Spatial Variability Of Soil Properties Research Articles

Small-scale spatial variability of hydro-physical properties of natural and degraded peat soils

The spatial variability of soil properties plays an important role in water and carbon cycles in peatlands. The objectives of this study were to analyze the spatial variation of hydro-physical properties of peat soils and to establish pedotransfer functions (PTFs) to estimate the hydraulic properties of peat using readily available soil properties. We selected three study sites, each representing a different state of peat degradation (natural, degraded and extremely degraded). At each site, 72 undisturbed soil cores were collected from 5 m by 5 m grid cells in an area of 35 m by 40 m. The saturated hydraulic conductivity (Ks), soil water retention curves, total porosity, macroporosity (pore diameter >30 μm), bulk density and soil organic matter content (SOM) were determined for all sampling locations. The Van Genuchten (VG) model parameters (θs, α, and n) were optimized using the RETC software package. A strong positive correlation between macroporosity and Ks was observed irrespective of the degradation stage of the peat. However, the relationships between macroporosity and Ks differed between the natural and the drained peatlands. Adding macroporosity to the PTFs substantially improves the prediction of Ks as well as VG parameters. Results show that the soil physical and hydraulic properties (e.g. Ks and VG model parameters) exhibit different levels of spatial heterogeneity depending on the peat degradation stage. The geostatistical analysis suggests that the spatial dependence of soil hydro-physical properties varies depending on the considered property as well as land management (e.g. drainage). Bulk density and SOM are spatially dependent, whereas Ks and macroporosity are spatially independent if the peat is severely degraded. In conclusion, the peat degradation stage plays an important role and should be generally considered in the spatial analysis of peatlands. The obtained semivariograms may serve as a basis for 2D and 3D hydrological modelling as well as peatland restoration studies.

Performance of linear mixed models and random forests for spatial prediction of soil pH

Digital soil maps describe the spatial variation of soil and provide important information on spatial variation of soil properties which provides policy makers with a synoptic view of the state of the soil. This paper presents a study to tackle the task of how to map the spatial variation of soil pH across Zambia. This was part of a project to assess suitability for rice production across the country. Legacy data on the target variable were available along with additional exhaustive environmental covariates as potential predictor variables. We had the option of undertaking spatial prediction by geostatistical or machine learning methods. We set out to compare the approaches from the selection of predictor variables through to model validation, and to test the predictors on a set of validation observations. We also addressed the problem of how to robustly validate models from legacy data when these have, as is often the case, a strongly clustered spatial distribution. The validation statistics results showed that the empirical best linear unbiased predictor (EBLUP) with the only fixed effect a constant mean (ordinary kriging) performed better than the other methods. Random forests had the largest model-based estimates of the expected squared errors. We also noticed that the random forest algorithm was prone to select as “important” spatially correlated random variables which we had simulated.

Effect of spatial variability of soil properties on permanent seismic displacements of slopes with uniform load

The purpose of this study is to investigate the development of permanent displacements of earth slopes, with spatially varying properties and a uniform surface load, subjected to a strong seismic excitation. The uncertainties associated with the determination of the mechanical properties of the slopes and the intensity of the seismic excitation, make the use of stochastic methods very attractive. As opposed to deterministic methods, stochastic methods allow an acceptable risk level based on the specifications of each project. Recent research described the spatial variability of soil parameters using the Random Field Theory. Apart from the prescribed statistical characteristics and cross-correlation of the various soil properties, the generated random field variables exhibit autocorrelation, a trend in which the soil properties of a point appear to be correlated with the soil properties of neighbouring points. Among the various random field algorithms, a particularly effective one is the Local Average Subdivision (LAS) method by Fenton and Vanmarcke [1]. To this end, a large number of random fields of soil properties is generated for a natural slope, having prescribed mean value μ, standard deviation σ, cross-correlation coefficients pij of properties i and j, and spatial autocorrelation lengths lx and ly for the vertical and horizontal directions, respectively. The numerical simulations are achieved using an automated procedure, based on collaboration of Mathematica, the incorporated LAS algorithm, and the finite difference program FLAC. The studied slope is loaded with a uniform load and subjected to a seismic shaking of various intensities using a series of acceleration records obtained from the five different earthquakes. The paper focuses mainly on the development of permanent seismic displacements of the slope at the end of ground shaking. It is demonstrated that the spatial variability of soil properties has a significant effect on the values of permanent displacements, resulting to a wide range of displacement variation.

