Topsoil Texture Research Articles

Adaptation of regional digital soil mapping for precision agriculture

In the initial phase of a national project to map clay, sand and soil organic matter (SOM) content in arable topsoil in Sweden, a study area in south-west Sweden comprising about 100 000 ha of arable land was assessed. Models were created for texture, SOM and two estimated variables for lime requirement determination (target pH and buffering capacity), using a data mining method (multivariate adaptive regression splines). Two existing reference soil datasets were used: a grid dataset and a dataset created for individual farms. The predictor data were of three types: airborne gamma-ray spectrometry data, digital elevation from airborne laser scanning, and legacy data on Quaternary geology. Validations were designed to suit applicability assessments of prediction maps for precision agriculture. The predictor data proved applicable for regional mapping of topsoil texture at 50 × 50 m2 spatial resolution (root mean square error: clay = 6.5 %; sand = 13.2 %). A novel modelling strategy, ‘Farm Interactive’, in which soil analysis data for individual farms were added to the regional data, and given extra weight, improved the map locally. SOM models were less satisfactory. Variable-rate application files for liming created from derived digital soil maps and locally interpolated soil data were compared with ‘ground truth’ maps created by proximal sensors on one test farm. The Farm Interactive methodology generated the best predictions and was deemed suitable for adaptation of regional digital soil maps for precision agricultural purposes.

采矿废弃地利用适宜性评价研究—以永年县超贫磁铁矿为例

土地适宜性评价是根据研究区土地自然属性和经济属性，从土地利用的要求出发，全面衡量土地对某种用途的适宜性及适宜程度，为科学调整用地结构，合理开发未利用土地提供依据。文章以永年县超贫磁铁矿采矿废弃地为例，依据土地限制因素的影响大小，选取地貌特征、土壤有机质(%)、表土质地、灌溉保证率、有效土层厚度(cm)等5个评价因子，采取极限条件法，划分了露天采坑、废石堆、废弃尾矿库等3个评价单元，通过分析评价得出一套行之有效的采矿废弃地利用适宜性评价研究方案。 Land suitability evaluation is based on the natural and economic attributes of land in the study area, from the requirements of land use, gives a full measure of the suitability and suitability of the land for a particular purpose, and provides the basis for scientific adjustment of land use structure and reasonable exploitation of unused land. The article takes Yongnian County super poor magnetite mining wasteland as an example, according to the influence of land restriction factors, selects the evaluation factors, including geomorphic features, soil organic matter (%), topsoil texture, irrigation guarantee rate and effective soil thickness (cm) and so on. It takes the limit condition method and selects three evaluation units, including open pit, waste rock pile and abandoned tailings reservoir which were divided. Finally, the article gives a set of effective evaluation of the use of mining waste evaluation program.

Is It Possible to Classify Topsoil Texture Using a Sensor Located 800 km Away from the Surface?

It is often difficult for pedologists to “see” topsoils indicating differences in properties such as soil particle size. Satellite images are important for obtaining quick information for large areas. However, mapping extensive areas of bare soil using a single image is difficult since most areas are usually covered by vegetation. Thus, the aim of this study was to develop a strategy to determine bare soil areas by fusing multi-temporal satellite images and classifying them according to soil textures. Three different areas located in two states in Brazil, with a total of 65,000 ha, were evaluated. Landsat images of a specific dry month (September) over five consecutive years were collected, processed, and subjected to atmospheric correction (values in surface reflectance). Non-vegetated areas were discriminated from vegetated ones using the Linear Spectral Mixture Model (LSMM) and Normalized Difference Vegetation Index (NDVI). Thus, we were able to fuse images with only bare soil. Field samples were taken from bare soil pixel areas. Pixels of soils with different textures (soil texture classifications) were used for supervised classification in which all areas with exposed soil were classified. Single images reached an average of 36 % bare soil, where the mapper could only “see” these points. After using the proposed methodology, we reached a maximum of 85 % in bare areas; therefore, a pedologist would have proper conditions for generating a continuous map of spatial variations in soil properties. In addition, we mapped soil textural classes with accuracy up to 86.7 % for clayey soils. Overall accuracy was 63.8 %. The method was tested in an unknown area to validate the accuracy of our classification method. Our strategy allowed us to discriminate and categorize different soil textures in the field with 90 % accuracy using images. This method can assist several professionals in soil science, from pedologists to mappers of soil properties, in soil management activities.

