INTRODUCTION
Erosion, driven by water, wind, and gravity, significantly impacts Vietnam's steep lands, which encompass around 25 million hectares with an erosion potential of approximately 10 tons per hectare per year Systematic monitoring since 1960 reveals that 10-20% of the area experiences moderate to severe erosion, resulting in substantial soil loss annually in mountainous regions This process depletes surface soil, diminishes soil fertility, and contributes to soil exhaustion Additionally, erosion can transport suspended solids to lower-lying areas, adversely affecting water quality and sedimentation The Bui River, a vital water source for local communities, underscores the need for assessing erosion potential to ensure sustainable resource management.
Historically, researchers calculated soil loss from erosion by constructing costly and time-consuming reservoirs Various methods have emerged for studying soil erosion, with a focus on modeling the dynamics of the erosion process Notable models based on the Universal Soil Loss Equation (USLE) include MUSLE, ANSWERS, SLEMSA, SOILOSS, and RUSLE, each with its own advantages and disadvantages The USLE model, an empirical tool widely used globally, estimates soil erosion caused by raindrop impact and surface runoff In Vietnam, studies by Tu (2011) and Ha (2009) successfully applied the USLE model to assess soil erosion levels.
Research has identified effective solutions to mitigate erosion, including ground cover, ladder fields, and wetland methods, which have been successfully implemented worldwide These studies are highly regarded as reliable references, offering valuable scientific insights for land use planning This thesis aims to utilize the Universal Soil Loss Equation (USLE) to assess soil erosion in the Bui River watershed, located in Lam Son commune, Luong Son, Hoa Binh, characterized by over 70% mountainous terrain with significant erosion potential As there are currently no studies on soil erosion in this area, applying the USLE to evaluate erosion is essential.
OBJECTIVES
General objective
This study provides scientific basic to propose possible solutions for soil erosion management in Bui river watershed at Lam Son commune, Luong Son district, HoaBinh city.
Specific objectives
Forming maps of rainfall erosion index (R), soil erodibility index (K), slope index to form potential and present condition map
Giving estimation about eroding rate as well as proposing the best solution for limiting soil erosion at Bui basin
STUDY AREA AND METHODS
Study area
The study area is carried out in Lam Son commune- belongs to Luong Son district, Hoa Binh province to the west
The study area has border:
- To the West: DanHoa, DanHa of KySon, HoaBinh
- To the North: TienXuan, DongXuan commune
- To the East: LuongSon district
- To the South: TanVinh, TruongSon commune of LuongSon, HoaBinh
Lam Son lies about 10km along the 6 highway, it is about 45km far from HaNoi city
The study area features numerous rivers, streams, ponds, and lakes, supported by 1,796.3 hectares of forest, which includes 1,692.2 hectares of planted forest, 22.4 hectares of bamboo forest, and 91.7 hectares of mixed forest, resulting in a forest cover of 56.1% The land allocation includes 55.1 hectares for domestic use, 562.3 hectares for other purposes, and 557.9 hectares of vacant land (Forest Inventory and Planning Institute of VFU) The region experiences a rainy season that accounts for over 70% of total rainfall, leading to flooding in the headwater catchment of the Bui River, while dry seasons often result in water shortages for both production and daily living Lam Son commune is home to two main ethnic groups, the Kinh and Muong people, with the local economy heavily reliant on agriculture, forestry, and services such as golf and crafts Residents engage in the cultivation of rice, maize, fruit trees, and woody trees, alongside livestock farming.
Table1.1 Basis criteria datum of weather in the study area Weather criteria
(Source: Bioclimatology software-Institute of ecological forest resources and environment- Vietnam National University of Forestry)
Lam Son has tropical monsoon climate with two seasons:
-Rainy season with more vapors and last from April to October
-In dry season, humidity is low, dry and last from October to March in the next year
Temperature regime: Average temperature per year is 23.1 o C, July is the hottest month on average and the coldest month is in January
The average annual precipitation is 1913mm, with the rainy season predominantly occurring during the summer months of July, August, and September December experiences the least amount of rainfall, while the annual humidity averages 84% Notably, October records the lowest humidity levels, whereas August sees the highest at 86%.
The wind regime in the study area is characterized by two primary directions: the Southeast wind, which occurs during the rainy season from April to October, bringing hot air and moisture, and the Northeast wind, a dry breeze that prevails from November to April Additionally, the region experiences influences from hot and dry westerly winds and northern winds.
