Article Type: Research Article Article Citation: Tamirneh Kifle, Asnakew Deres, Assefa Birku,
and Demke Mengist. (2020). EVALUATION OF THE IMPACTS OF LAND USE AND LAND COVER
CHANGES USING EROSION ASSESSMENT MODEL AT TIKUR WUHA WATERSHED. International
Journal of Research -GRANTHAALAYAH, 8(6), 75 – 91. https://doi.org/10.29121/granthaalayah.v8.i6.2020.79 Received Date: 01 May 2020 Accepted Date: 22 June 2020 Keywords: Erosion USLE Land Cover Land Use SWC and Watershed Rapid increases in population, forest clearing and continuing search for a farm land have induced pressure on natural resource. In order to reverse such kind of problem assessing the level of problem and finding solution at watershed level is necessary. The studies of land use land cover changes and their effects on soil erosion and runoff patterns at the watershed level are essential in water resource planning and management. This study provides an approach to identify the effects of land use land cover changes on runoff and sediment in Tikur wuha watershed. The changes in land use land cover were associated with growing demand of wood for fire, charcoal, construction materials, household furniture, pulp and paper industries, and expansion of farming and grazing land. The study was conducted the impacts of land use land cover changes, to identify the main cause erosion, to assess soil loss rate in different slope classes, agricultural activities and its effect on land resource by using erosion assessment model. USLE is important to predict the annual soil loss by using different parameters. The necessary data were generated from mean annual rainfall, previous study of the area, erosivity factor, erodibilty factor, topographic factor (LS), the cropping management factor, erosion control factor, both primary and secondary data as well as key informants interview, field observation, by distribution of structural survey questionnaire and field measurement. The result of the analysis showed that the amount of soil loss at Tikur wuha watershed is about 5.58 ton/ ha/yr. The study finding suggest that understanding of some of the socio- economic, institutional and biophysical factors that determine land cover change of the area would contribute to advice appropriate strategies to achieve the desired change in SWC process and to alleviate damage land cover change in the study area. In selecting priority intervention areas in the rehabilitation of land use land cover, strategies should considered the socio-economic and specific land characteristics as well as farmers preference.
1. INTRODUCTIONThe LULC pattern of a region is a result of natural and socio-economic factors and their utilization by man in time and space. From systems theory thinking and the observed interconnectedness within our natural systems, linking LULC and water resources is imperative (Wei et al, 2007). Land use land cover change (LULCC) can be easily observed in forestry on a global scale, the largest change in terms of land area, and arguably also in terms of hydrologic effects, is from deforestation and a forestation. Deforestation, rapid land use change for farming and overgrazing are likely to affect the hydrologic regime of the rift lakes (Tenalem, 2007). The amount and peak intensity are two main important characteristics of a rainstorm that influence its potential ability of causing erosion. Volume and peak rate of runoff are measures of runoff erosive (Foster, 1988). Soil erosion by water is also a function of steepness (gradient), slope length, and shape, which modify the energy of the hydrologic inputs. Edibility is the specific property of soil, which can be quantitatively evaluated as the vulnerability of the soil to erosion under specific circumstances (Hudson, 1996). Physical process-based models are intended to represent the essential mechanisms controlling erosion process by solving the corresponding equations. These models are the synthesis of individual components that affect the erosion processes and it is argued that they are highly capable to assess both the spatial and temporal variability of the natural erosion processes. The physical based models include AGNPS (Young et al., 1987), ANSWERS (Beasley et al., 1980). Empirical models are like the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1965), the Modified Universal Soil Loss Equation (MUSLE) (Williams, 1975) and Agricultural Nonpoint Source Pollution Model (AGNPS) (Young et al., 1987) are examples of commonly used watershed models based on USLE methodology to compute soil erosion. Erosion is a problem at Tikur Wuha water shed Therefore, this study evaluates the impacts of land use and land cover changes 2. MATERIAL AND METHODS2.1. DESCRIPTION OF THE STUDY AREATikur wuha watershed located is 266km from Addis Ababa, 16k.m from Shashemne in southern direction; 4km from Hawassa in northern direction. 