andosols/Udands was desired to express the properties of pedons on the plateau in the lower categories of the recent soil classification systems.     

Soil mapping for land‐use planning in a karst area of N Thailand with due consideration of local knowledge

For the development of sustainable land‐management systems in the highlands of N Thailand, detailed knowledge about soil distribution and soil properties is a prerequisite. Yet to date, there are hardly any detailed soil maps available on a watershed scale. In this study, soil maps on watershed level were evaluated with regard to their suitability for agricultural land‐use planning. In addition to common scientific methods (as underlying the WRB classification), participatory methods were used to exploit local knowledge about soils and to document it in a “Local Soil Map”. Where the WRB classification identified eight soil units, the farmers distinguished only five on the basis of soil color and “hardness”. The “Local Soil Map” shows little resemblance with the detailed, patchy pattern of the WRB‐based soil map. On the contrary, the “Local Soil Map” is fairly similar to the petrographic map suggesting that soil color is directly related to parent material. The farmers' perception about soil fertility and soil suitability for cropping could be confirmed by analytical data. We conclude that integrating local soil knowledge, petrographic information, and knowledge of local cropping practices allows for a rapid compilation of information for land‐evaluation purposes at watershed level. It is the most efficient way to build a base for regional land‐use planning.     

Digital soil mapping using artificial neural networks

In the context of a growing demand of high‐resolution spatial soil information for environmental planning and modeling, fast and accurate prediction methods are needed to provide high‐quality digital soil maps. Thus, this study focuses on the development of a methodology based on artificial neural networks (ANN) that is able to spatially predict soil units. Within a test area in Rhineland‐Palatinate (Germany), covering an area of about 600 km², a digital soil map was predicted. Based on feed‐forward ANN with the resilient backpropagation learning algorithm, the optimal network topology was determined with one hidden layer and 15 to 30 cells depending on the soil unit to be predicted. To describe the occurrence of a soil unit and to train the ANN, 69 different terrain attributes, 53 geologic‐petrographic units, and 3 types of land use were extracted from existing maps and databases. 80% of the predicted soil units (n = 33) showed training errors (mean square error) of the ANN below 0.1, 43% were even below 0.05. Validation returned a mean accuracy of over 92% for the trained network outputs. Altogether, the presented methodology based on ANN and an extended digital terrain‐analysis approach is time‐saving and cost effective and provides remarkable results.     

Predicting Cationic Exchange Capacity in Calcareous Soils of East-Azerbaijan Province,Northwest Iran

The aim of this research is to study the efficiency of pedotransfer functions (PTFs) and artificial neural networks (ANNs) for cationic exchange capacity (CEC) prediction using readily available soil properties. Here, 417 soil samples were collected from the calcareous soils located in East-Azerbaijan province, northwest Iran and readily available soil properties, such as particle size distribution (PSD), organic matter (OM) and calcium carbonate equivalent (CCE), were measured. The entire 417 soil samples were divided into two groups, a training data set (83 soil samples) and test data set (334 soil samples). The performances of several published and derived PTFs and developed neural network algorithms using multilayer perceptron were compared, using a test data set. Results showed that, based on statistics of RMSE and R², PTFs and ANNs had a similar performance, and there was no significant difference in the accuracy of the model results. The result of the sensitivity analysis showed that the ANN models were very sensitive to the clay variable (due to the high variability of the clay). Finally, the models tested in this study could account for 85% of the variations in cationic exchange capacity (CEC) of soils in the studied area.

Abbreviations: ANN: arti?cial neural networks; MLP: multilayer perceptron; MLR: multiple linear regression; PTFs: Pedotransfer Functions; RBF: Radial Basis Function; MAE: mean absolute error; MSE: mean square error; CEC: cationic exchange capacity     

Technosols in the World Reference Base for Soil Resources – history and definitions

ABSTRACT

The World Reference Base for Soil Resources (WRB) is an international soil classification system for naming soils and creating legends for soil maps. The currently valid version is the update 2015 of its third edition. WRB has two levels: first and second. The first level comprises 32 Reference Soil Groups (RSGs), identified using a Key. At the second level, the soil names are constructed by adding a set of qualifiers to the name of the RSG. In the WRB, diagnostic horizons, properties and materials are defined. Diagnostic materials are materials that significantly influence soil-forming processes. Diagnostic properties and horizons have a combination of attributes that mostly reflect results of soil-forming processes.

The RSG Technosols was introduced in the second edition of the WRB in 2006. In the current version of the WRB, two diagnostic materials are defined for Technosols: artefacts and technic hard material. Artefacts are substances that are created or substantially modified by humans or brought to the surface from a depth, where they were not influenced by surface processes. The technic hard material is a (relatively) continuous consolidated material resulting from an industrial process. The Technosols are at the third place in the Key after Histosols and Anthrosols. A soil is a Technosol if it has technic hard material within 5 cm or a geomembrane or a significant amount of artefacts within 100 cm. If a soil has no technic hard material and no geomembrane but a layer with artefacts that has undergone enough soil formation to develop a diagnostic horizon typical for advanced pedogenesis, the soil is excluded from the Technosols. There are specific qualifiers to further characterise the Technosols. They are also important to characterise soils other than Technosols that have artefacts or technic hard material. Human-transported natural soil material does not qualify as Technosol.     

