IJERD – International Journal of Environmental and Rural Development (2022) 13-2

Research article
erd

Evaluation of Soil Erosion Risk in the City of Cobija,
Bolivian Amazonia Using RUSLE and GIS
JUAN UEGO PEREZ
Graduate School of Agro-Environmental Science, Tokyo University of Agriculture, Japan
E-mail: uego.juan@gmail.com

MACHITO MIHARA*
Faculty of Regional Environment Science, Tokyo University of Agriculture, Japan
E-mail: m-mihara@nodai.ac.jp
Received 17 May 2022

Accepted 4 July 2022(*Corresponding Author)

Abstract In recent years, deforestation has increased remarkably in the region of BolivianAmazonia. So, research interests have been focused on how the deforestation affects thesoil erosion risk. Accordingly, this study was conducted in the city of Cobija, Bolivian
Amazonia to evaluate the erosion risk employing the Revised Universal Soil Loss Equation(RUSLE). For calculating the rainfall-runoff erosivity factor (R), an emperical model basedon measured annual precipitation was applied. To calculate the soil erodibility factor (K),the experimental models based on soil properties (composition of sand–silt–clay
percentages, organic matter, structure, and permeability of the soil proﬁle) was employed.
Also, to generate the topographic factor (LS), a digital elevation model (DEM) image wasapplied. In addition, the cover management factor (C) of each land use and the support
practice factor (P) for different conservation practices were based on the outcomes fromformer studies. The results of RUSLE analysis, the annual soil loss per unit area (A) was in
the range of of the medium soil loss from 5 < 7 t/ha/year. With accelerating soil erosion,soil organic matters as well as soil aggregate have been lost, and soil loss would be severergradually. So, effective soil conservation measures should be implemented for minimizing
the loss of soil organic matters as well as soil aggregate.
Keywords RUSLE, soil loss, amazonia, erosion

INTRODUCTION
Soil erosion is one of the main global threats to water and food security (Amundson et al., 2015).
Soil provisioning, regulatory, and supporting services are essential for sustaining water, food, andenergy security nexus (Keesstra et al., 2016). In recent years, Bolivia has been recognized as one of
the countries that has the highest rate of deforestation in the world, the city of Cobija is located in
the Amazonia, has experienced annually expansion of the city simultaneously with the expansionof the agricultural frontier and generated deforestation in the areas. The adverse influences ofwidespread soil erosion on soil degradation, agricultural production, water quality, hydrologicalsystems, and environment, have long been recognized as severe problems for human sustainability.
However, estimation of soil erosion loss is often difficult due to the complex interplay of manyfactors, including climate, land cover, soil, topography, and human activities. This paper describes
research that seeks to evaluate the erosion risk in the city of Cobija - Pando, Bolivian Amazonia
and apply the Revised Universal Soil Loss Equation (RUSLE) for estimate the soil loss.
OBJECTIVE
The objective is to apply the Revised Universal Soil Loss Equation (RUSLE), and geographic
information system (GIS) to determine the risk of erosion and the soil loss in the city of Cobija
Bolivian Amazonia area. Cobija is a Bolivian City, capital of the Department of Pando and theⒸ ISERD55

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Province of Nicolás Suárez. Cobija has experienced high deforestation rates during the past twodecades (Bolivian Institute, 2015) following the national strategy of regional initiated by theBolivia Government in 2015 played a major role in this process. The local farmers transformed theforested landscape slowly into urban city the climate in this study area is classiﬁed as equatorial hotand humid with tropical transition. A well-deﬁned dry season lasts from June to August, and theannual average precipitation is 2016 mm (Mayoralty of Cobija, 2020). The surface of the researcharea is 411 km2, the annual average temperature is 32˚C - 22˚C, and monthly averages for air
moisture range from 80 to 85 percent. The terrain is undulating, ranging from 100 to 120 m above
sea-level.