2.2. LAND USE AND LAND COVERMost of the watershed area is agricultural land, with rain fed agro forestry practices (mostly perennial and annual cropping). The forests besides supplying the needs of the nation for fuel wood, timber and grazing, served a very important purpose of protecting the hill sides against erosion and the valley lands against floods. The bar hill sides flood water during the rain. The result is that the rain water instead of percolating in to the soil runs over the surface of the soil, with great velocity, causing floods, erosion and sedimentation (SCRP, 1995). The concept of different aspects of effects of land use change on hydrology at local, regional and global scale. Land use change could have an effect on decrease or increase of the quantity of water (Maidment, 1993). Land covers refer to the land surface cover characterized main vegetation type. Thus, include crop land forest, wood lands, bush lands and water bodies without reference to how this cover is used. On the other hand, land use refers to the use made of the various land cover types taking in to accounts the type of management linked to economic consideration. In many cases, land cover and land uses are described interrelated. 2.3. CLIMATEBased on the Moisture Index Classification of climate, the climate of the watershed in general is dry sub-humid in the northern part of the high lands and moist sub-humid in the eastern and southern part of the catchment area. Depending on the local climate information the mean annual temperature of the Watershed is about 19.5 0c and altitude range 1680 to 3000 meters above sea level, the area is predominantly categorized as Woinadega Zone and classified as temperate. The main rainy season in the catchment area is from May to October, but the dry season goes from November to February, similar to the rain pattern of most Ethiopian plateaus. The average annual rainfall estimated to be about 975mm.The mean monthly temperature varies from 170c to 22oc. The mean maximum temperature is 300c. 2.4. SOIL CLASSIFICATIONSoils have many physical and chemical characteristics that are useful in describing and differentiating, among them the most important characters include; effective soil depth, soil color, texture, acidity or alkalinity, electrical conductivity, cat ion exchange capacity and base saturation. 2.5. DATA COLLECTION METHODData availability as well as quality for a watershed can increase the accuracy of model predication. The data require from this study were collected from both primary and secondary sources. Primary data: were generated from selected sample house using as structured questionnaire in the watershed face to face interviews in which trained enumerators administer the structured questionnaires, were used to collected primary data. The data collection were carried out from house hold characteristics, farmer perception of soil erosion, land cover change by interviewing, field observation and by measurements such as land slope, soil bulk density, soil depth and soil texture. House hold characteristics: the necessary data related to the demographic, socioeconomic, agricultural activities related to crop type; cultivation system different investors and institutional factors affecting the land cover changes collected using structured questionnaire through interviewing the house hold. Field observation: were made to collect on the community and biophysical resources of selected sample house hold within the watershed. In addition through it the land cove change problem, (extent and distribution) and potential opportunities of the area and the status of soil conservation measures, different agricultural practice and currently the land cover in the watershed were observed. Farmer’s perception on soil erosion hazard and land cover change was assessed using formal interviews with sampled households. To obtain information about the same fact from multiple methods and to increase reliability of the data by using group discussion, detailed personal interviews, random sampling technique was used. A structured questionnaire (appendix-1) was used for the tiled interview and the interview was conducted in the homestead of each interviewed farmer. For the farmer to develop as strong trust on the enumerator, each farmer was well informed about the purpose of the survey and why he /she is chosen for interview, by the Development agents of the kebel. After the formal interview, group discussion has been held in different disciplines such as Agronomy, soil and water conservation experts and natural resources manager in the Woreda of agricultural office and 5 members from farmer, elder people, woman, kebel of administration and development agents of the study area were held to have the detail information about the attitude of farmers on soil erosion hazard and land cover change and factors that constrain practicing soil and water conservation measures. Land slope measure: in the study area we were measured the land slope using by leveling instruments and staff rod methods. 