The region is characterized by numerous streams, ponds, and gullies due to its fragmented topography Despite receiving over 70% of its precipitation, the area frequently experiences flash floods in the upstream of the Bui River Conversely, there is often a shortage of water available for agricultural and domestic purposes.
Generalizations about soil erosion
Erosion is a natural process driven by the energy of water, wind, and gravity, leading to the detachment, transport, and deposition of soil particles This detachment happens when the forces maintaining a soil particle's position are surpassed by the impact of raindrops, flowing water, or wind.
3.2.2 Summary about history of soil erosion research
Research on soil erosion dates back to German studies in 1877 (Baver, 1939; Huson, 1995), with significant advancements occurring in 1907 when the U.S Department of Agriculture implemented soil protection policies Laws (1941) conducted the first detailed analysis of how raindrops impact soil, outlining the erosion process, while Zingg (1940) developed a mathematical equation to evaluate the effects of slope and slope length on erosion.
In 1947, Musgrave et al introduced the Musgrave equation, which was widely used until it was succeeded by the Universal Soil Loss Equation (USLE) in 1958 From the mid-1980s to the early 1990s, various erosion models based on USLE emerged globally, including the Soil Loss Equation Model for South America (SLEMSA) developed by Elwell in 1981, the SOILOSS model created by Rosewell in 1993 in Australia, and the ANSWERS model, which was expanded in the late 1970s to evaluate aggradation levels in river basins (Beasley et al., 1980).
In Vietnam, erosion occurs gradually because the country has mountainous topography so, researches about this problem have been carried out early Tung and Moorman
(1958) had some basic researches about soil erosion After completing the study, they
Terraced farming is an effective method for reducing soil erosion, as concluded by various studies Research conducted up until 1960 highlighted the significant impact of slope on soil erosion, leading to the establishment of soil protection criteria for the use of steep lands Chu Dinh Hoang's studies in 1962 and 1963 focused on the effects of rainfall on soil erosion and explored farming techniques to mitigate this issue.
Since the 1980s, research has increasingly utilized the Universal Soil Loss Equation (USLE) developed by Wischmeier and Smith (1978) to assess soil erosion potential Notably, Dung (1991) applied the USLE to predict soil erosion in Tay Nguyen and propose preventive measures Additionally, Xiem and Phien (1996) conducted studies focused on the mountainous regions of Vietnam, further contributing to the understanding of soil erosion in the country.
The Universal Soil Loss Equation (USLE) is a groundbreaking advancement in soil and water conservation from the 20th century, widely used globally to estimate soil erosion caused by raindrop impact and surface runoff Its creation resulted from extensive soil erosion research conducted by university faculty and federal scientists throughout the United States.
The Universal Soil Loss Equation (USLE), developed by Walter H Wischmeier and Dwight D Smith at the USDA National Runoff and Soil Loss Data Center at Purdue University, is a vital tool for estimating long-term average annual soil loss (A) This equation is grounded in extensive erosion data collected from studies across the United States and consists of six key factors that contribute to soil erosion assessment.
Since its introduction in Vietnam in 1990, the application of Geographic Information Systems (GIS) for soil erosion assessment has primarily utilized the Universal Soil Loss Equation (USLE) model, as demonstrated by researchers such as Dung (1991), Ha (2009), and Vinh and Minh (2009) This model, which is based on empirical data from 47 regions across 21 states in the USA, takes into account various factors influencing erosion, including climate, topography, soil composition, and human activities, represented by six key coefficients: rainfall (R), slope length (L), slope steepness (S), cover and management (C), and support practices (P) Researchers in Vietnam and globally have enhanced the USLE model to better fit local conditions, as seen in Ha's work from 1996 Today, the integration of GIS and advanced technologies in calculating these coefficients has significantly improved the accuracy of soil erosion assessments.
According to researches of soil erosion process of Ellision 1994, Wishmeier and Smith 1978 etc, soil erosion factors include: rain, topography, ground cover, human
After doing many researches of soil erosion systematically, scientists found that the most factor of soil erosion is raindrop
The first person pointed out that raindrop caused erosion is Elision (Elision 19940 In
1985, Hudson N W concluded that raindrops have the dynamic of 256 times more than its surface flow
The impact of raindrops on topsoil primarily causes structural breaks due to their kinetic energy, leading to the detachment of grains from the ground Additionally, rainfall facilitates the flow that transports these grains to sediment areas.