Temporal and Spatial Variation of Soil Moisture in Upland Soil using AMSR2 SMC

Temporal and spatial variation of soil moisture is important for understanding patterns of climate change, for developing and evaluating land surface models, for designing surface soil moisture observation networks, and for determining the appropriate resolution for satellite-based remote sensing instruments for soil moisture. In this study, we measured several soil moistures in upland soil using Advanced Microwave Scanning Radiometer 2 (AMSR2) Soil Moisture Content (SMC) during eight-month period in Chungbuk province. The upland soil moisture properties were expressed by simple statistical methods (average, standard deviation and coefficient of variation) from the monthly context. Supplementary studies were also performed about the effect of top soil texture on the soil moisture responses. If the results from this study were utilized well in specific cities and counties in Korea, it would be helpful to establish the countermeasures and action plans for preventing disasters because it was possible to compare with the relationship between soil moisture and top soil texture of each region. And it would be the fundamental data for estimating the effect of future agricultural plan.

Mapping of topsoil texture in Hungary using classification trees

ABSTRACTSpatial information about physical soil properties is in great demand, being basic input data in numerous applications. Soil texture can be characterized by different approaches, such as particle size distribution, plasticity index or soil texture classification. In accordance with the increasing demands for spatial soil texture information, our aim was to compile a topsoil texture class map for Hungary with an appropriate spatial resolution, using the United States Department of Agriculture soil texture classes. The ‘Classification and Regression Trees’ method was applied because it is widely used in Digital Soil Mapping, and has numerous advantages. Primary soil data were provided by the Hungarian Soil Information and Monitoring System. A digital elevation model and its derived components, geological and land cover map, and appropriate remotely sensed products together with the soil map featuring overall physical properties provided by the Digital Kreybig Soil Information System were used as auxiliary environmental co-variables. The resulting map can be used as direct input data in meteorological and hydrological modelling as well as in spatial planning.

Mapping topsoil physical properties at European scale using the LUCAS database

The Land Use and Cover Area frame Statistical survey (LUCAS) aimed at the collecting harmonised data about the state of land use/cover over the extent of European Union (EU). Among these 2·105 land use/cover observations selected for validation, a topsoil survey was conducted at about 10% of these sites. Topsoil sampling locations were selected as to be representative of European landscape using a Latin hypercube stratified random sampling, taking into account CORINE land cover 2000, the Shuttle Radar Topography Mission (SRTM) DEM and its derived slope, aspect and curvature.In this study we will discuss how the LUCAS topsoil database can be used to map soil properties at continental scale over the geographical extent of Europe. Several soil properties were predicted using hybrid approaches like regression kriging. In this paper we describe the prediction of topsoil texture and related derived physical properties. Regression models were fitted using, along other variables, remotely sensed data coming from the MODIS sensor. The high temporal resolution of MODIS allowed detecting changes in the vegetative response due to soil properties, which can then be used to map soil features distribution. We will also discuss the prediction of intrinsically collinear variables like soil texture which required the use of models capable of dealing with multivariate constrained dependent variables like Multivariate Adaptive Regression Splines (MARS).Cross validation of the fitted models proved that the LUCAS dataset constitutes a good sample for mapping purposes leading to cross-validation R2 between 0.47 and 0.50 for soil texture and normalized errors between 4 and 10%.

A Study on Soil Characteristics of Paddy Fields with Re-established Soils

Six study sites in Gumi, Goryeong in Gyeongbuk province and Naju in Jeonnam province were selected to investigate soil properties of poorly drained horizons in paddy soils. The horizons were re-established layers which were parent material layers originated from fluvial deposits. Topsoil layers were differentiated from piled parent materials while soil structure of the topsoil layer was massive with striated microstructure. Compaction at soil re-establishment and a lack of structure and aggregate development in these soils may cause the limitation of vertical water movement and result in poorly drained horizons. Soil samples were taken from paddy fields with top soils of sandy loam, silt loam and silty clay loam and re-established soils of coarse and fine texture. The samples were taken from each horizon for the analyses of soil chemical and mineral properties. Soils with re-established soils of coarse texture had greater amounts of sands from top soil texture distributions, while soils with fine texture had greater amounts of silts. Chemical properties of top soils were analyzed from rice cultivated soils at the time of re-establishments and one year after the re-establishments. The coarse texture of the re-established horizons decreased in EC values from 0.23 to 0.11 (dS m -1 ), available phosphate values from 112 to 54 (mg kg -1 ), and exchangeable Ca values from 6.6 to 4.9 (cmolc kg -1 ). On the other hand, soils with fine texture showed decrease only in pH and exchangeable Ca values. Especially, organic matter and available phosphate contents showed heterogeneous distributions from each horizon. This result may be caused by mixture of plough layer and subsurface layer during and consolidation. Hydraulic conductivity values were low at the boundaries of top soil and parent material layers except SL/coarse soil. Soil microstructure was massive structure without soil clods or pores and showed striated structure. Therefore, re-established paddy fields with fluvial deposits as parent material layers showed limited vertical movements of soil water because of occurrence of compacted layers and less-development of soil clods and aggregates.

