Ordinary Kriging Technique Research Articles

Prediction of grain size distribution using ordinary kriging and compositional kriging methods

Grain size analysis plays a crucial role in understanding the geological characteristics of the coastal environments that influence the optimizations for oil and gas production operations. This paper aims to explore a sophisticated geostatistical approach using the ordinary kriging and compositional kriging techniques, to forecast the grain size fluctuations of sediments in the Long Island region located in the United States. In addition, utilizing a comprehensive dataset collected from the same region about an integrated seventeen compositional components for investigation using the spatial model of the grain size distribution. Moreover, a variogram and the scatter plot predicted a distinctive spatial dependency was achieved. The compositional kriging method used to predict the grain size distribution in the coastal areas presented an accurate result based on the shape of the histogram, Root Mean Square Error (RMSE), and the Mean Squared Error (MSE). In conclusion, the geo-statistics assisted in the integration of the sedimentological analysis in the coastal settings and showed an effective configuration for the decision-making in the oil and gas industry business.

Reliability-based seismic liquefaction hazard mapping of Kolkata Metropolitan City, India, using ordinary kriging technique

Reliability-based seismic liquefaction hazard mapping of Kolkata Metropolitan City, India, using ordinary kriging technique

Lake Water Quality Assessment Through GIS based Interpolation Method: A Case Study of Beris Dam, Kedah, Malaysia

Preliminary research on physicochemical and water quality in the Beris dam shows that the water catchment area has the best standards and quality during the observation of the wet season and dry season mainly in the northern states of peninsular Malaysia. The analysis of water quality assessment using the DOE-WQI and Carlson Trophic State Index (CTSI) resulted that during the dry season (February), the average DOE-WQI reading is 88 or Class II; while during the wet season, the average DOE-WQI reading recorded 87.1 (October). Although the CTSI score was 34.2 - 27.0 during wet season and dry season, both seasons were classified as oligotrophic conditions. Some physicochemical parameters also show a high correlation between them and indicates minor water quality issues. The use of more efficient interpolation techniques in water quality studies also reinforces the evaluation of water quality. As a reference point for future water research, GIS interpolation application methods can be more effectively and efficiently used with Inverse Distance Weighted (IDW), Ordinary Kriging and Spline techniques. HIGHLIGHTS Investigation of physicochemical and water quality in lake water in Beris Dam Assessment by using water quality index by DOE-WQI and Carlson Trophic State Index (CTSI) to acknowledge water quality and trophic condition. Analyzing physicochemical data into mathematical method to compare between physicochemical data and correlate between them. Application of GIS based interpolation methods to analyze and referencing physicochemical data into spatial interpolation maps. Comparing appropriate spatial interpolation techniques to analyze physicochemical observation data and index. GRAPHICAL ABSTRACT

Practical implications in the interpolation methods for constructing the regional mean sea surface model in the eastern Mediterranean Sea

Abstract This investigation estimates a regional Mean Sea Surface (MSS) model, named SY21MSS, over the eastern Mediterranean Sea using satellite altimetry data from nine Exact Repeat Missions (ERM) and two Geodetic Missions (GM). Two interpolation methods, Least Squares Collocation (LSC) and Ordinary Kriging (OK), were employed, and statistical metrics were applied to assess their performance within a 15 km buffer from the coast. The comparison between LSC and OK techniques in the context of regional MSS modeling primarily centers on the covariance functions used by these methods. Furthermore, generalized cross-validation results indicate that OK outperforms LSC in this region. Consequently, the study recommends adopting the Kriging-based model for calculating regional MSS models in this region due to its superior performance. The investigation further explored the disparities between estimated regional MSS models and the global model DTU18MSS, highlighting a pronounced similarity between OK-SY21MSS and DTU18MSS, as evidenced by a lesser standard deviation (SD) difference compared to LSC-SY21MSS. The practical implications of this research underscore the importance of selecting an appropriate interpolation technique based on data characteristics and study area specifics. While both LSC and OK techniques are deemed viable for MSS modeling, the study emphasizes the superior performance of OK, particularly concerning covariance functions. Additionally, the results emphasize caution when applying global models in regions with significant local variations.