3D stability analyses of Skjeggestad landslide

In 2015, a sudden landslide caused the failure of one of the pillars supporting the southern lanes of the Skjeggestad Bridge near Mofjellbekken on Expressway E18. The transportation corridor was closed to traffic for 17 months. To investigate the cause of the failure, an assessment of slope stability is necessary. Usually, limit equilibrium analysis of the middle two-dimensional (2D) cross-section is modelled. The Skjeggestad landslide geometry was not close to a 2D case, and three-dimensional (3D) modelling is more appropriate to analyse the slope. The paper calculates the stability of the slope that failed and compares the results of 3D finite element analyses with classical limit equilibrium and 2D finite element analyses. The analyses were run in the Novapoint GeoSuite Stability software. The soil parameters, including their statistical values, were obtained with the GeoSuite Soil Data Interpretation (SDI) module. A companion paper at this conference analyses the pillar neighbouring the landslide with the module GeoSuite Piles. The paper illustrates the importance of 3D effects in a stability analysis. The results of the analyses also illustrate the need to include appropriate consideration of any strain-softening and spatial variation of soil properties.

Preliminary study of a bridge abutment settlement considering spatial variability of soil properties

The paper focuses on two-dimensional (2D) analyses of a bridge abutment based on a spatially variable soil. The analysis was carried out to evaluate the settlement of the bridgehead. The numerical model consists of a reinforced concrete support structure, embankment and natural soil. Moreover, frictional contact between the soil and the structure is assumed. As it is well known, the natural soils are spatially highly variable materials, and their mechanical and physical parameters in geotechnical designing, such as a bridge abutment, must be considered variable. Thus, the presented analyses will be devoted to determining the settlement of the structure under study, in probabilistic terms due to soil variability. To this end, Sequential Gaussian Simulation was used to describe the spatial variability of soil parameters. This approach uses geostatistical methods to produce different realizations of soil parameters. A data set of several Cone Penetration Tests (CPT) was used. Simulations were used to calculate the histogram of a bridgehead settlement and search the worst case. Abaqus program and some ad hoc scripts written in Python language were used to perform the analyses. The analysis reflects several stages of abutment’s construction. This allows for a comprehensive analysis of a bridge abutment.

Predicting the bearing capacity of pile installed into cohesive soil concerning the spatial variability of SPT data (A case study)

Nowadays, in situ tests have played a viable role in geotechnical engineering and construction technology. Besides lab tests conducted on undisturbed soil samples, many different kinds of in-situ tests were used and proved to be more efficient in foundation design such as pressuremeter PMT, cone penetration test CPT, standard SPT, etc. Among them, a standard penetration test (SPT for short) is easy to carry out at the site. For decades, it has proved reliable to sandy soil, but many viewpoints and opinions argued that the test was not appropriately applicable to cohesive soil because of scattered and dispersed data of SPT blow counts through different layers. This paper firstly studies how reliable the SPT data can predict the physical and mechanical properties; secondly, the soil strength is determined in terms of corrected N-SPT values, and finally the bearing capacity of a pile penetrating cohesion soil. By analyzing data from 40 boreholes located in 18 projects in Ho Chi Minh City, South VietNam, coefficients of determination between SPT numbers and physical and mechanical properties of different soil kinds are not the same: R2 = 0.623 for sand, =0.363 for sandy clay and =0.189 for clay. The spatial variability of soil properties is taken into account by calculating the scale of fluctuation θ=4.65m beside the statistically-based data in horizontal directions. Finally, the results from two theoretical approaches of predicting pile bearing capacity were compared to those of finite element program Plaxis 3D and static load test at site. Correlation between the capacity computed by using corrected N-values instead of soil strength and results of static load test has proved to be well suitable in evaluating the bearing capacity of driven and jack-in piles, particularly installing in the cohesive soil using the SPT blows.

