United States

Department of

Agriculture

Forest Service 

Pacific Northwest

Research Station

General Technical

Report

PNW-192

March 1986

About This File: 

This file was created by scanning the printed publication. Misscans identified

by the software have been corrected; however, some mistakes may remain.

A Method of Site

Quality Evaluation

for Red Alder

Constance A. Harrington
CONSTANCE A. HARRlNGTON is a research forester, U.S. Department of Agriculture,

Forest Service. At the time this research was conducted she was with the Pacific

Northwest Research Station, Forestry Sciences Laboratory, 3625 93d Avenue S.W.,

Olympia, Washington 98502. She is currently with the Southern Forest Experiment

Station, Forestry Sciences Laboratory, Box 3516, Monticello, Arkansas 71655.

Author
Harrington, Constance A. A method of site quality evaluation for red alder. Gen. Tech. 

Rep. PNW-192. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific

Northwest Research Station; 1986. 22 p.

A field guide to predict site index for red alder (Alnus rubra Bong.) was developed for

use in western Washington and Oregon. The guide requires the user to evaluate 14

soil-site properties that are grouped into three general factors: (1) geographic and

topographic position, (2) soil moisture and aeration during the growing season, and (3)

soil fertility and physical condition. Construction of the guide was modeled after a

method of site evaluation developed for several southern hardwood species. The red

alder model is accurate when used properly. The correlation (r) between predicted and

measured site index was 0.97 for the basic data set of 25 plots and 0.96 for the 15 plots

used for verification. Estimated site index should be within ± 2 meters of measured site

index 95 percent of the time. Use of a second independent data set for model verification

resulted in a somewhat lower correlation (r = 0.89) between measured and predicted

site index than was achieved with the original data set, but the model continued to meet

the accuracy standard of ± 2 meters (p≤0.05).

Keywords: Site class, site index, guidebooks, models, red alder, Alnus rubra.

Abstract
1 lntroduction

2 Field and Laboratory Methods 

4 Model Construction

11 Model Validation

12 Use of the Site Evaluation Tables

16 Applicability

16 English Equivalents

17 Acknowledgments

18 Literature Cited

20 Appendix 1

20 Definition of Terms Used in Tables 

22 Appendix 2

22 Sample Field Worksheet

Contents
Forest managers need site-specific information on site quality to make intelligent 

decisions regarding species selection and management practices. The most commonly

used measure of potential site productivity is site index (mean height of upper crown

class trees that have been free to grow in an even-aged stand at a specified index age).

It is, however, difficult or impossible to accurately assess site index in stands that are

uneven aged, mixed species, very young or very old, or on sites where the species of

interest is not growing.

Several methods have been developed for predicting site quality for a species when it

cannot be directly measured (Mader 1965). The most common methods use: (1) 

measured soil and site characteristics in a mathematical equation, (2) soil mapping

units, or (3) the presence or growth of other plant species. Prediction of site index by

use of soil and topographic characteristics in a multiple regression equation (method 1)

was popular in the United States from the 1930’s through the 1960’s; Coile (1952),

Ralston (1964), Berglund (1968), and Carmean (1975) provide good reviews of the 

subject. These studies were useful in identifying the soil and site characteristics 

correlated with site index. In most studies the selection of variables for the final 

regression equation was based on mathematical relationships. Composite or transformed variables, as well as simple variables, were often utilized. Many of these

mathematical soil-site studies resulted in final prediction equations with high multiple

correlation coefficients. The biological interpretation of the resulting complex equations

was often difficult, however, and the applicability of the equations was usually limited to

small uniform areas. In addition, equations that accounted for the majority of observed

variations in site quality could not always be developed. This type of classical soil-site

study has been criticized for both mathematical and biological reasons (Broadfoot 1969,

Hodgkins 1959, Lloyd and Lemmon 1970) and is currently used much less commonly

than in the past. Use of existing soil mapping units or other plant species has been

helpful in distinguishing between broad classes of productivity; however, in most cases

these approaches have not yielded the desired precision for estimating site quality

(Harding and Baker 1983, Mader 1965, Youngberg and Scholz 1949).

In 1977, Baker and Broadfoot of the Southern Forest Experiment Station published a

new method of site quality evaluation that combined both subjective and objective

approaches. They first evaluated the relative importance of four major soil factors on

growth of a particular species. The soil factors were: (1) physical condition, (2) moisture

availability during the growing season, (3) nutrient availability, and (4) aeration. Next,

they identified (and later quantified) the specific soil-site properties that best described

or summarized the effect of each soil factor. They then developed field guides that could

be used in evaluating site quality for 14 southern hardwood species; verification with

field data indicated high accuracy over a broad range of sites (Baker and Broadfoot

1977, 1979).