Spatial Variability of Soil Macronutrients and Chemical Properties in Ujjain Tehsil of Ujjain District of Madhya Pradesh, India

Understanding the characteristics of soil and how it may be utilised sustainably makes soil property mapping a crucial process. To improve crop yield and preserve healthy soil, it is essential to understand the heterogeneity of the soil in each location in order to establish site-specific nutrient management strategies for that area. The current research was carried out in the Ujjain tehsil (Ujjain) district of Madhya Pradesh state, India, with the aim of measuring the spatial variability of several soil macronutrients and soil chemical characteristics. Throughout the research region, 150 geo-coded surface soil samples with a depth of 0 to 15 cm were collected. The soil characteristics, namely pH, electrical conductivity (EC), soil organic carbon (SOC), available nitrogen (Av-N), available phosphorous (Av-P), available potassium (Av-K), and available sulphur (Av-S), were assessed in these samples using standard methods. The research region's ranges for soil pH, EC, SOC, Av-N, Av-P, and Av-K were 7.01 to 8.15, 0.10 to 0.79 dSm-1, 0.30-0.60%, 139.00 – 235.00 kg ha-1, 8.00 – 25.60 kg ha-1, and 301.00 – 463.00 kg ha-1, in that order. The data were analysed using classic statistics and geo--statistics by constructing semi-variograms and mapping by ordinary kriging techniques. Semi-variograms were calculated for soil characteristics and their spatial distributions were mapped. Best-fit models for measured soil properties were Exponential, Circular, Gaussian and Hole effect with Nugget/Sill (Co/Co+C) ratio for modelled variables indicated strong and moderate spatial dependences. The distribution maps of soil attributes could be utilized as a guide for site-specific crop management in similar soils. Further, this study demonstrates the usefulness of GIS- application in soil variability studies.

Quantitative Evaluation of the Spatial Variation of Surface Soil Properties in Continuous Paddy Growing Fields

Soil degradation caused by poor land management practices is a major impediment to optimal land productivity. Soil spatial variability is required for agricultural productivity, food safety and environmental modeling. Rice is one of the important food resources for most of the world’s population, especially in India and feeds more than 60 per cent population of the country. Telangana is on track to become India's rice bowl as rice production is expected to reach 1.3 crore tons in 2019–20.The present study was conducted in continuous paddy cultivated field of Machapur village of Siddipet district, Telangana, India to know the spatial variability of soil properties with a help of geostatistical model. For this, a total of 100 composite samples at 20*20 m grids in an area of 4 ha were collected. The pH of the soil, electrical conductivity (EC), organic carbon (OC), available nitrogen (N), phosphorus (P) and potassium (K) were all determined. The semivariogram model was used to create surface maps of soil properties using the ordinary kriging technique. The skewness values showed a normal distribution for all analyzed parameters except for Available K. Coefficient of variation ranged from 1.92% for pH to 34.08% for EC in topsoil indicating the heterogeneity of soil properties. Spherical model fits well with experimental semivariogram of pH, EC and AK. Exponential model better described the variation of soil OC and AN while the variation of AP was best described by Gaussian model. The soil pH, OC and available P were moderately spatially dependent whereas EC, available N and K were strongly spatially dependent. The cross validation results demonstrated the spatial prediction's smoothing effect. According to the findings of this study, a geostatistical model can directly reveal the spatial variability of lateritic soils and will assist farmers and decision makers in improving soil-water management.

