pa. United States @ Department of 

Agriculture 

RSlTE-A New Method af Site 

Evaluation for Loblolly Pine: Model 

Forest Service opment and User's Guide 

Southern Forest 

Experiment Station 

New Orleans, 

Louisiana 

General Technical Report Constame A. Harrington SO-81 
SUMMARY 

A model was developed to predict site index age 50 years) for Sublol'iy pine {Pinus 

taeda L.) on the Southern Coastal Plain. The model, named PTSITE, was based on soil characteristics, site location on the landscape, and land history. The model was constrained so 

that the relationship between site index and each soil-site variable was consistent with what 

was known about the biology of the species. The model was quantified empirically with information available from the literature or other sources and with data collected for this study 

from 50 stands. Because of the way the model was developed, the PTSITE model is an example of a knowledge-based or expert system. The final model was tested with data reserved by 

the author for testing and with data from other organizations. For the original set of 50 

stands, the difference between site index estimated from tree ages and heights and site index 

predicted by the model averaged 1.7 feet with the chi-square test of accuracy, indicating that 

site index prediction should be within + 4 feet (p<0.05). PTSITE was modified to predict site 

index for a 25-year base age; site index predictions were judged to be within -+ 3 feet (p<0.05) 

for these younger stands. 

Testing the model with reserved data from an additional 13 stands sampled by the author 

indicated that the model was consistent in its accuracy. In addition, soil-site data from two 

other sources were used to provide another test of the accuracy of the PTSITE model. These 

additional data sets did not have values for all variables; thus, some of the information used 

in the predictions was based on estimates. Despite probable errors introduced by estimation, 

the model performed quite well with two independent data sets. 

The PTSITE model is available as an IBM-compatible interactive computer program that 

can be requested from the author. Supplemental information is provided in an appendix to 

assist users in operating the program and in understanding the structure of the underlying 

model. 

ACKNOWLEDGMENTS 

Financial support and field assistance in the collection of most of the soil and 

site data were provided by the Southern Region of the National Forest System. 

Assistance from the following soil scientists is gratefully acknowledged: Neal R. 

Babik (Kisatchie National Forest), William L. Barnhill (Soil Conservation Service, retired), Don C. Dagnan (National Forests in Mississippi), Arthur Goddard 

(National Forests in Alabama), Dennis Law (National Forests in South Carolina), Dan M. Manning (National Forests in North Carolina), Rodney Peters 

(National Forests in Texas), and M. L. Weeks (National Forests in Mississippi). 

Some additional funding was provided by the Fusiforrn Rust Team of the Southern Forest Experiment Station. Appreciation is also extended to the Soil Conservation Service for providing plot data from their Soil Woodland data base and to 

Douglas M. Crutchfield, Westvaco, and Marilyn Buford, Southeastern Forest 

Experiment Station, for providing data used in verifying the model. Finally, the 

programming assistance of Bettina M. Casson and Kirby F. Sneed (Southern 

Forest Experiment Station) is deeply appreciated. 

January 1991 
CONTENTS 

INTRODUCTION ................................................................. 1 

FIELD AND LABORATORY METHODS ............................................ 2 

MODEL CONSTRUCTION ......................................................... 3 

MODEL VERIFICATION .......................................................... 6 

USING THE PTSITE PROGRAM .................................................. 8 

MODEL APPLICABILITY ......................................................... 9 

PROGRAM AVAILABILITY ....................................................... 11 

LITERATURE CITED ............................................................ 11 

Appendix A-Additional information to help users respond to the questions 

and requests for information of the Erl'SITE program .................... 13 

Appendix B-Definitions and ranges for program variables ........................... 23 

Appendix C-Example of sample output ........................................... -27 
PTSITE-A New Method of Site Evaluation for Loblolly Pine: 

Model Development and User's Guide 

Constance A. Harrington 

INTRODUCTION 

Knowledge of site quality for each unit of land to be 

managed is an important criterion for modem forest 

management, Although other meaures of site cguality 

have been proposed, site index is the measure most 

crtmmonly used in the TJnited States. Site index is the 

mean height of upper-crown-class trees that have been 

free to grow in an even-aged stand at a specified index 

age. Most modem growth and yield models require site 

index (or a related measure of site quality) as input to 

their prediction systems. In addition, site index often 

needs to be estimated for land not currently in timber 

production. Based on the estimated site index value9 

decisions are made on which species to plant or 

whether to plant at all. It is, however, very difficult to 

accurately assess site index in stands that are unevenaged, of mixed species, very young or very old, or on 

sites where the tree species of interest is not growing, 

Several methods have been developed for predicting 

site index for a species when it cannot be directly measured (Mader 19651, The most common methods are: 

(1) prediction of side index from a mathematical equatlon using measured soil and site characteristics as 

independent variables, (2) association of site-quality 

chasses with soil series or soiB mapping unit, and 

13) prediction of site quality from the presence or 

growth of other paant species. Rediction of site index 

by use of soil and topographic characteristics in a mu1- 

tiple regression equation was popular in the United 

States beginning in the 1930"; good reviews of the 

subject are available from several sources (Camean 

1975, Coile 1952, Ralston 1964). These classical soilsite studies were useful in identifying the soil and site 

characteristics that influence tree grovvth (Hodgkins 

1956). However, the biological interpretation of many 

of the equations developed in classical soil-site studies 

was often obscure, and t&le applicability of these ewations was usually limited to small unifom areas. In 

addition, the equations developed in most madhernatical soil-site studies were not verified with independent 

data (McQuilkin 1976). This type of mathematical 

soif-site study has been criticized on both matkernatical and biologicaS' gounds (Broadfoot 1969, Hdgkins 

1959, Lloyd and Lemrnon 1970) and is currently used 

much less than in the past, 

Use of soil series, soiB mapping units, or other planit; 

species has been useful in distinguishing among 

broad classes of productivity, but in most cases these 

approaches have not yielded the desired precision for 

predicting site index (Harding and Baker 1983, Mader 

1965, Youngberg and Seholz, 1949). Over the years, 

many modifications and refinements have been made 

in these types of studies. For example, McKee (1917) 

demonstrated that a substantial amount of the variation in site index values among plots could be 

accounted for by the chemical and physical properties 

of soils if the plots were first grouped by soil series. 

However, this type of approach is not practical when 

sites representing many soil series need to be evaIuated because it requires that the soil series be knom 

for each site and that the simificanl reiationships 

between site index and the soil-site factors be determined for each soil series of interest. Smalley (1979) 

divided sites into landtypes within: selected geographic areas; this landtype classification system 

incorporated several physiographic katures, could be 

used in areas where soil series had not been mawed, 

and provided information on soil-based management 

considerations as well as productivity for each Eandtype. However9 a landtype classification system is 

unlikely to yield the precision in site index estimabs 

required for some forest management decisions. Storie 

and Wieslander 11948) stratified sites into soil profile 

groups, then evaluated site quality fbr four California 

conifers on the basis of several site factors. This 

approach was usable over a wick range of site conditions, but their timber soil ratings were more in terns 

of site-quality classes than of specific site in&x 

values. 

Baker and Broadfoot (1977, 1979) pubIished field 

guides to evaluate site quality for southern ha 

that combined both subjective and objective 

approaches to site-quality evaluation, nese researchers first identified, and later quantified, the spifific 

Constance A. Ifarfington, previously research forester at the Forestry Sciences Laboratory, Southern Fbrest Experiment Station, USDA Forest 

Srvice, Monticello, AR 71655, is now research foresbr, Forestry Science La'boratory, Pacific Northwest Research Station, USDA Forest Service, 

Olympia, WA 98502. This research uras done in cooperation with the Department of Forest Resources and Arkansas Agricultural Exprinaent 

Station, University of Arkansas at Monticello. 
soil-site properties that influenced site quality for a 

parrLicul ar species. The approach used uras different 

from past work in that: (1) the values used in quantifying these models were constrained by the authors' 

knowledge of the silvies of the speeies involved, (21 it 

was not necessary to know soil series to use these 

models, and (3) the number of soil-sik characteri~tics 

evaluakd by these models wzts high enough to allow 

accurate prediction ( rlr 5 ft) wer a wide rage of site 

conditions. The Baker-Broadfoot field pi&s were 

later made available in the form of a computer program (Harrington and Casson f 986). A Baker-Bmadfoot tme of approach to site-quality evaluatim wm 3 

used by Harrington (1986) to develop a field pide for 

evaluating red alder (Alnus rubra Bong.) in the Pacific 

Northwest. Figure I.-General tcacr-rtion of l's6101I3~ pine stands used in tk 

A site quality evaluation system for loblolly pine &velopmnd and testing of th PTSfTE model. In most 

(Pinus tae& L.) is presented in this paper, This system cases, several szks were sampEe?d at eaeh beation. 

was based on a Baker-Broadfoot typeof site model in 

that the basic framework of thev&o&l wm derived 

from an understanding of the site requirements of the On each plot, seven dominant or codominant trees 

speeies, and the relationship between exh variable wre selected to be measured for site index. These 

and site index was constrained to be biolo@cally rea- trees were apparently healthy and free from past 

sonable. The model, named PTSITE, was quantified height damage. The trees were bored with an increwith concepts from the literrdure, published and ment borer do determine age at breast height; total 

unpublished data sets, and with data specifically col- height was measured with an altimeter. The rings on 

lected for this purpose in 50 loblolly pine stands in the the cores were counted in the laboratory; any trees 

Southern Coastal Plain, The PTSITE model was having cores showing signs of were 

tested with data from 23 additional stands that were deleted from dl-re sample, Site index at a 50-year base 

reserved from the original model development and (SIS0) was calculated from the equations of Farrar 

with similar soil-site data collected by other organiza- (1975), which were based on Miscellaneous Publications. The 50-year version of the model vvas convehed tion 50 (USDA FS 1976). fir natural stands, 3 years 

to a 25-year version with data from 11 additional mre added to age at breast height to estimate totaii 

younger stands. A computer program based on the age (as recommended in USDA FS 1976); for older 

mSITE model was then produced for use on IBM-corn- plantations, the breasdheight age correction factor 

patible personal computers. was reduced to 2 years. (The reasonableness of this 

correction factor ws verified with groundline inereFIELD AND LABORATOR'fP METHODS 

Even-aged, vvell-stocked stands of loblolly pine were 

selected for sampling in the Atlantic Coastal Plain 

(fig. 1). A deliberate attempt wils made to sample (I 

wide range of soil conditions and productivity (table I). 

The mean and range in site index values are similar do 

the regional mean and range in site index valws 

reported for loblolly pine in a large range-wide sample 

of stands (USDA FS 1976). Most sampled stands were 

pure loblolly pine. When mixed-species stads wetre 

sampled, the other species were not in a cram position 

or of an age to have suppressed past height &rovvth of 

pines. Within each stand, a 0.25-aere temporary plot 

was established. Plot boundaries were kephway from 

roads and did not cross any obvious stand boundaries 

or changes in stand or site conditions. On side slopes, 

plots were laid out along the contour to avoid crossing 

slope-position categories within a plot boundary. 

ment cores from several stands). Most stands were 35 

to 60 years old. Site index (25-year base) was calculated for stands 930 years old with the equations in 

Golden and others (1981). These younger stands were 

used only to convert the model from a 50-year prediction system to a 25-year prediction system; they were 

not used in quantifying or testing the original 

(50-year) model. 