This paper presents a set of site evaluation tables for predicting site index for red alder

(Alnus rubra Bong.) The tables contain 14 soil and site properties organized into three

major factors: (1) geographic and topographic position, (2) soil moisture and aeration

during the growing season, and (3) soil fertility and physical condition. The red alder

model was patterned after Baker and Broadfoot’s (1977, 1979) approach; that is, the

basic framework was subjectively derived based on an understanding of the site

requirements of the species. The model was developed and tested with data from 40

natural stands located over a range of site conditions in western Washington and

Oregon.

lntroduction

1
Forty natural, even-aged, well-stocked red alder stands in western Washington and

northwestern Oregon were selected for sampling (fig. 1). Stand ages ranged from 25 to

50 years; mean age of all stands was 36 years. A deliberate attempt was made to 

sample a wide range of soil conditions and of productivity (table 1). All sampled stands

were pure red alder, or when mixed, the other species were not in a crown position to

have suppressed past alder height growth. Within each selected stand one 0.10-ha plot

was established. Plot boundaries were kept away from roads and did not cross any

obvious stand boundaries or changes in stand or site conditions. When soil and stand

conditions were fairly uniform, plots were square (31.6 m on each side). Plots along

streams or on terraces were rectangular, the long axis of the plot paralleling the stream

or the long axis of the terrace. Rectangular plots were 50 x 20 m.

Field and Laboratory

Methods

2
On each plot, 10 dominant or codominant trees were selected to be measured for site

index. Selected trees appeared healthy and free from past top damage. Trees were

bored with an increment borer to determine age at breast height; total height was 

measured with a telescopic abney. Site index (50-year base) was calculated from the

equation of Worthington and others (1960).

Physiographic data recorded for each plot included aspect, slope percent, and physiographic position. Depth to water table during the growing season was estimated for sites

along rivers and streams and for areas with poor drainage. Associated major and minor

vegetation was also recorded along with comments on past history of the site (for 

example, evidence of burning, logging, or flooding).

In each plot at least two soil pits were dug, and their soil profiles were described. Pits

were located on what appeared to be representative microsites; rotting logs, old skid

trails, or other nontypical conditions were avoided. Pits were dug to a depth of 1 m or to

an impermeable layer, whichever came first. Pit descriptions included depth, structure,

texture, color, and coarse fragment content of each horizon. Presence of charcoal or

organic debris, and extent of rooting were also noted by horizon. If the two pits on a plot

differed in number, type, or texture of the horizons, a third pit was dug and described. 

All horizons 4 cm or thicker were sampled for bulk density. Soil volume for bulk density

samples was determined in the field by use of a water-filled graduated cylinder with a

rubber balloon and valve system (Blake 1965); soil weights were determined in the 

laboratory after samples were dried at 105 °C to constant weight. Bulk density samples

were sieved for gravel and rock (>2 mm in diameter) the coarse fragments were

weighed and their volume was determined by water displacement. Bulk density was 

calculated from both nonsieved and rock-free bases.

3
Two sampling transects were established in each plot. The transects were spaced to

divide the plot into equal thirds. Ten sampling points were located on each transect so

that there was an equal distance between the sampling points on the line and between

the end points and the plot boundary. Sampling points were adjusted as necessary to

avoid stumps, large rocks, or other obstacles. At each spot, samples of the forest floor

and of mineral soil at 0 to 10 cm, 10 to 30 cm, and 30 to 50 cm were collected. Forest

floor samples were collected with a circular template (13 cm in diameter). Heavy-gauge

stainless steel tubes (7.5 cm in diameter) were used to extract the mineral horizons on

most plots. Some plots were very rocky, however, and sampling spots had to be dug out

with trowels and shovels. Soil samples were bulked by sampling layer, brought 

back to the laboratory, and air dried.

After the soil samples were air dried they were sieved and separated into soil (<2 mm 

in diameter), small gravel (2 to 8 mm), and large gravel (>8 mm). Each size component

was weighed. The soil was then mixed well, and representative subsamples were

extracted for determination of selected chemical and physical characteristics. Soil

chemical characteristics were determined by the Cooperative Chemical Analytical

Laboratory, Corvallis, Oregon, using the following procedures: total Kjeldahl nitrogen

(Jackson 1958k Bray #1 extractable phosphorus (Jackson 1958); Walkely-Black 

organic carbon (Allison 1965k ammonium acetate cation exchange capacity (Jackson

1958); and exchangeable K, Ca, Mg, and Fe by ammonium acetate extraction, atomic

absorption spectrophotometry (North Central Regional Soil Testing Committee 1980).

Soil texture and pH were determined at the Forestry Sciences Laboratory, Olympia,

Washington. Soil texture was determined by the hydrometer method of mechanical

analysis (Bouyoucos 1962) with sodium hexametaphosphate as the dispersing agent;

all samples were treated with 30 percent H2O2 to remove organic matter before particle

size analysis. The determination of pH was made with a standard glass electrode on a

1:1 (v/v) mixture of distilled water and soil (USDA Soil Conservation Service 1972). A

subsample of the forest floor from each site was dried at 105 °C to constant weight, and

the ovendry equivalent weight of the total sample was determined.

