A synthesized approach for estimating the C-factor of RUSLE for a mixed-landscape watershed: A case study in the Gongshui watershed, southern China

Yan, Hua; Wang, L.; Wang, Tianwei; Wang, Z.; Shi, Zhanghong

doi:10.1016/j.agee.2020.107009

Cited by 34 publications

(12 citation statements)

References 45 publications

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“…This approach can be useful in increasing the accuracy of the calculation of the C-factor [67,92,93]. Our results show how, for the estimation of the C-factor in the Apulia region, the values are comparable to those present in the literature for ACL [67,74,94,95], which fluctuate between 0.01 and 0.44 for ACL, but with a much more accurate regional scale of detail. Concerning the difference between the CM and CA systems, our results demonstrate how, on average, the adoption of CA reduces the C-factor by 4.2% in line with other analyzed scenarios [34].…”

Section: Cover-management (C-factor)supporting

confidence: 74%

Impact of Conservation Agriculture on Soil Erosion in the Annual Cropland of the Apulia Region (Southern Italy) Based on the RUSLE-GIS-GEE Framework

et al. 2022

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The processes of soil erosion and land degradation are more rapid in the case of inappropriate agricultural management, which leads to increased soil loss rates. Moreover, climatic conditions are one of the most important determining factors affecting agriculture, especially in the Mediterranean areas featuring irregular rainfall and high summer temperatures. Conservation agriculture (CA) can make a significant contribution to reducing soil erosion risk on the annual cropland (ACL) of the Mediterranean region in comparison with conventional management (CM). The objective of this study is to provide soil loss rate maps and calculate the values for each altitude and slope class and their combination for the Apulia region in four annual production cycles for the scenarios CM and CA. The present study estimates the significance of the adoption of CA on soil erosion assessment at regional scale based on the Revised Universal Soil Loss Equation (RUSLE) model. The parameters of the RUSLE model were estimated by using remote sensing (RS) data. The erosion probability zones were determined through a Geographic Information System (GIS) and Google Earth Engine (GEE) approach. Digital terrain model (DTM) at 8 m, ACL maps of the Apulia region, and rainfall and soil data were used as an input to identify the most erosion-prone areas. Our results show a 7.5% average decrease of soil loss rate during the first period of adoption of the four-year crop cycle—from 2.3 t ha−1 y−1 with CM to 2.1 t ha−1 y−1 with the CA system. CA reduced soil loss rate compared to CM in all classes, from 10.1% in hill class to 14.1% for hill + low slope class. These results can therefore assist in the implementation of effective soil management systems and conservation practices to reduce soil erosion risk in the Apulia region and in the Mediterranean basin more generally.

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Section: Cover-management (C-factor)supporting

confidence: 74%

Impact of Conservation Agriculture on Soil Erosion in the Annual Cropland of the Apulia Region (Southern Italy) Based on the RUSLE-GIS-GEE Framework

et al. 2022

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“…For other crops, the P factor was assigned as 1 because the relevant data on soil conservation on a global scale is lacking. We followed the method developed by Panagos et al to estimate the C factor of cropland. , Besides, we divided main crops into six categories according to the FAO classification . Then, the C factor of cropland in each country was evaluated as follows: where the C cropland represents the weighted average of the six types of crops in each region, C crop i , g is the C factor of the i th crop belonging to the g type (Table ), and S crop i , g represents the proportion of the harvested area of the i th crop to the cropland area in the region.…”

Section: Methods and Datamentioning

confidence: 99%

“…We followed the method developed by Panagos et al 28 to estimate the C factor of cropland. 6,31 Besides, we divided main crops into six categories according to the FAO classification. 32 Then, the C factor of cropland in each country was evaluated as follows:…”

Section: Rusle Modelmentioning

confidence: 99%

Production-Based and Consumption-Based Accounting of Global Cropland Soil Erosion

Cui

Wang

Yan

et al. 2022

Environ. Sci. Technol.

