**5. Soil Loss Estimation**

with both wisdom and prudence, is not antithetical to good stewardship. "All of us have vested interests in making forest management a wise and efficient use of resources. Soil in‐

Characterization of soil loss is very important for environment and natural resources. In erosion control planning, soil loss estimates for a particular site are determined using a prediction model and compared with a T-value for that site [31]. The Universal Soil Loss Equation (USLE) is an example of a model used extensively to predict erosion from crop‐ lands and rangelands. More recently, the Agricultural Research Service, Forest Service, and the Bureau of Land Management have joined in a cooperative effort, the Water Erosion Prediction Project (WEPP). WEPP has been implemented to develop an improved model based on modern technology for estimating soil erosion by water. WEPP technology is based on fundamental hydrologic and soil erosion processes and is designed to replace the wide‐

Until recently, prediction of soil loss rates on National Forest lands involved using the USLE [8, 22]. Soil losses were evaluated in the context of potential soil losses, natural soil losses, current soil losses and tolerable soil losses. Potential losses were those that would occur af‐ ter complete removal of the vegetation and litter. Natural losses were associated with the potential natural vegetation community. Current losses were those occurring with current management. Tolerable loss was assumed to be the rate that can occur while sustaining in‐

The Universal Soil Loss Equation (USLE) is a widely used method for calculating annual soil losses, based on rainfall, runoff, slope, runoff length, soil type and landuse parameters. The equation originally developed on small agricultural plots, but has been adopted for evaluat‐

where *A* represents the soil loss, commonly expressed in tonnes ha-1 year-1. *R* refers to the rainfall erosivity factor, calculated by the summation of the erosion index EI30 over the peri‐ od of evaluation. EI30 is a compound function of the kinetic energy of a storm and its 30 min maximum intensity. The latter factor is defined as the greatest average rainfall intensity experiences in any 30-min period during a storm. *K* is the soil erodibility factor reflecting the susceptibility of a soil type to erosion. It is expressed as the average soil loss per unit of the R factor. *L* is an index of slope length, *S* is a slope gradient index, *C* is an index for the pro‐ tective coverage of canopy and organic material in direct contact with the ground. It is meas‐ ured as the ratio of soil loss from land cropped under specific conditions to the corresponding loss from tilled land under clean-tilled continuous fallow conditions. Finally, the protective factor P represents the soil conservation operations or other measures that

*A RKLSCP* = ´ ´´´´ (1)

ing erosion from large watersheds under a wide range of land uses. [41]

formation can immeasurably help us be good stewards of the land" [12].

**4. Soil Loss Characterization**

ly used USLE [8].

92 Research on Soil Erosion Soil Erosion

herent site productivity [8].

The soil loss expressed as ton ha-1 year-1 is determined using the Universal Soil Loss Equa‐ tion (USLE). Soil samples are collected from sample plots and analyzed in a laboratory for soil properties including; silt %, sand %, clay %, organic matter %, and classes for structure and permeability. The soil erodibility factor K values of soil samples are calculated using the following equation [41]:

$$K = \frac{2.1 \times M^{1.14} \times 10^{-4} \times (12 - OM) + 3.25 \times (S \cdot 2) + 2.5 \times (P \cdot 3)}{100} \tag{2}$$

where *OM* is soil organic matter content, *M* is (%silt + %very fine sand)x(100-%clay), *S* is soil structure code and *P* is permeability class. If soil organic matter content was greater or equal to 4%, *OM* was considered constant at 4%. Moreover, the influence of rock fragments on soil loss was accounted for by a subsurface component in the soil erodibility *K* factor [29]. The rainfall erosivity was differently obtained from average annual rainfall erosivity map for countries or locations.

The slope length factor L, accounts for increases in runoff volume as downslope runoff lengths increase. The slope stepness factor S accounts for increased runoff velocity as slope stepness increases. These factors were obtained from digitized topographic maps of scale 1:25 000.

