United States

Department of Positive Returns from Investment in

Agriculture Fusiform Rust Research

Forest Service

Southern

Research Station

John M. Pye, John E, Wagner, Thomas P. Holmes,

and Frederick W. Cubbage

Research Paper

SRS-4
The Authors

John M. Pye is an Ecologist, Southern Research Station, U.S.

Department of Agriculture, Forest Service, Research Triangle Park,

NC 27709; John E. Wagner is an Assistant Professor, State

University of New York, Syracuse, NY 132 10; Thomas P. Holmes is

a Research Forester, Southern Research Station, 1J.S. Department of

Agriculture, Forest Service, Research Triangle Park, NC 27709; and

Frederick W. Cubbage is Department of Forestry Head and

Professor, North Carolina State University, Raleigh, NC 27695.

Cover:

Research plot in Olustee, FL--control group (L), rust resistant

family (R).

May 1997

Southern Research Station

P.O. Box 2680

Ashville, NC 28802
Summary

Fusiform rust [Cronartium quercuum (Berk.) Miy. ex Shirai f.

sp. fusiforme Burdsall et Snow] is a widespread and damaging

disease of loblolly and slash pine across much of the Southern

United States. Research by government and university

scientists has identified families of these species with

improved genetic resistance to infection by the disease,

allowing production and planting of resistant seedlings in

areas at risk. This paper describes an evaluation that

compared the cost of fusiform rust research to the simulated

benefits of rust resistant seedlings in plantations established

Southwide between 1970 and 2020.

Forest Inventory and Analysis (FIA) records from 1968 to

I986 provided the frequency of various infection rates and site

qualities among young plantations before substantial effects of

improved resistance. These site quality and infection levels

formed the inputs to stand-level yield models that simulated

the disease’s effects on mortality and product degrade as

stands mature. Yields at successive ages were merchandised

to three products and valued to permit identification of optimal

rotation age. We extrapolated stand-level yields to regional

levels using the FIA frequency data plus historical and

projected planting rates, The value of these regional yields

indicated plantation values in the absence of improved

resistance and formed our baseline scenario.

To estimate the impact of genetically improved resistance on

this baseline, we asked seedling producers from across the

South to estimate their production of resistant seedlings and

associated gains in resistance since 1970 and to project those

gains forward to 2020. We used these estimates to reduce

infection rates in the various years of the baseline scenario to

simulate the effect of improving resistance over time.

We handled selected uncertainties by running three separate

sets of simulations based on different assumptions. First, we

tested the impact of rotations longer than economically

optimal by adding a set of simulations that assumed a fixed

35-year rotation. Second, because we had no empirical

information on how well the industry is able to target resistant

seedlings into areas at greatest risk, we simulated two different

resistance scenarios: (I) uniform, where resistant seedlings

were placed uniformly across plantations of the South; and

(2) optimal, where resistant seedlings first went to conditions

where resistance would yield the greatest benefit. An

additional resistance scenario, total resistance, separately

valued rust damage and showed the maximum potential

benefits achievable from future research. Third. in the absence

of clear evidence on how efficiently loggers and mills utilize

infected stems, we simulated stand and regional yields under

four different levels of utilization: poor, pulpwood, sawtimber,

and full.

From the information supplied by seedling producers, we

found that production of rust-resistant seedlings rose rapidly

beginning around 1980, although gains in resistance were

initially small. By 1990, approximately 66 million resistant

slash and 144 million resistant loblolly seedlings were being

produced annually, representing 39 percent of slash seedling

production and 17 percent of loblolly. For those resistant

seedlings, the gains in resistance in 1990 were about 39 and

28 percent for slash and loblolly, respectively. Seedling

producers anticipated that both production and resistance

numbers would continue to improve for the next decade or

two.

Forest Inventory and Analysis data from three surveys between

1968 and 1993 indicated fusiform infection has been

widespread although rarely severe. About one-third of young

plantations have experienced fusiform infection rates of 5

percent or more, but infection rates of more than 45 percent

have typically occurred on less than 10 percent of the

plantation acres. For most of the period surveyed, infection

incidence in plantations was greater on high-quality sites but

similar for loblolly and slash pine once these site-quality

summary differences were taken into account. Neither of

these patterns was apparent in the most recent survey,

however, possibly due to increased deployment of resistant

seedlings and a southwestward shift in planting activity within

the region. Site quality of new plantations generally improved

over the three surveys.