Soil series or mapping unit was determined for each plot by comparing mapped 

information from USDA Soil Conservation Service, Washington State Department of

Nautral Resources, USDA Forest Service, or private timber companies, with descriptions

obtained in the field. Appropriate local personnel were contracted for assistance when

profile descriptions of the soil mapping unit did not match the descriptions obtained in

the field. Rainfall from April 1 through September 30 and number of frost-free days were

taken from isohyetal maps (Dick 1955). Plot elevations were determined from topographical maps.

Construction of a model to predict red alder site index was accomplished in stages. First,

a very general theoretical model, which listed the soil and climatic characteristics that

could influence tree growth, was developed. This first model had five major components—soil physical condition, moisture availability during the growing season, soil 

fertility, soil aeration, and climatic characteristics.

Model Construction

4
The second step was to plot the relationships between site index and each of the

measured variables. The graphs were visually examined, with particular attention given

to the maximum values of site index that occurred over the range of values for the

independent variable being examined. In addition, a rough tabulation was made for each

property of: (1) the known variation in values throughout the natural range of the species

and (2) the variation sampled in this study.

The next step in model construction was data analysis for the primary purpose of

hypothesis generation. Standard statistical programs (Dixon 1981, Hull and Nie 1981)

were used to perform the following analyses:

1. Simple correlation analysis (all possible combinations).

2. Stepwise discriminant analysis of three groups based on low, medium, and high site

index.

3. Cluster analysis of the soil and site variables measured and of the individual 

locations.

The next stages in model construction required determination of the variables most

closely related to site index, and for each of these variables determination of the levels

associated with high and low site index. Twenty-five of the plots were randomly selected

and used as a reference data set to aid in assigning quantitative values to selected

variables or site characteristics. The characteristics used in this stage of model 

development had to meet the following criteria: (1) were theoretically important in 

modeling growth, (2) had substantial range in observed characteristics (at least ± 20

percent of the mean), (3) had differences in the levels of the characteristic associated

with good and poor site index, and (4) were independent of (that is, poorly correlated

with) other selected variables. One of the goals of this project was to provide a field

guide for site evaluation that could be used by practicing foresters. Thus, when possible

without reducing the accuracy of the model, the soil-site properties selected for inclusion

in the model were those that could be determined with a minimum of special equipment.

The variable selection process can be illustrated by describing the steps involved in the

selection of elevation, the variable with the most weight in the final model. The first 

theoretical model did not include elevation; it did, however, include several variables

closely correlated with elevation such as length of the growing season and growing

season temperatures. Generally a variable such as length of the growing season would

be preferred over elevation because it more directly measures a property important to

tree growth. Because of the varied and rugged topography in the region, however, many

more climatic recording stations than currently exist would be necessary to provide good

estimates of climatic variables. Thus, the imprecision associated with available estimates of climatic variables negated the theoretical value of having a variable that 

influenced growth in a direct manner. For research purposes it would be possible to

estimate climatic variables from information on elevation and geographic location. This

type of calculation did not seem to be warranted as it was unlikely that calculation would

increase the accuracy of the relationship. In addition, users of the guide would find it

easier to determine elevation than to calculate such variables as number of days in the

growing season.

5
Elevation was highly correlated with site index and was the first variable used in the

stepwise discriminant analysis; that is, of the measured variables, elevation was the

most effective in separating poor, medium, and good sites. Red alder naturally occurs

over a wide range of elevations, and this study sampled sites over much of the range.

Thus, elevation was selected as the first variable for the model. Then, because of its

high correlation with site index, elevation was determined to have a high weight in the

model. Based on graphical analyses, the relationship between site index and elevation

was determined to be linear. A line was drawn to correspond to the maximum values of

site index (this technique is sometimes called boundary line analysis). The change in

site index along this line corresponding to the maximum possible range in elevation was

taken as a guide to the maximum number of site points that elevation could account for

in the model. Because elevation was correlated with other variables used in the model,

however, the whole range in site points observed in this two-variable relationship was

not used in the final model.

Selection of the other variables to be included in the model and a preliminary determination of the maximum range in site points associated with each variable followed the

same general procedure outlined for elevation.

Once the first set of variables had been selected and quantitative values were assigned,

model refinement was basically an iterative procedure. The current version of the model

was used to predict site index for the 25 reference plots. Soil-site properties on plots

where the difference between predicted and measured site index was large were 

compared with soil-site properties on plots where the difference was small. This 

comparison facilitated the identification of the properties or levels of properties that

appeared to be associated with poor model performance. Based on this information, the

model was changed. The adjusted version of the model was then used to repredict site

index for the 25 plots. Model adjustment involved primarily changing the values

assigned to the levels of specific properties; however, two additional variables—bulk

density and pH—were later added to the group originally selected.

Site conditions considered unsuitable for the species or the minimum and maximum 

levels for a specific soil-site property were sometimes determined from sources other

than the plot data collected for the study. These other information sources included 

published reports on the silvics of the species (for example, Worthington and others

1962); personal communication with foresters, ecologists, and researchers familiar with

the species; and personal observation.

