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1 PREDICTING

- RAINFALL

EROSION

LOSSES

f

A GUIDE TO CONSERVATION PLANNING

mK UNITED STATES AGRICULTURE PREPARED BY

|] DEPARTMENT OF HANDBOOK SCIENCE AND

' AGRICULTURE NUMBER 537 EDUCATION

ADMINISTRATION
U.S. Department of Af^' '

National AgricuJtural L..

Division of Lending

PRFDirTINC ^^'^'""^''^^'^^'^"'^ ^^^^^

RAINFALL

EROSION

LOSSES

A GUIDE TO CONSERVATION PLANNING

Supersedes Agriculture Handbook No. 282,

''Predicting Rainfall-Erosion Losses From Cropland East of the Rocky Mountains"

Science and Education Administration

United States Department of Agriculture

in cooperation with

Purdue Agricultural Experiment Station

USDA policy does not permit discrimination because of age, race, color, national origin, sex, or

religion. Any person who believes he or she has been discriminated against in any USDA-related

activity should write immediately to the Secretary of Agriculture, Washington, D.C. 20250.

December 1978
ABSTRACT

Wischmeier, W. H., and Smith, D.D. 1978. Predicting rainfall erosion losses—a

guide to conservation planning. U.S. Department of Agriculture, Agriculture

Handbook No. 537.

The Universal Soil Loss Equation (USLE) enables planners to

predict the average rate of soil erosion for each feasible alternative combination of crop system and management practices

in association with a specified soil type, rainfall pattern, and

topography. When these predicted losses are compared with

given soil loss tolerances, they provide specific guidelines for

effecting erosion control within specified limits« The equation

groups the numerous interrelated physical and management

parameters that influence erosion rate under six major factors

whose site-specific values can be expressed numerically. A half

century of erosion research in many States has supplied information from which at least approximate values of the USLE

factors can be obtained for specified farm fields or other small

erosion prone areas throughout the United States. Tables and

charts presented in this handbook make this information readily

available for field use. Significant limitations in the available

data are identified.

The USLE is an erosion model designed to compute longtime

average soil losses from sheet and rill erosion under specified

conditions. It is also useful for construction sites and other nonagricultural conditions, but it does not predict deposition and

does not compute sediment yields from gully, streombank, and

streambed erosion.

Keywords: Conservation practices, conservation tillage, construction sites, crop canopy, crop sequence, delivery ratios, erosion factors, erosion index, erosion prediction, erosion tolerances, erosivity, gross erosion, minimum tillage, no-till,

rainfall characteristics, rainfall data, residue mulch, runoff,

sediment, sediment delivery, slope effect, water quality,

soil erodibility.

For sale by the Superintendent of Documents, U.S. Government Printing OflBice

Washington, D.C. 20402

Stock Nimiber 001-000-03903-2
CONTENTS

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Purpose of handbook 1

History of soil loss equations 1

Soil loss tolerances 2

Soil loss equation 3

Rainfall and runoff factor (R) 5

Rainfall erosion index 5

R values for thaw and snowmelt 7

Soil erodibility factor (K) 8

Definition of factor K 8

Values of K for specific soils 8

Soil erodibility nomograph 10

Topographic factor (LS) 12

Slope-effect chart 12

Slope-length effect 14

Percent slope 15

Irregular slopes 16

Changes in soil type or cover along the slope 16

Equation for soil detachment on successive segments of a slope 17

Cover and management factor (C) 17

Definition of factor C 17

Cropstage periods , 18

Quantitative evaluations of crop and management effects 18

Soil loss ratios 20

Erosion index distribution data 21

Procedure for deriving local C values 28

Support practice factor (P) 34

Contouring 34

Contour stripcropping 36

Terracing 37

Applying the soil loss equation 40

Predicting cropland soil losses . 40

Determining alternative land use and treatment combinations 42

Construction sites 44

Estimating upslope contributions to watershed sediment yield 45

Accuracy of USLE predictions 47

References 48

Appendix 50

Estimating percentages of canopy and mulch covers 50

Probability values of El in the United States 50

Computing the erosion index from recording-rain gage records 50

Conversion to metric system 51

'D5
TABLES

1 .—Computed K values for soils on erosion research stations 9

2.—Approximate values of the soil erodibility factor, K, for 10 benchmark soils in Hawaii 9

3.—Values of the topographic factor, LS, for specific combinations of slope length and steepness 12

4.~Estimated relative soil losses from successive equal-length segments of a uniform slope ...... 15

5.—Ratio of soil loss from cropland to corresponding loss from continuous fallow 22

5—A.-~Approximote soil loss ratios for cotton 25

5--B.—Soil loss ratios for conditions not evaluated in table 5 26

5—C—Soil loss ratios (percent) for cropstage 4 when stalks are chopped and

distributed without soil tillage 2Ó

5--D.—Factors to credit residual effects of turned sod 26

6.—Percentages of the average annual El which normally occurs between

January 1 and the indicated dates. Computed for the geographic areas shown in figure 9 . . 28

7.—Monthly distribution of El at selected rain gage locations 29

8.—Sample working table for derivation of a rotation C value 30

9.—Mulch factors and length limits for construction slopes 31

10.—Factor C value for permanent pasture, range, and idle land 32

11 .—Factor C values for undisturbed forest land 33

12.-Factor C for mechanically prepared woodland sites 34

13.—P values and slope-length limits for contouring 3*

14.—P values, maximum strip widths, and slope-length limits for contour stripcropping 36

15.—P values for contour-farmed terraced fields 37

16.—Maximum permissible C values (T/RKLS) for R = 180, K = 0.32, and 1 = 5 43

17.—Observed range and 50-, 20-, and 5 percent probability values of erosion

index at each of 181 key locations 52

18.—Expected magnitudes of single-storm El index values 54

19.—Kinetic energy of rainfall expressed in foot-tons per acre per inch of rain 56

20.—Kinetic energy of rainfall expressed in metric ton-meters per hectare per centimeters of rain . 56

FIGURES

1.—Average annual values of the rainfall erosion index (map) between pages 6 and 7

2.—Estimated average annual values of the rainfall erosion index in Hawaii 7

3,—The soil erodibility nomograph 11

4.—Slope-effect chart (topographic factor, LS) ... 13

5.—Influence of vegetative canopy on effective El values 19

6.—Combined mulch and canopy effects when average fall distance of drops

from canopy to the ground is about 40 inches 19

7.—Combined mulch and canopy effects when average fall distance of drops

from canopy to the ground is about 20 inches 19

8.—Typical El-distribution curves for three rainfall patterns 27

9.—Key map for selection of applicable El-distribution data from table 6 27

10.—Relation of percent cover to dry weight of uniformly distributed residue mulch 50

11 .—Slope-effect chart for metric system 58
PREDICTING RAINFALL EROSION LOSSES—

A GUIDE TO CONSERVATION PLANNING

Waiter H. Wischmeier and Dwight D. Smith^

PURPOSE OF HANDBOOK

Scientific planning for soil and water conservation requires knowledge of the relations between

those factors that cause loss of soil and water and

those that help to reduce such losses. Controlled

studies on field plots and small watersheds have

supplied much valuable information regarding

these complex factor interrelations. But the greatest possible benefits from such research can be

realized only when the findings are converted to

sound practice on the numerous farms and other

erosion prone areas throughout the country. Specific guidelines are needed for selecting the control practices best suited to the particular needs of

each site.

The soil loss prediction procedure presented in

this handbook provides such guidelines. The procedure methodically combines research information from many sources to develop design data

for each conservation plan. Widespread field experience for more than two decades has proved it

highly valuable as a conservation planning guide.

The procedure is founded on an empirical soil loss

equation that is believed to be applicable wherever numerical values of its factors are available.

Research has supplied information from which at

least approximate values of the equation's factors

can be obtained for specific farm fields or other

small land areas throughout most of the United

States. Tables and charts presented in this handbook make this information readily available for

field use.

This revision of the 1965 handbook (64) updates

the content and incorporates new material that has

been available informally or from scattered research reports in professional journals. Some of

the original charts and tables are revised to conform with additional research findings, and new

ones are developed to extend the usefulness of

the soil loss equation. In some instances, expanding a table or chart sufficiently to meet the needs

for widespread field application required projection of empirical factor relationships appreciably

beyond the physical limits of the data from which

the relationships were derived. Estimates obtained

in this manner are the best information available

for the conditions they represent. However, the

instances are identified in the discussions of the

specific erosion factors, tables, and charts. Major

research needs are suggested by these discussions

and were recently summarized in an available

publication by Stewart and others (42).

HISTORY OF SOIL LOSS EQUATIONS

Developing equations to calculate field soil loss

began about 1940 in the Corn Belt. The soil loss

estimating procedure developed in that region

between 1940 and 1956 has been generally re-

^ Retired. Former research statistician (water management). Science and Education Administration (SEA), and professor emeritus,

agricultural engineering, Purdue University, West Lafayette, Ind.;

and agricultural engineer, SEA, Beltsville, Md.

ferred to as the slope-practice method. Zingg (64)^

published an equation in 1940 relating soil loss

rate to length and percentage of slope. The following year. Smith (38, 39) added crop and conservation practice factors and the concept of a specific

soil loss limit, to develop a graphical method for

" Numbers in parentheses refer to References p. 48.
UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

determining conservation practices on Shelby and

associated soils of the Midwest. Browning and associates (Ó) added soil and management factors

and prepared a set of tables to simplify field use

of the equation in Iowa. Research scientists and

operations personnel of the Soil Conservation Service (SCS) in the North Central States worked together in developing the slope-practice equation

for use throughout the Corn Belt.

A national committee met in Ohio In 1946 to

adapt the Corn Belt equation to cropland in other

regions. This committee reappraised the Corn Belt

factor values and added a rainfall factor. The

resulting formula, generally known as the Musgrave Equation (31), has been widely used for

estimating gross erosion from watersheds in flood

abatement programs. A graphical solution of the

equation was published in 1952 (19) and used by

the SCS in the Northeastern States.

The soil loss equation presented in this handbook has become known as the Universal Soil

Loss Equation (USLE). Regardless of whether the

designation is fully accurate, the name does distinguish this equation from the regionally based

soil loss equations. The USLE was developed at the

National Runoff and Soil Loss Data Center established in 1954 by the Science and Education Administration (formerly Agricultural Research Service) in cooperation with Purdue University. Federal-State cooperative research projects at 49 locations^ contributed more than 10,000 plot-years of

basic runoff and soil loss data to this center for

summarizing and overall statistical analyses. After

1960, rainfall simulators (22) operating from Indiana, Georgia, Minnesota, and Nebraska were used

on field plots in 16 states to fill some of the gaps in

the data needed for factor evaluation.

Analyses of this large assembly of basic data

provided several major improvements for the soil

loss equation (53): (a) a rainfall erosion index

evaluated frorn local rainfall characteristics; (b) a

quantitative soil erodibility factor that is evaluated

directly from soil property data and is independent

of topography and rainfall differences; (c) a

method of evaluating cropping and management

effects in relation to local climatic conditions; and

(d) a method of accounting for effects of interactions between crop system, productivity level, tillage practices, and residue management.

Developments since 1965 have expanded the use

of the soil loss equation by providing techniques

for estimating site values of its factors for additional land uses, climatic conditions, and management practices. These have included a soil erodibility nomograph for farmland and construction

areas (58); topographic factors for irregular slopes

(72, 55); cover factors for range and woodland

(57); cover and management effects of conservation tillage practices (54); erosion prediction on

construction areas (61, 24, 25); estimated erosion

index values for the Western States and Hawaii |

(5, 2Î, 55); soil erodibility factors for benchmark^

Hawaii soils (9); and improved design and evaluation of erosion control support practices (Î7, 36).

Research is continuing with emphasis on obtaining a better understanding of the basic principles

and processes of water erosion and sedimentation

and development of fundamental models capable

of predicting specific-storm soil losses and deposition by overland fiow (JO, 7 7, 22, 26, 32). The

fundamental models have been helpful for understanding the factors in the field soil loss equation

and for interpreting the plot data.

SOIL LOSS TOLERANCES

The term "soil loss tolerance" denotes the maximum level of soil erosion that will permit a high

level of crop productivity to be sustained economically and Indefinitely.

^ The data were contributed by Federal-State cooperative research projects at the following locations: Batesville, Ark.; Tifton

and Watkinsville, Ga.; Dixon Springs, Joliet, and Urbana, III.; Lafayette, Ind.; Clarinda, Castaña, Beaconsfield, Independence, and

Seymour, Iowa; Hays, Kans.; Baton Rouge, La.; Presque Isle, Maine;

Benton Harbor and East Lansing, Mich.; Morris, Minn.; Holly

Springs and State College, Miss.; Bethany and McCredie, Mo.;

Hastings, Nebr.; Beemerville, Marlboro, and New Brunswick, N.J.;

Ithaca, Geneva, and Marcellus, N.Y.; Statesville and Raleigh, N.C.;

Coshocton and Zanesville, Ohio; Cherokee and Guthrie, Okla.;

State College, Pa.; Clemson and Spartanburg, S.C.; Madison,

S.Dak.; Knoxville and Greeneville, Tenn.; Temple and Tyler, Tex.;

Blacksburg, Va.; Pullman, Wash.; LaCrosse, Madison, and Owen,

Wis.; and Mayaguez, P.R.
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING

The major purpose of the soil loss equation is to

guide methodical decisionmaking in conservation

planning on a site basis. The equation enables

the planner to predict the average rate of soil

erosion for each of various alternative combinations of crop system, management techniques, and

control practices on any particular site. When these

predicted losses can be compared with a soil loss

tolerance for that site, they provide specific guidelines for effecting erosion control v/ithin the specified limits. Any cropping and management combination for which the predicted erosion rate is less

than the tolerance may be expected to provide

satisfactory erosion control. From the satisfactory

alternatives indicated by this procedure, the one

best suited to a particular farm or other enterprise may then be selected.

Soil loss tolerances ranging from 5 to 2 t/A/year

for the soils of the United States were derived by

soil scientists, agronomists, geologists, soil conservationists, and Federal and State research leaders at six regional workshops in 1961 and 1962.

Factors considered in defining these limits included

soil depth, physical properties and other characteristics affecting root development, gully prevention, on-field sediment problems, seeding losses,

soil organic matter reduction, and plant nutrient

losses. A deep, medium-textured, moderately permeable soil that has subsoil characteristics favorable for plant growth has a greater tolerance than

soils with shallow root zones or high percentages

of shale at the surface. Widespread experience

has shown these soil loss tolerances to be feasible

and generally adequate for sustaining high productivity levels indefinitely. Some soils with deep

favorable root zones may exceed the 5-t tolerance

without loss of sustained productivity.

Soil loss limits are sometimes established primarily for water quality control. The criteria for

defining field soil loss limits for this purpose are

not the same as those for tolerances designed to

preserve cropland productivity. Soil depth is not

relevant for offsite sediment control, and uniform

limits on erosion rates will allow a range in the

quantities of sediment per unit area that are delivered to a river. Soil material eroded from a field

slope may be deposited in the field boundaries, in

terrace channels, in depressional areas, or on flat

or vegetated areas traversed by the overland flow

before it reaches a river. The erosion damages the

cropland on which it occurs, but sediment deposited near its place of origin is not directly relevant for water quality control.

If the soil loss tolerance designed for sustained

cropland productivity fails to attain the desired

water quality standard, flexible limits that consider

other factors should be developed rather than

uniformly lowering the soil loss tolerance. These

factors include distance of the field from a major

waterway, the sediment transport characteristics

of the intervening area, sediment composition,

needs of the particular body of water being protected, and the probable magnitude of fluctuations

in sediment loads (42). Limits of sediment yield

would provide more uniform water quality control than lowering the limits on soil movement

from field slopes. They would also require fewer

restrictions on crop system selection for fields from

which only small percentages of the eroded soil

become off-farm sediment.

SOIL LOSS EQUATION

The erosion rate at a given site is determined

by the particular way in which the levels on numerous physical and management variables. are

combined at that site. Physical measurements of

soil loss for each of the large number of possible

combinations in which the levels of these variable

factors can occur under field conditions would not

be feasible. Soil loss equations were developed to

enable conservation planners to project limited

erosion data to the many localities and conditions

that have not been directly represented in the research.

The USLE is an erosion model designed to predict the longtime 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 such nonagricultural conditions as construction sites.

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 cropstage period within

a crop year. It computes the soil loss for a given
UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

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,

hov/ever, present soil loss equations are substantially less accurate for prediction of specific events

than for prediction of longtime averages.

The soil loss equation is

A=:RKLSCP

where

0)

A is the computed soil loss per unit area, expressed in the units selected for K and for the period selected for R. In practice, these are usually so selected that they compute A in tons

per acre per year, but other units can be

selected.

R, the rainfall and runoff factor, is the number of

rainfall erosion index units, plus a factor for

runoff from snowmelt or applied water where

such runoff is significant.

K, the soil erodibility factor, is the soil loss rate

per erosion index unit for a specified soil as

measured on a unit plot, which is defined as

a 72.6-ft length of uniform 9-percent slope

continuously in clean-tilled fallow.

L, the slope-length factor, is the ratio of soil loss

from the field slope length to that from a 72.6-

ft length under identical conditions.

S, the slope-steepness factor, is the ratio of soil

loss from the field slope gradient to that from

a 9-percent slope under otherwise identical

conditions.

C, the cover and management factor, is the ratio

of soil loss from an area with specified cover

and management to that from an identical

area in tilled continuous fallow.

P, the support practice factor, is the ratio of soil

loss with a support practice like contouring,

stripcropping, or terracing to that with

straight-row farming up and down the slope.

The soil loss equation and factor evaluation

charts were initially developed in terms of the

English units commonly used in the United States.

The factor definitions are interdependent, and direct conversion of acres, tons, inches, and feet to

metric units would not produce the kind of integers

that would be desirable for an expression of the

equation in that system. Therefore, only the English

units are used in the initial presentation of the

equation and factor evaluation materials, and

their counterparts in metric units are given in the

Appendix under Conversion to Metric System.

Numerical values for each of the six factors

were derived from analyses of the assembled research data and from National Weather Service

precipitation records. For most conditions in the

United States, the approximate values of the factors for any particular site may be obtained from

charts and tables in this handbook. Localities or

countries where the rainfall characteristics, soil

types, topographic features, or farm practices are

substantially beyond the range of present U.S.

data will find these charts and tables incomplete

and perhaps inaccurate for their conditions. However, they will provide guidelines that can reduce

the amount of local research needed to develop

comparable charts and tables for their conditions.

The subsection on Predicting Cropland Soil Losses, page 40 illustrates how to select factor values

from the tables and charts. Readers who have had

no experience with the soil loss equation may wish

to read that section first. After they have referred

to the tables and figures and located the values

used in the sample, they may move readily to the

intervening detailed discussions of the equation's

factors.

The soil loss prediction procedure is more valuable as a guide for selection of practices if the user

has a general knowledge of the principles and

factor interrelations on which the equation is

based. Therefore, the significance of each factor is

discussed before presenting the reference table or

chart from which local values may be obtained.

Limitations of the data available for evaluation of

some of the factors are also pointed out.
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING

RAINFALL AND RUNOFF FACTOR (R)

Rills and sediment deposits observed after an

unusually intense storm have sometimes led to the

conclusion that the significant erosion is associated

with only a few storms, or that it is solely a function of peak intensities. However, more than 30

years of measurements in many States have shown

that this is not the case (51). The data show that

a rainfall factor used to estimate average annual

soil loss must include the cumulative effects of the

many moderate-sized storms, as well as the effects

of the occasional severe ones.

The numerical value used for R in the soil loss

equation must quantify the raindrop impact effect

and must also provide relative information on the

amount and rate of runoff likely to be associated

with the rain. The rainfall erosion index derived

by Wischmeier (49) appears to meet these requirements better than any other of the many rainfall

parameters and groups of parameters tested

against the assembled plot data. The local value

of this index generally equals R for the soil loss

equation and may be obtained directly from the

map in figure 1. However, the index does not include the erosive forces of runoff from thaw, snowmelt, or irrigation. A procedure for evaluating R

for locations where this type of runoff is significant

will be given under the topic R Values for Thaw

and Snowmelt.

Rainfall Erosion Index

The research data indicate that when factors

other than rainfall are held constant, storm soil

losses from cultivated fields are directly proportional to a rainstorm parameter identified as the

El (defined below) (49). The relation of soil loss to

this parameter is linear, and its individual storm

Values are directly additive. The sum of the storm

El values for a given period is a numerical measure of the erosive potential of the rainfall within

that period. The average annual total of the storm

El values in a particular locality is the rainfall erosion index for that locality. Because of apparent

cyclical patterns in rainfall data (33), the published

rainfall erosion index values were based on 22-

year station rainfall records.

Rain showers of less than one-half inch and

separated from other rain periods by more than

6 hours were omitted from the erosion index

computations, unless as much as 0.25 in of rain fell

in 15 min. Exploratory analyses showed that the El

values for such rains are usually too small for

practical significance and that, collectively, they

have little effect on monthly percentages of the

annual El. The cost of abstracting and analyzing

4,000 location-years of rainfall-intensity data was

greatly reduced by adopting the 0.5-in threshold

value.

El Parameter

By definition, the value of El for a given rainstorm equals the product, total storm energy (E)

times the maximum 30-min intensity (I30)/ where E

is in hundreds of foot-tons per acre and I30 is in

inches per hour (in/h). El is an abbreviation for

energy-times-intensity, and the term should not be

considered simply an energy parameter. The data

show that rainfall energy, itself,-is not a good indicator of erosive potential. The storm energy indicates the volume of rainfall and runoff, but a

long, slow rain may have the same E value as a

shorter rain at much higher intensity. Raindrop

erosion increases with intensity. The I30 component

indicates the prolonged-peak rates of detachment

and runoff. The product term. El, is a statistical

interaction term that refiects how total energy and

peak inensity are combined in each particular

storm. Technically, it indicates how particle detachment is combined with transport capacity.

The energy of a rainstorm is a function of the

amount of rain and of all the storm's component

intensities. Median raindrop size increases with

rain intensity (Ó2), and terminal velocities of freefalling waterdrops increase with increased dropsize (73). Since the energy of a given mass in motion is proportional to velocity-squared, rainfall

energy is directly related to rain intensity. The

relationship is expressed by the equation,

E=: 916+ 331 logio I, (2)

where E is kinetic energy in foot-tons per acreinch and I is intensity in inches per hour (62). A

limit of 3 in/h is imposed on I by the finding that

median dropsize does not continue to increase

when intensities exceed 3 in/h (7, 75). The energy
FIGURE 1.—^Average annual values of the rainfall erosion index.
UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

of a rainstorm is computed from recording-rain

gage data. The storm is divided into successive increments of essentially uniform intensity, and a

rainfall energy-intensity table derived from the

above formula (app., table 19) is used to compute

the energy for each increment. (Because the energy

equation and energy-intensity table have been

frequently published with energy expressed in

foot-tons per acre-inch, this unit was retained in

table 19. However, for computation of El values,

storm energy is expressed in hundreds of foot-tons

per acre. Therefore, energies computed by the published formula or table 19 must be divided by 100

before multiplying by I30 to compute EÍ.)

Isoerodent Maps

Local values of the rainfall erosion index may

be taken directly from the isoerodent maps, figures

I and 2. The plotted lines on the maps are called

isoerodents because they connect points of equal

rainfall erosivity. Erosion index values for locations

between the lines are obtained by linear interpolation.

The isoerodent map in the original version of

this handbook (64) was developed from 22-year station rainfall records by computing the El value for

each storm that met the previously defined threshold criteria. Isoerodents were then located between

these point values with the help of published rainfall intensity-frequency data (47) and topographic

maps. The 11 Western States were omitted from

the initial map because the rainfall patterns in

this mountainous region are sporadic and not

enough long-term, recording-rain gage records

were available to establish paths of equal erosion

index values.

The isoerodent map was extended to the Pacific

Coast in 1976 by use of on estimating procedure.

Results of investigations at the Runoff and Soil Loss

Data Center at Purdue University showed that the

known erosion index values in the Western Plains

and North Central States could be approximated

with reasonable accuracy by the quantity 27.38

p2.i7^ where P is the 2-year, 6-h rainfall amount

(55), This relationship was used with National

Weather Service isopluvial maps to approximate

erosion index values for the Western States. The

resulting isoerodents are compatible with the few

point values that had been established within the

II Western States and can provide helpful guides

for conservation planning on a site basis. However, they are less precise than those computed

for the 37-State area, where more data were available and rainfall patterns are less erotic. Also,

linear interpolations between the lines will not

always be accurate in mountain regions because

values of the erosion index may change rather

abruptly with elevation changes. The point values

that were computed directly from long-term station rainfall records in the Western States are included in table 7, as reference points.

Figure 2 was developed by computing the erosion index for first-order weather stations in Hawaii

and deriving the relation of these values to National Weather Service intensity-frequency data for

the five major islands. When the present shortterm, rainfall-intensity records have been sufficiently lengthened, more point values of the index

should be computed by the standard procedure.

Figure 1 shows that local, average-annual values of the erosion index in the 48 conterminous

States range from less than 50 to more than 500.

The erosion index measures the combined effect of

rainfall and its associated runoff. If the soil and

topography were exactly the same everywhere,-

average annual soil losses from plots maintained

in continuous fallow would differ in direct proportion to the erosion index values. However, this potential difference is partially offset by differences

in soil, topography, vegetative cover, and residues.

On fertile soils in the high rainfall areas of the

Southern States, good vegetal cover protects the

soil surface throughout most of the year and

heavy plant residues may provide excellent cover

also during the dormant season. In the regions

where the erosion index is extremely low, rainfall

is seldom adequate for establishing annual meadows and the cover provided by other crops is often

for relatively short periods. Hence, serious soil

erosion hazards exist in semiarid regions as well

as in humid.

Frequency Distribution

The isoerodent mops present 22-year-average

annual values of El for the delineated areas. However, both the annual and the maximum-storm values at a particular location vary from year to year.

Analysis of 181 station rainfall records showed

that they tend to follow log-normal frequency distributions that are usually well defined by continu-
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING

ous records of from 20 to 25 years (49). Tables of

specific probabilities of annual and maximumR Values for Thaw

The standard rainfall erosion index estimates

the erosive forces of the rainfall and its directly

associated runoff. In the Pacific Northwest, as much

as 90 percent of the erosion on the steeply rolling

wheatland has been estimated to derive from runoff associated vs^ith surface thaws and snowmelt.

This type of erosion is not accounted for by the

rainfall erosion index but is considered either predominant or appreciable in much of the Northwest

and in portions of the central Western States. A

linear precipitation relationship would not account

for peak losses in early sprinig because as the winter progresses, the soil becomes increasingly more

erodible as the soil moisture profile ¡s being filled.

storm El values at the 181 locations are presented

in the appendix (tables 17 and 18).

and Snowmelt

the surface structure is being broken down by

repeated freezing and thawing, and puddling

and surface sealing are taking place. Additional

research of the erosion processes and means of

control under these conditions is urgently needed.

