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.