## 3 Results
### Principal Component Analysis  
PCA was first performed separately on the set of chemical and physical variables, and then carried out on the whole dataset. In the analysis of the chemical indicators, the first three components (PCs) explained about 65.16% of total variance, whereas in the analysis of the physical indicators, the first two PCs were able to explain 80.12% of total variance (Table 3). The score plots of the first two components showed that both chemical and physical variables were able to discriminate the different soil management compared (Figure 1). The inspection of the loadings of the first PCs highlighted that no-tilled soils (NT) were characterized by a greater TOC and TEC, together with exchangeable K\({}^{+}\) content, and by a larger bulk density and RFC, whereas a lower P \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Source of Variation** & **BD** & **P\({}_{\text{MAC}}\)** & **AC** & **RFC** & **Clay** & **Sand** \\  & **g cm\({}^{-3}\)** & **cm\({}^{3}\) cm\({}^{-3}\)** & **cm\({}^{3}\) cm\({}^{-3}\)** & **-** & **g 100 g\({}^{-1}\)** & **g 100 g\({}^{-1}\)** \\ \hline Soil management (mean) & 0.96729 & 0.03329 & 0.08473 & 0.8151 & 48.15 & 11.03 \\ Pr(P=F) & 0.0316 * & 0.0992 & 0.0370 * & 0.0302 * & 0.1854 & 0.5042 \\ \hline No-tillage (NT) & 1.04516 a & 0.00890 & 0.04112 b & 0.90814 a & 45.44 & 11.41 \\ Minimum tillage (MT) & 0.90204 b & 0.05362 & 0.12107 a & 0.73764 b & 50.85 & 10.65 \\ \hline \hline \end{tabular}  
* and ** indicate, respectively, differences at P \(\leq\) 0.05 and P \(\leq\) 0.01. Means followed by different letters are significantly different according to the SNK test (P = 0.05). Pr(P-F) indicates the probability value (p-value) to determine whether to reject the null hypothesis. BD = dry bulk density; P\({}_{\text{MAC}}\) = macroporosity; AC = air capacity; RFC = relative field capacity. (\(n=22\) for BD, PMAC, AC, RFC. \(n=24\) for clay and sand).  
\end{table} 
Table 2: Results of nested analysis of variance carried out on soil physical and hydrological parameters. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{4}{c}{**(a) Eigenvalues of the Correlation Matrix: Total**} & \multicolumn{4}{c}{**(b) Eigenvalues of the Correlation Matrix: Total**} \\ \multicolumn{4}{c}{**=14 Average = 1**} & \multicolumn{4}{c}{**=6 Average = 1**} \\ \hline  & **Eigenvalue** & **Difference** & **Proportion** & **Cumulative** & & **Eigenvalue** & **Difference** & **Proportion** & **Cumulative** \\ \hline
1 & 5.1000 & 2.6932 & 0.3643 & 0.3643 & 1 & 3.7885 & 2.7698 & 0.6314 & 0.6314 \\
2 & 2.4068 & 0.7911 & 0.1719 & 0.5362 & 2 & 1.0187 & 0.1054 & 0.1698 & 0.8012 \\
3 & 1.6158 & 0.4237 & 0.1154 & 0.6516 & 3 & 0.9133 & 0.6757 & 0.1522 & 0.9534 \\
4 & 1.1921 & 0.2644 & 0.0851 & 0.7368 & 4 & 0.2376 & 0.1971 & 0.0396 & 0.993 \\
5 & 0.9278 & 0.2123 & 0.0663 & 0.803 & 5 & 0.0404 & 0.0389 & 0.0067 & 0.9998 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Eigenvalues and variance explained by the first five principal components (PCs) of the analysis carried out on the set of (**a**) chemical and biological indicators (14 variables) and (**b**) physical indicators (6 variables). The analysis on the whole set of soil indicators (chemical, physical and biological) confirmed these results with an even clearer treatment discrimination and the same highly weighted variables showing the greatest loadings (Tables 4 and 5; Figure 2). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Eigenvalues of the Correlation Matrix Total**} \\  & \multicolumn{4}{c}{**=20 Average = 1**} \\ \hline  & **Eigenvalue** & **Difference** & **Proportion** & **Cumulative** \\ \hline
1 & 8.2424 & 5.7211 & 0.4121 & 0.4121 \\
2 & 2.5213 & 0.3911 & 0.1261 & 0.5382 \\
3 & 2.1302 & 0.5153 & 0.1065 & 0.6447 \\
4 & 1.6149 & 0.4059 & 0.0807 & 0.7254 \\
5 & 1.2089 & 0.2821 & 0.0604 & 0.7859 \\ \hline \hline \end{tabular}
\end{table}