2.6. SOIL BULK DENSITY MEASUREIn the study area we were taken the soil sample in the field. To convert the volume of the soil lost by sheet, rill and gully erosion in to tone/catchments area, the soil bulk density of the study area were investigated. In order to assess and investigate soil bulk density of the study area, total of 6 soil sample were taken from the cultivated land. The samples were taken from up, mid and down position of each of the land. It was investigated by core sampling method. To find the dry mass weight, the samples were weighed before getting into a 105co oven dry for 24 hours in Hawassa University College of agriculture soil laboratory and weighed after dried. The mean of the total samples was taken as the bulk density for the soil on the study area. Bulk density (gm/cm3) =Md/Vt Where, Md mass of dry soil sample (in gram) VT, total volume of the soil sample (in cm3) Secondary data: were collected from which related to biophysical and socio-economic features of the watershed was obtained from shasmene Woreda agricultural and rural development office, development agents (DA) as well as published and unpublished documents. From biophysical
data, the status of land use and land cover change, slope type, status of
different soil and water conservation measures both physical and biological. A physical measure
includes, level bund, stone bund, area closer and mostly common to apply in the
area is soil bund. Biological measures
such as, plantation activity, area closer, grass and tree etc… The farming system
of the watershed is characterized as a rain fed subsistence and mixed farming
which type of cereal crops such as, maize, teff, sorghum and related cereal
crops and livestock productions. The major livestock
type as composition of cattle, sheep, goats, horse, donkey and chickens. The major trees that
are growing on different land use such as Eucalyptus, Acacia and others. In the study area
the land uses type and area in hectares shown in the table below. Table 3: land uses type and area
From demographic
characteristics such as age, sex, marital status, house hold and family size is
shown in the table below. Table 4: demographic characteristics
3. DATA ANALYSISThe data was
analyzed using descriptive, quantitative, qualitative and sampling methods. 3.1. USLE MODEL DESCRIPTIONThe USLE is
empirically based model developed in the United States by using data on soil
erosion rates (Wischmeier and Smith, 1978). Mathematically the
equation is donated as: A=R*K*L*S*C*P Where, A is the mean
annual soil loss (tons/ha/year), R, is the rainfall and runoff factor (MJ mm/ha/yr),
K is the soil erodibility factor (ton hr/MJ mm), L is the slope length factor
(-), S is the slope steepness factor (-), C is the land cover and management
factor (-) and P, is the support practice factor (-). 3.2. EMPIRICAL DESCRIPTION
3.2.1. SOIL LOSS PREDICTING MODEL The
Universal Soil Loss Equation (A model for predicting soil loss) This
equation developed in USA to predict average annual soil loss from interrill
and rill erosion. It is given as A =
RKLSCP Where: - A = the annual soil loss, R
= the rainfall erosivity factor, K = the soil erodibility factor L
= the slope length factor C = the cropping management factor, and P
= the erosion control practice factor. R: The
erosivity factor it can be
predicts based on the rain fall data obtained from Hawassa metrological station
and adapted for Ethiopian standard condition.
K: The erodibility factor
the erodibility factor can be estimated by
filed observation of the soil colour in the watershed.
3.3. TOPOGRAPHIC FACTOR, LS FACTOR
The combination of the length of the slope (L) and the degree of the slope (S) is known as topographic factor (LS factor). LS factor is dimensionless ratio, which allows comparison of the site being estimated with the standard conditions. Can be computed by filed measurement: Slope length (l); L2 =
∑HI2 +∑VI2 Land slope
(s); S % =
3.4. THE CROPPING MANAGEMENT FACTOR, CThe
cropping management factor can be
computed by formal interview and filed observation method. The erosion control
practice factor, P The erosion control practice factor can be computed by formal interview and filed observation method. 4. RESULTS AND DISCUSSION4.1. ANALYSES OF LAND USE LAND COVER CHANGEThe analysis of land LULC shows that there was significant change in the period between1965 to 2004. The result has revealed that using dominant LULC which contributes more than 15% of the area, the dominant LULC of 1965 were the Dense Wood Land (DSWL), Bushy Wood Land (DBWL), Dense Shrub Land (DSSL) and Wetlands-Non-Forested (WETN) changed in to Bare Land (BRLD), Open Bush Land (OPBL), Open Shrub Land (OPSL), and Swampy Grass Land (SWGL) in 2004.