In the meantime, the early spring erosion by

runoff from snowmelt, thaw, or light rain on frozen soil may be included in the soil loss computations by adding a subfactor, Rg, to the location's

erosion index to obtain R. Investigations of limited

data indicated that an estimate of Rg may be obtained by taking 1.5 times the local Decemberthrough-March precipitation, measured as inches

of water. For example, a location in the NorthMAUl

KAUAI

OAHU

MOLOKAI

FIGURE 2.—Estimated average annual values of the rainfall erosion index in Hawaii.
8 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

west that has an erosion index of 20 (fig. 1)

and averages 12 in of precipitation between December 1 and March 31 would have an estimated

average annual R of 1.5(12) + 20, or 38.

This type of runoff may also be a significant

factor in the northern tier of Central and Eastern

States. Where experience indicates this to be the

case, it should be included in R and also in the

erosion index distribution curves as illustrated on

page 27.

SOIL ERODIBIUTY FACTOR (K)

The meaning of the term "soil erodibility" is

distinctly different from that of the term "soil erosion." The rate of soil erosion. A, in the soil loss

equation, may be influenced more by land slope,

rainstorm characteristics, cover, and management

than by inherent properties of the soil. However,

some soils erode more readily than others even

when all other factors are the same. This difference, caused by properties of the soil itself, is referred to as the soil erodibility. Several early attempts were made to determine criteria for scientific classifications of soils according to erodibility

(6, 18, 28, 35), but classifications used for erosion

prediction were only relative rankings.

Differences in the natural susceptibilities of soils

Definition

The soil erodibility factor, K, in the USLE is a

quantitative value experimentally determined. For

a particular soil, it is the rate of soil loss per erosion index unit as measured on a "unit" plot, which

has been arbitrarily defined as follows:

A unit plot is 72.6 ft long, with a uniform lengthwise slope of 9 percent, in continuous fallow, tilled

up «nd down the slope. Continuous fallow, for this

purpose, is land that has been tilled and kept free

of vegetation for more than 2 years. During the

period of soil loss measurements, the plot is plowed

and placed in conventional corn seedbed condition

each spring and is tilled as needed to prevent

vegetative growth and severe surface crusting.

When all of these conditions are met, L, S, C, and

P each equal 1.0, and K equals A/El.

The 72.6 ft length and 9 percent steepness were

selected as base values for L, S, and K because

they are the predominant slope length and about

the average gradient on which past erosion meato erosion are difficult to quantify from field observations. Even a soil with a relatively low erodibility factor may show signs of serious erosion

when it occurs on long or steep slopes or in localities with numerous high-intensity rainstorms.

A soil with a high natural erodibility factor, on the

other hand, may show little evidence of actual erosion under gentle rainfall when it occurs on short

and gentle slopes, or when the best possible management is practiced. The effects of rainfall differences, slope, cover, and management are accounted for in the prediction equation by the symbols R, L, S, C, and P. Therefore, the soil erodibility

factor, K, must be evaluated independently of the

effects of the other factors.

of Factor K

surements in the United States had been made.

The designated management provides a condition

that nearly eliminates effects of cover, management, and land use residual and that can be duplicated on any cropland.

Direct measurements of K on well-replicated,

unit plots as described refiect the combined effects

of all the soil properties that significantly influence

the ease with which a particular soil is eroded by

rainfall and runoff if not protected. However, K is

an average value for a given soil, and direct measurement of the factor requires soil loss measurements for a representative range of storm sizes

and antecedent soil conditions. (See Individual

Storm Soil Losses under APPLYING THE SOIL LOSS

EQUATION.) To evaluate K for soils that do not

usually occur on a 9-percent slope, soil loss data

from plots that meet all the other specified conditions are adjusted to this base by S.

Values of K for Specific Soils

Representative values of K for most of the soil available research information. These tables are

types and texture classes can be obtained from

tables prepared by soil scientists using the latest

available from the Regional Technical Service Centers or State offices of SCS. Values for the exact
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING

TABLE 1.—Computed K values for soils on erosion

research stations

Soil Source of data Computed K

Dunkirk silt loam Geneva, N.Y. ^O.ó9

Keene siit loam Zanesviile, Ohio .48

Shelby loam Bethany, Mo. .41

Lodi loam Blacksburg, Va. .39

Fayette silt loam LaCrosse, Wis. \38

Cecil sandy clay loam . . Watkinsville, Ga. .36

Marshall silt loam Clarinda, Iowa .33

Ida silt loam Castaña, Iowa .33

Mansic clay loam Hays, Kans. .32

Hagerstown silty clay loam State College, Pa. ^.31

Austin clay Temple, Tex. .29

Mexico silt loam McCredie, Mo. .28

Honeoye silt loam Marcellus, N.Y. \28

Cecil sandy loam Clemson, S.C. .28

Ontario loam Geneva, N.Y.« ^.27

Cecil clay loam Watkinsville, Ga. .26

Boswell find sandy loam Tyler, Tex. .25

Cecil sandy loam Watkinsville, Ga. .23

Zaneis fine sandy loam Guthrie, Okla. .22

Tifton loamy sand Tifton, Ga. .10

Freehold loamy sand Marlboro, N.J. .08

Bath fiaggy silt loam with surface Arnot, N.Y. \05

stones 1> 2 inches removed ....

Albia gravelly loam Beemerville, N.J. .03

* Evaluated from continuous fallow. All others were computed

from rowcrop data.

soil conditions at a specific site can be computed

by use of the soil erodibility nomograph presented

in the next subsection.

Usually a soil type becomes less erodible with

decrease in silt fraction, regardless of whether the

corresponding increase is in the sand fraction or

the clay fraction. Overall, organic matter content

ranked next to particle-size distribution as an indicator of erodibility. However, a soil's erodibility

is a function of complex interactions of a substantial number of its physical and chemical properties

and often varies within a standard texture class.

Values of K determined for 23 major soils on

which erosion plot studies under natural rain were

conducted since 1930 are listed in table 1. Seven

of these values are from continuous fallow. The

others are from row crops averaging 20 plot-years

of record and grown in systems for which the

cropping effect had been measured in other studies. Other soils on which valuable erosion studies

have been conducted^ were not included in the

table because of uncertainties involved in adjustments of the data for effects of cropping and management.

Direct measurement of the erodibility factor is

both costly and time consuming and has been

feasible only for a few major soil types. To achieve

a better understanding of how and to what extent each of various properties of a soil affects its

erodibility, an interregional study was initiated

in 1961. The study included the use of field-plot

rainfall simulators in at least a dozen States to obtain comparative data on numerous soils, laboratory determinations of physical and chemical properties, and operation of additional fallow plots

under natural rain. Several empirical erodibility

equations were reported (3, 60), A soil erodibility

nomograph for farmland and construction sites

(58) provided a more generally applicable working tool. Approximate K values for 10 benchmark

soils in Hawaii are listed in table 2.

* See footnote 3, p. 2.

TABLE 2.—Approximate values of the soil erodibility factor, K, for 10 benchmark soils in tiawaii

Order Suborder Great group Subgroup Family Series K

Ultisols Humults Tropohumults Humoxic Tropohumults Clayey, kaolinitic, isohyperthermic Waikane 0.10

Oxisols Torrox Torrox Typic Torrox Clayey, kaolinitic, isohyperthermic Molokai .24

Oxisols Ustox Eutrustox Tropeptic Eutrustox clayey, kaolinitic, isohyperthermic Wahiawa .17

Vertisols Usterts Chromusterts Typic Chromusterts Very fine, montmorillonitic, isohyperthermic Lualualei

Kawaihae

.28

.32

Aridisols Orthids Camborthids Ustollic Camborthids Medial, isohyperthermic (Extremely stony phase)

Inceptisols Andepts Dystrandepts Hydric Dystrandepts Thixotropic, isothermic Kukaiau .17

Inceptisols Andepts Eutrandepts Typic Eutrandepts Medial, isohyperthermic Naolehu (Variant) .20

Inceptisols Andepts Eutrandepts Entic Eutrandepts Medial, isohyperthermic Pakini .49

Inceptisols Andepts Hydrandepts Typic Hydrandepts Thixotropic, isohyperthermic Hilo .10

Inceptisols Tropepts Ustropepts Vertic Ustropepts Very fine, kaolinitic, isohyperthermic Waipahu .20

SOURCE; El-Swaify and' Dangler (9).
10 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

Soil Erodibility

The soil loss data show that very fine sand (0.05-

0.10 mnn) is comparable In erodibility to silt-sized

particles and that mechanical-analysis data are

much more valuable when expressed by an interaction ternr» that describes the proportions in which

the sand,.silt, and clay fractions are combined in

the soil. When mechanical analysis data based on

the standard USDA classification are used for the

nomograph in figure 3, the percentage of very fine

sand (0.1-0.05 mm) must first be transferred from

the sand fraction to the silt fraction. The mechanical analysis data are then effectively described by

a particle-size parameter M, which equals percent

silt (0.1-0.002 mm) times the quantity 100-minuspercent-clay. Where the silt fraction does not exceed 70 percent, erodibility varies approximately

as the 1.14 power of this parameter, but prediction

accuracy is improved by adding information on

organic matter content, soil structure, and profile

permeability class.

For soils containing less than 70 percent silt and

very fine sand, the nomograph (fig. 3) solves the

equation:

TOO K = 2.1 M'-'* (10-') (12 ~ a) -f- 3.25 (b - 2) + 2.5 (e - 3) (3)

where

M = the particle-size parameter defined above,

a = percent organic matter,

b = the soil-structure code used in soil classification, and

c = the profile-permeability class.

The intersection of the selected percent-silt and percent-sand lines computes the value of M on the

unidentified horizontal scale of the nomograph.

(Percent clay enters into the computation as 100

minus the percentages of sand and silt.)

The data indicate a change in the relation of

M to erodibility when the silt and very fine sand

fraction exceeds about 70 percent. This change was

empirically reflected by inflections in the percentsand curves at that point but has not been described by a numerical equation.

Readers who would like more detail regarding

the data and relationships underlying the nomograph equation may obtain this from journal articles (58, 60).

Nomograph Solution

With appropriate data, enter the scale at the

Nomograph

left and proceed to points representing the soil's

percent sand (0.10-2.0 mm), percent organic matter, structure code, and permeability class as illustrated by the dotted line on the nomograph.

The horizontal and vertical moves must be made

in the listed sequence. Use linear interpolations

between plotted lines. The structure code and permeability classes are defined on the nomograph

for reference.

Many agricultural soils have both fine granular

topsoil and moderate permeability. For these soils,

K may be read from the scale labeled ''first approximation of K," and the second block of the

graph is not needed. For all other soils, however,

the procedure must be completed to the soil erodibility scale in the second half of the graph.

The mechanical analysis, organic matter, and

structure data are those for the topsoil. For evaluation of K for desurfaced subsoil horizons, they pertain to the upper 6 in of the new soil profile. The

permeability class is the profile permeability.

Coarse fragments are excluded when determining

percentages of sand, silt, and clay. If substantial,

they may have a permanent mulch effect which

can be evaluated from the upper curve of the

chart on mulch and canopy effects (p. 19, fig. 6)

and applied to the number obtained from the

nomograph solution.

Confidence Limits

In tests against measured K values ranging from

0.03 to 0.69, 65 percent of the nomograph solutions

differed from the measured K values by less than

0.02, and 95 percent of them by less than 0.04.

Limited data available in 1971 for mechanically

exposed B and C subsoil .horizons indicated about

comparable accuracy for these conditions. However, more recent data taken on desurfaced hîghclay subsoils showed the nomograph solution to

lack the desired sensitivity to differences in erodibilities of these soil horizons. For such soils the

content of free iron and aluminum oxides ranks

next to particle-size distribution as an indicator of

erodibility (37), Some high-clay soils form what

has been called irreversible aggregates on the

surface when tilled. These behave like larger primary particles.
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING n

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12 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

TOPOGRAPHIC FACTOR (LS)

Both the length and the steepness of the land

slope substantially affect the rate of soil erosion by

water. The two effects have been evaluated separately in research and are represented in the soil

loss equation by L and %, respectively. In field

applications, however, considering the two as a

single topographic factor, IS, is more convenient.

Slope-Effect Chart

LS is the expected ratio of soil loss per unit area

from a field slope to that from a 72.6-ft length of

uniform 9-percent slope under otherwise identical

conditions. This ratio for specified combinations of

field slope length and uniform gradient may be

obtained directly from the slope-effect chart (fig.

4). Enter on the horizontal axis with the field slope

length, move vertically to the appropriate percentslope curve, and read LS on the scale at the left.

For example, the LS factor for a 300-ft length of

10-percent slope is 2.4. Those who prefer a table

may use table 3 and interpolate between listed

values.

To compute soil loss from slopes that are appreciably convex, concave, or complex, the chart

LS values need to be adjusted as indicated in the

section LS Values for Irregular Slopes. Figure 4

and table 3 assume slopes that have essentially

uniform gradient. The chart and table were derived by the equation

IS = (X/72.6)"* {65.4] sin' 8 + 4.56 sin 6 -j- 0.065) (4)

where X = slope length in feet;

6 = angle of slope; and

m = 0.5 if the percent slope is 5 or more, 0.4 on

slopes of 3.5 to 4.5 percent, 0.3 on slopes of 1 to

3 percent, and 0.2 on uniform gradients of less

than 1 percent.

The basis for this equation is given in the subsection discussing the individual effects of slope

length and steepness. However, the relationships

expressed by the equation were derived from data

obtained on cropland, under natural rainfall, on

slopes ranging from 3 to 18 percent in steepness

and about 30 to 300 ft in length. How far beyond

these ranges in slope characteristics the relationships derived from the data continue to be accurate has not been determined by direct soil loss

measurements.

The Palouse Region of the Northwest represents

TABLE 3.—Values of the topographic factor, LS, for specific combinatioris of slope length

and steepness^

Slope length (feet)

^^lo^r ^^ ^ ^^ ^^ ^^ ^°° ^^° ^^^ ^°° ^°^ ^^° ''^°^

0.2 0.060 0.069 0.075 0.080 0.086 0.092 0.099 0.105 0.110 0.114 0.121 0.126

0.5 073 .083 .090 .096 .104 .110 .119 .126 ,132 .137 .145 .152

0.8 086 .098 ,107 ,113 .123 .130 .141 .149 .156 .162 .171 .179

2 133 .163 .185 .201 .227 .248 .280 .305 .326 .344 .376 .402

3 190 .233 .264 .287 .325 .354 .400 .437 .466 .492 .536 .573

4 .230 .303 .357 .400 .471 .528 .62\ .697 .762 .820 .920 1.01

5 268 ,379 ,464 .536 .656 .758 .928 1.07 1.20 1.31 1.52 1.69

6 .336 .476 ,583 .673 .824 .952 1.17 1.35 1.50 1.65 1.90 2.13

8 496 ,701 .859 .992 1.21 1.41 1.72 1.98 2,22 2.43 2.81 3.Î4

10 685 .968 1.19 1.37 1.68 1.94 2.37 2.74 3.06 3.36 3.87 4.33

12 903 1.28 1.56 1.80 2.21 2.55 3.13 3.61 4.04 4.42 5.11 5.71

14 1.15 1.62 1.99 2.30 2.81 3.25 3,98 4.59 5.13 5.62 6.49 7.26

16 1.42 2.01 2.46 2.84 3.48 4.01 4.92 5.68 6.35 6.95 8.03 8.98

18 1.72 2.43 2.97 3.43 4.21 3.86 5.95 6.87 7.68 8.41 9.71 10.9

20 2.04 2.88 3.53 4.08 5.00 5.77 7.07 8.16 9.12 10.0 11.5 12.9

^ is = (X/72.6)"* (65.41 sin' 6 -f 4.56 sin 0 -\- 0.065) where X = slope length in feet; m = 0.2 for

gradients < 1 percent, 0.3 for 1 to 3 percent slopes, 0.4 for 3.5 to 4.5 percent slopes, 0.5 for 5 percent

slopes and steeper; and 6 = angle of slope. (For other combinations of length and gradient, interpolate

between adjacent values or see fig. 4.)
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING 13

TOPOGRfiPHIC FRCTOR - LS

cn

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14 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

a different situation. The rainfall erosion index is

quite low because most of the rain comes as small

drops and at low intensities. But many of the cropland slopes are long or steep, and substantial erosion occurs because of runoff from snowmelt or

light rains over saturated soil surfaces. Limited

erosion data from this region, mostly observational, strongly indicate that for this type of runoff

(not accompanied by raindrop impact) the effects

of percent and length of slope are of lower magnitude than indicated by the humid region data. Investigations designed to develop a more accurate

LS equation for this region are underway at Pullman, Wash. (2Î). In the meantime, the researchers

are temporarily recommending using a modified

equation which computes LS values that are close

to those that would be calculated by the equation

given above if sin^^ Ö were substituted for sin^ 0

and the length-exponent, m, were assumed to

equal 0.3. Intuitively, these changes seem reasonable for the conditions under which about 90 percent of the erosion in this region occurs.

Slope-Length Effect

Slope length is defined as the distance from the

point of origin of overland flow to the point where

either the slope gradient decreases enough that

deposition begins, or the runoff water enters a

well-defined channel that may be part of a drainage network or a constructed channel (40). A

change in land cover or a substantial change in

gradient along a slope does not begin a new slope

length for purposes of soil loss estimation.

The effect of slope length on annual runoff per

unit area of cropland may generally be assumed

negligible. In some of the studies runoff per unit

area was slightly lower on the longer slopes during the growing season and slightly higher during

the dormant season, but the differences were relatively small and neither of the relationships was

consistent (52).

However, the so/7 loss per unit area generally

increases substantially as slope length increases.

The greater accumulation of runoff on the longer

slopes increases its detachment and transport capacities.

The plot data showed average soil loss per unit

area to be proportional to a power of slope length.

Because L is the ratio of field soil loss to the corresponding loss from 72.6-ft slope length, its value

may be expressed as L = (X/72.6)'", where X is the

field slope length in feet, and m assumes approximately the values given in the LS equation in the

preceding section. These are average values of m

and are subject to some variability caused by

interaction effects which are not now quantitatively predictable.

The existing field plot data do not establish a

general value greater than 0.5 for m on slopes

steeper than 10 percent, as was suggested in 1965

(64). Although apparent values up to 0.9 were observed in some of the data (63), the higher values

appear to have been related to soil, crop, and

management variables rather than to greater slope

steepness. However, basic modeling work has suggested that m may appreciably exceed 0.5 on

steep slopes that are highly susceptible to rilling,

like some construction slopes (Î0). Additional research data are greatly needed to quantify the

significant interaction effects so that specific site

values of iti can be more precisely computed. Subdividing erosion between interrill (or sheet) erosion

and rill erosion, being done in recent modeling

work (10, 11, 22), promises to be quite helpful for

solving this problem.

Some observations have indicated that the values of the length exponent that were derived from

the plot data may overestimate soil loss when applied to lengths in the range of a quarter of a mile

or more. This is logical because slopes of such

lengths would rarely have a constant gradient

along their entire length, and the slope irregularities would affect the amount of soil movement

to the foot of the slope. By the definition of slope

length quoted earlier, such slopes would usually

consist of several lengths, between points where

deposition occurs.

Slope length is difficult to determine for long

slopes with an average gradient of less than 1

percent, unless they are precisely formed with a

land leveler. On flat slopes, reflecting both the

erosion and the deposition accurately by a length

factor may not be possible. However, on a nearly

zero-percent slope, increased length would have

minor effect on runoff velocity, and the greater

depths of accumulated runoff water would cushion

the raindrop impact. An exponent of 0.2 for gradients of less than 1 percent is compatible with the
PREDICTING RAINFALL EROSION LOSSES~A GUIDE TO CONSERVATION PLANNING 15

scarce data available for such slopes and was used

to derive figure 4 and table 3.

Distribution of Length Effect

IS values from figure 4 or table 3 predict the

averoge erosion over the entire slope. But this erosion is not evenly distributed over the entire length.

The rate of soil loss per unit of area increases as

the m**^ power of the distance from the top of the

slope, where m is the length exponent in the preceding equation.

An equation by Foster and Wischmeier (12) estimates the relative amounts of soil loss from successive segments of a slope under conditions

where there is no deposition by overland flow.

When the gradient is essentially uniform and the

segments are of equal length, the procedure can

be shortened (55), Table 4, derived by this procedure, shows the proportionate amounts of soil

detachment from successive equal-length segments

of a uniform slope.

Table 4 is entered with the total number of

equal-length segments, and the fraction of the

soil loss for each segment is read beneath the applicable value of m. For example, three equallength segments of a uniform 6-percent slope

would be expected to produce 19, 35, and 46 percent, respectively, of the loss from the entire slope.

Percent

Runoff from cropland generally increases with

increased slope gradient, but the relationship is

influenced by such factors as type of crop, surface

roughness, and profile saturation. In the natural

rain slope-effect studies, the logarithm of runoff

from row crops was linearly and directly proportional to percent slope. With good meadow sod

and with smooth bare surfaces, the relationship

was insignificant. The effect of slope on runoff decreased in extremely wet periods.

So/7 loss increases much more rapidly than runoff as slopes steepen. The slope-steepness factor,

S, in the soil loss equation is evaluated by the

equation

S = 65.41 sin' e + 4.56 sin 0 -f 0.065 (5)

where 6 is the angle of slope.

This equation was used to develop the slopeeffect chart. The values reflect the average effect of

slope steepness on soil loss in the plot studies. The

relation of percent slope to soil loss is believed to

TABLE 4.—Estimated relative soil losses from successive

equal-length segmer)ts of a uniform slope^

Number of segments

Sequence number

of segment

Fraction of soil

m - 0.5 m = 0.4

loss

m = 0.3

2 . 1 0.35 0.38 0.41

2 .65 .62 .59

3 . 1 .19 .22 .24

2 .35 .35 .35

3 .46 .43 .41

4 . 1 .12 .14 .17

2 .23 .24 .24

3 .30 .29 .28

4 .35 .33 .31

5 . 1 .09 .11 .12

2 .16 .17 .18

3 .21 .21 .21

4 .25 .24 .23

5 .28 .27 .25

^ Derived by the formula;

Soil loss fradHon =

m

i

+1 'm+1

m+1

N

where j = segment sequence number; m = slope-length exponent

(0.5 for slopes > 5 percent, 0.4 for 4 percent slopes, and 0.3 for

3 percent or less); and N = number of equal-length segments into

which the slope was divided.

Four segments would produce 12, 23, 30, and 35

percent, respectively. Segment No. 1 is always at

the top of the slope.

Slope

to be influenced by interactions with soil properties

and surface conditions, but the interaction effects

have not been quantified by research data. Neither

are data available to define the limits on the equation's applicability.

This equation can be derived from the formerly

published equation for S. Expressing the factor as

a function of the sine of the angle of slope rather

than the tangent is more accurate because raindrop-impact forces along the surface and runoff

shear stress are functions of the sine. Substituting

100 sin Ô for percent slope, which is 100 tan G, does

not significantly affect the initial statistical derivation or the equation's solutions for slopes of less

than 20 percent. But as slopes become steeper, the

difference between the sine and the tangent becomes appreciable and projections far beyond the

range of the plot data become more realistic. The

numerator was divided by the constant denominator for simplification.
16 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

Irregular

Soil loss is also affected by the shape of a slope.

Many field slopes either steepen toward the lower

end (convex slope) or flatten toward the lower end

(concave slope). Use of the average gradient to

enter figure 4 or table 3 would underestimate soil

movement to the foot of a convex slope and would

overestimate it for concave slopes. Irregular slopes

can usually be divided into segments that have

nearly uniform gradient, but the segments cannot

be evaluated as independent slopes when runoff

flows from one segment to the next.

However, where two simplifying assumptions

can be accepted, LS for irregular slopes can be

routinely derived by combining selected values

from the slope-effect chart and table 4 (55). The

assumptions are that (1) the changes in gradient

are not sufficient to cause upslope deposition, and

(2) the irregular slope can be divided into a small

number of equal-length segments in such a manner that the gradient within each segment for

practical purposes can be considered uniform.

After dividing the convex, concave, or complex

slope into equal-length segments as defined earlier, the procedure is as follows: List the segment

gradients in the order in which they occur on the

slope, beginning at the upper end. Enter the slopeeffect chart with the total slope length and read LS

for each of the listed gradients. Multiply these by

Changes in Soil Type or

The procedure for irregular slopes can include

evaluation of changes in soil type within a slope

length (55). The products of values selected from

table 3 or figure 4 and table 4 to evaluate LS for

irregular slopes are multiplied by the respective

values of K before summing. To illustrate, assume

the K values for the soils in the three segments

of the convex slope in the preceding example were

0.27, 0.32, and 0.37, respectively. The average KLS

for the slope would be obtained as follows:

Slopes

the corrresponding factors from table 4 and add

the products to obtain LS for the entire slope. The

following tabulation illustrates the procedure for

a 400-ft convex slope on which the upper third has

a gradient of 5 percent; the middle third, 10 percent; and the lower third, 15 percent:

Segmenf Percent slope Table 3 Table 4 Product

1 5 1.07 0.19 0.203

2 10 2.74 .35 .959

3 15 5.12 .46

LS

2.355

= 3.517

For the concave slope of the same length, with

the segment gradients in reverse order, the values

in the third column would be listed in reverse order. The products would then be 0.973, 0.959, and

0.492, giving a sum of 2,42 for LS.

Research has not defined just how much gradient change is needed under various conditions for

deposition of soil particles of various sizes to begin, but depositional areas can be determined by

observation. When the slope breaks are sharp

enough to cause deposition, the procedure can be

used to estimate LS for slope segments above and

below the depositional area. However, it will not

predict the total sediment moved from such an

interrupted slope because it does not predict the

amount of deposition.

Cover Along the Slope

Within limits, the procedure can be further extended to account for changes in cover along the

slope length by adding a column of segment C

values. However, it is not applicable for situations

where a practice change along the slope causes

deposition. For example, a grass buffer strip across

the foot of a slope on which substantial erosion is

occurring induces deposition. The amount of this

deposition is a function of transport relationships

(JO) and cannot be predicted by the USLE.

Segment No. Table 3 Table 4 K Product

1 1.07 0.19 0.27 0.055

2 2.74 .35 .32 .307

3 5.12 .46 .37

KLS

.871

= 1.233
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING 17

Equation for Soil Detachment on

This procedure is founded on an equation (72)

that can be applied also when the slope segments

are not of equal length. Concepts underlying this

equation include the following:

Sediment load at a location on a slope is controlled either by the transport capacity of the runoff and rainfall or by the amount of detached

soil material available for transport. When the

amount of detached material exceeds the transport

capacity, deposition occurs and the sediment load

is determined primarily by the transport capacity

of the runoff at that location. Where upslope deSuccessive Segments of a Slope

tachment has not equaled the transport capacity,

sediment load at a given location is a function of

erosion characteristics of the upslope area and can

be computed by the USLE. Soil loss from a given

segment of the slope can then be computed as the

difference between the sediment loads at the lower

and upper ends of the segment.

Foster and Wischmeier (12) present a procedure

for using this equation to evaluate LS for irregular

slopes and to account for the effects of the soil or

coverage changes along a slope, so long as the

changes do not cause deposition to occur.