Delineating the spatial distribution of soil liquefaction potential in a cross-section from limited cone penetration tests

The damage caused by soil liquefaction during earthquakes is substantially affected by the total volume and spatial distribution of the liquefied soils. Therefore, a proper characterization of the spatial distribution of liquefaction potential of subsurface soils plays a critical role in earthquake hazard assessment and mitigation. In engineering practice, in situ tests, such as the cone penetration test (CPT), are widely used for evaluating soil liquefaction potential through, e.g., factor of safety (FS) against liquefaction, and the results of liquefaction assessment in a cross-section are needed for subsequent earthquake hazard analysis and mitigation. Traditional simplified methods only provide the liquefaction potential of soils at individual locations, and the results of liquefaction triggering evaluation at individual locations are often used to represent the whole site. This might lead to an unconservative estimation of earthquake-induced deformations and damage at a site where spatial variation of soil properties is significant. In addition, although CPT provides almost continuous sounding along vertical direction, measurements along horizontal directions are usually limited. This leads to the difficulty in proper application of geostatistical methods in liquefaction potential analysis. To address these challenges, a non-parametric and data-driven method is developed in this study to interpret a cross-section of FS with high spatial resolution directly from sparse CPT soundings. Based on the interpreted cross-section of FS, the predicted total volume and spatial distribution of liquefied soils are estimated. Both simulated and real CPT data are used to demonstrate and validate the proposed method, and the results show that the proposed method performs reasonably well.

Geostatistical Properties of Liquefaction Vulnerability Index for Reclaimed Land in South Korea

Various liquefaction vulnerability indices (LVIs) have been proposed to quantify the severity of liquefaction, and used to create the liquefaction hazard map. In producing the liquefaction hazard map, the spatial variability of LVI is important because the number of available in-situ data is typically limited, and the values at the unmeasured locations are predicted using kriging operations that consider the spatial correlation of observed values. In addition, the liquefaction hazard map may vary significantly depending on how LVI was spatially interpolated. This study aims to evaluate the spatial variability of soil properties based on cone penetration test (CPT) data, and to investigate the changes in geostatistical properties of LVIs depending on shaking intensity. A simplified CPT-based procedure was used to calculate the factor of safety against liquefaction, and the geostatistical properties of LVI according to shaking intensity were evaluated using the parameters of semivariogram. In addition, the LVIs at unmeasured locations were predicted using two geostatistical modeling approaches, and their results were compared to investigate the accuracy of geostatistical approaches in predicting LVIs according to the peak ground acceleration (PGA).

Resilient modulus descriptive analysis and estimation for fine-grained soils using multivariate and machine learning methods

ABSTRACT The adoption of mechanistic-empirical approach to pavement design requires the use of resilient modulus of subgrade soils as a crucial input. The determination of in the laboratory is inexpedient due to the nature of the existing test protocols. This prompted the use of estimated values, which inadvertently has gained popularity lately. However, the accuracy of estimated values is questionable due to spatial variability of soil properties. This necessitated the aggressive search for robust and thorough approaches for predictive modelling of the . In the present study, a systematic approach was adopted for the descriptive analysis and estimation of . from routine soil properties using data from Long-Term Pavement Performance (LTPP) and considering the spatial variability of the soil properties. Descriptive analysis was executed using non-parametric correlation and principal component analysis (PCA), while the estimation was done using three machine learning methods which include gradient boosting regression (GBR), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Based on the PCA, four factors which explained a total of 77.5% variance in the data had significant influence on the . These include the effect of moisture-induced changes on the soil consistency limits and physical condition, effect of the soil clay content, effect of the soil gradation and effect of the soil stress state. Various factors of the machine learning methods such as the learning rate, number of clusters and number of hidden layers had a significant effect on the prediction accuracy. The three machine learning methods were satisfactory for the prediction based on R 2 values which were generally above 0.9. Also, when considering spatial variability of routine soil properties, the GBR and ANFIS have a comparative advantage over the ANN, since they exhibited a high stability in the prediction for both the training and testing dataset.

Probabilistic Bearing Capacity of a Pavement Resting on Fibre Reinforced Embankment Considering Soil Spatial Variability

This paper intends to examine the influence of spatial variability of soil properties on the probabilistic bearing capacity of a pavement located on the crest of a fibre reinforced embankment. An anisotropic random field, in combination with the finite difference method, is used to carry out the probabilistic analyses. The cohesion and internal friction angle of the soil are assumed to be lognormally distributed. The Monte Carlo simulations are carried out to obtain the mean and coefficient of variation of the pavement bearing capacity. The mean bearing capacity of the pavement is found to decrease with the increase in horizontal scale of fluctuation for a constant vertical scale of fluctuation; whereas, the coefficient of variation of the bearing capacity increases with the increase in horizontal scale of fluctuation. However, both the mean and coefficient of variation of bearing capacity of the pavement are observed to be increasing with the increase in vertical scale of fluctuation for a constant horizontal scale of fluctuation. Apart from the different scales of fluctuation, the effects of out of the plane length of the embankment and randomness in soil properties on the probabilistic bearing capacity are also investigated in the present study.