Soil erodibility is the factor, which determines the level of erosion Raindrops cause two effects on soil erosion process:
- Energy of raindrops splash and break the combination of soil, it also impacts on chemical and biological property of soil and integrate soil particles
Well-structured soil maintains equilibrium, making it resilient against disruption In contrast, unstructured soil lacks cohesion among particles, leading to increased vulnerability to erosion from raindrop impact Soil erodibility is influenced not only by its composition but also significantly by its structural integrity.
The slope of the land significantly influences soil erosion, as it determines the movement of soil particles and surface flow Steeper slopes experience stronger surface flow, making them more susceptible to erosion due to factors like soil splashes, surface erosion, and the movement of larger soil blocks Different slope shapes have varying effects on erosion; for instance, flat slopes tend to erode more when they are concave, while protruding slopes experience less erosion Additionally, the length of the slope also plays a crucial role in the extent of erosion.
Under the impact of rain, bare lands with steep slope have high potential of erosion But when there are ground cover, vegetation layer will have two functions:
- Firstly, ground cover absorb the energy impact of raindrops, disintegrate the impact of rain, water move along the stem of tree will be reduced the impact on soil
- Secondly, leaves and branches when fall down will deposit and form a slime layer, which limit the surface runoff
Human activities significantly affect nature, resulting in both positive and negative consequences, with soil erosion being a direct or indirect outcome Practices such as ploughing, deforestation, and long-term livestock raising contribute to this environmental issue.
Soil erosion has made huge impact on agricultural activities, natural and ecosystem:
Soil erosion leads to significant soil and nutrient loss, severely impacting agricultural productivity The erosion of surface nutrients deprives trees of essential elements for growth, while also altering the physicochemical properties of the soil.
- Plant productivity: the productivity of plant is reduced due to nutrient loss Seriously, some area lost all crop because of erosion
Soil erosion poses a significant threat to the environment and ecosystems, as nutrient runoff carries soil particles, often enriched with phosphorus and nitrates, into water bodies This process can lead to harmful algal blooms, which, upon decomposition, deplete oxygen levels in the water, endangering fish and other aquatic life Furthermore, the presence of pesticides in eroded soil exacerbates water pollution, posing serious health risks to humans and disrupting the delicate balance of aquatic ecosystems.
Methodology
To create a soil erosion map for the study site using the Universal Soil Loss Equation (USLE) and Geographic Information Systems (GIS), we must first generate individual maps for the R, K, LS, and C indices These maps will then be combined to produce a potential erosion map Finally, by integrating the C index map with the potential erosion map, we can develop a present condition erosion map.
The R factor, which stands for rainfall and runoff erosivity, is essential for assessing the strength of rain erosion and surface runoff It encompasses not only the total precipitation but also incorporates rainfall intensity, making it a critical component in understanding erosion dynamics.
After many researches, with 8.250 experimental indice of 35 stations, Wishmeier
(1985) found the product between kinetic energy of rain and maximum rainfall intensity in 30 minutes singned EI 30 This value reflect the relationship between ammount of soil loss and rain mode
Wishmeier proposed equation to calculate R fsctor based on EI30 as follow:
In which: R: Rainfall and runoff erosivity index
I30: Maximum rainfall in 30 minutes (mm/h)
Table 3.1 Some equations to calculate R factor
Roose (1975)- Erosion index calculated by annual precipitation (P)
Morgan (1974)- Erosion index calculated by annual precipitation (P)
Foster et al (1981) - Erosion index calculated by annual precipitation (P) and I30
El-Swaify and others (1985) - Erosion index calculated by annual precipitation (P)
Wanapiryarat et al (1986)- Erosion index calculated by daily precipitation (X)
Ha Nguyen Trong equation (Water resources
University)- Erosion index calculated by annual precipitation (P)
The calculation of the R factor is influenced by the specific location of research areas, taking into account variations in rainfall, distribution, and characteristics Increased rainfall intensity and duration elevate the potential for erosion Since the R factor fluctuates annually, accurately determining it poses challenges To obtain precise values, it is essential to gather long-term data on precipitation and rainfall patterns Additionally, selecting the most appropriate equation for the specific research area is crucial when calculating the R factor.
The R factor map illustrates the distribution of rainfall and flow within the Bui River watershed To calculate the R factor, annual precipitation and the 30-minute rainfall intensity (I30) as defined by Wishmeier (1985) are typically required However, due to insufficient data on 30-minute rainfall intensity, the R factor for the Bui River watershed will be determined using average precipitation and the equation proposed by Ha (1996).