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The effective control of cropland soil erosion is urgent for all countries because of its threat to global food security. Cropland soil erosion is caused by agricultural production and driven indirectly by consumption. Analyzing the causes and preventive strategies from the consumption side is essential for soil erosion control. However, there is not yet sufficient research or practice. In this study, we estimated global cropland soil erosion with the revised universal soil loss equation, allocated it to specific types of crops, and quantified the cropland soil erosion footprint of the economies with a multiregional input–output analysis model. Our results showed that developed economies, usually importing cropland soil erosion from developing or agriculturally developed economies, are the beneficiaries in the current crop trading system. The European Union is the largest net importer, while Brazil is the largest exporter. The indirect and induced sectors are the main contributors, consuming approximately 70.48% of the total cropland soil erosion. Our results revealed the region- and product-specific contributors that could inform the reduction of global cropland soil erosion for sustainable food production and consumption.

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“…Among them, the Revised Universal Soil Loss Equation (RUSLE) model is the most commonly used empirical model for predicting soil erosion (Fernández & Vega, 2018;Ranzi, Le, & Rulli, 2012;Renard, Foster, Weesies, McCool, & Yoder, 1997). Even though the model was designed for use on a runoff plot or at a single-hillslope scale, the combination of geographic information system (GIS) and the RUSLE model was used to estimate the magnitude and spatial distribution of erosion in ungauged catchments (Bircher, Liniger, & Prasuhn, 2019;Napoli, Cecchi, Orlandini, Mugnai, & Zanchi, 2016;Yan, Wang, Wang, Wang, & Shi, 2020). Panagos et al (2015) reported that the mean soil loss rate in the European Union in 2010 was found to be 2.46 t hm −2 a −1 by using the RUSLE model.…”

Section: Introductionmentioning

confidence: 99%

Dynamic changes in soil erosion risk and its driving mechanism: A case study in the Loess Plateau of China

Xin-shi

Song

et al. 2020

European J Soil Science

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The Loess Plateau has long been considered as a very fragile area that suffers from serious water erosion. With the onset of global climate fluctuations and regional implementation of soil and water conservation measures, it is of great significance to effectively reveal the variations in soil erosion risk and its driving mechanism on the Loess Plateau. Taking the Yanwachuan watershed as a case study and using rainfall, soil, digital elevation model (DEM) and land‐use data, this study applied the Revised Universal Soil loss Equation (RUSLE) model with geographic information system (GIS) technology to analyse the temporal and spatial variations of soil erosion risk and to evaluate the effects of land‐use change and climate change on soil erosion from 1981 to 2016. Future soil erosion risk was also assessed under different land‐use and climate‐change scenarios. Results showed that annual soil loss in the Yanwachuan watershed presented a significant decreasing trend with a rate of 47.928 t km−2 a−1 and that average annual soil loss was 3,543.7 t km−2 from 1981 to 2016. However, some areas, even with good vegetation cover, still had a rather high soil erosion risk (exceeding the severe erosion rate of 5,000 t km−2 in 2000–2016) if located in hilly and gully slope regions. Land‐use change and climate variation contributed 37.2% and 62.8%, respectively, to reducing soil erosion, showing that climate variation played a leading role in current soil‐erosion reduction. In addition, the average soil losses in the 2020s, 2030s and 2040s under different scenarios are predicted to increase by 8.6–42.6% compared with the average soil loss (2,993.8 t km−2) in 2001–2016. Future climate change may be the main driving factor in enhancing the risk of soil erosion. Hence, there is an urgent need to strengthen soil and water conservation research and management in future years.Highlights This study assessed current and future water erosion risk on the Loess Plateau using the RUSLE model. Areas with steep slopes were still experiencing serious soil erosion risk in 2000–2016. Climate change was the main driving factor for soil erosion variation. Future erosion risk is expected to increase under different climate and land‐use scenarios.

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A synthesized approach for estimating the C-factor of RUSLE for a mixed-landscape watershed: A case study in the Gongshui watershed, southern China

Cited by 34 publications

References 45 publications

Impact of Conservation Agriculture on Soil Erosion in the Annual Cropland of the Apulia Region (Southern Italy) Based on the RUSLE-GIS-GEE Framework

Impact of Conservation Agriculture on Soil Erosion in the Annual Cropland of the Apulia Region (Southern Italy) Based on the RUSLE-GIS-GEE Framework

Production-Based and Consumption-Based Accounting of Global Cropland Soil Erosion

Dynamic changes in soil erosion risk and its driving mechanism: A case study in the Loess Plateau of China

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