COVER AND MANAGEMENT FACTOR (C)

Cover and management effects cannot be independently evaluated because their combined effect

is influenced by many significant interrelations.

Almost any crop can be grown continuously, or it

can be grown in rotations. Crop sequence influences the length of time between successive crop

canopies, and it also influences the benefits obtained from residual effects of crops and management. The erosion control effectiveness of meadow

sod turned under before a row crop depends on

the type and quality of the meadow and on the

length of time elapsed since the sod was turned

under. Seedbeds can be clean tilled, or they can be

protected by prior crop residues. They can be left

rough, with much available capacity for surface

storage and reduction of runoff velocity, or they

can be smoothed by secondary tillage.

Definition

Factor C in the soil loss equation is the ratio of

soil loss from land cropped under specified conditions to the corresponding loss from clean-tilled,

continuous fallow. This factor measures the combined effect of all the interrelated cover and management variables.

The loss that would occur on a particular field

if it were continuously in fallow condition is computed by the product of RKLS in the soil loss equation. Actual loss from the cropped field is usually

much less than this amount. Just how much less

depends on the particular combination of cover,

crop sequence, and management practices. It alCrop residues can be removed, left on the surface, incorporated near the surface, or plowed

under. When left on the surface, they can be

chopped or dragged down, or they can be allowed

to remain as left by the harvesting operation. The

effectiveness of crop residue management will depend on the amount of residue available. This, in

turn, depends on the amount and distribution of

rainfall, on the fertility level, and on the management decisions made by the farmer.

The canopy protection of crops not only depends

on the type of vegetation, the stand, and the quality of growth, but it also varies greatly in different

months or seasons. Therefore, the overall erosionreducing effectiveness of a crop depends largely

on how much of the erosive rain occurs during

those periods when the crop and management

practices provide the least protection.

of Factor C

so depends on the particular stage of growth and

development of the vegetal cover at the time of

the rain. C adjusts the soil loss estimate to suit

these conditions.

The correspondence of periods of expected

highly erosive rainfall with periods of poor or

good plant cover differs between regions or locations. Therefore, the value of C for a particular

cropping system will not be the same in all parts

of the country. Deriving the appropriate C values

for a given locality requires knowledge of how the

erosive rainfall in that locality is likely to be distributed through the 12 months of the year and
18 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

how much erosion control protection the growing

plants, crop residues, and selected management

practices will provide at the time when erosive

rains are most likely to occur. A procedure is presented for deriving local values of C on the basis

of available weather records and research data

Cropstage

The change in effectiveness of plant cover within the crop year is gradual. For practical purposes,

the year is divided into a series of cropstage periods defined so that cover and management effects

may be considered approximately uniform within

each period.

Initially, five periods were used, with the seedling and establishment periods defined as the first

and second months after crop seeding (50). Because of the existing ranges in soil fertility, row

spacing, plant population, and general growing

conditions, however, soil loss prediction accuracy

is improved when the cropstage periods are defined according to percentage of canopy cover

rather than for uniform time periods. The lengths

of the respective periods will then vary with crop,

climate, and management and will be determined

by conditions \n a particular geographic area.

The soil loss ratios presented in the next subsecthat reflect effects of crops and management in

successive segments of a rotation cycle. The cropping and weather data needed for this purpose

appear in reference form in the subsections entitled. Soil Loss Ratios and Erosion Index Distribution Data.

Periods

tion for computation of C were evaluated for six

cropstage periods defined as follows:

Period F (rough fallow)—Inversion plowing to secondary tillage.

Period SB (seedbed)—Secondary tillage for seedbed

preparation until the crop has developed 10

percent canopy cover.

Period 1 (establishment)—End of SB until crop has

developed a 50 percent canopy cover. (Exception: period 1 for cotton ends at 35 percent

canopy cover.)

Period 2 (development)—End of period 1 until canopy cover reaches 75 percent. (60 percent for

cotton.)

Period 3 (maturing crop)—End of period 2 until crop

harvest. This period was evaluated for three

levels of final crop canopy.

Period 4 (residue or stubble)—Harvest to plowing

or new seeding.

Quantitative Evaluations of

More than 10,000 plot-years of runoff and soil

loss data from natural rain,^ and additional data

from a large number of erosion studies under simulated rainfall, were analyzed to obtain empirical

measurements of the effects of cropping system

and management on soil loss at successive stages

of crop establishment and development. Soil losses

measured on the cropped plots were compared

with corresponding losses from clean-tilled, continuous fallow to determine the soil loss reductions

ascribable to effects of the crop system and management. The reductions were then analyzed to

identify and evaluate influential subfactors, interactions, and correlations. Mathematical relationships observed for one crop or geographic region

were tested against data from other research sites

for consistency. Those found compatible with all

the relevant data were used to compute soil loss

* See footnote 3, p. 2.

Crop and Management Effects

reductions to be expected from conditions not directly represented in the overall plot studies.

The value of C on a particular field is determined

by many variables, one of which is weather. Major variables that can be influenced by management decisions include crop canopy, residue mulch,

incorporated residues, tillage, land use residual,

and their interactions. Each of these effects may be

treated as a subfactor whose numerical value is

the ratio of soil loss with the effect to corresponding loss without it (57), C is the product of all the

pertinent subfactors.

Crop Canopy

Leaves and branches that do not directly contact the soil have little effect on amount and velocity of runoff from prolonged rains, but they reduce the effective rainfall energy by intercepting

falling raindrops. Waterdrops falling from the

canopy may regain appreciable velocity but usually less than the terminal velocities of free-falling
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING 19

1.00

.60

.40

.20

"^C**v^ * •». ^

'^

S ^

^^ "^ "^ -^^ ^ ^

N N

\^

^~*^ 4rii*

\

\

V

N

\

\^

N 'V

V

V

V

^2m*

\

V

N

N.

\

\,

Jm*

*Aver

drop

1

age fall hei

s from canop

ght of

y

\0.5m*

\

20 40 60

PERCENT GROUND COVER BY CANOPY

100

FIGURE 5.—Influence of vegetative canopy on effective El values.

Canopy factor is a subfactor of C.

raindrops. The amount by which energy expended

at the soil surface is reduced depends on the

height and density of the canopy. The subfactor

for canopy effect can be estimated for specified

conditions by reference to figure 5.

Residue Mulch

Residue mulches and stems from ciose-growmg

vegetation are more effective than equivalent percentages of canopy cover. Mulches intercept falling

raindrops so near the surface that the drops regain

no fall velocity, and they also obstruct runoff flov^

and thereby reduce its velocity and transport capacity. Measurements of the effectiveness of several types and rates of mulch have been published

{1,2, 20, 27, 43), Average subfactors for specific

percentages of surface cover by plant materials at

the soil surface are given by the upper curve of

figure 6. Guides for estimating percent cover are

given in the appendix.

If the cover includes both canopy and mulch,

the two are not fully additive; the impact energy

of drops striking the mulch is dissipated at that

point regardless of whether canopy interception

has reduced its velocity. The expected effects of

mulch and canopy combinations have been com^

puted and are given in figures 6 and 7. Figure 6

applies to corn, sorghum, and cotton in the maturw

\

\ \

\\

\ N

\

\ \\ '40-'

\

N \, .CANOPY

\ 1 % 1

\\

\

\

S

s \,\

\

s. \

\

<

\, \

s \

S

40

S

\

\

\

S \

\

N,

\

\ 60

N \

^ \

\

NN. 80 \

^

X X^\

->.. _ ^ , X,

^

\, X:NN

-^ 100 ^^ ^ ^^s

^ ^ -^^:::^^

"~~ =^

^T^l^^^^^

^ ^

1—

0 20 40 60 80 100

% COVER BY MULCH

FIGURE Ó.—Combined mulch and canopy effects when average fall

distance of drops from canopy to the ground is about 40 inches

(1 m).

ing stage. Figure 7 applies to small grain, soybeans, potatoes, and the establishment period for

taller row crops. Enter either figure 6 or 7 along

the horizontal scale, move vertically to the approrv '

\

\

\ \

\

N

\,

\ \

\ \ \, 20"

\ \, \

CA NO%

PY

\

s

s \

\

N, ^

^.

\

\ \

\

20 \,

\ K \

S

s.

\

\

s*» \. \

\,

^

-- ^S

i^X:'^^ s \

X S

s

\ N

s,

N^

^ 80

^ ^ ^S

-^ ^

^v.

>^N ^^ L> 100 "^ ^ "VJ .v.

^

— JH ^

^^^^

40 60

% COVER BY MULCH

FIGURE 7.—Combined mulch and canopy effects when average fall

distance of drops from canopy to the ground is about 20 inches

(0.5 m).
20 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

priate percent-canopy curve, and read at the left

the soil loss ratio from cover effect. This ratio is a

subfactor that may be combined with other pertinent subfactors to account for the cropstage soil

loss of table 5 or to estimate others.

Incorporated Residues

The plot data indicate that, at least during the

seedbed and establishment periods, the erosionreducing effectivensss of residues mixed into the

upper few inches of soil by shallow tillage is appreciably greater than the residual effect of longterm annual incorporation with a moldboard plow.

However, the incorporated residues are less effective than if left on the surface.

Tillage

The type, frequency, and timing of tillage operations influencé porosity, roughness, cloddiness,

compaction, and microtopography. These, in turn,

affect water intake, surface storage, runoff velocity, and soil detachability, all of which are factors in potential erosion. These effects are highly

correlated with cropland residual effects.

Land Use Residuals

These include effects of plant roots; long-term

residue Incorporation by plowing; changes in soil

structure, detachability, density, organic matter

content, and biological activity; and probably

other factors. The residual effects are most apparent during seedbed and establishment periods.

Some residual effect will be apparent on nearly

any cropland, but the magnitude of its erosionreducing effectiveness will differ substantially with

crops and practices. Tillage and land use residuals

are influenced by so many factor interrelations

that development of charts like those for canopy

and mulch has not been feasible. However, apparent values of these subfactors for some situations were derived from the data and used for expansion of the soil loss ratio table to include conditions somewhat different from those directly represented in the plot studies.

Plowing residues down is far less effective than

leaving them on the surface but better than burning them or removing them from the land. After

several years of turning the crop residues under

with a moldboard plow before row crop seeding

in plot studies under natural rainfall, both runoff

and soil loss from the row crops were much less

than from similar plots from which cornstalks and

grain straw were removed at harvesttimes {52, 54,

59).

Short periods of rough fallow in a rotation will

usually lose much less soil than the basic, cleantilled, continuous fallow conditions for which C =

1. This is largely because of residual effects and

is also partly because of the roughness and cloddiness.

The most pronounced residual effect is that from

long-term sod or forest. The effect of a grass-andlegume rotation meadow turned under diminishes

gradually over about 2 years. In general, the erosion-reducing effectiveness of sod residual (from

grass or grass-and-legume meadows) in the plot

studies was directly proportional to hay yields. Site

values of the subfactor for sod residuals in rotations can be obtained from soil loss ratio table 5-D.

The effectiveness of virgin sod and of long periods

of alfalfa in which grass became well established

was longer lasting. Mixtures of grasses and legumes

were more effective than legumes alone.

Residual effectiveness of winter cover crops

plowed under in spring depends largely on the

type and quality of the crop and its development

stage at the time it is plowed under. The effectiveness of grass-and-legume catch crops turned under

in spring was less and of shorter duration than

that of full-year rotation meadows. Covers such

as vetch and ryegrass seeded between corn or

cotton rows before harvest and turned under in

April were effective in reducing erosion during the

winter and showed some residual effect in the following seedbed and establishment periods. Small

grain seeded alone in corn or cotton residues

showed no residual effect under the next crop.

Small grain or vetch on fall-plowed seedbed and

turned at spring planting time lost more soil than

adjacent plots with undisturbed cotton residues on

the surface.

Soil Loss Ratios

Factor C is usually given in terms of its average system, management, and rainfall pattern. To deannual value for a particular combination of crop rive site values of C, soil loss ratios for the indi-
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING 21

vidual cropstage periods must be combined with

erosion-index distribution data, as demonstrated later. Ratios of soil losses in each cropstage period of

specified cropping and management systems to

corresponding losses from the basic long-term fallow condition were derived from analysis of about

a quarter million plot soil loss observations. The

ratios are given in table 5 as percentages.

The observed soil loss ratios for given conditions

often varied substantially from year to year because of influences of unpredictable random variables and experimental error. The percentages

listed in table 5 are the best available averages

for the specified conditions. To make the table inclusive enough for general field use, expected ratios had to be computed for covi^r, residue, and

management combinations that were not directly

represented in the plot data. This was done by

using empirical relationships of soil losses to the

subfactors and interactions discussed in the preceding subsection. The user should recognize that

the tabulated percentages are subject to appreciable experimental error and could be improved

through additional research. However, because of

the large volume of data considered in developing the table, the listed values should be near

enough to the true averages to provide highly

valuable planning and monitoring guidelines. A

ratio derived locally from 1-year rainfall simulator

tests on a few plots would not necessarily represent the true average for that locality more accurately. Small samples are more subject to bias by

random variables and experimental error than

larger samples.

Table for Cropland

Table 5, with its supplements 5A, B, C, and D,

replaces tables 2, 3, and 4 in the 1965 edition.

The supplements had to be separated from the

main table to accommodate changes in format

requirements. The ratios are expressed as percentages in the tables to eliminate decimal points.

More than half the lines in table 5 are for conErosion Index

The rainfall factor, R, in the soil loss equation

does not completely describe the effects of local

differences in rainfall pattern on soil erosion. The

erosion control effectiveness of a cropping system

ditions associated with conservation tillage practices (65), which were not included in the 1965

edition. Also, it provides a direct means of crediting effects of faster and more complete canopy

development by improved fertility, closer row spacing, and greater plant population. Because the table includes several times as many specific conditions as the table in the 1965 edition and defines

applicable field conditions more accurately, some

simplicity has been sacrificed. However, it is not

intended for direct use by each field technician or

farmer.

Table 5 as presented here is designed to provide

the details needed by a trained agronomist to develop simple handbook tables of C values for conditions in specific climatic areas. It is designed for

use of the revised definitions of cropstage periods

given in the preceding section. The agronomist will

first determine, for the particular climatic area, the

number of weeks normally required for the crop

canopies to attain 10, 50, and 75 percent surface

cover, respectively. The table will then be used

as illustrated in the next major section. Linear interpolation between ratios listed in the table is

recommended where appropriate.

Semiarid Regions

Water erosion is a serious problem also in subhumid and semiarid regions. Inadequate moisture

and periodic droughts reduce the periods when

growing plants provide good soil cover and limit

the quantities of plant residue produced. Erosive

rainstorms are not uncommon, and they are usually concentrated within the season when cropland is least protected. Because of the difficulty of

establishing rotation meadows and the competition

for available soil moisture, sod-based rotations are

often impractical. One of the most important opportunities for a higher level of soil and moisture

conservation is through proper management of

available residues. The effects of mulch-tillage

practices in these areas can be evaluated from

lines 129 to 158 of table 5 and item 12 of 5-B.

Distribution Data

on a particular field depends, in part, on how the

year's erosive rainfall is distributed among the

six cropstage periods of each crop included in the

system. Therefore, expected monthly distribution
TABLE 5.-—Ratio of soil loss from (cropland to corresponding loss from continuous fallow

Cover Soi 1 loss ratio* for cropstage Cover Soil loss ratio* for cropstage

Line Cover, crop sequence. Spring after period and canopy cover^ Line

No.

Cover, crop sequence. Spring

and management^ residue^ after period and canopy cover^ c

No. and management^ residue^ plants F SB 1 2 3:80 90 96 4L« plant^ F SB 1 2 3:80 90 96 4L« z

Lb Pet Pet Pet Pcf Pet Pet Peí Pet Pet Lb Pet Pet Pet Pet Pet Pet Pet Pet Pet m

CORN AFTER C, GS, G OR COT CORN AFTER WC OF RYEGRASS

IN MEADOWLESS SYSTEMS OR WHEAT SEEDED IN

Moldboard plow, conv t'tlh C STUBBLE s 1 Rdl, sprg TP 4,500 — 31 55 48 38 — — 20 23 WC reaches stemming stage: -1m 2 3,400 — 36 60 52 41 — 24 20 30 79 No-till pi in killed WC 4,000 — — 7 7 7 — 7 6

(13)

3 2,600 — 43 64 56 43 32 25 21 37 80 3,000 —. — 11 11 11 11 9 7

O 4 2,000 — 51 68 60 45 33 26 22 47 81 2,000 — — 15 15 14 14 11 9 m

5 RdL, fall TP HP2 ^_ 44 65 53 38 20 82 1,500 — — 20 19 18 18 14 11

> 6 GP — 49 70 57 41 — 24 20 — Strip till one-fourth row space 5ö

7 FP — 57 74 61 43 32 25 21 — 83 Rows U/D slope 4,000 — — 13 12 11 — 11 9

(13) -H

m

Z

8 LP — 65 78 65 45 32 26 22 .— 84 3,000 — — 18 17 16 16 13 10

9 RdR, sprg TP HP , 66 74 65 47 22 '56 85 2,000 — — 23 22 20 19 15 12

10 GP — 67 75 66 47 — 27 23 62 86 1,500 — — 28 26 24 22 17 14 -1

11 FP — 68 76 67 48 35 27 — 69 87 Rows on contour" 4,000 — — 10 10 10 — 10 8

(13) 0

12 LP — 69 77 68 49 35 — — 74 88 3,000 — — 15 15 15 15 12 9 -n

13 RdR, fall TP HP _ 76 82 70 49 22 . 89 2,000 — — 20 20 19 19 15 12 >

14 GP 77 83 71 50 27 23 90 1,500 — — 25 24 23 22 17 14 O

15 FP 78 85 72 51 35 27 — 91 TP, conv seedbed 4,000 — 36 60 52 41 — 24 20 (13) 2

16 LP — 79 86 73 52 35 —. — — 92 3,000 — 43 64 56 43 31 25 21 n

c:

c

17 V/heelfrack ph RdL, TP^ 4,500 31 27 25 18 23 93 2,000 — 5} 68 60 45 33 26 22

18 3,400 — — 36 32 30 — 22 18 30 94 1,500 — 61 73 64 47 35 27 23

19 2,600 — — 43 36 32 29 23 19 37 WC succulent blades only: TO

20 2,000 — — 51 43 36 31 24 20 47 95 No-till pi in killed WC 3,000 — — 11 11 17 23 18 16 (13) m

21 Deep offset disk or 4,500 10 45 38 34 20 23 96 2,000 __ — 15 15 20 25 20 17

22 disk plow 3,400 10 ^

—

52 43 37 — 24 20 30 97 1,500 — — 20 20 23 26 21 18

23 2,600 5 — 57 48 40 32 25 21 37 98 1,000 — — 26 26 27 27 22 19 ;o

24 2,000 — — 61 51 42 33 26 22 47 99 Strip till one-fourth row space 3,000 — — 18 18 21 25 20 17 (13) n

25 No-till plant in crop residue^ 6,000 95 2 2 2 2 14 100 2,000 — — 23 23 25 27 21 18 c

r—

26 6,000 90 3 3 3 3 14 101 1,500 — — 28 28 28 28 22 19 -H

C

27 4,500 80 — 5 5 5 — — 5 15 102 1,000 _ — 33 33 31 29 23 20

28 3,400 70 — 8 8 8 — 8 6 19 CORN IN SOD-BASED SYSTEMS m

29 3,400 60 — 12 12 12 12 9 8 23 No-ii7/ pi in killed sod: X

30 3,400 50 — 15 15 14 14 11 9 27 103 3 to 5 tons hay yid — — — 1 1 1 — 1 1 1 >

31 2,600 40 — 21 20 18 17 13 11 30 104 1 to 2 tons hay yId — — — 2 2 2 2 2 2 2 Z

32 2,600 30 — 26 24 22 21 17 14 36 Strip till, 35 ton M: o

Chisel, shallow disk, or 105 50 percent cover, tilled strips — — — 2 2 2 — 2 2 4 o

fid cult, as only tillage: 106 20 percent cover, tilled strips — — — 3 3 3 — 3 3 5 O

33 On moderate slopes 6,000 70 — 8 8 7 — — 7 17 Strip till, 1-2 fon M: 7^

34 60 — 10 9 8 — — 8 17 107 40 percent cover, tilled strips — __ — 4 4 4 4 4 4 6 Z

35 50 — 13 11 10 — — 9 18 108 20 percent cover, tilled strips — — — 5 5 5 5 5 5 7 C

36 40 , 15 13 11 10 19

37 30 18 15 13 12 20 09 Oiher tillage after sod: (14) (14) {'') (14) (14, (14) (14) (14)

38 20 — 23 20 18 16 21 CORN AFTER SOYBEANS m70

39 Do. 4,500 70 ^ 9 8 7 _ _ 7 18 109 Sprg TP, conv till HP — 40 72 60 48 — __ 25 29

40 60 12 10 9 8 18 CO 110 GP — 47 78 65 51 30 25 37

41 50 14 13 11 — — 9 19 111 FP — 56 83 70 54 40 31 26 44 ^

42 40 — 17 15 13 — — 10 20 112 Fall TP, conv till HP — 47 75 60 48 — — 25 —

43 30 _ 21 18 15 — — 13 21 113 GP — 53 81 65 51 — 30 25 —

44 20 — 25 22 19 — — 16 22 114 FP — 62 86 70 54 40 31 26 —
45 Do. 3,400 60

-

~ 13 11 10 .— 10

8 20 115 Fall

& sprg chisel or cult HP 1330 __- 40 35 29

—— 23^ 29

46 50

-- 16 13 12

— 12

9 24 116 GP 25

— 45 39 33

— 27 23 37

47 40

^- 19 17 16 14 11 25 117 GP 20

— 51 44 39 34 27 23 37

48 30

'

- 23 21 19 __ 17 14 26 118 FP 15

— 58 51 44 36 28 23 44

49 20

~- 29 25 23 __ 21 16 27 119 LP 10

— 67 59 48 36 28 23 54

50 10

-

- 36 32 29

— 24 20 30 120 No-till pi in crop res'd HP 1540— 25 20 19 __ 14 11 26

51 Do. 2,600 50

-- 17 16 15 15 13 10 29 121 GP 30

— 33 29 25 22 18 14 33 "13

75

52 40

-- 21 20 19 19 15 12 30 122 FP 20

— 44 38 32 27 23 18 40

man

53 30

-

- 25 23 22 22 18 14 32 BEANS AFTER CORN

54 20

-- 32 29 28 27 22 17 34 123 Sprg IP, RdL, conv till HP

— 33 60 52 38

— 20 17 (16)

55 10

-

- 41 36 34 32 25 21 37 124 GP— 39 64 56 41— 21 18

z

56 Do. 2,000 40

~

~ 23 21 20 20 15 12 37 125 FP

— 45 . 68 60 43 29 22—

O

57 30

-

~ 17 25 24 23 19 15 39 126 Fall IP, Rc/t, conv till HP

— 45 69 57 38~ 20 17 (16)

ya

SB 20

-

- 35 32 30 28 22 18 42 127 GP

— 52 73 61 41

— 21 18

>

59 10

-

- 46 42 38 33 26 22 47 128 FP

— 59 77 65 43 29 22

—

Z

On slopes> 12 percent. -n Chisel or fíd cult: (17) (17) (17) (17) (17) (17) n

(16)

60 Lines 33-59 times factor of:

—

Disfe or fiarrow af/er spring

chisel or fid cult:

- 1.3 1.3 1.1 1.0 1.0 1.0 1.0

BEANS AFTER BEANS

GRAIN AFTER C, G, GS, COr»

(18) (18) (18) (18) (18) (18) (18) (16)

>I—

r-m

Lines 33-59 times factor of: 129 In disked residues: 4,500 70 12 12 11

7

4

2

(20)

o

61

62

On moderate slopes—

On slopes> 12 percent—

—

- 1.1 - 1.41.1

1.4

1.1

1.2

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

130

131

132

3,400 60

50

40

—

16

22

27

14

18

21

12

14

16

789

455

233

Ridge p/onf.-io 133 30 32 25 18

9

6

3

z

Lines 33-59 times factor of: 134 20 38 30 21 10

6

3 r"

63 Rows on contour^^

—

_ .7 .7 .7 .7 .7 .7 .7

O

64 Rows U/D slope

< 12 percent—

_ .7 .7 1.0 1.0 1.0 1.0 1.0 135 Do. 2,600 40 29 24 19

9

6

3

(20)

65 Rows U/D slope

> 12 percent—

—_ .9 .9 1.0 1.0 1.0 1.0 1.0 136

137

20

10

~

43

52

34

39

24

27

11

12

77

44

ml

Till plant:

Lines 33-59 times factor of: 138 Do. 2,000 30

— 38 30 23 11

7

4

n

66 Rows on contour"

—

~ .7 .85 1.0 1.0 1.0 1.0 1.0 139 20 46 36 26

30

12

13

7

45

67 Rows U/D slope<7 percent—

—

~ 1.0 1.0 1.0 1.0 1.0 1.0 1.0 140 10 56 43

8

OCom

Strip tilt one-fourth of row spacing: 141 ¡n disked stubble, RdR

"

— 79 62 42 17 11

6

(20)

68 Rows on contour^^ 4,500 1260

-

- 12 10

9 .— __

8 23 142 Winter

G after fall IP, RdL HP

— 31 55 48 31 12

7

5

(20)

69 3,400 50-

- 16 14 12

— 11 10 27 143 GP— 36 60 52 33 13

85

-H

70 2,600 40

-- 22 19 17 17 14 12 30 144 FP

— 43 64 56 36 14

O

9

5

71 2,000 30

-

- 27 23 21 20 16 13 36 145 LP

— 53 68 60 38 15 10

6

nOz

72 Rows U/D slope 4,500 1260

-

- 16 13 11 . —

9 23 GRAIN AFTER SUMMER FALLOW

73 3,400 50

-

- 20 17 14

— 12 11 27 146 With grain residues 200 10— 70 55 43 18 13 11 (21)

74 2,600 40

-

- 26 22 19 17 14 12 30 147 500 30

— 43 34 23 13 10

8 Ui

75 2,000 30

-

- 31 26 23 20 16 13 36 148 750 40

— 34 27 18 10

7

7

m70

Vari-till: 149 1,000 50

— 26 21 158

76

<

76 Rows on contour^i 3,400 40

-- 13 12 11 , . . 11 22 150 1,500 60

— 20 16 12

7

5

5

oz

77 3,400 30

-- 16 15 14 14 13 12 26 151 2,000 70

— 14 11

97

5

5

78 2,600 20-- 21 19 19 19 16 14 34 152 With row crop residues 300

5

— 82 65 44 19 14 12 (21)

153 500 15

— 62 49 35 17 13

n

154 750 23

— 50 40 29 14 11

9 •V

155 1,000 30

— 40 31 24 13 10

8

>

156 1,500 45

— 31 24 18 108

7

Z

157 2,000 55

— 23 19 14

8

75

z

158

POTATOES

2,500 65

— 17 14 12

7

5

4

Zo

159 Rows with slope

Contoured rows, ridged when

canopy cover is about

160 50 percenfii

64 56 36 26 19 16

43 64 56 IS 13 108

See footnotes, p. 24. to
K5

4^

c

z

H

m

O

c/>

-H

>

Foofnotes for table 5.

