Estimation of exchangeable bases and base saturation from soil physical and chemical data

Abstract

Soil physical, chemical, and taxonomic data of about 12,000 pedons from the continental U.S., Hawaii, Puerto Rico, and some foreign countries were used to develop multiple regression equations to estimate sum of exchangeable bases (SUMBAS) and base saturation (BS). Soils were grouped according to their taxonomic classification at order and suborder categories. Multiple regression equations using organic carbon and clay contents, and soil pH in water ratio 1:1 accounted for more than 50% of the variation in SUMBAS in 11 of 35 suborders included in this study. Regression equations using organic carbon and clay contents, pH, 1M KC1 extractable Al or percent Al saturation, and cation exchange capacity accounted for more than 70% of the variation in SUMBAS in 18 of 35 suborders. Percent Al saturation and organic carbon content were negatively related to BS. These two parameters accounted for more than 70% of the variation in BS in 16 of 32 suborders. Regression equations using soil pH alone, in turn, accounted for more than 70% of the variation in BS in 4 of 6 suborders with a formative element Aquic in their taxonomic names.     

Classification of Ferrallitc Soils in Chinese Soil Taxonomy

The development of the classification of ferrallitic soils in China is reviewed and the classification of Ferralisols and Ferrisols in Chinese Soil Taxonomy is introduced in order to discuss the correlation between the ferrallitic soil classification in the Chinese Soil Taxonomy and those of the other soil classification systems.In the former soil classification systems of China,the ferrallitic soils were classified into the soil groups of Latosols,Latosoilc red soils,Red soils,Yellow soils and Dry red soils,according to the combination of soilforming conditions,soil-forming processes,Soil features and soil properties.In the Chinese Soil Taxonomy,most of ferrallitic soils are classified into the soil orders of Ferralisols and Ferrisols based on the diagnostic horizons and /or diagnostic characteristics with quantitatively defined properties.Ferralisols are the soils that have ferralic horizon,and they are merely subdivided into one suborder and two soil groups.Ferrisols are the soils that have LAC-ferric horizon but do not have ferralic horizon.and they are subdivided into three suborders and eleven soil groups.Ferralisols may correspond to part of Latosols and Latosolic red soils.Ferrisols may either correspond to part of Red soils,Yellow soils and Dry red soils,or correspond to part of Latosols and Latosolic red soils.     

中国土壤分类法中铁铝土的分类

The development of the classification of ferrallitic soils in China is reviewed and the classification of Ferralisols and Ferrisols in Chinese Soil Taxonomy is introduced in order to discuss the correlation between the ferrallitic soil classification in the Chinese Soil Taxonomy and those of the other soil classification systems. In the former soil classification systems of China, the ferrallitic soils were classified into the soil groups of Latosols, Latosolic red soils, Red soils, Yellow soils and Dry red soils, according to the combination of soil-forming conditions, soil-forming processes, soil features and soil properties. In the Chinese Soil Taxonomy, most of ferrallitic soils are classified into the soil orders of Ferralisols and Ferrisols based on the diagnostic horizons and/or diagnostic characteristics with quantitatively defined properties. Ferralisols are the soils that have ferralic horizon, and they are merely subdivided into one suborder and two soil groups. Ferrisols are the soils that have LAC-ferric horizon but do not have ferralic horizon, and they are subdivided into three suborders and eleven soil groups. Ferralisols may correspond to part of Latosols and Latosolic red soils. Ferrisols may either correspond to part of Red soils, Yellow soils and Dry red soils, or correspond to part of Latosols and Latosolic red soils.     

Soil-forming materials and soil texture as shown on small-scale soil maps

Information about the soil-forming materials and soil texture shown on pages of the State Soil Map (SSS) on a 1: 1 M scale, the Soil Map of the Russian Federation on a 1: 2.5 M, and several other small-scale soil maps is analyzed. Certain discrepancies exist in the approaches used to display this information, in the names of the soil textural classes and genetic groups of soil-forming materials, and in the degree of detail of their representation on separate pages of the SSS and on other soil maps compiled by different authors. To eliminate these discrepancies, it is suggested that the soil classification should be supplemented with a special section containing systematized information on the soil-forming materials existing in Russia. The manuals on soil mapping on different scales should be supplemented with recommendations on showing the soil texture and the composition of the soil-forming materials on the maps.     

How good is a reconnaissance soil map for agronomic purposes?