Stand-level simulations showed rust affects both product mix

and gross yield. High rates of rust infection shifted harvest

volumes away from sawtimber and into lower valued products,

and except under full utilization in Ioblolly, also lowered total

harvested volumes. Changes in product mix and volume

resulted in reduced harvest values with the largest losses

occurring on higher quality sites and when infected stems were

poorly utilized. In slash pine, stands with low infection levels

at age 5 eventually produced financial returns equal to or

greater than similar uninfected stands. Generally, only when

initial infection rates exceeded 20 to 30 percent did rust result

in a net damage to stand value and yield. This presumed

competitive release effect was less common in loblolly

simulations, where low levels of rust generally produced at

least some financial damage.
The effect of genetically improved resistance on Southwide

plantation values depended on the year and on the assumptions

regarding rotation standard, resistance deployment, and

utilization efficiency, but several broad conclusions are

warranted. The total resistance scenario showed that fusiform

rust had a relatively small effect on region-wide plantation

volumes, generally less than 1 percent, although product shifts

caused additional loss of value. Rust effects on the value of

plantations Southwide depended on how efficiently the

industry utilizes infected stems. Rust reduced total plantation

values by 2.1 percent under the full utilization assumption but

by 12.7 percent under poor utilization. Small shifts in supply

imply small price effects, suggesting that plantation owners

would capture most of the benefits of improved resistance.

Improvements in resistance resulted in gradually increasing

benefits over time but the amount depended on the

assumptions used. Regional benefits for plantations

established in 1990 were $4 to $55 million but rose to $8 to

$67 million for planting in the year 2020. Placing resistant

seedlings optimally resulted in benefits 2% to 4 times higher

than deploying them uniformly. For most scenarios, projected

improvements in resistance did not capture all potential

benefits of rust elimination, indicating substantial room for

improvement through further research and development,

When annual benefits were aggregated over time with

appropriate discounting, region-wide benefits of improved

resistance totaled $108 to $999 million, depending on

assumptions of rotation length, utilization, and resistance

deployment. Such benefits were the direct result of

investments in research and development-investments that

cost government, universities, and industry an estimated $49

million in 1992 constant dollars. Comparing the various

benefit estimates with these costs showed net returns to

investment were $58.9 to $949.5 million with benefit:cost

ratios ranging between 2.2 and 20.4, We conclude that even

under our most conservative assumptions, past investments in

fusiform rust research have resulted in substantial benefits to

owners of loblolly and slash pine plantations across the South.
Table of Contents

Summary . . . . . . .................................................. i

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .._......._........... 1

Objectives..,...........,,,....................,............................. 2

PreviousResearch....,.,...................................................... 2

Methods .................................................................... 3

The Simulation Systemm ...................................................... 3

Stand-Level Simulations ..................................................... 4

Growth and yield ........................................................ 4

Product merchandising .................................................... 4

Product prices .......................................................... 5

Plantation establishment costs .............................................. 5

Economic value calculations ............................................... 5

Aggregate Benefit Simulations ................................................ 6

Distribution of conditions across the region .................................... 7

Accounting for total pine plantation area ...................................... 8

Estimation of rust resistance adoption ........................................ 9

Rust-resistant seedling deployment ......................................... 9

The aggregation process .................................................. 10

Summing values over time ............................................... 11

Implementation details.................................................... 11

Benefits calculations ..................................................... 11

Research Co.sts ........................................................... 12

Benefit:Cost computations .................................................. 12

Results and Discussion . .. . .............. . ........... . . ............ , . .......... . 12

Adoption of Rust Resistance ............................., .................... 12

Total seedling production ......... , ................. ............. , .......... 13

Resistant seedling production ...... . .........................., ............. 13

Resistance gain ......................................................... 13

Regional Fusiform Rust Infection Patterns ............ , .......... , ............... 15

Location ............................................................... 15

Siteclass .......................... . . .................................. 17

Class and locationn ............... ..__..........._........................ 17

Shifts in site class and species .......... , ..........................., ....... 17

Regional distributions ................................................... 18

Stand-Level Impacts .............. . . ........................................ 18

