Global analysis of support practices in USLE-based soil erosion modeling

Abstract

Support practices (SPs) influence the magnitude of soil loss and can be readily influenced by human interventions to mitigate soil loss. The SPs factor is expressed as the P-factor in the widely used soil erosion model – the universal soil loss equation (USLE) – and its revised version. Although the effects of SPs on soil erosion are well recognized, the quantification of the P-factor for soil loss modeling remains challenging. This limitation of the P-factor particularly restricts the applicability of USLE-based models at large scales. Here, we analyzed the P-factor values in USLE-based models from 196 published articles. The results were as follows: (a) an increasing trend in the number of studies has been observed in recent years, especially at large scales; (b) the P-factor values for paddy fields, orchards, and croplands were 0.16 ± 0.15, 0.47 ± 0.12, and 0.49 ± 0.21, respectively, and in terms of different types of SPs, the P-factor values for terracing, contouring, and strip-cropping were 0.28 ± 0.18, 0.52 ± 0.24, and 0.49 ± 0.28, respectively; (c) various methods have been developed for P-factor qualification, although the methods that consider SP conditions were most frequently used in studies with relatively smaller areas (< 100 km2), suggesting that USLE-based models are in need of improvement via the quantification of the P-factor, particularly with respect to the regional and global scale; and (d) further improvements of the P-factor for soil erosion modeling should concentrate on building P-factor datasets at the regional level according to data on the effectiveness of SPs on soil loss control based on field experiments in published articles, using advanced image processing techniques based on higher-resolution satellite imagery and developing proxy indicators for P-factors at large scales.

Investigating the compounding effects of environmental factors on ecosystem services relationships for Ecological Conservation Red Line areas

“…The biodiversity maintenance ECRL areas are distributed in the high-elevation areas of western Beijing (Figure 1), where the leading ecological function is to protect vegetation diversity and species rich- and land-management decisions greatly influence services provision (White et al, 2012). Support practices (P factor) of different land use types directly influence the magnitude of soil loss (Xiong et al, 2019) (Table . 2), and the difference in photosynthesis capacity of plants in forestland, grassland, shrubland, and cultivated land has major impacts on carbon sequestration ability. Our research also found that land use is more responsible for the spatial distribution of trade-offs between SC and CS (Figure 5), which reminds decision-makers of the reasonable allocation of land use to mitigate the tradeoffs in these ECRL areas.…”

“…C is the vegetation coverage factor, expressing the effect of cropping and management C was determined by NDVI, using the methods of Cai et al ( 2000) To a certain extent, land use reflects the support practices taken by mankind. Most studies assign P factors based on land use type due to lack of observed support practices (Xiong et al, 2019). We refer to the P of the area with similar conditions as the study area and assign it to the land use map with a spatial resolution of 15 m.…”

Deriving the Main Cultivation Direction from Open Remote Sensing Data to Determine the Support Practice Measure Contouring

“…Despite the increased availability of remote sensing datasets and their higher spatial resolution, many studies concerning the USLE do not consider the P-factor for support practice management. Some use at least a constant input that is spatially uniform [10,11]. The P-factor accounts for the soil conservation practices, which can be managed and controlled by humans.…”

“…However, the lack of available data and methods causes the actual on-site condition of support practice measures to be disregarded. Therefore, the following alternative methods are often used to quantify the support practice management concerning the USLE [10]:…”

“…Advances in remote sensing and greater availability of earth observation data are important factors for future improvement in USLE results [9,10,19]. In addition, geographic information systems (GIS) ensure differentiated and significantly faster preparation meth-ods and processing of datasets with spatial reference.…”