Physiographic data collected for each plot included 

aspect, percent slope, and position on the slope (fig. 2)- 

man depth to the wader table during the growing season was estimated for sites along rivers or streams and 

for areas of poor drainage. Frequency of flooding and 

depth to a perched water table during winter months 

were estimated from physiographic position, soil characteristics, and local knowledge. Site descriptiolrs also 

included mention of site-specific or microsite topography such as the presenee of intermittent or ephemeral 

stream channels, small depressional areas (hummocks 

and hollowsl, and larger bowl-shaped depressions. Plot 

elevations were determined from topographical maps. 
Table l.-iWcen and range in selectcrd site eharmteristrGs for pbts 

used in the &~;eIoprnnd csf the PTSITE model 

Characteristic Mean Range 

Site index at age 50 iftl 

Slope (percent) 

Drainage class 

Elfavakiw fitf'l 

Sudace soil (0-6 in): 

pl-I 

Organic maiLter ipercenA 

Phosphorus ipprn) 

miekness ill or Ap fin) 

Texture of subsoil 

68- 122 

0-25 

1-7 

20-360 

4.0-6.5 

0.9-18.2 

0.5-9.9 

0.5-24 

sand-clay 

"M = not applicable 

LOWER - 

Figure 2.-Skqe-posction cakgorks used in the PTSITE model. The 

category "noninflueming" shuld be sekcZed when there 

is no real change in topography in the area around the site 

being evaluated. See appendix A for additional 

informtion on slope positbn catecpclrks. 

Mean precipitation (annual and for selected time periods) and water deficit (precipitation minus evapolkanspiration) values were assieed to each plot on the 

basis of published maps (USDA FS 1969 and U.S. 

Department of Commerce 1968). 

At each plot, a detailed soil profile description was 

made by a soil scientist. Soil profiles mre described to 

a minimum sf 60 inches (or to an impenetrable layer). 

Profile descriptions included depth, calm (inelrading 

mottles), texture, structure, consistence, pH, presence 

nnd size sf pim roots, and peseentage of rock fragments in each horizon, The presence of strati6"lction 

within the profile was a190 noted. Effective rooting 

depth was estimated fir each site, Field dedermina- 

"ions of kxdure were spsdeheeked in the labordory 

using the hydrometer method iBouyoucos 19621, with 

sodium hexametaphosphate being used as a dispersing agent, to ensure that individual soil scientists 

were not introducing a systematic bias into the d&a. 

Representative soil samples were taken from each 

horizon, Samples kvere air-dried and sieved to remove 

organic materials and rock frapenats. Samples from 

each horizon were analyzed for pH (standard glass 

electrode, 1: f -volume ratio of distilled water and soil) 

(USBA Soil Conservation Service 1972). Representative samples fmm each brizon were also sent to Wad 

Laboratories, ECearnq Nebraska, for analysis of 

Walkley-Black organic carbon (Allison 19651, doubleacid extractahte phosphorus (also called Mehlich 1 

phosphorus) (Olson and Sommers 19821, and total 

Kjefdahl nitrogen (Jackson 1958). 

Samples to determine the bulk &nsity of the soil 

were collected from three arbitrarily selected locations 

in the plot. At each location, two samples were collected from the surfme horizon and two collected from 

the top E3 or C horizon (or in the E hrizon if the depth 

to a B or C horizon was greater than 20 inchs). Thus, 

a total of 12 samples were c~llected in each plot. Samples were taken with care to avoid crossing horizon 

boundaries; in addition, samples crossing large roots 

or ani~sal burrows were discarded and new samples 

taken. Samples of known volume (5.5 in3) were 

extracted in metal cores using a drop-hanmner sampler; care ws taken to avoid compacting the core during sample collection. Ovendry vveights (constant 

weight at 220 OF) were determined in the ltlboratory, 

and bulk density was calculated. It wm necessary to 

dig small soil pits to collect bulk density samples from 

the subsoil, Depth of the surface horizons was measured in these small pits and at the spot where the 

complete profile description was being made; this 

allowed the vari&ility of the depth of the surface horizons to be measured and provided a cheek on the uniformity of the soil across the plot. If there was any 

indication that more than one soil series was present, 

several spots were checked with a soil auger to allow 

the plot boundaries to be relocated so that each plot 

encompassed only one series. 

MODEL GONSTRgJGTION 

The goal of this project was to incorporate what vvas 

known &out site quality for loblolly pine into a general site-prediction model, Developnne.11L of this tme of 

knowledge-based or expert system incorporated published' and unpublished observations on the silvics of 

the species by &resters and forestry researchers in the 

Southern United States. General observations and 

conelusions were supplemented by several dmes of 

data sets, which allowed specific relationships Le, be 

quantified. 

Actual construction of the model was aceomp2ished 

in stages. First, a general theoretical model that liskd 

'See Ebweiils 119651 and Wahlenberg (1960) for general infomation on the silvics of loblolly pine. More recent likratum can be 

accessed by consulting ILIu and others r 1983). 
the soil and climatic characteristics that could influence tree growth was developed. This was a subjective 

listing, but it was based on results from many published studies (e.g., Baker and Broadfoot 1977, 1979; 

Camean 1975; Coile 1952). Next, a subset of these 

variables was selected on the basis of the following criteria. (Pi variables "eeoreti~ally eonaidered to b the 

most important ones in influencing height growth, 

(2) variables that exhibited a meanindul range across 

the sampled sites, and (3) variables that were fairly 

independent of (that is, poorly correlated with) other 

selected variables. The goal at this stage of model construction was to reduce the number of variables to a 

manageable level without deleting classes of variables 

that could be important. 

The next stage of model construction was to quantify the relationship between site index and each variable being considered. The first step was to debmine 

what levels of each variable were associated with the 

highest values for site index. This was accomplished 

by examining the data graphically and via statistical 

analyses such as discriminant analysis. Graphical 

analyses included boundary-line analysis, which 

reveals the maximum values associated with various 

levels of a variable (Evanylo and Sumner 1987, Webb 

1972). This was helpful in detemining the general 

shape of the relationship and the amount of the range 

in site index values that may be attributable to that 

variable. When possible, data from sources other than 

the plots measured for this study were used to determine the preliminary values assigned to the various 

levels of each variable. For example, data from 1,086 

plots in the Soil Conservation Service (SCS) soil woodland data base were used to initially quantify the 

effects of soil drainage class, soil order, and texture of 

the surface horizon of the soil. (These were values 

recorded on individual plots and were not averages by 

soil series.) For example, the relationship between site 

index for loblolly pine and soil drainage class was plotted for the SCS soil woodland plots (fig. 3). Separate 

boundary lines were drawn for sites that had been 

coded as being on stream terraces or floodplains and 

for upland sites. When sites were not near rivers and 

streams (the solid line), poorly drained, moderately 

well-drained2, or well-drained, sites could achieve a 

maximum site index of about 110 feet; very poorly 

drained sites a maximum value of about 95 feet; sornewhat excessively drained sites a maximum value of 90 

feet; and excessively drained sites a maximum value of 

80 feet. The differences between the heights of the 

solid and dashed lines of figure 3 for mderately well 

to excessively drained sites were taken as an indica2The apparently lower value of maximum site index achieved on 

somewhat poorly drained sites was judged to be an artifact due to 

insufficient sample size. 

tion of the effect of subsurface irrigation. These differences were then used as guides in selecting the values 

initially assimed to the drainage classes and the 

values for extra water (a variable that included factors 

such as distance to a water table during the growing 

season). However, the actual values assigned did not 

EOFT~S~~~ directly to the &fferences in the mmimuwi 

values observed because it was wident that other variables being evaluated (e-g., soil texture) would be partially correlated with drainage class, 

General relationships between each variable and 

site index were constrained to be consistent with what 

is known about the silvics of the species (cf., Fowells 

1965) and general published relationships between 

plant growth and specific variables. Interrelationships 

between variables were also required to be consistent 

with what was known. For example, bulk density was 

not evaluated alone but only in the context of soil texture (Daddow and Wrrington 1983) and structure, 

and phosphorous values were evaluated in relation to 

soil drainage class (cf., Tiarks and Shoulders 1982). 

Apparent inconsistencies or "quirks" in the measured 

data or in other data used in model construction were 

not allowed to influence the structure of the model. If 

there was no biological reason to explain an odd shape 

in the relationship between site index and the variable 

being considered, the odd shape or quirk was ignored 

in defining the guiding shape of that relationship. 

Model quantification began by assigning the characteristics associated with the highest levels of site 

index (sites with S1502 115 ft) a value of zero. Then the 

amount of reduction in site index associated with 

other levels of the variable was estimated. The final 

weights assigned to the individual levels were determined empirically; more than 400 runs of the basic 

program were made before deciding on the values in 

the final model. The implied relationship between site 

index and each variable was always constrained to be 

biologically reasonable. For example, if evaluation of a 

preliminary run indicated that changing the variable 

weights in a manner inconsistent with the shape of 

the guiding relationship would improve the predictive ability of the model, such changes were not made. 

The starting maximum value used in the model for 

SIS0 was 122 feet. The values of some variables or combinations of variables were assumed to have additive 

effects; thus, the theoretical maximum value that the 

model can predict is 136 feet. This theoretical maximum value is a hypothetical condition that may not 

exist in the Southern Coastal Plain; however, a large 

range-wide study (USDA FS 1976) reported sampling 

one natural stand in the 130-foot site index class, so 

this value may not be completely unrealistic. 

When the model with the first group of selected variables predicted site index from the site characteristics 

to within tr: 15 feet of the value estimated from tree 

measurements, the original group of variables was 
DRAINAGE CLASS 

Figure 3.-Relationship between site index at age 50 and soil drainage class for plots from a Soil 

Conseruation Seruice duta base for Zoblolly pine. Boundary lines represent the theoretical 

mimum ualue of site index for each drainage class and physiogvhie position. 

reevaluated. Particular care was taken to evaluate 

whether or not plots with certain characteristics were 

underpredicted or overpredicted. The second round of 

model fitting included two new variables-aspect and 

previous land use. These variables had not qualified 

for the original group of variables because most plots 

were classified into only one or two categories of these 

variables. Thus, the effects of these variables were difficult to quantify until some of the other site variables 

had been accounted for. The third round of model 

quantification resulted in adding three more variables 

to the previous group. The first of these new variables 

coded for the depth, thickness, and texture of buried or 

stratified sandy layers; the second assessed frequency 

and timing of flooding; and the third coded for past 

erosion. These new variables were only important on a 

few sites but needed to be incorporated into the final 

model to improve its overall predictive ability. The 

final model was named mSITE, the PT portion of the 

name coming from the scientific name of loblolly pine 

(Pinus taeda). 

The variables included in the final model are listed 

in table 2. The ranges in site index values associated 

with the possible range in variable values are given in 

appendix B, which also indicates which site characteristics are associated with the most and least favorable 

model values. 

The model includes one climatic variable-mean 

annual water deficit. Water deficit is the difference 

between precipitation and potential evapotranspiration. The value used in the model is based on longterm records and does not incorporate any measure of 

year-to-year variability. Although the value is 

expressed as annual water deficit (based on the usual 

pattern of precipitation and evapotranspiration in the 

Southern Coastal Plain), all of the mean annual water 

deficit occurs during the growing season. Thus, this 

variable is actually assessing water deficit during the 

growing season. The values of water deficit used in the 

model were regional values appropriate for average 

sites and were not calculated on the basis of sitespecific characteristics. Characteristics that influence 

the actual value for water deficit on a specific site 

(such as soil texture) are accounted for in other p 

of the program. 