Abstract. Information about the soil fertility status in irrigated ricelands at regional scales (1:50 000–1:250 000) is commonly not contained in classical soil maps. To assess the agronomic suitability of two different reconnaissance soil maps, we conducted a detailed soil survey in the Nueva Ecija province, Philippines. Soil samples were collected from 384 farmers' fields, and soil properties were measured for topsoil and subsoil samples. For most soil properties, a soil map made in 1940 (1:125 000) had within-map unit variances that were smaller than the total variance, whereas a new soil map of 1992 (1:50 000) did not significantly reduce the within-class variance. In both soil maps, classification into mapping units accounted for 0–40% of the variance of 14 agronomically important soil properties and large within-map unit variabilities were found. Underlying strategies of classical soil survey supported the partition of variance for relatively stable soil properties, such as soil texture, CEC, and organic matter. If reconnaissance soil maps are used in quantitative land evaluation studies, existing maps require upgrading by adding quantitative information about relevant soil properties and their within-map unit variability The sampling demand for upgrading a reconnaissance soil map was large, but pedotransfer functions can be used as cost-saving tools. Measures of soil nutrient status were highly variable within all mapping units and differences among farmers were much greater than the differences between soil types. Therefore, nutrient management in the study region should be based on individual field or farm recommendations rather than on soil-map based recommendations.     

浙江省土壤发生分类与系统分类参比及制图研究

利用新建立的浙江省1∶5万土壤数据库,对土壤发生分类土种与中国土壤系统分类亚类进行了参比,编制了土壤系统分类亚类分布图.结果表明,发生分类基层分类单元归属较为清楚,但高级单元关系较为复杂.99个土属有62个参比归属唯一,277个土种有252个参比归属唯一,通过参比将大比例尺土壤普查成果转换成系统分类体系是可行的,可以满足1∶10万的系统分类亚类制图要求.浙江省土壤参比后归属于8个土纲,以雏形土土纲面积最大,占总面积的31.3％;人为土次之,占总面积的21.4％,有机土面积最小.在系统分类土纲层次,土壤区域分布规律较为明显.研究结果对指导土壤系统分类具有一定的参考价值,也为省域范围的系统分类制图提供了范例.     

Global mapping of volumetric water retention at 100, 330 and 15 000 cm suction using the WoSIS database

Present global maps of soil water retention (SWR) are mostly derived from pedotransfer functions (PTFs) applied to maps of other basic soil properties. As an alternative, ‘point-based’ mapping of soil water content can improve global soil data availability and quality. We developed point-based global maps with estimated uncertainty of the volumetric SWR at 100, 330 and 15 000 cm suction using measured SWR data extracted from the WoSIS Soil Profile Database together with data estimated by a random forest PTF (PTF-RF). The point data was combined with around 200 environmental covariates describing vegetation, terrain morphology, climate, geology, and hydrology using DSM. In total, we used 7292, 33 192 and 42 016 SWR point observations at 100, 330 and 15 000 cm, respectively, and complemented the dataset with 436 108 estimated values at each suction. Tenfold cross-validation yielded a Root Mean Square Error (RMSE) of 6.380, 7.112 and 6.485 10⁻²cm³cm⁻³, and a Model Efficiency Coefficient (MEC) of 0.430, 0.386, and 0.471, respectively, for 100, 330 and 15 000 cm. The results were also compared to three published global maps of SWR to evaluate differences between point-based and map-based mapping approaches. Point-based mapping performed better than the three map-based mapping approaches for 330 and 15 000 cm, while for 100 cm results were similar, possibly due to the limited number of SWR observations for 100 cm. Major sources or uncertainty identified included the geographical clustering of the data and the limitation of the covariates to represent the naturally high variation of SWR.     

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.     

关中塿土发生特性与分类研究进展

塿土是关中地区受人类活动影响深刻的重要农业土壤,而对其发生分类及系统分类的归属仍有较大争议。本文综述了塿土的形成过程和成土过程特点,塿土发生分类归属的变更,系统分类的主要诊断层、诊断特性和诊断现象,高级分类单元归属及空间分布,基层分类单元的主要诊断指标等方面的研究进展。在此基础上,展望了塿土需要进一步开展的研究工作包括3个方面:①深入理解人为影响下塿土的成土过程,定量化分析覆盖层的形成过程;②开展塿土覆盖层厚度调查,界定堆垫表层的厚度标准;③进行土壤普查,建立塿土代表土系。     

SOIL TEXTURE PATTERNS IN THE ALLUVIUM OF THE LOWER INDUS PLAINS

Soil texture was mapped in the immature alluvial soils of the Lower Indus plain. A disadvantage of soil texture as a mapping criterion in such soils is the great complexity of their textural patterns. The geomorphological background of the Lower Indus plains is examined, and a classification of texture and textural profiles is defined. The mapping unit proposed, the‘textural association', comprises a certain range of textural profiles within a given landform, related to each other by the modes of deposition which established that landform. It seems likely that the textural association could, in more detailed surveys, form the basis of a soil series classification.     

