Spatial variation of soil nutrients on sandy-loam soil

Bogunović, Igor; Mešić, Milan; Zgorelec, Željka; Jurišić, Aleksandra; Bilandžija, Darija

doi:10.1016/j.still.2014.07.020

Cited by 85 publications

(50 citation statements)

References 43 publications

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“…The average dissimilarity between data is separated by a vector and is measured by the semivariogram. However, anomalies in soil data and their deviation from normal (Gaussian) or log-normal distribution need to recognized for identification, accurate delineation, and modeling of soil properties for site specific soil management and precision agriculture (Fu et al, 2010;Bogunovic et al, 2014;Vasu et al, 2017a). Soil particles were assumed to have "self-similar" features, and fractal theories were employed to investigate their characteristics in several studies (e.g., Oleschko et al, 2008;Tuffour, 2015;Li et al, 2016;Miloš and Bensa, 2017).…”

Section: Introductionmentioning

confidence: 99%

Spatial and fractal characterization of soil properties across soil depth in an agricultural field, Northeast Iran

Keshavarzi

Tuffour

Bagherzadeh

et al. 2018

Eurasian Journal of Soil Science (Ejss)

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Article InfoThe present study was conducted to explore the fractal behavior and establish fractal dimensions of soil physical and chemical properties (i.e., sand, silt, and clay contents, bulk density, degree of moisture saturation, pH, organic carbon content, total nitrogen, available phosphorus, and available potassium) to characterize their spatial patterns. Soil samples were collected from 0-30 (surface) and 30-60 cm (subsurface) depths from an agricultural field, Mashhad Plain, Northeast Iran. Descriptive statistics and fractal analysis were used to describe the extent and form of variability. Spatial patterns of the soil properties were estimated using GS+ 10.0 software. Soil properties showed low to high variations in both surface and subsurface layers across the field, where bulk density and pH being the most reliable soil physical and chemical properties in the study area. The variability was high (CV > 35%) for total N, available P, available K and organic carbon in both surface and subsurface soils and it could be attributed to management practices and micro-topographical variations as these are the dynamic properties of soil. The fractal dimension (D) values of soil physical properties ranged from 1.398 to 1.913 at the surface, and from 1.874 to 1.934 at the subsurface indicating both short and long range variations. The D values for the chemical properties ranged from 1.331 to 1.975, and 1.148 to 1.990 in the surface and subsurface layers, respectively. The results showed that fractal analysis could be employed to effectively describe the structure of soil heterogeneity in spatial scale for effective agricultural and environmental management of soil.

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Section: Introductionmentioning

confidence: 99%

Spatial and fractal characterization of soil properties across soil depth in an agricultural field, Northeast Iran

Keshavarzi

Tuffour

Bagherzadeh

et al. 2018

Eurasian Journal of Soil Science (Ejss)

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“…The next and the important step in geostatistics is the interpolation which is the process of estimating values at unknown points using known values for the sample points. This method can be divided into deterministic (create surfaces from measured points) and geostatistical (utilize the statistical properties of the measured points) techniques (Pereira et al, 2013a,b;Bogunovic et al, 2014;Xie et al, 2011).…”

Section: Geostatistical Tools For Measuring Spatial Variabilitymentioning

confidence: 99%

An Overview of Spatial Variability of Soil Microbiological Properties using Geostatistics

Kirmani¹,

Mir²,

Rasool³

et al. 2017

Int.J.Curr.Microbiol.App.Sci

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“…It's meaningful to analyze the extent of temporal and spatial variation of soil nutrient contents for more reasonable fertilizer applications. Lots of works have been actualized on soils by measuring and analyzing the spatial dependencies on soil fertility [1][2][3][4]. For example, Weijun Fu et al studied the spatial variation of soil nutrients in a dairy farm in southeastern Ireland [5], Kelin Hu et al studied patterns of spatial and temporal variation of SOM in Beijing's urban-rural transition zone [6], and Zhang Xing-Yi studied the spatial variability of nutrient contents in northeast China where has black soils [7].…”

Section: Introductionmentioning

confidence: 99%

Study of Spatio-temporal Variation of Soil Nutrients in Paddy Rice Planting Farm

Chen

Dong

Jiang

et al. 2016

Computer and Computing Technologies in Agriculture IX

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It is significant to analyze the spatial and temporal variation of soil nutrients for precision agriculture especially in large-scale farms. For the data size of testing results is growing every time after sampling mostly by the frequency of once a year or several months, in order to discover the variation trends of specific nutrient which would be instructive for the fertilization in the future. In this study, theories of GIS and geostatistics were used to characterize the spatial and temporal variability of soil nutrients in paddy rice fields in the Erdaohe farm of Heilongjiang Province, China, which located in the north of Daxing'an Mountains, has an area of nearly 36.1 million hectares for paddy rice planting. The soil samples, collected from 2009 to 2013 once a year, were sampled based on the spatial distribution of paddy rice fields,

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Spatial variation of soil nutrients on sandy-loam soil

Cited by 85 publications

References 43 publications

Spatial and fractal characterization of soil properties across soil depth in an agricultural field, Northeast Iran

Spatial and fractal characterization of soil properties across soil depth in an agricultural field, Northeast Iran

An Overview of Spatial Variability of Soil Microbiological Properties using Geostatistics

Study of Spatio-temporal Variation of Soil Nutrients in Paddy Rice Planting Farm

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