scholarly journals Use of the K factor from the Universal Soil Loss Equation can show arable land in Palau

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 89
Author(s):  
Masato Oda ◽  
Yin Yin Nwe ◽  
Hide Omae
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Arable Land ◽  
Infiltration Rate ◽  
Water Infiltration ◽  
Universal Soil Loss Equation ◽  
Root Mass ◽  
Sweet Potatoes ◽  
K Factor ◽  
Soil Loss Equation

Palau is an island in the Micronesia region of the western Pacific Ocean. The island receives heavy rainfall and has steep slopes, so 92% of the land is categorized within the most erodible rank, with a T factor of 5. A recent study reported that the water infiltration rate is proportional to the root mass of the crop soil; therefore, we attempted to evaluate the performance of root mass for preventing soil erosion. We covered parts of the land, with a slope of 15.4° (13.4°–17.3°), with weed control fabric to prevent the growth of grass and roots, then removed the fabric, cultivated the land, planted sweet potatoes, and compared the amount of soil erosion with other areas. Surprisingly, there was no erosion at all in the test plots, although there were 24 rainfall events that caused erosion. For the parameters of the Universal Soil Loss Equation (USLE) equation used in the present study, only the K factor was not actually measured. This means the K factor was larger than the actual value. Land at low risk of soil erosion and suitable for agriculture can be found by measuring K factor locally, even if the area is categorized as unsuitable.

scholarly journals Use of the K factor from the Universal Soil Loss Equation can show arable land in Palau

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 89
Author(s):  
Masato Oda ◽  
Yin Yin Nwe ◽  
Hide Omae
Keyword(s):  
Soil Erosion ◽  
Arable Land ◽  
Western Pacific Ocean ◽  
Sweet Potatoes ◽  
Rainfall Events ◽  
Erosion Level ◽  
K Factor ◽  
Steep Slopes ◽  
The Western Pacific ◽  
Soil Loss Equation

From the viewpoint of sustainability, the annual soil erosion must be controlled below an erosion level. Palau is an island in the Micronesia region of the western Pacific Ocean. The island receives heavy rainfall and has steep slopes, so 80% of the land is categorized within the most fragile rank, with at most 1 ton per acre per year (T factor = 1). We tested several methods of preventing soil erosion on the land, with a slope of 15.4° (13.4°–17.3°), cultivated the land, planted sweet potatoes, and compared the amount of soil erosion. Surprisingly, there was no erosion at all in all plots (including control plots), although there were 24 rainfall events and the USLE equation predicted 32 ton per ha of the soil erosion in the cropping period. For the parameters of the USLE equation used in the present study, only the K factor was not actually measured. This means the K factor was larger than the actual value. Land at low risk of soil erosion and suitable for agriculture can be found by measuring K factor locally, even if the area is categorized as unsuitable.

scholarly journals Estimation of annual soil erosion dynamics (2005 - 2015) in Pakistan using Revised Universal Soil Loss Equation (RUSLE)

2021 ◽  
Author(s):  
Hammad Gilani ◽  
Adeel Ahmad ◽  
Isma Younes ◽  
Sawaid Abbas
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Management Practices ◽  
Climatic Factors ◽  
Universal Soil Loss Equation ◽  
Rainfall Erosivity ◽  
Slope Length ◽  
Conservation Practice ◽  
Administrative Units ◽  
Soil Loss Equation

Abrupt changes in climatic factors, exploitation of natural resources, and land degradation contribute to soil erosion. This study provides the first comprehensive analysis of annual soil erosion dynamics in Pakistan for 2005 and 2015 using publically available climatic, topographic, soil type, and land cover geospatial datasets at 1 km spatial resolution. A well-accepted and widely applied Revised Universal Soil Loss Equation (RUSLE) was implemented for the annual soil erosion estimations and mapping by incorporating six factors; rainfall erosivity (R), soil erodibility (K), slope-length (L), slope-steepness (S), cover management (C) and conservation practice (P). We used a cross tabular or change matrix method to assess the annual soil erosion (ton/ha/year) changes (2005-2015) in terms of areas and spatial distriburtions in four soil erosion classes; i.e. Low (<1), Medium (1–5], High (5-20], and Very high (>20). Major findings of this paper indicated that, at the national scale, an estimated annual soil erosion of 1.79 ± 11.52 ton/ha/year (mean ± standard deviation) was observed in 2005, which increased to 2.47 ±18.14 ton/ha/year in 2015. Among seven administrative units of Pakistan, in Azad Jammu & Kashmir, the average soil erosion doubled from 14.44 ± 35.70 ton/ha/year in 2005 to 28.03 ± 68.24 ton/ha/year in 2015. Spatially explicit and temporal annual analysis of soil erosion provided in this study is essential for various purposes, including the soil conservation and management practices, environmental impact assessment studies, among others.

scholarly journals Modeling Soil Erosion and Landscape Metric Analysis of River Catchments in Pulau Pinang, Malaysia

2020 ◽  
Vol 55 (3) ◽  
Author(s):  
Sumayyah Aimi Mohd Najib
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Soil Loss ◽  
Diversity Index ◽  
Land Use Changes ◽  
Shannon Diversity Index ◽  
Universal Soil Loss Equation ◽  
Data Sampling ◽  
Shannon Diversity ◽  
Soil Loss Equation

