Variability Of Soil Properties Research Articles

Investigation of Topographic Amplification on Ground Motions Considering Spatial Variability of Soil Properties

Investigation of Topographic Amplification on Ground Motions Considering Spatial Variability of Soil Properties

Applying a Python script to predict the geotechnical properties of the Nasiriyah soil

Soil is a natural material that suffers from intrinsic spatial variability resulting from natural factors and their influence on the soil. It became controversial and debated how to estimate the characteristic value of soils to obtain a reliable geotechnical design with low cost and less effort. Usually, foundations are not built on the same site as the screening; investigations are carried out to excavate a little at essential sites. In this paper (423), test wells were collected in the study area to be obtained and tabulated in Excel. The kriging statistics is applied using a python script to predict the values of geotechnical site properties and reliability of the method in estimating spatially varying soil properties values based on measurement data and prior knowledge. The program implements probabilistic kriging statistics and predicts the desired value by entering the coordinates of the locations whose properties you want to know and based on the previously prepared Excel file of known points, coordinates, and property values. The program will be used in two soil sites in the city of Nasiriyah to predict its properties. These points were selected from the examination of soil investigation reports to determine the reliability and accuracy of the program in predicting values. To get more reliable probability values using the kriging method and python scripts. A huge database of prepared and analyzed engineering soil properties has been created based on field investigation reports for projects in Nasiriyah.

Predicting Soil Properties Using Topographic and Climatic Variables

Aim of study: The present study aimed to model soil physical and chemical properties through multiple linear and regression tree techniques.
 Area of study: The study area is located between 41,07 – 41,33 N latitude and 41,74 – 42,27 E longitude in Artvin, which is in the Colchis part of the Black Sea Region of Turkey.
 Material and methods: The multiple linear regression and regression tree models were used to predict soil properties using topographic and climatic features as independent variables. Besides, the relationships between soil properties and independent variables were determined by Pearson correlation.
 Main results: The study results revealed that model accuracy by regression tree generally was higher than those of multiple linear regression. Up to 56% and 59% of the variance in soil properties was accounted for by multiple linear regression and regression tree, respectively. The easting, northing, elevation, and minimum temperature parameters were key drivers of both models. Increasing soil depth significantly increased the pH and reduced the organic carbon, total nitrogen, and carbon/nitrogen ratio.
 Highlights: Topographic and climatic variables accounted for Up to 59% and 56% of the variance in soil properties such as texture, pH, organic carbon, total nitrogen, and carbon/nitrogen ratio by regression tree and multiple linear regression techniques. The most influential factors on soil properties were the minimum temperature, latitude, actual
 evapotranspiration, mean temperature, distance to the ridge, and radiation index.

Rice crop response to site variability in a multi-locational trial: A call for site specific management

Yield is a net expression of genotype (G) x environment (E) interactions including management. However, the segregation of 'E' into respective causes is seldom done while 'G' is a constant. Soil is a component of 'E' with imminent variability in attributes among multiple locations. Data on yield response of varieties to a set of treatments in different soils from multi-locational yield maximisation trial under All India Coordinated Rice Improvement Project were regularly gathered. A dataset pertaining to a trial conducted in Karaikal district of Puducherry Union Territory was analysed to ascertain the site-specific crop responses with inherent variability in soils. Rice varieties, ADT 46, BPT 5204 and CR 1009 were tested for responses at 17 sites with farmer fertiliser practices (FFP), regional recommended fertiliser dose (RDF) and software, 'Nutrient Expert®' (2016) (NE) derived fertiliser quantities. Analysis of variance showed that test sites explained 59.3% variability in yield. A multivariate technique, Factor Analysis extracted two factors, which are linear combinations of soil attributes those explained 76% of variance in soils. Factor scores classified soils into four groups, owing to variability in soil properties. Soil texture influenced yield significantly (across varieties and treatments) (R2 = 11.1%). Sites varied in excess duration in nursery ranging from 2 - 26 days. However, this excess duration reduced number of panicles m-2 only in CR 1009 (r = -0.328**). General linear model with sites and treatments as fixed factors, their interactions and panicles m-2 as covariate predicted better (R2 = 90.3%) with their significant contribution to the model. The order of R2 (%) was Sites (59.3) > Varieties (27.4) > Treatments (13.6%) in explaining variability in yield highlighting site-specific responses. Mean differences between ADT 46 and BPT 5204; BPT 5204 and CR 1009 were significant. Yield significantly changed across sites and treatments when fertiliser management shifted from non-specific (FFP) to site-specific NE based calculations through RDF (region specific). Results of this trial placed emphasis on soil test-based crop management to realise the uniform best, which clearly is site specific crop management.

