Determining and mapping some soil physico-chemical properties using geostatistical and GIS techniques in the Naqade region, Iran

Mousavifard, Seyyedeh Mahbube; Momtaz, H. R.; Sepehr, Ebrahim; Davatgar, Naser; Rasouli-Sadaghiani, MirHassan

doi:10.1080/03650340.2012.740556

Cited by 42 publications

(23 citation statements)

References 32 publications

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“…Liu et al (2009) found that spatiotemporal variations in soil nutrients were caused by an altered land tenure system in China and concluded that the spatial variability in soil properties may be affected by both intrinsic and extrinsic factors. Mousavifard et al (2012) reported the similar results. Similar to our study, Walter et al (2001) used ordinary kriging to create a map of soil salinity.…”

Section: Interpolation Using Krigingsupporting

confidence: 67%

Characterizing spatial variability of soil properties in salt affected coastal India using geostatistics and kriging

et al. 2015

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Soil salinization is a major problem affecting 955 Mha globally and 7 Mha in India. Soil properties vary spatially and knowing the extent of spatial variability of soil physicochemical characteristics is highly essential for management of these soils and crop cultivation. This study was conducted in salt-affected coastal parts of eastern India, with the following objectives: (i) to explore the spatial variability of soil properties (soil electrical conductivity (ECe), soil pH, soil organic carbon (SOC), available soil nitrogen, available soil phosphorus, and available soil potassium) and fitting the semivariogram models; (ii) to estimate the values of soil properties at unsampled locations using geostatistical tools; and (iii) to prepare the spatial maps of soil properties using parameters of best fit semivariogram model and interpolation by ordinary kriging technique. A total of 132 soil samples were collected. Gaussian, exponential, circular, spherical, K-Bessel, and spherical semivariogram models were found to be the best fit for assessing the spatial variability in ECe, soil pH, SOC, available soil nitrogen, available soil phosphorus, and available soil potassium, respectively. The best fit model parameters were used to create the spatial maps for these soil properties by ordinary kriging. It was concluded that geostatistical and kriging tools can be used to estimate the value of soil properties at unsampled locations and ultimately to develop spatial maps for site-specific nutrient management.

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Section: Interpolation Using Krigingsupporting

confidence: 67%

Characterizing spatial variability of soil properties in salt affected coastal India using geostatistics and kriging

et al. 2015

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“…Sun et al (2003) documented that soil Av.P showed the highest variability while soil pH showed the lowest. In addition, Tsegaye and Hill (1998), Aimrun et al (2007), and Mousavifard et al (2013) reported a lower variance of soil pH compared to other soil chemical attributes.…”

Section: Resultsmentioning

confidence: 86%

Evaluation of the physicochemical properties of alluvial and colluvial soils formed under ustic moisture regime using multivariate geostatistical techniques

Sağlam

2014

Archives of Agronomy and Soil Science

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The purpose of this study, which was carried out in the areas of Amasya Gökhöyük Agricultural Enterprise, Turkey, was to determine area management factors that will induce management practices unique to alluvial and colluvial areas using multivariate geostatistical techniques. Physical and chemical analyses were done for 224 soil samples taken at 0-30 cm depth from Entisol and Inceptisol soils. Based on the factor analysis results of the data set prepared, factors were determined, and these factors were named to as soil texture, soil organic matter, fertilization, and pH. While the texture factor accounted for the highest variability with 32.17% and the pH factor accounted for the lowest variability with 10.45%, the four factors together accounted for 79.17% of the total variance. According to the spatial distribution maps, soil texture and soil organic matter factors reached the highest values in alluvial areas close to Çekerek Creek while fertilization factor reached the highest values in colluvial areas. However, pH showed a relatively more homogenous distribution and reached its highest values in areas close to the transition zone of alluvial and colluvial areas. Based on these results, it can be seen that the aforementioned factors are the most important soil factors for these areas.

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“…The minimum CV value belonged to pH, since pH is the logarithm of H + concentration in soil solution and if this parameter is reflected directly by proton concentration, it could show more changes. Other researchers have also observed less variation in soil pH than other soil parameters (Tesfahunegn et al, 2011;Mousavifard et al, 2013). To determine the relationship between annual and seasonal DDR and surface soil properties in the study area, correlation coefficients were calculated (Table II).…”

Section: Statistical Description Of the Studied Propertiesmentioning

confidence: 99%

Relationship between dust deposition rate and soil characteristics in an arid region of Iran

Aghasi

Jalalian

Khademi

et al. 2019

Atm

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La formación de polvo es uno de los procesos que causan mayor impacto ambiental en zonas áridas y semiáridas. En este estudio se utilizó el algoritmo para la detección automática de interacción de chi-cuadrado (CHAID, por sus siglas en inglés) para determinar las relaciones no lineales entre las propiedades físicas y químicas del suelo y la tasa de sedimentación de polvo (TSP) tanto estacional como anual en la subcuenca del pantano de Gavkhouni, Irán central. Los resultados se compararon con los obtenidos por el método de regresión lineal múltiple (RLM). Ciento veinticuatro muestras de polvo atmosférico se tomaron estacionalmente en 31 emplazamientos. También se tomaron 96 muestras de suelo superficial. Se evaluaron tanto la tasa de sedimentación y la distribución del tamaño de partículas del polvo, como las propiedades físicas y químicas de las muestras de suelo. Los resultados mostraron que la TSP más alta y más baja corresponden al verano y al otoño, respectivamente. Con base en los resultados del algoritmo CHAID, las propiedades del suelo más importantes que afectaron la TSP en otoño, invierno, primavera y verano, así como la TSP anual, fueron el contenido de materia orgánica (coeficiente de importancia [IC] = 0.34), de yeso (IC = 0.42), de arena (IC = 0.39), de limo (IC = 0.31) y de arena (IC = 0.23) del suelo, respectivamente. A partir de los resultados del algoritmo CHAID, parece que la distribución del tamaño de partículas del suelo, especialmente el contenido de arena, es un factor que afecta de forma determinante la TSP estacional y anual en el área de estudio. En este estudio, el modelo RLM mostró una exactitud no aceptable en comparación con el método no lineal del algoritmo CHAID. Por lo tanto, se puede concluir que en las áreas con alta complejidad ecológica y complejas relaciones no lineales entre los datos de entrada y salida, los métodos no lineales como el algoritmo CHAID son mejores que los métodos lineales como el RLM.

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Determining and mapping some soil physico-chemical properties using geostatistical and GIS techniques in the Naqade region, Iran

Cited by 42 publications

References 32 publications

Characterizing spatial variability of soil properties in salt affected coastal India using geostatistics and kriging

Characterizing spatial variability of soil properties in salt affected coastal India using geostatistics and kriging

Evaluation of the physicochemical properties of alluvial and colluvial soils formed under ustic moisture regime using multivariate geostatistical techniques

Relationship between dust deposition rate and soil characteristics in an arid region of Iran

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