3.2 Study methods
3.2.1 Revised Universal Soil Loss Equation (RUSLE)
RUSLE is an empirical model for estimating soil erosion, which has been
widely used in current situation evaluation, risk estimation, simulation
and prediction (Prasannakumar et al., 2012; Panagos et al., 2015; Zare
et al., 2017). The specific form is shown in equation (1).
\(A=100\times R\times K\times L\times S\times C\times P\) (1)
Where A is average annual soil loss per unit area
(t·km-2·a-1); R is rainfall
and runoff erosivity factor
(MJ·mm·hm-2·h-1·a-1),K is soil erodibility factor
(t·hm2·h·hm-2·MJ-1·mm-1),L is slope length factor, S is slope steepness factor,C is cover and management factor, these factors are calculated
from equation (2) ~ (6) (Table 1); P is support
practice factor, which is assigned according to land use type (Mallick
et al., 2014; Kumar et al., 2014) (Table 2).
[Table 1 near here]
[Table 2 near here]
3.2.2 Soil conservation potential (SCP)
In this study, the SCP was defined as the difference between the current
value and the theoretical minimum value of soil erosion per unit area of
a region, reflecting the improvement space of soil erosion control in a
region. According to the RUSLE, in order to control soil erosion, the
values of the above factors must be effectively controlled. In fact,
precipitation condition is difficult to manipulate because it is nearly
constant over a short period. It takes a lot of capital to artificially
influence soil properties and topographic conditions, so it is
uneconomical to change the behavior of these factors. Vegetation cover
and land use types are directly affected by the implementation of GFG,
and they are also important factors affecting soil erosion. Therefore,
in the calculation of SCP, C factor and P factor will be
introduced into the model as the main variables.
The soil erosion amount when the C factor and P factor in
region i reach the ideal state is defined as the theoretical
minimum soil erosion level of the region (Ai0 ),
and the soil erosion calculated from the actual values of Cfactor and P factor in the year t was defined as the soil
erosion level (Ait ) of the region in the yeart . Then the formula of SCP can be defined as equation (7).
\(\text{AP}_{\text{it}}=A_{\text{it}}-A_{i0}=R_{i}\times K_{i}\times L_{i}\times
S_{i}\times\left(C_{\text{it}}\times P_{\text{it}}-C_{i0}\times P_{i0}\right)\)(7)
APit represents the SCP of region i in
year t . Ri , Ki ,Li , and Si are
respectively the rainfall and runoff erosivity factor, soil erodibility
factor, slope length factor and slope steepness factor of regioni . Cit and Ci0 are
respectively the values of the cover and management factor of regioni in year t and in the ideal state. Among them, theEVI corresponding to Ci0 comes from the
theoretical maximum value of vegetation restoration obtained by
constructing similar habitat units based on the spatial sliding window,
and references for specific calculation methods (Zhang et al., 2019; Xu
et al., 2020). Pit and Pi0are respectively the values of the support practice factor of regioni in year t and in the ideal state. Among them, the land
use types corresponding to Pi0 are defined based
on the standard of ensuring 1 mu (1/15 hm2) of grain
ration field per capita. Based on the resident population data of each
county or district of Yan’an in 2015, the scale of grain ration fields
to be reserved in each county or district was calculated, and the
reserved grain ration fields should occupy the lowest part of the
originally cultivated land slope. Finally, the remaining cultivated land
will be converted to forest. The layers of each factor are shown in
Figure 2.
[Figure 2 near here]
According to the definition and calculation method of SCP, it can be
considered that if a region has higher SCP, it indicates that the gap
between the current soil erosion level and the theoretical minimum soil
erosion level in the region is more obvious, so the GFG investment in
the region should be higher, thereby enhancing the effectiveness of soil
erosion control.
In addition, this study uses village-level administrative districts as
statistical units for calculation results, thereby enhancing data
analysis and visualization.