United States

Department of

Agriculture

Forest Service

Southern

Research Station

Research Paper

SRS-36

VB Merch-Slash: A Growth-and-Yield

Prediction System with a Merchandising

Optimizer for Planted Slash Pine in the

West Gulf Region

S.J. Chang, R.L. Busby, P.R. Pasala, and J.C. Goelz
The Authors

June 2005

Southern Research Station

P.O. Box 2680

Asheville, NC 28802

S.J. Chang, Professor, Louisiana State University, School of Renewable Natural

Resources, Baton Rouge, LA 70803; R.L. Busby, Principal Economist, U.S.

Department of Agriculture, Forest Service, Southern Research Station, New

Orleans, LA 70113; P.R. Pasala, Graduate Student, Louisiana State University,

School of Renewable Natural Resources, Baton Rouge, LA 70803; and J.C. Goelz,

Principal Mensurationist, U.S. Department of Agriculture, Forest Service, Southern

Research Station, Pineville, LA 71360.

The VB Merch-Slash Program is available upon request with the understanding that the U.S. Department of

Agriculture cannot assure its accuracy, completeness, reliability, or suitability for any other purpose than

that reported. Neither the Southern Research Station nor the authors will be responsible for any damages

arising from the use of or inability to use this software. The recipient may not assert any proprietary rights

thereto nor represent the program to anyone as other than a Government-produced computer program. The

program may be obtained by sending a request to Southern Research Station, RM T-10034, P.S.B., 701

Loyola Avenue, New Orleans, LA 70113.

Use of firm, company, or trade names is for the reader’s information and convenience and does not constitute official endorsement or approval by the U.S. Department of Agriculture to the exclusion of any other

suitable product.
1

Abstract

A Visual Basic computer model that can be used to estimate the harvest

value of slash pine plantations in the west gulf region is presented. The

model uses a dynamic programming algorithm to convert stand tables

predicted by COMPUTE_P-SLASH into a listing of seven products that

maximizes the harvested value of the stand.

Keywords: Dynamic programming, economics, merchandising, optimization, Pinus elliottii Engelm. var. elliottii.

Summary

A new growth-and-yield model with a merchandising

optimizer can predict volumes and values of thinned or

unthinned slash pine (Pinus elliottii Engelm. var. elliottii).

Visual Basic (VB) Merch-Slash forecasts product volumes

and stand values for stands partitioned into 1-inch diameter

at breast height (d.b.h.) classes at any stage of plantation

development from ages 10 through 50 years. The minimum

initial variables necessary to run the program are age,

density, site quality, minimum and maximum dimensions of

products, and product prices. The merchandising optimizer

converts predicted stand tables into an estimated optimum

product mix with a dynamic programming algorithm that

maximizes the selling value of the stand.

Introduction

A new computer program, VB Merch-Slash, can estimate the

product mix and per-acre stumpage values of planted,

thinned or unthinned, slash pine. VB Merch-Slash is the

counterpart of COMPUTE_MERCHLOB (Busby and others

1990). This program is written in Visual Basic.

Slash pine is one of the most important commercial tree

species and also a widely planted southern pine. Managers

of slash pine plantations need accurate and complete

predictions of product volumes and values to formulate

management guidelines. For example, they must decide

whether or not to thin; if the decision is to thin, they must

decide when, how much, and how often. The effects of

management actions on the types of products that can be

produced dictate timing, quantity, and frequency of thinning.

The stand must be premerchandised.

Zarnoch and others (1991) developed a yield prediction

system for thinned and unthinned slash pine plantations for

the west gulf region. A computer program for this system,

called Comprehensive Outlook for Managed Pines using

Simulated Treatment Experiments for Plantation Slash Pine

(COMPUTE_P-SLASH) (Baldwin and Ferguson 1991)

appeared shortly thereafter. This growth-and-yield prediction system provides both weight and volume yields in a

stand/stock table format for the aboveground portion of the

trees by 1-inch d.b.h. classes for 10- to 50-year-old slash

plantations on cutover sites. Many users want yields to be

separated into product classes.