The original version of WSITE predicted site index 

for a base age of 50 years. A second version was developed to predict site index for a base age of 25 years. 

The model for base age 25 was developed primarily by 

scaling down the variable effects by a factor3 of one3This scaling factor was based on a rule-of-thumb used by some 

foresters-convert a 50-year site index value for loblolly pine to a 

25-year value by multiplying the 50-year value by two-thirds (0.67). 
%ble 2.-Infbrmatbn me&d to run PI'SITE, a mdel for predicting 

oflobloEZy pine from soil and si& ccharae&ristks - 

Main cakgory and specific infomation needed 

Physical properties oaf soils: 

Texture of surface horizon 

Texture of subsoil 

Buried sand or sLr&ifi~d layers 

Bulk density of first subsoil horizon 

Thickness of A1 or Ap horizon 

Thickness of E horizon (if present, 

Depth to first subsoil horizon 

Effective rooting depth 

Structure of sudace and subsoil horizons 

Consistence of sudace and subsoil horizons 

Rock frapents in rooting zone 

Soil properties influencing nutrient availability: 

pH of surface soil (0-6 inches) 

Change in p1-I frorn surface soil to pH at 12 inches 

Extractable phosphorus in surface soil 

Organic rnatter in surfslee soil 

Soil order (from Soil Survey Staff 1975) 

Site location on the landscape and in relation to climate: 

Slope position and percent slope 

Aspect 

Drainage classification 

Access to extra water 

Water deficit* 

Occurrence of flooding 

Land history: 

fievious land use 

Erosional history 

* The user is asked for county (or parish) and State name for site 

to be evaluated; an internal file provides the actual values for water 

deficit. 

third (0.33). However, several variables were weighted 

differently in the version of the model for younger 

stands. Some variables were given the same weight in 

the 25-year model as in the 50-year model. These ""fulleffect variablesw-previous land use, past erosion, parent material, and thickness of the E horizon-were 

considered to have a proportionately greater effect on 

early grovvLh than on later grodh of the stand. Thus, 

the effect of these variables was not scaled down in 

conversion to a younger index age. Values for two other 

variables, aspect and soil order, had proportionately 

less impact in the younger stands than in the older 

stands and were scaled down by a factor of 0.50. The 

conversion from the 50-par model to the 25-year 

model also incorporated a "drainage conversion" factor 

to account for the difference in height growth patterns 

of trees on sites that differ in soil drainage classification. For example, trees on imperfectly drained sites 

have proportionately poorer height growth at young 

ages and attain. greater height at older ages than trees 

on better drained sites having the same site index (cf., 

Golden and others 1981, Pienaar and Shiver 1980). 

The final model of mSITE fit the original data set 

well (table 3). The correlation coefficient between site 

index predicted from f)T"SITE and site index predicted 

from tree heiglrL and age was 0.98 for the initial group 

of 50 plots used in quantifying %he model (f~g. 4). 

Based on the chi-square test of accuracy (Freese 1960), 

the model was judged to predict site inciCex within -?- 4 

feet (pc0.05). More than half the sites wem predicted 

within -P-2 feet. The portion of the model predicting 

25-year site index achieved a similar level of accuriae3i, 

with the predictions being within ~?r 3 feet (pC0.05). 

PTSITE also fit the reserved data set with the same 

level of accuracy ( 1- 4 ft., p<0.05, chi-square test of 

accuracy) as the original data (table 3, fig. 4). The 

reserved data included sites from North Carolina to 

Texas and drainage classes somewhat poorly drained, 

moderately well drained, and well drained. Thus, the 

model was tested and verified with independent data 

that were representative of many loblolly pine sites in 

the Southern Coastal Plain. 

Soil-site data from Westvaco and from the USDA 

Forest Servic& Southeastern Forest Experiment Station (SEFES) mre made anilable to the author for the 

purpose of testing the PTSITE model. The data in 

these two data sets were from coastal South Carolina 

and included poorly drained to moderately well 

drained drainage classes and a wide range in values 

for extractable phosphorus. Thus, these data sets provide an additional test of the model's predictive ability. 

The Westvaco and SEFES data sets did not contain 

information on bulk density, access to extra water, or 

soil structure or consistence; they did have slightly different coding systems for slope position. These data 

sets also had information on fewer trees per plot for 

site index calculation than the aulhor's original and 

reserved data sets. For the purposes of this evaluation, 

bulk density values were assumed to be at optimal 

levels. Access to extra water, soil structure, and soil 

consistence were estimated frorn soil textures, position 

on the landscape, and soil series descriptions (each plot 

had been classified into soil series or soil mapping 

unit). In addition, the values for extractable phosphorus in the data set from SEFES were obtained using a 

different analysis procedure (Bray 2 in Jackson 1958). 

The values for extractable phosphorus were assumed 

to be roughly equivalent to what would have been 

obtained using the double-acid (Mehlich 1) procedure 

based on the results in Tiarks (1982). This assumed 

equivalence in phosphorus values may have introduced a bias into the predictions; however, it was 

apparent that having approximate values for phosphorus would be better than not having any information 

on this variable. 

Considering that information was missing for some 

variables and had to be estimated and that there were 
Table 3.-Prdietiue ability ofPTSITE for seueral data sets* 

Number Accuracy 

Data source and of AS1 

age for site index plots Location &tin' IhBS@ R 1 2 3 4 5 10 

Original data 

56-yea1 base 

Reserved data 

50-year base 

Reserved data 

25-year base 

Westvaeo" 

50-year base 

25-year base 

SEFES"." 

50-year base 

Forested sites 

Old-field sites 

13 Southwide 0.05 1.7 0.96 31 62 92 100 100 100 

6 

11 Mostly Mississippi - 0.01 1.5 0.97 36 73 100 100 100 100 

4 South Carolina 1.50 1.5 0.99 50 100 100 100 100 100 

3 South Carolina 1.10 1.9 0.71 33 67 100 100 100 100 

16 South Carolina - 1.38 1.7 0.95 21 71 79 93 93 100 

15 South Carolina - 2.61 5.2 0.73 24 24 29 29 53 94 

* All data came from the Southern Coastal Plain; AS1 is the difference between site index estimated from tree heights and ages and site 

index predicted by the PTSITE model. 

Mean of positive and negative values of AS1 numbers. 

Wean of absolute values of AS1 numbers. 

"ome variables were not measured on these plots; see text for details. 

'" SEFES = Southeastern Forest Experiment Station. 

PT SITE 3150 (FEET) 

Figure 4,-Relationship between site i at age 50 (tS15J cakukated from tree helights and ages 

(Tree SISoi and Sho predickd by PTSITE; diagonal line indicates where the two vdws 

for SISo would be eguivaknt. 
differences in data collection and coding systems, the 

plots from the Westvaco and SEFES data sets were 

predicted quite well by the model. Because of differences in the procedures used to collect the data, these 

plots did not provide as rigorous a test of the model as 

the plots in the reserved data set; however, they may 

be indicative of the accuracy that users might achieve 

when using available soil-site data that were not collected specifically to run through the PTSITE model. 

The Westvaco plots were generally underpredicted 

(mean change in site index was positive), which may 

have been caused by errors in assiping slope position 

and access-to-extra-water codes. Several of the possible alternative codes would result in slightly higher 

prediction values. On the other hand, site index values 

for the plots from SEFES were generally overpredicted 

by the model, which may indicate that there is a consistent difference in phosphorus values obtained by 

the different extraction procedures. 

The correlation coefficient between measured and 

predicted site index for the previously forested plots in 

the SEFES data set (0.95) was similar to the values 

obtained in analyzing the data sets collected by the 

author. The lower correlation obtained for the old-field 

sites may indicate that past agricultural use altered 

the physical properties of soils to the extent that it was 

inaccurate to assume that bulk density values were at 

optimum levels or that the typical values for soil structure, soil consistence, and rooting depth associated 

with the soil series were appropriate. The 25-year-base 

plots from the Westvaco data set also had lower correlation than the plots in the author's data; however, 

with only three plots in the group and a small range in 

the values being predicted, that statistic is probably 

not very meaningful. 

USING THE PTSITE PROGRAM 

An interactive computer program was developed 

from the ITSITE model. The program, also named 

PTSITE, is run on IBM and IBM-compatible 

machines. The version of the program available at the 

time this user's guide was prepared (version 10-89) has 

a menu format. The program is accessed by keying in 

the name of the program, PTSITE, and pressing the 

<RETURN> (or <ENTER> or <NEW LINE>) key. 

The program prompts the user to answer questions or 

requests for infomation regarding various soil and 

site characteristics (table 2). Most answers can be 

selected from a multiple choice format. Menus "pop 

up" with the possible choices as they are needed (fig. 

5). To respond to each question or request for information, the user should press the number key associated 

with the appropriate answer and then press the 

<RETURN> (or <ENTER> or <NEW LINE>) key. 

The program can be teminated at any point by pressing control F'P (is., by holding down the <CONTROL>. 

key and pressing the F1 key). Some questions require 

infomation to be keyed in rather than just selecting a 

category. Examples of this type of question are site 

name or number, county (or parish) and Stak location, 

values for depth of specific horizons, and bulk density* 

Some questions are asked only if the answer to a previous question falls into a specific category; thus, some 

questions do not appear on every run of the program. 

A question will be repeated if the response is not a 

reasonable answer. For example, if a multiple choice 

question offers the user the choices of 1 to 5 and the 

user inputs 35, the question will be asked again. On 

most computers an inappropriate answer is also signaled by a beep. PLEASE READ THE SCREEN 

CAREFULLY!! Users should also be careful when 

keying in county (or parish) names and State abbreviations because the program will not recognize misspellings. 

Certain answers will trigger a waming message 

indicating that an unusual or possibly unsuitable 

value has been selected. In most cases the user is then 

given the opportunity to change the answer. Additional information on running the program is provided 

in appendix A, which explains how to answer each 

question and provides suggestions on how to collect 

information or answer questions dealing with unusual 

or unfamiliar characteristics. Users should read 

appendix A before running the program. 

Many of the questions require users to use their 

judgment in selecting the answer. Users must be careful to accurately evaluate the conditions on each site. If 

users "know"that the site being evaluated is a "good 

site" or a "bad site," they may tend to choose more 

favorable or less favorable characteristics than are 

warranted. Careful evaluation of each characteristic is 

essential for accurate site evaluation. 

When all the questions on site characteristics have 

been answered, the user is given the opportunity to 

change any of the values before running the program. 

Each of the input variables is numbered on the screen 

(fig. 5). For example, if users want to change the value 

input for texture of the surface horizon, they should 

press 11 (and press <RETURN>) upon seeing the 

message at the bottom of the screen that reads: "Enter 

variable number you want to change 10 for site ID) or 

press F1 to process." The menu of choices corresponding to texture of the surface horizon will appear, and 

users can select another value. There is no limit to the 

number of variables that can be changed; however, 

changes can be made only when all of the questions 

have been answered initially. When users are satisfied 

with all the values, the F1 key should be pressed to 

process the program. 

