IJERD – International Journal of Environmental and Rural Development (2023) 14-1

Research articleerd

Evaluation of Soil Erosion Risk from Weathering Effects onK Factor of RUSLE in Cobija, Bolivian Amazonia
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 15 January 2023

Accepted 12 May 2023(*Corresponding Author)

Abstract Soil erosion by water is a serious problem in the Amazonia region of Bolivia. In thearea of Cobija, Bolivia, around 8 Mg ha-1 of Amazon soil is lost to the nearby watershedannually. The physical weathering of soils under natural conditions is one of the key processes
for understanding structural changes in the soil. The soil erodibility (K) factor in RUSLE
represents the susceptibility of soils to erosion, which can be measured with the standard unit
plot experiment or through calculation with particle sizes and organic matter. Soil weathering
affects soil particle distribution that is closely related to the K factor. Therefore, the purposeof this research is to evaluate and determine the change in the distribution of soil particles aswell as the K value. The experiment was conducted in a laboratory setting. A total of 120 canscontaining 1 g of soil each were used and treated with different periods and volumes of water
added to represent weathering. The simulated weather conditions were set in line withconditions observed in Bolivian Amazonia. The pipette method was used to determine thedistribution of soil particles. The results indicated slight changes in the distribution of the soilparticles, reducing the percentage of fine sand and increasing the silt content, which affected
the K factor as well as the estimate of soil losses based on RUSLE.
Keywords soil erosion, weathering, RUSLE, soil particle, Amazonia, Bolivia

INTRODUCTION
Weathering soils under natural conditions is a pivotal process to quantify sustainability assessmentsof forestry and land use, as well as to evaluate the rate of soil losses in the field. Accelerated
weathering occurs as a consequence of deforestation and the loss of plant cover, which exposes thesoil to the direct impact of temperature and wet-drying processes. The impact of raindrops on the soil
causes changes in the percentage distribution of soil particles, ultimately altering the values in the Kfactor of the Revised Universal Soil Loss Equation (RUSLE) and influencing the results of soil loss
calculations. For this study, a total of 120 containers filled with the soil from the Bolivian Amazon
were subjected to weathering conditions identical to the study area, maintaining the same temperature
and applying water for various periods. The objective was to determine if the changes observed in
the percentage distribution of soil particles. After a 100-days experimental period, the pipette method
was used to assess particle distribution in the soil, followed by the calculation of the K factor as wellas other factors in the RUSLE. Accordingly, this study aimed to discuss the weathering effects on
the K factor and the amounts of soil loss.
OBJECTIVE
The objective of this study was to assess soil erosion risk, focusing on the weathering effects on theK factor and the amounts of soil loss in Cobija, Bolivian Amazonia, using the Revised Universal Soil
Loss Equation (RUSLE).
Ⓒ ISERD59

IJERD – International Journal of Environmental and Rural Development (2023) 14-1

METHODOLOGY
Site Description

Fig. 1 Location of the soil sample collection in the city of Cobija, Bolivian Amazonia
Cobija is a Bolivian city, serving as the capital of the Department of Pando and the Province ofNicolás Suárez. Over the past two decades, Cobija has experienced substantial deforestation rates(Bolivian Institute, 2015). This process was greatly influenced by the regional development strategy
initiated by the Bolivian Government in 2015. Local farmers gradually transformed the forested
landscape into an urban city. The climate in the study area is classified as equatorial, characterizedby hot and humid conditions with a tropical transition. There is a distinct dry season from June toAugust, and the average annual precipitation is 2016 mm (Mayoralty of Cobija, 2020). The researcharea spans 14 km2, with an average annual temperature ranging from 32˚C to 24˚C. The monthly
average air moisture ranges from 80 to 85 percent. The terrain is undulating, with elevations ranging
from 100 to 120 meters above sea level.
Soil Sample Source and Preparation
Twenty soil samples were collected from an area of 60 ha in the Bolivian Amazonia that was recentlydeforested. And after mixing to give a homogenous sample the soil was air-dried. A quantity of 5.4
kg of each soil sample was transported from Bolivia to Japan for physical experiments conducted in
the Land and water laboratory using engineering equipment at the Tokyo University of Agriculture,
Tokyo, Japan.
Weathering Experiment
A weathering experiment was undertaken to replicate the climatic conditions prevalent in theBolivian Amazon. A total of 120 soil containers were meticulously prepared, with each container
containing 1 g of soil. These cans were then subjected to a controlled environment in an incubator,
simulating the temperature conditions found in the Bolivian Amazon.
To mimic the effects of rainfall, 0.4 ml of water was carefully added to each soil can at specificintervals of 2, 4, and 12 hrs. This process was repeated 100 times for each treatment, ensuring
sufficient replication for reliable analysis and accurate data collection.
Determination of Soil Particle Distribution Experiment using the pipette method
To determine the percentage distribution of particles in the soil samples before and after the treatment,
the pipette method was employed for each treatment and repetition. This method allows for thedetermination of the percentage of coarse sand, fine sand, silt, and clay. The experiment involvedⒸ ISERD