Busby and Ward (1989) developed a merchandising optimization routine called MERCHOP for growth-and-yield

prediction systems. The routine gives users a wide selection

of product categories and mathematically derives optimum

product mixes for any yield output. The algorithm divides a

stand table into six products plus residual wood. Users

specify product prices, maximum and minimum product

sizes, minimum harvest volumes, and a factor to account for

degrade due to form and other defects.

VB Merch-Slash represents the first attempt to merge

MERCHOP with the slash pine stand growth-and-yield

prediction system. Zarnoch and others (1991) earlier

presented a general overview of the growth-and-yield model

system. This paper now provides essential background

information about the growth-and-yield prediction system

from Baldwin and Ferguson (1991) and the merchandising

optimization system from Busby and Ward (1989). We also

provide the input requirements to run the model, an example

of output, and a typical forest management example to guide

system users.

Program Components

Growth-and-Yield Subroutines

The growth-and-yield subroutines are based on measurement data from study plots in unthinned and thinned slash

pine plantations. The data pool consisted of 507 unthinnedstand yield observations and 543 thinned-stand growthperiod observations from 0.10- to 0.25-acre plots in slash

pine plantations established on problem-free, cutover

forestland. The growth period was generally 5 years, but

VB Merch-Slash: A Growth-and-Yield Prediction

System with a Merchandising Optimizer for Planted

Slash Pine in the West Gulf Region

S.J. Chang, R.L. Busby, P.R. Pasala, and J.C. Goelz
2

ranged from 3 to 11 years. The plots avoided areas where

survival was poor or where heavy insect, disease, or other

damage was present. For the most part the thinning was

from below, but at later ages a good distribution of tree

diameters across the plot was also a criterion. In addition,

there were 530 residual-stand observations of postthinning

conditions (Zarnoch and others 1991).

The three-parameter Weibull function (Bailey and Dell

1973) describes the d.b.h. distribution in a plantation. At any

time in a stand’s development, the model predicts the

minimum (X1), quadratic mean (Xqmd), and 93rd percentile

(X93) stand diameters as functions of the number of trees

surviving (TS), stand age from planting (A), and dominant

height (HD) or site index (SI). Algebraic and numerical

techniques then estimate Weibull parameters (Zarnoch and

others 1991). Thus, given the prediction of the number of

trees surviving, the model can partition the total number of

trees into 1-inch d.b.h. classes, and construct a stand table.

Growth and Yield of Unthinned Stands

When starting with an unthinned stand, the model requires

A, HD, and TS as input. If HD is not available as input, a SI

function can predict HD. If the user does not know TS; it

can be predicted as a function of trees planted per acre

(TP), A, and HD, or as a function of HD and basal area per

acre (BA).

Given this initial information, the prediction of X1, Xqmd,

and X93 as functions of HD, TS, and A can describe the

current stand. The next step estimates the Weibull parameters and produces stand/stock tables.

To estimate future stand attributes and future stand/stock

table information in unthinned stands, the first step projects

TS from time A1 to time A2 using a survival function TS2 = f

(TS1, SI, A1, A2). The SI equation then produces HD2 at A2.

These values produce estimates of X1, Xqmd, and X93 at time

A2. In the final step, the model yields new Weibull parameters and derives a new stand/stock table.

Stand Attributes After Thinning

The model determines the residual stand after thinning by

predicting “thinned” values for X1,a, Xqmd,a, X93,a, TSa, and

BAa (“a” denotes an after thinning value). The next step uses

these values to obtain the Weibull parameters. Data were

available only for a modified low thinning. Because there

was a significant difference in the resulting stand between

the first thinning and subsequent thinnings, Zarnoch and

others (1991) developed two separate sets of equations. One

predicts the stand characteristics following the first thinning,

and the other predicts the residual stand after any subsequent thinning.