After the F1 key is pressed, the user is given the 

choice of having the output from the program sent to a 
SITE: Crossett Experimental Forest, Compartment 3, Stand 7 

1. COUNTY ASHLEY 

3. SWPE POSITION 2 

5. ASPECT 0 

7. PAST EROSION 1 

9 ' FrnODTNG 1 

11. TEXTURE OF SURFACE 14 

2, STATE 

4. PERCENT SLOPE 

8. DMINAGE CLASS 

10. EXTRA WATER 

12. TEXTURE OF SUBSOIL 

Sand 

Coarse sand 

Fine sand 

Very fine sand 

Loamy coarse sand 

Loamy sand 

Loamy fine sand 

Loamy very fine sand 

Coarse sandy loam (17) Clay loam 

Sandy loam (18) Silty clay loam 

Fine sandy loam (19) Sandy clay 

Very fine sandy loam (20) Clay 

~oam (21) Silty clay 

Silt Loam (22) Muck or peat 

Silt 

Sandy clay loam 

]Enter variable number you want to change (0 for site ID) or press F1 to process 

Figure 5.-Appearance of data on the screen and one oftk '>opup" mnus fmm the PTSITE progntm. 

printer4 or saved in a file. An example of a sample output is included in appendix 6. If the option of saving to 

a file is selected, the program will ask for the path5 

and file name. File names can be any combination of 

alpha or numeric characters (up to a maximum of 

eight characters); however, file names cannot begin 

with a number or contain blank spaces or punctuation 

characters. 

After the decision has been made as to where to 

send the output from a run of the program, users are 

then given the choice to: (1) evaluate another sitel 

starting with the values from the last run; (2) evaluate 

another site, but delete the old values; or (3) exit the 

program. Selecting option 1 enables users to make 

another run of the program without having to input 

all the variables again. For exaqle, if a user wanted 

to run the program for the same soil but on two differ4Tf the user selects the option of sending the output to a printer 

and a printer is not attached, the program will print an error message. If the print option is selected and a printer is attached but not 

online, the output will be lost. The popup menu includes a reminder 

to the user that the printer must be online. 

'The path is the drive where the user wants the outl~ut file to be 

stored ie.g., A:). If the user does not specify a path, the file will be 

saved on the current drive. If the user does not specify a file exlension, the program will add an extensions of .pts to the fife name (e.g., 

Jones,pts). 

ent slope positions, the program would first be run 

with one of the values for slope position; at the end of 

the first run; option 1 would be selected. The other 

value for slope position would be input for variable 3, 

and F1 would be pressed to process the new value. If 

several variables need to be changed for the new run, 

it is probably safer for users to select option 2-that 

way incorrect values cannot be inadvertently retained 

from the previous run. 

RIODEL APPLICABILITY 

The PTSITE model was developed with data from 

stands of loblolly pine in the Southern Coastal Plain 

from North Carolina to Texas. Sites were not sampled 

in the Piedmont or in the floodplains of major river 

systems such as the Mississippi River. In addition, all 

sites sampled wre within the native range of loblolly 

pine iFowells 1965). The program should not be relied 

upon outside this geographic area until local users 

determine the applicability of the program ;to their 

site conditions. 

An attempt vvw made to sample a wide range of soil 

conditions and site indices. Hot all possible conditions 

were sampled, however, and the user is cautioned from 

relying on the model's prediction for unusual or nontypical coditions until the model can be tested under 
those conditions. For example, only a few sites had pH 

values below 4.0 or above 6.5, and no samples were 

taken on sites that had slopes greater than 30 percent 

or that were on very deep sands or deep organic soils. 

In addition, samples were not taken on sites that had 

problems with sodium toxicity6 or with knomnutrient deficiencie~ ather than phoaphoms ~r nitragen. 

The model may be modified in the future, and the 

author would welcome input on how the model performed under different conditions, 

The program was developed on the basis of plots 

located on apparently uniform site conditions. Plot 

boundaries were laid out to avoid changes in slope, 

aspect, drainage, or other visible 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 to first roughly 

map areas that appear to be uniform and then to sample within each area. Sampling intensity should vary 

with the user's need for accuracy; however, soil characteristics can be extremely variable, and in some areas 

several point determinations may be required to accurately assess potential site index. 

The PTSITE model was developed with data from 

stands that were considered to accurately represent 

the potential site quality of the area. Stands having 

uneven-aged structure or a significant component of 

upper-canopy hardwoods were not included. Trees 

showing evidence of major top damage or ring patterns 

indicating a period of early suppression were deleted, 

and uniformity in tree ages within a stand was one of 

the criteria used for selecting plots to be used in model 

development or testing. In addition, very young or very 

old stands were not included because of the possible 

errors in accurately estimating site index from such 

stands. 

The PTSITE model can be used to provide estimates 

of potential site index in stands having the criteria 

mentioned above-uneven-aged structure, upper-canopy hardwoods, suppression, or damage, as well as 

stands that are very young or very old. In fact, a soilbased prediction system like PTSITE is the only way 

to accurately estimate potential site index for most of 

these stand conditions. Users are cautioned, however, 

to follow the same criteria for stand and tree selection 

described above when testing the applicability of the 

model in new situations. 

Most stands used in developing the model had 

regenerated naturally; however, some of the stands 

had been planted in the 1930's by the Civilian Conservation Corps. Stands used in scaling down the 50-year 

model to a 25-year model were also of both natural and 

artificial origins. 

Actual or apparent changes in site quality associated with forest management practices such as thinning are not included in the model. However, if a forest 

management practice such as intensive site prepmation sipificantly alters long-term soil properties, the 

change in these properties should result in a change ia 

the site index predicted by mSETE. Thus, the mdel 

can be run with different values for depth of the A1 

horizon, percentage of organic matter, phosphorus concentration, bulk de~sity; past use, or erosion to determine its sensitivity to changes in those factors7. Some 

users may wish to use PTSITE as a rough method of 

predicting or monitoring the effects of management 

practices on long-term productivity. For example, if 

onsite investigation or other information indicates 

that a certain site preparation practice reduces depth 

of the A horizon and organic matter content of the surface soil, the change in the value of site index predicted by the model could be used as a measure of the 

change in site productivity. 

The PTSITE model was developed and tested with 

data from mature stands where soil characteristics 

would be quite stable. For example, organic matter in 

the top 6 inches of a very sandy soil may decrease 

sharply when the overstory is removed and the soil 

surface opened up to full sun. Then, as the regeneration increases in size and crown closure occurs, 

organic matter contents will increase. In contrast, 

extractable phosphorus levels will be quite high 

immediately after fertilization, then will decrease and 

eventually level off at a new equilibrium value. Thus, 

users are cautioned not to overestimate or underestimate the long-term effects of a management practice 

on site productivity based on a soil characteristic that 

is changing over time. For most variables, measurements made 10 or more years after disturbance will 

probably provide estimates close to equilibrium 

values. Measurements made before that time may 

need to be modified on the basis of experience or specific information on the behavior of a soil variable over 

time. 

The PTSITE model does not take genetic variability 

of loblolly pine into account. Actual increases in the 

measured site index may be realized when specific 

plant materials are present. These increases may 

result from higher overall growth rates associated 

with some genotypes or from using genotypes having 

tolerances for specific conditions. The program can be 

used to rank sites in order of potential site index; however, how accurately the program will predict actual 

site index under a variety of forest management practices is unknown. Users having some knowledge of 

programming can add or alter sections of the PTSITE 

'Some soils having suspected high sodium levels were analyzed, 

but sodium levels were not high enough to be a problem. 

'See the sections in appendix A on past land use, access to extra 

water, and organic matter for additional ideas on how to handle specific management practices. 
mode1 so that its predictions are in line with their 

experience. The basic framework of the model is 

expected to remain intact, but additional modules 

could be added txr take other site conditions or specialized plant materials into account. 

PROGRAM AVAILABILITY 

A copy of the WSITE program may be obtained by 

contacting the Southern Forest Experiment Station, 

Box 3516, Montieello, AR 71655 (501-367-3464). 

Requests by mail should include the user's name, 

address, and telephone number as well as a blank formatted diskette (3.5- or 5.25-inch). The requester's 

name MUST be on the diskette. Unless otherwise 

specified, a compiled version of the program will be 

provided. Upon request, a program listing or an interpreted version of the program is available. The interpreted program version can be changed by the user; 

however, to actually run an interpreted program, 

users must have a BASIC interpreter software program available on their computer. The compiled program can be run without additional software but cannot be altered. 

LITERATURE CITE11 

Allison, L.E. 1965. Organic carbon. In: Black, C.A., ed. 

Methods of soil analysis. Agronomy. Madison, WI: 

American Society of Agronomy: 1367-1378. Vol. 9, 

pt* 2. 

Baker, James B.; Broadfoot, WM. 1977. 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. 

31 p. 

Baker, James B.; Broadfoot, W.M. 1979. 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. 51 p. 

Bouyoucos, G. J. 1962. Hydrometer method improved 

for making particle size analysis of soils. Agronomy 

Journal. 54: 464-465. 

Broadfoot, W:M. 1969. Problems in relating soil to site 

index for southern hardwoods. Forest Science. 15: 

354-364. 

Carmean, Willard H. 1975. Forest site quality evaluation in the U.S. Advances in Agronomy. 27: 

209-269. 

Coile, T.S. 1952. Soil and the growth of forests. 

Advances in Agronomy. 16: 329-398. 

Daddow, Richard L.; Warrington, Gordon E. 1983. 

Gmhh-limiting soil bulk densities as influenced by 

soil texture. WSDG Rep. WDG-TN-00005. Fort 

Collins, CO: US. DeparLment of Agriculture, Forest 

Service, Watershed Systems Development Group. 

17 p. 

Evanylo, G.K.; Sumner; M.E. 1987. Wilization of the 

boundary line approach in the development of soil 

nutrient norms for sofiean production. Communications in Soil Science and Plant Analysis. 18(12): 

1379-1401. 

Farrar, R.M., Jr. 1975. Southern pine site-index computing program. Res. Note SO-197. New Orle 

LA: U.S. Department of Agriculture, Forest %~ce, 

Southern Forest Experiment Station. 8 p. 

Fowells, H.A., comp. 1965. Silvics of forest trees of the 

United States. Agric. Handb. 271. Washington, DC: 

U.S. Department of Agriculture. 761 p. 

Freese, Frank. 1960. Testing accuracy. Forest Science. 

612): 139-145. 

Golden, M.S.; Meldahl, R.; Knowe, S.A.; Boyer, W.D. 

198 1, Predicting site index for old-field loblolly pine 

plantations. S~uthern Journal of Applied Forestry. 

513): 109-114. 

Harding, R.B.; Baker, R.D. 1983. The soil survey and 

its value in the ad valorem taxation of privately 

owned timberland. Southern Journal of Applid Forestry. 714): 204-208. 

Harrington, Constance A. 1986. 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. 22 p. 

Harrington, Constance A.; Casson, Bettina M. 1986. 

SITEQUAL-a user's guide. Computerized site evaluation for 14 southern hardwood species. Gen. Tech. 

Rep. SO-62. New Orleans, LA: U.S. Department of 

Agriculture, Southern Forest Experiment Station. 

13 p. 

Hodgkins, Earl J. 1956. Testing soil-site index tables 

in southwestern Alabama. Journal of Forestry. 

5414): 261-266. 

Hodgkins, Earl J. 1959. Forest site classification in the 

Southeast. In: Proceedings, 8th annual symposium 

on southern forest soils; 1958 June; Baton Rouge, 

LA. Baton Rouge, LA: Louisiana State University 

Press: 34-48. 

Hu, Shih-Chang; Lilieholm, R.J.; Burns, Paul Y. 1983. 

Loblolly pine: a bibliography, 1959- 1982. Res. Rep2. Baton Rouge, LA: School of Forestry and Wildlife 

Management, Louisiana State University Agrieultural Center. 199 p. 