A soil quality index using Vis-NIR and pXRF spectra of a soil profile

A soil quality index (SQI) can be used to manage the soil for potential uses and remediate limitations of soil for sustainable agriculture and ecology. The objectives of this study were to (i) characterize vertical and horizontal variations of some soil properties and SQI in a Fluventic Haploxerepts profile in Eskişehir, Turkey, (ii) compare interpolation methods for mapping the distribution of soil properties and SQI in the profile wall, and (iii) assess cubist model for predicting soil properties and SQI using individual and combined Vis-NIR and pXRF spectra in the profile. The soil profile wall (1 m deep × 1 m wide) was divided into a raster of 10 × 10 cm intervals for collecting a total of 100 soil samples. The soil samples were analyzed for soil physical, chemical properties, and elemental concentrations and scanned using Vis-NIR (350–2500 nm) and pXRF (0–8 and 0–45 kV) spectrometers. Standard scoring function was used for standardization of indicators. Weighted linear combination method was used to calculate SQI and F-AHP for assigning weights of indicators. The 100 samples were split into calibration (70%) and validation (30%) datasets. Individual or combined Vis-NIR and pXRF spectra were used in cubist model to evaluate the feasibility for predicting soil properties and SQI in the Inceptisol. Results suggested that the soil profile had a considerable variation of soil properties and SQI with depth and within horizons. The A horizon had the highest SQI. In addition, Vis-NIR had better prediction performances for pH, SOM, Fe, and Mn (R2 = 0.42–0.88), whereas pXRF had better prediction performances for EC, CaCO3, P, and Zn (R2 = 0.18–0.90). In addition, combining Vis-NIR, pXRF8, and pXRF45 had better prediction performance for sand, silt, clay, K, Cu, and SQI (R2 = 0.81–0.97). We concluded that Vis-NIR and pXRF spectra can be successfully used to predict SQI in Fluventic Haploxerepts. Combined Vis-NIR and pXRF spectra had better prediction performance for SQI.

Soil properties and topographic features influence within‐field variation in potato tuber yield in New Brunswick, Canada

AbstractReduced within‐field potato (Solanum tuberosum L.) yield variation may lead to increased productivity and reduced environmental impact. Using soil samples collected from 88 site‐years in commercial fields in New Brunswick, Canada from 2013–2017, this study examined how within‐field variation in potato tuber yield was related to soil properties and topographic features. At each of 774 sampling locations, a wide range of soil physical and chemical properties was measured in the lab and topographic features were assessed using a regional digital elevation model. Principal component (PC) analysis identified three PCs, which accounted for 79.1% of the total variation. The PC1 (41.3% of total variance) was dominated by soil texture (i.e., sand, silt) and the quantity and quality of soil organic matter (i.e., soil organic C, particulate organic matter C, and soil C/N ratio). Under rain‐fed potato production in New Brunswick, finer soil texture and increased soil organic matter pools are expected to enhance soil water availability and thereby improve yield. The PC2 (22.7% of total variance) was related primarily to parameters associated with soil fertility, and PC3 (15.1% of total variance) was related primarily with concave or convex landforms, which may influence yield through drought or excess water. This study demonstrated the value in using multivariate approaches to identify the factors that control within‐field yield variability in the presence of significant regional variation in soil properties and environmental conditions. The findings point to the value of enhancing the quantity and quality of soil organic matter as a key strategy to overcome yield limitations under rain‐fed production.

Effects of spatial autocorrelation structure for friction angle on the runout distance in heterogeneous sand collapse

This paper proposes a stochastic method for analyzing the runout distance of sand collapse considering the spatial variability of shear strength, in which random field theory and generalized interpolation material point method are integrated into a Monte-Carlo simulation basis. The random field is generated by Cholesky matrix decomposition method and implemented into the material point level, hence heterogeneity and large deformations are simultaneously considered in the modeling process. A sand collapse case is simulated with both homogeneous and heterogeneous condition assumptions by the proposed method. The effect of five theoretical autocorrelation functions (ACFs) on the runout distance of the collapse is highlighted since the ACFs are commonly adopted to characterize the spatial variability of soil properties due to sparse site observation data. It is shown that the deterministic analysis may underestimate the runout distance, while the heterogeneous model provides realistic results. Moreover, five ACFs and different coefficients of variation of friction angle (COVφ) are compared to investigate their influences on the runout distance modeling. The results show that the uncertainty of runout distance increases with the increase in COVφ. Meanwhile, the variances of the runout distance also become larger with COVφ increasing. Based on the proportion of the runout distance which exceeds the deterministic value, the results indicate that the deterministic analysis notably underestimates the risk induced by large runout distances in real heterogeneous granular flows (e.g., landslide, debris-avalanches).

