Spatial variability of isoproturon mineralizing activity within an agricultural field: Geostatistical analysis of simple physicochemical and microbiological soil parameters

2007 ◽  
Vol 145 (3) ◽  
pp. 680-690 ◽  
Author(s):  
T. El Sebai ◽  
B. Lagacherie ◽  
G. Soulas ◽  
F. Martin-Laurent
Keyword(s):  
Spatial Variability ◽  
Geostatistical Analysis ◽  
Soil Parameters ◽  
Agricultural Field ◽  
Mineralizing Activity

scholarly journals Geostatistical Analysis of the Permeability Coefficient in Different Soil Textures

2018 ◽  
Vol 67 (2) ◽  
pp. 119-124 ◽  
Author(s):  
Lida Issazadeh ◽  
Mustafa Ismail Umar ◽  
Said I.A. Al-Sulaivany ◽  
Jian Hassanpour
Keyword(s):  
Spatial Variability ◽  
Permeability Coefficient ◽  
Soil Samples ◽  
Geostatistical Analysis ◽  
Soil Parameters ◽  
Kurdistan Region ◽  
Soil Permeability ◽  
Interpolation Methods ◽  
Soil Hydraulic ◽  
Irrigation Drainage

Summary Estimating soil hydraulic properties are so important for hydrological modeling, designing irrigation-drainage systems and soil transmission of soluble salts and pollutants, although measurements of such parameters have been found costly and time-consuming. Owing to a high spatial variability of soil hydraulic characteristics, a large number of soil samples are required for proper analysis. Nowadays, geostatistical methods are used to estimate soil parameters on the basis of limited data. The purpose of this research is to investigate the spatial variability of the permeability coefficient in different soil textures (26 soil samples) found in the Kurdistan region of Iraq. The parameter values obtained indicated a normal trend in particle size distribution, whereas the values of permeability coefficient showed aberrant distribution patterns. Geostatistical analysis results indicated the best fitted theoretical model was Gaussian model and the proportion of sill/(sill + nugget) was 0.17 indicated strong spatial dependency of soil permeability. Furthermore, the optimal distance for estimating the soil permeability coefficient was 109,119 meters. A comparison of the kriging and IDW interpolation methods showed that both methods can estimate soil permeability with high accuracy and less error. The prediction maps of the applied methods indicated that high soil permeability rates were recorded in the south-east of the Kurdistan region of Iraq compared to low soil permeability rates recorded in the remainder of this region. It is recommended other interpolation methods such as co-kriging and indicator or simple kriging methods could be used to simulate data in large scale areas as well.

Modelling spatial variability of soil parameters

1990 ◽  
Vol 27 (5) ◽  
pp. 617-630 ◽  
Author(s):  
M. Soulié ◽  
P. Montes ◽  
V. Silvestri
Keyword(s):  
Shear Strength ◽  
Spatial Variability ◽  
Undrained Shear Strength ◽  
Geostatistical Analysis ◽  
Earth Dam ◽  
Soil Parameters ◽  
Bay Area ◽  
Measurements Errors ◽  
Weak Zones ◽  
Undrained Shear

The purpose of this study is to show that geostatistics can help in finding the structure of the spatial variability of the undrained shear strength within a clay deposit. The site under study, B-6, owes its name to the earth dam that will be constructed on it; the site is located on the shore of the Broadback River in the James Bay area of Quebec. The geostatistical analysis is carried out on the unaltered zone of the B-6 clay; it shows an anisotropic structure for the spatial variability. The knowledge of the structure (variogram) of the undrained shear strength is used in the kriging theory to compute estimations at points of the deposit where experimental measurements are not available. Kriging is also used to identify weak zones within the B-6 clay. The geostatistical analysis of the B-6 clay gives the opportunity to test the capability of the method. Even if the errors of measurements were small, the variogram has permitted detection and correction of a bias that affected a certain number of vane profiles. Key words: clay, geostatistics, undrained shear strength, variogram, measurements errors, kriging.

scholarly journals Consolidation Inverse Analysis Considering Spatial Variability and Non-Linearity of Soil Parameters

2002 ◽  
Vol 42 (3) ◽  
pp. 45-61 ◽  
Author(s):  
Shin-ichi Nishimura ◽  
Kiyoshi Shimada ◽  
Hiroaki Fujii
Keyword(s):  
Spatial Variability ◽  
Inverse Analysis ◽  
Soil Parameters

Classical Statistical and Geostatistical Analysis of Soil Nitrate-N Spatial Variability

2015 ◽  
pp. 175-186
Author(s):  
G. W. Hergert ◽  
R. B. Ferguson ◽  
C. A. Shapiro ◽  
E. J. Penas ◽  
F. B. Anderson
Keyword(s):  
Spatial Variability ◽  
Geostatistical Analysis ◽  
Soil Nitrate ◽  
Nitrate N

scholarly journals Spatial variability of vegetation index and soil properties in an integrated crop-livestock system

2017 ◽  
Vol 21 (8) ◽  
pp. 513-518 ◽  
Author(s):  
Alberto C. de C. Bernardi ◽  
Célia R. Grego ◽  
Ricardo G. Andrade ◽  
Ladislau M. Rabello ◽  
Ricardo Y. Inamasu
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Management Strategies ◽  
Exchange Capacity ◽  
Soil Parameters ◽  
Forage Production ◽  
Soil P ◽  
Livestock System

ABSTRACT The knowledge of soil property spatial variability is useful for determining the rational use of inputs, such as the site-specific application of lime and fertilizer. The objective of this study was to evaluate the vegetation index and spatial variability of physical and chemical soil properties in an integrated crop-livestock system (ICLS). Soil samples were taken from a 6.9 ha area in a regular hexagon grid at 0-0.20 m depths. Soil P, K, Ca, Mg, and cation exchange capacity - CEC; base saturation; clay and sand were analyzed. Soil electrical conductivity (ECa) was measured with a contact sensor. The site was evaluated at the end of the corn season (April) and during forage production (October) using Landsat 5 images, remote sensing techniques and a geographic information system (GIS). Results showed that the normalized difference vegetation index (NDVI) was associated with ECa and soil parameters, indicating crop and pasture variations in the ICLS. Geostatistics and GIS were effective tools for collecting data regarding the spatial variability of soil and crop indicators, identifying variation trends in the data, and assisting data interpretation to determine adequate management strategies.

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