The final model contains 14 soil-site properties divided into three general factors: (1)

geographic and topographic position (which includes climatic properties), (2) soil 

moisture and aeration during the growing season, and (3) soil fertility and physical 

condition. The site evaluation tables developed from the model are given in tables 2, 3,

and 4.

6
7
8
9
10
Most of the measured soil chemical characteristics were not used in the final model.

There are several possible reasons for this. First, soil chemical characteristics may have

been correlated with more general soil properties that were included in the model. For

example, percent organic matter was used in the model with both high and low levels

considered suboptimal. High organic matter content was associated with poorly drained

soils and with high elevation sites; in both these situations the high organic matter 

content probably indicates that mineral cycling rates on these sites are low. Cation

exchange capacity is another soil property that theoretically would influence tree 

nutrition and could have appeared in the model. On these sites, however, cation

exchange capacity and organic matter content were highly correlated (r = 0.97); thus,

very little additional information would have been gained by including cation exchange

capacity in addition to organic matter content. Second, some differences between soils

in chemical characteristics would be accounted for by the categories “Parent material

and age” and “pH.” Finally, the majority of forest soils in the Pacific Northwest are 

considered geologically young and relatively unweathered; thus, mineral deficiencies

other than nitrogen may not be common. Because red alder has root nodules capable 

of fixing atmospheric nitrogen, it is not sensitive to soil nitrogen levels.

The red alder site evaluation model fit the original data well. The correlation between predicted and measured site index was 0.97 for the 25 plots used to quantify 

the model and 0.96 for the 15 plots used for verification. For the original combined 

data set, the model accounted for 93 percent of the observed variation in site 

quality. Freese’s (1960) chi-square test of accuracy indicated that true site index 

should be within ±2 m of the predicted value 95 percent of the time.

While I was conducting this study, a similar study—with the same purpose of evaluating

site quality for red alder—was being independently conducted by another researcher.1/

The Crown Zellerbach study was conducted in a smaller geographic area than my study

area but it sampled sites with approximately the same mean site index (27.6 m vs. 

28.6 m) and covered a similar range in values for many of the soil-site properties. For

example, the sites in my study had a range in elevation from 5 to 1025 m, the Crown

Zellerbach plots from 5 to 950 m. Although the two studies collected information on

many of the same variables, different field and laboratory methods were used. In 

addition, some variables (for example, bulk density) were measured in one study but not

in the other. In spite of these differences in measurements and methodology, the data

from this second study provided a unique opportunity to further test the red alder soil-site

(RASS) model.

1/ Walter J. Shields, Research Forester, Crown Zellerbach, 

Wilsonville, Oregon.

Model Validation

11
Plots from the Crown Zellerbach study were used if they met the following criteria: mean

tree age greater than 25 years, within plot range in tree age less than or equal to 5 years,

and range in site index between trees less than 2 m. Adhering to these criteria helped

eliminate trees with irregular height growth and reduced within-plot variation in site 

index. Plots were deleted if they had missing values for variables that could not be 

estimated from other plot information. Other missing or unmeasured variable values 

were estimated when possible. For example, bulk density was roughly estimated from 

the moist consistence category assigned to each soil horizon. The RASS model was 

then used to predict site index for 20 of the Crown Zellerbach plots. The model

performed well in the test. The correlation (r) between measured and predicted site 

index was 0.89. In addition, the model continued to meet the accuracy standard of 

±2 m (p≤0.05).

The use of the site evaluation tables (table 2, 3, and 4) is straightforward and fairly 

simple. To evaluate potential site index of an area requires determination of several

characteristics of the actual location or position of the site, and of various soil properties.

Site points are assigned to different levels of each property. Potential site index is 

predicted by summing the appropriate site points for all properties. Definitions of the

terms used in the site evaluation tables are given in appendix 1.

The red alder model differs from the models developed by Baker and Broadfoot (1977,

1979) in two major respects. First, it places considerable importance on the actual 

location or position of the site to be evaluated. Working on the southern Atlantic Coastal

Plain where climate and topography are relatively uniform, Baker and Broadfoot did not

include properties such as elevation, percent slope, aspect, or climatic characteristics;

such properties are included in the red alder model. Second, the Baker and Broadfoot

models were designed to evaluate each factor independently; this required site 

characteristics or properties that influenced more than one factor to reoccur in different

places in the model. The red alder model evaluates each property only once; thus, the

factors are additive rather than independent. For example, factor 2 for red alder (soil

moisture and aeration during the growing season) can have a maximum value of 12

points. This should not be interpreted as implying that soil or site characteristics that 

influence soil moisture and aeration only explain or influence about 30 percent (that is, 

12 of 40 site points) of the variation in site quality for red alder. The interpretation should

be that the characteristics in factor 2 of the red alder model account for or explain an

additional 30 percent of the variation not accounted for in factor 1 (geographic and 

topographic position), but some soil-site properties in factors 1 and 3 also influence soil

moisture and aeration. Similarly, the soil-site properties in red alder factor 3 (soil fertility

and physical condition) represent the site characteristics that influence soil fertility and

physical condition in addition to the properties considered in factors 1 and 2.