南四湖区农田土壤有机质和微量元素空间分布特征及影响因素分析Spatial distribution and influencing factors of farmland soil organic matter and trace elements in the Nansihu Region

PDF HTML阅读 XML下载 导出引用 引用提醒 南四湖区农田土壤有机质和微量元素空间分布特征及影响因素 DOI: 10.5846/stxb201307181906 作者: 作者单位: 山东农业大学资源与环境学院,山东农业大学资源与环境学院,山东农业大学资源与环境学院,平邑经济开发区管理委员会,诸城国土局 作者简介: 通讯作者: 中图分类号: 基金项目: 山东省自然科学基金项目（Y2008H03）；山东省科技攻关项目支持（2009GG10006006） Spatial distribution and influencing factors of farmland soil organic matter and trace elements in the nansihu region Author: Affiliation: College of Resources and Environment Shandong Agricultural University,Tai’an,College of Resources and Environment Shandong Agricultural University,Tai’an,College of Resources and Environment Shandong Agricultural University,Tai’an,Administration Commission of Pinyi Economic Development Area,Pinyi,Zhucheng Land and Resources Bureau,Zhucheng Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:基于地统计学和GIS技术相结合的方法，研究了南四湖区农田土壤有机质和微量元素的空间分布特征及其影响因素。结果表明，土壤有机质和微量元素均属中等变异程度，除硼符合正态分布外，其余土壤属性均符合对数正态分布。结构分析表明，除硼为纯块金效应外，土壤有机质和其它微量元素空间自相关性较强，其中结构性因素起主导作用。克里格插值结果表明，土壤有机质分布总体趋势为由北向南逐渐降低，锰、铜、锌分布总体趋势为中部高，南北两端低。影响因素分析表明，土壤类型、耕层质地、坡度、土地利用类型和地貌类型对土壤有机质均有显著影响。土壤类型主要是由于成土母质的差异影响土壤有机质的高低与分布，随质地由砂变粘、坡度由低变高，土壤有机质含量逐步升高，田间管理水平的差异是造成不同土地利用类型下土壤有机质含量差异的主要原因。微量元素中，除硼不受影响外，铁、锰、铜和锌与土壤类型、耕层质地、坡度、土地利用类型和地貌类型密切相关。 Abstract:Soil organic matter (SOM) and trace elements are important indicators of soil quality for sustainable agriculture. The study of spatial distribution and influencing factors of soil organic matter and trace elements provided references for understanding regional soil characteristics,soil fertility and quality optimization and farmland ecological environment protection. A total of 1021 soils samples were taken in the Nansihu region, China, for SOM and trace elements analysis. The spatial distribution of SOM and these trace elements was determined using geostatistics and GIS technology such as semivariogram, ordinary Kriging interpolation, and spatial analysis methods. Factorings related with the spatial variations including soil types, soils texture, slope, land uses, and land forms were examined. Descriptive statistics showed that SOM averaged 14.66 g/kg with a coefficient of variation (CV) of 13.2%. Among the five trace elements, the average content was highest for Mn (10.66 mg/kg) and lowest for B (0.57 mg/kg). The CV was smallest for Fe(35.1%) and largest for B (96.5%). The normal distribution test showed that B was accorded with normal distribution while SOM, Fe, Mn, Cu and Zn conformed to logarithmic normal distribution. Semivariogram analyses indicated that B was of the pure nugget effect, with constant variation in the study scale. SOM, Fe, Mn, Cu and Zn displayed strong spatial autocorrelation and the structural factors played a leading role. The spatial correlation distance was 84 m for SOM, and ranged from 162 m (Fe) to 2572 m (Mn) for trace elements. The ordinary kriging interpolation results showed that the overall trend of SOM distribution was gradually decreased from north to south. The higher soil organic matter values were mainly distributed in the flat area. The values of Fe were highest in the northern mountainous area and hilly land (>14 mg/kg) and lowest in the South (3-9 mg/kg). A spatial pattern with higher concentrations in the central area than in the north and south area was observed for Mn, Cu and Zn. This was especially so for Mn ranging from <5 mg/kg on the north and south sides to >13 mg/kg in the central area. No significant spatial variation was observed with B (mostly >0.55 mg/kg).Soil type and topsoil texture were the predominant factors influencing spatial variations of SOM and trace elements except for B. SOM content was highest in the paddy soil. Fe, Mn contents were highest in the cinnamonic soil. Cu and Zn contents were highest in the mortar black soil. SOM increased with increasing clay content. Fe and Mn were highest in heavy loam. Cu was highest in slight loam and Zn was highest in sandy loam. Slope had a certain influence on some indicators, but the effect was small. The organic matter content increased with increasing slope. Land use types had significant influence on SOM, Fe and Cu, but had no effect on Mn, Zn and B. Contents of SOM, Fe and Cu was higher in paddy field. Topographic types had a greater influence on SOM, Fe, Mn and Zn, but had no impact on Cu and B, and the contents of trace elements were higher in depression. 参考文献 相似文献 引证文献