Fig. 1 Location of the study area in the city of Cobija, Bolivian Amazonia
METHODOLOGY
Brief Description of RUSLE
The RUSLE represents how climate, soil, topography, and land use affect rill and interrill soil
erosion caused by raindrop impact and surface runoff (Renard et al., 1997). It has been extensively
used to estimate soil erosion loss, to assess soil erosion risk, and to guide development andconservation plans in order to control erosion under different land-cover conditions, such as
croplands, rangelands, and disturbed forest lands (Millward and Mersey, 1999; Boggs et al., 2001;
Mati and Veihe, 2001; Angima et al., 2003). The RUSLE is expressed by Equation 1.
A= R × K× L× S × C×P

Eq. 1

Where A is the average annual soil loss in tons per hectare; R is the rainfall-runoff erosivity
factor; K is the soil erodibility factor; L is the slope length factor; S is the slope steepness factor; C
is the cover management factor; and P is the support practice factor. Table 1 summarized thecalculating methods for estimating each factor.
Table 1 Summary of calculating methods for applying RUSLE in this study
Methods

References

RUsing average monthly precipitation and average annual
precipitation Using a regression model based on measured
annual precipitation

Renard and Fremund, 1994

KUsing the experimental models based on soil properties(composition of sand–silt–clay percentages, organic matter,structure, and permeability of the soil proﬁle)Wischmeier and Smith, 1978

LS

Estimated from actual ﬁeld measurements of length andsteepness Calculated from DEM data with various approaches
Land use comparison table

Hickey, 2000; Van Remortel et al., 2001

Table of P values for different conservation practices

Bertoni and Lombardi Neto, 1985CPⒸ ISERD

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Silva et al., 2007

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RESULTS AND DISCUSSION
Rainfall and Runoff Erosivity R Factor
The rainfall runoff erosivity was calculated from Equation 2 as shown below.

𝑃𝑚2𝐸𝐼𝑚𝑜𝑛𝑡 ℎ𝑙𝑦 = 89.823
𝑃𝑎0.759

Eq. 2

Where EI is the monthly mean of the erosion index for the month considering (mm/ha), Pm isthe average precipitation of the month considering (mm), Pa is the average annual precipitation(mm) at the Pelvimetric Station located in Cobija. Based on monthly results, the rainfall runoff
erosivity was calculated annually. Using the equation, the annual average of rainfall runoff
erosivity was determined with the data of the last 5 years as shown in Table 2, the average R factor
determined was 8929.2 MJ mm/h ha per year and it was considered the EI value was in the highest
class describing the potential of intense erosion.
Table 2 Annual rainfall and rainfall runoff erosivity
Year2017
2018
2019
2020
2021
AverageAnnual Rainfall Pa (mm)1903.11687.5
2002.21385.6
1950.31785.7

Annual EI (Mj.mm/h ha)
9054.67955.39628.67867.210140.3
8929.2

Soil Erodibility K Factor
Soil erodibility K factor was calculated using inherent soil properties following the procedure fortropical soils (El-Swaify and Dangler, 1976), which uses the percent-modified silt (0.002 - 0.1 mm),percent modified sand (0.1 - 2 mm), base saturation, percent unstable aggregates, and percent veryfine sand. The calculation for measuring soil erodibility were indicated in Equation 3.
K=−0.03970 + 0.00311X1 + 0.00043X2 +0.00185X3 + 0.00258X4 − 0.00823X5

Eq. 3

Where X1 is the percent unstable aggregates <0.250 mm, X2 is the product of the percent ofsilt (0.002-0.01 mm) and sand (0.1-2 mm) present in the sample, X3 is the percent base saturationof the soil, X4 is the percent silt present (0.002-0.050 mm), and X5 is the percent sand in the soil
(0.1-2 mm).
As shown in Table 3, soil erodibility K factor for the soil was calculated as 0.0150Mg h MJ −1−1mm (0.1139 t acre h [hundreds of acre ft-tonf in.] −1).
Table 3 Soil properties for use in calculating the RUSLE k – Factor for the Cobija soil
Fraction
Clay (<0.002 mm)
Silt (0.002–0.05 mm)
Sand (0.05–2 mm)
Sand (0.1–2 mm)
Base saturation
Unstable aggregates
Permeability class

Percentage
53342855
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Slope Length and Steepness LS Factor
The length and steepness LS factor represents the loss of soil per unit area on any slope,corresponding to a unit plot with 22 m long and 9% slope as shown in Equation 4.