Use of the Site

Evaluation Tables

12
The site evaluation tables for red alder have been set up to predict a maximum value of

40 (site index in meters at 50 years). Factor 1 at its maximum level can account for 20

site points or half the maximum total number of points. The site characteristics in factor 

1 can be easily and quickly determined in the field or by using readily available 

information sources such as topographic maps or weather records. Thus, if a user thinks

it desirable to do so, a preliminary rating of sites can be done without determination of

soil properties. This preliminary rating may not be correlated with the final or total rating;

however, it will indicate the maximum score the site could receive if all the soil characteristics in factors 2 and 3 are at optimum levels. For example, if a site receives 8 points

in factor 1 (or a maximum of 20), then the maximum total rating the site could 

achieve is 28. If a user only wishes to consider planting or managing the species 

on sites with site index values of 30 m or more, then this site can be deleted from 

consideration without the necessity of examining the soil.

The site evaluation tables were deliberately designed to require the user to interpolate

between site points for many properties. For example, a site at an elevation of 150 m

would fall between the categories “Less than 100 m” (worth 8 points) and “200 to 

300 m” (worth 7 points). In this case a value of 7.5 points would be reasonable. Similarly,

a soil profile with horizons of different textures should receive an interpolated value if the

individual textures are associated with different numbers of site points.

If a particular piece of information is not known or is not known very precisely, users

should consider using their “best guess.” Which properties have the greatest spread in

point values and thus the largest impact on determination of site quality can be 

determined from an examination of the site evaluation tables. The properties with the

greatest influence should be determined or measured accurately. “Best guess” determinations can be based on a knowledge of the most common value or range of a soil or

site property. For example, if detailed weather information is not available, precipitation

during the growing season is probably in the 30- to 40-cm range unless the area being

evaluated is in a rain shadow, along the Pacific Coast, or in a geographic location where

orographic precipitation is likely to be significant. For users who are not familiar with 

estimating various soil properties, spending some time in the field with a soil scientist

should be very beneficial for increasing the accuracy of their determinations.

A final word of caution. Users should try to evaluate each property as objectively as 

possible. If before using the site evaluation tables, the user has “decided” or “knows”

that the site is poor, there may be a tendency for subjective decisions to have a negative

bias and thus for actual site quality to be underestimated. The reverse is also true. When

evaluating a “good” site, users may tend to be overly generous in assigning site points

and may overestimate site quality.

Two examples of the use of the site evaluation tables are given below. Appendix 2 

contains a sample field worksheet.

13
Area A to be evaluated is on a northeast-facing slope of 20 percent. Elevation is 45 m,

and average precipitation during the growing season is 31 cm. The location is inland

(near Olympia, Washington) in a midslope position and does not appear to be subject 

to any special hazards. The soil is a gravely to very gravely loam in the surface 35 cm, 

a gravely sandy loam from 35 to 95 cm (a clay pan encountered at 95 cm). Silt content 

is 25 percent, gravel and rock content is 50 to 70 percent by weight, and the soil is well

drained to somewhat excessively drained. The water table is deep, probably at 6 to 

8 m. The soil formed from coarse glacial outwash material. Horizons are easily 

distinguished, but there is no evidence of a leached horizon (A2). In the surface 10 cm,

pH is 5.0 and organic matter content is 4.0 percent. No compaction is evident in the 

surface 50 cm; bulk density in 82 (rock-free basis) is 0.9 g/cm3.

Factor 1 is easily evaluated for area A (table 5). All the information fits within the specified

ranges in site characteristics. Evaluation of some properties in factor 2 requires the user

to interpolate. The soil was described as well drained to somewhat excessively drained

and was given a rating of 1.5 (that is, halfway between the 1 rating for somewhat 

excessively drained and the 2 value for well drained without special features). Texture

was rated as 1.75 percent. In the surface 35 cm the soil was loam with less than 40

percent silt, which would earn a rating of 2; however, since the texture from 35 to 50 cm

was sandy loam, the texture rating for the surface 50 cm was reduced. The observed

range in rock and gravel content crossed the level given in the table so the intermediate

value of -1 was selected. Determination of the values associated with other properties 

in factors 2 and 3 was straightforward as no interpolation was necessary.

Summing each factor for area A gives individual factor values of 16, 5.25, and 6 for a

total site rating or site index of 27.25 m. Rounding to the nearest meter results in an 

estimate of 27 m. A quick check of the values assigned to each factor with the total points

possible indicates that area A received 80 percent of the maximum for factor 1, 44 

percent of the maximum for factor 2, and 75 percent of the maximum for factor 3. Thus,

we can conclude: Area A is located in a relatively favorable position in terms of 

geography and topography (factor 1) moisture availability during the growing season

probably plays a major role in limiting growth (factor 2), and the characteristics rated for

soil fertility and physical condition (factor 3) had less than optimum but not markedly

unfavorable values.