Comparison of Three Supervised Learning Methods for Digital Soil Mapping: Application to a Complex Terrain in the Ecuadorian Andes

A digital soil mapping approach is applied to a complex, mountainous terrain in the Ecuadorian Andes. Relief features are derived from a digital elevation model and used as predictors for topsoil texture classes sand, silt, and clay. The performance of three statistical learning methods is compared: linear regression, random forest, and stochastic gradient boosting of regression trees. In linear regression, a stepwise backward variable selection procedure is applied and overfitting is controlled by minimizing Mallow’s Cp. For random forest and boosting, the effect of predictor selection and tuning procedures is assessed. 100-fold repetitions of a 5-fold cross-validation of the selected modelling procedures are employed for validation, uncertainty assessment, and method comparison. Absolute assessment of model performance is achieved by comparing the prediction error of the selected method and the mean. Boosting performs best, providing predictions that are reliably better than the mean. The median reduction of the root mean square error is around 5%. Elevation is the most important predictor. All models clearly distinguish ridges and slopes. The predicted texture patterns are interpreted as result of catena sequences (eluviation of fine particles on slope shoulders) and landslides (mixing up mineral soil horizons on slopes).

Spatial Prediction of Topsoil Texture in a Mountain-plain Transition Zone Using Unvariate and Multivariate Methods Based on Symmetry Logratio Transformation

High-resolution soil texture maps are essential for land-use planning and other activities related to forestry, agriculture and environment protection. The objective of the article was to find suitable methods for predicting soil texture through comprehensive comparison of different prediction methods (e.g., univariate and multivariate methods) by completely taking account of its characteristics as composition data with the same auxiliary information. This article, taking elevation as auxiliary variable, predicted the soil texture using univariate [ordinary kriging (OK)] and multivariate [i.e. regression kriging (RK), simple kriging with locally varying means (SKlm), and cokriging (COK)] methods. Soil texture was transformed by symmetry log ratio (SLR) to meet the requirements of the spatial interpolation for the compositional data. The root mean squared errors (RMSE), the relative improvement (RI) values of RMSE and Aitchison's distance (DA) were utilized to assess the accuracies of different prediction ...