The model can express the target density for each thinning

as residual stand BA or TS. Zarnoch and others (1991)

developed the equations for X1,a,, Xqmd,a, X93,a as functions of

the before-thinned values of the predictor variable, HDb

, and

BAa/BAb (“b” denotes a before-thinning value). If the

chosen thinning target is TSa, the model first predicts the

value for BAa from an equation relating TSa / TSb = f (BAa /

BAb)

Growth and Yield of Thinned Stands

Equations in the thinned stand section of the yield system

take either predicted or user supplied after-thinning stand

information at age A1. The model projects these values into

the future to A2 to provide stand table predictions at the new

stand age. The model predicts the same stand diameter and

height attributes as elsewhere, and these values yield

Weibull parameters to describe the new stand.

The model predicts diameters at a second age (“2”) in

thinned stands (X1,2, Xqmd,2, and X93,2). Equations use the

initial values of the predicted variables X 1,1, Xqmd,l, or X93,1,

respectively, and TS1, A1, and A2. Another survival equation,

TS2 = f(TS1, SI, A1, A2), predicts survival in thinned stands.

Stand/Stock Table Estimation

The model generates stand tables for any age by apportioning the total number of surviving trees into 1-inch d.b.h.

classes according to the predicted Weibull distribution for

that age. Development of a stock table requires a total height

prediction equation, volume or weight equations, and taper

equations. We used two height prediction equations—one

for trees from unthinned stands and one for trees from

thinned stands. They predict total tree height as a function of

d.b.h., HD, A, and TS. The volume, volume ratio, weight,

weight ratio, and taper equations are obtained from Baldwin

and Ferguson (1991).

Merchandising Routines

The merchandising routines use dynamic programming to

find the optimal mix of products that can be cut from stands

described by the growth-and-yield routines. Users specify

allowable products, product prices, maximum and minimum

product sizes, minimum product harvest per-acre volumes,

and the percentage of defect due to form and other defects.

Six different products can be specified: (1) poles, (2) veneer

bolts, (3) saw logs, (4) chip-n-saw logs, (5) pulpwood bolts,

and (6) chips. Residual wood can account for any available
3

wood in the boles of the trees not accounted for in the other

six products.

The basic problem is to find the highest value of stumpage

that may be cut from a stand (STDVAL) given certain

constraints. The optimal solution, STDVAL, has a particular

combination of associated products (STDVOLi

). Each

STDVOLi volume must meet the minimum product-specific

harvest value (HARVSTi) constraint if the product is to be

considered in the analysis at all. The solution technique is

summarized in equation 1 (Busby and Ward 1989):

 (1)

subject to: STDVOLi ≥ HARVSTi

,

where

VALUEi = per-unit value of product i

All other variables are as previously defined.

Busby and Ward (1989) give details on how this problem is

solved.

Program Description

The model allocates the entire bole of each tree to one or

more of the six product categories or to residual wood.

The units of measurement for the products are cords, tons,

and board feet (in Doyle, Scribner, or International 1/4-

inch), depending upon the type of product being produced.

A three-step process calculates cords. The first step calculates the total cubic-foot volume of the solid wood inside

bark (i.b.) for each log using COMPUTE_P-SLASH

routines. The equation for thinned and unthinned stands

(Baldwin and Ferguson 1991) is:

 ln(TVib) = – 6.93385 + 2.07195(ln D)

 + 1.13645(ln H) (2)

where

TVib = the total stem volume (cubic feet, i.b.)

D = the d.b.h.

H = total tree height (feet)

The second step finds the cubic-foot volume of any particular log. Baldwin and Ferguson (1991) provide coefficients

for an equation to estimate the ratio of total merchantable

volume, to any top diameter, to the total volume in the tree.