Aggregate Benefits ......................................................... 21

Volume benefits by yearr .................................................. 21

Financial benefits by year ...........................................*................ 22

Benefits summed over time ................................... . ........... . 25

Research Costs ............................................................ 27

Benefit:Cost Analysis ....................................................... 27

iii
Conclusions .... . ..........................................................:. . . 29

Resistance Gains and Regional Conditions ....................................., . 29

Financial Analyses and Management Implications .............................. ... 30

Regional Research Benefits ................................................... 30

Maximum Potential Gains .................................................... 32

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . l.... . . . . . . . . . . . . . . . . 32

Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . * , , . , . , . . *. . . . . _ . _ . . _ _ _ _ _ _ _ _ _ . _ . . .._ _ 32

Appendix A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..~....~~~~~ 35

AppendixB..............,..........,.........,......,.............................. 39

iv
Positive Returns from Investment in

Fusifor’m Rust Research

John M. Pye, John E. Wagner,

Thomas P. Holmes, and Frederick W. Cubbage

Abstract

Fusiform rust [Cronartium quercuum (Berk.) Miy. ex Shirai f. sp. fusiforme

Burdsall et Snow] is a widespread and damaging disease of loblolly and slash

pine in the South. Research has identified families of these pines with

improved genetic resistance to the disease, allowing production and planting

of resistant seedlings in areas at risk. This study compared the cost of fusiform

rust research to the simulated benefits of rust-resistant seedlings in plantations

established Southwide between 1970 and 2020, Seedling producers provided

estimates of resistant seedling production and gains in resistance over the

period. Stand-level simulations evaluated the impact of various infection rates

on financial yield on low-, medium-, and high-quality sites of each species,

taking into account both motility and product degrade effects of the disease.

Two rotation regimes and four levels of infected stem utilization were

explored. Stand-level yields were extrapolated to regional values using longterm distributions of plantation conditions from Forest Inventory and Analysis

surveys. Simulation results showed that past investments in fusiform rust

research of $49 million will return benefits to plantation owners of between

$108 and $999 million in 1992 constant dollars. Expected improvements in

resistance will not eliminate all financial damages from the disease;

simulation results indicate substantial financial benefits yet remain for

additional research and development.

Keywords: Cronartium fusiforme, disease control, disease resistance,

economic damages, fusiform rust, loblolly pine, plantation valuation, research

benefits, slash pine.

Introduction

Fusiform rust [Cronartium quercuum (Berk.) Miy. ex Shirai f.

sp. fusiformeBurdsall et Snow] has long been recognized as

the most damaging disease of southern pine forests. Occurring

in a band across the heart of the South, the disease is prevalent

in the most productive stands-loblolly (Pinus taeda L.) and

slash (P. eliottii Engelm.) plantations on higher quality sites

(Anderson and others 1986a, Borders and Bailey 1986). Galls

on seedlings can cause early mortality, while survivors with

resulting stem cankers are subject to breakage and are

unsuitable for solid wood products (Get-on and Hafley 1988,

Holley and Veal 1977). The most effective means of reducing

damage from this pathogen has been to plant genetically

resistant seedlings.

For three decades, the U.S. Department of Agriculture, Forest

Service (Forest Service), academic institutions, and forest

industry have maintained extensive research efforts to improve

resistance of planted growing stock to fusiform rust. Research

has included characterizing the basic biology of the disease,

identifying resistant genotypes, establishing rust-resistant

orchards, and developing standardized methods for screening

seedlings for rust susceptibility. A casual evaluation suggests

that fusiform rust research has achieved considerable

success-increasing amounts of planted stock embody

improved genetic resistance, and infection rates in planted

southern pine stands appear to have waned during the 1980's.

A more thorough evaluation of this apparently successful

research program would analyze its economic benefits and

costs. Although a considerable body of literature exists on the

productivity of agricultural research expenditures, we found

fewer studies examining forestry research productivity, and

most of these investigate technical change in the forest

products industry. We are aware of two studies that examine

the productivity of research investments in timber growing

technology. Hyde and others (1992) examined aggregate

research investment effects on southern pine productivity,

Huang and Teeter (1990) evaluated net economic returns from

research on a single factor influencing pine productivityherbaceous weed control. Our analysis is more similar to the

latter study because it focuses on a single factor--fusiform

rust.