Area B is located on a gently rolling bench southeast of Mount Vernon, Washington, at

an elevation of 120 m. Slope averages 7 percent, and the aspect is southeast. The area

receives 30 cm of rainfall April 1 through September 30. No special hazards are 

apparent. The soil is 75-80 cm of loam (silt content 45-50 percent) over slightly 

weathered sandstone and siltstone. The soil has a rock and gravel content of 10 to 15

percent and is well drained but with downward water movement slowed by the presence

of the parent material close to the soil surface. Depth to a true water table is not known,

but it is greater than 4 m. In the surface 10 cm, pH is 4.9 and organic matter averages

7.0 percent. Soil is friable to firm; rock-free bulk density in the B2 averages 1.1 g/cm3

The site evaluation of area B is given in table 6. Two properties required interpolation.

Elevation was given a 7.75 rating, as it was closer to the “Less than 100 m” category

having an 8 value than the “200 to 300 m” category worth 7 (table 2). Aspect and slope

were the other properties requiring interpolation. A southeast aspect on a 7-percent 

slope was intermediate between “Any aspect when slope 5 percent or less” worth 4 and

“S, SE, or SW with slope 10-20 percent” worth 2 and was assigned a value of 3.

14
15
Rounded to the nearest meter, site index or total site rating for area B was 34 m; this

area would be a good site for red alder. It achieved 84, 83, and 88 percent of the 

maximum values for factors 1, 2, and 3. Thus, although area B was less than optimum

in all three factors, no one factor or property was identified as having a large potential

impact on growth.

These site evaluation tables were developed with data from stands growing west of the

crest of the Cascade Range in Washington and northern Oregon (latitude 45°0′ to

48°30′ N). These tables should not be used outside this geographic area until local users

determine the applicability of the tables to their site conditions. For example, it would

probably be necessary to modify the site points assigned to specific elevations for use

in areas substantially north of the Washington-British Columbia border. In addition,

when the user encounters site conditions not widely distributed in the Pacific Northwest—

such as soil derived from serpentine rocks—the site evaluation tables should not be

used until they are tested. Such nontypical conditions were not sampled in the study;

thus, the model does not account for them.

This site evaluation guide was developed from plots located on apparently uniform site

conditions. Plot boundaries were laid out to avoid changes in slope, aspect, drainage,

or other site conditions. The greatest accuracy in prediction of site index will be achieved

when users limit their evaluations to areas of similar uniformity. It may be helpful for a

user to first roughly map areas that appear to be fairly uniform and then to sample within

each of the major divisions or strata. Sampling intensity will vary with the user’s need for

precision; however, it should be recognized that soil characteristics can be extremely

variable and in some areas it may take several point determinations to accurately

assess potential site index.

All plots were in natural unmanaged stands. Actual or apparent changes in site quality

for red alder associated with forest management (including plantation culture) are not

known. In addition, the model does not take genetic variability of the species into

account. Apparent increases in site index may be realized in the future when genetically

improved plant materials are used. These apparent increases in site index may result

from higher overall growth rates associated with some genotypes or from using 

genotypes with tolerances for specific site conditions. The site evaluation tables (tables

2, 3, and 4) can be used to rank sites in order of potential site index; however, how

accurately the tables will predict the actual site index attained under intensive culture

cannot be judged until older plantations are available for evaluation.

1 cm = 0.394 in 

1 cm3 = 0.061 in3

1 g = 0.035 oz (avoirdupois) 

1 ha = 2.47 acres 

1 m = 3.281 ft 

1 mm = 0.039 in

Applicability

English Equivalents

16
I thank the following companies or agencies for their cooperation in this study: Crown

Zellerbach Corporation, Washington State Department of Natural Resources, Fort 

Lewis Military Reservation, Murray Pacific Corporation, Pope and Talbot, ITT Rayonier,

Inc., Weyerhaeuser Company, Pilchuck Tree Farm, Hama Hamma Farms, Columbia

White-tail Deer Refuge, and the Olympic, Mount Baker-Snoqualmie, Gifford Pinchot,

and Siuslaw National Forests. The assistance of the USDA Soil Conservation Service

and the Land Grading Department of the Washington State Department of Natural

Resources in identifying soil series or mapping units is also gratefully acknowledged.

The following personnel of the Pacific Northwest Research Station were very helpful in

accomplishing the field and laboratory work: David Bassett, Darci Birmingham, Mary

Culliton, Robert Deal, Daniel Johnson, Lynn Mills, and Thomas Turner. I especially thank

Walter J. Shields for help in locating suitable sites and for sharing his plot information; I

wish to also acknowledge the helpful comments and advice of James B. Baker, Paul

Heilman, E.S. Lyle, Richard E. Miller, M.A. Radwan, and John Shumway.

This study was supported in part under an interagency Agreement with the U.S.

Department of Energy (DE-A105-810R20914).

Acknowledgments

17
Allison, L.E. Organic carbon. In: Black, C.A., ed. Methods of soil analysis. Agronomy, 

vol. 9, part II. Madison, WI: American Society of Agronomy; 1965: 1367-1378.

Baker, James B.; Broadfoot, W.M. A practical field method of site evaluation for eight 

important southern hardwoods. Gen. Tech. Rep. SO-14. New Orleans, LA: U.S.

