1. SOIL LOSS ESTIMATION

2. Introduction Soil loss estimation is fundamental to soil conservation.
It enables concise objectives about soil conservation to be formulated and provides a means of achieving those objectives.
It forms the foundation of rational design in the field, provides data for extension programmes and planning exercises, and gives firm basis for natural resources legislation.
It is therefore an invaluable aid to decision making at all levels throughout agriculture and natural resources management.

3. Modelling and quantifying Wind Erosion:
The Wind Erosion Equation. (WEQ)
Wind Erosion is a phenomenon of Dry Climates.
Three major factors in wind erosion are;
Wind Velocity and turbulence
Nature of material over which the wind is blowing e.g. roughness of soil surface and soil texture,
Moisture content is a prime factor since a film of moisture tend to anchor soil particles
Other factors are;
Particle size distribution
Degree of soil aggregation
Amount of vegetation cover

4. Wind Erosion Factors, Cont.
These factors have been combined into the WEQ as follows;
E =f(I,C,K,L,V)
Where;
E= potential erosion loss usually quoted in t/acre (2.242t/ha)
I = Soil erodibility
C = local wind climate factor
K = Soil Roughness
L = Length of open wind blow
V= Vegetation cover
The solution to this equation requires the use of tables, figures , normographs, etc. ( More information can be obtained from Hudson, 1981)

5. Soil Erodibility Index (I)
Based on soil texture and degree of aggregation,
All soils contain some particles whose size is such that they are susceptible to erosion if occurring as single isolated particles.
Critical particle size is 0.84mm
The greater the proportion of detachable fine particles, the higher the soil erodibility index and the more likely is wind erosion to occur.

6. Typical values for the soil erodibility index (I)
% dry soil < or = 0.84mm
Erodibility Index (I)
(t/acre)
(t/ha) 99
310
695 95
180
404 80 98
220 50 38 85 29 2 4

7. Climatic factor (C) It is dimensionless. It depends on;
Wind speed (the higher the wind speed, the more likely is erosion to occur),
Effective soil moisture content (the drier the soil, the greater the risk of erosion occurring)
In the WEQ , wind speed is taken as the mean annual wind velocity at 30 feet (9.14m) above the ground level.
Moisture content is the Thornthwaite P-E (precipitation –Evaporation) index , calculated from climatic factors .
Typical values of the resulting climate factor, C, can range from 0.3 to 3 being higher in drier and more arid conditions.

8. Surface roughness K (Dimensionless)
Wind tends to pick up and transport particles more easily from smoother surfaces than from irregular surfaces.
Also, ridges tend to trap saltating grains.
K is calculated from the height of ridges produced by tillage and planting and the distance between them.
Typical values are 1.0 for a smooth surface (the most erodible) , 0.95 for a non-terraced unploughed stubble and 0.5 for a field with about 10cm vertical range in micro relief.

9. Length of open-wind blow L
 It is dimensionless
The more open the land, the more erosion is likely to take place.
L is calculated from field length, field width and orientation in relationship with the prevailing wind direction.
There is a special set of tables from which this can be derived, giving typical values from 0 (small protected areas) to 1.0 (wide open areas with several hundreds of meters of open ground).

10. Vegetation Cover (V)
Vegetation cover (living or dead) protects soil surfaces against wind erosion by creating a zone of relatively still air above the ground surface.
The WEQ takes account of standing live biomass, standing dead residue, and flattened crop residue.
The original work to quantify this was carried out for flattened wheat straw, and so all weights of living or dead vegetation matter need to be converted to “flattened straw equivalents”, defined as 10inch (254mm) row spacing and stalks oriented perpendicular to the wind direction.
In practice, the weight of the vegetation cover per unit area is determined and multiplied by a factor depending on its type and height.
For example for grain sorghum, the actual weight is multiplied by 1.5 if flat and by 2.3 if 30cm tall, by 3 if 50cm tall, or for standing wheat stable the weight is multiplied by 6.

