INTRODUCTION
Erosion, caused by water, wind, and gravity, significantly impacts Vietnam's steep lands, with approximately 25 million hectares at risk of losing around 10 tons of soil per hectare annually Systematic monitoring since 1960 reveals that 10-20% of the area experiences moderate to severe erosion, leading to substantial soil loss in mountainous regions This process depletes surface soil, diminishes soil fertility, and can result in sediment accumulation in low-lying areas, adversely affecting water quality Given the increasing severity of erosion, particularly affecting vital water sources like the Bui River, it is essential to assess and evaluate erosion potential for sustainable resource planning and management.
Historically, researchers relied on building reservoirs to measure soil loss from erosion, a method that proved to be both costly and time-consuming Various approaches to studying soil erosion have emerged, with a notable trend towards modeling the dynamics of erosion processes Numerous models, including MUSLE (William, 1975), ANSWERS (Beasley et al., 1980), SLEMSA (Elwell, 1981), SOILOSS (Rosewell, 1993), and RUSLE (Renard, 1997), have been developed to evaluate soil erosion, each with its own advantages and disadvantages The USLE model, an empirical method 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, such as ground cover, ladder fields, and wetland methods, which have been successfully implemented globally These studies are highly regarded as reliable references, offering substantial scientific content for land use planning This thesis aims to utilize the Universal Soil Loss Equation (USLE) to estimate soil erosion in the Bui River watershed in Lam Son commune, Luong Son, Hoa Binh, where over 70% of the terrain is mountainous and prone to significant erosion Currently, there is a lack of research on soil erosion in this area, making the application of USLE essential for assessing erosion levels.
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, encompassing 1796.3 hectares of forested land with a forest cover of 56.1%, features numerous rivers, streams, ponds, and lakes that provide essential water resources Of this area, 1692.2 hectares are dedicated to planted forests, 22.4 hectares to bamboo forests, and 91.7 hectares to mixed forests The land use includes 55.1 hectares for domestic purposes, 562.3 hectares for other activities, and 557.9 hectares of vacant land, according to the Forest Inventory and Planning Institute of VFU The region experiences over 70% of its rainfall during the rainy season, leading to flooding in the Bui River's headwater catchment, while the dry season often results in water shortages for both agricultural production and daily living Lam Son commune is home to two primary ethnic groups: the Kinh and the Muong, with the local economy heavily reliant on agriculture, forestry, and services such as golf and crafts Residents cultivate rice, maize, fruit trees, and woody plants, in addition to raising cattle and poultry.
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 region experiences an average annual precipitation of 1913mm, with the rainy season primarily occurring during the summer months of July, August, and September December records the least precipitation, while the average annual humidity stands at 84% However, humidity levels vary throughout the year, peaking at 86% in August and reaching their lowest in October.
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, which is dry and prevails from November to April Additionally, the region experiences influences from hot and dry westerly winds and North winds.
The region's fragmented topography features numerous streams, ponds, and gullies, contributing to its hydrology Despite receiving over 70% of its precipitation, the area frequently experiences flash floods upstream of the Bui River Conversely, there is often a scarcity of water available for agricultural and domestic needs.
Generalizations about soil erosion
Erosion is a natural process driven by water, wind, and gravity, which leads to the detachment, transport, and deposition of soil particles This detachment happens when the forces keeping a soil particle in place 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 studies conducted by German scientists in 1877 (Huson, 1995), with significant advancements occurring in 1907 when the U.S Department of Agriculture implemented soil protection policies Notably, Laws (1941) conducted the first comprehensive analysis of raindrop mechanics on soil, elucidating the erosion process Additionally, Zingg (1940) developed a mathematical equation to evaluate the impact of slope and the length of downhill gradients 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 the USLE emerged globally, including the Soil Loss Equation Model for South America (SLEMSA) developed by Elwell in 1981, the SOILOSS model created in Australia by Rosewell in 1993, 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 to 1960 highlighted the significant impact of slope on soil erosion, leading to the establishment of soil protection criteria for steep terrains Notably, Chu Dinh Hoang's studies in 1962 and 1963 focused on the effects of rainfall on soil erosion and explored preventive measures through innovative farming techniques.