Identifying the factors dominating the spatial distribution of water and salt in soil and cotton yield under arid environments of drip irrigation with different lateral lengths

The trend of secondary soil salinization has been increasingly concerned in the arid region under drip irrigation in recent decades in China. It remains unclear whether the potential increasing nonuniformity of irrigation water aroused by the increased acreage of subunit leads to salt harmfulness and yield reduction. Cotton field experiments were conducted in 2018 and 2019 to evaluate the effects of lateral length on the distribution of water and salt in soil as well as lint yield under mulched drip irrigation to identify the dominating factors using the random forest regression. Three lateral lengths of 40-, 80-, and 120-m were used. Along with a trigger point of 60–70% of field capacity (FC), irrigation amount was determined using three irrigation upper limits of 90%, 100%, and 110% of FC in the 2018 and of 90%, 110%, and 130% of FC in the 2019. The random forest analysis revealed that the nonuniform water applied by drip irrigation greatly affected the distribution of soil water content with an affecting weight exceeding 0.4. However, it imposed little impact on soil salt content along the lateral with the affecting weights less than 0.1. The relative importance of spatial variation of soil properties on soil water content decreased with increasing lateral length, while the nonuniformly applied water became the dominant factor affecting the distribution of soil water content for a field with lateral length of 120-m. The initial soil salt content dominated the spatial and temporal variation of soil salt content for treatments with all the three lengths of lateral. The long lateral did not produce obvious soil salt accumulation at the 0–60 cm soil depth along the lateral. The cumulative irrigation water and nitrogen applied imposed greater influence on the cotton lint yield than other factors. A medium lateral length is a promising selection to maintain sustainable production balancing possible salt accumulation and crop production in arid lands with relatively low initial soil salt content.

Analysis of Four Delineation Methods to Identify Potential Management Zones in a Commercial Potato Field in Eastern Canada

Management zones (MZs) are delineated areas within an agricultural field with relatively homogenous soil properties, and therefore similar crop fertility requirements. Consequently, such MZs can often be used for site-specific management of crop production inputs. This study evaluated the effectiveness of four classification methods for delineating MZs in an 8-ha commercial potato field located in Prince Edward Island, Canada. The apparent electrical conductivity (ECa) at two depths from a commercial Veris sensor were used to delineate MZs using three classification methods without spatial constraints (i.e., fuzzy k-means, ISODATA and hierarchical) and one with spatial constraints (i.e., spatial segmentation method). Soil samples (0.0–0.15 m depth) from 104 sampling points was used to measure soil physical and chemical properties and their spatial variation in the field were used as reference data to evaluate four delineation methods. Significant Pearson correlations between ECa and soil properties were obtained (0.22 < r < 0.85). The variance reduction indicated that two to three MZs were optimal for representing the field’s spatial variability of soil properties. For two MZs, most soil physical and chemical properties differed significantly between MZs for all four delineation methods. For three MZs, there was greater discrimination among MZs for several soil properties for the spatial segmentation-based method compared with other delineation methods. Moreover, consideration of the spatial coordinates of the data improved the delineation of MZs and thereby increased the number of significant differences among MZs for individual soil properties. Therefore, the spatial segmentation method had the greatest efficiency in delineation of MZs from statistical and agronomic perspectives.

Βehavior of underground energy pipelines under permanent ground displacements

The seismic performance of oil and natural gas steel pipelines embedded in earth slopes presents great uncertainty related both to the earthquake shaking characteristics and to the natural heterogeneity of geomaterials. Slope movements and landslides in general constitute a significant risk to pipelines because they can cause permanent deformations and rupture, resulting in spills, environmental problems, and long periods of outage. Moreover, significant risks for underground pipeline integrity may be caused by permanent ground displacements from tectonic fault action, lateral spreading of liquefied soils, where soil can move for a few meters and impose major deformation on the section of pipeline passing through the affected area. Similar risks appear also in the case of pipes embedded in a liquefiable soil layer passing below a river or ground aquifer. In all these cases, the pipe may be subjected to bending resulting to significant compression, with a high risk of local buckling, large plastic deformation and wall rupture. The magnitude of the imposed ground movement may depend significantly on the spatial variability of the soil properties in a zone along the path of the pipeline system. Also, the seismic response of a slope earth with embedded pipeline is investigated. The results compared with a similar slope with spatial variability of soil properties demonstrate that the influence of ground movement imposed on the embedded pipeline is very important and should be accounted for the evaluation of its seismic safety. The study is based on numerical modelling of the soil–pipeline systems, by considering the soil–pipe interaction during seismic shaking. The aim of this study is the investigation of the behaviour of underground energy pipelines under permanent ground displacements.

