scholarly journals Distribution of soil nutrients and erodibility factor under different soil types in an erosion region of Southeast China

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11630
Author(s):  
Man Liu ◽  
Guilin Han
Keyword(s):  
Soil Erosion ◽  
Soil Nutrients ◽  
Soil Ph ◽  
Major Elements ◽  
Soil Types ◽  
Soil Erodibility ◽  
Soil Evolution ◽  
Southeast China ◽  
Red Soils ◽  
K Factor

Background Soil erosion can affect the distribution of soil nutrients, which restricts soil productivity. However, it is still a challenge to understand the response of soil nutrients to erosion under different soil types. Methods The distribution of soil nutrients, including soil organic carbon (SOC), soil organic nitrogen (SON), and soil major elements (expressed as Al2O3, CaO, Fe2O3, K2O, Na2O, MgO, TiO2, and SiO2), were analyzed in the profiles from yellow soils, red soils, and lateritic red soils in an erosion region of Southeast China. Soil erodibility K factor calculated on the Erosion Productivity Impact Calculator (EPIC) model was used to indicate erosion risk of surface soils (0∼30 cm depth). The relationships between these soil properties were explored by Spearman’s rank correlation analysis, further to determine the factors that affected the distribution of SOC, SON, and soil major elements under different soil types. Results The K factors in the red soils were significantly lower than those in the yellow soils and significantly higher than those in the lateritic red soils. The SON concentrations in the deep layer of the yellow soils were twice larger than those in the red soils and lateritic red soils, while the SOC concentrations between them were not significantly different. The concentrations of most major elements, except Al2O3 and SiO2, in the yellow soils, were significantly larger than those in the red soils and lateritic red soils. Moreover, the concentrations of major metal elements positively correlated with silt proportions and SiO2 concentrations positively correlated with sand proportions at the 0∼80 cm depth in the yellow soils. Soil major elements depended on both soil evolution and soil erosion in the surface layer of yellow soils. In the yellow soils below the 80 cm depth, soil pH positively correlated with K2O, Na2O, and CaO concentrations, while negatively correlated with Fe2O3 concentrations, which was controlled by the processes of soil evolution. The concentrations of soil major elements did not significantly correlate with soil pH or particle distribution in the red soils and lateritic red soils, likely associated with intricate factors. Conclusions These results suggest that soil nutrients and soil erodibility K factor in the yellow soils were higher than those in the lateritic red soils and red soils. The distribution of soil nutrients is controlled by soil erosion and soil evolution in the erosion region of Southeast China.

scholarly journals Characteristics of Soil Erodibility K Value and Its Influencing Factors in the Changyan Watershed, Southwest Hubei, China

Land ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 134
Author(s):  
Xiaofang Huang ◽  
Lirong Lin ◽  
Shuwen Ding ◽  
Zhengchao Tian ◽  
Xinyuan Zhu ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Spatial Variability ◽  
Influencing Factors ◽  
Soil Type ◽  
Soil Survey ◽  
Purple Soil ◽  
Soil Erodibility ◽  
K Value ◽  
K Factor

Soil erodibility K factor is an important parameter for evaluating soil erosion vulnerability and is required for soil erosion prediction models. It is also necessary for soil and water conservation management. In this study, we investigated the spatial variability characteristics of soil erodibility K factor in a watershed (Changyan watershed with an area of 8.59 km2) of Enshi, southwest of Hubei, China, and evaluated its influencing factors. The soil K values were determined by the EPIC model using the soil survey data across the watershed. Spatial K value prediction was conducted by regression-kriging using geographic data. We also assessed the effects of soil type, land use, and topography on the K value variations. The results showed that soil erodibility K values varied between 0.039–0.052 t·hm2·h/(hm2·MJ·mm) in the watershed with a block-like structure of spatial distribution. The soil erodibility, soil texture, and organic matter content all showed positive spatial autocorrelation. The spatial variability of the K value was related to soil type, land use, and topography. The calcareous soil had the greatest K value on average, followed by the paddy soil, the yellow-brown soil (an alfisol), the purple soil (an inceptisol), and the fluvo-aquic soil (an entisol). The soil K factor showed a negative correlation with the sand content but was positively related to soil silt and clay contents. Forest soils had a greater ability to resist to erosion compared to the cultivated soils. The soil K values increased with increasing slope and showed a decreasing trend with increasing altitude.