English

Soil health in terms of plant available nutrients is crucial to estimate the yield potential of agricultural land in an area. Based on the availability of soil health data, it is possible to set yield targets using various yield prediction models. This study was initiated to estimate the concentration of DTPA-extractable micronutrients in Tehsil Muzaffargarh and to develop a database that can be used to develop predictive models. Soil samples (n=11537) were collected from Muzaffargarh (0-15 cm depth). The latitude and longitude values of sampling points were recorded for the purpose of georeferencing the soil samples. Micronutrients such as zinc (Zn), copper (Cu), iron (Fe) and manganese (Mn) were extracted with diethylenetriamine penta-acetate (DTPA) extraction solution and their concentrations were determined with atomic absorption spectrophotometer. Water soluble boron (B) was determined spectrophotometrically using Azomethine-H as colour developing reagent. Critical limits established by the National Agricultural Research Center (NARC) Islamabad, Pakistan, were used as a benchmark for grading micronutrient status. The ordinary kriging technique was used to visualize the micronutrient status in the surveyed area and digital maps were prepared using Quantum Geographic Information System (QGIS) software. Results showed that the range of DTPA-extractable micronutrients Zn, Fe, Mn, and Cu ranged from 0.10-5.92, 0.01-12.51, 0.09-10.00 and 0.03-8.08 µg/g, respectively. Boron concentration ranged from 0.02-1.98µg/g. Most of the soils in the studied area had adequate levels of Zn (61.9%) and Cu (48.6%) while 45.8, 43.3 and 55.4 % of the soil samples fell into the marginal category in terms of Cu, Mn, and B, respectively. The results revealed the widespread deficiency of DTPA-extractable Fe (< 4.5mg kg-1 in 74% soil samples) in Tehsil Muzaffargarh. The digitized maps developed from this study would serve as primary source to locate micronutrient deficient areas and also set site-specific yield targets along with other biophysical factors. However, these maps need ground truthing after crop harvest each year.

Spatial Variability of Soil Organic Carbon, pH and Electrical Conductivity and Its Influencing Factors in a Watershed of Coastal Region of Odisha, India

ABSTRACT Soil organic carbon (SOC), as a main component of the carbon cycle, contributes to biological and physico-chemical processes in the soil that influence plant growth and soil health. The pH of soil determines how acidic and basic the soil is, while the electrical conductivity (EC) of soil is used to determine the extent of soil salinity. Therefore, the quantification and spatial distribution of these soil properties are important for agricultural crop planning. In this study, the spatial variability of SOC, pH, and EC in soils of a watershed in Nayagarh (Odisha, India) was analyzed using geostatistical (ordinary kriging) techniques. The study revealed that the experimental semi-variogram of SOC and EC fits well with the spherical model; however, the Gaussian model was found to be the best fit for soil pH. The coefficients of variation for SOC, pH, and EC were 15%, 7.63%, and 40.3%, respectively, which suggests SOC may have moderate variability. SOC had moderate spatial variability, as shown by the nugget to sill ratio. The geostatistical method was able to accurately assess the spatial variability of SOC content and its influencing factors at the study watershed. The findings could help decision-makers and farmers in developing more effective land resource management.

Distribución espacial de Copturus aguacatae en el cultivo de aguacate en la región central de México

Introduction: Copturus aguacatae is one of the most important pests of avocado plantations in Estado de México. The presence of this insect represents a risk for avocado production.Objectives: To study the spatial behavior of adult population of C. aguacatae using geostatistical techniques in avocado plantations in Estado de México. Methodology: A total of 400 7-year-old cv. Hass trees were selected and georeferenced in the municipalities of Temascaltepec and Tenancingo, Estado de México. Adult insects of C. aguacataewere collected using sticky traps baited with a commercial adhesive. For geostatistical analysis, experimental semivariograms were performed and fitted with theoretical models using the Variowin program. Infected surface maps were generated with the ordinary kriging technique, and graphical representation was provided with the Surfer 16 software.Results: The spatial behavior of the insect is observed in clusters within the plots. In some months, infestations were distributed over 100 % of the surface, but with variable population densities. These results can improve pest detection and sampling programs, as well as control measures on specific areas of infestation.Study limitations: No insects during some sampling periods.Originality: Use of geostatistical methods as a tool to know the spatial distribution of pests in the avocado plantations in Mexico.Conclusions: Including geostatistical methods in the management of C. aguacatae can help avocado growers, because it it provides a more precise knowledge of their spatial behavior.