Characterization of the spatial heterogeneity of the agrochemical properties of arable chernozem soils in areas with a heterogeneous relief in the aspect of variable rate application

The heterogeneous relief leads to an uneven distribution of nutrients in the redistribution of crop rotation fields, which creates the prerequisites for contamination of adjacent environments and introduces risks to the production of eco-products when applying mineral fertilizers calculated for the entire. Taking into account the uncertainty introduced by the relief into the spatial distribution of soil fertility indices increases the accuracy of the VRA maps. However, it is often moved beyond the interests of introducing precision farming technologies. In this work, using various methods of regression modeling, the characteristic of the variability of the properties of chernozem soils on two arable lands with different heterogeneous relief is given. In the area with heights from 80 to 140 m, the relief determines from 24 to 58% of the spatial variability of soil properties, and in the area with a flat relief (110-150 m), the proportion of the dispersion of soil parameters described by the relief is much lower and amounts to 13-30%. It is shown that, in areas with significant elevation differences, it is possible to use regression techniques to describe the spatial distribution of soil properties, while in leveled areas, regression models need to be supplemented with a geostatistical description of spatial heterogeneity.

Probabilistic simulation of entire process of rainfall-induced landslides using random finite element and material point methods with hydro-mechanical coupling

Rainfall-induced landslides are a major geohazard worldwide, which involve a complicated process, starting from rainfall infiltration, to landslide triggering and initiation, and post-failure large deformation of soils. To effectively mitigate landslide hazards, quantitative risk assessment (QRA) of landslides may be performed, which includes assessment of landslide probability and consequences. Because rainfall-induced landslide probability is controlled by rainfall infiltration and landslide triggering, while landslide consequences are often dictated by post-failure large deformation of soils, it is necessary to model the entire process of a landslide in QRA. However, most previous QRA studies did not model the entire landslide process (e.g., without modeling of rainfall infiltration or post-failure large deformation of soils) due to the difficulty of slope stability analysis methods (e.g., limit equilibrium methods and finite element method (FEM)) in modeling large deformation of soils. This study proposes a probabilistic method for simulating the entire process of rainfall-induced landslides considering spatial variability of soil properties. A two-stage method containing FEM and material point method (MPM), both with hydro-mechanical coupling, is proposed to simulate a landslide from triggering by rainfall to post-failure large deformation of soils. It quantifies landslide probability and consequences simultaneously and provides new insights for QRA of rainfall-induced landslides.

Reliability of an engineered slope considering the Regression Kriging (RK)-based conditional random field

Many attempts have been made to apply random field theory to the slope reliability analysis in recent decades. However, there are only a few studies that consider real landslide cases by incorporating actual soil data in the probabilistic slope stability analysis with spatially variable soils. In this paper, an engineered slope located in Hong Kong was investigated using the probabilistic approach considering the Regression Kriging (RK)-based conditional random field. The slope had been assessed and considered to be safe by classical deterministic slope stability analyses but failed eventually. In this study, both deterministic slope stability analyses and probabilistic slope stability analyses were conducted, and the comparison was made between the probabilistic approach adopting RK-based conditional random field and that adopting Ordinary Kriging (OK)-based approach. The results show that the deterministic factor of safety (FS) for a slope may not be an adequate indicator of the safety margin. In particular, a slope with a higher deterministic FS may not always represent a lower probability of failure under the framework of probabilistic assessment, where the spatial variability of soil properties is explicitly considered. Besides, the critical portion of the slope could not be found using the OK-based approach that considers a constant trend structure.

Novel approach to efficient slope reliability analysis in spatially variable soils

The random field finite element method (RF-FEM) provides a robust tool for carrying out slope reliability analysis that incorporates the spatial variability of soil properties. However, it has a major drawback of being computationally very time-consuming. To address this common criticism, the current study proposes a novel metamodel-based method for efficient slope reliability analysis in spatially variable soils. The proposed method involves the use of Convolutional Neural Networks (CNNs) as metamodels of the random field finite element model. With proper training using a small but sufficient number of random field samples, the CNN can potentially replace the computationally demanding random field finite element analyses for Monte-Carlo simulations. This paper examines the capability of CNNs to learn high-level features that contain information about the random variabilities in both spatial distribution and intensity, and the accuracy of the subsequent predictions of the RF-FEM results. Application of the proposed method to slope reliability analysis in spatially variable soils is illustrated and compared against other metamodel-based approaches, using a case study involving a multi-layered soil system with randomly varying cohesion c and the friction angle ϕ. The results show that (i) the proposed CNN approach predicts a probability of slope failure that is within 5% of the corresponding value obtained using direct RF-FEM Monte-Carlo simulations, but at a small fraction of the computational cost, and (ii) the proposed method also compares favourably against other metamodel-based methods in terms of computational efficiency and accuracy.