According to (WWDSE, 2004) dens woodland and bush wood land has been changes to open bush land, open grass land and cultivated land. The growing demand of wood for fire, charcoal, construction materials and household furniture’s has led to changes. The evaluation of
the land use change shows that most of the woodlands were removed or changed as
compared to the situation of the watershed in 1965. The largest part of the
bare land is dominated by agricultural and grazing land; with agro forestry
practice mainly depend on rain fall. Some private small holding farmers are
occupying considerable large portion on the flatter lands. The southern part of
Lake Cheleleka, now a swampy area, serves as a very important grazing land.
Majority of the rural populations are farmers and their life is based on
farming with a considerable income generated from cattle production and cash
crops. The overall change
in the study area was dramatic; there were many reasons behind it, and the
major ones are during the fall of the Dreg regime the forest cover was invaded
by new settlers from different part of the country, these new settlers have
created high population pressure so that a need for searching of new farm land
became evident (WWDSE, 2001). The other factor
that contributed to the land use change was that most of the forest area was
border of two regional states (Oromo and SNNPR) and there were repeated clashes
among the nearby community and incidence of frequent forest fire. And there
also limited protection of different illegal timber processing around the area. 4.2. LAND SLOPE MEASUREMENTDetermination of
field slope is important in soil and water conservation since erosion is
affected by slope of a field. ×100 Were, VI is the
vertical interval between the extreme ends of the field. HI is the horizontal
distance between the extreme ends of the field. In the study area we
use the line level and staff rod method is to measure the land slope. 4.3. LINE LEVEL AND STAFF METHODA horizontal line is
fixed by the help of a line level and a string of convenient length. The string
is tied on staffs. Each staff is placed on each extreme ends. The vertical is
determined by two graduated staffs of each one meter in height. The horizontal
length of the string is measured using tape meter. The line level is placed on
the string of the middle of the staff or rods two persons, called head and rear
string men, hold the staffs and one person, called middle string man, hold the
line level at the middle and adjust the center. The rear string man holding the
staff till the bubble of the line level is centered. The middle string man
tells the rear string man to move the string up and down till the line level is
correctly leveled. The difference
between the rods reading both staffs gives the vertical interval. The distance between
the two rods gives the horizontal distance. Based on the above
procedures to measure the land slope in the study area was measure the grazing
and cultivated land slope. Grazing land slope
in the table shown Table 5: Grazing land slope
Vertical interval is
calculated in the above table. Then to calculating
the slope(S), 11m string
S % = ×100 = S% = 3.75 The slope ranges
between 2-5% is gentle slope. The calculated slope is between the ranges. Calculating the Slope length (L)
(L2) =
(∑VI) 2 + (∑HI) 2 = (0.35+0.4)2
+ (10+10)2 L2 = 0.752+202 L= 20.014 Cultivated land
slope in the table below shown Table 6: Cultivated land slope
Where, VI is
vertical interval HI is horizontal
distance The total vertical
interval is, ∑VI= VI1+
VI2 +VI3 + VI4 + VI5 =
0.58m+0.55m+0.5m+0.56m+0.43m = 2.61m
The total horizontal distance is, = HI1
+HI2+ HI3+ HI4+ HI5 =
10m+10m+10m+10m+10m =50m Then to calculate the slope
S%= S% =
= 5.22 The slope range
between 5-10% is rolling.