Stomatal regulation prevents plants from critical water potentials during drought: Result of a model linking soil–plant hydraulics to abscisic acid dynamics

AbstractUnderstanding stomatal regulation during drought is essential to correctly predict vegetation‐atmosphere fluxes. Stomatal optimization models posit that stomata maximize the carbon gain relative to a penalty caused by water loss, such as xylem cavitation. However, a mechanism that allows the stomata to behave optimally is unknown. Here, we introduce a model of stomatal regulation that results in similar stomatal behaviour without presupposing an optimality principle. By contrast, the proposed model explains stomatal closure based on a well‐known component of stomatal regulation: abscisic acid (ABA). The ABA level depends on its production rate, which is assumed to increase with declining leaf water potential, and on its degradation rate, which is assumed to increase with assimilation rate. Our model predicts that stomata open until the ratio of leaf water potential to assimilation rate, proportional to ABA level, is at a minimum. As a prerequisite, the model simulates soil–plant hydraulics and leaf photosynthesis under varying environmental conditions. The model predicts that in wet soils and at low vapour pressure deficit (VPD), when there is no water limitation, stomatal closure is controlled by the relationship between photosynthesis and stomatal conductance. In dry soils or at high VPD, when the soil hydraulic conductivity limits the water supply, stomatal closure is triggered by the sharp decline in leaf water potential as transpiration rate increases. Being adaptive to changing soil and atmospheric conditions, the proposed model can explain how plants are enabled to avoid critical water potentials during drought for varying soil properties and atmospheric conditions.

Spatial Distribution and Influencing Factors of Soil Fungi in a Degraded Alpine Meadow Invaded by Stellera chamaejasme

Alpine meadow degradation causes a notable decrease in palatable grasses and an increase in forbs and toxic plants in recent decades. Stellera chamaejasme is one of the most serious toxic weeds, which exerts an increasing threat on alpine meadow in Qinghai–Tibetan Plateau. Combined DNA sequencing with geostatistics was applied to analyze a typical degraded meadow invaded by S. chamaejasme in Qinghai Province, China. The study aimed to determine the spatial variation of soil fungi and its interrelationship with the plant–soil environment. Alpha diversity and relative abundance of fungal phyla and classes showed moderate or strong spatial dependency and were structured in patches of 19–318 m, and taxonomic composition exhibited much higher spatial variability than alpha diversity. Compared to plant cover, the matching of patch size showed a closer spatial link between soil properties and fungal community. Community coverage, SOM, TN, TP, and TK positively correlated to fungal diversity and taxonomic composition; no direct correlation was found between S. chamaejasme coverage and fungal community. The result suggested significant but weak association between plant–soil properties and soil fungal community at local scale. Patchy pattern of S. chamaejasme may disturb spatial variations of soil properties and fungal community, since S. chamaejasme in higher coverage corresponded to lower TK content, which contributed to a decrease in fungal diversity indirectly.

Analysis of site-specific N balances in heterogeneous croplands using digital methods

In this study, the spatial variability of nitrogen (N) balances were determined on heterogeneous arable fields in southern Germany using digital methods (combine yield sensing system, tractor mounted multispectral sensor and satellite data, vegetation indices, and models). The aim of this study is to precisely localize N surplus and nitrogen loss potentials through a high spatial resolution. The spatial variability of plant properties (yield, N uptake) were determined in order to calculate the N balances. The spatial variability of soil properties (soil organic carbon content, soil total nitrogen content, available water capacity) were determined to identify the causes of high or low N surplus on sub fields.N surplus, yield, and N uptake determined using different digital methods showed similar spatial variability. N surplus determined in test plots correlated best with N surplus calculated using tractor mounted multispectral sensor data (up to r = 0.67). Site-specific N balancing identified zones with high N loss potential (N surplus up to over 100 kg ha−1), even if, on average, N inputs and N outputs are almost balanced (N surplus = 0). Soil properties showed considerable spatial variation within the fields. Soil organic carbon and soil total nitrogen content was closely correlated in all fields (up to r = 0.97) and were most closely positively correlated with yield and N uptake (up to r = 0.62) and negatively correlated with N surplus (up to r = − 0.62). The combine yield sensor data showed weaker correlations with soil parameters than tractor mounted multispectral sensor and satellite data. Based on these results, digital technologies are suitable for the calculation of site-specific N surplus. However, their accuracy must be further improved before they can be used to make precise management decisions. Site-specific N balancing using digital methods makes it possible to identify N loss potentials at a sub field scale.