Jackson, M.L. 1958. Soil chemical analysis. 

Englewood Cliffs, NJ: Prentice-Hall, Inc. 498 p. 

Lloyd, W.J.; Lemmon, P.E. 1970. Rectifying mimuth 

(of aspect) in studies of soil-site index relationships. 

In: ~iee growth and forest soils; proceedings of the 
3rd North American forest soils confemnce; 1968 

August; Raleigh, NC. Corvallis, Ofit: Oregon Stab 

University Press: 453-558. 

Mader, D.L. 1965. Where are we in soil-site classification? In: Applications of soils infomation in forestry, Washington, DC: Society of American Foresters and Soil Conservation Society of America: 

25-32. 

McKw, W.L. 1977. Soil-site relationships for loblolly 

pine on selected soils. In: Roceedings 6th southem 

forest soils mrkshop; 1976 October 19-21; Charleston, SC. Atlanta, GA: U.S. Department of Agriculture, Forest Service, State and hivate Forestry, 

Southeastern Area: 1 15- 120. 

McQuilkin, Robert A. 1976. The necessity of independent testing of soi l-site equations. Soil Science 

Society of America Journal. 40: 783-785. 

Olsen, S.R.; Sommers, L.E. 1982. Phosphorus. In: 

Page, A.L., ed. Methods of soil analysis; pt. 2. Chemical and microbiological properties. Agronomy 

Monogr: 9,2d. ed. Madison, WI: American Society of 

Agronomy and Soil Science Society of America: 

403-430. 

Pienaar, Leon V; Shiver, Barry D. 1980. Dominant 

height growth and site index curves for loblolly pine 

plantations in the Carolina flatwoods. Southern 

Journal of Applied Iibrestnty 4(1): 54-59. 

Ralston, C.W 1964. Evaluation of forest site prductivity. International Review of Forestry Research. 1: 

171-201. 

Smalley, Glendon W 1979. Classification and evaluation of forest sites for timber production: introduction of a new system for classifying forest sites 

based on the physical features of the landscape. In: 

Proceedings of a forest soils and site quality workshop; 1979 May 8-9; Auburn, AL. Auburn, AL: 

Auburn University: 28-47, 

Soil Survey Staff. 1975. Soil taxonomy: a basic system 

of soil classification for making and interpreting 

soil surveys. Agric. Handb. 436. Washington, DC: 

U.S. Department of Agriculture. 754 p. 

Storie, R. Earl; Wieslander, A.E. 1948. Rating soils for 

timber sites. Soil Science Society of America Proceedings. 13: 499-509. 

Tiaartrs, A.E,; Shaulders, E, 1982. Effects of shallow 

water tables on height growth and phosphorus 

uptake by loblolly and slash pines. Res. Note 

SO-285. New Orleans, LA: U.S. Department of Agriculture, Forest Service, Southern Forest Experiment 

Station. 5 p. 

Tiarks, Alfen E. 1982. Phosphorus sorption curves for 

evaluating phosphorus requirements of loblolly pine 

(Pinus taed7a). Communications in Soil Science and 

Pliant Analysis. 13(8): 619-631. 

U.S. Department of Agriculture, Forest Service. 1969. 

A forest atlas of the South. New Orleans, LA: U.S. 

Department of Agriculture, Forest Service, Southern Forest Experiment Station and Southeastern 

Forest Experiment Station. 27 p. 

US. Department of Agriculture, Forest Service. 1976. 

Volume, yield, and stand tables for second-growth 

southern pines. Misc. hbl, 50. Washingeon, DC: 

U.S. Department of Agriculture. 202 p. 

U.S. Department of Agriculture, Soil Conservation 

Service. 1972. Soil survey laboratory methods and 

procedures for collecting soil samples. Soil Surv. 

Invest. Rep. 1. Wshington, DC: U.S. Government 

Printing Office. 63 p. 

U.S. Department of Commerce. 1968. Climatic atlas of 

the hited States. Ashevillie, NC: U.S. Department 

of Commerce. 80 p. 

Wahlenberg, W.G. 1960. Loblolly pine: its use, ecology, 

regeneration, protection, growth, and mmagement. 

Raleigh, NC: School of Forestry, Duke T_Tniversity. 

603 p. 

Webb, R.A. 1972. Use of the boundary line in the analysis of biological data. Journal of Horticultural Science. 47: 309-319. 

Youngberg, C.T.; Scholz, H.F: 1949. Relation of soil fertility and rate of growth of mixed oak stands in the 

driftless area of southwestern Wisconsin. Soil Science Society of America hoceedings. 14: 33 1-332. 

General note-Questions or requests for information 

listed below that are preceded by an asterisk (*) are 

asked only if the answer to a previous question or 

requesL for infomation fell into a specific category 

Thus, some questions will not appear in every run of 

the program. Screen prompts appear in bold-face type. 

What is your site name or number? 

A site identification in the form of a name or number 

is optional; any combination of letters, numbers, 

spaces, or special characters can be used, up to a maximum of 256 characters or spaces. If users do not wish 

their output to be labeled, they should press 

<RETURN> (or <ENTER> or <NEW LINE>) when 

this qilestisn is asked. 

Input county (or parish) name: 

Input the two-letter State abbreviation: 

The user should first enter the county or parish name 

(press <RETURN> after the name). Use of uppercase 

or lowercase letters is acceptable. Next, the user 

should enter the two-letter State abbreviation (the program will prompt the user). This information on 

county (or parish) and State names will appear on the 

output as an additional site identifier. The program 

also checks a supplemental file (included with the program) to determine whether lobloliy pine is native to 

that county (based on the range map in Fowells 1965). 

If loblolly pine is not native, a warning message is 

triggered, and the user is given the option of changing 

the site location or continuing with the same location. 

The warning message is also triggered if the county 

(or parish) name or State abbreviation is spelled incorrectly (e.g., Arkansas is AR not AK). Counties or parishes named for saints are spelled ST with no punctuation (e.g., ST CLAIR, ST TAMMANY, OR ST JOHNS). 

Spaces are optional between words in a county or parish name if the common spelling uses spaces (e.g., 

either REDRIVER or RED RIVER, or ST FRANCIS or 

STFRANCIS will work, but RICH LAND for 

RICHLAND will not). When in doubt, the spaces 

should be left out. 

The srapplemental file also contains the mean 

annual water deficit values1 for each county or parish 

in the Southern United States (based on the map in 

USI>A FS 1969). The program uses water deficit in 

conjunction with the codes for access to extra water in 

evaluating site quality, water deficit values are not 

included for States where foblolly pine is not native 

nor for central or urestern Oklahoma or Texas. If a 

'Water deficit is the difference between mean annual precipitation and mean annual potential evapotranspiration; the values for 

water deficit in the psite.dat file are in millimeters. 

water deficit value is not available for the county (or 

parish) selected, a warning message is triggered, and 

the user will be given the choice of selecting another 

county or exiting the program. 

The supplemental file (psite.dat) contains State 

names arranged in alphabetical order. If the user is 

evaltaa.f;ing a site with a Stake code near the end of the 

alphabet, the program will pause for several seconds 

while it checks the file. This delay is reduced if the 

program is run on a hard drive rather than on a disk 

drive. If users do not know what a hard drive is, they 

should ask a computer programmer or specialist for 

assistance. 

Select a value for slope position of the site from 

the following list: 

(I) Lowerslope 

(2) Lower noninfluencing 

(3) Midslope 

(4) Upper noninfluencing 

(5) Upperslope and ridgetop 

(6) Noninfluencing (flatwoods) 

Users can refer to figure 2 for assistance in categorizing slope position. This is an important variable 

and should be assessed carefully. Sites characterized as noninfluencing, lower noninfluencing, or upper 

noninfluencing should have slopes 55 percent. Noninfluencing (without a modifier) should be selected when 

there is no major change in topography in the area 

(such as flatwoods). The categories upper noninfluencing or lower noninfluencing should be selected for 

sites having slopes 55 percent that have an obvious 

position in relation to the surrounding topography. 

The information on slope position is used to assess 

relative water movement. Sites in depressional areas 

or with a bowl-shaped topography should be classified 

as having a "1oowr'~osition if the user perceives them 

as gaining moisture from adjoining areas, regardless 

of their elevational relationship to the overall topography of the area. Similarly, sites on terraces located in 

midslope positions should be classified as lower if the 

user believes these sites are likely to have a net gain 

in soil moisture due to down-slope water movement, 

Small differences in elevation can alter the appropriate slope position code if the differences mld influence water movement. For example, a large flatwoods 

area would generally be coded as noninfluencing; however, there may be: (a) depressional areas within the 

larger area that should be coded as lower or lower 

noninfluencing if they are in moisture-gaining positions or (b) elevated flats that should be coded as upper 

noninfluencing if they are in moisture-losing positions. It may be helpful to examine a topographical 

map having small contour intervals to pick out areas 

likely to be moisture gaining or losing. 
Select a value for percent slope from the 

following Pist: 

(1) 0-2 

(2) 3-5 

(3) 6-8 

(4) 9-11 

(51 12-15 

(6) 18-19 

(7) 20-29 

(8) > =30 

Users should select the percent-slope category that 

best fits the site being evaluated. The program is 

fairly sensitive to percent slope, so users should be as 

accurate as possible. They should select the value for 

the average slope of the general lay of the land in the 

area of interest rather than the value for the maximum slope. This is particularly important in eroded 

areas having gullies. It is also important to separate 

areas on terraces or steps or in bowl-shaped topography from those on nearby side slopes. The model was 

developed with data primarily from sites having gentle slopes; the program will print a warning message 

if category 8 (slopes 230 percent) is selected. 

*Select a vahe for aspect from the following list: 

(1) South or Southwest 

(2) All others 

This request only appears if percent slope is greater 

than 5. Future versions of the program may break 

aspect into more codes; additional codes were not warranted with the low number of Coastal Plain sites having steep slopes. 

Select a value for past land use from the 

following list: 

(1) Previously forested 

(2) Farming or pasture (include sites open and 

bare at time of regeneration) 

Past land use can have both negative and positive 

effects on growth of the current (or planned) forest 

stand. Negative effects such as compaction, erosion, 

and loss of organic matter are addressed in other parts 

of the program. This part of the program is primarily 

concerned with assessing the amount of early eompetition, particularly hardwood competition, present 

during stand establishment and early grourLh. 

Thus, sites on fresh alluvium or sites having site preparation and vegetation control sufficient to marlaedly 

reduce hardwood competition would fall into category 2. 

Please ebaraebrizre past erosion on the sik: 

(1) None or slight (include sibs with uniform 

sheet erosion) 

(2) Moderate, rill erosion or slightly gullied 

(ofhn with irregdar depths of A horbn 

on sideslopes) 

(3) Major, gdlies >2 feet and <5 feet deep 

(4) Severe, gullies >5 feet deep 

This part of the program categorizes rill and gully erosion induced by human activity. Sheet erosion is rxot 

included here because its effects are accounted for in 

the sections on depth of the A horizon and effective 

rooting depth. The model wzs not tested with data 

from any sites having severe gully erosion; thus, site 

evaluation for severely eroded sites should be eonsidered as approximate. 