The calculated slope
is between these ranges. Slope length (L) L2=
(∑VI) 2+ (∑ HI) 2 = (26.1m) 2+
(50m) 2 L= 50.068m To determine the
soil BULK density, Table 7: Soil bulk density
Calculation: Given
Required
Soil bulk density (b)? Core sampler
diameter (D) =5cm R= =2.5cm Length (Lc) =5cm Mass of core sampler
(MC) =97.6gm w=1gm/cm3at 20co Calculation to fill
the above table For sample No 1 we
have volume of core sampler=bulk volume (total volume) VC=Ac *Lc Where, AC is area of
core sampler it has cylindrical shape And LC, is the
length of core sampler Then AC=πDc2/4 =π (5cm) 2/4 =19.625cm2 VC = π (5cm) 2/4*5cm =98.17cm3
=bulk volume From the above table
to find the mass of water (Mw): Mw =sat-mass –oven
dry mass Mw =262.4gm-238gm
=24.4gm The mass of dry soil
(Md): Md=oven dry mass
–mass of core sampler (Mc) Md =238gm-97.6gm = 140.4gm Using the above
information we can determine the bulk density of the soil: b1=mass of dry soil (1)/Vtotal b1=Md1/VT =140.4gm/98.17cm3 = 1.43gm/cm3 Repeat the above
calculation for sample (6) and the soil sample with the same calculation: We are given for
sample (6) VT =98.17cm3 Mw 6=sat- mass6
–oven dry mass6 =264.2 gm-235.9gm =28.3gm The mass of dry soil
(Md6): Md 6= oven dry mass6
–mass of core sampler (Mc) = 235.9gm -97.6gm =138.3gm Then to determine
the bulk density of sample (6): b6=Md6/VT =138.3gm/98.17cm3=1.41gm/cm 4.4. TEXTURE ANALYSISTexture,
or size distribution of mineral particles (or its associated pore volume), is
one of the most important measures of a soil because finely divided soil
particles have much greater surface area per unit mass or volume than do coarse
particles. Soil (mineral) particles are
broadly segregated into three size classes (1) sand - individual particles
visible with the naked eye, (2) silt - visible with a light-microscope, and (3)
clay - some may not be visible with a light-microscope, especially the
colloidal size (i.e., < 1 micrometer or 0.001 millimeter).This sand, silt
and clay groups are commonly referred to as the soil separates; soil texture is
defined as the relative proportions of each class. In the study area the soil
type is sandy (by bulk density). The
soil texture of the study area was determined in the ACA campus school of
horticultural and plant science soil laboratory by using hydrometer method. Materials 1) Sieved soil (50 g dry wt.
sandy). 2) Electric mixer and cup. 3) Sedimentation cylinder (1000
mL). 4) Hydrometer 5) Thermometer (23°C). Reagents,
Hydrogen per oxide Procedure 1) Place 50g of soil (dry
weight) into a soil dispersing cup. 2) Fill cup to within two inches
of the cup with distilled water, should be at room temperature. 3) Add 5 ml of hydrogen per
oxide. 4) Allow to slake (soak) for 5
minutes sandy soils 5) Transfer suspension to
sedimentation cylinder; use distilled water from squirt bottle to get all of
sample from mixing cup 6) Fill cylinder to 1000-mL mark
with distilled water. 7) Carefully mix suspension with
plunger. After removing plunger, begin
timing. Carefully place hydrometer into
suspension; note reading at 40 seconds.