For direct application of the USLE a combined slope length and slope stepness (LS) factor was evaluated for each sample plots as [1]:

$$LS = I^{0.8} \times (0.0138 + 0.00965 \times S + 0.00138 \times S^2) \tag{3}$$

**6. Data Analysis and Modeling**

**1.** measures of physiographic structure and

**2.** measures of the stand level of structure and density.

have been used as measures of the stand level of structure.

ume (*V*), different stand density indexes [7, 28, 10, 5] may be tested.

to two groups:

generally hypothesized:

length), *S <sup>2</sup>* is the stand structure (*d*

The candidate variables modeling are numerous and diverse. Hartanto et al. [14] classified such variables in four groups: Soil characteristics, physiographic properties, climatic proper‐ ties and stand characteristics. The candidate variables of soil loss models can be divided in

Altitude, exposition, aspect, slope and exposure length have been used as measures of phys‐ iographic structure. Mean height, mean diameter, crown closure and stand density may

Several possibilities exist to describe stand density. Hamilton [13], Ojansuu et al. [26], Van‐ clay [38], Thus [37], all of whom used *BA*, and [3], who used *N*, have provided examples of models with stand density parameters as explicatory variables in modeling. Since *N* and *BA* were directly determined, and did not rely on functional relationships, as opposed to vol‐

The soil loss model should be applicable to different stand structures. Therefore, all varia‐ bles must be tested. Based on the discussion above, the following soil loss models have been

0 11 2 2 3 3 *A SSS* <sup>ˆ</sup> =+ + +

 b

where *S <sup>1</sup>* is the physiographic structure (altitude, exposition, aspect, slope and exposure

Relationship between magnitude of soil loss obtained from sample plots and stand charac‐ teristics have been used to model soil protection value one of the forest values for quantify‐ ing soil loss by using linear, nonlinear, mixed linear and mixed nonlinear procedures in Regression Analysis Method The significance of parameter estimates was tested by means of *t*=b/ASE, where *b* is the parameter estimate and *ASE* is the asymptotic standard error. The parameters of the model for data have been determined using a software package (e.g. SPPS, SAS). Only were variables which are significant (*P*<0.05) included in the equation. A soil loss model is constructed based on some site and stand characteristics as a predictor and possi‐ ble insignificant predictor are excluded. The predicted variable in the soil loss model is an‐ nual soil loss amount, which resulted in a linear or nonlinear relationship between the dependent and independent variables. The predictors of a soil loss model were chosen from stand level characteristics as well as their transformations. Some of them had to be signifi‐ cant at the 0.05 level without any systematic errors in residuals. The assumption of homo‐

 b

(4)

Modeling of Soil Erosion and Its Implication to Forest Management

http://dx.doi.org/10.5772/ 53741

95

*<sup>q</sup>*and crown closure) and *S* 3 is the stand density.

bb

¯ *<sup>q</sup>*, *h* ¯

scedasticity has been tested using the Durbin-Watson test.

where *l* is runoff length (meter), *S* is slope (percent).

Crop and management factor is the soil loss from an area with specified cover. *C* is a func‐ tion of landuse conditions such as vegetation type, before and after harvesting, crop resi‐ dues, and crop sequence. Forest management practices create a variety of conditions that influence sheet and rill erosion. The USLE has been used with varying degrees of success to predict these forms of erosion on forest land. Assigning a proper value to cover-manage‐ ment factor (C) in the USLE is a problem, however. An undisturbed, totally covered forest soil usually yields no surface runoff. What erosion does occur on undisturbed forest land comes from stream channels, soil creep, landslide, gullies, and pipes, none of which are evaluated by the USLE. Logging, road building, site preparation, and similar activities that disturb and destroy cover expose the soil to the erosivity of rainfall and runoff [41].

Tree categories of woodland are considered separately:


Factor C for undisturbed forest land may be obtained from Table 1 [9].


**Table 1.** Factor *C* for undisturbed forest land

The conservation practice factor *P*, is determined by the extend of conservation practices such as strip, cropping, contouring, and terracing practices, which tend to decrease the ero‐ sive capabilities of rainfall and runoff. Values of *P* range from zero to one.