Select a value for drainage class from the 

following list: 

(1) Very poorly drained 

(2) Poorly drained 

(3) Somewhat poorly drained 

(4) Moderately well drained 

(5) Well drained 

(6) Somewhat excessively drained 

(7) Excessively drained 

(8) Permanently swampy or flooded 

These drainage classes (except for 8) are the same as 

the standard soil drainage classes used by the Soil 

Conservation Service (SCS). Users should select the 

most appropriate drainage class for the specific site 

being evaluated rather than the average drainage 

class that might be associated with the soil series or 

mapping unit for the area. If users are not familiar 

with drainage classes or other soil terminology, they 

should contact their local SCS office for assistance. 

Select one of the following descriptions that best 

describes the timing and duration of flooding: 

(1) Never flooded 

(2) Flooded winkr only or if in early spring 

only for a few days 

(3) Frequently flooded in late spring or 

summear, flood wakrs cam cover the tops of 

seedlings for more than a week during the 

growing season 

(4) Gontimously flooded 

Users should select the category that the best 

describes the timing of flooding (if any) on the site. 

Sites continuously flooded are unsuitable for loblolly 

pine; selection of this category will trigger a warning 

message, and the user will be given the choice of 

selecting a new category. Sites subject to flooding for 

more than a week during the growing season will be 

difficult to regenerate; a warning message to that 

effect will appear on the output. 
Select one or two of the following categories for 

access ta extra wakr from the following list (if 

two characteristics are seleekd, input the value 

as a. tm-digit number with the smaller digit 

first-17 rather than 71): 

(1) Wakr table during the growing season of 

6-10 feet 

(2) Water table during the growing season of 

greater than 10 feet 

(3) Water table during the growing season at 5 

feet or less 

(4) Seasonally perched water table, wakr table 

= >2 feet during winter months 

(5) Seasonally perched water table, water table 

<2 feet during winter months 

(6) Microsik relief, mounds and depressions 

(7) Intermitknt or ephemeral stream channels 

on plot 

(8) Bowl-shaped topography 

The listed characteristics can markedly influence soil 

moisture and thus site quality. The possible categories 

for access to extra water describe summer water tables 

(choices 1-3), winter water tables (choices 4 and 51, 

and characteristics associated with microsite relief 

(choices 6-8). The user should carefully select the one 

or two characteristics considered to be most important 

in influencing soil moisture. The user can input information on both summer and winter water tables; for 

example, a code of 25 would indicate that the water 

table during the growing season is deeper than 10 feet 

and that the site has a perched water table2 during the 

winter months within 2 feet of the surface. The characteristics selected for access to extra water modify the 

values assigned for soil drainage; some characteristics 

also modify the values for water deficit. 

Users should carefully consider the possible characteristics for each site to be evaluated. Several of the 

extra water characteristics can be affected by management activities. The effects of some activities, such as 

ditching, are obvious, while the effects of other activities are more subtle and need to be carefully considered. For example, road construction activities can 

alter water movement patterns and, in some situations, can create bowl-shaped topography. The effects 

'A perched water table is a saturated layer that is separated from 

ground water (the true water table) by soil or rock layers that are not 

saturated. Perched water tables often develop during the winter on 

sites having well-developed fragipans or other soil layers of low permeability. County soil surveys (available from most loeal SCS 

offices) include information on the type and depth of water tables 

commonly associated with a soil series or association. CautionSCS soil surveys categorize sites having deep water tables as having 

a water table greater than 6 feet (their deepest category). Unless the 

selected site is on a stream terrace or in a floodplain, users should 

assume that this means that the water table during the growing 

season is deeper than 10 feet (category Z), rather than at 6-10 feet 

(category 1). 

of bedding can be accounted for by selecting ""Itemittent or eptremeral stream channels on plot" (choke 7) 

and the appropriate water table category. On bedded 

plats, the drainage classification. will also wed to be 

changed to reflect the altered water relations, and the 

value for effective rooting depth will need to be 

increased. 

Select a .value for texture of the surface horkon 

from the following list: 

( 1) Sand 

( 2) Coarse sand 

( 3) Fine sand 

( 4) Very fine sand 

( 5) Loamy coarse sand 

( 6) Loamy sand 

( 7) Loamy fine sand 

( 8) Loamy very fine sand 

( 9) Coarse sandy loam 

(10) Sandy loam 

(11) Fine sandy loam 

(12) Very fine sandy loam 

(13) Loam 

(14) Silt loam 

(15) Silt 

(16) Sandy clay loam 

(17) Clay loam 

(18) Silty clay lsam 

(19) Sandy clay 

(20) Clay 

(21) Silty clay 

(22) Muck or peat 

Users should first enter the one- or two-digit code for 

the texture of the uppermost or surface horizon (below 

an organic horizon or litter layer if present). Sand size 

should be carefully considered as the model distinguishes between textures based on sand size; for 

example, sand and fine sand are rated differently. 

The program will then request that the user select 

the code for the dominant texture of the subsoil horizons in the main rooting zone (the same list of codes 

will appear). Most soil profiles are finer textured as 

depth increases or have little change in texture in the 

profile; the presence of sand or sandy loam layers 

below a finer textured subsoil horizon will be 

addressed in a separate question. 

The program will then prompt the user to enter the 

code for the texture of the horizon where bulk density 

was measured. If bulk density was not measured, 

users should input a value of 0 (zero). 

*What is the bulk density in the main rooting 

zone of the subsoil? 

Bulk density should be measured in the first or top 

subsoil horizon unless the depth to the subsoil is 
greater than 20 inches. If the depth to the subsoii is 

greater than 20 inches, bulk density should be measured at a depth of 10 to 15 inches. Horizon boundaries 

should not be crossed when measurements are being 

taken. Bulk density should be expressed in gram per 

cubic centimeter (g/cm3) and the value input to the 

nearest 0.05 if possible ie.g,, "1.35;. The values used irr 

the program were derived from samples taken with a 

core sampler. The bulk density values associated with 

dedhlctions in site index vary by soil textural clms and 

the strength of the soil structure. 

If the user indic&ed that bulk density was not measured Cby inputting a O [zero] when asked for the texture of the horizon where bulk density vvas measured), 

the program will skip over the question on bulk density, When bulk density is not input, the program 

assumes that bulk density in the rooting zone was at 

an optimum level. 

Most accurate site determinations are made when 

all variables, including bulk density, are measured 

rather than estimated. If necessary, an experienced 

user can roughly estimate bulk density from soil texture, organic matter, and consistence. Use of the 

growth-limiting bulk density values based on soil texture (Daddow and Warrington 1983) can indicate 

approximate upper limits for bulk density values in 

structureless soils. Average bulk density values can 

sometimes be obtained from soil interpretation records 

available from the SCS. Obtaining accurate bulk density values is probably most important when the user 

knows or suspects that previous practices associated 

with soil compaction have occurred, for example, if the 

area was farmed for many years or had a lot of heavy 

equipment use on it. However, please note that some 

soils-especially silt loams, silty clay loams, and 

sandy clay loams-may have higher than optimum 

bulk densities independent of human activities. If 

users or others wish to measure bulk density for a site 

and have not taken this measurement before, a local 

Cooperative Extension or SCS office can be contacted 

for assistance. 

Is most of the soil profile stratified? 

(1) Yes, it is stratified 

(2) No, it is not stratified 

This question sets apart sites that have soil profiles 

consisting of numerous thin layers of deposited material. Stratified soil profiles are usually on stream terrace sites or in floodplains. These profiles are an indication that little in-place soil development has 

occurred; thus, stratified soils are usually classified as 

Entisols or Inceptisols. Most soil profiles are not stratified; if there is doubt as to whether the profile being 

evaluated is stratified, the answer ""(2) No. . ." should 

be selected. The user should not answer "(1) Yes. . ." if 

only one horizon in the profile is stratified. 

If the top subsoil horizon is not a sand or sandy 

loam, is there a sand, loamy sand, or sandy loam 

horhon below it? 

(1) Yes, there is a sand, loamy sand, or sandy 

loam horizon below a finer hxtured B 

horhon 

(2) No, $here are no briiried or stratified sand 

layers (includes soils that are sandy 

throughout) 

Some profiles will not be adequately characterized by 

surfaee and subsoil texture because of the presence of 

deeper horizons that are much sandier than horizons 

closer to the surface. These include soils with buried 

horizons, some bisequal soils, and soils with some 

stratified, mixed, or layered horizons. If the answer to 

the question is ""0 No. . .," the program skips down to 

the question on structure. If the answer is "(11) Yes. . .,'" 

two or three additional questions will follow to determine the depth and texture of the sandier horizon or 

layer. 

*Please characterize the soil using the following 

list as to the hxture and location of the sandy 

layer: 

(1) Within 30 inehes of the surface, texture 

changes from a finer texture to sandy loam 

(2) Within 30 inches of the surface, textpule 

changes from a finer texture to a sand or a 

loamy sand 

(3) Within 60 inehes of the surface, texture 

changes from a finer texture to a sandy 

loam 

(4) Within 60 inches of the surface, texture 

changes from a finer texture to sand or 

loamy sand 

(5) Pickup in sand content occurs at = >60 

inches 

This request for information only appears if the 

answer to the previous question was "(1) Yes. . . ." The 

purpose of this request is to determine the texture of 

the sandy layer and where in the profile it occurs. 

*How thick is the sandy loam layer before a finer 

texttured horizon is encounhred? 

*How thick is the sand or loamy sand layer 

before a finer textured horizon is encounfered? 

*What texture are the horizons below the sandy 

loam layer? 

*Characterize the profile from the sand or loamy 

sand layer on down. 

*Is the sand below 60 inches stratified, mixed, or 

layered? 

Depending on the response to the previous query on 

the location and texture of the sandy layer, one of these 
five requests for information%ill appear on the In addition to its effect as an independent variable, 

screen. structure is also used in evaluating bulk density. 

*Please indieah whether the soil profile has any 

of the following characbristics: 

(1) Many, ~noderahly $hick, esnti~uaus, clay 

bridges or films on sand grains 

(2) Discontinuous or patchy clay bridges or 

films om sand grains 

(3) Mkrnating bands of same textural class 

but different colors 

(4) Alternating bands of different textural 

classes (e.g, sl, Is, sl, Is) 

(5) Presence of one or more finer hxtmed 

layers at Ileast 3 inches thick 

(6) No special features present 

This request for information appears if infomation 

previously provided by the user indicated the soil was 

a well-drained or excessively drained sand. If the user 

selects answers 1 through 5, the following question is 

asked: 

*At what depth do the special sand 

characteristics first occur? 

(1) <65 inches 

(2) 65-85 inches 

(3) >85 inches 

This query will appear on the screen when the user 

selects answers 1-5, indicating the presence of special 

characteristics in deep sands that should be taken into 

account in predicting site index. 

Select a value for the structure of the surface soil 

from the following list. (Use a two-digit code 

with one digit coming from each list; for 

example, weak blocky structure would be coded 

as 23.): 

(1) Structureless (1) Platy 

(2) Weak (2) Prismatic 

(3) Moderah (3) Blocky 

(4) Strong (4) Granular or crumb 

(5) Single-grained 

(6) Massive 

This part of the program characterizes the strength 

and type of structure in the surface soil using standard codes. The user will then be requested to characterize the structure in the main rooting zone of the 

subsoil, and the list of choices will be presented again. 

Seleet a value for consisknce of the surface soil 

from the following list: 

(1) Loose 

(2) Very friable 

(3) Friable 

(4) Firm 

(5) Very firm 

(6) Extremely firm 

(7) Cemenkd 

Consistence of moist soil should be characterized for 

the surface horizon. If cementation or brittleness is 

present in the surface 6 inches, the consistence of the 

A horizon should be coded as "(7) Cemented." The consistence of the subsoil should also be characterized 

using the same codes (the program will prompt the 

user for this information). 