60

IJERD – International Journal of Environmental and Rural Development (2023) 14-1

using 10 g of dry soil, with the organic matter content initially removed with a previous treatmentusing H2O2. A 20 ml solution of sodium hexametaphosphate at a concentration of 6% was preparedfor each analysis. The gravel and sand content were collected by filtering the soil through sieves witha mesh size of 2.0 and 0.2 mm, respectively. The remaining silt and clay contents were placed in 500
ml measuring bottles, and 20 ml of the 6% sodium hexametaphosphate solution was added.
Subsequently, 10 ml of the solution was collected at different time intervals based on Stokes' law todetermine the content of clay, silt, and fine sand.
Brief Description of the RUSLE
The RUSLE represents how climate, soil, topography, and land use affect rill and inter-rill soil
erosion caused by raindrop impact and surface runoff (Renard et al., 1997). It has been extensively
used to estimate soil erosion loss, assess soil erosion risk, and guide development and conservationplans 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) The RUSLE is expressedby Eq. (1) as follows.
A = R × K × LS × C × P(1)

Table 1 summarizes the terms for estimating the factors using Eq. (1), where A is the averageannual soil loss in t/ha; R is the rainfall-runoff erosivity factor; K is the soil erodibility factor; L isthe slope length factor; S is the slope steepness factor; C is the cover-management factor; and P is
the support practice factor. Table 1 summarizes the methods for estimating these factors.
Table 1 Summary of methods for developing RUSLE used in this research
TermRKLS

Methods

References

A regression model based on the measurement of annual precipitation
uses averages monthly precipitation and average annual precipitation
Experimental models based on soil properties (composition of coarse
sand, fine sand, silt, clay, percentages.
Estimated from actual ﬁeld measurements of length and steepness
calculated from DEM data with various approaches

Renard and Fremund, 1994
El-Swaify and Dangler, 1976
Hickey, 2000;
Van Remortel et al., 2001

C

Land use comparison table

Silva et al., 2007

P

Table of P values for different conservation practices

Bertoni and Lombardi Neto,
1985

RESULTS AND DISCUSSION
Soil Erodibility K Factor
Soil Erodibility K Factor was calculated using inherent soil properties following the procedure fortropical soils and determined using inherent soil properties, following the El-Swaify and Dangler(1976) procedure for tropical soils, which uses the percent-modified silt (0.002-0.2 mm), percent
modified sand (0.1-2.0 mm), base saturation, percent unstable aggregates, and percent very fine sand.
The values for the measured soil properties were used to calculate K Factor using Eq. (2).
K = −0.03970 + 0.00311X1 + 0.00043X2 +0.00185X3 + 0.00258X4 − 0.00823X5(2)

Where X1 is the percent unstable aggregates < 2.0mm, X2 is the product of the percent ofmodified silt (0.002-0.01 mm) by the percent of modified sand (0.1-2.0 mm) present in the sample,
X3 is the percent base saturation of the soil, X4 is the percent silt present (0.002-0.05 mm), and X5 isthe percent of modified sand in the soil (0.1-2.0 mm). The calculated results of K factor weresummarized in Table 2.
Ⓒ ISERD

61

IJERD – International Journal of Environmental and Rural Development (2023) 14-1

Table 2 Changes in soil properties for calculating the RUSLE K-factor with weathering
Fraction
Silt (0.002-0.05 mm)
Modified silt (0.002-0.01 mm)
Fine sand (0.02-0.2 mm)
Modified sand (0.1-2.0 mm)
Aggregates (< 2.0 mm)
Base saturation (%)

