Mapping of the soil texture using geostatistical method (a case study of the Shahrekord plain, central Iran)

Safari, Yaser; Esfandiarpour-Boroujeni, I.; Kamali, Ardavan; Salehi, Mahdi; Bodaghabadi, Mohsen Bagheri

doi:10.1007/s12517-012-0559-9

Cited by 22 publications

(10 citation statements)

References 22 publications

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“…AN, AP, and AK were moderate spatially dependent (53-72 %), imprinted by intrinsic factor (soil forming process) and extrinsic factors (soil fertilization and cultivation practices) (Cambardella et al 1994). Some other researchers had also found the moderate spatial dependence of soil properties (Safari et al 2013;Liu et al 2015). OC exhibited weak spatial dependence (77 %), this indicated that the spatial patterns of this soil properties was mainly influenced by extrinsic factors such as fertilization and rainfall redistribution induced by canopy (Liu et al 2015).…”

Section: Geostatistical Analysismentioning

confidence: 89%

“…Variability in soil properties results mainly from the complex interactions between geology, topography and climate, as well as soil use (Quine and Zhang 2002;Liu et al 2015). In addition, variability may also occur as a result of land use and management strategies (Safari et al 2013). As a consequence, soils can exhibit marked spatial variability at the macro-scale and micro-scale (Brejda et al 2000;Vieira and Paz Gonzalez, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, geostatistics has been widely used by many researchers for preparation of spatial variability maps of soil properties like soil texture (Safari et al 2013), total soil nitrogen and phosphorus (Wang et al 2009), soil available phosphorus (Reza et al 2012b), soil organic carbon (Lui et al 2006), soil quality (Sun et al 2003), and soil physical properties (Santra et al 2008;Reza et al 2015). Thus, the objective of this study was to determine the spatial variability of selected soil properties, such as pH, organic carbon content (OC), available N (AN), available P (AP), and available K (AK), with the classical statistics and geostatistical analysis for Bongaigoan district of Assam, India.…”

Section: Introductionmentioning

confidence: 99%

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Spatial variability of soil properties using geostatistical method: a case study of lower Brahmaputra plains, India

et al. 2016

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Soil properties like pH, organic carbon (OC), available nitrogen (AN), available phosphorus (AP), and available potassium (AK) vary spatially from a field to a larger region scale and determine the soil fertility. This study addressed the spatial variability of soil properties in Brahmaputra plains, northeastern India using geostatistical method. For this, a total of 767 soil samples from a depth of 0-25 cm at an approximate interval of 1 km were collected over the entire Bongaigaon district of Assam. Data were analyzed both statistically and geostatistically on the basis of semivariogram. Soil properties showed large variability with greatest variation was observed in AP (86 %) where as the smallest variation was in pH (19 %). The semivariogram for all soil properties were best fitted by exponential models and showed a highest (2.7 km) range for OC and lowest (1.2 km) for AP. The nugget/sill ratio indicates a strong dependence for pH (12 %), moderate spatial dependence for available nutrients (53-72 %) and a weak spatial dependence for OC (77 %). Evaluation of spatial maps indicated that except for AN due to high root mean square error (61.8), kriging could successfully interpolate other soil properties. Soil pH highly negatively correlated with OC (−0.330**) and AN (−0.228**) and highly positive correlated with AP (0.334**) and AK (0.164**). A highly significant correlation was also found between OC and AN (0.490 ** ).

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Section: Geostatistical Analysismentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial variability of soil properties using geostatistical method: a case study of lower Brahmaputra plains, India

et al. 2016

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“…The collected qualitative and quantitative information was analysed by using MS Excel, the Statistical Package for the Social Science (SPSS) and ArcGIS 10.1 Desktop software was used in spatial analysis and map preparation. A geographical information system (GIS) was extensively used in assessing spatial and temporal variations of soil and water properties (Cemek et al, 2007;Guagliardi et al, 2012;Safari et al, 2013;Shouse et al, 2010;Zhou et al, 2010). After processing and integration of water quality data, the data analysis was carried out in Aquachem v4.0 and Rockworks 15 to understand the groundwater quality and subsurface conditions.…”

Section: Data Processing and Analysismentioning

confidence: 99%

Spatial and Temporal Variation of Soil and Water Salinity in the South‐Western and South‐Central Coastal Region of Bangladesh