The equation for thinned and unthinned stands is:

 (3)

where

MVib = the merchantable volume i.b.

d = the upper stem diameter limit (outside bark, in inches)

The equation calculates the cubic-foot volume for any log

by finding MVib /TVib to the top and bottom. The difference

between the two ratios represents the volume of that log.

Total cubic-foot volume is the product of multiplying the

ratio by the total volume in the tree, which equation 2

calculated.

The third step in calculating cord volume is to divide the

resulting cubic-foot volume by the appropriate cubic–foot–

per–cord ratio (CUFTCD), which is the number of cubic

feet of solid wood per cord. The default value of CUFTCD

is 76, which is the conversion factor for straight and smooth

slash pine that has midlength outside bark diameters of < 6

inches (Koch 1972). Users may supply their own values for

CUFTCD, but entries must range from 55 to 95, inclusive.

A two-step process calculates tons. First, the green weight of

the total stem, including bark (TGWob), comes from the

equation in Baldwin and Ferguson (1991):

 ln(TGWob) = – 2.00410 + 2.10282(ln D)

 + .96566(ln H) (4)

where

All variables are as previously defined.

The second step is to find the weight of the particular log.

Baldwin and Ferguson (1991) provide coefficients for

equations to estimate the ratio of merchantable green weight

outside bark (MGWob) to any top diameter and TGWob. The

equation for thinned and unthinned stands is:

 (5)

where

All variables are as previously defined.

( )( ) 1 i

I

i i STDVAL ∑ STDVOL VALUE = =

MV

TV

d

D

ib

ib

= − ⎛

⎝

⎜

⎞

⎠

⎟

⎡

⎣

⎢

⎢

⎤

⎦

⎥

⎥ exp 12 68894

5 96264

6 40722 .

.

.

MGW

TGW

d

D

ob

ob

=

− ⎛

⎝

⎜

⎞

⎠

⎟

⎡

⎣

⎢

⎢

⎤

⎦

⎥

⎥

exp

1.37971

5 15075

5 06016

.

.
4

The total weight in pounds for any log is calculated by

finding MGWob /TGWob, first to the top and then to the

bottom of the log being examined. The difference between

the two ratios is the ratio represented by that log. Multiplying that log’s ratio by the total weight of the tree (calculated

in equation 4) yields total log weight. Dividing total log

weight by 2,000 expresses the result in tons.

Doyle and Scribner volumes in board feet are calculated

with the formulae from Grosenbaugh (1952):

 Doyle board feet = 0.0625 DIB2 L

 – 0.500 (DIB)L + L (6)

 Scribner board feet = 0.0494 DIB 2 L

 – 0.124(DIB)L – 0.269 L (7)

where

DIB = diameter inside bark at the top end of the log

L = length of the log

International l/4-inch volumes in board feet are calculated

using the formula from Koch (1972):

 International

 1/4 – inch board feet = 0.0498 DIB2 L

– 0.185 (DIB) L + 0.0422 L

+ 0.00622 (DIB) L 2 (8)

+ 0.000259 L 3 – 0.0116 L 2

where

All variables are as previously defined.

The model then requires price and product dimensions. The

user must specify unit prices by product, minimum and

maximum top-end diameters, minimum and maximum piece

lengths, the piece-length increment, and allowance for trim.

These inputs allow the user to specify the size and value for

each product. For example, the user could say that poles

must have a top-end diameter between 4 and 10 inches and

be between 40 and 70 feet in length. The user is asked to

define only possible products. The model solves the

problem of finding the best product mix.

Two problems arise when attempting to estimate the best

product mix: (1) the growth-and-yield model does not

provide form and disease product degrade information, and

(2) small quantities of even high-valued products may be

too expensive to sort out and sell. Form and disease product

degrade may be modeled by including the input variable

DEFECT, defined as the percentage of properly sized trees

incapable of making a particular product. For example, not

all trees 40 feet tall to a 6-inch top are suitable for pole

production because some may have bad form or a defect that

precludes the log being used as a pole. If 60 percent of all

properly sized trees have defects, DEFECT would be set to

60.