scholarly journals Prediction of Land Erosion Events in the Down Stream Kreung Meureubo Watershed West Aceh District

2021 ◽  
Vol 1 (4) ◽  
pp. 70-76
Author(s):  
Muhammad Ikhsan ◽  
Meylis Safriani ◽  
Cut Suciatina Silvia ◽  
Refvina Dari
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Soil Erosion ◽  
Land Cover ◽  
Erosion Rate ◽  
Land Conservation ◽  
Soil Types ◽  
Soil Erodibility ◽  
Conservation Actions ◽  
Rain Splash

This study aims to predict the occurrence of erosion in the downstream Krueng Meureubo watershed, West Aceh Regency. Erosion is the loss of topsoil due to rain splash which is analyzed as a factor of rain erosivity, but the occurrence of erosion is not necessarily calculated by the occurrence of rain alone, but many other factors, such as soil erodibility, slope and length of land, land cover and the presence or absence of land conservation efforts. the. The Krueng Meurebo watershed shows a large sediment transport, with an indication that the river is getting shallower caused by sediment deposition at the riverbed, this sediment comes from sediment carried through the process of soil erosion. The method used in analyzing the occurrence of soil erosion in this study is the USLE method and uses a Geographic Information System (GIS). The results obtained are the distribution of erosion rate values in 228 polygons, with the largest erosion rate value occurring in polygon 1 with an erosion rate of 8495.308 tons/ha/year. The smallest erosion rate occurs in polygons 30, 34, 35, 179, and 180, with an erosion rate of 0 meaning that there is no land erosion event, which occurs in organosol and glehumus and regosol soil types, land cover is settlements and water bodies. It is concluded that the occurrence of erosion in a land is very dependent on the type of soil and the type of land cover. It is recommended for land with large erosion events to take serious land conservation actions so that erosion events can be minimized and do not occur continuously which of course can cause the watershed to become critical. Conservation efforts can be carried out in various ways, one of which is by vegetative means using plants that can reduce the rate of soil erosion.

scholarly journals Digital mapping of soil erodibility for water erosion in New South Wales, Australia

Soil Research ◽  
2018 ◽  
Vol 56 (2) ◽  
pp. 158 ◽  
Author(s):  
Xihua Yang ◽  
Jonathan Gray ◽  
Greg Chapman ◽  
Qinggaozi Zhu ◽  
Mitch Tulau ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Soil Properties ◽  
Soil Loss ◽  
New South ◽  
New South Wales ◽  
Field Measurements ◽  
Soil Erodibility ◽  
South Wales ◽  
Australian Continent ◽  
K Factor

Soil erodibility represents the soil’s response to rainfall and run-off erosivity and is related to soil properties such as organic matter content, texture, structure, permeability and aggregate stability. Soil erodibility is an important factor in soil erosion modelling, such as the Revised Universal Soil Loss Equation (RUSLE), in which it is represented by the soil erodibility factor (K-factor). However, determination of soil erodibility at larger spatial scales is often problematic because of the lack of spatial data on soil properties and field measurements for model validation. Recently, a major national project has resulted in the release of digital soil maps (DSMs) for a wide range of key soil properties over the entire Australian continent at approximately 90-m spatial resolution. In the present study we used the DSMs and New South Wales (NSW) Soil and Land Information System to map and validate soil erodibility for soil depths up to 100 cm. We assessed eight empirical methods or existing maps on erodibility estimation and produced a harmonised high-resolution soil erodibility map for the entire state of NSW with improvements based on studies in NSW. The modelled erodibility values were compared with those from field measurements at soil plots for NSW soils and revealed good agreement. The erodibility map shows similar patterns as that of the parent material lithology classes, but no obvious trend with any single soil property. Most of the modelled erodibility values range from 0.02 to 0.07 t ha h ha–1 MJ–1 mm–1 with a mean (± s.d.) of 0.035 ± 0.007 t ha h ha–1 MJ–1 mm–1. The validated K-factor map was further used along with other RUSLE factors to assess soil loss across NSW for preventing and managing soil erosion.

scholarly journals Pemetaan Erodibilitas Tanah Dan Korelasinya Terhadap Karakteristik Tanah Di Das Serang, Kulonprogo