11. The Universal Soil Loss Equation (USLE)

12. Introduction
This is an empirical soil loss estimation equation developed in the 1960s in the United States of America.
Research work on soil loss estimation that contributed to the development of this model had started in the USA around 1940.
The major purpose of the soil loss equation is to guide methodical decision making in conservation planning on a site basis.
The USLE is an erosion model designed to predict the long term average soil losses in runoff from specific field areas in specified cropping and management systems.
Widespread field use has substantiated its usefulness and validity for this purpose.
It is also applicable for soil loss estimation for non agricultural conditions such as construction sites.

13. Introduction cont.
With appropriate selection of its factor values, the equation will compute the average soil loss for a multicrop system, for a particular crop year in a rotation, or for a particular crop stage period within a crop year.
It computes the soil loss for a given site as the product of six major factors whose most likely values at a particular location can be expressed numerically.
Erosion variables reflected by these factors vary considerably about their means from storm to storm, but effects of the random fluctuations tend to average out over extended periods.
Because of the unpredictable short-time fluctuations in the levels of influential variables however, present soil loss equations are substantially less accurate for prediction of specific events than for prediction of long term averages.
The equation is based on the fact that erosion is a function of erosivity and erodibility.

14. PHYSICAL CHARACTERISTICS
The USLE Variables
Can be illustrated as shown in the diagram below;
EROSIVITY and ERODIBILITY
RAINFALL
ENERGY
PHYSICAL CHARACTERISTICS
LAND MANAGEMENT
MANAGEMENT
CROP MANAGEMENT A = R x K LS P C

15. The USLE Equation The equation is as follows; A = R*K*LS*P*C Where;
A = Soil loss in tonnes per acre per year,
R = Rainfall and Run off erosivity index = EI30/100
K= Soil erodibility : mean annual loss per unit of erosivity for a standard condition of bare soil, 9% slope and 22m long and with no conservation practice.
LS = Slope length factor,
P= Conservation Practice factor,
C= Crop management factor

16. Factors of the USLE : R, the rainfall factor.
More appropriately called the erosivity index.
It is a statistic calculated from the annual summation of rainfall energy in every storm.
Correlates with raindrop size times its maximum 30- minute intensity.
Empirically, this “EI-Index” was found to have the highest correlation with soil erosion from experimental plots.
As expected, it varies geographically, but its seasonal distribution is also important in calculating C- values.

17. Factors of the USLE Cont.
K, the soil erodibility factor.
This factor quantifies the cohesive character of a soil type and its resistance to dislodging and transport (particle size and density dependent) due to raindrop impact and overland flow shear forces.
Knowing these soil properties, we are able to estimate K.
LS, the topographic factor.
Steeper slopes produce higher overland flow velocities.
Longer slopes accumulate run off from larger areas and also result in high flow velocities.
Thus, both result in increased erosion potential, but in a non-linear manner.
For convenience, L and S are frequently lumped into a single term.

18. Factors of the USLE Cont.
C, the Crop management factor:
Represents the ratio of soil loss from land cropped under specified conditions to corresponding loss under tilled, continuous fallow conditions.
This incorporates effects of:
tillage management (dates and types) ,
crop,
cropping history (rotation),
crop yield level (organic matter production potential).
seasonal EI- index distribution,

19. Factors of the USLE Cont.
P, the conservation practice factor.
Practices included in this term are contouring, strip cropping (alternative crops on a given slope established on the contour), and terracing.
As a rule of thumb, contouring reduces to one- half the soil loss caused by up-and-down hill farming, strip cropping to one-half that of contouring and terracing to one-half that of strip cropping.

20. Procedure for Using the USLE
Determine the R factor.
Based on the soil texture determine the K value. If there is more than 1 soil type in a field and the soil textures are not very different, then use the soil type that represents the majority of the field.
Repeat for other soil types if necessary.
Divide the field into sections of uniform slope gradient and length.
Assign an LS value to each section.
Choose the crop type factor and tillage method factor for the crop to be grown.
Multiply these 2 factors together to obtain the C factor.
Select the P factor based on the support practice used.

