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.