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Chapter 2 Spatial variability of soil properties affected by grazing intensity in Inner Mongolia grassland
samples were free of carbonate so that the total C concentration equals the
organic carbon concentration. Soil particle size distribution was measured with
the pipette method.
2.3. Statistical analysis
Measured variables in the data set were firstly analyzed using descriptive
statistical methods. The Shapiro-Wilk test revealed that all measured variables
were approximately normally distributed, only K and WDPT showed a negative
skewness. Natural logarithmic transformation Y*=Ln(y+1) (where y is the
recorded value) for WDPT and Y*=Ln(y) for K were performed to obtain a nearly
normal distribution before proceeding with the geostatistical analysis (Jongman
et al., 1987). Data were not transformed back in order to simplify multiple linear
regression analysis. Correlations among different analyzed parameters were
tested using Pearson’s correlation coefficient. Semivariograms was used to
determine the degree of spatial variability. Appropriate model functions were
fitted to the semivariogram obtained by the maximum likelihood cross-validation
method (Samper and Carrera, 1990). Before applying the geostatistical tests,
each variable was also checked for drift, trend and anisotropy (Iqbal et al., 2005).
The semivariogram (γh) was calculated as follows:
N ( h)
⎧
⎫
1 2
γ ( h)
= 2N
( h)
⎨∑
[ Z(
xi
+ h)
− Z(
xi
)] ⎬
(1)
⎩ i=
1
⎭
where γ(h) is the semivariance for interval class h, N(h) is the number of
pairs separated by the lag distance h, Z(xi) and Z(xi+h) are values of the
measured variable at spatial locations i and i+h, respectively. A semivariogram
model consists of three basic parameters which describe the spatial structure:
γ(h)=Co+Cs. Co represents the nugget effect; Cs is the structural component;
Co+Cs is the sill; and the distance at which the sill is reached is the range. Value
of the proportion of spatial structure Cs/(C0+Cs) is a measure of the proportion
of sample variance (C0+Cs) that is explained by spatially structured variance
(Cs). Following Cambardella et al. (1994), The classes of spatial dependence
were distinguished: strongly spatial dependence (Cs/(C0+Cs) >75%),
moderately spatial dependence (Cs/(C0+Cs) >25% and