中国人为土的多样性

Human activities make strong effects on soil formation. Anthropogenic soils are much more intensive and extensive in China for their history of agricultural production can be dated back to more than 7 000 years ago. Owing to different physical conditions and land uses, the anthropogenic soil-forming processes are various. Anthrosols are proposed, and the corresponding soil order is set up in Chinese Soil Taxonomy (CST). Mainly based on 6 Anthropogenic diagnostic horizons, which are anthraquic epipedon, hydragric horizon, irragric epipedon, cumulic epipedon, fimic epipedon and agric horizon, the Anthrosols Order is subdivided into 2 soil suborders and 4 soil groups. Meanwhile the classification of Anthrosols in CST has been basically accepted as the classification of Anthrosols in World Reference Base for Soil Resources (WRB).     

对全国第二次土壤普查中土类、亚类划分及其调查制图的辨析

按照中国发生分类对新采集的68个北京市山区的土壤剖面进行了分类命名,并与剖面点所在土壤普查图上的分类名称进行比较,结果是只有18个剖面的分类名称一致。造成分类名称不一致的原因:1发生分类以区域典型土壤剖面分类命名,而区域内很多土壤不同于典型土壤剖面;2发生分类往往以现代生物气候带为主要分类标准命名区域土壤,而不是根据土壤性质;3分类不一致的最大原因可能是制图精度不够。研究认为,土壤分类必须依据土壤性质本身,而不是土壤形成因素;采取野外单土壤性质调查制图,室内叠加单土壤性质图形成多属性图斑,根据分类系统对它们进行综合分类,以提高分类制图精度。     

豫南白浆化黄褐土分类参比研究

根据土壤理化性质,分别按照《中国土壤系统分类(第三版)》中的诊断层和诊断特性和中国土壤分类系统的发生分类原则对河南省南部地区具有漂白层的4个代表性土壤剖面进行分类归属,确定其在中国土壤系统分类中属于淋溶土纲,湿润淋溶土亚纲、漂白湿润淋溶土土类及相应的亚类、土族、土系;在中国土壤分类系统中属于湿暖淋溶土亚纲,黄褐土土类,白浆化黄褐土亚类及相应的土属、土种。     

平原区土壤质地的反射光谱预测与地统计制图

基于地统计方法的土壤属性制图通常需要大量的采样与实验室测定。本研究提出利用可见光近红外(visible-nearinfrared spectroscopy,VNIR)光谱技术测定替代实验室测定,并与地统计方法相结合预测土壤质地的空间变异。通过建立砂粒(0.02 mm),粉粒(0.002~0.02 mm),黏粒(0.002 mm)含量的VNIR光谱预测模型,将模型预测得到的质地数据和建模点实测质地数据一同用于地统计分析和Kriging插值制图。以江苏北部黄淮平原地区为案例的研究结果表明,砂粒、粉粒、黏粒含量的预测值和实测值的均方根误差(RMSE)分别为8.67%、6.90%3、.51%,平均绝对误差(MAE)分别为6.46%、5.60%、3.05%,显示了较高的预测精度。研究为快速获取平原区土壤质地空间分布提供了新的可能的途径。     

土壤制图中土壤类型配色模型构建与应用

在全国1∶5万土壤图集制图中,土壤类型的配色既需表现土类等高级类型的分布特征,也要表现土属等较低级类型的差别。我国土壤低级类型众多,且1∶5万基本比例尺图幅达2万余幅,采用传统人工设色方法进行土壤制图,不仅效率低,而且难以保持图幅间土壤颜色的协调一致性。针对这一技术难题,本研究采用图幅间相似配色方法和人机交互的设计思想,通过建立1个多层级管理色库、人工设置土壤类型的Q配色单元及其多个近似色系(色组),建立了Q配色单元的避让选色和区域土壤特征分析等5个组件模型,构建了土壤类型配色模型(SCO-Model)。该模型在大比例尺土壤制图中不仅反映了区域土壤的总体分布特征,也表达了土壤类型间的差异,特别是实现了大比例尺土壤制图中土壤类型的快速智能配色,大大提高了制图效率。     