Spatial distribution model of terrestrial radiation imbalance measured by means of orbiting radiometers

A spatial distribution model was applied to net radiation levels in central Colombia obtained from remote sensing data; this model was based on the use of spatial interpolation geostatistical techniques and consists of three essential phases: an evaluation of the quality of the spectral scenes, the application of spatial interpolation techniques and the statistical evaluation of the model. These procedures allowed a more accurate analysis of the variables that influence the spatial patterns of terrestrial radiation imbalance (T.R.I) in specific areas of the country. The input data used in this model are called FlashFlux Time Integrated and Spatially Averaged (TISA) and come from observations of the Terra and Aqua satellites of the CERES (Clouds and the Earths Radiant Energy System) and MODIS (Moderate-Resolution Imaging Spectroradiometer) projects. In summary, the way in which this research was carried out was initially with an evaluation of the image quality, to analyse the potential and statistical characteristics of the FlashFlux TISA data; subsequently, a kriging interpolation method was applied adapting the radiometric data for a correct analysis and the patterns of terrestrial radiation imbalance in the centre of the country were analysed, trying to explore variables that influence these spatial dynamics. Finally, an evaluation of the spatial distribution model of terrestrial radiation imbalance was established, where the virtues, relevance and limitations of the techniques used in it were validated. This research was able to implement statistical methods of spatial interpolation, specifically the Ordinary Kriging technique, allowing the modelling of the recent situation of terrestrial radiation imbalance in central Colombia, identifying some processes and factors that are closely related to this atmospheric phenomenon responsible for the effects of regional warming.

Characterizing, predicting, and mapping of soil spatial variability in Gharb El-Mawhoub area of Dakhla Oasis using geostatistics and GIS approaches

Precision agriculture relies heavily on information concerning assessing and mapping the spatial variation of soil attributes to ensure soil and plant sustainability. The current study was undertaken in the Gharb El-Mawhoub area of Dakhla Oasis to determine, predict, map, and assess the spatial variation of physicochemical attributes. Thirty-four geo-referenced soil profiles yielded a sum of 131 representative samples. Soil physicochemical properties, i.e., electrical conductivity (ECe), texture, sand, silt, clay, calcium carbonate, organic matter saturation percentage (SP), pH, cation exchange capacity (CEC), organic matter (OM), exchangeable sodium percentage (ESP), gypsum, and sodium absorption ratio (SAR) were measured. Following data normalization, classical and geostatistical approaches have been performed to characterize soil parameters and their spatial distribution. Semi-variogram models were used to quantify the spatial variation of physicochemical properties, and the ordinary kriging technique was applied to generate the respective maps. Accuracy of the prediction performance of models was assessed employing the cross-validation technique. Results showed that soil characteristics differed considerably throughout the area under study, with significant positive or negative correlation coefficients (P < 0.01 and/or P < 0.05). Furthermore, Rational Quadratic, Circular, Hole Effect, Pentaspherical, Exponential, Tetrasperical, and J-Bessel semi-variogram models were chosen as the best-fitted models for the investigated soil properties. Cross-validation results indicated that the selected models are the best-suitable semi-variogram models for estimation and mapping the spatial pattern surfaces of the soil attributes studied. The generated prediction maps provide valuable information concerning precision agriculture for improved soil productivity and limitation reduction. Therefore, these predicted maps have a high potential for application in site-specific management. Overall, the findings demonstrated that geostatistics approaches are powerful techniques for determining, predicting, and mapping the spatial interrelationships of soil attributes.

Combination of Fuzzy Logic and Kriging Technique Under Uncertainty for Spatial Data Prediction

This paper deals with the spatial prediction in Geostatistics. This paper depend on interpellation methods of spatial statistic (ordinary kriging technique) to combination with fuzzy logic under uncertainty for spatial data prediction. This work includes the best linear unbiased estimator prediction by using formals of linear prediction and variance kriging to find prediction by Appling on real spatial data. The data adopted from real spatial data represented the depth of real underground water wells with real location from Mosul city/Iraq. We took (100) real data with locations in study area. We applied empiricism variogram function to get the properties of variogram function. We combination between kriging technique with fuzzy logic (Mamdani Fuzzy Model). To get the best Mathematical model under uncertainty. We getting the results between kriging and fuzzy logic using Matlab language.This study is a continuation of the research conducted in this context Which is very important to highlight.