Rahman

Sarker

Mia

2017

Irrigation and Drainage

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Salinity intrusion is one of the major water‐related problems in the coastal region of Bangladesh. The intention of this study was to discuss the spatial distribution of soil and water salinity with their severity levels using geospatial techniques. The spatial analysis shows about 222 300 ha of new land being affected by various degrees of soil salinity during the last four decades. The analysis revealed that the spatial extent of salinity has been following an increasing trend and the movement of soil salinity has moved north from the coast. An increasing trend of salinity concentration in the rivers has already been found in the Lower Meghna River, Alaipur Khal (creek) of Daratona River, Gorai Madhumati, Rupsa Passur River and Bishkhali River in different districts of coastal Bangladesh. The groundwater is contaminated with a high level of salinity, which affects sources of drinking water, agriculture and the irrigation sector. Increased soil and water salinity levels cause an unfavourable environmental and hydrological situation that restricts usual crop production throughout the year. This research will assist decision makers and planners in taking proper initiatives for developing integrated land and water resources management plans for the coastal regions, which have similar environmental settings along the Bay of Bengal. Copyright © 2017 John Wiley & Sons, Ltd.

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“…In a time series analysis, goodness of fit tests for testing normality is used to determine the probabilistic process that describes a particular time series (Iqbal and Quamar 2011). Moreover, in various fields of geosciences, testing normality with various goodness of fit tests is required because a normal distribution may lead to more reliable results, which are necessary for analyses (Abouelnaga et al 2014;Narany et al 2014;Safari et al 2013).…”

Section: Goodness Of Fit Tests For Testing Normalitymentioning

confidence: 99%

Testing the normality of the residuals of surface temperature data at VLBI/GPS co-located sites by goodness of fit tests

Kayıkçı

Sopacı

2015

Arab J Geosci

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Evaluating the distribution patterns of surface temperature data at Very Long Baseline Interferometry (VLBI)/ Global Positioning System (GPS) co-located sites w.r.t. normality is one of the most important issues in modeling surface temperature data over long periods. Such evaluation can generate algorithms for filling in missing data at measurement sites. Some algorithms in the literature, such as those in the study of Cho et al. J Coast Res 65. ), require trend, harmonic, and residual components to fill in the missing data. Trend and harmonic components estimate an optimal model that can be used to assist such algorithms when filling in missing data. The present study is based on the investigation of the normal distribution of the residuals of a surface temperature time series at VLBI/GPS co-located sites, after removing the trend and seasonal effects through harmonic components (inter-daily variations). This study uses surface temperature data collected from the VLBI/GPS colocated sites of two different regions in Europe: Matera (Italy) and Wettzell (Germany). The data collected from these sites form a time series, and time series analyses and conventional k-sigma outlier detection are implemented on these data sets before subjecting them to goodness of fit tests for normality. The residual components of the time series are acquired through a decomposing trend and signal effect from the original time series, assuming that the residuals of the time series are normally distributed. In testing the hypothesis that an observed frequency distribution fits the normal distribution, the following tests are used: Pearson χ 2 , KolmogorovSmirnov, Anderson-Darling, Shapiro-Wilk or ShapiroFrancia, D'Agostino, Jarque-Bera, skewness, and kurtosis tests. Some graphical methods are also applied to support the results of the goodness of fit tests for normality. Some proposals on the application of the goodness of fit tests are put forward, such as the evaluation of the estimation model for trend and harmonic components by considering the properties of the implemented goodness of fit tests. The results of this study can be used to determine the optimal model for estimating trend and harmonic components. The output of the present study is expected to have an important role in modeling surface temperature distributions at co-located VLBI/GPS sites for filling in missing data. Above all, meteorological data, such as temperature, pressure, and humidity, are of specific interest for modeling tropospheric delay, the main error factor in positioning in space geodesy, which in turn makes investigations on the distribution of meteorological data more attractive in geoscience.

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Mapping of the soil texture using geostatistical method (a case study of the Shahrekord plain, central Iran)

Cited by 22 publications

References 22 publications

Spatial variability of soil properties using geostatistical method: a case study of lower Brahmaputra plains, India

Spatial variability of soil properties using geostatistical method: a case study of lower Brahmaputra plains, India

Spatial and Temporal Variation of Soil and Water Salinity in the South‐Western and South‐Central Coastal Region of Bangladesh

Testing the normality of the residuals of surface temperature data at VLBI/GPS co-located sites by goodness of fit tests

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