Since the 1980s, research has increasingly utilized the Universal Soil Loss Equation (USLE) developed by Wischmeier and Smith (1978) to assess soil erosion potential Notable studies include Dung's (1991) investigation into applying the USLE to predict soil erosion and propose prevention strategies in Tay Nguyen, as well as the research conducted by Xiem and Phien (1996) focused on mountainous regions in Vietnam.
The Universal Soil Loss Equation (USLE) is a pivotal advancement in soil and water conservation from the 20th century, designed to estimate soil erosion caused by raindrop impact and surface runoff This empirical technology has been globally implemented, resulting from extensive soil erosion research conducted by university faculty and federal scientists throughout the United States over several decades.
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, offers a rapid method for estimating long-term average annual soil loss (A) based on comprehensive erosion data collected across the United States This equation incorporates six key factors that influence soil erosion rates.
Since 1990, the application of Geographic Information Systems (GIS) in assessing soil erosion in Vietnam has been developing, primarily utilizing the Universal Soil Loss Equation (USLE) model, as seen in studies by Dung (1991), Ha (2009), and Vinh and Minh (2009) This model, which is based on empirical data from 47 areas across 21 states in the United States, considers various factors influencing erosion, including climate, topography, soil composition and structure, and human activities, represented by six key erosion coefficients: rainfall (R), slope length (L), slope steepness (S), cover and management (C), and support practice (P) Researchers in Vietnam and worldwide have enhanced the USLE model to better fit specific regional conditions, such as in Ha's work (1996) Currently, the integration of GIS and advanced technologies in calculating model 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 causes structural breaks due to their kinetic energy, leading to the detachment of grains from the ground Furthermore, 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 its integrity and resists erosion, while unstructured soil lacks connectivity between particles, making it vulnerable to damage from raindrop impact The erodibility of soil is influenced not only by its components but also significantly by its structural arrangement.
Slope 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 affect erosion rates; for instance, erosion is more pronounced on flat concave slopes compared to protruding slopes 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, both positively and negatively, often resulting in soil erosion Key practices contributing to this issue include ploughing, deforestation, and prolonged livestock raising.
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 hinders tree 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-rich soil particles are washed away, often leading to algal blooms When these algae die, their decomposition depletes oxygen levels in the water, endangering fish and other aquatic life, ultimately disrupting the balance of the water ecosystem Additionally, soil erosion contributes to water pollution, as soil particles can contain harmful substances like phosphorus, nitrate, and pesticides, which pose risks to human health.
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 quantifies rainfall and runoff erosivity, serving as a crucial metric for assessing the strength of rain-induced erosion and surface runoff It encompasses not just the total precipitation but also incorporates rainfall intensity, making it essential for accurate erosion calculations.
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 EI30 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
I 30: 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 R factor equation varies based on the location of research areas due to differences in rainfall patterns, distribution, and characteristics Increased rainfall intensity and duration lead to a higher potential for erosion Since the R factor fluctuates annually, accurately determining it can be challenging To obtain precise values, it's essential to collect 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 30-minute rainfall intensity (I30) are typically required, as outlined by Wishmeier (1985) 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
This study focuses on the R factor equation tailored for Vietnam's climate, offering greater accuracy than alternative calculations Data from the Lam Son weather station indicates a high average annual precipitation in the Lam Son commune Utilizing this data, an interpolation algorithm was applied to create a precipitation distribution map for the area.
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 soil characteristics and the stability of the soil structure and its components Various methods exist for calculating the K factor.
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, assigning 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, focusing 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
L represents the distance from the start of water runoff on land to where sediment is deposited or where runoff enters a defined channel The slope length factor assesses how slope length influences erosion, but lengths exceeding 1000 feet are excluded from this interactive calculator due to potential reliability issues.
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 in cropping systems, incorporating elements such as plant cover, production levels, and cropping techniques This factor 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 to 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 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 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 Accordingly, we allocated the surface coating values corresponding to the C index for the study area, 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 mitigating soil erosion In the RUSLE model, the P factor is comprised of three sub-factors that further detail its impact on soil conservation methods.