^ Symbols: B, soybeans; C, corn; conv till, plow, disk and harrow for seedbed; cot, cotton;

F, rough fallow; fid cult, field cultivator; G, small grain; GS, grain sorghum; M, grass and

legume meadow, at least 1 full year; pi, plant; RdL, crop residues left on field; RdR, crop

residues removed; SB, seedbed period; sprg, spring; TP, plowed with moldboard; WC,

winter cover crop; —, insignificant or an unlikely combination of variables.

" Dry weight per acre after winter loss and reductions by grazing or partial removal:

4,500 lbs represents TOO to 125 bu corn; 3,400 lbs, 75 to 99 bu; 2,600 lbs, 60 to 74 bu;

and 2,000 lbs, 40 to 59 bu; with normal 30-percent winter loss. For RdR or fall-plow

practices, these four productivity levels are indicated by HP, GP, FP and LP, respectively

(high, good, fair, and low productivity). In lines 79 to 102, this column indicates dry

weight of the winter-cover crop.

^ Percentage of soil surface covered by plant residue mulch offer crop seeding. The

difference between spring residue and that on the surface after crop seeding is reflected

in the soil loss ratios as residues mixed with the topsoil.

* The soil loss ratios, given as percentages, assume that the indicated crop sequence

and practices are followed consistently. One-year deviations from normal practices do not

have the effect of a permanent change. Linear interpolation between lines is recommended

when justified by field conditions.

^Cropstage periods are as defined on p. 18. The three columns for cropstage 3 are for

80, 90, and 96 to 100 percent canopy cover at maturity.

^ Column 4L is for all residues left on field. Corn stalks partially standing as left by

some mechanical pickers. If stalks are shredded and spread by picker, select ratio from

table 5-C. When residues ore reduced by grazing, take ratio from lower spring-residue

line.

^ Period 4 values in lines 9 to 12 are for corn stubble (stover removed).

"* Inversion plowed, no secondary tillage. For this practice, residues must be left and

incorporated.

'* Soil surface and chopped residues of matured preceding crop undisturbed except in

narrow slots in which seeds are planted.

^"Top of old row ridge sliced off, throwing residues and some soil into furrow areas.

Reridging assumed to occur near end of cropstage 1.

^* Where lower soil loss ratios are listed for rows on the contour, this reduction is in

addition to the standard field contouring credit. The P value for contouring is used with

these reduced loss ratios.

'^ Field-average percent cover; probably about three-fourths of percent cover on undisturbed strips.

^^ If again seeded to WC crop in corn stubble, evaluate winter period as a winter

grain seeding (lines 132 to 148). Otherwise, see table 5-C.

^* Select the appropriate line for the crop, tillage, and productivity level and multiply

the listed soil loss ratios by sod residual factors from table 5-0.

^^ Spring residue may include carryover from prior corn crop.

'«See table 5-C.

'^ Use values from lines 33 to 62 with appropriate dates and lengths of cropstage

periods for beans in the locality.

^^ Values in lines 109 to 122 are best available estimates, but planting dates and

lengths of cropstages may differ.

'^When meadow is seeded with the grain, its effect will be reflected through higher

percentages of cover in cropstages 3 and 4.

^^ Ratio depends on percent cover. See table 5-C.

^'See item 12, table 5-B.

om-o

>

-H

m

Z

>

O

n

cI—

H

C

5Ö

>

OTO

O

C

-H

C

>

Z

w

o

O7^

Z

C

2:

09

Cd
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING 25

TABLE 5-A.—Approximate soil loss ratios for cotton

Expected final canopy percent cover: 65 80 95

Estimated initial percent cover from defoliation +

stalks down: 30 45 60

Practice

Number Tillage operation(s) Soil loss ratio^

COTTON ANNUALLY: Percent

I.... None;

Defoliation to Dec. 31 36 24 15

Jan. 1 to Feb. or Mar, tillage:

Cot Rd only 52 41 32

Rd & 20 percent cover vol veg- 32 26 20

Rd & 30 percent cover vol veg 26 20 14

2. . , . Chisel plow soon after cot harvest:

Chiseling to Dec. 31 40 31 24

Jan. 1 to sprg tillage 56 47 40

3 Fall disk offer chisel:

Disking io Dec. 31 53 45 37

Jan. 1 to sprg tillage 62 54 47

4....Cfi/se/ plow Feb-Mar, no prior tillage:

Cot Rd only 50 42 35

Rd & 20 percent vol veg 39 33 28

Rd & 30 percent vot veg 34 29 25

5....fied ("flip"} Fefa-Mor, no prior tillage:

Cot Rd only 100 84 70

Rd & 20 percent vol veg 78 66 56

Rd & 30 percent vol veg 68 58 50

Split ridges & plant after hip,, or

Disk & plant after chisel (SB)i

Cot Rd only 61 54 47

Rd & 20 percent vol veg 53 47 41

Rd & 30 percent vol veg 50 44 38

Cropstage 1:

Cot Rd only 57 50 43

Rd & 20 percent vol veg 49 43 38

Rd & 30 percent vol veg 46 41 36

Cropstage 2 45 39 34

Cropstage 3 40 27 17

6....Bed (hip) after 1 prior tillage:

Cot Rd only 110 96 84

Rd & 20 percent veg 94 82 72

Rd & 30 percent veg 90 78 68

Split ridges after hip (SB):

Cot Rd only 66 61 52

Rd & 20 to 30 percent veg 61 55 49

Cropstage 1:

Cot Rd only 60 56 49

Rd & 20 to 30 percent veg 56 51 46

Cropstage 2 47 44 38

Cropstage 3 42 30 19

7, . . . Hip after 2 prior tillages:

Cot Rd only 116 108 98

Rd & 20-30 percent veg 108 98 88

Split ridges after hip (SB) 67 62 57

8. , . .Hip after 3 or more tillages: 120 no 102

Split ridges after hip (SB) 68 64 59

9 Convent/ona/ moldboard plow and disk:

Fallow period 42 39 36

Seedbed period 68 64 59

Cropstage 1 63 59 55

Cropstage 2 49 46 43

Cropstoge 3 44 32 22

Cropstage 4 (See practtices 1, 2, and 3)

COTTON AFTER SOD CROP:

For the first or second crop after a grass or grass-and-legume

meadow has been turnplowed, multiply values given in the last five

lines above by sod residual factors from table 5-D.

COTTON AFTER SOYBEANS:

Select values from above and multiply by 1.25.

See footnotes at right.

of erosive rainfall at a particular location is an

element in deriving the applicable value of cover

and management, C.

Central and Eastern States

A location's erosion index is computed by summing El values of individual rainstorm^ over periods from 20 to 25 years. Thus, the expected monthly distribution of the erosion index can be computed from the same data. For each rainfall record

abstracted for development of the isoerodent map,

the monthly El values were computed and expressed as percentages of the location's average

annual erosion index. When the monthly percentages are plotted cumulatively against time, they

define El distribution curves such as illustrated in

figure 8 for three locations. The three contrasting

curves are presented to demonstrate how drastically the normal El distribution can differ among

climatic regions.

On the basis of observed seasonal distributions

of El, the 37 States east of the Rocky Mountains

were divided into the 33 geographic areas delineated in figure 9. The changes in distribution are

usually gradual transitions from one area to the

next, but the average distribution within any one

of the areas may, for practical purposes, be considered applicable for the entire area. The El distributions in the 33 areas, expressed as cumulative percentages of annual totals, are given in

table 6. The area numbers in the table correspond

to those in figure 9. The data in the table were

^ Alternate procedure for estimating the soil loss ratios:

The ratios given above for cotton are based on estimates for reductions in percent cover through normal winter loss and by the successive tillage operations. Research is underway in Mississippi to obtain

more accurate residue data in relation to tillage practices. This research

should provide more accurate soil loss ratios for cotton within a few

years.

Where th© reductions in percent cover by winter loss and tillage

operations are small, the following procedure may be used to compute

soil loss ratios for the preplant and seedbed periods: Enter figure 6 with

the percentage of the field surface covered by residue mulch, move

vertically to the upper curve, and read the mulch factor on the scale

at the left. Multiply this factor by a factor selected from the following

tabulation to credit for effects of land-use residual, surface roughness

and porosity.

Productivitty

level

No

tillage

Rough

surface

Smoothed

surface

High

Medium

Poor

0.66

.71

.75

0.50

.54

.58

0.56

.61

.65

Values for the bedded period on slopes of less than 1 percent should

be estimated at twice the value computed above for rough surfaces.

- Rd, crop residue; vol veg, volunteer vegetation.
26 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

TABLE 5-B.—So/7 loss ratios for conditions not evaluated

in table 5

COTTON:

See table 5-A.

CROPSTAGE 4 FOR ROWCROPS:

Stalks broken and partially standing: Use col. 4L.

Stalks standing after hand picking: Col. 4L times 1.15.

Stalks shredded without soil tillage: See table 5-C.

Fall chisel: Select values from lines 33-62, seedbed column.

CROPSTAGE 4 FOR SMALL GRAIN:

See table S-C.

DOUBLE CROPPING:

Derive annual C value by selecting from table 5 the soil loss percentages for the successive cropstage periods of each crop.

ESTABLISHED MEADOW, FULL-YEAR PERCENTAGES:

Grass and legume mix, 3 to 5 t hay 0.4

Do. 2 to 3 t hay .6

Do. 1 t hay 1.0

Sericea, after second year 1.0

Red clover 1.5

Alfalfa, tespedeza, and second-year sericea 2.0

Sweetclover 2.5

MEADOW SEEDING WITHOUT NURSE CROP:

Determine appropriate lengths of cropstage periods SB, 1, and 2 and

apply values given for small grain seeding.

PEANUTS:

Comparison v/ith soybeans is suggested.

PINEAPPLES:

Direct data not available. Tentative values derived analytically are

available from the SCS in Hawaii or the Western Technical Service Center at Portland, Oreg. (Reference 5).

SORGHUM:

Select values given for corn, on the basis of expected crop residues

and canopy cover.

SUGARBEETS:

Direct data not available. Probably most nearly comparable to potatoes, without the ridging credit.

SUGARCANE:

Tentative values available from sources given for pineapples.

SUMMER FALLOW IN LOW-RAINFALL AREAS, USE GRAIN OR ROW

CROP RESIDUES:

The approximate soil loss percentage after each successive tillage

operation may be obtained from the following tabulation by estimating the percent surface cover after that tillage and selecting

the column for the appropriate amount of initial residue. The

given values credit benefits of the residue mulch, residues mixed

with soil by tillage, and the crop system residual.

Percent cover \n\i\a\ residue (lbs/A)

TABLE 5-C.—So/7 loss ratios (percent) for cropstage 4

when stalks are chopped and distributed without soil

tillage

by mulch > 4,000 3,000 2,000 1,500

90 4 —

80 8 18 — —

70 12 . 13 114 —

60 16 17 MB 119

50 20 22 24 125

40 25 27 30 32

30 29 33 37 39

20 35 39 44 48

10 47 55 63 68

1 For grain residue only.

WINTER COVER SEEDING IN ROW CROP STUBBLE OR RESIDUES:

Define cropstage periods based on the cover seeding date and apply

values from lines 129 to 145.

Corn or

Tilled

Sorghum Soybeans

Mulch Tilled No-till in Grain

coveri seedbed^ No-till seedbed^ corn rd3 Stubble^

20 48 34 60 42 48

30 37 26 46 32 37

40 30 21 38 26 30

50 22 15 28 19 22

60 17 12 21 16 17

70 12 8 15 10 12

80 7 5 9 6 7

90 4 3 — — 4

95 3 2 — — 3

1 Part of a field surface directly covered by pieces of residue mulch.

^ This column applies for all systems other than no-till.

^ Cover after bean harvest may include an appreciable number of

stalks carried over from the prior corn crop.

^ For grain with meadow seeding, include meadow growth in percent

cover and limit grain period 4 to 2 mo. Thereafter, classify as established meadow.

abstracted from the published El distribution

curves.

The percentage of the annual erosion index that

is to be expected within each cropstage period

may be obtained by reading from the appropriate

line of table 6, the values for the last and first

date of the period, and subtracting. Interpolate

TABLE 5-D.—Factors fo credit residual effects of turned

sod^

Factor for cropstage period:

Crop Hay yield 7—

F SB and 1 2 3 4

Tons

First year after mead:

Row crop or grain ... 3-5 0.25 0.40 0.45 0.50 0.60

2-3 .30 .45 .50 .55 .65

1-2 .35 .50 .55 .60 .70

Second year after mead:

Row crop 3.5 .70 .80 .85 .90 .95

2-3 .75 .85 .90 .95 1.0

1-2 .80 .90 .95 1.0 1.0

Spring grain 3-5 — .75 .80 .85 .95

2-3 — .80 .85 .90 1.0

1-2 — .85 .90 .95 1.0

Winter grain 3-5 — .60 .70 .85 .95

2-3 ~ .65 .75 .90 1.0

1-2 — .70 .85 .95 1.0

1 These factors are to be multiplied by the appropriate soil loss percentages selected from table 5. They are directly applicable for sodforming meadows of at least 1 full year duration, plowed not more

than 1 month before final seedbed preparation.

When sod is fall plowed for spring planting, the listed values for all

cropstage periods are increased by adding 0.02 for each additional

month by which the plowing precedes spring seedbed preparation. For

example, September plowing would precede May disking by 8 months

and 0.02(8—1), or 0.14, would be added to each value in the table. For

nonsod-forming meadows, like sweetclover or lespedeza, multiply the

factors by 1.2. When the computed value is greater than 1.0, use as 1.0.
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING 27

l/l 2/1 3/1 4/1 5/1 6/1 7/1 8/1 9/1 lO/l I I/I 12/1 I/I

DATE

FIGURE 8.—Typical El-distribution curves for three rainfall patterns.

between values in the selected line when the desired dates are not listed.

Western States, Hawaii, and Puerto Rico

Normal rainfall patterns in these mountainous

States often change abruptly within a short distance. Figure 9 was not extended to include these

States because long-term intensity data were not

available for enough locations to delineate boundaries of homogeneous areas. However, El dist*-'"^- '!ons indicated by station records that were

abstracted are given in table 7 for reference.

FIGURE 9.—Key map for selection of applicable El-distribution data

from table 6.

Winter Periods

Site El values reflect only rain falling at erosive

intensities. Where the winter precipitation comes

as snow or light rain. El distribution curves may

show insignificant percentages for several winter

months. Yet, snowmelt and low intensity rains on

frozen soil may cause appreciable runoff that is

erosive even though the associated maximum 30-

minute rainfall intensity is extremely low or zero.

The section on isoerodent Maps pointed out that

where this type of runoff is significant its erosive

force must be reflected in an Rg value that is added

to the El value to obtain R. This additional erosive

force must also be reflected in the monthly disfnbufion

of R. Otherwise, poor management during the

winter period will not be reflected in the USLE

estimate of annual soil loss because a zero cropstage R value would predict zero soil loss regardless of the relevant soil loss ratio.

Soil erosion by thaw runoff is most pronounced

in the Northwest, where Rg values often exceed the

average annual El. However, it may also be significant in other Northern States. Probable amounts

of thaw runoff were not available for inclusion

in the calculations of the El distributions given in

tables 6 and 7, but the significance and probable

time of occurrence of such runoff can be estimated

by local people. The procedure for adjusting table

6 cumulative percentages to include this erosive

potential will be illustrated.

Based on the previously described estimating

procedure, Rg values in area No. 1, figure 9, appear to equal about 8 percent of the annual El.

Assuming that the thaw runoff in that area normally occurs between March 15 and April 15, the

percentage in table 6 for April 1 is increased by 4,

the April 15 and all subsequent readings are increased by 8, and all the adjusted readings are

then divided by 1.08. This procedure corrects the

data given in line 1, table 6, for dates April 1 to

September 1 to the following cumulative percentages listed in chronological sequence: 5, 9, 10, 13,

18, 29, 41, 53, 66, 79, 91. The other values are

unchanged. Such adjustments in monthly distribution of R where thaw runoff is significant will be

particularly helpful when the USLE is used to estimate seasonal distribution of sediment from agricultural watersheds.
28 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

TABLE 6.—Percentage of the average anr)ual El which normally occurs between January 1 and the indicated dátese

Computed for the geographic areas shown in figure 9

y^PgQ Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

No. rTs TTs ris 1 15 TTs 1 15 ~5 1 15 1 15 1 15 1 15 1 15

1 0 0 0 0 0 0 12 3 6 1123 36 49 63 77 90 95 98 99 100 100 100 100

2 0 0 0 0 11 2 3 6 10 17 29 43 55 67 77 85 91 96 98 99 100 100 100

3 0 0 0 0 11 2 3 6 13 23 37 5161 69 78 85 91 94 96 98 99 99 100

4 0 0 11 2 3 4 7 12 18 27 38 48 55 62 69 76 83 90 94 97 98 99 100

5 0 1 2 3 4 6 8 13 21 29 37 46 54 60 65 69 74 81 87 92 95 97 98 99

6 0 0 0 0 11 12 6 16 29 39 46 53 60 67 74 81 88 95 99 99 100 100

7 0 1 12 3 4 6 8 13 25 40 49 56 62 67 72 76 80 85 91 97 98 99 99

8 0 1 3 5 7 10 14 20 28 37 48 56 6164 68 72 77 81 86 89 92 95 98 99

9 0 2 4 6 9 12 17 23 30 37 43 49 54 58 62 66 70 74 78 82 86 90 94 97

10 0 1 2 4 6 8 10 15 2129 38 47 53 57 6165 70 76 83 88 91 94 96 98

11 0 1 3 5 7 9 11 14 18 27 35 41 46 51 57 62 68 73 79 84 89 93 96 98

12 00 00 11 23 59 15 27 38 50 62 74 84 91 95 97 98 99 99 100

13 0 0 0 1 12 3 5 7 12 19 33 48 57 65 74 82 88 93 96 98 99 100 100

14 0 0 0 1 2 3 4 6 9 14 20 28 39 52 63 72 80 87 9194 97 98 99 100

15 .00 12 34 68 1115 2231 4049 5969 7885 9194 96 98 99100

16 ..0 1 2 3 4 6 8 10 14 18 25 34 45 56 64 72 79 84 89 92 95 97 98 99

17 0 1 2 3 4 5 6 8 1115 20 28 41 54 65 74 82 87 92 94 96 97 98 99

18 0 1 2 4 6 8 10 13 19 26 34 42 50 58 63 68 74 79 84 89 93 95 97 99

19 0 1 3 6 9 12 16 21 26 31 37 43 50 57 64 71 77 81 85 88 91 93 95 97

20 0 2 3 5 7 10 13 16 19 23 27 34 44 54 63 72 80 85 89 91 93 95 96 98

21 0 3 6 10 13 16 19 23 26 29 33 39 47 58 68 75 80 83 86 88 90 92 95 97

22 0 3 6 9 13 17 2127 33 38 44 49 55 61 67 71 75 78 8184 86 90 94 97

23 ...03 57 1014 1823 2731 3539 4553 6067 7480 8486 88 90 93 95

24 0 3 6 9 12 16 20 24 28 33 38 43 50 59 69 75 80 84 87 90 92 94 96 98

25 .....0 1 3 5 7 10 13 17 2124 27 33 40 46 53 61 69 78 89 92 94 95 97 98

26 0 2 4 6 8 12 16 20 25 30 35 41 47 56 67 75 8185 87 89 91 93 95 97

27 0 1 2 3 5 7 10 14 18 22 27 32 37 46 58 69 80 89 93 94 95 96 97 99

28 0 1 3 5 7 9 12 15 18 21 25 29 36 45 56 68 77 83 88 91 93 95 97 99

29 0 1 2 3 4 5 7 9 1114 17 22 3142 54 65 74 83 89 92 95 97 98 99

30 0 1 2 3 45 68 10 14 19 26 34 45 56 66 76 82 86 90 93 95 97 99

31 0 0 0 12 3 4 5 7 12 17 24 33 42 55 67 76 83 89 92 94 96 98 99

32 0 1 2 3 4 5 6 8 10 13 17 22 3142 52 60 68 75 80 85 89 92 96 98

33 0 1 2 4 6 8 1113 15 18 2126 32 38 46 55 64 71 77 81 85 89 93 97

^ For dotes not listed in the table, interpolate between adjacent values.

Procedure far Deriving Local C Values

Factor C in the USLE measures the combined

effect of all the interrelated cover and management variables and is defined as the ratio of soil

loss from land cropped under specified conditions

to the corresponding loss from clean-tilled continuous fallow. It is usually expressed as an annual value for a particular cropping and management system. Soil loss ratios, as used in table 5,

express a similar ratio for a short time interval

within which cover and management effects are

relatively uniform. The cropstage soil loss ratios

must be combined in proportion to the applicable

percentages of El to derive annual C values.

To compute the value of C for any particular

crop and management system on a given field, one

needs first to determine the most likely seeding

and harvest dates, rate of canopy development,

and final canopy cover. Also, the system to be

evaluated must be carefully defined with regard

to crop and residue management details. Within

the broad limits of tables 5 and 6, these tables

then supply the research data needed to complete
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING 29

TABLE 7.—Monthly distribution of El at selected raingage locations

Average percentage of annual El occurring from 1/1 to:

Location^ 2/1 3/1 4/1 5/1 6/1 7/1 8/1 9/1 10/1 11/1 12/1

California

Red Bluff (69) 18 36 47 55 62 64 65 65 67 72 82

San Luis Obispo (51) 19 39 54 63 65 65 65 65 65 67 83

Colorado

Akron (91) 0 0 0 1 18 33 72 87 98 99 100

Pueblo (68) 0 0 0 5 14 23 40 82 84 100 100

Springfield (98) .... 0 0 1 4 26 36 60 94 96 99 100

Hawaii

Hiio (770) 9 23 34 44 49 51 55 60 65 72 87

Honolulu (189) 19 33 43 51 54 55 56 57 58 62 81

Kahului (107) 14 32 49 62 67 68 69 70 71 76 86

Lihue (385) ........ 19 29 36 41 44 45 48 51 56 64 80

Montana

Billings (18) 0 0 1 6 22 49 86 88 96 100 100

Great Falls (17) .... 1 1 2 6 20 56 74 93 98 99 100

Miles City (28) .... 0 0 0 1 10 32 65 93 98 100 100

New Mexico

Albuquerque (15) ..1 1 2 4 10 21 52 67 89 98 99

Roswell (52) 0 0 2 7 20 34 55 71 92 99 99

Oregon

Pendleton (6) 8 12 15 22 56 64 67 67 74 87 96

Portland (43) 15 27 35 37 40 45 46 47 54 65 81

Puerto Rico

Mayaguez (600) .... 1 2 3 6 15 31 47 63 80 91 99

San Juan (345) .... 5 8 11 17 33 43 53 66 75 84 93

Washington

Spokane (8) 5 9 11 15 25 56 61 76 84 90 94

Wyoming

Casper (11) 0 0 1 6 32 44 70 90 96 100 100

Cheyenne (32) 0 1 2 5 17 42 73 90 97 99 100

^ Numbers in parentheses are the observed average annual El.

12/31

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

the computation of C. The procedure will be explained by an example that, for illustration purposes, was selected to include many changes in

field conditions.

Problem. Evaluate C for a 4-year rotation of

wheat-meadow-corn-corn on moderately sloping

land in Central Illinois or Indiana, assuming the

following management details and dates: Wheat

is seeded October 15 in a 40-percent cover of

disked corn residue, and a grass and legume

meadow mix is seeded with the wheat. The wheat

would normally develop a 10-percent cover by November 1, 50 percent by December 1, 75 percent

by April 15, and nearly 100 percent in the maturing stage. It is harvested July 15, leaving an 80-

percent surface cover of straw and small grass.

The sod developed under 1 full year of meadow,

yielding more than 3 t of hay, is turned under

in April. The field is disked May 5 and is harrowed

and planted to corn May 10. The first-year corn,

harvested October 15, is followed by fall chiseling

about November 15 and spring disking for secondyear corn. Residue cover is 50 percent after fall

chiseling and 30 percent after corn planting on

May 10. Fertility, row spacing, and plant population for both corn years are such that 10, 50, and

75 percent canopy covers will be developed in 20,

40, and 60 days, respectively, from planting, and

final canopy cover is more than 95 percent.

Procedure. Set up a working table similar to

the one illustrated in table 8, obtaining the needed

information as follows:

Column 1. List in chronological sequence all the

land-cover changes that begin new cropstage periods, as previously defined.

Column 2. List the date on which each cropstage

period begins.

Column 3. Select the applicable area number
30 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

(Î) (2) (3)

Table 6,

(4)

Crop-

(5) (6) (7) (8) (9)

area stage El in Soil loss Sod Cropstage Crop

Event Date 16 period period ratio* Factor C value year

TABLE 8.—Sample working fable for derivation

ñ (2) (3) (4) (5) (óT

Table 6, Croparea stage El in Soil los

fent Date 16 period period ratio*

PI W' 10/15 92 SB 0.03 0.27(132)

10 percent c .11/1 95 1 .03 .21

50 percent c .12/1 98 2 .12 .16

75 percent c .4/15 10 3 .46 .03

Hv W 7/15 56 4 .28 .07(50)

Meadow 9/15 84 1.26 .004(5B)

IP 4/15 10 F .05 .36(2)

Disk 5/5 15 SB .10 .60

PI C 5/10 —

10 percent c .6/1 25 1 .13 .52

50 percent c .6/20 38 2 .14 .41

75 percent c .7/10 52 3 .40 .20

Hv C 10/15 92 4L .05 .30

Chisel n/15 97 4c .17 .16(46)

Disk 5/1 14 SB .11 .25(48 & 61)

PI C 5/10 —

10 percent c .6/1 25 1 .13 .23

50 percent c .6/20 38 2 .14 .21

75 percent c .7/10 52 3 .40 .14(48)

Hv C & pi W .10/15 92

Rotation totals 4.0

Average annual C value for rotation

^ Numbers in parentheses are line numbers in table 5.

^ Abbreviations: c, canopy cover; C, corn; hv, harvest; pi,

W, v/heat.

of a rotation C value

0.95 0.0077

.95 .0060

1.0 .0192

.0138

.0196 0.066

1.0 .0050 .005

.25 .0045

.40 .0240

.40 .0270

.45 .0258

.50 .0400

.60 .0090 .130

.60 .0163

.80 .0220

.80 .0239

.85 .0250

.90 .0504 .138

0.3392

.085

plant; IP, moldboard plow;

from figure 9, and from the line in table 6 having

the corresponding area number (in this case, 16),

read the cumulative percentage of El for each date

in column 2. Values for the corn planting dates

were omitted in table 8 because the seedbed periods had begun with the spring diskings. The El

percentage for May 5 was obtained by interpolating between readings from May 1 and 15.

Column 4. identify the cropstage periods.

Column 5. Subtract the number in column 3

from the number in the next lower line. If the

cropstage period includes a year end, subtract

from 100 and add the number in the next lower

line. The differences are percentages and may be

pointed off as hundredths.