Analysis of the IMERG-GPM Precipitation Product Analysis in Brazilian Midwestern Basins Considering Different Time and Spatial Scales

Precipitation products derived from satellites have emerged as a promising approach for obtaining precipitation estimates, enabling accurate long-term observations and describing the water cycle dynamics from a global scale to a local scale. The quality of these products has improved significantly in the last decades, especially with the emergence of TRMM missions and its successor GPM. The objective of this study was to evaluate the daily, monthly and annual precipitation estimates provided by IMERG version 05 of the GPM, with the data observed by the rainfall stations of the Brazilian Agency of Water and Sanitation (ANA) in the basins of the Brazilian midwest. In order to compare the data, the spatialization of the data of the rainfall stations was performed by means of the ordinary kriging technique, interpolating the data for grids of 0.1° × 0.1° that correspond to the specialized grids of the GPM satellite. The data were evaluated quantitatively by means of statistical metrics. The GPM satellite precipitation product performed relatively well on a daily scale for regions with smooth topography, and was able to describe the rainfall regime on larger time scales, regardless of the terrain conditions. However, the satellite retrievals were unable to reproduce rainfall extremes in virtually all situations, which may limit their application in frequency analyses.

THE PERFORMANCE OF ESTIMATION TECHNIQUES FOR NICKEL LATERITE RESOURCE MODELING

The choice of estimation technique according to geological conditions and mineralization character is the main problem in estimating block grade of nickel laterite. CV (coefficient of variance) and variogram determine the choice of estimation technique for nickel laterite resource classification. This study aims to evaluate various techniques for estimating block grades and to select the appropriate method for the classification of nickel laterite resources. The basic statistical analysis is to find out the description of the data, while the variography is to find out the spatial correlation between the data. Nickel grade estimation results are based on Near Neighbor Polygon (NNP), Inverse Distance Weighting (IDW), and Ordinary Kriging (OK) techniques to determine the classification of nickel resources. Accuracy levels are based on cross-sectional visualization comparisons, plan views, probability plots and linear regression analysis. The OK technique were not superior in grade estimation, especially in nickel laterite deposits. The results showed that the IDW technique was suitable to be applied to the limonite zone, while the NNP technique was suitable to be applied to the saprolite zone. Based on the performance of the estimation technique, the weighted average method can be applied for the classification of inferred, indicated, and measurable resources. The grade-tonnage curve shows the nickel laterite resource potential in the study area.

Assessing the Spatial Variability of Soil Properties to Delineate Nutrient Management Zones in Smallholder Maize-Based System of Nigeria

Spatially explicit information on soil variability is relevant for agronomic decisions; however, such information is limited in the northern Guinea savanna (NGS) agroecological zone of Nigeria. This study was conducted to delineate soil nutrient management zones (MZs), based on spatial variability of soils in the smallholder maize-based farming system within the NGS. Two hundred and eighty-nine soil samples were analyzed for some physical and chemical properties. Principal component analysis (PCA) was used to aggregate the soil properties into four principal components, which accounted for about 60% of the variation in the data, and spatial variability was assessed with a semivariogram. The ordinary kriging technique was used to predict soil properties at unsampled locations, while weighted overlay analysis was conducted to delineate nutrient management zones. Results showed that total nitrogen (0.06%), available phosphorus (5.6 mg kg−1), organic carbon (0.66%), and effective cation exchange capacity (5.6 cmol(+) kg−1) are below optimal requirement for maize production. Four MZs were identifiable in the region with the highest fertility (MZ3 and MZ4) associated with the northern area but covering a relatively small part (9.1%). The differences observed in soil properties among the MZs suggest that each zone requires different agronomic management, especially in relation to fertilizer application.