Investigating translated data of the German soil‐quality assessment using pinpoint field validation

AbstractIn contrast to modern soil‐profile characterization, alternative soil classifications, such as the German soil‐quality assessment (Bodenschätzung), bear a lower degree of scientific quality. However, despite originally created to determine the tax value of arable land and grassland, its high spatial resolution and complete areal coverage makes soil‐quality assessment a valuable tool. To assess its performance in a mountainous setting soil‐layer data of 60 soil pits, recorded in Bavaria (SE Germany) in the course of the soil‐quality assessment, were translated into German soil‐science terminology using the translation program NIBIS®. With regard to soil type and texture the translation was checked using pinpoint field validation based on soil‐science terminology. 57% of soil types and 61% of texture were correctly translated by NIBIS®. To obtain information about probable parameters that can explain the different results readily available parameters such as elapsed time between soil‐quality assessment and validation, altitude, slope, aspect, horizon thickness, lower edge of horizon, as well as weathering surface and silicate‐weathering rate derived from geological maps were used. Differences in topsoil texture were somewhat related to petrographic parameters, those of the lower subsoil showed a weak dependence to topographic parameters. The NIBIS® translation overrated the silt content to the expense of sand. Clay was the best‐matched texture class. The shift towards silty texture classes was the dominant factor for the differences of texture‐related values of the available water capacity and hydraulic conductivity. Both parameters as derived from the NIBIS® translation on the one and from field validation on the other hand were used to evaluate the water‐retention capacity of individual soil profiles. Despite differing input data the soils' water‐retention capacity was rated identical. Thus, a certain degree of disagreement between the texture data obtained from NIBIS® translation and from field validation is tolerable, if the eventual soil‐function evaluation is based on wide classes of texture or of secondary parameters derived from texture.

The use of scientific and indigenous knowledge in agricultural land evaluation and soil fertility studies of two villages in KwaZulu-Natal, South Africa

Local people and small-scale farmers have knowledge of their lands based on soil and land characteristics that remain largely unknown to the scientific community. It is therefore important for researchers to understand farmers’ knowledge of soil classification and management. To address this, indigenous knowledge was elicited by questionnaires from 59 households in two villages (Ezigeni and Ogagwini), near Durban in KwaZulu-Natal. Farmer vernacular soil and land suitability evaluations were compared to scientifically obtained soil and land suitability maps. Yield was used as a quantifiable indicator to test the effect of fertility management practices. It was found that farmers’ soil classification was based mainly on topsoil colour and texture. Slope position was the main factor determining land suitability. Crop yield, crop appearance, natural vegetation, soil colour, soil texture, and mesofauna were used to estimate soil fertility. Despite this, there was a correlation between farmers’ indigenous evaluation and scientific evaluation, implying that there are similarities between the two approaches. Key words: Local knowledge, scientific knowledge, soil properties, crop indicators, soil fertility.

Visible-Near Infrared Spectra as a Proxy for Topsoil Texture and Glacial Boundaries

Visible-Near Infrared Spectra as a Proxy for Topsoil Texture and Glacial Boundaries

Monsoon rains, drought periods and soil texture as drivers of soil N2O fluxes – Soil drought turns East Asian temperate deciduous forest soils into temporary and unexpectedly persistent N2O sinks

To quantify N2O fluxes between soil and atmosphere and understanding those processes driving them, is crucial if we aim to reliably predict one of earth's important greenhouse gases' origin and fate. Soil moisture has been identified as one major driver of N2O fluxes, drought has been observed to decrease soil N2O emissions and accounts for soil N2O consumption. We monitored N2O fluxes occurring at the soil/atmosphere interface of three temperate deciduous Korean forest sites experiencing a pronounced early summer drought followed by heavy East Asian monsoon rains. Because soil texture can enhance or mitigate soil drought effects, we selected sites which were different in topsoil texture. Therefore, we took closed chamber measurements of N2O fluxes during the growing season 2010 and determined N2O concentrations and δ15N values along soil profiles in the dry and monsoon season for a sandy-loam site. We observed N2O consumption at all of our study sites during early summer drought, which turned into N2O emission during the monsoon season. The N2O balance of the sandy-loam site remained slightly negative during the entire vegetation period. Soil moisture explained most of the measured N2O fluxes. For a sandy-loam forest soil we calculated a switch between N2O emission and consumption at an intermediate soil moisture (pF level of 3.02) which corresponds to a water filled pore space (WFPS) of 36%, but at half an order of magnitude moister soil (pF level: 2.57; WFPS 50%) at a loamy site. N2O concentration and δ15NN2O values along the soil profiles suggest that those processes driving the N2O fluxes at the soil/atmosphere interface most likely occurred in the topsoil. Our results contribute to our knowledge on the global N2O budget, because monsoon affected forests cover large areas worldwide and their soils' N2O emissions have so far been uninvestigated.