The second problem, the high price of sorting and selling

small quantities of even high-valued products, is solved by

including the HARVST variable. The user sets the minimum

harvest volume required in a stand if that product is going to

be produced at all (HARVST), and any solution to the model

must meet that constraint. For example, the user may

specify that a minimum of 1,000 board feet per acre of

sawtimber logs must be produced.

Program Operation

The VB Merch-Slash Program is currently available in

Visual-basic for use on the Microsoft Windows®-based

systems. Copies of the executable version of the program

are available from the authors. The appendix provides a

detailed example of executing the model.

Discussion and Conclusions

Diameter distribution and individual-tree-based growth-andyield prediction systems will each provide stand and stock

table output. Some, such as COMPUTE_P-SLASH, include

weight as well as volume yields. These features can help a

forest manager predict future product yield. However,

economic and product specification information are also

required to merchandise a stand effectively. Forest managers

have needed a method to estimate the economic impact of

proposed management practices (such as thinning), which

will not only alter total volume and weight yields, but also

alter the size and shape of tree boles and the ratio of

branchwood to bolewood. Thinning changes the product

mix of a stand that will ultimately be harvested. VB MerchSlash meets some of these management needs.

VB Merch-Slash provides two tables at the output stage—a

product yield table of specified product possibilities and a

value table predicting monetary amounts for the products.

The model describes the exact combination of products that

will yield the highest value. These represent the highest

value solutions, subject to the constraints imposed by the

users. Using model predictions, a manager can make profits

marketing wood products using plantation slash pine in the

west gulf region.
5

Literature Cited

Bailey, Robert L.; Dell, T.R. 1973. Quantifying diameter distributions with

the Weibull function. Forest Science. 19: 97-104.

Baldwin, V.C., Jr.; Ferguson, R.B. 1991. Comprehensive outlook for

managed pines using simulated treatment experiments for plantation

slash pine. (COMPUTE_P-SLASH) Computer program available for

download from www.srs.fs.fed.us/4111/techtrans.htm

Busby, Rodney L.; Ward, Keith B. 1989. MERCHOP: a dynamic

programming model for estimating the harvest value of unthinned

loblolly and slash pine plantations. Res. Pap. SO-254. New Orleans:

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

Experiment Station. 19 p.

Busby, R.L.; Ward, K.B.; Baldwin, V.C., Jr. 1990.

COMPUTE_MERCHLOB: a growth and yield prediction system with a

merchandising optimizer for planted loblolly pine in the west gulf

region. Res. Pap. SO-255. New Orleans: U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station. 22 p.

Grosenbaugh, L.R. 1952. Shortcuts for cruisers and scalers. Occas. Pap.

126. New Orleans: U.S. Department of Agriculture, Forest Service,

Southern Forest Experiment Station. 24 p.

Koch, Peter. 1972. Utilization of the southern pines. Agric. Handb. 420.

Washington, DC: U.S. Department of Agriculture. 1,663 p. 2 vol.

Zarnoch, Stanley J.; Feduccia, Donald P.; Baldwin, Virgil C., Jr.; Dell,

Tommy R. 1991. Growth and yield prediction for thinned and

unthinned slash pine plantations on cutover sites in the West Gulf

region. Res. Pap. SO-264. New Orleans: U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station. 32 p.
6

Input to run the model is entered on four forms. The first

three forms contain inputs to run the growth-and-yield

routines. The fourth form contains product information

needed by the merchandising routines. The output from the

model goes to a file or a printer.

Growth-and-Yield data—The growth-and-yield data needed

to run VB Merch-Slash are the same variables required to

run COMPUTE_P-SLASH.