Column 6. Obtain from table 5. Enter the table

with crop and management, pounds of spring residue or production level, and percent mulch cover

after planting, in that sequence. The data in the

selected line are percentages and are used as

hundredths in the computation of C. For cropstage

3, use the column whose heading corresponds with

expected final canopy. For conditions not listed in

the primary table, consult supplements 5-A to D.

Lines used for the examples are given in parentheses in column 6.

Column 7. From table 5-0.

Column 8. The product of values in columns 5,

6 and 7. The sum of these products is the value of

C for the entire rotation. Because C is usually desired as an average annual value, this sum is divided by the number of years in the rotation.

Column 9. The subtotals in this column are C

values for the individual crop-years. They also

show the relative contributions of the four crops

to the rotation C value.

Changes in geographic area or in planting dates

would affect the C value by changing columns 3

and 5. Changes in amount or disposition of residues, tillage practices, or canopy development

would change column 6. Thus C can vary substantially for a given crop system.

Values of C for one-crop systems are derived

by the same procedure but would require only a

few lines. Also, column 7 is omitted for meadowless systems.
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING 31

C-Value Tables for Cropland

It will rarely, if ever, be necessary for a field

technician or farmer to compute values of C. Persons experienced in the procedures outlined above

have prepared C value tables for specific geographic areas. Such a table v/ill list all the onecrop and multicrop systems likely to be found

v^ithin the designated area and will list the C

values for each system for each of the combinations of management practices that may be associated with it. They are usually listed in ascending

or descending order of magnitude of the C values.

The user can then quickly determine all the potential combinations of cropping and management

that have C values smaller than any given threshold value. Persons in need of C values for a particular locality can usually obtain a copy of the

applicable table from the nearest SCS state office.

C Values for Construction Areas

Site preparations that remove all vegetation and

also the root zone of the soil not only leave the

surface completely without protection but also remove the residual effects of prior vegetation. This

condition is comparable to the previously defined

continuous fallow condition, and C = 1. Roots and

residual effects of prior vegetation, and partial

covers of mulch or vegetation, substantially reduce soil erosion. These reductions are reflected in

the soil loss prediction by C values of less than 1.0.

Applied mulches immediately restore protective

cover on denuded areas and drastically reduce C

(?, 2, 20, 27, 43), Soil loss ratios for various percentages of mulch cover on field slopes are given

by the upper curve of figure 6. Where residual effects are insignificant, these ratios equal C. The

percentage of surface cover provided by a given

rate of uniformly spread straw mulch may be estimated from figure 10 (appendix).

Straw or hay mulches applied on steep construction slopes and not tied to the soil by anchoring

and tacking equipment may be less effective than

equivalent mulch rates on cropland. In Indiana

tests on a 20 percent slope of scalped subsoil, a

2.3-t rate of unanchored straw mulch allowed soil

loss of 12 t/A when 5 in of simulated rain was

applied at 2.5 in/h on a 35-ft plot (ó7). There was

evidence of erosion from fiow beneath the straw.

Mulches of crushed stone at 135 or more t/A, or

wood chips at 7 or more t/A, were more effective.

(Broadcast seedings of grass after the tests gave

good stands on the plots mulched with 135 or 240

t crushed stone, 70 t road gravel, 12 t wood chips,

or 2.3 t straw. Stands were poor on the no-mulch

and the 15-t rate of crushed stone mulch.)

Table 9 presents approximate C values for

straw, crushed stone, and woodchip mulches on

construction slopes where no canopy cover exists,

and also shows the maximum slope lengths on

which these values may be assumed applicable.

Soil loss ratios for many conditions on construcTABLE 9.—Mulch factors and length limits for

construction slopes^

Type of Mulch Land Factor Length

mulch Rate Slope C limits

Tons per acre Perceni Feef

None 0 all 1.0 —

Straw or hay. 1.0 1-5 0.20 200

tied down by 1.0 6-10 .20 100

anchoring and

tacking 1.5 1-5 .12 300

equipment^ 1.5 Ó-10 .12 150

Do. 2.0 1-5 .06 400

2.0 6-10 .06 200

2.0 11-15 .07 150

2.0 16-20 .11 100

2.0 21-25 .14 75

2.0 26-33 .17 50

2.0 34-50 .20 35

Crushed stone. 135 <16 .05 200

VA to V/2 in 135 16-20 .05 150

135 21-33 .05 100

135 34-50 .05 75

Do. 240 <21 .02 300

240 21-33 .02 200

240 34-50 .02 150

Wood chips 7 <16 .08 75

7 16-20 .08 50

Do. 12 <16 .05 150

12 16-20 .05 100

12 21-33 .05 75

Do. 25 <16 .02 200

25 16-20 .02 150

25 21-33 .02 100

25 34-50 .02 75

^ From Meyer and

shop group on the

data.

' Maximum slope

considered efFective.

application rate or

length is required.

^When the straw

values on moderate

than 0.30 should be

Ports (24). Developed by an interogency workbasis of field experience and limited research

length for which the specified mulch rate is

When this limit is exceeded, either a higher

mechanical shortening of the efFective slope

or hay mulch is not anchored to the soil, C

or steep slopes of soils having K values greater

taken at double the values given in this table.
32 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

tion and developmental areas can be obtained

from table 5 if good judgment is exercised in comparing the surface conditions with those of agricultural conditions specified in lines of the table.

Time intervals analogous to cropstage periods v^ill

be defined to begin and end with successive construction or management activities that appreciably

change the surface conditions. The procedure is

then similar to that described for cropland.

Establishing vegetation on the denuded areas as

quickly as possible is highly important. A good sod

has a C value of 0.01 or less (table 5-B), but such

a low C value can be obtained quickly only by

laying sod on the area, at a substantial cost. When

grass or small grain is started from seed, the

probable soil loss for the period while cover is

developing can be computed by the procedure

outlined for estimating cropstage-period soil losses.

If the seeding is on topsoil, without a mulch, the

soil loss ratios given in line 141 of table 5 are appropriate for cropstage C values. If the seeding is

on a desurfaced area, where residual effects of

prior vegetation are no longer significant, the

ratios for periods SB, 1 and 2 are 1.0, 0.75 and

0.50, respectively, and line 141 applies for cropstage 3. When the seedbed is protected by a mulch,

the pertinent mulch factor from the upper curve

of figure 6 or table 9 is applicable until good

canopy cover is attained. The combined effects of

vegetative mulch and low-growing canopy are

given in figure 7. When grass is established in

small grain, it can usually be evaluated as established meadow about 2 mo after the grain is cut.

C Values for Pasture, Rxtnge, and Idle Land

Factor C for a specific combination of cover

conditions on these types of land may be obtained

from table 10 (57). The cover characteristics that

must be appraised before consulting this table are

defined in the table and its footnotes. Cropstage

periods and El monthly distribution data are generally not necessary where perennial vegetation

has become established and there is no mechanical

disturbance of the soil.

Available soil loss data from undisturbed land

were not sufficient to derive table 10 by direct

comparison of measured soil loss rates, as was

done for development of table 5. However, analyses of the assembled erosion data showed that the

research information on values of C can be extended to completely different situations by combining subfactors that evaluate three separate and

distinct, but interrelated, zones of influence: (a)

vegetative cover in direct contact with the soil surface, (b) canopy cover, and (c) residual and tillage

effects.

Subfactors for various percentages of surface

cover by mulch are given by the upper curve of

TABLE 10.—Factor C for permanent pasture, range, and

idle land^

Vegetative canopy Cover that contacts the soil1 surface

Type and Percent

cover^

Percent ground cover

height^ Type^ 0 20 40 60 80 95+

No appreciable

canopy

Tall weeds or 25

short brush

with average

drop fall height 50

of 20 in

75

G

W

G

W

G

W

G

W

Appreciable brush 25 G

or bushes, with W

average drop fall

height of 6V2 ft 50 G

W

0.45 0.20 0.10 0.042 0.013 0.003

.45 .24 .15 .091 .043 .011

.36 .17 .09 .038 .013 .003

.36 .20 .13 .083 .041 .011

.26 .13 .07 .035 .012 .003

.26 .16 .11 .076 .039 .011

.17 .10 .06 .032 .011 .003

.17 .12 .09 .068 .038 .011

.40 .18 .09 .040 .013 .003

.40 .22 .14 .087 .042 .011

.34 .16 .08 .038 .012 .003

.34 .19 .13 .082 .041 .011

75 G .28 .14 .08 .036 .012 .003

W .28 .17 .12 .078 .040 .011

Trees, but no 25 G

appreciable low W

brush. Average

drop fall height 50 G

of 13 ft W

75 G

W

.42 .19 .10 .041 .013 .003

.42 .23 .14 .089 .042 .011

.39 .18 .09 .040 .013 .003

.39 .21 .14 .087 .042 .011

.36 .17 .09 .039 .012 .003

.36 .20 .13 .084 .041 .011

^ The listed C values assume that the vegetation and mulch are

randomly distributed over the entire area.

" Canopy height is measured as the average fall height of water

drops falling from the canopy to the ground. Canopy effect is inversely proportional to drop fail height and is negligible if fall

height exceeds 33 ft.

"^ Portion of total-area surface that would be hidden from view by

canopy in a vertical projection (a bird's-eye view).

* G: cover at surface is grass, grassiike plants, decaying compacted duff, or litter at least 2 in deep.

W: cover at surface is mostly broadleaf herbaceous plants (as

weeds with little lateral-root network near the surface) or

undecayed residues or both.
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING 33

TABLE 11.—Factor C for undisturbed forest iand^

Percent of area

covered by canopy of

trees and undergrowth

Percent of area

covered by duff

at least 2 in deep

Factor C^

100-75

70-45

40-20

100-90

85-75

70-40

.0001-.001

.002-.004

.003..009

^ Where effective litter cover is less than 40 percent or canopy

cover is less than 20 percent, use table 6. Also use table 6 where

woodlands are being grazed, harvested, or burned.

^ The ranges in listed C values are caused by the ranges in the

specified forest litter and canopy covers and by variations in effective canopy heights.

figure 6. Subfactors for various heights and densities of canopy cover are given in figure 5. The

subfactor for residual effects of permanent pasture,

range, idle land, or grazed or harvested woodland

has been estimated to vary from 0.45 to 0.10 (57).

Major influences on this subfactor are plant roots,

organic matter buildup in the topsoil, reduced soil

compaction, and surface stabilization after long

periods without soil disturbance. The C values

given in table 10 were derived by combining subfactors for specified combinations of type, height,

and density of canopy cover; type and density of

cover at the soil surface; and probable residual

effects of longtime existence of the specified cover

on the land. They are compatible with the rather

scarce existing soil loss data from undisturbed land

areas.

C Values for Woodland

Three categories of woodland are considered

separately: (1) undisturbed forest land; (2) woodland that is grazed, burned, or selectively harvested; and (3) forest lands which have had site

preparation treatments for re-establishment after

harvest.

In undisturbed forests, infiltration rates and organic matter content of the soil are high, and much

or all of the surface is usually covered by a layer

of compacted decaying forest duff or litter several

inches thick. Such layers of duff shield the soil from

the erosive forces of runoff and of drop impact

and are extremely effective against soil erosion.

Where cover by trees and litter is incomplete, the

spots with little or no litter cover are partially protected by undergrowth canopy. Factor C for undisturbed forest land may be obtained from table

11. These estimated C values are supported by the

quite limited existing data and also by the subfactor-evaluation procedure discussed in the preceding subsection.

Woodland that is grazed or burned, or has been

recently harvested, does not merit the extremely

low C values of table 11. For these conditions, C

is obtained from table 10. However, the buildup

of organic matter in the topsoil under permanent

woodland conditions is an added factor that

should be accounted for by a. reduction in the C

value read from table 10. An earlier publication

(57) recommended a factor of 0.7 for this purpose.

Site preparation treatments for re-establishing

trees on harvested forest land usually alter the

erosion factors substantially. Canopy effect is initially greatly reduced or lost entirely, and its restoration is gradual. Some of the forest litter is

incorporated in the soil, and it may be entirely

removed from portions of the area. A surface

roughness factor is introduced. Windrowed debris,

if across slope, may function as terraces by reducing effective slope length and inducing deposition

above and in the windrows. The amount of residual effect retained depends on the amount and

depth of surface scalping. Some of the changes

are analogous to cropland situations. Some of the

relationships available from tables 5 and 10 can

be used to evaluate C for these conditions, but

neither table is directly applicable.

Table 12 presents C values computed for Southern Pine Forests that have had site preparation

treatments after harvesting. This table was jointly

developed (in 1977) by representatives of SEA, SCS,

and Forest Service, using factor relationships from

tables 5, 10, and 11 as basic guides. Its application

on forest lands in other climatic regions may require some modifications of factor values. Research

designed to refine and improve tables 10, 11, and

12 is underway.

Tree plantings on converted cropland should, in

the initial years, be evaluated similarly to cropland

because the forest residual effect which underlies

tables 10 to 12 will not be applicable. The subfactor for residual effects may be estimated by

selecting from lines 1 to 16 of table 5 the line that

most nearly describes the condition of the converted cropland and assuming a residual subfactor equal to the seedbed-period value given in that

line. If the cropland has most recently been in
34 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

TABLE 12.—Facfor C for mechanically prepared

woodfand sites

Mulch

cover*

Soil condition^ and weed cover^

Site

preparation Excellent Good Faiir Poor

NC WC NC WC NC WC NC WC

Disked, raked.

Percent

or bedded* None 0.52 0.20 0.72 0.27 0.85 0.32 0.94 0.36

10 .33 .15 .46 .20 .54 .24 .60 .26

20 .24 .12 .34 .17 .40 .20 .44 .22

40 .17 .11 .23 .14 .27 .17 .30 .19

60 .11 .08 .15 .11 .18 .14 .20 .15

80 .05 .04 .07 .06 .09 .08 .10 .09

Burned^ None .25 .10 .26 .10 .31 .12 .45 .17

10 .23 .10 .24 .10 .26 .11 .36 .16

20 .19 .10 .19 .10 .21 .11 .27 .14

40 .14 .09 .14 .09 .15 .09 .17 .11

60 .08 .06 .09 .07 .10 .08 .11 .08

80 .04 .04 .05 .04 .05 .04 .06 .05

Drum chopped ' None .16 .07 .17 .07 .20 .08 .29 .11

10 .15 .07 .16 .07 .17 .08 .23 .10

20 .12 .06 .12 .06 .14 .07 .18 .09

40 .09 .06 .09 .06 .10 .06 .11 .07

60 .06 .05 .06 .05 .07 .05 .07 .05

80 .03 .03 .03 .03 .03 .03 .04 .04

^ Percentage of surface covered by residue in contact with the

soil.

^ £xce//enf soil condition—Highly stable soil aggregates in topsoil with fine tree roots and litter mixed in.

Good—Moderately stable soil aggregates in topsoil or highly

stable aggregates in subsoil (topsoil removed during raking), only

traces of litter mixed in.

Fair—Highly unstable soil aggregates in topsoil or moderately

stable aggregates in subsoil, no litter mixed in.

Poor—No topsoil, highly erodible soil aggregates in subsoil, no

litter mixed in.

^ NC—No live vegetation.

WC—75 percent cover of grass and weeds having an average

drop fall height of 20 in. For intermediate percentages of cover, interpolate between columns.

* Modify the listed C values as follows to account for effects of

surface roughness and aging:

First year after treatment: multiply listed C values by 0.40 for

rough surface (depressions >6 in); by 0.65 for moderately

rough; and by 0.90 for smooth (depressions <C2 in).

For 1 to 4 years after treatment: multiply listed factors by 0.7.

For 4-4- to 8 years: use table 6.

More than 8 years: use table 7.

" For first 3 years: use C values as listed.

For 3+ to 8 years after treatment: use table 6.

More than 8 years after treatment: use table 7.

meadow, the selected seedbed soil loss ratio is

multiplied by a factor frorh table 5-D. If mulch

is applied, a subfactor read from the upper curve

of figure 6 is multiplied by the residual subfactor

to obtain C. When canopy develops, a canopy subfactor from figure 5 is also included.

SUPPORT PRACTICE FACTOR (P)

In general, whenever sloping soil is to be cultivated and exposed to erosive rains, the protection offered by sod or close-growing crops in the

system needs to be supported by practices that will

slow the runoff water and thus reduce the amount

of soil it can carry. The most important of these

supporting cropland practices are contour tillage,

stripcropping on the contour, and terrace systems.

Stabilized waterways for the disposal of excess

rainfall are a necessary part of each of these

practices.

By definition, factor P in the USLE is the ratio

of soil loss with a specific support practice to the

corresponding loss with up-and-down-slope culture. Improved tillage practices, sod-based rotations, fertility treatments, and greater quantities

of crop residues left on the field contribute materially to erosion control and frequently provide

the major control in a farmer's field. However,

these are considered conservation cropping and

management practices, and the benefits derived

from them are included in C.

Contouring

The practice of tillage and planting on the contour, in general, has been effective in reducing

erosion. In limited field studies, the practice provided almost complete protection against erosion

from storms of moderate to low intensity, but it

provided little or no protection against the occasional severe storms that caused extensive breakovers of the contoured rows. Contouring appears

to be the most effective on slopes in the 3- to 8-

percent range. As land slope decreases, it approaches equality with contour row slope, and the

soil loss ratio approaches 1,0. As slope increases,

contour row capacity decreases and the soil loss

ratio again approaches 1.0.
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING 35

Effectiveness of contouring is also influenced by

the slope length. When rainfall exceeds Infiltration and surface detention in large storms, breakovers of contour rows often result in concentrations

of runoff that tend to become progressively greater

v/ith increases in slope length. Therefore, on slopes

exceeding some critical length the amount of soil

moved from a contoured field may approach or

exceed that from a field on which each row carries

its own runoff water down the slope. At what slope

length this could be expected to occur would depend to some extent on gradient, soil properties,

management, and storm characteristics.

P Values for Contouring

A ¡oint SEA and SCS workshop group, meeting

at Purdue University in 1956, adopted a series of

contour P values that varied with percenf slope.

The P values were based on available data and

field observations supplemented by group judgment. Subsequent experience indicated only a few

minor changes. Current recommendations are

given in table 13. They are average values for the

factor on the specified slopes. Specific-site values

may vary with soil texture, type of vegetation,

residue management, and rainfall pattern, but data

have not become available to make the deviations

from averages numerically predictable.

Full contouring benefits are obtained only on

fields relatively free from gullies and depressions

other than grassed waterways. Effectiveness of

this practice is reduced if a field contains numerous small gullies and rills that are not obliterated

by normal tillage operations. In such instances,

land smoothing should be considered before contouring. Otherwise, a judgment value greater than

TABLE 13.—P vaiues ond slope-length Umits for

confouring

^°"^''**f^ P value Maximum lengtM percent

Feef

\ to 2 0.60 400

3 to 5 50 300

6 to 8 .50 200

9 to 12 60 120

13 to 16 70 80

17 to 20 80 60

21 to 25 .90 50

^ Limit may be increased by 25 percent tf residue cover after crop

seedlings will regularly exceed 50 percent.

shown in table 13 should be used when computing

the benefits for contouring.

Slope-Length Limits

After the 1956 workshop, the SCS prepared reference tables for use with the Corn Belt slopepractice procedure. They included guides for slopelength limits for effective contouring, based largely

on judgment. These limits, as modified with later

data and observations {16, 42), are also given in

table 13. Data to establish the precise limits for

specific conditions are still not available. However,

the P values given in table 13 assume slopes short

enough for full effectiveness of the practice. Their

use for estimating soil loss on unterraced slopes

that are longer than the table limits specified is

speculative.

Contour Listing

Contour listing, with corn planted in the furrows,

has been more effective than surface planting on

the contour (29). However, the additional effectiveness of the lister ridges applies only from the date

of listing until the ridges have been largely obliterated by two corn cultivations. Therefore, it can be

more easily credited through C than through P. This

is done by a 50-percent reduction in the soil loss

ratios (table 5) that apply to the time interval during which the ridges are intact. The standard P

value for contouring is applicable in addition to the

C value reduction.

Potato rows on the contour present a comparable condition from lay-by time until harvest. However, this ridging effect has been already credited

in table 5, line 160, and should not be duplicated.

Controlled-Row Grade Ridge Planting

A method of precise contouring has been developed that provides effective conservation on

farm fields where the land slope is nearly uniform,

either naturally or by land smoothing, and runoff

from outside the field can be diverted. The practice uses ridge planting with undiminished channel capacity to carry water maintained throughout

the year. It is being studied in Texas {26), Arkansas, Mississippi (8), and Iowa {30), In Texas, the

channel cross section, with 40-in row spacing, was

nearly 0.5 ft^ and row grades varied from nearly

zero at the upper end to 1 percent at the lower end
36 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

of a 1,000-ft length. Measured soil loss compared

favorably with that from an adjacent terraced

watershed. Soil loss measurements in Mississippi

and Iowa showed similar effectiveness during the

test periods.

Because each furrow functions as an individual

terrace, P values similar to those for terracing seem

appropriate. Slope-length limits for contouring

would then not apply, but the length limits would

be applicable if the channel capacity were only

sufficient for a 2-year design storm.

Contoured-Residue Strips

Contoured strips of heavy crop-residue mulch,

resembling contour stripcropping without the sod,

may be expected to provide more soil loss reduction than contouring alone. P values equal to

about 80 percent of those for contouring are recommended if fairly heavy mulch strips remain

throughout the year. If the strips are maintained

only from harvest until the next seedbed preparation, the credit should be applied to the soil loss

ratio for cropstage 4 rather than the P value.

Contour Stripcropping

Stripcropping, a practice in which contoured

strips of sod are alternated with equal-width

strips of row crops or small grain, is more effective than contouring alone. Alternate strips of grain

and meadow year after year are possible with a

4-year rotation of corn-wheat with meadow seeding-meadow-meadow. This system has the added

advantage of a low rotation C value. A stripcropped rotation of corn-corn-wheat-meadow is

less effective. Alternate strips of winter grain and

row crop were effective on flat slopes in Texas

(74), but alternate strips of spring-seed grain and

corn on moderate to steep slopes have not provided better erosion control than contouring alone.

Observations from stripcrop studies showed that

much of the soil eroded from a cultivated strip

was filtered out of the runoff as it was slowed and

spread within the first several feet of the adjacent

sod strip. Thus the stripcrop factor, derived from

soil loss measurements at the foot of the slope,

accounts for off-the-field soil movement but not

for all moyemenf within the field.

P Values, Strip Widths, and Length Limits

Recommended P values for contour stripcropping

are given in table 14. The system to which each

column of factors applies is identified in the table

footnotes. The strip widths given in column 5 are

essentially those recommended by the 1956 slopepractice workshop and are to be considered approximate maximums. Reasonable adjustments to

accommodate the row spacing and row multiple

of the planting and harvesting equipment are

permissible. Slope-length limit is generally not a

critical factor with contour stripcropping except

on extremely long or steep slopes. The lengths

given in column 6 are judgment values based on

field experience and are suggested as guides.

Buffer Stripcropping

This practice consists of narrow protective strips

alternated with wide cultivated strips. The location

of the protective strips is determined by the width

and arrangement of adjoining strips to be cropped

in the rotation and by the location of steep, severely eroded areas on slopes. Buffer strips usually occupy the correction areas on sloping land

and are seeded to perennial grasses and legumes.

This type of stripcropping is not as effective as

contour stripcropping (4).

TABLE 14.—P values, max/mum strip widths, and slopelength limits for contour stripcropping

Land slope ^!1^ strip wîdth^ Maximum length

percent ABC

Feet Feet

1 to 2 0.30. 0.45 0.60 130 800

3 to 5 25 ,38 ,50 100 600

6 to 8 25 .38 .50 100 400

9 to 12 30 .45 .60 80 240

13 to 16 .35 .52 .70 80 160

17 to 20 .40 .60 .80 60 120

21 to 25 45 .68 .90 50 100

^ P values:

A For 4-year rotation of row crop, small grain with meadow

seeding, and 2 years of meadow. A second row crop can replace the small grain if meadow is established in it.

B For 4-year rotation of 2 years row crop, winter grain with

meadow seeding, and 1-year meadow.

C For alternate strips of row crop and small grain.

" Adjust strip-width limit, generally downward, to accommodate

widths of farm equipment.
PREDICTING RAINFALL EROSION LOSSES~A GUIDE TO CONSERVATION PLANNING 37

Terracing

The most common type of terrace on gently

sloping land is the broadbase, with the channel

and ridge cropped the same as the interterrace

area. The steep backslope terrace is most common on steeper land. Difficulty in farming point

rows associated with contoured terraces led to

developing parallel terracing techniques (?ó). Underground outlets, landforming, and variable

channel grades help establish parallel terraces.

The underground outlets are in the low areas along

the terrace line. The ridge is constructed across

these areas. Another type of terrace, using a level

and broad channel with either open or closed ends,

was developed to conserve moisture in dryland

farming areas.

Terraces with underground outlets, frequently

called impoundment terraces, are highly effective

for erosion control. Four-year losses from four such

terrace systems in Iowa (17) averaged less than

0.4 t/A/year, which was less than 5 percent of the

calculated soil movement to the channel. Comparable losses were measured from installations in

Nebraska.

Terracing combined with contour farming and

other conservation practices is more effective than

those practices without the terraces because it positively divides the slope into segments equal to the

horizontal terrace interval. The horizontal terrace

interval for broadbase terraces is the distance from

the center of the ridge to the center of the channel

for the terrace below. For steep backslope terraces

with the backslope in sod, it is the distance from

the point where cultivation begins at the base of

the ridge to the base of the frontslope of the terrace below (44), y/Uh terracing, the slope length

is this terrace interval; with stripcropping or contouring alone, it is the entire field slope length.

P Values

Values of P for contour farming terraced fields

are given in table 15. These values apply to contour farmed broadbase, steep backslope, and level

terraces. However, recognize that the erosion control benefits of terraces are much greater than indicated by the P values. As pointed out earlier,

soil loss per unit area on slopes of 5 percent or

steeper is approximately proportional to the square

root of slope length. Therefore, dividing a field

slope into n approximately equal horizontal terrace intervals divides the average soil loss per

unit area by the square root of n. This important

erosion control benefit of terracing is not included

in P because it is brought into the USLE computation through a reduced LS factor obtained by using

the horizontal terrace interval as the slope length

when entering figure 4 or table 3.

Erosion control between terraces depends on the

crop system and other management practices evaluated by C. The total soil movement within a contour-farmed terrace interval may be assumed

equal to that from the same length of an identical

slope that is contoured only. Therefore, if a control

level is desired that will maintain soil movement

between the terraces within the soil loss tolerance

limit, the P value for a contour-farmed terraced

field should equal the contour factor (col. 2, table

15), and use of these values for farm planning

purposes is generally recommended.