The Variability of Nitrogen Forms in Soils Due to Traditional and Precision Agriculture: Case Studies in Poland.

The soil and human health issues are closely linked. Properly managed nitrogen (N) does not endanger human health and increases crop production, nevertheless when overused and uncontrolled, can contribute to side effects. This research was intended to highlight that there is a need for carrying out monitoring studies in agricultural areas in order to expand the available knowledge on the content of N forms in agricultural lands and proper management in farming practice. The impact of two types of fertilization, concerning spatially variable (VRA) and uniform (UNI) N dose, on the distribution of N forms in soils was analyzed. The analysis was performed on the basis of soil monitoring data from agricultural fields located in three different experimental sites in Poland. The analyses performed at selected sites were supported by statistical evaluation and recognition of spatial diversification of N forms in soil. It was revealed that the movement of unused N forms to deeper parts of the soil, and therefore to the groundwater system, is more limited due to VRA fertilization. Finally, it was also concluded that the management in agricultural practice should be based on the prediction of spatial variability of soil properties that allow to ensure proper application of N fertilizers, resulting in the reduction of possible N losses.

Fertigation in citrus, a toolkit for sustainable quality production

Techniques and managements of perennial fruits production are nowadays directed towards the need to conserve resources, energy and a commitment to the environment. In this sense, fertigation has risen as a valuable tool in recent years spreading around the world in major fruit belts. The million dollar question strikes why fertigation out-performs conventional basin method of irrigation carrying fertilizers. Citrus trees do not exhibit visible symptoms of water stress until most of the available soil moisture has been depleted. However, fruit set can be adversely affected at soil moisture depletion levels of only 40 to 50 percent, particularly from pre-bloom through June. Consequently, extensive research in the citrus production areas has led to the recommendation that water be applied at one-third depletion during January through June and at two-thirds depletion over the remainder of the year.Fertigation is an effective method of fertilizing in arid climates. Fertilizer levels in the soil are maintained at constant levels of 35–60 mg/kg throughout the irrigation period. Studies suggest that optimum nitrogen rate for mature Shamouti orange trees is about 170kg/ha/year. Higher nitrogen (N) rates increased yields but decreased external fruit quality by delaying peel color development and increasing peel thickness. However, juice quality, specifically total soluble solids, was not affected by nitrogen rate.A plant exposed to uniform regime of moisture and nutrient flow within rhizosphere zone has to spend much less energy than the growing conditions constantly changing over time. Fertigation, is thus, energy efficient as well. While addressing these concerns, however, the major concern emerges as orchard efficiency vis-a-vis FUE/WUE by adopting variable rate fertilizer application taking into consideration the spatial variability in soil properties in a time domain manner known as automated fertigation.This has led to an increase in both fertilizer use efficiency (FUE) and water use efficiency (WUE).

Effect of Savanna windrow wood burning on the spatial variability of soil properties

ABSTRACT Tropical Savannas cover an area of approximately 1.9 billion hectares around the word and are subject to regular fires every 1 to 4 years. This study aimed to evaluate the influence of burning windrow wood from Cerrado (Brazilian Savanna) deforestation on the spatial variability of soil chemical properties, in the field. The data were analysed by using geostatistical methods. The semivariograms for pH(H2O), pH(CaCl2), Ca, Mg and K were calculated according to spherical models, whereas the phosphorus showed a nugget effect. The cross semi-variograms showed correlations between pH(H2O) and pH(CaCl2) with other variables with spatial dependence (exchangeable Ca and Mg and available K). The spatial variability maps for the pH(H2O), pH(CaCl2), Ca, Mg and K concentrations also showed similar patterns of spatial variability, indicating that burning the vegetation after deforestation caused a well-defined spatial arrangement. Even after 20 years of use with agriculture, the spatial distribution of pH(H2O), pH(CaCl2), Ca, Mg and available K was affected by the wood windrow burning that took place during the initial deforestation.