Read the hydrometer at the top of the meniscus rather than at the bottom. 8) After final 40-second
reading, remove hydrometer, carefully lower a thermometer into the suspension
and record the temperature (°C). Record the temperature for both hydrometer
readings (40 sec and 2 hr.) 9) Mix suspension again and
begin timing for the two-hour reading 10) Make up a blank cylinder with
water and hydrogen per oxide. Record the
blank hydrometer reading 11) Take a hydrometer reading at
2 hours, followed by a temperature reading. Calculations 1) Temperature correction
factor, T (may be different for each reading): T
= (Observed temperature - 23°C) * 0.3=6.9 2) Corrected 40-second reading:
40-sec(c) =
40-sec - Blank + T =29+6.9=35.9 3) Corrected 2-hour reading:
2-hr(c) =
2-hr - Blank + T =10+6.9=16.9 4) %of sand (2-0.05mm) =
(OD soil wt)-(corr-40-second reading)*100 a. OD soil wt = *100% =28.2% 5) % clay(<0.002mm) = *100% =
33.8% 6) % Silt (0.05 - 0.002 mm) = 100% - (% sand + % clay) = 100 % - (28.2%+33.8%) =100% - 62% =38% According
to the percentage of soil triangle the soil textural class is clay loam. Table 8: Soil depth
The soil depth of the study area measuring by tape meter from top soil to bottom layers of the soil. Depth (D1) =0.35m, Depth (D2) =0.5m, Depth (D3) =0.25m, Depth (D4) =0.3m Then to calculate the average soil depth, Dav = = 0.35m The study area of the soil depth ranges between 20-50cm and the class of soil depth is shallow. The rain fall data for the study area obtained from Hawassa metrological station from (2003-2012) in (appendix-2) The annual rain fall is shown in the table below Table 9: annual rain fall
In the above table the annual rain fall in 2012 is 785.4. Then to calculate the rain fall factor(R) based on the adapted Ethiopian condition of the annual rain fall and rain fall factor. Use interpolation method to calculate the rain fall factor (R), Table 10: rainfall factor
== = 385.4(R-441) =14.6(217-R) 385.4R-169961.4=3168.2-14.6R 385.4R+14.6R=173129.6 400R=173129.6 R=432.824 ·
K (the
soil erodibility) This factor is prepared to be estimated based on the soil color. The soil color is brown then K value is 0.2 · L: slope length of the study area we take the cultivated land slope
Using Pythagoras theorem L2=∑HI2 +∑VI2 L2 = 502+2.612 L2=2506.8121 L = L = 50.068m The calculated slope length occurs between 40 and 80 slope length of Ethiopian conditions. By using interpolation to calculate the slope length factor (L). Table 11: slope length factor
Using interpolation method =S = 10.068(L-1.9) = 29.932(1.4-L) 10.068L-10.068*1.9 = 29.932*1.4-29.932L 10.068L+29.932L = 10.068*1.9+29.932*1.4 40L = 61.034 L = L=1.5 ·
S:
slope gradient The slope of the cultivated land of the study area can be calculated as follows.
S% = S% = S% = 5.22 The calculated slope gradient occurs between 5 and 10% of slope gradient of Ethiopian conditions. By using interpolation the slope gradient can be calculated as follows. Table 12: Slope gradient factor
= = 0.22(S-1) =4.78(0.4-S) 0.22S-0.22=4.78*0.4-4.78*S 5S=2.132 S=0.43 · C: the land cover of the study area commonly sorghum and maize. Then the C value is 0.1 ·
P:
management factor Management practice commonly in the farms ploughing up and down then the P value is 1.00 Based on the above information, to calculate the annual soil loss by using USLE equation method in cultivated land
A= Annual soil loss (ton/ha/yr.) R=Erosivity factor (R=432.824) K=Erodibility factor (K=0.2) L=Slope length factor (L=1.5) S=Slope gradient factor(S=0.43) C= Land cover factor (C=0.1) P=Management factor (P=1.0) Then the annual soil loss (A) A=432.824*0.2*1.5*0.43*0.1*1.0 =5.58 ton/ha/yr. Accordingly, the degree of soil erosion or loss can be expressed as weak; medium; serious; severe and Very severe erosion.