Impact of flood deposits on earthworm communities in alder forests from a subalpine floodplain (Kandersteg, Switzerland)

In many ecosystems, bioindication is a tool to estimate biodiversity and quality of environment. In soils, invertebrates are generally suitable bioindicators, especially earthworms. In floodplains, young alluvial soils are exposed to sedimentation and erosion, and little is known about soil bioindication. Moreover, a reference state is now needed to evaluate river restoration projects. The aim of our study was thus to establish an “undisturbed” floodplain reference at the subalpine level based on earthworm communities and to test if they are indicators of fluvial dynamics. Seven plots were chosen along a stretch of the Kander River (BE, Switzerland). At each plot, a soil profile was described (carbonated Fluvisols) and topsoil was analysed. Earthworms were extracted in each plot using standard mustard extraction (3 × 1 m2) and “hand sorting” method (20 × 20 × 20 cm). Eight species were identified, and Lumbricus meliboeus was found for the first time in a carbonated environment. The absence of anecics was considered, at the subalpine level, as a bioindication of the fluvial dynamics (erosion and sedimentation processes). Biomass of epigeics was positively correlated to topsoil texture and organic matter quality, and thus epigeics, sensitive to variations of topsoil composition, are bioindicators of the latest flood event at the subalpine level.

Effectiveness of the GAEC cross-compliance standard Ploughing in good soil moisture conditions in soil structure protection

Researches have been carried out within the framework on the EFFICOND Project, focused at evaluating the effectiveness of the standards of Good Agricultural and Environmental Conditions (GAECs) established for Cross Compliance implementation under EC Regulation 1782/2003. In particular the standard 3.1b deals with soil structure protection through appropriate machinery use, with particular reference to ploughing in good soil moisture conditions. The study deals with the evaluation of soil structure after tillage in tilth and no-tilth conditions at soil moisture contents other than the optimum water content for tillage. The Mean Weight Diameter (MWD) of water stable aggregates was used as an indicator of tillage effectiveness. The study was carried out in the period 2008-2009 at six experimental farms belonging to Research Centres and Units of the Italian Agricultural Research Council (CRA) with different pedo-climatic and cropping conditions. Farm management and data collection in the different sites were carried out by the local CRA researchers and technicians. The comparison of MWD values in tilth and no tilth theses showed statistically significant differences in most cases, depending on topsoil texture. On clay, clay loam, silty clay, and silty clay loam topsoils a general and significant increase of MWD values under no tilth conditions were observed. No significant differences were observed in silt loam and sandy loam textures, probably due to the weak soil structure of the topsoils. Moreover, ploughing in good soil moisture condition determined higher crop production and less weed development than ploughing in high soil moisture conditions.

Grazing changes topography-controlled topsoil properties and their interaction on different spatial scales in a semi-arid grassland of Inner Mongolia, P.R. China

Semiarid steppe ecosystems account for large terrestrial areas and are considered as large carbon (C) sinks. However, fundamental information on topsoil sensitivity to grazing is lacking across different spatial scales including the effects of topography. Our interdisciplinary approach considering soil chemical, physical, and vegetation properties included investigations on pit scale (square-metre scale), plot scale (hectare scale), and the scale of a landscape section (several hectares). Five different sites, representing a grazing intensity gradient, ranging from a long-term grazing exclosure to a heavily grazed site were used. On the pit scale, data about aggregate size distribution, quantity of different soil organic carbon (SOC) pools, SOC mineralisation, hydraulic conductivity and shear strength was available for topsoil samples from representative soil profiles. Spatial variability of topographical parameters, topsoil texture, bulk density, SOC, water repellency, and vegetation cover was analysed on the basis of regular, orthogonal grids in differently grazed treatments by using two different grid sizes on the plot scale and landscape section. On the pit scale, intensive grazing clearly decreased soil aggregation and the amount of fresh, litter-like particulate organic matter (POM). The weak aggregation in combination with animal trampling led to an enhanced mineralisation of SOC, higher topsoil bulk densities, lower infiltration rates, and subsequently to a higher risk of soil erosion. On the plot scale, the effects of soil structure disruption due to grazing are enhanced by the degradation of vegetation patches and resulted in a texture-controlled wettability of the soil surface. In contrast, topsoils of grazing exclosures were characterised by advantageous mechanical topsoil characteristics and SOC-controlled wettability due to higher POM contents. A combined geostatistical and General Linear Model approach identified topography as the fundamental factor creating the spatial distribution of texture fractions and related soil parameters on the scale of a landscape section. Grazing strongly interfered with the topography-controlled particle relocation processes in the landscape and showed strongest effects on the aboveground biomass production and biomass-related soil properties like SOC stocks. We conclude that interdisciplinary multi-scale analyses are essential (i) to differentiate between topography- and grazing-controlled spatial patterns of topsoil and vegetation properties, and (ii) to identify the main grazing-sensitive processes on small scales that are interacting with the spatial distribution and relocation processes on larger scales.