Influence of horizontally variable soil properties on nonlinear seismic site response and ground motion coherency

AbstractThe objective of this study is to quantify the effect of the spatial variability of soil properties on site response and ground motion coherency. A 2D random field theory is used to simulate soil parameters, including shear wave velocity (Vs) and nonlinear stress–strain curves. Different levels of spatial variability are controlled by coefficient of variation (COV) and correlation length (CL). The wave prorogation is simulated through the 2D finite difference software FLAC2D. Different types of analysis conditions, such as 1D or 2D model, linear or nonlinear analysis, and input motion, are investigated. The increase of Vsvariability leads to a decrease in the mean amplification ratio (surface response spectrum to bedrock response spectrum) but an increase in its variation. The effect of the variability of soil nonlinearity on the mean amplification ratio is marginal compared with that induced by Vsvariability. However, as the ground motion intensities increase (or more nonlinearity induced), the standard deviation of the simulation normalized by the baseline response increases. Compared with the 2D result, the 1D site response analysis that is commonly used in practice may potentially underestimate the mean amplification ratio but overestimate its variability due to the ignorance of the horizontal soil property variability. In the evaluation of the spatially correlated site response, the ground motion coherency decays significantly, as the COV of Vsand the ground motion intensity increase. The effect of CL on ground motion coherency is minor and limited to a separation distance within two‐times of CL. Therefore, a coherency model, which eliminates the CL term but includes the ground motion intensity (i.e., nonlinear effect) parameter, is proposed in this study. It is revealed to yield a better agreement with the simulation results than other coherency models.

Assessment of reclamation-induced consolidation settlement considering stratigraphic uncertainty and spatial variability of soil properties

Consolidation analysis is a key task for reclamation design. Although consolidation is a long-term process, acceleration of consolidation is often preferred for speeding up the reclamations. Before proposing measures to accelerate the consolidation and reclamation process, it is imperative to have an accurate prediction of consolidation settlement for fine-grained materials, which is greatly affected by spatial distribution of subsurface zones with different soil types (i.e., stratigraphic heterogeneities and uncertainty) and spatial variability of soil properties. In current practice, calculation of consolidation settlement often uses simplified stratigraphic boundaries and deterministic consolidation parameters without considering stratigraphic uncertainty or soil property spatial variability. The oversimplified practice might result in unconservative estimations of consolidation settlement and pose threats to safety and serviceability of constructed facilities on reclaimed lands. In this study, a stochastic framework is proposed for consolidation settlement assessment with explicit modeling of stratigraphic uncertainty and spatial variability of soil properties by machine learning and random field simulation from limited site investigation data. Performance of the proposed framework is demonstrated using an illustrative example. Results indicate that the proposed method effectively generates multiple realizations of geological cross section and random field samples of geotechnical properties from limited measurements and offers valuable insights into spatial distribution of the estimated total primary consolidation settlement curves and angular distortion.

Site-scale liquefaction potential analysis using a sectional random field model

This paper presents a new random field modeling to assess the site-scale spatial variability of soil properties applied in liquefaction mapping. In this procedure, the initial random fields are primarily generated, based on in-situ measured data. These initial fields are considered as the virtual known values to construct interior random fields, section-by-section and around a central axis. Assembling these sections results in a three-dimensional random field model. Through this approach, more accurate vertical semivariogram models can be available, in addition to a higher number of measured data involved in the generation of random fields. Based on a verification procedure, computational cost reduction and a more appropriate prediction of the layering characteristics are the two most important beneficial points of changing the modeling strategy from planar to sectional. The proposed approach is implemented in a case study in Oceano, California and the spatial distribution of liquefaction probability is estimated. It is concluded that the sectional random field can efficiently locate the liquefiable zone through the soil volume, based on liquefaction evidence. In addition to performing the liquefaction severity assessment, it is also illustrated that the hazard assessments based on liquefaction severity alone cannot provide the comprehensive prediction of liquefaction potential and may lead to unconservative engineering judgments.