𝐿𝑆 =𝐿 𝑚0.065 + 0.0454. 𝑆 + 0.0065𝑆 2
22.1

Eq. 4

Where m=0.2 for S<1%; m=0.3 for 1%≤S≤3%; m=0.4 for 3%<S<5and m=0.5 for S ≥5%; S
average slope of the land (%), and L is the slope length (m).

Fig. 2 Digital elevation map (DEM) in Cobija area
For determinate the slope and steepness LS factor, the average slope in the drainage basin area
and the average length of the slope are necessary. The result obtained for the slope and steepness
LS factor was 0.5987 (Fig. 2).
Cover Management C Factor
The cover management C factor was determined from analyzed land use in the city of Cobija. The
main outcomes from the observation of land use changes and the cover management C factor
indicated that 66.9% deforested for timber resources, with land use change such as 20.7% for
pastures to raise cattle and 12.4% for agriculture as shown in Fig. 3.

Fig. 3 Land use changes from 2010 to 2020 in the city of CobijaⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2022) 13-2

Although the vegetal cover acts as a shield against the impact of the raindrop on the soil
surface, this cover was lost and left the soil exposed to the impact of the raindrop. These trends
increased the risk of erosion due to advancing deforestation. The observed result of the covermanagement C factor in the city of Cobija was 0.368.
Support Practice P Factor
For choosing the support practice P factor of conservation in the city of Cobija, the observations ofsatellite images and site investigation were conducted. As the results of these observations, thesupport practice P factor of conservation at 0.2 was decided averagely, as the soil surface wascovered with permanent vegetation (Bertoni and Lombardi, 1985).
Average Annual Soil Loss in the City of Cobija
These factors were integrated into RUSLE model (Eq. 1) for obtaining the annual soil loss per unit
area (A). The changes in annual soil loss in the city of Cobija from 2017 to 2021 were summarizedin Table 4. The annual average was 5.902 t/ha year that was in the range of the medium soil loss
from 5 < 7 t/ha/year according to Silva (2008).
As the soil surface was covered with permanent vegetation after the deforestation, the annual
soil loss per unit area (A) was in the range of the medium soil loss from 5 < 7 t/ha/year. However,with accelerating soil erosion, soil organic matters as well as soil aggregate would be lost, and soilloss would be severer gradually. So, effective soil conservation measures should be implemented
for minimizing the loss of soil organic matters as well as soil aggregate.
Table 4 Changes in annual soil loss in the city of Cobija from 2017 to 2021
Year2017
2018
2019
2020
2021
TotalA (t/ha year)
5.984
5.258
6.364
5.200
6.702
5.902

CONCLUSION
Soil erosion by water is a serious global problem, especially in Amazonia. This study wasconducted to evaluate soil losses by precipitation and runoff in the city of Cobija, Bolivian
Amazonia with the RUSLE model. Although the topography showed relatively flat relief, the soil
type has medium erodibility and the support practice of conservation was maintaining strands ofpermanent vegetation. So, the annual soil loss per unit area (A) was in the range of the medium soil
loss from 5 < 7 t/ha/year.
In the city of Cobija, Bolivian Amazonia, intense deforestation has been advanced. With
accelerating soil erosion, soil organic matters as well as soil aggregate have been lost, and soil losswould be severer gradually. So, effective soil conservation measures should be implemented forminimizing the loss of soil organic matters as well as soil aggregate.
ACKNOWLEDGMENTS
We like to express special gratitude to research fellows in the Laboratory of Land and Water Use
Engineering, Tokyo University of Agriculture for meaningful discussion always.

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