Avaliação de equações de intensidade-duração-frequência e técnicas de espacialização na Bacia do Rio Grande no estado da Bahia, Brasil

ABSTRACT Understanding the probabilistic behavior of extreme rainfall on a fine temporal and spatial scales is crucial for design and risk assessment of hydraulic structures. However, information at appropriate resolutions is frequently limited or unavailable at the locations of interest, thereby requiring the estimation of Intensity-Duration-Frequency (IDF) curves at the regional scale. In this paper, we resort to simplified approaches for rainfall disaggregation and spatialization for deriving a regional IDF equation for the Grande River catchment, in the Brazilian state of Bahia. Our results suggest that, at the daily time scale, the maximum rainfall amounts can be reasonably described by the light-tailed Gumbel distribution in the study region. The spatialization procedures indicated that, whereas both the Inverse Distance Weighting (IDW) and the ordinary kriging techniques could capture the spatial variability of rainfall quantiles, for several durations of practical interest, only the former was able to model the spatial variability of the IDF parameters. Finally, despite the simplifying assumptions, we were able to derive smooth spatial surfaces for the aforementioned quantities, which might be useful for the design of hydraulic structures at ungauged sites.

Integrating water quality index, GIS and multivariate statistical techniques towards a better understanding of drinking water quality

Groundwater is considered as an imperative component of the accessible water assets across the world. Due to urbanization, industrialization and intensive farming practices, the groundwater resources have been exposed to large-scale depletion and quality degradation. The prime objective of this study was to evaluate the groundwater quality for drinking purposes in Mewat district of Haryana, India. For this purpose, twenty-five groundwater samples were collected from hand pumps and tube wells spread over the entire district. Samples were analyzed for pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), turbidity, total alkalinity (TA), cations and anions in the laboratory using the standard methods. Two different water quality indices (weighted arithmetic water quality index and entropy weighted water quality index) were computed to characterize the groundwater quality of the study area. Ordinary Kriging technique was applied to generate spatial distribution map of the WQIs. Four semivariogram models, i.e. circular, spherical, exponential and Gaussian were used and found to be the best fit for analyzing the spatial variability in terms of weighted arithmetic index (GWQI) and entropy weighted water quality index (EWQI). Hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA) were applied to provide additional scientific insights into the information content of the groundwater quality data available for this study. The interpretation of WQI analysis based on GWQI and EWQI reveals that 64% of the samples belong to the “poor” to “very poor” bracket. The result for the semivariogram modeling also shows that Gaussian model obtains the best fit for both EWQI and GWQI dataset. HCA classified 25 sampling locations into three main clusters of similar groundwater characteristics. DA validated these clusters and identified a total of three significant variables (pH, EC and Cl) by adopting stepwise method. The application of PCA resulted in three factors explaining 69.81% of the total variance. These factors reveal how processes like rock water interaction, urban waste discharge and mineral dissolution affect the groundwater quality.

SITE INDEX IN EUCALYPTUS STANDS APPLYING ORDINARY KRIGING: AN APPROACH WITH DIFFERENT MODELS AND METHODS OF CLASSIFICATION

The objective of this work was to evaluate the efficiency of models and methods to obtain the site index, associated with ordinary kriging, to classify productive capacity in eucalyptus stands. Thus, the site quality was performed considering the traditional modeling in clonal stands (2,119 hectares) located in Minas Gerais state, Brazil. 170 plots of 400m2 were randomly allocated, representing a sampling intensity of 0.32%. The dominant height of trees (Assmann) was measured at 24, 36, 48, 60, 72, and 84 months. The site index (S) was estimated by the guide curve and algebraic difference methods, using the models of Schumacher, Chapman and Richards, and Bailey and Clutter. 136 plots were used in the fit and 34 plots in the predictive validation. The spatial dependence of site index was evaluated by experimental semivariogram and adjustment of exponential, spherical, and gaussian models. After confirming the spatial dependence, ordinary kriging was performed to spatialize the site index. For the predictive validation, the dominant height values at 72 months were used. The algebraic difference method provided excellent estimates of site index, which showed spatial dependence in all adjustments, from moderate to strong. In most cases, the gaussian model was the most accurate. It is concluded that the algebraic difference method was more efficient and the site index showed strong spatial dependence at all ages, regardless of the model used. Thus, regression models for site index estimation can be used in combination with ordinary kriging techniques.