Forest-to-pasture conversion modifies the soil bacterial community in Brazilian dry forest Caatinga

Soils comprise a huge fraction of the world's biodiversity, contributing to several crucial ecosystem functions. However, how the forest-to-pasture conversion impact soil bacterial diversity remains poorly understood, mainly in the Caatinga biome, the largest tropical dry forest of the world. Here, we hypothesized that forest-to-pasture conversion would shape the microbial community. Thus, the soil bacterial community was assessed using the 16S rRNA gene sequencing into the Illumina MiSeq platform. Then, we analyzed ecological patterns and correlated the bacterial community with environmental parameters in forest, and two distinct pastures areas, one less productive and another more productive. The variation in soil properties in pastures and forest influenced the structure and diversity of the bacterial community. Thus, the more productive pasture positively influenced the proportion of specialists and the co-occurrence network compared to the less productive pasture. Also, Proteobacteria, Acidobacteria, and Verrucomicrobia were abundant under forest, while Actinobacteria, Firmicutes, and Chloroflexi were abundant under pastures. Also, the more productive pasture presented a higher bacterial diversity, which is important since that a more stable and connected bacterial community could benefit the agricultural environment and enhance plant performance, as can be observed by the highest network complexity in this pasture. Together, our findings elucidate a significant shift in soil bacterial communities as a consequence of forest-to-pasture conversion and bring important information for the development of preservation strategies.

Kolmogorovʼ entropy to investigate variation of forest soil properties in the Czech Republic

A measure of variation in values of physico-chemical properties of different soil groups is influenced by deterministic uncertainty. In this study, we assessed variation level of soil horizon properties in the most wide-spread forest soil groups on territory of the Czech Republic in the period 1953–2010. The assessment of variation was based on the selection of potentially correlating grain-size and chemical properties and a regression analysis (p < 0.05) between normal values (1981–2010) and Kolmogorov (K-) entropy of the forest soil quantities. Cambisols (73%), Stagnosols (10%) and Leptosols (4%) were the most frequently sampled soil groups in the database of 15,287 pits from the state pilot forest surveys during the normal period. The K-entropies of clay, Ntot, CaO and MgO indicated the mean contents in the soil groups more significantly than the entropies of loam, pH and Al2O3. The low variable clay content differentiated the series of Cambisols-Podzols (CM-PZ) from other soil groups. On the contrary, the higher stable clay content differentiated the Luvisols-Retisols-Stagnosols (LV-RT-ST) series. The low stable clay content and the higher Ntot and CaO contents were characteristic of Fluvisols and Gleysols. The CM-PZ series varied from LV-RT-ST by more variable contents of finer grain particles, Ntot and CaO. Relationships between soil properties means and K-entropies suggest different development series.

Analysis of Calcareous Soils for Two Various Facies within the Nature Reserve of El-Mergueb (M’sila, Algeria)

Abstract This research was conducted on the protected area of El-Mergueb at M’sila province. The zone of El-Mergueb is located about 180 km south of the capital Algiers, at an altitude from 550 to 800 m, and is characterised by arid climate. The zone of El-Mergueb has a landscape from the steppe to Alfa that lies just as well in the flat ridges hills that in their slopes and in the top part of the ravines. Alfa tenacissima dominates the most geographical space of El-Mergueb. This study is based on the analysis of the homogenous and heterogeneous facies by the determination of the content of calcium carbonate (CaCO3), active calcium carbonate (ACCE) and gypsum through monitoring these chemical parameters during 3 years: 2017, 2018, 2019. Six soil profiles of 15 soil samples collected in the site of study were studied and several chemical soil properties were considered. These factors included: soil pH, calcium carbonate (CaCO3), active total carbonate (CA), gypsum (CaSO4) and electrical conductivity (EC). Principal component analysis (PCA) was used to identify the variations in soil properties. Results showed that there are significant relationships between some soil factors and two PCA axes. The results thus obtained showed that the content of CaCO3 and CA analysed ranged from average to high. The average of gypsum and salinity was low. Floristic diversity defined 284 species. The data on the chemical characteristics of the soil studied have been the subject of a detailed statistical analysis (PCA). Our database consisted of quantitative variables. The implementation of the PCA to the soils studied showed a major dominance of the limestone in the two stations, which explains the alkaline pH and the low concentration of gypsum. Finally, these types of high total limestone are the most observed in the majority of steppe and arid soils.

