Statistical Methods for the Analysis of Soil Spatial and Temporal Variability

Douaik, Ahmed; Meirvenne, Marc Van; Tóth, Tibor

doi:10.5772/28927

Cited by 4 publications

(2 citation statements)

References 98 publications

(104 reference statements)

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“…Soil quality mainly depends on its intrinsic properties, its geochemical and climatic environments, and its use by humans. In addition, as soilforming factors vary with space and time, soil quality will not be uniform for a given area and its spatial and temporal variability could be assessed by using GPS, remote sensing, GIS, and field and laboratory data (Douaik et al, 2005;2011).…”

Section: Introductionmentioning

confidence: 99%

Gis and Statistical Evaluation of Soil Quality of Sidi Yahya, Gharbplain (Morocco)

Lahmar

Khodrani²,

Omrania

et al. 2021

JEBAS

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The study of soil quality in irrigated areas is necessary to evaluate the sustainability of the agricultural production system. Indeed, the assessment of this quality is based on the physicochemical and biological characterization of soil parameters, as well as the knowledge of their spatial distribution and their evolution over time. This work aims to make a diagnosis of the current situation of soil quality of SidiYahya in the Gharb plain, Morocco. For this, sampling was carried out from 33 sites distributed over the studied plain during 2019. In this study, different soil properties including specifically texture, pH, electrical conductivity (EC), organic matter (OM), phosphorus (P2O5), and potassium (K2O) were measured while exchangeable sodium percentage (ESP) was calculated using the standard formula. Based on the observed soil properties a map was prepared by using a geographic information system (GIS), which was based specifically on the inverse distance weighted (IDW) spatial interpolation method. Data were processed using different statistical tools like descriptive statistics, correlation, and principal component analysis (PCA). Results of the study revealed that 70% of the soils have a heavy clayey texture with a predominance of vertisols (55%). Further, the study area soil is mainly alkaline (70%), poor in organic matter (61%) and phosphorus (52%), while very rich in potassium (70%), and non-saline (88%) contents. Soil pH was reported to be the least variable whereas sand, phosphorus, and salinity were the highest variable. IDW allowed mapping the soil properties by moving from punctual information to whole extent information. Furthermore, correlations were found between various soil properties by using PCA, 3 principal components (PCs) were able to extract 76% of the information from the 9 initial soil properties. Collected soil samples were grouped into 3 groups, based on their scores on the 3 PCs. Based on these two kinds of information, delineation of management zones can be established for a site-specific supply of agricultural inputs leading to better management of soil and water resources for securing their sustainable use.

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Section: Introductionmentioning

confidence: 99%

Gis and Statistical Evaluation of Soil Quality of Sidi Yahya, Gharbplain (Morocco)

Lahmar

Khodrani²,

Omrania

et al. 2021

JEBAS

Self Cite

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“…Drainage connected with forest management causes multidirectional changes in the physical and chemical properties of organic soils (Glina et al 2016b). In studies of organic soil degradation, alongside the conventional methods used in soil science, an increase in the importance of advanced statistical methods can be observed (Douaik et al 2011). The precise selection and execution of statistical analyses are significant tools which can confirm the findings arising from field and laboratory observations.…”

Section: Introductionmentioning

confidence: 99%

Application of multivariate statistical methods in the assessment of mountain organic soil transformation in the central Sudetes

Zawieja

Glina

2017

Biometrical Letters

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SUMMARYIn studies of organic soil degradation and transformation, alongside the conventional methods used in soil science, an increase in the importance of advanced statistical methods can be observed. In this study some multivariate statistical methods were applied in an investigation of organic soil transformation in the central Sudetes. Andrews curves, linear and kernel discriminant variable analysis and cluster analysis were used. The similarities among peatland soils and their layers were determined. It can be stated that the application of statistical methods in soil science research related to organic soil transformation is a valuable tool. The use of various statistical methods (such as Andrews curves, linear and kernel discriminant variables and cluster analysis) can with high probability confirm earlier laboratory or field observations. This is particularly justified in the case of organic soils derived from varied geobotanical peat materials, different types of peatlands and water supply types, which impact the primary properties of the soil.

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Quantifying specific capacity and salinity variability in Amman Zarqa Basin, Central Jordan, using empirical statistical and geostatistical techniques

Shaqour

Ta’any

Rimawi

et al. 2015

Environ Monit Assess

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Modeling groundwater properties is an important tool by means of which water resources management can judge whether these properties are within the safe limits or not. This is usually done regularly and in the aftermath of crises that are expected to reflect negatively on groundwater properties, as occurred in Jordan due to crises in neighboring countries. In this study, specific capacity and salinity of groundwater of B2/A7 aquifer in Amman Zarqa Basin were evaluated to figure out the effect of population increase in this basin as a result of refugee flux from neighboring countries to this heavily populated basin after Gulf crises 1990 and 2003. Both properties were found to exhibit a three-parameter lognormal distribution. The empirically calculated β parameter of this distribution mounted up to 0.39 m(3)/h/min for specific capacity and 238 ppm for salinity. This parameter is suggested to account for the global changes that took place all over the basin during the entire period of observation and not for local changes at every well or at certain localities in the basin. It can be considered as an exploratory result of data analysis. Formal and implicit evaluation followed this step using structural analysis and construction of experimental semivariograms that represent the spatial variability of both properties. The adopted semivariograms were then used to construct maps to illustrate the spatial variability of the properties under consideration using kriging interpolation techniques. Semivariograms show that specific capacity and salinity values are spatially dependent within 14,529 and 16,309 m, respectively. Specific capacity semivariogram exhibit a nugget effect on a small scale (324 m). This can be attributed to heterogeneity or inadequacies in measurement. Specific capacity and salinity maps show that the major changes exhibit a northwest southeast trend, near As-Samra Wastewater Treatment Plant. The results of this study suggest proper management practices.

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Statistical Methods for the Analysis of Soil Spatial and Temporal Variability

Cited by 4 publications

References 98 publications

Gis and Statistical Evaluation of Soil Quality of Sidi Yahya, Gharbplain (Morocco)

Gis and Statistical Evaluation of Soil Quality of Sidi Yahya, Gharbplain (Morocco)

Application of multivariate statistical methods in the assessment of mountain organic soil transformation in the central Sudetes

Quantifying specific capacity and salinity variability in Amman Zarqa Basin, Central Jordan, using empirical statistical and geostatistical techniques

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