Department of Agriculture, Forest Service, Southern Forest Experiment Station; 

1977. 31 p.

Baker, James B.; Broadfoot, W.M. A practical field method of site evaluation for 

commercially important southern hardwoods. Gen. Tech. Rep. SO-26. New Orleans,

LA: U.S. Department of Agriculture, Forest Service, Southern Forest Experiment

Station; 1979. 51 p.

Berglund, John V. The use of modal soil taxonomic units for the prediction of sugar 

maple site productivity in southern New York. Syracuse, NY: State University of New

York, College of Forestry; 1968. Ph. D. dissertation.

Blake, G.R. Bulk density. In: Black, C.A., ed. Methods of soil analysis. Agronomy, vol. 

9, part I. Madison, WI: American Society of Agronomy; 1965: 374-390.

Bouyoucos, G.J. Hydrometer method improved for making particle size analysis of 

soils. Agronomy Journal. 54: 464-465; 1962.

Broadfoot, W.M. Problems in relating soil to site index for southern hardwoods. Forest 

Science. 15: 354-364; 1969.

Carmean, Willard H. Forest site quality evaluation in the U.S. Advances in Agronomy. 

27: 209-269; 1975.

Coile, T.S. Soil and the growth of forests. Advances in Agronomy. 16: 329-398; 1952.

Dick, James. Climatic zones of the Douglas-fir region. Centralia, WA: Weyerhaeuser 

Timber Company; 1955. 4 p.

Dixon, W.J., chief ed. BMDP statistical software. Berkeley, CA: University of California 

Press; 1981. 726 p.

Freese, Frank. Testing accuracy. Forest Science. 6: 139-145; 1960.

Harding, R.B.; Baker, R.D. The soil survey and its value in the ad valorem taxation of 

privately owned timberland. Southern Journal of Applied Forestry. 7: 204-208; 1983.

Hodgkins, E.J. Forest site classification in the Southeast. in: 8th annual forestry 

symposium southern forest soils. Baton Rouge, LA: Louisiana State University Press;

1959: 34-48.

Hull, C. Hadlai; Nie, Norman H. SPSS update 7-9: new procedures and facilities for 

releases 7-9. New York: McGraw Hill Book Company; 1981. 402 p.

Jackson, M.L. Soil chemical analysis. Englewood Cliffs, NJ: Prentice-Hall, Inc.; 1958. 

498 p.

Lloyd, W.J., Lemmon, P.E. Rectifying azimuth (of aspect) in studies of soil-site index 

relationships. In: Tree growth and forest soils. Proceedings of the 3d North American 

forest soils conference. Corvallis, OR: Oregon State University Press; 1970: 435-558.

Literature Cited

18
Mader, D.L. Where are we in soil-site classification? In: Applications of soils information 

in forestry. Washington, DC: Society of American Foresters and Soil Conservation

Society of America; 1965: 23-32.

North Central Regional Soil Testing Committee. Recommended chemical soil 

procedures for the north central region. North Cent. Reg. Publ. 221. Fargo, ND: North

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Ralston, C.W. Evaluation of forest site productivity. lnternational Review of Forest 

Research. New York: Academic Press; 1: 171-201; 1964.

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interpreting soil surveys. Agric. Handb. 436. Washington, DC: U.S. Department of

Agriculture, Soil Conservation Service; 1975. 754 p.

U.S. Department of Agriculture, Soil Conservation Service. Soil survey laboratory 

methods and procedures for collecting soil samples. Soil Surv. Invest. Rep. 1.

Washington, DC: U.S. Government Printing Office; 1972. 63 p.

Worthington, Norman P.; Johnson, Floyd A.; Staebler, George R.; Lloyd, William J.

Normal yield tables for red alder. Res. Pap. 36. Portland, OR: U.S. Department of

Agriculture, Forest Service, Pacific Northwest Forest and Range Experiment Forest

Station; 1960.

Worthington, Norman P.; Ruth, Robert H.; Matson, Elmer E. Red alder—its 

management and utilization. Misc. Publ. 881. Washington, DC: U.S. Department of

Agriculture, Forest Service; 1962. 44 p.

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America Proceedings. 14: 331-332; 1949.

19
Factor 1 

Elevation: Mean elevation aboVe sea level in meters. Should be determined to nearest

25 m.

Precipitation, April 7 through September 30: Can be determined from weather records 

or maps of rainfall distribution. Users should try to compensate for differences in 

precipitation between the site being evaluated and the nearest weather station or map

averages. In particular, the effect of elevation on precipitation should be taken into

account.

Physiographic position:

Flood plain—On a level plain associated with a river system, the width of a flood plain 

is determined by the size of the river and the associated topography. A site should 

probably not be considered to be on a flood plain unless it is subject to flooding during

high water at least once every 10 to 15 years.

Terrace or bench—Site generally level or somewhat rolling with slope less than 15 

percent, and far enough from a river or high enough to be rarely or never flooded. Also

includes lower slope positions on side slopes when slope is less than 20 percent. In 

contrast to midslope positions, sites in this category are considered to be in a generally

moisture-gaining position.

