where Wi is the weighting value of the soil indicators selected by the
PCA, Si is the indicator score calculated by Eq. (1) and
n is the number selected in MDS.
Statistical analysis
A one-way analysis of variance (ANOVA), followed by a Tukey mean
comparison test (P ≤ 0.05) was used to examine and to compare the
differences in soil indicators and SQIs among different land use type at
p < 0.05 level. The principal component analysis (PCA) and
correlation matrices between soil indicators were evaluated using
Pearson’s correlation analysis. procedure. An additional ANOVA was
performed on the overall SQI and MDS-scored soil quality indicators to
reveal the effect of different land use type on soil quality. All
statistical analyses were performed by SPSS 21.0 (SPSS Inc., Chicago,
USA).
RESULTS
3.1. Soil physical Indicators under different land use types
The structural quality of the soil was analysed according to different
physical properties under different land-cover uses whose average values
are shown in Table 2. It can be seen that bulk density (BD) only showed
significant differences in the surface layer (P≤0.05), ranging from 0.30
to 0.51 Mg m-3, for the uses of forest and Chakra_C
respectively. Regardless of use, a slight increase was recorded, ranging
from 0.45 to 0.57Mg m-3. At both depths the forest
cover presented the lowest value. Saturated hydraulic conductivity
(Ksat) presented significant differences at both depths
(P<0.05), with a higher penetration speed in the surface layer
(0-10cm). In said horizon, the order obtained according to type of land
use was: Chakra_B > Forest > Chakra_A
> Cattle_C > Cattle_B >
Chakra_C > Cattle_A, while for the subsurface layer it
was: Forest > Chakra_B > Cattle_C
> Chakra_C > Cattle_A >
Cattle_B > Chakra_A.