soil lead levels in orchards and roadsides of mission peninsula,michigan

The Pb content in orchard soils at Mission Peninsula, Michigan was determined to assess the impact of historical lead arsenate applications. Soil samples at 72 sites located in five orchards were collected at depths of 2-, 20-, 50-, and 100 cm. Atomic absorption spectroscopy was used to quantify Pb levels (μg g^-1). Mean surface Pb levels at individual orchards ranged from< 1–136 μg g^-1 and rapidly decreased with depth, to <1–5 μg g^-1 at 100 cm. The impact of textural class and slope angle on Pb levels was also analyzed. Correlation coefficients linking Pb levels with textural class were weak, ranging from 0.21 to –0.07. Varying slope steepness and slope position within orchards failed to affect the spatial pattern of soil Pb. Soil Pb levels were also compared at 5 sites along local roads with varying levels of automobile traffic. Samples were collected 1 m from the roadside at the same depth intervals studied in orchards. Average daily traffic along the busiest roadsites ranged from 8200 to 16 000; these sites had Pb levels of 90–210 μg g^-1. Such locales had Pb levels similar to the more intensively sprayed orchards.     

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.     

Regional-scale modelling of the spatial distribution of surface and subsurface textural classes in alluvial soils using Markov chain geostatistics

Soil texture is directly associated with other soil physical and chemical properties and can affect crop yield, erodibility and water and pollutant movement. Thus, maps of soil textural class are valuable for agricultural management. Conventional spatial statistical methods do not capture the complex large-scale spatial patterns of multi-class variables. Markov chain geostatistics (MCG) was recently proposed as a new approach for the conditional simulation of categorical variables. In this study, we apply an MCG algorithm to simulate the spatial distribution of textural classes of alluvial soils at five different depths in a 15-km² area on the North China Plain. Soil texture was divided into five classes – sand, sandy loam, light loam, medium loam and clay. Optimal prediction maps, simulated maps and occurrence probability maps for each depth were generated from sample data. Simulated results delineated the distribution of the five soil textural classes at the five depths and quantified related spatial uncertainties caused by limited sample size (total of 139 points). These results are not only useful for understanding the spatial distribution of soil texture in alluvial soils, but also provide valuable quantitative information for precision agriculture, soil management and studies on environmental processes affected by surface and subsurface soil textures.     

Quantification and depth distribution analysis of carbon to nitrogen ratio in forest soils using reflectance spectroscopy

Forest soils have large contents of carbon (C) and total nitrogen (TN), which have significant spatial variability laterally across landscapes and vertically with depth due to decomposition, erosion and leaching. Therefore, the ratio of C to TN contents (C:N), a crucial indicator of soil quality and health, is also different depending on soil horizon. These attributes can cost-effectively and rapidly be estimated using visible–near infrared–shortwave infrared (VNIR–SWIR) spectroscopy. Nevertheless, the effect of different soil layers, particularly over large scales of highly heterogeneous forest soils, on the performance of the technique has rarely been attempted. This study evaluated the potential of VNIR–SWIR spectroscopy in quantification and variability analysis of C:N in soils from different organic and mineral layers of forested sites of the Czech Republic. At each site, we collected samples from the litter (L), fragmented (F) and humus (H) organic layers, and from the A₁ (depth of 2–10 cm) and A₂ (depth of 10–40 cm) mineral layers providing a total of 2505 samples. Support vector machine regression (SVMR) was used to train the prediction models of the selected attributes at each individual soil layer and the merged layer (profile). We further produced the spatial distribution maps of C:N as the target attribute at each soil layer. Results showed that the prediction accuracy based on the profile spectral data was adequate for all attributes. Moreover, F was the most accurately predicted layer, regardless of the soil attribute. C:N models and maps in the organic layers performed well although in mineral layers, models were poor and maps were reliable only in areas with low and moderate C:N. On the other hand, the study indicated that reflectance spectra could efficiently predict and map organic layers of the forested sites. Although, in mineral layers, high values of C:N (≥ 50) were not detectable in the map created based on the reflectance spectra. In general, the study suggests that VNIR–SWIR spectroscopy has the feasibility of modelling and mapping C:N in soil organic horizons based on national spectral data in the forests of the Czech Republic.