Global Analysis of the Cover-Management Factor for Soil Erosion Modeling

Land use and management practices (LUMPs) play a critical role in regulating soil loss. The cover-management factor (C-factor) in Universal Soil Loss Equation (USLE)-type models is an important parameter for quantifying the effects of LUMPs on soil erosion. However, accurately determining the C-factor, particularly for large-scale assessments using USLE-type models, remains challenging. This study aims to address this gap by analyzing and comparing the methods used for C-factor quantification in 946 published articles, providing insights into their strengths and weaknesses. Through our analysis, we identified six main categories of methods for C-factor quantification in USLE-type modeling. Many studies have relied on empirical C-factor values for different land-use types or calculated C-factor values based on vegetation indices (VIs) in large study areas (>100 km(2)). However, we found that no single method could robustly estimate C-factor values for large-scale studies. For small-scale investigations, conducting experiments or consulting the existing literature proved to be more feasible. In the context of large-scale studies, employing methods based on VIs for C-factor quantification can enhance our understanding of the relationship between vegetation changes and soil erosion potential, particularly when considering spatial and spatiotemporal variations. For the global scale, we recommend the combined use of different equations. We suggest further efforts to develop C-factor datasets at large scales by synthesizing field-level experiment data and combining high-resolution satellite imagery. These efforts will facilitate the development of effective soil conservation practices, ensuring sustainable land use and environmental protection.

Automated summary

Land use and management practices are crucial for regulating soil loss. This study analyzes and compares methods used to quantify the cover-management factor (C-factor) in Universal Soil Loss Equation (USLE)-type models, providing insights into their strengths and weaknesses. The findings highlight the challenges of accurately determining C-factor values for large-scale assessments and suggest the need for future research to develop datasets and combine different methods for more robust estimation.