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 the influence of natural factors on erosion Utilizing the Universal Soil Loss Equation (USLE) model, this map is created by integrating R, K, and LS factor maps Geographic Information System (GIS) software is employed to combine these maps effectively.
Current erosion is influenced not only by natural factors but also by socio-economic elements, including 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 a comprehensive 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 )
In Lam Son commune, the K coefficient values range from 0.2 to 0.3, indicating that the soil types in the study area exhibit similar characteristics Consequently, the soil erosion resistance coefficients are also relatively uniform across the region.
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 C index 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 earlier; however, due to limited satellite data, it will be categorized based on the land use map and supplemented with information from other sources The land use map facilitates the distribution of surface cover types across the study area, aligning with the corresponding 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 values for different plants are relatively similar, with the C coefficient in Lam Son commune being approximately 0.01 This low value contributes significantly to reducing erosion within 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 erosion classification standards outlined in Vietnam's TCVN 5299-1995, the Bui River watershed has been assessed for potential erosion The analysis reveals that a significant portion of the area is experiencing severe erosion, classified into three primary types of potential erosion.
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 combining the C factor map with the potential erosion map Utilizing the Raster Calculator tool in ArcGIS 10.1, we generated this map, which provides critical insights into erosion patterns in the area.
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 demonstrate the effectiveness of ground cover in reducing soil erosion, with both potential and current erosion values varying As indicated 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 results indicate that forest land experiences the least erosion, highlighting the crucial role of forests in erosion prevention beyond economic benefits Despite often having steep slopes and a high potential for erosion, 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 waterways or reservoirs, it can cause sedimentation, leading to detrimental effects on the aquatic environment and disrupting the balance of the ecosystem.
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 predominantly found near Phoenix Golf Resort, Kem Village, and Doan Ket Village The land use in these three villages indicates that erosion is primarily caused by inadequate planning in tree exploitation and planting I examined seven areas that clearly illustrate soil erosion as depicted in the current erosion map and calculated the extent of erosion in each area Additionally, I compared the similarities and differences in the erosion areas.
Table 4.7 Comparison of current erosion in map and by observation
The discrepancies in erosion area between Levels IV and V arise because the land use map was created in 2015, while the thesis was conducted in August 2016, making the C factor map representative of 2015 data Additionally, the comparison indicates that ground cover increased from 2015 to 2016, which explains why the observed erosion area is smaller than what is depicted on 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
To enhance canopy cover and combat soil erosion, effective methods include utilizing crop residue for ground cover and planting trees, shrubs, and grasses Particularly, establishing and safeguarding watershed forests or planting on hilltops significantly reduces soil erosion These strategies 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 local community protection To maintain this stability, it is essential to implement a suitable planting and crop management plan that preserves the existing ground cover.
Erosion-prone areas, particularly bare land and steep forest slopes that have been recently clear-cut or replanted, require immediate attention To combat soil erosion, it is essential to plant fast-growing species like Acacia and bamboo, which can effectively enhance the land Additionally, it is crucial to minimize negative impacts that contribute to soil erosion in these vulnerable regions.
CONCLUSION AND RECOMMENDATION
Conclusion
Soil erosion is a natural process; however, human activities have significantly exacerbated this issue, turning it into a critical challenge for both the environment and society This article presents key conclusions based on the processes and outcomes of soil erosion.
- 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 area is covered by vegetation, which is a crucial factor in assessing erosion potential Therefore, it is essential to develop an effective strategy for forest management and planting.
The thesis results are reliable as they have been validated within the research area, making them a valuable resource 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, highlighting the need for comprehensive data and current surveys Research emphasizes the importance of canopy cover, suggesting that an effective cropping schedule can enhance canopy density during the rainy season To effectively limit erosion, strategies must be both stable and efficient, utilizing natural elements and human resources while maintaining these criteria Additionally, further research on soil erosion should incorporate GIS technology at broader scales, such as district or national levels, to improve analysis and solution selection Future studies should also integrate GIS with real-time erosion assessments to increase the practical applicability of findings.