3.7.Redundancy discriminatory analysis
The CANOCO software was used to analyze the data in relation to carbon
sources and environmental factors. The maximum gradient length in the
four axes was 0.464, with a value below 3. Therefore, linear model
analysis (RDA) was selected. The eigenvalues of the first two ordination
axes (carbon source diversity an environmental factors) were 0.573 and
0.213 (Table 4), while the correlation coefficients of the two
ordination axes were 1. The first two ordination eigenvalues accounted
for 99.8% of the total eigenvalues. The correlation coefficients
between the first two ranking axes and environmental factors were
extremely high, accounting for 99.9% of the total variance. The axes
RDA1 and RDA2 explained 46.4 and 34.9% of the variation among
intercropping system communities, respectively (Table 5).
The greatest differences between treatment communities were observed
when comparing mulberry and alfalfa and related to differences in pH,
OM, SWC and AN, according to axis length and angle. The second greatest
differences were observed between ANE and AN0 and were due to the
differences in the activities of PPO, CAT, and POD (Fig. 6). Moreover,
POD, CAT, and OPP were positively related to A0 and AN0, while pH, AN,
and SWC were negatively related to AE and ANE. The third greatest
differences were observed between ME and MN0, and between MNE and M0,
which were related to differences in SWC, AN, SUR, POD, pH, and OM,
respectively. The parameters SUR, AN, and SWC were positively related
with M0, while pH, OM, and SWC were positively related to M0 and MNE.