What is the average thickness of the A1 or A;p 

horizon (in inches)? 

The thickness of the surface (topsoil) horizon should be 

input. Only A1 and Ap horizons should be counted (i.e., 

do not include A2 or E horizons). The user shouId input 

the value to the nearest 0.5 inch if the horizon thickness is less than 2 inches (e.g., 1.5). If the horizon 

thickness is more than 2 inches, values can be rounded 

to the nearest inch. Thickness of the A1 or Ap horizon 

should be checked at several locations across the site 

and an average value used. If an A1 or Ap horizon is 

not present, the user should enter a value of 0. If an 

A/E horizon is present, the user should add two-thirds 

(0.67) of the thickness of the AIE horizon to the thickness of the A1 or Ap horizon. For example, if the site 

has a 2-inch-thick A horizon and a 3-inch-thick ME 

horizon, input the thickness of the A horizon as 4 [i.e., 

2 + (2/3)(3) = 41. 

The model evaluates topsoil thickness in relation to 

soil order and slope position. Shallow topsoil depth 

will be most detrimental to site quality when soil horizons are well developed (e.g., Ultisols) and sites are in 

rnidslope or upperslope positions. 

What is the depth to the first I3 horizon (or 

subsoil) in the soil profile (in inches)? 

The answer to this question is used in conjunction 

with slope position and with the change in texture 

between the surface soil and subsoil to calculate a cor3~~ the response provided for these rection factor to the texture value for the surface horirequests are not listed here. he response choices are provided in a zon. Note-use depth to the first C horizon when evalmultiple-choice format. uating young soils without B horizons. 
Haw thick is the E horizon (in inches)? 

The E horizon (formerly called the A2 horizon) has 

been leached and is usually lighter in color than the 

underlying I3 horizon, If an E (or A2) horizon is nod 

presenl, users should enter a value of 0 (zero). Users 

should include all E horizons above the first B horizon; that is, the thckness of an El horizon ad an E2 

horizon are added together if both are wesent. A B/E 

horizon should not be included unless it has the sme 

bxture as the E horizon above it and differs fmm the 

B horizon below. Buried E horizons should not be 

ineluded because they are accounted for in another section of the program. 

Estimate percentage of rock fragments in the A 

horizon(s), express as a percentage, not as a 

decimal. 

This request requires the user to categorize the percentage of soil volume in the topsoil occupied by rock 

fragments. The values should be input as whole numbers and as 0 (zero) if no rock fragments are present. 

The program also requests the user to estimate the 

percentage of rock fragments in the subsoil (the program will prompt the user for this information). 

Please estimate the maximum effective rooting 

depth for loblolly pine (in inches) using the 

following list: 

(1) <= 15 

(2) 16-20 

(3) 21-25 

(4) 26-30 

(5) 31-35 

(6) 36-40 

('7) 41-45 

(8) 46-60 

(9) >60 

Effective rooting depth is the maximum general 

depth of root penetration and not the depth where 

most of the root system is located. Effective rooting 

depth should be measured from the level of the mot 

collar to the depth of maximum root penetration. Estimating maximum rooting depth is fairly easy if the 

site is being evaluated using a large soil pit and if 

mature loblolly pine trees are present on the site. 

Effective rooting depth is probably similar for many 

tree species, so rooting depth for loblolly pine could be 

estimated from rooting depth of sweetgum, Liquidambar styrmiflua L., or slash pine, P. elliottii Engelm. var. 

elliottii, (for example) if loblolly pine were not on the 

site. Tree species have low rooting densities compared 

to grasses or many agricultural plants; thus, the person doing the estimating should be careful not to 

underestimate rooting depth based on roots seen when 

using an auger.4 

If roots fm mature trees are not present on the 

site, rooting depth must be estimated from soil eh 

teristies (and personal experience). Loblolly pine mots 

do penetratr! into and through some horizons having 

cemented pans. w1u3, rooting depth should BO* be 

taken as the depth to the top of the first horizon having cementation or brittleness. If better infomation is 

no?; milable, effective rooting depth for sites with 

well-developed pans can be estimated by adding 10 

inches to the depth of the first horizon that has a welldeveloped pan. (Horizons having only partial or moderate pan development should be included in the estimation of rooting depth). Tree rooting is restricted by 

firm or very firm clay layers; however, effective rooting depth should not be estimated as the depth to the 

top of the clay layer because: (a) the clay layer will 

supply some water to overlying soil layers via capillary rise and 6) some rooting will probably be fonned 

in the clay layer along ped faces5 or in the areas where 

organic materials are decomposing. Estimating effective rooting depth as the depth to a clay layer would 

imply that the clay layer is as inert as rock. Thus, to 

determine an equivalent value for effective rooting 

depth when other information is not available, the user 

should add 15 inches to the depth of the clay layer 

when the consistence of the clay is firm or 10 inches 

when the consistence is very firm. For example, if a 

very firm clay layer begins at 20 inches, one can 

assume that effective rooting depth is 30 inches (category 4,26-30 inches). 

Effective rooting depth is evaluated in conjunction 

with soil drainage classification. One should be careful not to underestimate rooting depth on bedded sites 

or when microtopography is present. On poorly or very 

poorly drained sites having microrelief (natural or 

artificial), most trees will be on the high spots. One 

should keep this relationship in mind when estimating effective rooting depth li.e., remembering to measure from the root collar). 

Very shallow rooting depths (1) (15 inches, will 

trigger a warning message that the site is unsuitable 

for loblolly pine. The user will be given the option of 

selecting another value for rooting depth or exiting the 

program. 

4When medium-size mots are present in an auger sample, this is 

usually an indication that fine roots are present at greater depths. 

5Peds are the natural units of soil structure, such as a block or 

granule. A ped face is the surface of this structural unit. 
Select a value for soil order from the following 

list: 

(1) Entisol or Inceptisol 

(2) Alfisol or IJlttisoP 

(3) Spodosol 

(4) ertisol 

(53 Hf~b80l 

(6) Mollisol 

These soil orders are those used in IJSDA Soil Taxonomy (Soil Survey Staff 19451, If the area is mapped by 

soil series, soil association, or soil mapping unit, the 

appropriate order can probably be determined from 

that information. It is not necessary to know soil 

series. Soil order is used in the model as an indication 

of weathering (i.e., nutrient availability). In addition to 

its use as an independent variable, soil order is also 

used to evaluate the effects of both topsoil depth and 

percentage of organic matter on site quality. 

The model was developed and tested primarily with 

data from Entisols, Inceptisols, Alfisols, Ultisols, and 

Spodosols. Selection of other soil orders will trigger 

warning messages to be printed on the program output. The warning message for Vertisols is that trees 

may have form problems, especially on side slopes, due 

to the presence of shrink-swell clays. Selection of 

Histosols results in a warning message that model 

values for organic soils should be viewed as preliminary. Selection of Mollisols results in a message that 

the model has not been tested with data from Mollisols. 

Select a value for average pH of the surface soil 

(from 0 to 6 inches): 

(1) c3.5 

(2) 3.5-3.89 

(3) 3.9-4'29 

(4) 4.3-5.59 

(5) 5.6-6.79 

(6) 6'8-7.59 

(7) 7.6-8.49 

(8) > = 8.5 

First, the user should select a value for pH (measured 

in urater) for the surface soil. If the top horizon is less 

than 6 inches thick, the pH value should be weighted 

according to the depths of the lborizons in the zero to 6 

inch layer. Soil pH is used as an indication of nutrient 

availability Very low or high pH values ke., choices 1 

or 8) will trigger a warning message thd the site is 

unsuitable for loblolly pine. The user is ~ven the 

choice of changing the pH value for the sik or exiting 

the propam. 

kxt, the user should select a value for average pH 

of the soil at 12 inches (the program will prompt the 

user). This infomation is used to identify sites with 

major pH changes in the portion of the profile whese 

heavy rooting occurs. Most sites exhibit little change 

or a small decrease in pH with depth. Sites that have 

been limed may exhibit a major drop in pH belovv the 

surface layer. In contrast, some sites change fmm 

acidic to basic because of differences in parent material. Although the program asks for the pH at 12 

inches, ur;ers shonld use their judgrfrtint in ~eapondirng. 

For example, if a major change in pH occurs between 

the surface soil and a horizon that begins at 14 inches, 

the pH value from the horizon beginning at 14 inches 

is probably a better choice than the value at exactly 12 

inches. On the other hand, even major changes in pH. 

that occur deep in the profile are probably not significant in their effects on nutrient awilability. 

Select a value for extractable phosphrus 

concentration of the surface soil (0 to 6 inches) 

from the following list: 

( 1) c0.5 

( 2) 0.5-0.9 

( 3) 1.0-1.3 

( 4) 1.4-1.7 

( 5) 1.8-2.1 

( 6) 2.2-3.0 

( '7) 3.1-5.0 

( 8) 5.1-7.0 

( 9) 7.1-10.0 

(10) 10.1-15.0 

(1 1) 15.1-25.0 

(12) 25.1-50 

(13) >50 

(14) I have no idea 

The values for extractable-phosphorus concentration 

(phosphorus soluble in dilute hydrochloric acid and 

sulfuric acid, Olsen and Sommers 1982-also knom 

as double-acid phosphorus or Mehlich-1 phosphorus) 

are expressed in parts per million (ppm). The progrm 

evaluates phosphorus values in relation to drainage 

class. If extractable P is not known for the site being 

evaluated, values from similar sites in the area or published values from the soil series can be used am an 

estimate if the same chemical analysis procedure vvaa 

used. However, some soil series6 have wide ranges in 

P concentrations; use of average values for these se~es 

may markedly reduce the accuracy of the site index 

prediction. In. addition, for imperfectly drained sites, 

the PYTSITE model predicts greater changes in sib 

index based on P concentraLion than changes bd on

any other variable. Thus, the user is strongly eneouraged to obtain site-specific information on extractable 

P if accurate estimates of site index are desired. If no 

'Soils with the widest range in P values appear to be lmated primarily on the emtern half of the Atlantic Coastal Plain. 
information is available on P concentration, the user 

can select "I have no idea'bption. The program will 

then give the user the choice of having the program 

select a "BEST GUESS" value for P concentration or 

exiting the program. ""BEST GUESS" values are b 

on averages by drainage class and should be considered as enly very apprsxirnrate '47alue~~ 

Users or others who would like to obtain a value for 

extractable phosphorus but have not collected soil 

samples before should use the following procedure: collect soil from the 0- to &inch layer at several (5 to f 0) 

spots in the area of interest. Remember to remove the 

organic layer before taking the sample. An easy way to 

take the samples is with a push probe. (Many county 

Cooperative Extension or SCS offices have push 

probes that can be borrowed). Remove any roots, other 

organic material (such as needles, leaves, and twigs), 

and rock fragments (with a 2-mm sieve or by picking 

the pieces out by hand). Thoroughly mix together the 

soil from the different sampling spots. Let the sample 

air-dry, and then send a representative subsample to a 

laboratory for analysis. 