2018 ◽  
Vol 2 (1) ◽  
pp. 135
Author(s):  
Efrinda Ari Ayuningtyas ◽  
Ainul Fahmi Nur Ilma ◽  
Rindhang Bima Yudha
Keyword(s):  
Soil Erosion ◽  
Soil Properties ◽  
Spatial Information ◽  
Organic Soil ◽  
Soil Erodibility ◽  
Soil Permeability ◽  
K Value ◽  
Gis Tools ◽  
Soil Structures ◽  
K Factor

Soil erosion was happened caused by many factors, such as rainfall intensity, soil erodbility, steepness and length of slope, land cover, and conservation practices. In other case, the soil properties also influence the vulnerability of soil to be detached. This soil properties characteristics is classified as soil erodibility. Erodibility factor (K) from the Universal Soil Loss Equation (USLE) in this study was the result of soil erodibility estimation or soil capability to be dispersed by rain. K factor was affected by soil organic, soil permeability, soil structures, and soil textures. This study was contributed in Serang Watershed because of the main fuction of this watershed to supply water resources especially in Sermo Reservoir in Ngrancah Subwatershed. This reservoir was used to distribute water and irrigation to all Kulonprogo District and especially to keep the sustainability of sedimentation of soastal area di Glagah Beach. All of soil properties was collected in each landform of Serang Watershed and was analyzed by laboratory measurement. By using K factor formula, the K value can be estimated. Geographic Information System (GIS) tools were used to map and represent the spatial information of soil erodibility of Serang Watershed. The result of this study showed that the high value of K factor was distributed in the area which has genesis of structural, denudated structural, and sedimented denudational. Furthermore, this study can be strived to analyze soil erosion hazard which was influenced by soil erodibility.

scholarly journals Effects of Soil Properties on K Factor in the Granite and Limestone Regions of China

2020 ◽  
Vol 17 (3) ◽  
pp. 801
Author(s):  
Man Liu ◽  
Guilin Han ◽  
Xiaoqiang Li ◽  
Shitong Zhang ◽  
Wenxiang Zhou ◽  
...  
Keyword(s):  
Soil Properties ◽  
Soil Ph ◽  
Structural Equation ◽  
Principal Component ◽  
Guizhou Province ◽  
Karst Region ◽  
Ecological Problem ◽  
Soil Profiles ◽  
Soil Erodibility ◽  
K Factor

Soil erosion has become a serious ecological problem in many catchments. Soil erodibility K factor can be estimated based on a series of soil properties, however, the identification of dominant soil properties that affect K factor prediction at different soil types has been little concerned. In this study, 3 soil profiles from the Jiulongjiang River Catchment (JRC) of granite region in Fujian province and 18 soil profiles from the Chenqi Catchment (CC) of karst region in Guizhou province were selected. Soil properties, including soil particle size distribution, soil organic carbon (SOC) and soil organic nitrogen (SON) content, and soil pH, were determined, and the K factors were estimated in the erosion productivity impact calculator (EPIC) model. The soils in the granite region were characteristic for coarse texture, low SOC and SON, and strong acidity compared with limestone soils. Although the K factors in both regions ranged from 0.009 to 0.018, they were overestimated in limestone soils due to frequent soil aggregation, which enhanced soil permeability, hence reduced soil erodibility. The results of principal component analysis (PCA) and structural equation model (SEM) showed that (1) K factor estimation in the soils of the granite region mainly depended on soil texture, of which silt was the most important factor; (2) while K factor in limestone soils was mainly controlled by soil organic matter (SOM) content, other soil properties, including soil pH, clay and silt contents, could indirectly affect prediction of K factor by affecting SOM accumulation.

scholarly journals Análisis del efecto de variables ambientales en la estimación de la erosionabilidad (Factor K) = Analysis of the Effect of Environmental Variables on the Soil Erodibility Estimation (K Factor)

2018 ◽  
pp. 195
Author(s):  
Pedro Perez Cutillas ◽  
Gonzalo G. Barberá ◽  
Carmelo Conesa García
Keyword(s):  
Remote Sensing ◽  
Environmental Variables ◽  
Input Data ◽  
Vegetation Index ◽  
Mineralogical Composition ◽  
Soil Erodibility ◽  
Estimation Algorithms ◽  
K Factor ◽  
Remote Sensing Techniques