World Reference Base for Soil Resources—Its fourth edition and its history

The fourth edition of the international soil classification system World Reference Base for Soil Resources (WRB) was released in 2022. It maintains the 32 Reference Soil Groups at the first classification level. Most qualifiers (second level) and most diagnostic horizons, properties and materials were maintained but some were abolished and new ones introduced. The main part of the fourth edition is followed by six annexes, most of them are new. For the first time, the WRB has a Field Guide (Annex 1) to facilitate field survey and to assure that all field characteristics required in the classification are reported. The fourth edition also provides designations for horizons and layers (Annex 3), which was not the case in the second and the third edition. The wordings of the definitions were harmonized, and the same features are worded in the same way throughout the text (including the annexes). Ambiguities have been corrected and many definitions written in a more concise and a more didactical way. The WRB has a long history. Four editions have been published: 1998, 2006 (with update 2007), 2014 (with update 2015) and 2022. Editor is the Working Group WRB of the International Union of Soil Sciences. The WRB followed the Legend and the Revised Legend of the Soil Map of the World. This map was edited by FAO (Food and Agriculture Organization of the United Nations) and UNESCO, and the system is known as the FAO Soil Classification System. In addition, WRB incorporated ideas from the former Working Group International Reference Base for Soil Classification that existed from 1982 to 1994.     

皖南紫红色砂石岩上发育土壤的系统分类研究

对皖南不同时期紫红色砂石岩上发育的8个土壤剖面,按中国土壤系统分类体系,进行鉴别、检索、分类定名。阐述发生分类紫色土的2个亚类级土壤类型在中国土壤系统分类中归为3个土纲,即均腐土、雏形土和新成土;3个亚纲,即湿润均腐土、湿润雏形土和正常新成土;4个土类和6个亚类。提出按中国土壤系统分类的紫色土分类系统;并与美国土壤系统分类、国际土壤分类参比基础进行了参比。讨论了皖南紫红色砂石岩上发育为均腐土的成土环境条件,同时对发生分类紫色土在中国土壤系统分类类别检索提出修订建议。     

Spatial prediction of soil classes using digital terrain analysis and multinomial logistic regression modeling integrated in GIS: Examples from Vestfold County,Norway

The main objectives of this study were to model the relationship between WRB-1998 soil groups and terrain attributes and predict the spatial distribution of the soil groups using digital terrain analysis and multinomial logistic regression integrated in GIS in the Vestfold County of south-eastern Norway. A digital elevation model of 25 meter grid resolution was used to derive fifteen terrain attributes. A digitized soil map of thirteen WRB soil groups at the scale of 1:25,000 was used to obtain the reference soil data for model building and validation. First, the relationships between the soil groups and the terrain attributes were modeled using multinomial logistic regression. Then, the probability that a given soil type is present at a given pixel was determined from the logit models in ARCGIS to continuously map each soil group's spatial distribution. Elevation, flow length, duration of daily direct solar radiation, slope, aspect and topographic wetness index were found to be the most significant terrain attributes correlating with the spatial distribution of the soil groups. The prediction showed higher mean probability values for each soil group in the areas actually covered by that soil group compared to other areas, indicating the reliability of the prediction. However, the prediction performed poorly for soil groups that are not greatly influenced by topography but by other factors such as human activities.     

Automatic Recovery Estimation of Degraded Soils by Artificial Neural Networks in Function of Chemical and Physical Attributes in Brazilian Savannah Soil

ABSTRACT

The Oxisols is predominant in 54% of Brazilian territories and characterized by high weathering, relatively low chemical properties, and adequate structure. This study aimed to analyze the Oxisols through an Artificial Neural Network (ANN) with the purpose of estimating its recovery in function to soil chemical and physical attributes. The chemical attributes considered were: pH, cation exchange capacity (CEC), base saturation (V%), phosphorus (P), magnesium (Mg²⁺), and potassium (K⁺) and for the physical attributes, bulk density, soil porosity and soil resistance to penetration. The ANN used in this study is the Multilayer Perceptron (MLP), composed of three layers, input, intermediate and the output and with backpropagation training algorithm (supervised training). The intermediate layer is composed by 10 neurons and the layer of exit by 1 neuron, which has a function of informing the levels of chemical recovery (high, medium and low chemical attributes of the soil) and soil physics (recovered, partially recovered or not recovered). From the results obtained by ANN showed that the network reached an adequate training, with low mean square error (MSE). Therefore, ANN is a powerful and automatic alternative for the recovery estimation of degraded soils.     

基于行走式近红外反射光谱和偏最小二乘法的冰碛土中土壤碳和粘粒含量制图研究

Characterizing spatial variability of soil attributes, using traditional soil sampling and laboratory analysis, is cost prohibitive. The potential benefit of managing soils on a site-specific basis is well established. High variations in glacial till soil render detailed soil mapping difficult with limited number of soil samples. To overcome this problem, this paper demonstrates the feasibility of soil carbon and clay mapping using the newly developed on-the-go near-infrared reflectance spectroscopy (NIRS). Compared with the geostatistics method, the partial least squares regression (PLSR), with NIRS measurements, could yield a more detailed map for both soil carbon and clay. Further, by using independent validation dataset, the accuracy of predicting could be improved significantly for soil clay content and only slightly for soil carbon content. Owing to the complexity of field conditions, more work on data processing and calibration modeling might be necessary for using on-the-go NIRS measurements.