Control T0(0 h)29.05%
29.60%
28.68%
7.24%
0.78%
25.56%

Treatment T1(2 h)
30.85%
31.50%
27.89%
5.89%
0.52%
25.56%

Treatment T2(4 h)
31.88%
32.50%
26.84%
5.78%
0.29%
25.56%

Treatment T3(12 h)
33.48%
33.60%
24.70%
5.56%
0.25%
25.56%As shown in Table 2, the results were used to calculate the K factor and also indicated a rise in
the percentage of soil clay and silt particles following the treatment. These notable increases served
as an indication that the soil was exposed under the harsh climatic conditions prevalent in theBolivian Amazon region, where has a discernible impact on the changes in soil particle distribution.
The climate in the study area plays a pivotal role in enhancing the weathering effects, leading tochanges in the soil composition and structure. The heightened exposure to the Amazonian climate
accelerates the breakdown of soil particles, making them more vulnerable to weathering processes.
Thus, the relationship between climate and the weathering effect is crucial in understanding thealterations in soil particle distribution and their implications for soil erosion.
Table 3 Changes in particle size-dependent variations in RUSLE K-factor with weathering
Value
X1
X2
X3
X4
X5
K values (hundreds of acre ft ton in.)K values (Mg h MJ-1mm-1)

Control T00.78
214.30
25.56
29.05
7.24
0.11750.0154Treatment T1
0.52
185.53
25.56
30.85
5.86
0.12030.0158

Treatment T2
0.29
187.85
25.56
31.88
5.78
0.12390.0163Treatment T3
0.25
186.82
25.56
33.48
5.56
0.12930.0170In Table 3, the impact of varying K values in the Revised Universal Soil Loss Equation(RUSLE) is elucidated concerning alterations in particle size distribution. These modifications areintricately linked to shifts in the soil particle distribution following the weathering experimental
treatment. The data presented therein offers a comprehensive insight into the progressive changes inK values, thereby providing valuable information about the soil erosion susceptibility resulting fromshifts in particle size distribution due to the experimental weathering treatment.

Fig. 2 Changes in soil particle distribution with weathering
As shown in Fig. 2, the changes in the percentage distribution of soil particles in the soil revealed
an interesting trend; there is a tendency for the proportion of fine sand to decrease while the silt
Ⓒ ISERD

62

IJERD – International Journal of Environmental and Rural Development (2023) 14-1

content experiences a slight increase. This suggests that the weathering processes and exposure tothe climatic conditions in the study area contribute to the transformation of soil particle composition.
Specifically, the finer sand particles exhibit a decreasing presence, potentially due to theirsusceptibility to weathering and erosion. On the other hand, the slight increase in silt content could
be attributed to the redistribution of soil particles caused by weathering processes. Overall, theseobservations highlight the intricate relationship between weathering, soil particle distribution, andthe impact of climatic conditions in shaping the composition of soil samples.
Table 4 Annual soil losses in the city of Cobija
Year20172018
2019
2020
2021
Average

A0 (t/ha year)
9.36
8.41
10.36
8.48
11.11
9.54

A1 (t/ha year)
9.81
8.82
10.86
8.89
11.65
10.01

A2 (t/ha year)
10.03
9.02
11.10
9.09
11.91
10.23

A3 (t/ha year)
10.15
9.12
11.23
9.20
12.05
10.35

Average Annual Soil Loss in the City of Cobija
The results applied all the factors of the RUSLE on the different treatments to obtain the annual soil
loss per unit area are summarized in Table 4. The average annual soil losses were in the range of 10< t/ha/year before the effects of weathering, but the values were beyond 10 < t/ha/year after affecting
weathering. Also, there was a tendency that the values of Treatment 3 affected higher weathering
were higher than those of other treatments affected lower weathering. Figure 3 showed the increasing
trends in the average annual soil losses, A value, in the City of Cobija, based on the changes in soil
particle distribution due to weathering processes.

Fig. 3 Increase in A values (annual soil loss) with weathering
CONCLUSION
Soil erosion by water remains a big concern in the Amazonia region. This study aimed to assess theweathering effects on the K factor of the Revised Universal Soil Loss Equation (RUSLE) in Cobija,Bolivian Amazonia. The experiments utilized Bolivian Amazon soil were conducted to replicate themeteorological conditions prevalent in the study area. The pipette method was employed to measure
the distribution of soil particles in a sample. The experimental results indicated remarkable changes
in soil particle distribution with weathering, and the changes affected K value as well. This trend
influences the final results of RUSLE, the average annual soil losses, A value.
In conclusion, this study highlights the significant impact of weathering on the K factor of theRevised Universal Soil Loss Equation (RUSLE) in the Cobija region of Bolivian Amazonia. TheⒸ ISERD63

