3. Results
The results showed that gathered data satisfy assumptions of normal
distribution (all P > 0.05) and homogeneity of
variance (all P > 0.05) (Table 3). Based on the
results, vegetation components significantly varied between control and
mulch treated sites (all P < 0.05), in which total
cover and litter increased both about 76% while bare soil decreased
about 26% under mulch treated (Fig. 2). Species richness was not varied
between control and mulch treated sites (t -value= -0.067 andP =0.947). In contrast, species evenness, Shannon, and Simpson
diversity indices were negatively affected by mulch treatment (Table 3
and Fig. 2). For example, applying mulch treatment resulted in a decline
in species evenness, Shannon, and Simpson diversity indices about 88%,
63%, and 71%, respectively (Fig. 2).
Table 3. Results of normal distribution (Shapiro-Wilk Test),
homoscedasticity (Levene’s Test), and independent t-test between
vegetation and diversity component in control and mulch treated sites.
significant values are shown by bold numbers. The non-significant values
in Shapiro-Wilk and Levene’s tests indicate normal distribution and
homogeneity in variance, respectively.
Figure 2. Mean ± SE comparison of vegetation and diversity components in
control and mulch treated sites. significant differences are indicated
by different letters.
All RCS parameters (BS, LF and BP) and total RCS significantly differed
between control and mulch treated sites (all P < 0.001)
while WPC was not significantly different between control and mulch
treated sites (t -value= 1.226 and P = 0.236) (Table 4). In
addition, RCS class in control and mulch treated areas were very poor
and poor, respectively.
Table 4. The comparison results of RCS and their components.
BS: bare soil, LF: litter
frequency, WPC: weighted palatability classes, BP: biomass production,
RCS: rangeland condition score. Significant values are shown by bold
numbers.
Before calculating MSAVI maps of 2017 and 2019, the slope and intercept
of soil lines regression were calculated by fitting a linear regression
between the red and infrared bands (Table 5).
Table 5. The soil line parameters (slope and intercept) are based on a
linear regression between the red (as the dependent variable) and
infrared bands (as an independent variable) and good of fitness indices.
Then, the MSAVI maps of 2017 and 2019 were produced and reclassified
(Fig. 3). As shown in Fig. 4, the vegetation cover significantly
increased in 2019 compared to those found in 2017.
Figure 3. The vegetation cover maps based on remotely sensed MSAVI index
The results of map differencing showed that vegetation cover of extent
area of studied regions was not varied (52.4%) or increased (44.8%)
between 2017 and 2019; however, vegetation cover in some small areas
(northwest) was decreased (2.8%) (Fig. 4).
Figure 4. Vegetation cover change monitoring using the differencing
method
The results of map differencing showed that vegetation cover of extent
area of our studied regions was not varied (52.4%) or increased
(44.8%) between 2017 and 2019; however, vegetation cover in some small
areas (northwest) was decreased (2.8%) (Fig. 4).
Finally, two-way ANOVA results showed that years (2017 vs. 2019), treats
(control vs. mulch), and their interaction had significant effects on
vegetation cover value extracted by the MSAVI index (Fig. 5). Based on
the results, vegetation cover was greater in 2019 than that of 2017 and
also greater in the mulched site in 2019 than the controls site, while
there was no difference between vegetation cover in controls and mulch
sites in 2017 (Fig. 5).
Figure 5. Results of two-way ANOVA of vegetation cover extracted by
MSAVI index for pre and post applying mulching. The significant
differences are shown by different letters. ***, indicate significant
effects at P < 0.001.