El objetivo principal de este trabajo se centra en la determinación y análisis de las variables ambientales que influyen en las divergencias de las estimaciones de erosionabilidad a partir de dos métodos, aplicando tres algoritmos de estimación del Factor K. La exploración de esta información permite conocer el peso que ejerce el origen de los datos de entrada a los modelos en el cómputo de erosionabilidad y qué importancia tiene en función del algoritmo elegido para la estimación del Factor K. Los resultados muestran que las pendientes, así como los índices de vegetación (NDVI) y de composición mineralógico (IOI) obtenidos mediantes técnicas de teledetección han   mostrado los valores de asociación más elevados entre ambos métodos.The main goal of this work is to determine and analyze the influence of environmental variables on the changes of two erodibility methods, through the application of three estimation algorithms of K Factor. The analysis of this information allows knowing the significance of the input data to the models in the erodibility estimation, and likewise the consequence of the algorithm selected for the estimation of K Factor. The results show that the slopes, as well as the vegetation index (NDVI) and the mineralogical composition index (IOI), generated both by remote sensing techniques, have shown the highest values of association between methods.

scholarly journals Estimation of soil loss using remote sensing and GIS-based universal soil loss equation in northern catchment of Lake Tana Sub-basin, Upper Blue Nile Basin, Northwest Ethiopia

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Veera Narayana Balabathina ◽  
R. P. Raju ◽  
Wuletaw Mulualem ◽  
Gedefaw Tadele
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Soil Erosion ◽  
Soil Loss ◽  
Erosion Rate ◽  
Soil Erodibility ◽  
Lake Tana ◽  
Erosion Rates ◽  
Soil Erosion Rate ◽  
Soil Loss Equation

Abstract Background Soil erosion is one of the major environmental challenges and has a significant impact on potential land productivity and food security in many highland regions of Ethiopia. Quantifying and identifying the spatial patterns of soil erosion is important for management. The present study aims to estimate soil erosion by water in the Northern catchment of Lake Tana basin in the NW highlands of Ethiopia. The estimations are based on available data through the application of the Universal Soil Loss Equation integrated with Geographic Information System and remote sensing technologies. The study further explored the effects of land use and land cover, topography, soil erodibility, and drainage density on soil erosion rate in the catchment. Results The total estimated soil loss in the catchment was 1,705,370 tons per year and the mean erosion rate was 37.89 t ha−1 year−1, with a standard deviation of 59.2 t ha−1 year−1. The average annual soil erosion rare for the sub-catchments Derma, Megech, Gumara, Garno, and Gabi Kura were estimated at 46.8, 40.9, 30.9, 30.0, and 29.7 t ha−1 year−1, respectively. Based on estimated erosion rates in the catchment, the grid cells were divided into five different erosion severity classes: very low, low, moderate, high and extreme. The soil erosion severity map showed about 58.9% of the area was in very low erosion potential (0–1 t ha−1 year−1) that contributes only 1.1% of the total soil loss, while 12.4% of the areas (36,617 ha) were in high and extreme erosion potential with erosion rates of 10 t ha−1 year−1 or more that contributed about 82.1% of the total soil loss in the catchment which should be a high priority. Areas with high to extreme erosion severity classes were mostly found in Megech, Gumero and Garno sub-catchments. Results of Multiple linear regression analysis showed a relationship between soil erosion rate (A) and USLE factors that soil erosion rate was most sensitive to the topographic factor (LS) followed by the support practice (P), soil erodibility (K), crop management (C) and rainfall erosivity factor (R). Barenland showed the most severe erosion, followed by croplands and plantation forests in the catchment. Conclusions Use of the erosion severity classes coupled with various individual factors can help to understand the primary processes affecting erosion and spatial patterns in the catchment. This could be used for the site-specific implementation of effective soil conservation practices and land use plans targeted in erosion-prone locations to control soil erosion.

Modification of the USLE K factor for soil erodibility assessment on calcareous soils in Iran

Geomorphology ◽  
2016 ◽  
Vol 273 ◽  
pp. 385-395 ◽  
Author(s):  
Yaser Ostovari ◽  
Shoja Ghorbani-Dashtaki ◽  
Hossein-Ali Bahrami ◽  
Mehdi Naderi ◽  
Jose Alexandre M. Dematte ◽  
...  
Keyword(s):  
Calcareous Soils ◽  
Soil Erodibility ◽  
K Factor
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