A 10-year monitoring of soil properties dynamics and soil fertility evaluation in Chinese hickory plantation regions of southeastern China

Monitoring the temporal and spatial variation of soil properties is helpful to understand the evolution of soil properties and adjust the management method in time. Soil fertility evaluation is an urgent need to understand soil fertility level and prevent soil degradation. Here, we conducted an intensive field investigation in Chinese hickory (Carya cathayensis Sarg.) plantation to clarify the spatial and temporal variation of soil properties and its influencing factors, and to evaluate the change of soil fertility. The results showed that the soil pH and soil organic carbon (SOC) significantly increased from 2008 to 2018, while available nitrogen (AN) significantly decreased from 2008 to 2018. The semi-variance revealed that except available phosphorus (AP), the spatial dependencies of soil properties increased from 2008 to 2018. An increasing south-north gradient was found for soil AN, AP, available potassium (AK) and SOC and a decreasing south-north gradient was found for soil pH. The average soil fertility in the whole area was increased from 2008 to 2018. Our findings demonstrated that the changes of the management measures were the reason for the change of soil properties from 2008 to 2018. Therefore, rational fertilization strategies and sod cultivation are recommended to maintain the long-term development of the producing forest.

SOIL PROPERTIES MAPPING USING PROXIMAL AND REMOTE SENSING AS COVARIATE

Obtaining knowledge about the distribution of spatial variability of soil properties is crucial to the proper site-specific management. One way to improve the quality of soil mapping is by using auxiliary information (covariate). The objective of this study was to test whether remote and proximal sensing data can assist in soil properties mapping through geostatistical prediction. We worked in an experimental area cultivated with sugarcane located in Sao Paulo State, Brazil, and selected five soil properties: organic matter, CEC, base saturation, K and P availability. Two covariates often used to express soil variation were chosen, one obtained by remote sensing (SWIR2 band) and the other by proximal sensing (apparent soil electrical conductivity – ECa). These covariates were individually and together used in geostatistical interpolation method (kriging with external drift). We found that ECa is a more promising covariate than SWIR2 band from orbital imaging. Such proximal sensing can identify the soil short-range spatial variability. However, when the soil property variability is well explained by the sampling procedure, multivariate geostatistical methods may not improve the mapping accuracy.

Spatial variation of the (moist) damp soil properties in Al-Qadisiyah Province, Iraq

Spatial variation of the (moist) damp soil properties in Al-Qadisiyah Province, Iraq

Spatial prediction of soil acidity and nutrients for site-specific soil management in Bedele district, Southwestern Ethiopia

BackgroundUnderstanding the spatial variability of soil properties is useful to tailor site-specific agricultural inputs to enhance crop production on a sustainable basis. This study was aimed to assess and map the spatial patterns of soil acidity and nutrients using geostatistical methods and support site-specific lime and fertilizer recommendations in Bedele district, Southwestern Ethiopia.MethodsSoil samples were collected from agricultural land at a depth of 20 cm using grid sampling technique. The semivariogram analysis was performed for accurate spatial prediction and the kriging technique was used for interpolation of soil parameters.ResultsSoil pH varied between 4.5 and 6.8. Soil organic carbon (OC) content ranged from 0.3 to 5.6% and the mean soil OC density was 0.81 kg m−2. Available phosphorus (AvP) ranged from 0.8 to 38.6 mg kg−1 and nearly 80.23% of the soils exhibited very low to low AvP that could be due to fixation by strong acidity. Soils of the study area exhibited very high exchangeable potassium (K), but very low exchangeable calcium (Ca) and magnesium (Mg). The potassium to magnesium ratio (K:Mg) ranged from 0.2:1 to 10.9:1, while the values of calcium to magnesium ratio (Ca:Mg) varied between 0.3 and 3.4. Among the soil parameters, exchangeable Ca (CV = 54%) and K:Mg ratio (CV = 57.62%) were more variable than other soil parameters. Spatial variability was lowest for soil pH (CV = 10%).ConclusionsMajor portions of the study site were affected by strong acidity (pH ≤ 5.5). Accordingly, about 89% of the soils require lime that varied between 0.09 and 3.6 tons ha−1. In addition to soil acidity, deficiency of available P, Ca, and Mg were the major liming factors affecting crop production in the study area. Digital soil mapping was used to show the spatial variability of soil acidity and nutrients across agricultural land and applied for efficient lime and nutrients advisory works.