Initially, VB Merch-Slash requires the user to provide the

age of the stand (A), an indication of site quality, and a

measure of stand density. There are two ways to indicate site

quality. The user may supply the mean dominant and codominant height of the stand (HD) to calculate site index

(SI). Alternatively, the user may supply the stand’s site

index and base age for the site index measure (BSAG). Note

the BSAG must be either 25 or 50 years. If the user has

estimates of both HD and SI, HD is preferred, because the

growth-and-yield model COMPUTE_P-SLASH would

ignore SI and BSAG if both measures of site productivity

were supplied. The user must also supply a measure of stand

density by choosing to provide either the basal area (BA),

trees surviving at the starting stand age (TS), or the number

of trees planted (TP). If the user has more than one measure

of stand density, it is advised to use the same priorities that

COMPUTE_P-SLASH uses, if all three measures are

provided: (1) TS, (2) BA, and finally (3) TP (fig. A.1).

Next, the user must answer the question: Has the stand been

thinned before? If it has, the user must state the number of

previous thinnings, the age of the last thinning, and either

the residual BA or the number of trees per acre remaining

after the last thinning (fig. A.1).

The title of the run to be printed in the output is entered in

the second screen. Users may also provide the number of

thinnings desired with this run and the thinning method as

residual basal area or residual number of trees. Note that the

model allows the user to specify up to seven different

thinnings. The remaining entries on that form are the ages of

thinnings and their corresponding target values (fig. A.2).

The model will automatically print both before and afterthinning tables plus the cut table for each thinning.

The third form (fig. A.3) allows the user to specify the ages

other than thinning ages for which the model should

generate output. The user supplies the number of tables to be

generated and the ages when the information is required. A

single run may produce a maximum of 15 tables. Because

the model will generate a before-thinning table and afterthinning table at each thinning, these will be subtracted from

the total of 15 tables.

Figure A.1—Input information is entered for the existing stand to be modeled.

Appendix

Input Specifications for VB Merch-Slash
7

Figure A.2—The user supplies a title to the run and indicates the number and type of thinnings to be modeled.

Figure A.3—The user then specifies the years desired for a stand stable. Stand tables will

automatically be produced for years in which a thinning occurs.

eighth requested table

seventh
8

The fourth form (fig. A.4) gathers information for the

model’s economic subroutines. The first listed variable is the

name of each product. The user may analyze six products.

He or she chooses a particular product by marking a “Y” in

the “PRODUCE?” column; “N” means that the product will

not be considered. The user should choose “Y” or “N” for

each product.

Five scale measures are possible in the model. They are: (1)

cords (C), (2) Doyle MBF (D), (3) International l/4-inch

MBF (I), (4) Scribner MBF (S), or (5) tons (T). Users

express their choice by placing the proper letter in “Scale”

column. The following are the default and allowed output

measurement scales for each product:

Model Measurement scale

Product default allowed

Poles Scribner Scribner, Doyle, or

International

Veneer bolts Scribner Scribner, Doyle,

International, or Cords

Sawtimber Scribner Scribner, Doyle, or

International

Chip-n-saw logs Cords Scribner, Doyle,

International, or Cords

Pulpwood bolts Cords Cords or Tons

Chips Cords Cords or Tons

Figure A.4—The user then provides key economic and product information needed to merchandise the stand.

A user marks the price per unit of each product, in column

“Price”; note that residual wood is not priced. A user places

the minimum and maximum scaling diameters for product i

in the next two columns; note, that these are expressed in

inches. If the log segment’s top end diameter is within the

range of the minimum and maximum scaling diameter, then

that product is considered for production. Note that the

ranges of scaling diameters will normally overlap. This

allows for the same diameter log to be used for more than

one product. The exact combination of products depends on

which combination will yield the highest value.

The minimum, maximum, and increment of log lengths for

each product examined are marked in the next three columns, respectively.

The final input requirement for the product dimensions is

TRIMi

, which is the trim allowance for each product.

The value for the stage interval used in the dynamic programming subroutines is 0.25 foot. Therefore, the total

length of all logs, including trim allowance, must be a

multiple of the stage interval. The model will print an error

message if this condition is violated.