Extracting Topsoil Information from EM38DD Sensor Data using a Neural Network Approach

Electromagnetic induction soil sensors are an increasingly important source of secondary information to predict soil texture. In a 10.5‐ha polder field, an EM38DD survey was performed with a resolution of 2 by 2 m and 78 soil samples were analyzed for sub‐ and topsoil texture. Due to the presence of former water channels in the subsoil, the coefficient of variation of the subsoil clay content (45%) was much larger compared with the topsoil (13%). The horizontal (ECa–H) and vertical (ECa–V) electrical conductivity measurements displayed a similar pattern, indicating a dominant influence of the subsoil features on both signals. To extract topsoil textural information from the depth‐weighted EM38DD signals we turned to artificial neural networks (ANNs). We evaluated the effect of different input layers on the ability to predict the topsoil clay content. To identify the response of the topsoil, both EM38DD orientations were used. To examine the influence of the local neighborhood, contextual ECa information by means of a window around each soil sample was added to the input. The best ANN model used both ECa–H and ECa–V data but no contextual information: a mean squared estimation error of 2.83%2 was achieved, explaining 65.5% of the topsoil clay variability with a variance of 0.052%2 So, with the help of ANNs, the prediction of the topsoil clay content was optimized through an integrated use of the two EM38DD signals.

A pedotransfer function to evaluate the soil profile textural heterogeneity using proximally sensed apparent electrical conductivity

Since soil texture is difficult to quantify, its determination is often restricted to the topsoil. To improve its mapping accuracy, observations of the apparent electrical conductivity (ECa) obtained from proximal soil sensors are increasingly being used due to its close relationship with soil texture under non-saline conditions. However, a single ECa measurement does not allow an unambiguous interpretation of both the topsoil texture and its vertical distribution, because of its bulk support over the entire profile. Therefore we selected 88 soil profiles with a ‘pseudo-homogeneous’ textural composition from a database containing 1500 profiles with nine textural fractions and the organic matter content analysed for every horizon. These profiles were revisited and the ECa was measured with an EM38DD sensor. With these data we obtained the following pedotransfer function (PTF): ECa-V = 16.2 + 1.314% clay, with R 2 = 0.81. This function allows converting a topsoil clay percentage into a reference ECa value measured with the vertical coil orientation (ECa-V), assuming standard temperature and moisture conditions and a dominance of mica clay minerals. The ratio between the observed ECa-V and this reference ECa-V value is a measure of the soil profile textural heterogeneity: if this ratio is near to one, the profile is quasi-homogeneous; a ratio exceeding one indicates the presence of a more conductive material deeper in the soil profile, and vice versa. To explore this idea we considered an intensively surveyed area of 3000 km 2 in Belgium with a wide soil textural variation. Based on our PTF, a regional-scale topsoil clay map was converted into a reference ECa-V map which was found to be a valuable aid in evaluating the soil profile textural heterogeneity by field-measured ECa-V data.

Comparison of soil maps with different scales and details belonging to the same area

Two different soil maps prepared by different institutes at scales of 1:200 000 and 1:25 000 covering identical areas were compared to determine the accuracy of reconnaissance. These soil maps are widely used in land resources assessment studies in Turkey. For this purpose, the soil maps were digitised and performed a data set. Then the map layers were compared by using GIS technology in order to assess the soil properties and land characteristics. The reconnaissance soil map at the scale of 1:200 000 has the highest accuracy for the slope due to the fact that topographic maps have been used as basic maps for the field studies. The accuracy of other properties in descending order is as follows; slope &amp;gt; depth &amp;gt; salinity &amp;gt; texture &amp;gt; drainage &amp;gt; top soil texture. In addition, physiographic and topographic patterns of soils also affect the accuracy of maps. The reconnaissance soil map was found to be less accurate in flood plains where the slope does not affect other soil properties.