In which: R is erosion coefficients of rain and flow
The equation studied in the context of Vietnam's climate provides a more accurate calculation of the R factor compared to other equations Data from the Lam Son weather station indicates that the average annual precipitation in Lam Son commune is relatively high Utilizing this average precipitation data, I applied an interpolation algorithm to create a map illustrating the distribution of precipitation throughout Lam Son commune.
The result was carried out by Arcgis 10.1 software The process is as follow:
Spatial Analys Interpolate to Raster Inverse Distance Weighted…
After interpolation, we conducted to overlay the class area of rain on the boundary map and digitize them to form the map of average precipitation
Figure 3.3 Process of forming R factor map in Arcgis 10.1
The K factor indicates soil erodibility, with higher values signifying a greater potential for erosion This factor is influenced by the soil's characteristics and the stability of its structure and components Various methods exist to calculate the K factor effectively.
In which: M is defined as: (%) M = (%limon + %fine sand)(100% - %clay) a: percent organic matter b: classes for structure c: permeability
For more easily in calculating K factor, Wischmeier and Smith proposed nomogram based on equation of Wischmeier and Smith (1987) to look up for K factor
Figures 3.4 Nomogram for calculatin K factor of Wischmeier and Smith (1978)
In this thesis, K factor is referenced from multiple sources
To create a K index map in ArcGIS 10.0, I utilized an algorithm to query various soil types from the soil map and assign K index values according to Table 4.2 Once the K index values were populated, I converted the data from vector to raster format using the Feature to Raster tool based on the K fields.
LS factor represents for the effect of slope steepness factor (S) and length (L) to the process of erosion
S is the slope steepness, soil loss much when the slope is steeper
The distance (L) from the point of water runoff initiation to the site of sediment deposition is crucial in understanding erosion dynamics The slope length factor evaluates how the length of the slope impacts erosion rates For accuracy, slope lengths exceeding 1,000 feet are excluded from this interactive calculator, as their calculations may yield unreliable results.
L and Sare two factors to be considered when calculating erosion
Wischmeier and Smith (1978) proposed formula o calculate LS factor as follow:
In which: x: the length of the slope (m)
19 s: percent of slope n: actual parameter n = 0.5 when S > 5%; n = 0.4 when 3.5 < S< 4.5% n = 0.3 when 1% < S < 3.5%; n = 0.3 when S < 1%
Figure 3.5 Process of forming LS factor in Arcgis 10.1
The C factor, as defined by Wischmeier and Smith (1978), represents the relationship between erosion on bare soil and erosion under various cropping systems This factor incorporates elements such as plant cover, production levels, and the cropping techniques used Its value ranges from 1 for bare soil to as low as 1/1000 in forested areas, 1/100 in grasslands and cover crops, and between 1 and 9/10 for root and tuber crops.
To define C factor for the study area, it is necessary to have long-time observations There are two methods to calculate C factor o Surveying method Wischmeier and Smith (1978)
20 o Using current land use status map or satellite figures to forming plant cover, after that collecting C factor of each status from other documents
In this thesis, the variable C represents the cover of surface vegetation, indicating that a thicker cover layer correlates with a larger cover area and reduced erosion The C index map is generated using the methodology outlined previously; however, due to limited satellite data, the C index will be categorized based on the land use map and supplemented with information from other sources The distribution of surface coating across the study area is derived from the land use map, aligning with the corresponding C index values as detailed in Table 3.2.
Table 3.2 C index of Bui river basin
Soil Type C index Total area(km 2 )
In the USLE equation, the P factor evaluates the effectiveness of agricultural practices in protecting against soil erosion In the RUSLE model, the P factor is comprised of three sub-factors that further detail these protective practices.
P st: Contour plant sub-factor
P ter : sub-factor of embankment to prevent erosion
Due to the limit of the thesis, P factor is considered equal to 1
The potential erosion map illustrates how natural factors contribute to erosion, utilizing the Universal Soil Loss Equation (USLE) model, which integrates the R, K, and LS factors By employing GIS software, we can effectively combine these maps to assess erosion risk comprehensively.
Current erosion is influenced not only by natural factors but also by socio-economic elements like land use and farming practices To assess soil loss at a specific time, we combine the C factor map with the potential erosion map to create an updated current erosion map.