With contour stripcropping, the soil deposited in

the grass strips is not considered lost because it

remains on the field slope. With terraces, most of

the deposition occurs in the terrace channels, but

research measurements have shown that this deposition may equal 80 percent of the soil moved from

the contour-farmed slopes between the terraces

(Ó7). Use of the contour factor as the P value for

terracing assumes that all of the eroded soil deposited in the terrace channels is lost from the productive areas of the field. With broadbase terraces,

the channels and ridges are cropped the same as

TABLE 15.—-P values for contour-farmed terraced fields^

Land slope

(percent)

Farm planning

Contour Stripcrop

factor^ factor

Computing sediment yield^

Graded channe

sod outlets

ils Steep backslope

underground

outlets

\ to 2 0.60 0.30 0.12 0.05

3 to 8 .50 .25 .10 .05

9 to 12 .60 .30 ,12 .05

13 to 16 .70 .35 .14 .05

17 to 20 .80 .40 .16 .06

21 to 25 .90 .45 .18 .06

' Slope length is the horizontal terrace interval. The listed values

ore for contour farming. No additional contouring factor is used in

the computation.

" Use these values for control of interterrace erosion within specified soil loss tolerances.

' These values Include entrapment efficiency and are used for

control of ofFsite sediment within limits and for estimating the field's

contribution to watershed sediment yield.
38 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

the interterrace slopes, and some of the material

deposited in the channels is moved to the ridges in

terrace maintenance. The 1956 slope-practice group

felt that some of the deposition should be credited

as soil saved and recommended use of a terracing

practice factor equal to the stripcrop factor (64),

However, the more conservative values given in

column 2 are nov/ commonly used in conservation

planning.

When the USLE is used to compute a terraced

field's contribution to offsite sediment or watershed

gross erosion, the substantial channel deposition

must be credited as remaining on the field area.

For this purpose, the P values given in the last two

columns of table 15 are recommended unless an

overland fiow deposition equation based on transport relationships is used with the USLE.

With widespread use of large multirow equipment, farming with field boundaries across nonparallel terraces is not uncommon in some regions.

When terraces are not maintained and overtopping is frequent, P = 1 and the slope length is the

field slope length. However, if the terraces are

periodically maintained so that overtopping occurs only during the most severe storms, LS is

based on the horizontal terrace interval. If farming across terraces is at an angle that approximates contour farming, P values less than 1.0 but

greater than the contour factors would be appropriate.

Soil Loss Terrace Spacing

Traditionally, terrace spacing has been based on

slope gradient; however, some recent spacing

guides have included modifying factors for severity of rainfall and for favorable soil and tillage

combinations. A major objective of cropland conservation planning is to hold the productive topsoil in place. Extending this objective to terrace

system design suggests limiting slope lengths between terraces sufficiently so that specified erosion

tolerances will not be exceeded. Using the USLE

in developing spacing guides will make this possible.

The USLE may be written as LS = T/RKCP,

where T is the tolerance limit. If T/RKP == Z, then

LS = Z/C, and C = Z/LS. The values T, R, K and

P are constant for a given location and can be

obtained from handbook tables and charts as illustrated in the section Predicting Cropland Soil

Losses. Factor C can be selected as the C value of

the most erosion-vulnerable crop system that a

farmer is likely to use on the terraced field. LS can

be computed by solving the equation as written

above and, with the percent slope known, the maximum allowable length can be read from the slopeefl^ect chart, figure 4.

To illustrate the procedure, assume a ó-percent

slope at a location where R = 175, K = 0.32, T =

5, P = 0.5, and the most erodible crop expected to

occur on the field has a C value of 0.24. (An assumption that the field will always be in a sod

based rotation or that the operator will always

make the best possible use of the crop residues

would be too speculative to serve as a guide for

terrace spacing.) With these assumptions, Z =: 5/175

(0.32X0.5) = 0.179 and LS = 0.179/0.24, or 0.744.

Enter the slope-effect chart, figure 4, on the LS scale

with a value of 0.744, move horizontally to intersect the 6 percent-slope line and read the corresponding slope length, 120 ft, on the horizontal

scale. Add to this value the width of the terrace

frontslope and compute the vertical interval:

i 16 = 7.9 ft. However, the horizontal in100 ;

terval should not exceed the slope-length limit for

effectiveness of contouring. From table 13 the

length limit for contouring on a 6-percent slope is

200 ft, so the computed terrace interval is satisfactory. A small modification in spacing may be

made to adjust to an even multiple of machinery

width.

The maximum C value that will allow a horizontal terrace spacing equal to the length limit

for effective contouring on the given slope can also

be determined by using figure 4 and table 13. For

the conditions in the illustration above, C = 0.179/

LS. The maximum acceptable length for contouring

is 200 ft. From figure 4, the LS value for a 200-ft

length of 6-percent slope is 0.95. Therefore, the

maximum allowable C = 0.179/0.95, which is

0.188. With terraces spaced at 200-ft intervals, any

cropping and management system with a C value

of less than 0.188 should provide the level of conservation prescribed by the assumed soil loss tolerance limit of 5 t/A/year.

One additional consideration is important. For

a terrace to function satisfactorily, the channel
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING 39

capacity must be sufficient to carry the runoff

safely to a stabilized outlet without excessive

channel scour or overtopping of the ridge. SCS

engineering practice standards specify a capacity

sufficient to control the runoff from a 10-year-frequency, 24-hour storm without overtopping. Some

SCS practice standards may require a shorter terrace interval than would be indicated by the foregoing procedure.

The discussion of the topographic factor pointed

out that the erosion rate increases as slope length

increases. Table 4 lists the relative soil losses for

successive equal-length increments of a uniform

slope divided into 2, 3, 4, or 5 segments. The third

column of table 4 shows that if a uniform 6-percent

slope were controlled at a tolerance of 5 t average

soil loss, the average loss per unit area from the

lower third of the slope would exceed the tolerance

by about 38 percent. Soil loss from the upper third

would be 43 percent less than the tolerance limit.

To have an average rate of 5 t from the lower'

third, the T values used in the spacing calculation

would need to be 1/1.38 times the 5-t tolerance,

or 3.Ó t. This is an approach that can be used to

calculate terrace spacings for a higher level of conservation.

Effect of Terraces on Amount

and Composition of Offsite Sediment

By reducing runoff velocity and inducing deposition of sediment in the channels, terraces have a

profound effect on the amount and composition of

offsite sediments from cultivated fields. The type

of terrace, the channel grade, and the type of outlet influence the magnitude of the effect.

The greatest reduction in sediment is attained

with the impoundment type terrace systems that use

underground outlets. With the outlets in the lower

areas of the field and terrace ridges built across

these areas, temporary ponds are created around

the risers of the outlet tile. The outlets are designed

to drain the impounded runoff in 1 to 2 days. Thus,

the ponds provide a maximum stilling effect, and

only the smallest and lightest soil particles are

carried off the field in the runoff water. The increased time for infiltration also reduces runoff.

Sediments collected from four impoundment terrace systems over 4 years in Iowa (17) showed the

following percentages of fine materials:

< 0.002 mm < 0.008 mm

boil type -

Percenf Percenf

Fayette silt loam 78 91

Sharpsburg sîlty clay loam 68 96

Floyd loam 31 82

Clarion loam 35 78

Sediment concentrations in the runoff ranged

from about 1,300 p/m on the Fayette soil to 6,300

p/m on the Clarion. Average annual sediment

from the outlets was less than 800 lb/A for all

four systems.

Farm chemical losses in runoff vary with type

and formulation, amount, placement, and time of

rainfall in relation to time of application, as well

as with the usual runoff and erosion factors. Principal chemicals are the fertilizers, insecticides,

fungicides, and herbicides. Losses are by solution

and by suspension of chemical granules or adsorption on soil particles suspended in the runoff water.

Terracing exerts its greatest influence in reducing offsite pollution from those chemicals that are

adsorbed on soil particles. Examples of these are

the phosphates, organic nitrogen, and persistent

organochlorine insecticides. Reductions in offsite

sediment by terrace systems with contouring are

estimated to range from 82 to 95 percent. However, the reductions in chemical transport are generally not proportional to reductions in soil loss

because of an enrichment process that applies to

the suspensions. The nutrient content of sediments

is often 50 percent greater than that of the soil.

Offsite delivery of sediment is also affected by

watershed characteristics, particularly size of the

drainage area. This reduction is measured by a

''delivery ratio" that ranges from 0.33 for an area

of one-half square mile to 0.08 for a 200-mi2 area

(45).

Terracing has the least effect on offsite pollution

from those chemicals transported primarily in solution. Annual runoff reductions by terracing and

contour farming, at 21 locations throughout the

United States, have been estimated to vary only

from 9 to 37 percent {42). Examples of farm chemicals transported primarily in solution are the nitrates and some herbicides such as 2,4-D ((2,4-dichlorophenoxy) acetic acid). The predominate

transport modes for an extensive list of pesticides

are listed in volumes 1 and 2 of "Control of Water

Pollution From Cropland" (42).
40 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

APPLYING THE SOIL LOSS EQUATION

The major purpose of the soil loss prediction

procedure is to supply specific and reliable guides

for selecting adequate erosion control practices for

farm fields and construction areas. The procedure

is also useful for computing the upland erosion

phase of sediment yield as a step in predicting

rates of reservoir sedimentation or stream loading,

but the USLE factors are more difficult to evaluate

for large mixed watersheds. Specific applications

of the soil loss equation are discussed and illustrated below.

Predicting Cropland Soil Losses

The USLE is designed to predict longtime-average soil losses for specified conditions. This may be

the average for a rotation or for a particular crop

year or cropstage period in the rotation. Where the

term "average loss" is used below, it denotes the

average for a sufficient number of similar events

or time intervals to cancel out the plus and minus

effects of short-time fluctuations in uncontrolled

variables.

Rotation Averages

To compute the average annual soil loss from

a particular field area, the first step is to refer to

the charts and tables discussed in the preceding

sections and select the values of R, K, LS, C, and P

that apply to the specific conditions on that field.

For example, assume a field on Russell silt loam

soil in Fountain County, Ind. The dominant slope

is about 8 percent with a length of 200 ft. Fertility

and crop management on this field are such that

crop yields are rarely less than 85 bu corn, 40 bu

wheat, or 4 t alfalfa-brome hay. The probability

of meadow failure is slight.

Factor R is taken from the isoerodent map (fig.

1). Fountain County, in west-central Indiana, lies

between isoerodents of 175 and 200. By linear interpolation, R = 185. K is taken from a table of

K values that were derived either by direct research measurement or by use of the soil erodibility nomograph (fig. 3). For the Russell silt loam

soil, K = 0.37. The slope-effect chart, figure 4,

shows that an 8 percent slope 200 ft long has an

LS of 1.41. If the field were continuously in cleantilled fallow, the average annual soil loss from the

dominant slope would equal the product RKLS;

that is, 185(0.37X1.41) =: 96.5 t/A.

Next, we need to know the effect of the cropping and management system and support practices existing on the field. This effect is represented

by factors C and P. The C value for the field may

either be derived by the procedure previously presented, using data from tables 5 and Ó, or it may

be obtained from a centrally prepared C value

table available from the SCS. For convenience,

assume the same crop system and management

as were assumed for the problem illustrating the

derivation of locality C values. From table 8, C

then equals 0.085. If rows and tillage are in the

direction of the land slope, factor P=:1.0. The

computed average soil loss is then 96.5(0.085)(1.0)

= 8.2 t/A/year.

From table 13, contour farming on 8 percent

slopes not exceeding 200 ft ¡n length has a P value

of 0.5. Therefore, if farming were on the contour,

the computed average soil loss for the field would

be 96.5(0.085)(0.5) = 4.1 t. If the length of 8-percent slope was appreciably greater than 200 ft,

the effectiveness of contouring could not be assumed, and the P value of 0.5 would not be applied unless the slope length was broken by terraces or diversions. Any change in either the crop

sequence or the management practices would likely increase or decrease soil loss. This would be

reflected in the USLE solution through a change in

the C value.

When C is used at its average annual value for

a rotation that includes a sod crop, as was done

in the example given in table 8, the heavier losses

experienced during row crop years are diluted by

trivial losses in the meadow year(s). For holding

longtime-average soil losses below some prescribed tolerance limit, this dilution poses no problem. But from the viewpoint of offsite water quality, it may not be desirable. The USLE may also

be used to compute the average soil loss for each

crop in the rotation or for a particular cropstage

period.

Crop-Year Averages

The subtotals in column 9 of table 8 show that
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING 41

with the assumed management system, C for the

first-year corn would be 0.130 and for the secondyear, 0.138. For the second-year corn, without

contouring, the expected average soil loss would

equal 185(0.37){1.41)(0.138), or 13.3 t. If, in the

same crop system, the corn residues were plowed

down in fall, the C value for second-year corn

would be 0.29, and the soil loss would average

28 t. On the other hand, no-till planting the

second-year corn in a 70-percent cover of shredded

cornstalks would reduce the C value for this crop

to 0.08 and the soil loss to about 8 t. This would

also reduce the rotation average for straight row

farming to 7 t. Killing the meadow instead of turning it under, and no-till planting, would reduce the

C value for the first-year corn to 0.01 and the soil

loss to less than 1 t. Thus, crop-year C values can

be helpful for sediment control planning.

Cropstage Averages

Additional information can be obtained by computing the average annual soil loss for each cropstage period. First, the computed cropstage soil

losses will show in which portions of the crop year

(or rotation cycle) improved management practices

would be most beneficial. Second, they provide information on the probable seasonal distribution of

sediment yields from the field. When a tabulation

like table 8 has been prepared, the values in column 8 will be directly proportional to the cropstage soil losses. They can be converted to tons per

acre for a specific field by multiplying them by the

product of factors R, K, LS, and P.

To estimate the average soil loss for a particular

cropstage when such a table has not been prepared, the cropstage soil loss ratio from table 5

is used as C. The annual El fraction that is applicable to the selected period is obtained from table

6 and is multiplied by the location's annual erosion

index value (fig. 1) to obtain the relevant R value.

K, IS, and P will usually be assumed to have the

same values as for computation of average annual soil losses.

Suppose, for example, that one wishes to predict the average soil loss for the seedbed and

establishment periods of corn that is conventionally

planted about May 15 on spring plowed soybean

land in southwestern Iowa (area No. 13, fig. 9).

Suppose also that the corn is on a field for which

the combined value of factors K, LS, and P is 0.67

and the fertility and crop management are such

that corn planted by May 15 usually develops a

10 percent canopy cover by June 5, 50 percent by

June 25, and a final canopy cover of more than

95 percent. Interpolating between values in line

13 of table 6 shows cumulative El percentages of

12, 23, and 43 for these three dates. Therefore, on

the average, 11 percent of the annual El would

occur in the seedbed period, and 20 percent would

occur in the establishment period. From line 109

of table 5, the soil loss ratios for these two cropstage periods under the assumed management are

0.72 and 0.60. From figure 1, the average annual

El is 175. The soil loss would be expected to average 0.11(175)(0.72)(0.67) = 9.3 t/A in the seedbed

period and 0.20(175)(0.60)(0.67) = 14 t in the establishment period. The cropping assumed for this

example represents an extremely erodible condition. For second-year corn with good residue management, the applicable soil loss ratios and the

predicted soil losses would be much lower.

Individual Storm Soil Losses

The USLE factors derived from tables and charts

presented herein compute longtime-average soil

losses for specified cover and management on a

given field. The USLE is not recommended for prediction of specific soil loss events.

If it is applied to a specific rainstorm, using the

storm El for R and the relevant cropstage soil loss

ratio for C, it will estimate the average soil loss for

a large number of storms of this size occurring on

that field and in that cropstage period. However,

the soil loss from any one of these events may differ widely from this average because of interactions with variables whose values fluctuate randomly over time (56).

When rain falls on relatively dry, freshly tilled

soil> most of the water may infiltrate before runoff begins, resulting ¡n a low-average soil loss

per unit of El for that storm. When rain falls on

presaturated soil, runoff begins quickly, and most

of the rain becomes runoff. Such rains usually

produce above-average soil loss per El unit. Some

rains are accompanied by high winds that increase

the impact energy of raindrops; others occur in a

fairly calm atmosphere. Some storms begin with a

high intensity and seal the surface quickly so that

trailing lower intensities encounter a low infiltration rate. In other storms the moderate intensities
42 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

precede the high ones. In some seasons the soil is

cultivated when wet and remains cloddy; in other

seasons it is cultivated when soil moisture is ideal

for fine pulverization. A claypan or fragipan subsoil may substantially influence permeability in

early spring or in a wet growing season and yet

have no significant effect on infiltration rates during intense thunderstorms on dry soil.

The soil loss ratios of table 5 are averages for

cropstage periods that cover several weeks to several months. Early in a cropstage period, the ratio

will usually be higher than the average because

the development of cover is gradual. Later in the

period it will be lower than average. In a poor

growing season the ratio will be above average

because cover and water use by transpiration are

below normal. In a favorable growing season, the

ratio will be below average. Cover effect in a specific year may be substantially influenced by abnormal rainfall. A crop canopy or conservation

tillage practice may delay the start of runoff long

enough to be 100 percent effective for moderate

storms on a given field and yet allow substantial

erosion by prolonged runoff periods.

The irregular fluctuations in these and other

variables can greatly influence specific-storm soil

losses. However, they do not invalidate the USLE

for predicting long-term-average soil losses for

specific land areas and management conditions.

Their positive and negative effects tend to balance

over a longtime period, and their average effects

are reflected in the factor-evaluation tables and

charts.

Two recent research reports are recommended

references for those who find it necessary to estimate specific-storm soil losses {34, 10). The authors

present modifications of R and IS that are designed

to account for some random effects discussed.

Specific-Year Soil Losses

In any given year, both the annual El and its

monthly distribution may differ substantially from

the location averages. Therefore, R values from

figure T and El distribution data from table ó will

not correctly refiect specific-year values of these

variables. The most accurate procedure is to compute the El value for each storm from a recordingrain gage record for the location and year by the

method given in the appendix. The storm values are

summed for each cropstage period, and the subtotals are combined with soil loss ratios from table

5 to estimate the soil loss for each cropstage period.

The sum of the cropstage soil losses then reflects

the effects of possible abnormal El distribution, as

well as the corrected R value for the specific year.

However, the irregular fluctuations in variables

discussed in the preceding subsection are often related to abnormalities in rainfall. The plus and

minus effects on soil loss may not average out

within 1 year but may appreciably bias specificyear soil losses. These biases will not be evaluated

by the USLE. Therefore, specific-year estimates of

soil loss will be less accurate than USLE estimates

of long-term, crop-year averages.

Soil Loss Probabilities

Soil loss probabilities are a function of the combination of the probabilities for annual El, seasonal distribution of the erosive rains, abnormal

antecedent soil moisture conditions, favorable or

unfavorable conditions for soil tillage and crop

development, and other factors. The section on

the Rainfall Erosion Index pointed out that a location's annual and maximum storm El values tend

to follow log-normal frequency distributions and

that specific probability values are listed in tables

17 and 18 for 181 key locations. When these

probabilities of El are used for R in the USLE, the

equation will estimate the soil loss that would

occur if all the other factors were at their normal

levels. However, the seasonal distribution of erosive rains, and the surface conditions in the field,

may also be abnormal in years of rainfall extremes. Deriving probable relationships of these

variables to extremes in annual El would require

longer records than were available.

Stochastic modeling techniques (66) are available that could be used to generate synthetic data

having the same statistical properties as historical

data. Such data could be used to estimate the

probable range in specific-year soil losses in a

particular rainfall area.

Determining Alternative Land Use and Treatment Combinations

The soil loss prediction procedure supplies the tables from which he can ascertain, for each parpracticing conservationist with concise reference ticular situation encountered, which specific land
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING 43

use and management combinations will provide

the desired level of erosion control. A number of

possible alternatives are usually indicated. From

these, the farmer will be able to make a choice

in line with his desires and financial resources.

Management decisions generally influence erosion losses by affecting the factor C or P in the

erosion equation. L is modified only by constructing terraces, diversions, or contour furrows

with sufficient capacity throughout the year to

carry the runoff water from the furrow area above.

R, K, and S are essentially fixed as far as a particular field is concerned.

When erosion is to be limited within a predetermined tolerance, T, the term A in the equation

is replaced by T, and the equation is rewritten in

the form CP = T/RKLS. Substituting the site values

of the fixed factors in this equation and solving

for CP give the maximum value that the product

CP may assume under the specified field conditions. With no supporting practices, P= 1, and the

most intensive cropping plan that can be safely

used on the field is one for which C just equals

this value. When a supporting practice like contouring or stripcropping is added, the computed

value of T/RKLS is divided by the practice factor,

P, to obtain the maximum permissible cover and

rnanagement factor value. Terracing increases the

value of T/RKLS by decreasing the value L.

A special USLE calculator, originally designed in

Tennessee (41) and recently updated, enables

rapid and systematic calculation of either average

annual soil loss or T/RKLS for any specific situation.

Many practicing conservationists prefer to use

handbook tables. C-value tables for specific geographic areas (fig. 9) are centrally prepared by

persons who are experienced in the procedures

outlined in a preceding section and who obtain the

needed data from tables 5 and 6. Values of T/RKLS

are also centrally computed and arranged in twoway classification as illustrated in table 16 for R =

180, K=:0.32, and T = 5. Similar tables are prepared for other combinations of R, K, and T.

A conservationist working in the field usually

carries a pocket-sized handbook which includes

the R value(s), T and K soil values, applicable

tables of T/RKLS values, and a table of C values

for the area. These items will provide all the information needed to use this procedure as a guide

TABLE 16.—Maximum permissible C values fT/RKLS^ for

R = 180, K = 0,32 and 1 = 5

GrcíA'tfínt Values for slope lengths (feet)

percent 50 75 100 150 200. 250 300 400

STRAIGHT ROW

2 . 0.53 0.47 0.43 0.38 0.35 0.33 0.31 0.28

4 . .29 .24 .22 .18 .16 .15 .14 .12

6 . .18 .15 .13 .11 .091 .082 .074 .064

8 . .12 .10 .087 .072 .062 .055 .050 .044

10 . .090 .073 .063 .052 .045 .040 .037 .032

12 . .068 .056 .048 .039 .034 .030 .028 .024

14 . .054 .044 .038 .031 .027 .024 .022 .019

16 . .043 .035 .030 .025 .022 .019 .018 .015

CONTOURED'

2 . 0.89 0.78 0.72 0.64 0.58 0.55 0.52 0.47

4 . .57 .49 .43 .37 .33 .30 .28 .25

6 . .36 .30 .26 .21 .18 .16 C) __

8 . .25 .20 .17 .14 .12 .11 — —

10 . .15 .12 .11 .086 0 — —- —

12 . .11 .093 .080 .065 — — — —

14 . .077 .062 .054 0 — — — —

16 . .062 .050 .044 — — — — —

'The values for contour farming are T/RKLSP, where P is dependent on percent slope (see table 13).

^ Omission of values indicates that the slope-lengths exceed the

limits for effectiveness of contouring. Use corresponding values from

upper half of table.

for selecting conservation practices in each field.

Solving the equation or performing field computations rarely will be necessary.

Example. The first step is to ascertain the soil

type, percent slope, and slope length for the field

being planned. From his handbook data, the conservationist can then obtain the values of R, K, and

T. To complete the illustration, assume that R ==

180, K = 0.32, T = 5, and the field slope is 400 ft

long with a nearly uniform gradient of 6 percent.

For this combination, the T/RKLS table shows a

value of 0.064 for straight-row farming with the

land slope (table 16). This is the maximum C value

that will hold the average annual soil loss from

that field within the 5-t tolerance limit, if no supporting practices are used. Consulting the C value

table will show that a C as low as 0.064 can be

attained only with well-managed, sod-based crop

systems, or with no-till planting in residue covers

of at least 70 percent.

A logical improvement is to add contouring.

Table 13 shows a slope-length limit of 200 ft (250

ft if residue cover after seeding exceeds 50 percent) for contouring on 6-percent slope. Therefore,
44 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

the P value of 0.5 for contouring will not be applicable on the 400-ft slope without terracing. Construction of three, equally spaced terraces across

the slope would divide it into four 100-ft slope

lengths. Shortening the slope lengths to 100 ft will

assure contour effectiveness and will also reduce

the site value of L. For a 100-ft length of 6-percent

slope farmed on the contour, table 16 shows a

T/RKLSP value of 0.26. Any combination of cropping and management practices having a C value

less than 0.26 will now be acceptable. Consulting

the table of C values will shovy that with the terraces and contouring, the conservationist can recommend a range of possibilities for land use and

management. If a system with a C value appreciably less than 0.26 is selected, a higher level of

conservation will be attained than required by the

5-t tolerance limit.

Had the slope length in the example been only

200 ft, the contour P value of 0.5 (table 13) would

have been applicable without the terraces. Table

16 shows that this combination would have per-

. mitted use of any system having a C value less

than 0.18.

Thus, by this procedure a conservationist can

list all the alternative crop system and management combinations that would control erosion on a

field at an acceptable level. Study of this list will

show how an erosion control program can be improved and still increase crop yields or decrease

labor and fuel costs. In making a selection from

this list, practices needed for control of nutrient

and pesticide losses in the runoff (42) should also

be considered.

Construction Sites

Procedures and data have been presented for

predicting erosion losses from specific cropland

areas and logically determining alternative ways

in which the losses from each field may be held

below given tolerance limits. These procedures and

data can also be adapted to conditions on highway, residential, and commercial developing

areas. The USLE will show under which development plan the area will produce the least sediment, and it will also show about how much sediment the developer will need to trap in sediment

basins (46) during construction to prevent excessive soil movement to streams or reservoirs.

Evaluating the erosion factors for construction

site conditions is discussed below. However, those

primarily concerned with this particular phase of

sediment control should also read the preceding

discussions of the USLE factors and the procedures

for predicting cropland soil losses.

Factor R. For a construction project extending

over several years, the average annual R value for

the site is obtained directly from figure 1. Probabilities of El values greater than average are given

in table 17. Using El probabilities for R was discussed in the subsection Soil Loss Probabilities.

For construction periods of less than 1 year, the

procedure outlined for predicting cropland soil

losses for specific cropstage periods is appropriate.

The portion of the annual R value that is applicable

to the construction period is obtained from table 6

as illustrated on p. 41 for cropstage averages.

Factor K. Because the soil surface is often unprotected during construction, this factor assumes even

greater importance than for cropland. The soil erodibility nomograph (fig. 3) can be especially helpful

for sediment prediction and erosion control planning on construction sites because it can predict the

changes in erodibility when various subsoil horizons

are exposed in the reshaping process. Some subsoils

are substantially more erodible than the original

topsoil, and others are less erodible. The planner

can usually obtain a detailed description of the successive horizons of his soil from published soil survey data. By using the data for each soil horizon

separately to follow the steps of the nomograph

solution, the K value can be determined after

various depths of desurfacing. Soil losses from the

successive soil horizons, if exposed on similar

slopes, would be directly proportional to the horizon K values. Information on the subsoil K values

not only shows the depths of cut that would result

in the most or the least soil erosion but also indicates whether return of stockpiled topsoil on the

exposed subsoil would be profitable on the particular site.

When a chemical soil additive is used that stabilizes the soil and makes it less erodible, the K

value is the nomograph solution times a factor for

the effectiveness of the chemical additive.