21. Organic Matter Content
Table b1
K Factor Data
Organic Matter Content
Textural Class
Average
Less than 2%
More than 2%
Clay
0.22
0.24
0.21
Clay Loam
0.3
0.33
0.28
Coarse Sandy Loam
0.07 --
Fine Sand
0.08
0.09
0.06
Fine Sandy Loam
0.18
0.17
Heavy Clay
0.19
0.15
Loam
0.34
0.26
Loamy Fine Sand
0.11
Loamy Sand
0.04
0.05
Loamy Very Fine Sand
0.39
0.44
0.25
Sand
0.02
0.03
0.01
Sandy Clay Loam
0.2
Sandy Loam
0.13
0.14
0.12
Silt Loam
0.38
0.41
0.37
Silty Clay
0.27
Silty Clay Loam
0.32
0.35
Very Fine Sand
0.43
0.46
Very Fine Sandy Loam

22. Table b2 LS Factor Calculation Slope Length ft (m) Slope (%) LS Factor
100(31) 10
1.3800 8
0.9964 6
0.6742 5
0.5362 4
0.4004 3
0.2965 2
0.2008 1
0.1290
0.0693
200(61)
1.9517
1.4092
0.9535
0.7582
0.5283
0.3912
0.2473
0.1588
0.0796
400(122)
2.7602
1.9928
1.3484
1.0723
0.6971
0.5162
0.3044
0.1955
0.0915
800(244)
3.9035
2.8183
1.9070
1.5165
0.9198
0.6811
0.3748
0.2407
0.1051

23. Equation for Calculation of LS
Used if not using table above;
LS = [ S S2 ][L/C]NN
Where ;
S = Slope steepness (%)
L = length of slope
C =Constant= 72.5 for imperial units or 22.1 for metric units
NN see table below;

24. Table b4: Crop Type Factor
NN Values S
<1 (%)
1≤ Slope<3
3≤ Slope<5 ≥5 NN
0.2
0.3
0.4
0.5
Table b4: Crop Type Factor
Crop Type
Factor
Grain Corn (Maize)
0.40
Silage Corn, Beans &Canola
0.50
Cereals (spring and Winter)
0.35
Seasonal Horticultural Crops
Fruit Trees
0.10
Hay and Pasture
0.02

25. Table b5: Tillage Method Factor
Fall Plough
1.0
Spring plough
0.90
Mulch Tillage
0.60
Ridge Tillage
0.35
Zone Tillage
0.25
No-Till

26. Table b6: P Factor Data Support Practice P Factor Up & Down Slope 1.0
Cross Slope
0.75
Contour Farming
0.50
Strip cropping, cross slope
0.37
Strip cropping, contour
0.25

27. Table b7: Soil Loss Tolerance Rates
Soil Erosion Class
Potential Soil Loss (ton/acre/year)
Very Low (Tolerable)
<3
Low
3-5
Moderate
5-10
High
10-15
Severe
>15

28. Table b8 Management Strategies to Reduce Soil Loss
Factor
Management Strategies
Example R
The R factor for a field cannot be altered. - K
The K factor for a field cannot be altered. LS
Terraces may be constructed to reduce the slope length resulting in lower soil losses.
Terracing requires additional investment and will cause some inconvenience in farming. Investigate other soil conservation practices first. C
The selection of crop types and tillage methods that result in the lowest possible C factor will result in less soil erosion.
Consider cropping systems that will provide maximum protection for the soil. Use minimum tillage systems where possible. P
The selection of a support practice that has the lowest possible factor associated with it will result in lower soil losses.
Use support practices such as cross-slope farming that will cause deposition of sediments to occur close to the source.
Table b8 Management Strategies to Reduce Soil Loss

29. Example: Calculation of Soil Loss Using the USLE
The following is given;
Rainfall and Runoff Factor (R)
For the sample field in a particular place, obtain the tables from local weather Station. Say we have R =100 for our particular field.
Soil Erodibility Factor(K)
The sample field consists of fine sandy loam soil with average organic matter content. The K factor is obtained from table b1 or from normographs. K factor = 0.18 for our case.
Slope Length-Gradient Factor (LS)
The sample field is 800ft long with a 6% slope. The LS factor can be obtained directly from Table b2 or may be calculated using the equation. The NN value from table b3 to be used in the equation is 0.5. LS factor = 1.91.
Crop/Vegetation and Management Factor (C)
The sample field was ploughed in the spring and grain maize was planted. The C factor is obtained from the crop type factor (Table b4.) and the tillage method factor (Table b5).
Crop type factor for grain maize = 0.4
Tillage method factor for spring plough =0.9
C Factor = 0.4x0.9= 0.36
Support Practice Factor
Cross slope farming is used on this sample field. The P factor was obtained from table b6. P factor = 0.75.