A log with sufficient dimensions to produce a particular

product might have certain defects that preclude its use for

that product. A user marks the value for DEFECTi, an

estimate of the defect in the product i sized trees, in column

“Defect” for each product-specific line. If a log can provide

product i, then all products i+l are assumed from that log.

Hence, as one inputs the data, DEFECTi must be

≤ DEFECTi+l for all products.
9

If a user does not allocate part of the bole of the tree to one

of the first six products, the model automatically produces

residual wood.

The next required input is HARVSTi

. This is the minimum

stand volume of a particular product. The units of measure

for this variable are the same as the units of SCALEi. This

input value should be placed in column “Harvest”.

The user can save the results of the run to a file, print the

run, exit the program, or run the program again using the

original defaults of the program or with the previous

settings.

To illustrate the application of the VB Merch-Slash system,

a user can practice a relatively simple but realistic example.

It assumes an unthinned slash pine plantation with a site

index of 60 (base age 25) that is 15 years old and has 500

surviving trees per acre (fig. A.1). The user contemplates

immediately thinning the stand to 70 square feet of basal

area (fig. A.2). In addition to the information about thinning

at age 15 stand, the user enters stock, and value tables

without thinning for ages 20, 25, and 30 years (fig. A.3).

Sawtimber logs on Scribner scale, chip-n-saw logs in cords,

pulpwood bolts in cords, and chips in cords are the intermediate and final products desired (fig. A.4). The example

produces the amount and value of each product produced,

for each time period requested (fig. A.5).
10

Figure A.5—Example output of VB Merch-Slash (continued to next page).
11

Figure A.5—Example output of VB Merch-Slash (continued to next page).
12

Figure A.5—Example output of VB Merch-Slash (continued to next page).
13

Figure A.5—Example output of VB Merch-Slash (continued to next page).
14

Figure A.5—Example output of VB Merch-Slash (continued to next page).
15

Figure A.5—Example output of VB Merch-Slash (continued to next page).
16

Figure A.5—Example output of VB Merch-Slash.
Chang, S.J.; Busby, R.L.; Pasala, P.R.; Goelz, J.C. 2005. VB Merch-Slash: a growthand-yield prediction system with a merchandising optimizer for the planted slash pine

in the west gulf region. Res. Pap. SRS–36. Asheville, NC: U.S. Department of

Agriculture, Forest Service, Southern Research Station. 16 p.

A Visual Basic computer model that can be used to estimate the harvest value of slash

pine plantations in the west gulf region is presented. The model uses a dynamic programming algorithm to convert stand tables predicted by COMPUTE_P-SLASH into a listing

of seven products that maximizes the harvested value of the stand.

Keywords: Dynamic programming, economics, merchandising, optimization,

Pinus elliottii Engelm. var. elliottii.
The Forest Service, United States Department of

Agriculture (USDA), is dedicated to the principle of

multiple use management of the Nation’s forest resources

for sustained yields of wood, water, forage, wildlife, and recreation.

Through forestry research, cooperation with the States and private

forest owners, and management of the National Forests and

National Grasslands, it strives—as directed by Congress—to

provide increasingly greater service to a growing Nation.

The USDA prohibits discrimination in all its programs and activities

on the basis of race, color, national origin, sex, religion, age,

disability, political beliefs, sexual orientation, or marital or family

status. (Not all prohibited bases apply to all programs.) Persons

with disabilities who require alternative means for communication

of program information (Braille, large print, audiotape, etc.) should

contact USDA's TARGET Center at (202) 720-2600 (voice and

TDD).

To file a complaint of discrimination, write USDA, Director, Office

of Civil Rights, Room 326-W, Whitten Building, 1400

Independence Avenue, SW, Washington, D.C. 20250-9410 or call

(202) 720-5964 (voice and TDD). USDA is an equal opportunity

provider and employer.