FcÉctor LS. Within limits, the LS value for a given

length and steepness of uniform slope can be obtained directly from figure 4 or table 3. When the
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING 45

slope is concave or convex, the figure 4 value

needs to be adjusted by the procedure outlined

for irregular slopes in the section on The Topographic Factor.

Development planning may include measures

designed to reduce sediment yield by lowering LS.

The effect of shortening slope lengths by diversions or stabilized drainageways is credited by

entering figure 4 with the reduced slope length.

A slope graded to flatten toward the bottom (concave) will lose less soil than an equivalent uniform slope whereas one that steepens toward the

bottom (convex) will lose more. Reduction or increase in soil loss can be predicted by the procedure illustrated in the subsection Irregular Slopes.

Data are not available to evaluate LS on very

steep slopes, like 2:1 and 3:1 roadbank slopes, in

relation to soil and rainstorm characteristics. The

best presently available estimates of LS for these

slopes can be obtained by the LS equation presented earlier. However, values projected by this

equation for steep slopes are speculative because

the equation was derived from data obtained on

slopes of less than 20 percent.

Factor C. Procedures for selecting C values for

construction sites were given in the Cover and

Management Factor section.

Factor P. This factor as used for soil conservation planning on cropland would rarely have a

Estimating Upslope Contributions

The importance of predicting watershed sediment yields and identifying the major sediment

sources was increased by the Federal Water Pollution Control Act Amendments of 1972, Public Law

92-500. Sources, causes, and potentials of sediment,

nutrient, and pesticide losses from cropland, and

measures that may be necessary to control these

pollutants, are dealt with in depth in a two-volume

manual developed by SEA and the Environmental

Protection Agency (EPA) (42). Volume II, ''An Overview," also includes an extensive list of other relevant publications. Only sediment yield prediction

will be considered here.

Estimates show that about one-fourth of the

amount of sediment moved by flowing water in

the United States annually reaches major streams

(42). The USLE can be used to compute average

sheet and rill erosion in the various parts of a

watershed, but deposition and channel-type erosion must be estimated by other means. A fully

counterpart during construction on development

areas, and P will usually equal 1.0. Erosion-reducing effects of shortening slopes or reducing slope

gradients are accounted for through the LS factor.

If the lower part of a grass or woodland slope

on a development area can be left undisturbed

while the upper part is being developed, the procedure outlined for computing the value of LSC on

irregular slopes is applicable, and sediment deposition on the undisturbed strip must be accounted

for separately. For prolonged construction periods,

bufl^er strips of grass, small grain, or high rates of

anchored mulch may also be feasible to induce

deposition within the area. Such deposition is important for water quality or ofi^site sediment control, but it should be evaluated from soil-transport

factors rather than by a P factor.

Alternative plans. When appropriate numerical

values of the six erosion factors are combined,

their product is the soil loss estimate for the particular area in tons per acre and for the time interval for which R was evaluated. With the information supplied by the tables and charts in this

handbook, the six factor values can be derived for

each feasible alternative plan. Successive solutions

of the equation will then provide comparative soil

loss estimates to help guide decisions by the developer.

to Watershed Sediment Yield

tested equation for sediment transport to use on

agricultural land is not now available. One presented by Neibling and Foster (32) is perhaps the

best now available for use with the USLE. It estimates transport capacity for sand and large siltsized particles and does not consider the transport

of clay particles.

Of the several methods now used for estimating

sediment yield, the Gross Erosion-Sediment Delivery Method uses the USLE. A brief description

of this method follows. More details are available

from the SCS National Engineering Handbook (45).

The equation is

Y = E(DR)/W, (Ó)

where Y is sediment yield per unit area,

E is the gross erosion,

DR is the sediment delivery ratio, and

Ws is the area of the watershed above the point

for which the sediment yield is being computed.
46 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

Gross Erosion

Gross erosion is the summation of erosion from

oil sources within the watershed. It includes sheet

and rill erosion from tilled cropland, meadows,

pastures, woodlands, construction sites, abandoned

acreages, and surface-mined areas; gully erosion

from all sources; and erosion from streambeds and

streambanks. The relative importance of each of

these sources of gross erosion will vary between

watersheds.

The USLE can be used to estimate the sediment

generated by sheet and rill erosion that is usually,

but not always, the major portion of a watershed's

gross erosion. Sediment from gully, streambank

and streambed erosion, and from uncontrolled

roadsides must be added to the USLE estimates.

Methods for estimating sediment yields from these

sources are discussed in Section 3 of the SCS National Engineering Handbook (45).

For small areas like farm fields or construction

sites, the six USLE factors can usually be evaluated

directly from the information presented in this

handbook. For a large heterogeneous watershed,

the factors are more difficult to define. Several

methods of computing the average slope length

and gradient for a large drainage area are available. Using LS values based on such averages, together with estimated watershed-average soil and

cover factors, simplifies the computing procedure,

but the saving in time is at the expense of substantial loss in accuracy. Erosion hazards are highly

site specific. The parameters that determine the

USLE factor values vary within a large watershed,

and the variations are often not interrelated. Combining overall averages in the equation does not

reflect the particular way in which the factors are

actually combined in different parts of the watershed. Neither does it show which portions of the

drainage area are contributing most of the sediment.

A more accurate procedure is to divide the heterogeneous drainage area into subareas for which

representative soil type, slope length, gradient,

cover, and erosion-control practice factors can be

defined. The USLE is then used to compute the

sheet and rill erosion on each subarea. For this

purpose, eroded soil that is entrapped within the

field area by terrace systems is not soil loss. An

estimate of the entrapped sediment can be excluded from the USLE soil loss estimates by using

values from the last two columns of table 15 as

the P values. An alternate procedure is to estimate

the channel deposition by sediment-transport relationships and subtract this amount from the soil

loss computed by using the standard terracing factor (col. 2, table 15) in the USLE. By this procedure,

the subarea soil loss computations identify the portions of the drainage area that contribute most of

the sediment and also show how much of the sediment derives from tracts that receive heavy applications of agricultural chemicals.

Procedures for computing soil losses from

cropped, idle, pasture, range, or wooded areas

and from construction or development areas were

outlined in the preceding sections. Factor values

derived by the prescribed procedures are assumed

applicable also for surface-mined areas. However, the effect of mining processes on soil erodibility, K, has not been determined. Length and

percent slope and deposition within the area also

are hard to determine for rugged strip mine spoils.

Sometimes nearly all the sediment may be trapped

within the bounds of the area. The USLE can be

quite useful for predicting the effectiveness of each

feasible reclamation plan for such areas.

Sediment Delivery Ratio

Eroded soil materials often move only short distances before a decrease in runoff velocity causes

their deposition. They may remain in the fields

where they originated or may be deposited on

more level slopes that are remote from the stream

system. The ratio of sediment delivered at a given

location in the stream system to the gross erosion

from the drainage area above that location is the

sediment delivery ratio for that drainage area. A

general equation for computing watershed delivery ratios is not yet available, but the ratios for

some specific drainage areas have been computed

directly from local data. Helpful guides for estimating this factor for other drainage areas were

published by SCS in Section 3 of their National

Engineering Handbook {45), and most of these

guides were also included in a publication by SEA

and EPA (42). Therefore, the relationships involved

will be only briefly summarized here.
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING 47

Available watershed data indicate that the delivery ratio varies approximately as the 0.2 power

of drainage-area size, with representative values

of about 0.33 for 0.5 mi^; 0.18 for 10 mi^; and 0,10

for 100 mi^. There were indications that the exponent in this relationship nriay be as small as 0.1 for

very large areas. But the ratio may vary substantially for any given size of drainage area. Other

important factors include soil texture, relief, type

of erosion, sediment transport system, and areas of

deposition within the watershed. Fine soil texture,

high channel density, and high stream gradients

generally indicate delivery ratios that are above

average for the drainage-area size.

A substantial reduction in sediment delivered to

a stream may sometimes result in a compensatory

increase in channel erosion. Channel erosion produces sediment that is immediately available to

the transport system and that may remain in motion as bedload and suspended sediment. The composition of sediment derived from channel erosion

will usually differ substantially from that derived

from cropland erosion. This is particularly important from the viewpoint of transported chemical

pollutants.

With reference to a field-sized area, the delivery

ratio can closely approach 1.0 if the runoff drains

directly into a lake or stream system with no intervening obstructions or flattening of the land

slope. On the other hand, a substantial width of

forest litter or dense vegetation below the eroding

area may cause deposition of essentially all the

sediment except colloidal material. Anything that

reduces runoff velocity (such as reduction in gradient, physical obstructions, vegetation, and ponded

water) reduces its capacity to transport sediment.

When the sediment load exceeds the transport capacity of the runoff, deposition occurs.

From analysis of runoff and soil loss data from

small single-cropped watersheds, Williams (48)

concluded that the need for a sediment delivery

ratio could be eliminated by using the watershed

runoff times peak rate as the storm R value in the

USLE.

Accuracy of

Soil losses computed with the USLE are best

available estimates, not absolutes. They will generally be most accurate for medium-textured soils,

slope lengths of less than 400 ft, gradients of 3 to

18 percent, and consistent cropping and management systems that have been represented in the

erosion plot studies. The farther these limits are

exceeded, the greater will be the probability of significant extrapolation error.

An indication of the accuracy of the equation,

tables, and charts presented herein was obtained

by using them to compute longtime average soil

losses for plots in past erosion studies and comparing these with the actually measured losses on

each plot. About 53 percent of the differences were

less than 1 t/A, 84 percent were less than 2 t, and

5 percent were as much as 4.6 t (53). The mean

annual soil loss for this 2,300 plot-year sample

was 11.3 t. Of those differences that exceeded 1

t/A, 67 percent were from comparisons with plot

records whose duration was less than half of a

normal 22-year rainfall cycle {33). Such short records are subject to bias by cyclical effects and ranUSLE Predictions

dom fluctuations in uncontrolled variables whose

effects are averaged in the USLE factor values {56).

Testing the complete equation against the assembled plot data was statistically valid because the

equation for each factor, as a function of several

parameters, was independently derived from only

selected portions of the data.

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. Large-scale averaging of parameter values

on mixed drainage areas will usually also reduce

accuracy. For reasons previously pointed out and

discussed in depth in another publication {56), specific-storm or specific-year soil losses and short-term

averages may differ substantially from the longtime

average predicted by the USLE for the specified

physical and management conditions.
48 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

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National Conservation Tillage Conference Proceedings, Soil

Conservation Society of America, pp. 133-141.

1974. New developments in estimating water erosion. 29th Annual Meeting of the Soil Conservation Society

of America Proceedings, pp. 179-186.

1976. Use and misuse of the universal soil loss

equation. Journal of Soil and Water Conservation 31(1):

5-9.

1975. Estimating the soil loss equation's cover and

management factor for undisturbed areas. Sediment Yield

Workshop Proceedings, Oxford, Miss. U.S. Department of

Agriculture, ARS-S-40.

Johnson, C. B., and Cross, B. V. 1971. A soil erodibility nomograph for farmland and construction sites. Journal of Soil and Water Conservation 26:189-193.

and Mannering, J. V. 1965. Effect of organic matter content of the soil on infiltration. Journal of Soil and

Water Conservation 20:150-152.

and Mannering, J. V. 1969. Relation of soil properties to its erodibility. Soil Science Society of America

Proceedings 33:131-137.

— and Meyer, L. D. 1973. Soil erodibility on construction areas. Highway Research Board, National Academy of Science Special Report 135:20-29.

and Smith, D. D. 1958. Rainfall energy and its relationship to soil loss. American Geophysical Union Transactions 39:285-291.

Smith, D. D., and Uhland, R. E. 1958. Evaluation

of factors in the soil loss equation. Agricultural Engineering

39:458-462, 474.

and Smith, D. D. 1965. Predicting rainfall-erosion

losses from cropland east of the Rocky Mountains—Guide

for selection of practices for soil and water conservation.

Agricultural Handbook No. 282.

{65) Witmuss, H. D., Triplett, G. B., Jr., and Creb, B. W. 1973.

Concepts of conservation tillage systems using surface

mulches. National Conservation Tillage Conference Proceedings, Soil Conservation Society of America 5-12.

{66) Woolhiser, D. G. 1975. Simulation of unsteady overland

flow. In Unsteady Flow in Open Channels. Water Resources Publications, Fort Collins, Colo. Vol. II, Ch. 12, pp.

485-508.

{67) Zingg, R. W. 1940. Degree and length of land slope as It

affects soil loss in runoff. Agricultural Engineering 21:59-64.
50 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

APPENDIX

Estimating Percentages of

"Percent canopy cover" is the percentage of the

field area that could not be hit by vertically falling

raindrops because of canopy interception. It is the

portion of the soil surface that would be covered

by shadows if the sun were directly overhead.

Because the blades from adjacent rows intertwine

does not necessarily indicate 100 percent canopy

cover,

"Percent mulch cover" \s the percentage of the

field area that is covered by pieces of mulch lying

on the surface. Researchers in Indiana attempted

to relate percent cover to mulch rate by photographing numerous small, equal-sized areas in

harvested corn fields. The residues on the photographed areas were carefully picked up, dried,

and weighed to measure mulch rates, and the

photographs were projected on grids to determine

Canopy and Mulch Covers

percent cover. The indicated average relation of

percent cover to dry weight of well-distributed

corn stover mulch is shown by the solid-line curve

in figure 10. However, observed differences between samples were appreciable. The average relation of percent cover to dry weight of straw

mulch uniformly distributed over research plots is

shown by the broken-line curve.

A simple method of estimating percent mulch

cover on a field is with a cord, preferably not

shorter than 50 ft, that has 100 equally spaced

knots or other readily visible markings. The cord

is stretched diagonally across several rows, and

the knots that contact a piece of mulch are counted.

This procedure is repeated at randomly selected

spots on the field, and the data are averaged to

obtain a representative value for the field.

Probability Values of El in the United States

The annual and maximum-storm values of El

at any given location differ substantially from year

to year. The observed ranges and 50 percent, 20

percent and 5 percent probabilities of annual El

values from 22-year precipitation records at 181

locations in 44 States are listed in table 17. Other

Computing the Erosion Index

Soil loss prediction by the method presented in

this handbook does not require computation of El

values by application personnel, but the procedure

is included here for the benefit of those who may

wish to do so.

100

£60

>

o

Ü

aR 40

0 1 2 3 4 5 6

MULCH (THOUSAND-POUNDS/ACRE)

FIGURE 10.—Relation of percent cover to dry weight of uniformly

distributed residue mulch.

^ - -- --"l^

• -" " 1

STRAW y" y

X*'

/

.

/"

/

/ /

^STOVER

/

/

/

/ /

/

/

///

.7

probabilities can be derived by plotting the 50

percent and 5 percent values on log-probability

paper and joining the two points by a straight line.

Annual maxima storm probabilities for the same

locations are given in table 18.

from Recording-Rain Gage Records

The kinetic energy of a given amount of rain

depends on the sizes and terminal velocities of the

raindrops, and these are related to rainfall intensity. The computed energy per inch of rain at each

intensity is shown in table 19. The energy of a

given storm depends on all the intensities at which

the rain occurred and the amount that occurred at

each intensity. A recording-rain gage record of the

storm will provide this information. Clock time and

rain depth are read from the chart at each point

where the slope of the pen line changes and are

tabulated as shown in the first two columns of the

sample computation below. Clock times (col. 1)

are subtracted to obtain the time intervals given

in column 3, and the depths (col. 2) are subtracted

to obtain the incremental amounts tabulated in

column 4. The intensity for each increment (col. 5)

is the incremental amount times 60, divided by

column 3.
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING 51

Chart rea clings

Depth

(inch)

For each increment Energy

Time

Duration

(minute)

Amount

(inch)

Intensity

(in/hr)

Per

inch Total

4:00 0

:20 0.05 20 0.05 0.15 643 32

:27 .12 7 .07 .60 843 59

:36 .35 9 .23 1.53 977 225

:50 1.05 14 .70 3.00 1074 752

,57 1.20 7 .15 1.29 953 143

5:05 1.25 8 .05 .38 777 39

:15 1.25 20 0 0 0 0

:30 1.30 15 .05 .20 685 34

Totals To 1.30 1,284

Kinetic energy of the storm := 1,284(10-') = 12.84

The energy per inch of rain in each interval

(col. 6) is obtained by entering table 19 with the

intensity given in column 5. The incremented energy amounts (col. 7) are products of columns 4

and 6. The total energy for this 90-minute rain Is

1,284 foot-tons per acre. This is multiplied by a

constant factor of 10"^ to convert the storm energy

to the dimensions in which El values are expressed.

The maximum amount of rain falling within 30

consecutive minutes was 1.08 in, from 4:27 to

4:57. I30 is twice 1.08, or 2.16 in/h. The storm El

value is 12.84(2.16) = 27.7. When the duration of

a storm Is less than 30 minutes, I30 is twice the

amount of the rain.

The El for a specified time is the sum of the

computed values for all significant rain periods

within that time. The average annual erosion index for a specific locality, as given in figures 1 and

2, is the sum of all the significant storm El values

over 20 to 25 years, divided by the number of

years. For erosion index calculations, 6 h or more

with less than 0.5 in of precipitation was defined

as a break between storms. Rains of less than 0.5

in, separated from other showers by 6 h.or more,

were omitted as insignificant unless the maximum

15-min intensity exceeded 0.95 in/h.

Recent studies showed that the median dropsize

of rain does not continue to increase for intensities

greater than about 2.5 to 3 in/h (7, 15). Therefore,

energy per unit of rainfall also does not continue

to increase, as was assumed in the derivation of

the energy-intensity table published in 1958 (62).

The value given in table 19 for rain at 3 in/h {7.6

cm/h in table 20) should be used for all greater

intensities. Also, analysis of the limited soil loss

data available for occasional storms with 30-min

intensities greater than 2.5 in/h showed that placing a limit of 2.5 in (6.35 cm)/h on the I30 component of El improved prediction accuracy for

these storms. Both of these limits were applied in

the development of figure 1. They slightly lowered

previously computed erosion index values in

the Southeast, but average-annual El values for

the U.S. mainland other than the Southeast were

not significantly affected by the limits because

they are rarely exceeded.

Conversion to

Metric equivalents were not included in the

procedures and tables presented in this handbook

because direct conversion of each English unit

would produce numbers that would be awkward

and undesirable. Converting the USLE as a whole

is more appropriate. Metric units can then be selected so that each of the interdependent factors

will have a metric counterpart whose values will

be expressed in numbers that are easy to visualize

and to combine in computations.

A convenient unit for measuring cropland soil

losses is metric tons per hectare per year. El values

of convenient magnitude can be obtained by expressing rainfall energy in metric ton-meters per

hectare, expressing intensities in centimeters per

hour, and retaining the constant factor of 10"^

Metric System

that has been used consistently for El calculations

in English units. Factor K will then be in metric

tons per hectare per metric El unit. If 22 meters is

taken as the basic slope length and 9 percent is

retained as the basic slope gradient, the LS factor

will not be significantly affected. Using these units

is recommended and is assumed in the following

paragraphs.

The USLE factors will normally be derived directly in these units by procedures outlined below.

However, the following conversion factors will facilitate comparisons of the metric factor values

with the English values published in this handbook. Factors expressed in the recommended metric

units are identified by the subscript, m.

Text continues on page 56.
52 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

TABLE 17.—Observed range and 50-, 20-, and 5- percent probability values of erosion index at each of 181

key locations

Values of erosion index {El) Values of erosion index {El)

Location Observed

22-year 50-percent 20-percent 5-percent

range probability probability probability

8-247

5-291

4-246

Alabama:

Birmingham 179-601

Mobile 279-925

Montgomery 164-780

Arkansas:

Fort Smith 116-818

Little Rock 103-625

Mountain Home .... 98-441

Texarkana 137-664

California:

Red Bluff 11-240

San Luis Obispo .... 5-147

Colorado:

Akron

Pueblo

Springfield

Connecticut:

Hartford 65-355

New Haven 66-373

District of Columbia . . 84-334

Florida:

Apalachicola 271-944

Jacksonville 283-900

Miami 197-1225

Georgia:

Atlanta 116-549

Augusta 148-476

Columbus 215-514

Macon 117-493

Savannah 197-886

Watkinsville^ 182-544

Illinois:

Cairo 126-575

Chicago 50-379

Dixon Springs^ 89-581

Moline 80-369

Rantoul 73-286

Springfield 38-315

Indiana:

Evansville 104-417

Fort Wayne 60-275

Indianapolis 60-349

South Bend 43-374

Terre Haute 81-413

Iowa:

Burlington 65-286

Charles City 39-308

Clarinda^ 75376

Des Moines 30-319

Dubuque 54-389

Sioux City 56-336

Rockwell City 40-391

354

673

359

254

308

206

325

54

43

72

44

79

133

157

183

529

540

529

286

229

336

282

412

278

231

140

225

158

152

154

188

127

166

137

190

162

140

162

136

175

135

137

461

799

482

400

422

301

445

98

70

129

93

138

188

222

250

663

693

784

377

308

400

357

571

352

349

212

326

221

201

210

263

183

225

204

273

216

205

220

198

251

205

216

592

940

638

614

569

432

600

171

113

225

189

233

263

310

336

820

875

1136

488

408

473

447

780

441

518

315

465

303

263

283

362

259

302

298

389

284

295

295

284

356

308

335

Location Observed

22-year

range

See footnote at end of table.

Kansas:

Burlingame 57-447

Coffeyville 66-546

Concordia 38-569

Dodge City 16-421

Goodland 10-166

Haysi 66-373

Wichita 42-440

Kentucky:

Lexington 54-396

Louisville 84-296

Middlesboro 107-301

Louisiana:

Lake Charles 200-1019

New Orleans 273-1366

Shreveport 143-707

Maine:

Caribou 26-120

Portland 36-241

Skowhegan 39-149

Maryland:

Baltimore 50-388

Massachusetts:

Boston 39-366

Washington 65-229

Michigan:

Alpena 14-124

Detroit 56-179

East Lansing 35-161

Grand Rapids 33-203

Minnesota:

Alexandria 33-301

Duluth 7-227

Fosston 22-205

Minneapolis 19-173

Rochester 46-338

Springfield 37-290

Mississippi:

Meridian 216-820

Oxford 131-570

Vicksburg 165-786

Missouri:

Columbia 98-419

Kansas City 28-361

McCredie^ 64-410

Rolla 105-415

Springfield 97-333

St. Joseph 50-359

St. Louis 59-737

Montana:

Billings 2-82

Great Falls 3-62

Miles City 1-101

Nebraska

Antioch 18-131

Lincoln 44-289

Lynch 34-217

North Platte 14-236

Scribner 69-312

Valentine 4-169

-percent 20-percent 5-percent

•bability probability probability

176 267 398

234 339 483

131 241 427

98 175 303

76 115 171

116 182 279

188 292 445

178 248 340

168 221 286

154 197 248

572 786 1063

721 1007 1384

321 445 609

58 79 106

91 131 186

78 108 148

178 263 381

99 159 252

116 153 198

57 85 124

100 134 177

86 121 166

84 123 178

88 147 240

84 127 189

62 108 184

94 135 190

142 207 297

96 154 243

416 557 737

310 413 543

365 493 658

214 297 406

170 248 356

189 271 383

209 287 387

199 266 352

178 257 366

168 290 488

12 26 50

13 24 44

21 40 72

60 86 120

133 201 299

96 142 205

81 136 224

154 205 269

64 100 153
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING 53

TABLE 17.—Observed ronge and 50-, 20- aná 5- percent probabiiity values of erosion

key iocafions—Continued

idex of each of 181

Values of erosion index (El)

Location Observed

22-year

range

50-percent 20-percent 5-percent

probability probability probability

New Hampshire:

Concord 52-212 91 131 187

New Jersey:

Atlantic City 71-318 166 229 311

Marlboro! 58-331 186 254 343

Trenton 37-382 149 216 308

New Mexico:

Albuquerque 0-46 10 19 35

Roswell 5-159 41 73 128

New York:

Albany 40-172 81 114 159

Binghamton 20-151 76 106 146

Buffalo 20-148 66 96 139

Geneva^ 33-180 73 106 Ï52

Marcellus^ 24-241 74 112 167

Rochester 22-180 66 101 151

Salamanca 31-202 70 106 157

Syracuse 8-219 83 129 197

North Carolina:

Asheville 76-238 135 175 223

Charlotte 113-526 229 322 443

Greensboro 102-357 184 244 320

Raleigh 152-569 280 379 506

Wilmington 196-701 358 497 677

North Dokota:

Bismarck 9-189 43 73 120

Devils Lake 21-171 56 90 142

Fargo 5-213 62 113 200

Williston 4-71 30 45 67

Ohio:

Cincinnati 66-352 146 211 299

Cleveland 21-186 93 132 185

Columbiana 29-188 96 129 173

Columbus 45-228 113 158 216

Coshoctoni 72-426 158 235 343

Dayton 56-245 125 175 240

Toledo 32-189 83 120 170

Oklahoma:

Ardmore 100-678 263 395 582

Cherokee^ 49-320 167 242 345

Guthrie^ 69-441 210 316 467

McAlester 105-741 272 4T1 609

Tulsa 19-584 247 347 478

Oregon:

Pendleton 2-28 4 8 16

Portland 16-80 40 56 77

Pennsylvania:

Erie n-534 96 181 331

Franklin 50-228 97 135 184

Harrisburg 48-232 105 146 199

Philadelphia 72-361 156 210 282

See footnote at end of table.

Pittsburgh 43-201 111 148 194

Reading 84-308 144 204 285

Scranton 52-198 104 140 188

Puerto Rico:

San Juan 203-577 345 445 565

Values of erosion index {El)

Location Observed

22-year 50-percent 20-percent

range probability probability

Rhode Island:

Providence 53

South Carolina:

Charleston 174

Clemsoni 138

Columbia 81

Greenville 130

South Dakota:

Aberdeen 19'

Huron 18

Isabel 16

Rapid City 10

Tennessee:

Chattanooga 163

Knoxville 64

Memphis 139

Nashville 116

Texas:

Abilene 27.