Midslope—lncludes midslope positions and lower slope positions with slope greater 

than 20 percent. Such sites are considered to have minimal net change in soil moisture;

that is, gains in soil moisture caused by additions from upslope positions would be

approximately balanced by losses to sites farther down the slope. Would probably be 

the most common physiographic position encountered.

Marsh or bog—Depression in landscape with limited downward movement of water,

often in a position to gain soil moisture from surrounding area.

Upper slope—Side slope positions just below the top of the hill. Considered to have a

net loss in soil water but to be less exposed than ridgetop positions.

Ridgetop—Along top of ridge or hill. Considered to have a net loss in soil moisture and

to be the position most exposed to wind. True ridgetop positions are the most unfavorable physiographic positions in terms of tree growth. The user should assign a higher

value for physiographic position if the site being evaluated is on top of a hill or ridge but

is located on a fairly level plateau, is in a minor depression, or is sheltered by surrounding

features. The value assigned should be based on the user’s evaluation of water

movement and wind exposure.

Special hazards: Recognition of special hazards and assignment of negative site points

can best be done by users with knowledge of local conditions. Sites with special hazards

would include those located in frost pockets (either natural or created by cutting). Windy,

exposed sites—such as those close to the Pacific Ocean without blocking topographic

features, or areas in the vicinity of mountain passes—are examples of other conditions

that need to be taken into account.

Appendix 1

Definition of Terms Used

in Tables

20
Factor 2 

lnternal drainage: Well-drained and excessively drained soils should be carefully 

evaluated for the presence of special soil features; such evaluation can be done from a

detailed profile description or from examination of a soil pit.

Texture: As determined from a textural triangle after mechanical analysis. With practice,

texture can be estimated fairly well in the field, especially with the use of published 

definitions of soil textural classes based on feel and field experience (Soil Survey Staff

1975).

Soil depth: Mean soil depth that roots and water can easily penetrate, can be determined

by use of an auger or by examination of a soil pit. The user should try to determine

effective rooting depth rather than depth to bedrock.

Rock and gravel content: Can be determined by sieving soil samples or by visual and

textural examination. If the soil is not compacted and the rocks do not contain a lot of

trapped air (for example, pumice or similar material), multiplying the percentage of rock

and gravel volume by 2 will approximate the percentage of rock and gravel weight.

Depth to water table: The average depth to water table during the growing season; can

be determined by soil boring or by comparison of site elevation in relation to the water

level in nearby streams or lakes. Use of intermittent or very shallow streams to evaluate

water table level is not recommended.

Factor 3 

Parent material and age: Published soil survey information can be helpful in determining

parent material; however, many categories can be easily recognized in the field.

pH: Measure of soil acidity can be determined in the field or on fresh samples in the

office with a pH meter or a pH testing kit. Can be estimated from soil survey information

if description of mapped soil agrees well with what is observed at the site.

Organic matter: The organic portion of a mineral soil composed of plant and animal

remains in various stages of decomposition. Most accurately determined in the 

laboratory, but with practice can be estimated in the field. Surface soils low in organic

matter do not differ much in color from lower layers and generally are light colored. Soils

high in organic matter are usually very dark in color and both well-decomposed material

(greasy feeling) and partially decomposed materials (origin somewhat evident) can be

distinguished.

Bulk density: Measure of soil compaction expressed as ovendry soil weight per unit of

volume. Most accurately measured with equipment designed for such determination, but

with practice can be estimated in the field. Soils with low bulk density are loose, porous,

or friable. Soils with high bulk density are tight and strongly compacted.

21
22
Harrington, Constance A. A method of site quality evaluation for red alder. Gen. 

Tech. Rep. PNW-192. Portland, OR: U.S. Department of Agriculture, Forest

Service, Pacific Northwest Research Station; 1986. 22 p.

A field guide to predict site index for red alder (Alnus rubra Bong.) was developed for

use in western Washington and Oregon. The guide requires the user to evaluate 14

soil-site properties that are grouped into three general factors: (1) geographic and

topographic position, (2) soil moisture and aeration during the growing season, and

(3) soil fertility and physical condition Construction of the guide was modeled after a

method of site evaluation developed for several southern hardwood species. The red

alder model is accurate when used properly. The correlation (r) between predicted

and measured site index was 0.97 for the basic data set of 25 plots and 0.96 for the

15 plots used for verification. Estimated site index should be within ±2 meters of

measured site index 95 percent of the time. Use of a second independent data set for

model verification resulted in a somewhat lower correlation (r = 0.89) between

measured and predicted site index than was achieved with the original data set, but

the model continued to meet the accuracy standard of ±2 meters (p≤0.05).

Keywords: Site class, site index, guidebooks, models, red alder, Alnus rubra.

The Forest Service of the U.S. Department of 

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use management of the Nation’s forest resources 

for sustained yields of wood, water, forage, wildlife, 

and recreation. Through forestry research, 

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and National Grasslands, it strives — as directed by

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