Cooperative Extension agents have access to a soil 

testing service, so users or others interested should 

contact their local Extension office for more information. The Extension office will specify how much soil 

to send, and they may provide shipping containers for 

this purpose. Remember to specify that the laboratory is to use the double-acid extractable-phosphorus 

procedure (also known as Mehlich 1) and ask that the 

results be reported in parts per million (ppm).7 Users 

will probably also want to have the laboratory analyze 

the sample for organic matter content. If suitable soil 

analyses8 are not available through the local Extension office or through one's organization, the local 

Extension agent should be asked to check with a soils 

specialist, or one can contact the soils department in a 

nearby college or university and ask for the name of a 

reliable laboratory. 

*Select a value for the percentage of organic 

atratter in the surface soil (from 0 to 6 inches) 

from the following list: 

(1) <1 

(2) 1-1.9 

(3) 2-3.9 

(4) 4-6.9 

(5) '7-9.9 

(6) > = 10 

Choices are given for percentage of organic matter content in the top 6 inches of soil. If the user has values 

for individual horizons, a weighted value for the top 6 

inches of surface soil should be used. Organic matter 

content is evaluated in relation to soil order. Concentrations of 1.5 to 2.5 percent are common on welldrained forest; sites %?hen soil orders are not Spodosols 

or Histosols. If the user indicated in the question on 

soil order that the soil is a Histosol, the model assumes 

that the organic matter content of the soil is high and 

skips over this part of the program. Sites having fresh 

alluvium, a history of agricultural use, or intensive 

site preparation will probably have lower values. 

Organic matter in piles or windrows should not be eonsidered as part of the site. 

'Users may want to have the so41 tested zit a laboratory for organic matter content. They should refer to 

the discussion above on phosphorus for the procedure 

to be used in collecting the samples. If the user wants 

to obtain accurate values for organic matter determinations, it is especially important that roots, charcoal, 

and other organic material be carefully removed 

before analysis. 

Select a value for the parent material of the soil 

from the following list: 

(1) Coastal Plain sediments (or organic 

materials) 

(2) Loessal material deposited over Coastal 

Plain sediments (loess less than 4 ft thick) 

(3) Silty alluvium 

(4) Thick loessal deposits (loess greater than 4 

ft thick) 

This information on parent material helps characterize the general fertility of the soil. In addition, thin 

loess deposited over Coastal Plain sediments is much 

less favorable for tree growth than thick loess deposits 

because of both the thinner layer of nutrient-rich 

material and the presence of fragipans that commonly 

develop when this combination of materials is present 

(particularly on sites having slow internal water 

movement). Sites having thickness of loess at approximately 4 feet should be classified only as "(4) Thick 

loessal deposits. . ." if a pan is not present within 4 

feet of the surface. If the user indicated that the soil 

order is a Histosol, the program assumes the parent 

material is organic and skips this portion of the 

program. 

7Users having extractable-phosphorus values in units of pounds 

per acre or pounds per acre-furrow-slice should divide by 2 to convert 

to parts per million. 

'For example, some laboratories are primarily set up for analyzing nutrient requirements for agricultural crops and may not be 

able to provide information on double-acid extractable phosphorus. 
---Definikions and ranges for program variables 
alorvd-Evaluation of the thickness of A1 or Ap 

horizon in relation to soil order and slope position. 

Possible values range from 0 (any A horizon thickness value if soil order is Entisol or Inceptisol or an 

A horizon at least 5 inches thick if any other order) 

to - 7.5 (A horizon less than 1 inch thick, soil order 

not Entisol or TnceptisolL and midslope rrs upperclop9 

positions). 

aspval-Evaluation of aspect in relation to percent 

slope. Possible values range from 0 (aspects other 

than south or southwest) to - 3 (south or southwest 

aspects when slope is 220 percent). 

consval-Evaluation of soil consistence. Possible 

values range from 0 (any consistence if rooting 

depth is more than 40 inches) to - 6 (rooting depth 

less than 40 inches with firm soil consistence) to 

- 13 (rooting depth less than 40 inches and A horizon is cemented, or cementation occurs in the top 6 

inches of soil). 

drconv-Conversion factor based on drainage class 

used to convert 50-year site index predictions to 

25-year predictions. Values range from + 5 (excessively drained) to - 6 (very poorly drained). 

drexval-Evaluation of soil drainage class and access 

to extra water. Possible values range from + 5 (moderately well or well drained with summer water 

table at 6 to 10 feet and intermittent stream channels present) to - 8 (excessively drained with no 

special features). 

drppval-Evaluation of extractable phosphorus in 

relation to soil drainage class. Possible values range 

from -t 4 (P>50 ppm, any drainage class) to - 16 

(P<0.5 ppm and drainage class is very poor). 

Within a drainage class the relationship between 

phosphorus class and drppval is exponential. 

eroval-Evaluation of past gully erosion. Possible 

values range from O (no or slight erosion) to - 10 

(severe gully erosion). 

floodval-Evaluation of frequency and timing of 

flooding. Possible values range from + 2 (flooded in 

winter or early spring) to - 10 (flooded in late 

spring or summer). Continuous flooding will result 

in the site being judged unsuitable for loblolly pine. 

omorval-Evaluation of organic matter in relation to 

soil order. Possible values range from 0 to - 8. Low 

amounts of organic matter are considered less detrimental when the soil order is an Entisol or an Inceptisol than when it is one of the other possible orders 

for mineral soils. Histosols are automatically 

assigned a value of O for omorval. 

orval-Evaluation of soil order. Possible values range 

from 0 (Entisols or Inceptisols having a soil texture 

that is not sand1) to - 12 (Spodosols) and - 17 

'The purpose of this textural restriction is to separate sands that 

have low nutrient-holding capacity from other young soils that generally have higher nutrient-supplying abilities. 

(Histosols). Several soil orders result in warning 

messages being printed on the output. 

phval-Evaluation of surface pH. This value is modifIed if pH chmges with depth. Possible values range 

from 0 to - 12. The highest values of phval are 

assigned for sites with pH values between 4.39 and 

5.5 Phval ssn also resulf in that site being judged 

unsuitable for loblolly pine (pH ~3.5 or pH >8.5). 

pmval-Evaluation of soil parent material. Possible 

values range! from +6 (thick loess deposits) to 0 

(Coastal Plain sediments or organic parent material). 

puval-Evaluation of past land use. Possible values 

range from +4 (agricultural past use or intensive 

competition control) to 0 (past land use was forest). 

rfval-Eva!uation of rock frawent content. Possible 

values range from 0 to -8. No deduction is made 

unless rock content in the A horizon or average rock 

content in the soil profile is equal to or greater than 

20 percent. 

rtdval-Evaluation of effective rooting depth. This is 

done separately for each drainage class. Possible 

values range from 0 to - 15. Extremely shallow 

rooting depths (less than 15 inches) will result in 

the site being judged unsuitable for loblolly pine. 

sbandval-Evaluation of special features in deep, 

well-drained sands that improve site quality. The 

type of feature and the depth in the soil profile 

where the feature is first observed are used in the 

evaluation. Values range from 0 (no special features) 

to +5 (one of the following occurring within 65 

inches of the soil surface: continuous clay films on 

sand grains, layers of alternating texture, or presence of at least one finer textured layer at least 3 

inches thick). 

slslval-Evaluation of slope position and percent 

slope. Possible values range from O to - 20. Sites in 

lower or lower noninfluencing slope positions having 

slopes of 0 to 2 percent receive the best rating. Sites 

in upperslope positions having slopes greater than 

25 percent receive the worst rating. Slope position is 

also considered in the evaluation of alorval. 

stratval--Evaluation of stratified soil profile. Possible 

values are + 3 (stratified) and 0 (not stratified). 

stval-Evaluation of soil structure. Possible values 

range from 0 Qranular or crumb structure) to - 6 

(massive, with clayey subsoil texture). StruGLure is 

also considered in the evaluation of texsval. 

tckeval-Evaluation of thickness of the E horizon. 

Possible values range from 0 to - 5. Any E horizons 

less than 6 inches thick are assimed a value of 0. 

The value for tckeval is constrained so that the sum 

of texaval and tckeval is equal to or greater than the 

most negative value possible for texaval. 

texaval-Evaluation of texture of the surface horizon. 

Possible values range from 0 to - 13. Soil textures 

receiving the highest values for texaval are fine 
sandy loam, very fine sandy loam, loam, silt loam, 

and silt. Soil textures receiving the lowest values 

are sand and coarse sand. 

kxcval-Evaluation of change in texture from the 

surface horizon to the subsoil. This is evaluated in 

relation to depth to subsoil, texture of the topsoil, 

aad slope position. Possible v81ue~ range fmm -1-5 

(sites having sandy topsoil, much finer text 

subsoil close to the surface, and in midslope or 

upperslope positions) to -2 (sandy clay or sandy 

clay loam subsoil when surface soils are not sandy). 

hsand-Evaluation of the presence of a sand or 

sandy loam horizon below the first subsoil horizon. 

Possible values range from 0 to - 12. Thick, coarsetextured horizons close to the soil surface are more 

detrimenkal than thin, finer textured horizons deep 

in the profile. 

texsval-Ewluation of bulk density of the subsoil 

with soil texture and structure taken into aceout. 

Possible values range from 0 to - 8. For a given bulk 

density and soil texture class, the values of temwil 

are higher (i.e., less negatix~e) \vhepar mil 

strongly developed. 

wdval-Evaluation of the effects of water deficit. The 

value is modified by the codes used for accea to 

extra water. Possible values range from 0 to -8. 

Mean annual water deficits of less than 2 inches are 

optimum. Wdval is lowest for sites having water deficits of 4 inches or more and seasonally perched 

water tables close to the soil su~ace during the 

winter. 

WSITE 

Soil-site Rediction System $lor LobIoBly Pine 

Site ID-Csossett EwerimentaB Forest, Compas"e;er;C; 3, Stawd 7 

Input Values 

county 

Slope position 

Aspect 

Past emsion 

Flooding 

Texture of sudace 

Texture for bulk density 

Stratification 

Stmcture of the surface 

Consistence of the surface 

Thickness of A1 or Ap 

Thickness of E horizon 

Subsoil rock content 

Special features in sands 

pH surface 

Extractable phosphorus 

Parent material 

Staee 

Percent slope 

Past land use 

Drainage class 

Extra water 

Texture of subsoil 

Bulk density 

Resence of sand. layer 

Structure of the subsoil 

Consistence of the subsoil 

Depth to s~nbsoi%. 

Surface rock eontent 

Effective rooting depth 

Soil order 

pH subsoil 

Organic matter 

Water deficit 

Site index (50-year) = 93 feet 

Site index (25-year) = 63 feet 

Rograrn Variables 

alorval 

consval 

drexvaI 

eroval 

omorval 

ghval 

puval 

rtdval 

slslval 

stval 

texaval 

texsand 

vvdval 

aspval 

drconv - 

drppval 

floodval 

QTV~ 

prnva1 

rfvd 

sbandval 

stsadval 

tckeval 

texcval 

texsval 
Harrirrgton, Constaxace A, 1991. mSITE-a new method of site evaluation for loblolily pine: mode1 development and user9s guide. Gen. Tech. 

Rep. SO-81, New Orleans, LA: US. Departme.int of Agriculture, Forest 

Service, Southern Forest Experimernf; Statim. 28 p. 

A model, named WSZTE, was developed to predict site index for loblcslIg pine bmed on soil eharactentstics site location on. the landscape, 

and land history. The model ww tested with data from several 

sources and judged to predict site index within + 4 feet (p<0.05). A 

computer program for IBM-compatible personal computers was detreloped baed on the model; tfae program is availa$le by request. 

Keywords: computer pmgrams, expert systems, models, Ptnus h&, 

site index, site quality,