Amarillo 33

Austin 59

Brownsville 46

Corpus Christi 124-

Dallas 93

Del Rio 19

El Paso 4-

Houston 176

Lubbock 17

Midland 35

Nacogdoches 153

San Antonio 77

Temple^ 81

Victoria 108

Wichita Falls 79

Vermont:

Burlington 33

Virginia:

Blacksburgi S]

Lynchburg 64

Richmond 102-

Roanoke 78'

Washington:

Pullman^ 1-

Spokane 1-

West Virginia:

Elkins 43-

Huntington 56-

Parkersburg 69-

Wisconsin:

Green Bay 17

LaCrosse^ 61-

Madison 38-

Milwaukee 31-

Rice Lake 24-

Wyoming:

Casper 1-

Cheyenne 8-

-225

-270

119

72

-245 126

-366 164

373 208

-283 129

•30 6

19 7

223 118

228 127

303 120

148 77

385 153

251 118

193 93

-334 122

-24 9

66 28

167

114

5-percent

probability

232

-1037 387 559 795

-624 280 384 519

461 213 298 410

-589 249 350 487

295 74 129 219

145 60 91 136

141 48 78 125

140 37 64 108

468 269 348 445

370 173 239 325

-595 272 384 536

381 198 262 339

554 146 253 427

-340 no 184 299

-669 270 414 624

-552 267 386 549

559 237 330 451

-630 263 396 586

405 121 216 374

85 18 36 67

-1171 444 674 1003

-415 82 158 295

260 82 139 228

769 401 571 801

635 220 353 556

-644 261 379 542

-609 265 385 551

558 196 298 447

178

168 221

232 324

275 361

176 237

12 21

11 17

158 209

173 233

165 226

107 147

228 331

171 245

139 202

202 327

15 26

43 66

Computations based on SEA rainfall records. All others are based on Weather Bureou records.
54 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

TABLE 18.—Expected magnitudes of single-storm erosion index values

Index values normally exceeded once in— Index values normally exceeded once in—

Location year

1

years

2

years years years

5 10 20

Location year

1

years

2

years years years

5 10 20

Alabama:

Birmingham 54

Mobile 97

Montgomery 62

Arkansas:

Fort Smith 43

Little Rock 41

Mountain Home 33

Texarkana 51

California:

Red Bluff 13

San Luis Obispo 11

Colorado:

Akron 22

Pueblo 17

Springfield 31

Connecticut:

Hartford 23

New Haven 31

District of Columbia 39

Florida:

Apalachicola 87

Jacksonville 92

Miami 93

Georgia:

Atlanta 49

Augusta 34

Columbus 61

Macon 53

Savannah 82

Watkinsville 52

Illinois:

Cairo 39

Chicago 33

Dixon Springs 39

Moline 39

Rantoul 27

Springfield 36

Indiana:

Evansville 26

Fort Wayne 24

Indianapolis . 29

South Bend 26

Terre Haute 42

Iowa:

Burlington 37

Charles City 33

Clarinda 35

Des Moines 31

Dubuque 43

Rockwell City 31

Sioux City 40

77

122

86

65

69

46

73

21

15

36

31

51

33

47

57

124

123

134

67

50

81

72

128

71

63

49

56

50

39

52

38

33

41

41

57

48

47

48

45

63

49

110

151

118

101

115

68

105

36

22

63

60

84

50

73

86

180

166

200

92

74

108

99

203

98

101

77

82

89

56

75

56

45

60

65

78

62

68

66

67

91

76

84

140

172

145

132

158

87

132

49

28

87

88

112

64

96

108

224

201

253

112

94

131

122

272

120

135

101

105

116

69

94

71

56

75

86

96

72

85

79

86

114

101

105

170

194

172

167

211

105

163

65

34

118

127

152

79

122

136

272

236

308

134

118

152

146

358

142

173

129

130

145

82

117

86

65

90

111

113

81

103

94

105

140

129

131

Kansas:

Burlingame 37

Coffeyville 47

Concordia 33

Dodge City 31

Goodland 26

Hays 35

Wichita 41

Kentucky:

Lexington 28

Louisville 31

Middlesboro 28

Louisiana:

New Orleans 104

Shreveport 55

Maine:

Caribou 14

Portland 16

Skowhegan 18

Maryland:

Baltimore 41

Massachusetts:

Boston 17

Washington 29

Michigan:

Alpena 14

Detroit 21

East Lansing 19

Grand Rapids 24

Minnesota:

Duluth 21

Fosston 17

Minneapolis 25

Rochester 41

Springfield 24

Mississippi:

Meridian 69

Oxford 48

Vicksburg 57

Missouri:

Columbia 43

Kansas City 30

McCredie 35

Rolla 43

Springfield 37

St. Joseph . .

Montana:

Great Falls .

Miles City . .

Nebraska:

Antioch . . . .

Lincoln

Lynch

North Platte

Scribner ... .

Valentine ,..

45

4

7

19

36

26

25

38

18

51 69 83 100

69 101 128 159

53 86 116 154

47 76 97 124

37 53 67 80

51 76 97 121

61 93 121 150

46 80 114 151

43 59 72 85

38 52 63 73

149 214 270 330

73 99 121 141

20 28 36 44

27 48 66 88

27 40 51 63

59

27

35

21

31

26

28

34

26

35

58

37

92

64

78

58

43

55

63

51

62

8

12

26

51

37

38

53

28

86

43

41

32

45

36

34

53

39

51

85

60

125

86

111

77

63

89

91

70

86

14

21

36

74

54

59

76

45

109

57

45

41

56

43

38

72

51

65

105

80

151

103

136

93

78

117

115

87

106

20

29

45

92

67

78

96

61

133

73

50

50

68

51

42

93

63

78

129

102

176

120

161

107

93

151

140

102

126

26

38

52

112

82

99

116

77
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING 55

TABLE 18.—Expecfed magnitudes of singie-sform erosion index values—Continued

Index values normally exceeded once în- Index values normally exceeded once in—-

Location year

1

years

2

years

5

years years

10 20

Location

New Hampshire:

Concord 18

New Jersey:

Atlantic City 39

Marlboro 39

Trenton 29

New Mexico:

Albuquerque 4

Roswell 10

New York:

Albany 18

Binghamton 16

Buffalo 15

Marceltus 16

Rochester 13

Salamanca 15

Syracuse 15

North Carolina:

Asheville 28

Charlottte 41

Greensboro 37

Raleigh 53

Wilmington 59

North Dakota:

Devils Lake 19

Fargo 20

Williston ..

Ohio:

Cincinnati

Cleveland

Columbiana

Columbus

Coshocton .

11

27

22

20

27

27

Dayton 21

Toledo 16

Oklahoma:

Ardmore 46

Cherokee 44

Guthrie 47

McAlester 54

Tulsa 47

Oregon:

Portland 6

Pennsylvania:

Franklin 17

Harrisburg 19

Philadelphia 28

Pittsburgh 23

Reading 28

Scranton 23

Puerto Rico:

San Juan 57

Rhode Island:

Providence 23

27

55

57

48

6

21

26

24

23

24

22

21

24

40

63

51

77

87

27

31

16

36

35

26

40

45

30

26

71

59

70

82

69

24

25

39

32

39

32

87

34

45

77

85

76

11

34

38

36

36

38

38

32

38

58

100

74

no

129

39

54

25

48

53

35

60

77

44

42

107

80

105

127

100

^ 13

35

35

55

45

55

44

131

52

62

97

111

102

15

45

47

47

49

49

54

40

51

72

131

92

137

167

49

77

33

59

71

41

77

108

57

5i7

141

97

134

165

127

15

45

43

69

57

68

53

169

68

79

117

136

131

21

53

56

58

61

62

75

49

65

87

164

113

168

206

59

103

41

69

86

48

94

143

70

74

179

113

163

209

154

18

54

51

81

67

81

63

216

83

year

1

years

2

years years years

5 10 20

South Carolina:

Charleston 74

Clemson 51

Columbia 41

Greenville 44

South Dakota:

Aberdeen 23

Huron 19

Isabel 15

Rapid City 12

Tennessee: '

Chattanooga 34

Knoxville 25

Memphis 43

Nashville . .

Texas:

Abilene

Amarillo

Austin

Brownsville

35

31

27

51

73

Corpus Christi 57

Dallas

Del Rio .

El Paso

Houston

Lubbock

Midland

53

44

6

82

17

23

Nacogdoches 77

San Antonio 57

Temple 53

Victoria 59

Wichita Falls 47

Vermont:

Burlington 15

Virginia:

Blacksburg 23

Lynchburg 31

Richmond 46

Roanoke 23

Washington:

Spokane 3

West Virginia:

Elkins 23

Huntington 18

Parkersburg 20

Wisconsin:

Green Bay

LaCrosse

Madison

Milwaukee

Rice Lake

Wyoming:

Casper

Cheyenne

106

73

59

65

35

27

24

20

49

41

55

49

49

47

80

113

79

82

67

9

127

29

35

103

82

78

83

63

22

31

45

63

33

31

29

31

26

67

42

35

45

7

14

154

106

85

96

55

40

38

34

72

68

70

68

79

80

125

181

114

126

108

15

208

53

52

138

122

123

116

86

35

41

66

86

48

42

49

46

38

99

61

50

70

9

21

196

133

106

124

73

50

52

48

93

93

82

83

103

112

169

245

146

166

144

19

275

77

69

164

155

162

146

106

47

48

83

102

61

51

69

61

49

125

77

62

92

11

27

240

163

132

153

92

61

67

64

114

122

91

99

138

150

218

312

171

213

182

24

359

103

85

194

193

206

178

123

58

56

103

125

73

60

89

76

59

154

95

74

119

14

34
56 UNITED STATES DEPARTMENT OF AGRICULTURE, AGRICULTURE HANDBOOK NUMBER 537

1 t/ha = 2.242 tons per acre (7)

1 t-m/ha/cm = 0.269 ft-tons per acre per inch

1 E =0.683 E

m

1 I =2.54 l„

30m 30

1 (El)

n

1 K

1.735 El

: 1.292 K

Factor R. The procedure for computing (El)^ for

a given rain period is similar to that described in

the preceding section for computation of El, but

the input data will be in different units. If the rain

gage chart used for the preceding example had

been calibrated in millimeters, the computation

would have been as follows:

Chart readings

Depth

Storm increments Energy

Duration Amount Intensity Per For

Time (mm) (m'ln) (cm) (cm/b) cm increment

4:00 0

:20 1.2 20 0.12 0.36 175 21

:27 3.0 7 .18 1.54 226 41

:36 8.8 9 .58 3.87 263 153

:50 26.6 14 1.78 7.68 289 514

.57 30.4 7 .38 3.26 256 97

5:05 31.7 8 .13 .98 220 29

:15 31.7 10 0 0 0 0

:30

Total

33.0

s

15

"90

.13

3.30

.52 184 24

879

Kinetic energy of th e storm = 879(10"') = 8.79

TABLE 19.—Kinetic energy of rainfall expressed in footfons per acre per inch of rain^

Intensity

inch per 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09

hour

0 — 254 354 412 453 485 512 534 553 570

0.1 585 599 611 623 633 643 653 661 669 677

.2 685 692 698 705 711 717 722 728 733 738

.3 743 748 752 757 761 765 769 773 777 781

.4 784 788 791 795 798 801 804 807 810 814

.5 816 819 822 825 827 830 833 835 838 840

.6 843 845 847 850 852 854 856 858 861 863

.7 865 867 869 871 873 875 877 878 880 882

.8 884 886 887 889 891 893 894 896 898 899

.9 901 902 904 906 907 909 910 912 913 915

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1 916 930 942 954 964 974 984 992 1000 1008

2 1016 1023 1029 1036 1042 1048 1053 1059 1064 1069

3 '1074

^ Computed by the equation, E = 916 + 331 logi,, I, where E —

kinetic energy in foot-tons per acre per inch of rain, and I = rainfall intensity in inches per hour.

^ The 1074 value also applies for all intensities greater than 3

in/h (see text).

TABLE 20.—Kinetic energy of rainfall expressed in

metric ton-meters per hectare per centimeter of rain^

Intensity

cm/h .0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

0 .. 0 121 148 163 175

1 ..210 214 217 220 223

2 .. 237 239 241 242 244

3 ..253 254 255 256 258

4 ..264 265 266 267 268

5 .. 273 273 274 275 275

6 .. 280 280 281 281 282

7 .. 286 286 287 287 288

184 191 197 202 206

226 228 231 233 235.

246 247 249 250 251

259 260 261 262 263

268 269 270 271 272

276 277 278 278 279

283 283 284 284 285

288 ^289

^ Computed by the equation E = 210 + 89 logiol,

where E = kinetic energy in metric-ton meters per hectare per centimeter of rain, and

I = rainfall intensity in centimeters per hour.

^ The 289 value also applies for all intensities greater than 7.6

cm/h.

Values for column 6 are obtained by entering

table 20 with the intensities listed in column 5, and

their sum, 879, is the kinetic energy (Em) of the 3.30

cm of rain expressed in metric ton-meters per hectare. The constant factor of 10"^ used for the English system should be applied here also so that

storm (El)in values will usually not exceed 100. The

maximum amount of rain in any 30-minute period

was 2.74 cm, from 4:27 to 4:57. Therefore Isom =

2(2,74 = 5.48 cm/h. (EIL = 8.79(5.48) = 48.17

The procedure for combining storm El values for

local erosion index values was fully described in

the preceding section. For predicting average annual soil losses from rainfall and its associated

runoff, R equals the erosion index. Where runoff

from thaw, snowmelt, or irrigation is significant,

an Rs factor must be added to the El value as

previously discussed.

Where adequate rainfall intensity data are not

available, the erosion index cannot be estimated

solely from annual precipitation data. It is a function of the sizes and intensities of the individual

rainstorms, and these are not closely related to annual precipitation. Therefore a given annual rainfall will indicate only a broad range of possible

values of the local erosion index. However, the

United States data indicate that the range of likely

values can be somewhat narrowed by knowledge

of the general climatic conditions in the particular

geographic area.
PREDICTING RAINFALL EROSION LOSSES-A GUIDE TO CONSERVATION PLANNING 57

In the U.S. Northern and Northeastern States,

the winter precipitation generally comes as snow

and low-intensity rains, but erosive intensities occur during the spring and summer. There, the local

erosion index values, (El)in/ have ranged from 2P52 to 2.6P, where P is the average annual precipitation expressed in centimeters. In several

Northwestern States, where rain intensities rarely

exceed 2.5 cm/h, the annual (El)m is generally less

than P, but R« values are high. Near the Gulf of

Mexico and along the southern half of the Atlantic Coast, the rainfall characteristics are substantially influenced by coastal storms, 24-h rainfall

exceeds 10 cm at least once in 2 years, on the

average, and erosive rains occur in nearly every

month of the year. There, erosion index values

range between 4.2P and 6.7P. Values computed

from the few long-term, recording-raingage records available for the Islands of Hawaii and

Puerto Rico were also within this range. In the

large region between the northern and southern

extremes mentioned above, the annual (El)m values

range from 2.5P to 4.5P. Brief, high-Intensity thunderstorms are common in this region during the

.summer months, but general rains of longer duration also occur.

Where data are adequate to determine 2-year

probabilities of 6-hour rainfall, these probabilities

may provide more specific estimates of the local

erosion index values. In the U.S. data, local erosion index values were approximately equal to the

quantity 27.38 P-^', where P = the 2-year, 6-hour

precipitation in inches. Converted to the recommended metric units, (El)n^ equals approximately

6.28P2^^ where P is expressed in centimeters. However, this estimating procedure should not be substituted for the standard erosion index calculation

procedure where adequate intensity data are available.

Factor K. This factor is the average soil loss in

metric tons per hectare per unit of (El),^!, measured

on unit plots of the given soil. A unit plot is a 22-m

length of uniform 9 percent slope that has been

in clean fallow for more than 2 years and is tilled

to prevent vegetative growth and surface crusting

during the period of soil loss measurement. If a

gradient other than 9 percent must be used, the

data are adjusted by an LS factor available from

figure 11. If the soil-erodibility nomograph (fig. 3)

is used to evaluate Km, the K value read from the

nomograph is multiplied by a conversion factor

of 1.292.

The most accurate direct measurement of K for

a given soil is obtained by measuring soil losses

from unit plots under natural rain for at least 5

years, beginning 2 years after the clean-fallow

condition was established. This permits averaging

the interactions of soil erodibility with antecedent

soil moisture, storm size, and other randomly distributed variables. The fallow plots receive the

same annual tillage as conventionally tilled row

crops.

Using rainfall simulators to evaluate K is quicker

and less costly, but it requires caution. A one-time

simulator test, even though replicated on several

plots, measures soil loss from only one storm size

and rain intensity, on one set of antecedent conditions, and these may or may not represent natural rainfall patterns. When simulated rainfall is

used to evaluate K, measuring the soil losses for

four or five successive 30-minute periods is helpful

so that the segmented data can be rearranged to

represent small, intermediate, and large storms

beginning at various antecedent soil moisture

levels. These can be weighted according to their

probability of occurrence in natural rainfall (58).

Factor LS. Selecting 22 m as the basic slope

length and retaining 9 percent as the basic slope

gradient leaves the LS values essentially unchanged from those usqd in the English system of

units. For uniform slopes, LS may be obtained by

entering figure 11 with the field slope length expressed in meters. For concave or convex slopes,

the value read from figure 11 should be modified

by the procedure given in the subsection Irregular

Slopes.

Factors C and P. Soil loss ratios (table 5) and P

values (tables 13, 14, 15) are not affected by the

units selected for the other factors. However, in

countries where crops and farming techniques are

different from those reflected in table 5, measurements of soil loss reductions attainable with feasible changes in crop system, tillage methods, and

residue management may merit priority over establishing El and K values.
•lua^sXs Dij|9ui jo¿ 4JDi^5 4Daj[ja-ado|s—• 11 B^inON

10

00

20 40 60

SLOPE LENGTH (METERS)

100 200 500
/ y

/f755//

PREDICTING RAINFALL EROSION LOSSES—

A GUIDE TO CONSERVATION PLANNING

Supplement to Agriculture Handbook No. 537

METRICATION OF THE USLE IN THE INTERNATIONAL SYSTEM OF UNITS (SI)

The metric conversion originally presented in

this handbook and in prior publications {S3, 60)

is not completely in the international System of

Units (SI), which is expected to gain widespread

usage. This supplement presents an alternative

conversion in which all the Universal Soil Loss

Equation (USLE) factors are expressed in standard

SI units or approved multiples thereof, and the order of magnitude of each new unit is similar to the

old.

Both conversion systems are authentic, and conservationists who have adopted the originally

recommended metric units would not improve their

USLE accuracy by changing to the new units. For

future conversions, however, the revised procedure, which is fully outlined below, is recommended because its use will facilitate standardization of units.

The USLE terms A, LS, C, and P need no change

from the recommendations in the preceding section.

Strictly, the SI units for mass and area are kilograms and square meters. Because of common use,

however, metric ton (a special name for megagram)

and hectare (a special name for square hectometer)

will be used. Soil loss (A) wirbe expressed in metric

tons per hectare, and factor K in metric tons per

hectare per metric El unit. Factors LS, C, and P are

following reasons: With I30 expressed in mm/h, the

metric El values would be 17 times the magnitude

of El in U.S. customary units. Annual erosion index

values would be in four- or five-digit numbers,

which are harder to visualize and compare mentally than the present smaller numbers. Of greater

importance, the large metric El values would result

in extremely small metric K values, ranging down-*

ward from a maximum of about 0.09. Absolute differences between K values fepiiId be so small that

many casual users of the USLfrwould tendeo neglect

important soil differences as jfrsignificalf^

Reducing the magnitude of I30 by a factor of 10

alleviates these disadvantagefcand doe^j?ot preclude the use of mm as the unit forrainfall ajnounts

and incremental intensities in en§rgy. compi/^btions.

The energy equation or table wilt also be expressed

in MJ/ha per mm of rain. Only bkwjH be converted

to cm as a matter of expedîencér;fliîs is directly

comparable to the U.S. customaYy procedure of

computing energy \r\ ft-tons/acre and dividing by

100 to obtain more convenient magnitudes. The

metric El will then equal storm energy in MJ/ha

times I30 in cm/h.

Assuming use of the metric units specified above,

a comparison of U.S. customary and SI dimensions

for the terms in the USLE is as follows:

term US customary dimensions SI dimensions Symbol

A ton/acre metric ton/hectare t/ha

R

100 foot-ton inch megajouie centimeter AAJ cm

acre hour hectare hour ha h

K

.01 ton acre hour

acre foot-ton inch

metric ton hectare hour t ha h

hectare megajouie centimeter ha AAJ cm

L,S,C,P dimensionless dimensionless

dimensionless. L is expressed relative to slope

lengths measured in meters, but selecting 22 m as

the basic slope length and retaining 9 percent as

the basic slope gradient leaves the LS values essentially unchanged. C and P are not affected by

the units selected for the other factors.

Factor R will be in different units than previously

recommended. In the SI system, energy is measured

in joules and rainfall in millimeters. The use of

"centi" as a multiple is minimized. Metric El values

can be obtained in standard SI units by expressing

rainfall energy in megajoules (MJ) per hectare and

maximum 30-minute intensity (I30) in mm/h, but use

of cm/h to express I30 is more expedient for the

The USLE terms will usually be derived directly in

the SI units by procedures outlined below. However,

the following conversion factors will facilitate comparisons of the metric factor values with the U.S.

customary values published in this handbook. Terms

expressed in metric units are identified by the subscript m.

To convert from.- muliipiy by: to obtain:

A in tons/acre l^Al Am in t/ha

E in 100 ft-tons/acre 0.670 Em in AAJ/ha

I30 in in/h 2.540 iaom in cm/h

El in 100 f\-ion in 1.702 (El)m in MJ cm

acre hi ha h

K in .01 ton acre hi 1.313 Km in t ha h

acre ft-ton in AAJ ho cm

UNITED STATES

DEPARTMENT OF

AGRICULTURE

AGRICULTURE

HANDBOOK

NUMBER 537

PREPARED BY

SCIENCE AND

EDUCATION

ADMINISTRATION
Factor R. The procedure for computing (El)m for o

given ram period îs similar to that described in the

preceding section for computing El, but the input

data will be in different units. If the raingage chart

used for the example on page 51 had been calibrated in millimeters, the computation would have

been as follows:

Chart Storm increments Energy

readings

Per mm Increment

Time Depth Duration Amount Intensity of rain total

(mm) (min) (mm) (mm/h) (MJ/tia mm) (MJ/tia)

(1) (2) (3) (4) (5) (6) (7)

4:00 0

:20 1 20 1 3 0.161 0.161

:27 3 7 2 17 .226 .452

:36 9 9 6 40 .259 1.554

:50 27 14 18 77 .283 5.094

:57 30 7 3 26 .242 .726

5:05 32 8 2 15 .222 .444

:15 32 10 0 0 0 0

:30 33 15 1 4 .172 .172

Totals 90 33 8.603

Kinetic energy of the storm: 8.60 AU/ha

Values for column 6 are obtained by entering the

revised table 20 with the intensities listed in column

5. The sum of the products of corresponding values

from columns 4 and 6 (8.60) is the kinetic energy,

Emf of the 33 mm of rain expressed in megajoules

per hectare. The maximum amount of rain in any

30-minute period was 27 mm, from 4:27 to 4:57.

Therefor« the maximum 30-minute intensity was 2 X

27, or 54, mm/h, and Isom =54/10 == 5.4 cm/h. (El)m

= 8.Ó0 X 5.4 = 46.4 (MJ cm)/(ha h).

For the El computations, the rain occurring between two successive periods of 6 hours or more

with less than 1.3 mm (0.05 in) of precipitation is

considered one storm. Rain showers of less than 12

mm are omitted as insignificant unless they include

a 15-minute intensity of at least 25 mm/h. The erosion index at a given location, as mapped in figures

1 and 2, is the average annual total of storm El

values over 20 to 25 years. For predicting average

annual soil losses from rainfall and its associated

runoff, R equals the erosion index. Where runoff

from thaw, snowmelt, or irrigation is significant, R

TABLE 20. (revised).—Kinetic energy of rainfall at

specified intensities, expressed in megajoules per

hectare per millimeter of rain^

Intensity

(mm/h) 0 1 8

0 .. 0 0.119 0.145 0.1610.172 0.180 0.187 0.193 0.198 0.202

10 .. .206 .210 .213 .216 .219 .222 .224 .226 .229 .231

20 .. .233 .234 .236 .238 .240 .241 .242 .244 .245 .247

30 .. .248 .249 .250 .252 .253 .254 .255 .256 .257 .258

40 .. .259 .260 .261 .262 .262 .263 .264 .265 .266 .267

50 .. .267 .268 .269 .270 .270 .271 .272 .272 .273 .274

60 .. .274 .275 .276 .276 .277 .277 .278 .278 .279 .280

70 .. .280 .281 .281 .282 .282 .283 .283'

* Computed by the equation e = 0.119—0.0873 iogio i, where

e = kinetic energy in mega¡ouies/(hectare millimeter) and i =

rainfall intensity in mm/h.

'The value of 0.283 also applies for all intensities greater than

76 mm/h.

equals the El plus an Rs value as discussed on

page 7.

The erosion index cannot be reliably estimated

from annual-rainfall data alone. It is a function of

the sizes and intensities of the individual rainstorms, and these have no common relationship to

annual rainfall totals. However, later analyses of

the U.S. annual erosion index values that had been

derived by the above procedure indicated that they

were roughly equal to the quantity 27.38 P^^^

where P = the 2-year, 6-hour rainfall expressed in

inches. By direct conversion, the average annual

(El)m would be roughly estimated by 0.0416 P^",

where P is expressed in mm. This estimating formula is appreciably less accurate than the standard

erosion index calculation procedure and should not

be substituted for it where intensity data are available.

Factor K. The soil-erodibility factor K is the average soil loss in metric tons per hectare per unit of

metric El, measured on unit plots of the given soil.

A unit plot (see p. 8) is a 22-m length of uniform 9

percent slope that has been in clean fallow for more

than 2 years and is tilled to prevent vegetative

growth and surface crusting during the period of

soil loss measurement. If a gradient other than 9

pertent must be used, the data are adjusted by the

appropriate LS factor. If the soil-erodibility nomograph (fig. 3) is used to evaluate Km, the K value

read from the nomograph must be multiplied by a

conversion factor of 1.313.

The basic slope length used for K and L in this

handbook is 72.6 ft, which equals 22.134 m. For experimental evaluation of factor K in metric units,

rounding this to 22.0 m is more convenient and

introduces no error when 22.0 m is also used as the

basic length for L, as in figure 11. The slight reduction in basic length increases factor L by 0.3 of

1 percent and decreases factor K by the same percentage, so the product of K and L is unchanged.

For conversion of the U.S. customary K values in

this handbook to metric K values based on a 22.0 m

length, the relatively insignificant potential error is

avoided by including an L-value of 0.997 in the conversion factor. The K-conversion factor of 1.313

given above has been so adjusted.

Factor LS. The preceding paragraph applies here,

also. For uniform slopes, LS may be obtained by

entering figure 11 with the field slope length expressed in meters or it may be computed by the

equation

LS: : (\/22) (65.41 sin'' B + 4.56 sin 0 + 0.065)

where A = slope length in m; 6 = angle of slope;

and m = 0.5 if the percent slope is 5 or more, 0.4

on slopes of 3.5 to 4.5 percent, 0.3 on slopes of 1 to

3 percent, and 0.2 on uniform gradients of less than

I percent. For concave, ¿bnvex, or mixed-gradient

slopes, the value so computed or read from figure

II should be modified by the procedure outlined on

page 16.

Factor C and P. Soil loss ratios (table 5) and P

values (tables 13, 14, 15) are not affected by the

units selected for the other factors and therefore

need no conversion.

Washington, D.C. January 1981
U.S. DEPARTMENT OF AGRICULTURE

SCIENCE AND EDUCATION ADMINISTRATION

HYATTSVIULE, MARYLAND 20782

OFFICIAL BUSINESS

PENALTY FOR PRIVATE USE $300

POSTAGE AND FEES PAID

U. S. DEPARTMENT OF

AGRICULTURE

AGR 101