RESULTS AND DISCUSSION
Mapping factors
Figure 4.1 Annual Precipitation Interpolation map of Lam Son commune
After apply the equation (3.1) for map of annual interpolated precipitation map of Bui river watershed in Lam Son commune, we get the R factor in the whole commune from
Table 4.1 R factor of Lam Son commune
R factor in the study area is medium and decreases from the North to the South Calculating R factor did not reflect the effect of rain and flow to on erosion
Figure 4.2 R factor map in Lam Son commune
Table 4.2 K index of some types of soil in Lam Son Commune
Symbol K index Total area (km 2 )
The findings indicate that the K coefficient values in Lam Son commune range from 0.2 to 0.3 Additionally, the K coefficient values for various soil types in the study area are similar, resulting in comparable soil erosion resistance coefficients.
Figure 4.3 K factor map of Lam Son commune
Figure 4.4 Steep map in Bui river watershed Table 4.3 Slope analysis in Bui river watershed Steep ( 0 ) Total area (km 2 ) Percentage of steep (%)
Slope in the study area is almost from 8 0 – 25 0 so I choose n = 0.5
Table 4.4 LS coefficient table in Lam Son commune
Figure 4.5 LS factor map in Lam Son commune
In this thesis, the cover of surface vegetation, referred to as the C index, indicates that a thicker cover layer correlates with a larger cover area and reduced erosion The C index map is constructed following the outlined methodology; however, due to limited satellite data, the C index is categorized based on the land use map and supplemented by external resources The distribution of surface coating across the study area is aligned with the government classifications corresponding to the C index values, as detailed in Table 3.2.
Figure 4.6 Land use map in Lam son commune
To create a C factor map in ArcGIS 10.1, the process mirrors that of generating a K factor map The C coefficient for various plants shows minimal variation, with the C coefficient in Lam Son commune approximately equal to 0.01 This value contributes significantly to minimizing erosion in the study area.
It required long time and money to survey to calculate P factor Due to the limit of the research, P factor in the thesis is considered equal to 1
Erosion map
4.2.1 Potential erosion map based on R, K LS factor
As I mentioned in chapter 3, potential erosion map is formed by gathering R, K, LS map together After calculating and using Arcgis 10.1 to integrated maps of factor by Raster Calculator tool The result is shown as follow:
Figure 4.8 Potential erosion map based on R,K, LS factor
Table 4.5 Classifying potential erosion in Bui river watershed
The amount of soil loss (tons/ha/year)
Percentage of potential erosion area (%)
According to the potential erosion map and the classification regulations outlined in Vietnam standard TCVN 5299-1995, the potential erosion classification for the Bui River watershed reveals that a significant portion of the area is experiencing severe erosion The analysis identifies three primary types of potential erosion present in this region, as detailed in Table 4.6.
Level 2: 1-5 tons/year: In the research area, the potential erosion area count for 9.6% of the whole region, erosion area concentrate mainly from the North to the Northwest This area has low rate of erosion thanks to the low of slope, main type of soil is valley land by slope (D)
Level 3 (5-10 tons/year): Count for km2 (0.05%), this area has low potential of erosion due to the not steep of slope and high density of ground cover
Level 5 (>50 tons/year) Count for 90.33%, in general, almost the region is eroded seriously Lam son is a small commune with main soil type is yellow-red soil on clay with K index = 0.31, high of average precipitation per year and steep of slope, so the potential erosion is very high
The current erosion map of the Bui River watershed illustrates the severity of soil erosion by integrating the C factor map with the potential erosion map Utilizing the Raster Calculator tool in ArcGIS 10.1, we processed the data to generate this comprehensive map.
According to regulation of classifying current erosion follow Vietnamese standard
(TCVN 5299-1995) in the study area, we can divide into level of erosion:
Figure 4.10 Distribution of erosion area
30,40% level 1 level 2 level 3 level 4 level 5
Statistics indicate that ground cover significantly reduces soil erosion The potential and current erosion values fluctuate As shown in Table 4.7 and the Current Erosion Map, Lam Son exhibits three primary levels of current erosion: level 1, level 2, and level 3.
Level 1(30tons/ha): This area distributed from the North to the South with total area of 10,602 km 2 Almost this area belongs to abandoned hill or still waiting for plan
The findings indicate that forest land experiences minimal erosion, highlighting its crucial role in erosion prevention beyond economic benefits Despite often having steep slopes and a high erosion potential, unprotected forest areas can suffer severe erosion when ground cover is lost This assessment focuses on the Bui River watershed in Lam Son commune.