30. Solution Therefore; A = RKLSCP =100x0.18x1.91x0.36x0.75
=9.28tons/acre/year.
Referring to table b7, it will be seen that this soil loss rate of 9.28 tons/acre/year is in the moderate range and considerably higher than the “tolerable loss level” of 3 tons/acre/year.
To reduce the soil losses for this sample field below 3 tons/acre/year, we will make the following changes to the above example.
Change the tillage method from “spring plough (0.9)” to “no-till (0.25)”.
Therefore, C factor (revised)= 0.4x0.25=0.10.

31. Solution Cont. Hence the adjusted annual soil loss value is;
A = RKLSCP
=100x0.18x1.91x0.10 x0.75
=2.58tons/acre/year.
Thus by changing the tillage practice, the average annual predicted soil loss for this field is below the “tolerable soil loss” of 3 tons/acre/year.

32. Accuracy of USLE Predictions
Soil losses computed with the USLE are best available estimates, not absolutes:
The predictions will be generally most accurate for medium- textured soils, slope lengths of less than 400ft (122m), gradients of 3 to 18%, and consistent cropping and management systems that have been represented in the plot studies.
The further these limits are exceeded, the greater will be the probability of significant extrapolation error.

33. Accuracy of USLE Predictions Cont.
Because of the unpredictable short-time fluctuations in the levels of influential variables however, present soil loss equations are substantially less accurate for prediction of specific events than for prediction of long term averages.
The accuracy of a predicted soil loss will depend on how accurately the physical and management conditions on the particular piece of land are described by the parameter values used to enter the factor evaluation tables and charts.
An error in the selection of a factor value will produce an equivalent percentage error in the soil loss estimate.

34. Later Developments concerning the use of the USLE
In the early 1980s, the United States Department of Agriculture (USDA) used the USLE and field-collected data from more than a million sample points to estimate soil loss from all non-Federal lands throughout the United States.
Based upon this analysis, they realised the need for improved erosion-prediction technology and requested an overhaul of the USLE.
The Revised Universal Soil Loss Equation (RUSLE) resulted from a 1985 workshop of government agency and university soil erosion scientists.
The workshop participants concluded that the USLE should be updated to incorporate the considerable amount of erosion information that had accumulated after the USLE had been developed, and to specifically address the application of the USLE to land uses other than agriculture.
This effort resulted in the computerized technology in the form of RUSLE.

35. Later Developments concerning the use of the USLE Cont.
The development of RUSLE included several USLE modifications of importance to mined lands, construction sites, and reclaimed land applications.
The climate data set was greatly expanded to include weather bureau stations at many more locations.
The K factor was modified to account for the variability of soil erodibility during the year.
Both the K and C factors now take into account the multivariate influence of rock fragment covers within soil profiles and fragments resting upon hill slope surfaces.
The equations used to estimate the LS factor were reconstituted to improve their accuracy and extended to include steeper hill slope gradients than the equations contained in the USLE.

36. Later Developments concerning the use of the USLE Cont.
The method of determining C factor values was modified using a sub-factor approach that incorporates input values describing the main features of a cover-management system as it influences soil-loss rates.
Consequently, RUSLE now can be applied to many more field conditions, and provides much more site-specific C values than does the USLE.
New process-based equations were developed to estimate P values, overcoming a major limitation of the USLE.
These equations accommodate a wide range of site-specific practice conditions and can estimate sediment yield for concave hill slopes.
Like its predecessor the USLE, The RUSLE estimates soil loss from a hill slope caused by raindrop impact and overland flow (collectively referred to as "interrill" erosion), plus rill erosion.
It does not estimate gully or stream-channel erosion.
In summary, the Revised Universal Soil Loss Equation is a technology for estimating soil loss from most undisturbed lands experiencing overland flow, from lands undergoing disturbance, and from newly or established reclaimed lands.
RUSLE also may be used to assess success of land reclamation efforts.

37. THE SOIL LOSS ESTIMATION MODEL for SOUTHERN AFRICA (SLEMSA)