Source: (plesnik 1958) · Weak erosion: - causing annual soil loss from 0.05-0.5mm depth or from 0.5- 5 ton/ha/yr. · Medium erosion: - it causes annual soil loss ranging from 0.5- 1.5mm of soil depth or from 5 -15ton/ha/yr. · Serious erosion: - it indicates great danger to the soil because top or upper fertile soil is removed in this erosion. The range of soil loss in this erosion varies from 1.5-5mmof soil depth or from 15-50 ton/ha/yr. · Severe erosion: -it is a case of extreme soil erosion; in which extreme danger to soil takes place. The range of annual soil loss varies from 5-20mm of soil depth or from 50 -200 ton/ha/yr. · Very severe erosion: - in this erosion; the erosion intensity is much greater; as result there is greater consequence than the severe erosion. The average soil removal is found more than 2000ton/ha/yr. According to the degree of soil erosion in the study area the annual soil loss is 5.58ton/ha/yr. Then the degree of soil erosion is medium. 4.5. SEDIMENT YIELDThe amount of eroded materials which completes the journey from origin point to the downstream control point such as reservoir is called sediment yield. The sediment yield from watershed is always less than gross soil erosion. The ratio of amount of sediment yield to the gross erosion is called sediment delivery ratio (SDR). SDR= The SDR is expressed as the percent of sediment yield to gross erosion. The value of SDR is less than 1, because sediment yield is less in magnitude than the gross erosion. The measurements show that, as little as 5% and as much as100% of materials eroded in some watersheds can be delivered to a downstream point. In the above annual soil loss of the watershed is 5.58ton/ha/yr. Let assume the SDR value is (35%, 65% and95%) and can be estimated the sediment yield of the watershed. For SDR =35%, Sediment yield =SDR*Gross erosion (annual soil loss) =35%*5.58ton/ha/yr =1.95 ton/ha/yr For SDR =65%, Sediment yield =65%*5.58ton/ha/yr = 3.62ton/ha/yr For SDR =95%, Sediment yield =95%*5.58ton/ha/yr =5.3ton/ha/yr Then the average sediment yield in the watershed is, = =3.62 ton/ha/yr 4.6. FARMER’S PERCEPTION OF LAND USE AND LAND COVER CHANGE AND SOIL EROSION HAZARDSThe result from farmer’s focus group discussion and key informer’s interviews should that farmers are well aware of what land cover change is, were able to identify the causes, indicators and impacts. Participant’s perceived land cover change as the removal of forest, loss of soil and water recourses in the watershed. The main cause’s suggested by farmers were population pressure, deforestation, rapid expansion of urbanization and the land occupied by investors. The investors have positive and negative impacts of land use and land cover. The positive impacts of the investors · To create the job for the people around the area · To reduce independent of the others · To reduce poverty · To developed the economy etc. The negative impacts of the investors · To reduce land cover change · To facilitate soil erosion · Poor management of SWC measures of the area etc. The participants also identify that major impacts as loss of soil fertility, decreasing production, depletion of water recourse, flooding and siltation. The table below shows the degree of erosion problem Table 14: degree of soil erosion
The rank of major causes of soil erosion in the area Table 15: the rank of major causes of soil erosion
4.7. THE WOREDA AGRICULTURAL EXPERTS AND DEVELOPMENT AGENT’S PERCEPTION FOR THE CAUSE OF SOIL EROSION AND LAND COVER CHANGESBefore 20 years the land was covered by green area or natural forests. From year to year the land cover rapidly decreases by different causes, such as population growth, deforestation, expansion of urbanization construction and road construction. Due to the removal of the land cover results initiation of soil erosion. The major causes of soil erosion, deforestation, over grazing, cultivation, poor agricultural practice, lack of knowledge of SWC measures etc. the effects of soil erosion such as loss of soil fertility, decreasing production, increasing poverty and climate change. 