IJERD – International Journal of Environmental and Rural Development (2023) 14-1

experimental investigation involving Bolivian Amazon soil, conducted to mimic the local
meteorological conditions, revealed alterations in soil particle distribution due to weathering. While
the specific changes in K values were not presented in this manuscript, the observed trend of
weathering-induced shifts in soil particle distribution undoubtedly plays a crucial role in influencing
the accuracy of the RUSLE model's predictions, particularly in terms of average annual soil losses.
On the basis of this research outcomes, it is suggested that the soils should not be exposed forlong period after deforestation. Soils should be covered by some sheets or residues as mulching for
decrease weathering effects on soil structure.
ACKNOWLEDGMENTS
We would like to express special gratitude to research fellows in the Laboratory of Land and Water
Use Engineering, Tokyo University of Agriculture for always meaningful discussion.
REFERENCES
Bertoni, J. and Lombardi, N.F. 1985. Conservação do solo. Piracicaba-SP, 392, Retrieved from https://www.passeidireto.com/arquivo/21438632/conservacao-de-solo/12Boggs, G., Devonport, C., Evans, K., and Puig, P. 2001. GIS-based rapid assessment of erosion risk in a small
catchment in the wet/dry tropics of Australia. Land Degradation and Development, 12 (5), 417-434,
Retrieved from DOI https://doi.org/10.1002/ldr.457
Bolivia National Statistical Institute. 2015. Annual report, Retrieved from URL https://www.ine.gob.bo/El-Swaify and Dangler, E.W., S.A. 1976. Erosion of selected Hawaii soils by simulated rainfall. Soil Science
Society of America Journal, 40, 769-773, Retrieved from DOI https://doi.org/10.2136/sssaj1976.0361599
5004000050040x
Hickey, R. 2000. Slope angle and slope length solutions for GIS. Cartography, 29 (1), 1-8, Retrieved from DOI
https://doi.org/10.1080/00690805.2000.9714334
Keesstra, S.D., Bouma, J., Wallinga, J., Tittonell, P., Smith, P., Cerdà, A., Montanarella, L., Quinton, J.N.,
Pachepsky, Y., van der Putten, W.H., Bardgett, R.D., Moolenaar, S., Mol, G., Jansen, B. and Fresco, L.O.
2016. The significance of soils and soil science towards the realization of the United Nations’ sustainable
development goals. Soil, 2 (2), 111-128, Retrieved from DOI https://doi.org/10.5194/soil-2-111-2016
Millward, A.A. and Mersey, J.E. 1999. Adapting the RUSLE to model soil erosion potential in a mountainous
tropical watershed. Catena, 38 (2), 109-129, Retrieved from DOI https://doi.org/10.1016 /S03418162(99)00067-3
Mortatti, J. and Probst, J.L. 2002. Silicate rock weathering and atmospheric/soil CO 2 uptake in the Amazon
basin estimated from river water geochemistry, Seasonal and spatial variations. Chemical Geology, 197 (14), 177-196, Retrieved from https://doi.org/10.1016/S0009-2541(02)00349-2
Renard, K.G., Foster, G.R., Weesies, G.A. and Porter, J.P. 1991. RUSLE, Revised universal soil equation.
Journal of Soil and Water Conservation, 46 (1), 30-33, Retrieved from URL https://www.jswconline.
org/content/46/1/30
Renard, K.G. and Fremund, J.R. 1994. Using monthly precipitation data to estimate the R-factor in the revised
USLE. Journal of Hydrology, 157 (1-4), 287-306, Retrieved from DOI http://dx.doi.org/10.1016/ 00221694(94)90110-4
Ryzak M., Bieganowski A., 2011. Methodological aspects of determining soil particle-size distribution using
the laser diffraction method. Journal of Plant Nutrition and Soil Science, 174 (4), 624-633, Retrieved fromDOI https://doi.org/10.1002/jpln.201000255
Silva, A.M., Schulz, H.E. and De Camargo, P.B. 2007. Erosão e hidrossedimentologia em bacias hidrográficas,Segunda edição. Revista e ampliada. RiMa, ISBN 978-85-7656099-9, Brasil.
Soil Survey Staff. 1999. Soil taxonomy. A Basic System of Soil Classification for Making and Interpreting
Soil Surveys, USDA, 869. USA.
Wischmeier, W.H. and Smith, D.D. 1978. Predicting rainfall erosion losses, A guide to conservation planning.
U.S. Department of Agriculture, Washington, USA.

Ⓒ ISERD64