- Current erosion of the research area is uneven between levels of erosion Total area suffered from erosion is up to 80%
Erosion levels 4 and 5 can reach up to 60% in areas with sparse population density and predominantly mountainous terrain, resulting in significant annual soil erosion When this eroded soil enters water flows or reservoirs, it causes sedimentation, which negatively impacts the aquatic environment and disrupts the ecological balance.
Figure 4.11 Comparison between potential erosion and current erosion
Figure 4.12 Location in Doan Ket village (Level IV in the map)
Figure 4.13 Location behind Phoenix golf resort (Level V in the map)
Figure 4.14 Location in Kem village (Level V in the map)
Figure 4.15 Location near Phoenix golf resort (Level V in the map)
The soil erosion map was validated through observation, revealing that erosion levels 4 and 5 are most prevalent near Phoenix Golf Resort, Kem Village, and Doan Ket Village The analysis of land use in these villages indicates that erosion is primarily driven by poor planning in tree exploitation and planting I conducted field visits to seven areas that prominently exhibit soil erosion, calculating the affected areas and comparing the similarities and differences in erosion levels across these sites.
Table 4.7 Comparison of current erosion in map and by observation
The discrepancies in erosion areas between Levels IV and V can be attributed to the land use map being created in 2015, while the thesis was conducted in August 2016, making the C factor map relevant to 2015 data Additionally, the comparison indicates an increase in ground cover from 2015 to 2016, which explains why the observed erosion area is smaller than what is represented in the map.
After observation, I moved to estimate the amount of soil erosion for observed locations follow:
Figure 4.16 Observed locations Table 4.8 Soil Erosion estimation at observed locations
Location Level of erosion Total area (ha) Amount erosion estimated (tons)
Solutions
Implementing strategies to enhance canopy cover, such as utilizing crop residue and planting trees, shrubs, and grasses, is vital for soil protection Specifically, cultivating and safeguarding watershed forests or hilltops effectively reduces soil erosion These practices not only shield the soil from raindrop impact but also contribute to improved soil fertility.
In addition, to increase the ability of erosion resistance we can also improve the fertility of soil by liming, fertilizing to increase the quality of soil
Erosion in these areas is minimal due to the high level of ground cover and strong protection from the local community To maintain this stability, it is essential to implement a suitable planting and crop management plan that preserves the existing ground cover.
Erosion primarily affects bare land and steep forest areas that have recently been clear-cut or replanted To combat this issue, it is essential to plant fast-growing trees like Acacia and bamboo, which can enhance soil quality Additionally, minimizing activities that contribute to soil erosion is crucial for maintaining healthy land.
CONCLUSION AND RECOMMENDATION
Conclusion
Soil erosion, a natural process, has been significantly exacerbated by human activities, leading to severe consequences for both the environment and society This article outlines key findings related to the causes and effects of soil erosion, highlighting its growing threat due to negative human impact.
- By using USLE model and GIS technology, the thesis formed some maps of R, K,
LS, C factor of the research area, then made the potential erosion map and current erosion map for Bui river watershed
- R factor in the study area is medium fluctuating from 950 – 1050mmand decrease from the North to the South
- Main soil type in the research area is yellow-red soil on sand (Fs) with K coefficient = 0.27
Approximately 65% of the land is covered by vegetation, which is a crucial factor in assessing erosion potential Therefore, it is essential to develop a strategic plan for forest management and planting.
The thesis results are reliable as they have been validated within the research area, making them a valuable document for land use planning in that region.
- The thesis proposed some solutions for each level of erosion Due to the limitation of time and budget, some solutions may not enough
- The research area has total potential erosion (level 5) up to 89% and current erosion is 89%divided equally for 3 levels (level 1, level 4 and level 5)
- The area of erosion tends to decrease due to good farming method and conscious of local people
Recommendation 41 REFERENCES
Soil erosion is a gradual process influenced significantly by rainfall, necessitating comprehensive data collection and surveys Research highlights the crucial role of canopy cover, emphasizing the importance of scheduling crops to maximize canopy during the rainy season Effective erosion control must be stable and utilize both natural and human resources while adhering to these principles Further research on soil erosion should incorporate GIS technology at larger scales, such as district or national levels, to enhance analysis and solution selection Future studies should combine GIS with real-time erosion assessment to improve the practical applicability of the findings.