5. CONCLUSION AND RECOMMENDATIONLand use land cover is a term that includes categories of land use and land cover. Land cover is the physical or other kind of material that covers of the land surface. Land use is the purpose of human activity on the land. Land use land cover change can be easily observed in forestry on global scale, the largest change in terms of land area, and arguably also in terms of hydraulic effects, is from deforestation, rapid land use change for poor farming activity are, overgrazing and lack of SWC awareness. Changes in land use from 1965 to 2004 land use pattern at Tikurwuha catchment have caused a higher sediment yield. From this study, it can be conclude that there were drastic land use land cover change between1965 and 2004 due to land use competition between cultivated land and wood land, grazing land and lives toke production, fuel wood collection and wood land regeneration etc. The increasing need for fuel wood charcoal, construction pole and the expansion of cultivated land adversely affect the natural vegetation cover. The rapid expansion of farm lands, deforestation and high population growth in the area resulted in high rate of soil erosion in the catchment area. The high soil loss rate in the catchment can be attributed to the deforested lands, the poor land cover, the shallow soil depth, and high rain fall intensity. Moreover the cultivated areas have the highest soil loss rate, followed by grass land, shrub land, urban area and forest respectively. To maximize the available resources integrating the effect of soil erosion in soil loss, those land uses and slope classes having high rate of erosion should be given first priority during the introduction of intensive and well-designed SWC interventions at Tikurwuha watershed. From the research we recommend that the water shed will be plan to develop biological and physical soil conservation measures. SOURCES OF FUNDINGNone. CONFLICT OF INTERESTNone. ACKNOWLEDGMENTNone. REFERENCES[1] Admassu, D. and Casselman J. (2000). Otolith age determination for adult tilapia, Oreochromisniloticus L. from Lake Hawassa (Ethiopian Rift Valley) by interpreting biannual and differentiating biannualrecruitment. Hydrobiologia; 418:15-24. [2] Beasle D.B Huggins L.F and Monke E.J. (1980). ANSWERS: a model for water shade planning Trans ASAE; 23(4): 938-944. [3] Food and Agricultural Organization (FAO), (1984). Ethiopian highlands reclamation study (EHRS). Final Report Rome: 1–2. [4] Foster, G. R. (1988). Modeling soil erosion and sediment yield, (Lal, R. (Ed.), Soil erosion research methods, Soil and Water Conservation Society. [5] Hurni, H. (1988). Degradation and conservation of the resources in the Ethiopian high lands.Mountain Research and Development; 8:2-3. [6] Hudson, N. (1996). Soil conservation (Second edition) London: B.T. Bats ford. Lal and [7] Morgan RPC. (2001). a simple approach to soil loss prediction: a revised Morgan–Morgan–Finney model. Catena; 44: 305–322. [8] Tenalem Ayenew. (2007): Some Improper Water Resources Utilization Practices and Environmental Problems in the Ethiopian Rift. Africa water journal; 1 (1):80-105. [9] Wei, W., Chen, L., Fu, B., Huang, Z., Wu, D and Gui, L, (2007). The effect of land uses and rain fall regimes on runoff and soil erosion in the semi-arid loess hilly area, China. Journal of Hydrology 335, 247-258. [10] Williams, J.R. (1975). Sediment yield prediction with universal equation using run off energy factor. In Present and prospective technology for predicting sediment yield and sources: Proceedings of the sediment yield workshop, USDA [11] Wischmeier W.H., Smith DD. (1965). Predicting rainfall-erosion losses from crop land east of Rocky Mountains: guide for selection of practices for soil and water conservation. US department of agriculture, Agricultural hand book 282. [12] Young RA.Onstad CA, Bosch DD. Anderson WP. (1987). AGNPS: an agricultural point source pollution model. Conservation research report 35, US Dept. Agric. Res.Services, Washington, DC, USA. [13] Yemane G. (2004). Assessment of Water Balance of Lake Hawassa Catchment, MSc ITCEnschede, the Netherlands
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