To determine the soil erosion in ungauged catchments, the author used 2 methods: Universal Soil Loss Equation model and sampling data. Sampling data were used to verify and validate data from model. Changing land use due to human activities will affect soil erosion. Land use has changed significantly during the last century in Pulau Pinang. The main rapid changes are related to agriculture, settlement, and urbanization. Because soil erosion depends on surface runoff, which is regulated by the structure of land use and brought about through changes in slope length, land-use changes are one of many factors influencing land degradation caused by erosion. The Universal Soil Loss Equation was used to estimate past soil erosion based on land uses from 1974 to 2012. Results indicated a significant increase in three land-use categories: forestry, built-up areas, and agriculture. Another method to evaluate land use changes in this study was by using landscape metrics analysis. The mean patch size of built-up area and forest increased, while agriculture land use decreased from 48.82 patches in 1974 to 22.46 patches in 2012. Soil erosion increased from an estimated 110.18 ton/km2/year in 1974 to an estimated 122.44 ton/km2/year in 2012. Soil erosion is highly related (R2 = 0.97) to the Shannon Diversity Index, which describes the diversity in land-use composition in river basins. The Shannon Diversity Index also increased between 1974 and 2012. The findings from this study can be used for future reference and for ungauged catchment research studies.

scholarly journals Application of Revised Universal Soil Loss Equation (Rusle) Model to Assess Soil Erosion in “Kalu Ganga” River Basin in Sri Lanka

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
D. L. D. Panditharathne ◽  
N. S. Abeysingha ◽  
K. G. S. Nirmanee ◽  
Ananda Mallawatantri
Keyword(s):  
Land Use ◽  
Sri Lanka ◽  
Soil Erosion ◽  
River Basin ◽  
Soil Loss ◽  
Management Practices ◽  
Agricultural Land ◽  
Universal Soil Loss Equation ◽  
Rusle Model ◽  
Soil Loss Equation

Soil erosion is one of the main forms of land degradation. Erosion contributes to loss of agricultural land productivity and ecological and esthetic values of natural environment, and it impairs the production of safe drinking water and hydroenergy production. Thus, assessment of soil erosion and identifying the lands more prone to erosion are vital for erosion management process. Revised Universal Soil Loss Equation (Rusle) model supported by a GIS system was used to assess the spatial variability of erosion occurring at Kalu Ganga river basin in Sri Lanka. Digital Elevation Model (30 × 30 m), twenty years’ rainfall data measured at 11 rain gauge stations across the basin, land use and soil maps, and published literature were used as inputs to the model. The average annual soil loss in Kalu Ganga river basin varied from 0 to 134 t ha−1 year−1 and mean annual soil loss was estimated at 0.63 t ha−1 year−1. Based on erosion estimates, the basin landscape was divided into four different erosion severity classes: very low, low, moderate, and high. About 1.68% of the areas (4714 ha) in the river basin were identified with moderate to high erosion severity (>5 t ha−1 year−1) class which urgently need measures to control soil erosion. Lands with moderate to high soil erosion classes were mostly found in Bulathsinghala, Kuruwita, and Rathnapura divisional secretarial divisions. Use of the erosion severity information coupled with basin wide individual RUSLE parameters can help to design the appropriate land use management practices and improved management based on the observations to minimize soil erosion in the basin.

Alternative production systems' effects on the K-factor of the Revised Universal Soil Loss Equation

1997 ◽  
Vol 12 (2) ◽  
pp. 55-58 ◽  
Author(s):  
Kim L. Fleming ◽  
William L. Powers ◽  
Alice J. Jones ◽  
Glenn A. Helmers
Keyword(s):  
Organic Matter ◽  
Soil Loss ◽  
Production Systems ◽  
Sandy Loam ◽  
Organic Matter Content ◽  
Universal Soil Loss Equation ◽  
Soil Erodibility ◽  
Silty Clay ◽  
K Factor ◽  
Soil Loss Equation

AbstractThe soil erodibility factor (K) of the Revised Universal Soil Loss Equation is currently considered a constant for all soils in the same type, regardless of production practice. To examine the effect of alternative production systems on the K-factor we compared pairs of alternatively and conventionally farmed fields on a Judson silt loam (Fine-silty, mixed, mesic Cumulic Hapludolls), a Yutan silty clay loam (Fine-silty, mixed, mesic Mollic Hapludalf), and a Wann fine sandy loam (Coarse-loamy, mixed, mesic Fluvaquentic Haplustolls). Soil cores were taken from the surface 10 cm and analyzed for organic matter, permeability, structure, and texture. These data were used to estimate K-factors from a nomograph. All soils in the study had a fine granular structure. Organic matter content and permeability were significantly higher for the alternatively managed field at every location, except for no difference in permeability on the Judson soil. However, the K-factor was significantly lower for the alternative system on the Judson soil. Of all the parameters, texture has the greatest influence in determining K-factors within the nomograph, with soils higher in silt being more erodible than soils higher in sand or clay. Thus, the influences of alternative production systems affected the Judson soil to a greater degree than other textures because of its higher inherent susceptibility to erosion. This study shows that alternative production systems affect the K-factor of some soil types and can reduce soil erodibility, and therefore should be considered when developing conservation plans.

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