Modeling and Assessing the Spatial and Vertical Distributions of Potentially Toxic Elements in Soil and How the Concentrations Differ.

A healthy soil is a healthy ecosystem because humans, animals, plants, and water highly depend upon it. Soil pollution by potentially toxic elements (PTEs) is a serious concern for humankind. The study is aimed at (i) assessing the concentrations of PTEs in soils under a long-term heavily industrialized region for coal and textiles, (ii) modeling and mapping the spatial and vertical distributions of PTEs using a GIS-based ordinary kriging technique, and (iii) identifying the possible sources of these PTEs in the Jizerské Mountains (Jizera Mts.) using a positive matrix factorization (PMF) model. Four hundred and forty-two (442) soil samples were analyzed by applying the aqua regia method. To assess the PTE contents, the level of pollution, and the distribution pattern in soil, the contamination factor (CF) and the pollution load index load (PLI) were applied. ArcGIS-based ordinary kriging interpolation was used for the spatial analysis of PTEs. The results of the analysis revealed that the variation in the coefficient (CV) of PTEs in the organic soil was highest in Cr (96.36%), followed by Cu (54.94%) and Pb (49.40%). On the other hand, the mineral soil had Cu (96.88%), Cr (66.70%), and Pb (64.48%) as the highest in CV. The PTEs in both the organic soil and the mineral soil revealed a high heterogeneous variability. Though the study area lies within the “Black Triangle”, which is a historic industrial site in Central Europe, this result did not show a substantial influence of the contamination of PTEs in the area. In spite of the rate of pollution in this area being very low based on the findings, there may be a need for intermittent assessment of the soil. This helps to curtail any excessive accumulation and escalation in future. The results may serve as baseline information for pollution assessment. It might support policy-developers in sustainable farming and forestry for the health of an ecosystem towards food security, forest safety, as well as animal and human welfare.

English

Detailed study on soil fertility assessment based on the understanding of the soil system are not available for the most part of Ethiopia. With the cognizance of this fact, the present study was conducted to assess the soil fertility status and to prepare the fertility map of Argo-Gedilala Sub watershed in Dugda District, Central Rift Valley of Ethiopia. Eighteen composite soil samples (0-20cm depth) were taken for fertility assessment of the soil from the land mapping units of the subwatershed and mapping the status of selected soil fertility parameters was done using ordinary kriging technique in ArcGIS 10 software. The bulk density is at an acceptable range 1.3 to 1.4 g cm-3 for mineral agricultural soils. The soil organic matter contents of LUs (04, 05 and 06) 680.47 ha (64.77%) in the study area can be categorized in the range of low soil organic matter content and LUs (01, 02 and 03) 369.31 ha (35.23%) can be categorized in the medium range at the sub-watershed. The average percent total N content of the LUs (02, 04, and 05) 567.7ha (54.05%) were&nbsp;found to&nbsp;be&nbsp;low; LUs (01,&nbsp;03&nbsp;and 06)&nbsp;482.61ha&nbsp;45.95%)&nbsp;were&nbsp;found&nbsp;to be&nbsp;moderate. The mean available P&nbsp;of&nbsp;soils&nbsp;of&nbsp;LUs&nbsp;(04and05) 443.17 ha (42.19%)&nbsp;was&nbsp;categorized&nbsp;as&nbsp;very&nbsp;low range and LUs&nbsp;(01,&nbsp;02, 03&nbsp;and&nbsp;06)&nbsp;607.15&nbsp;ha (57.82%) classified as&nbsp;low&nbsp;range. Exchangeable Ca2+, LUs (01, 02, 03, 05 and 06) 824.88 ha (78.55%) were very high while LU04 which covered an area of 225.44 ha (21.46%) were rated as high. The soils of the study area had low organic matter, available P and total nitrogen content, which can be seen as production constraints of all cultivated lands. To overcome the identified limitations, increasing the organic matter levels through continuous application of manure and/or compost should be integrated with chemical fertilizers. &nbsp; Key words: Fertility status, land-mapping units, ordinary kriging.