Fusiform rust affects both the quantity and quality of timber

produced per unit area; therefore, increased rust resistance

translates directly to increased economic value. Because

rotation ages in southern pine plantations are relatively short,

financial gains from genetic improvements are realized

quickly. Moreover, discounting in short rotation species

affects these gains less than in species with long rotations, We

expect the benefits of improved resistance to fusiform rust in

southern pine to be greater than the benefits of research

investments in many other timber types.

The methods we develop to evaluate fusiform rust research in

the South are applicable to other timber types and timber

investments. In this approach, we simulate timber production

and merchandising processes at the stand level and then

aggregate quantities and values across the region to estimate

aggregate production and value functions. By comparing a

base level scenario representing no investments in fusiform

rust research with a variety of scenarios representing potential

impacts of fusiform rust research, we can estimate the

aggregate production and value impacts of fusiform rust

research. Using data collected on research program costs, we

evaluate the economic efficiency of this research by computing

a benefit:cost ratio for each of the potential outcomes.
In reporting our research, we divide this paper into five

sections. First, we present the overall goals and objectives.

Second, we review previous work related to fusiform rust

evaluations. Third, we describe our research methods. Fourth,

we present the results of our analysis. Finally, we discuss our

conclusions and their implications.

Objectives

The overall goal of this study is to evaluate the economic

efficiency of historical fusiform rust resistance programs. This

research evaluation includes the following specific objectives:

1. Estimate the aggregate biological effects of fusiform rust

resistance breeding programs.

2. Estimate the stand-level (microeconomic) impacts of

fusiform rust on timber yields and timber growing

investments.

3. Estimate the resulting aggregate (macroeconomic) increases

in volume and value of southern softwood timber supply,

resulting from improved rust resistance.

4. Estimate the costs of fusiform rust research.

5. Compute benefit:cost ratios and discuss their implications

for future research strategies.

Previous Research

Several studies have valued the impacts of fusiform rust, its

control, or other issues related to the economic effectiveness of

forestry research. Studies made use of a variety of empirical

and simulation approaches. Some studies were conducted at

the stand level, while others evaluated regional impacts.

Powers and others (1974) used Forest Inventory and Analysis

(FIA, previously Forest Survey) data from South Carolina,

Georgia, and Florida to extrapolate an estimated loss from

fusiform rust of $28 million across the South in 1972. Gross

volume losses to galls in plantations and natural stands were

valued without explicit treatment of product shifts or mortality

effects. The authors considered the estimated loss conservative

because it excluded the costs of spraying protective fungicide

in tree nurseries and replacing infected trees in seed orchards

and did not separately account for losses of higher value

products.

Holley and Veal (1977) concluded that the amount of

quantitative data on the effects of fusiform rust on growth,

yield, and degrade in pine stands across the South was

insuffkient to estimate the economic impacts of fusiform rust.

However, they concluded the major effect of rust-associated

mortality is a reduction in yield. Lowered quality of the

infected stem also results in loss. They speculated that damage

from fusiform rust, on a Southwide basis, would run into the

tens of millions of dollars annually and would increase over

time.

Anderson and others (1986b) also used FIA data to estimate

the economic losses caused by fusiform rust. They estimated

that total losses for the South Central States (Texas and

Oklahoma through Tennessee and Alabama) equaled $35

million per year based on a discounted cash flow, The harvest

volumes estimated were gross volumes because they only

merchandised the individual stems into sawtimber and

pulpwood products.

Busby and Haines (1989) developed a model to estimate the

damage of fusiform rust infection in slash pine stands. The

model required the user to specify the percent reduction in

sawtimber to account for degrade from fusiform rust but this

information was unobtainable; therefore, degrade was not

included in their evaluation. Geron and Hafley (1988) used the

North Carolina State University (NCSU) Managed Loblolly

Pine Plantation Growth and Yield Simulator to examine the

stand-level impacts of fusiform rust on loblolly yields with two

different merchandising assumptions. They concluded that the

majority of the stem galls occur below 8 feet (It), the most

valuable portion of the stem. As the percentage of infection

increases, solid wood losses increase as well.

Redmond and Anderson (1986) examined the economic

benefits of the fusiform rust screening center in Asheville, NC.

They concluded that the screening process can identify

superior resistant seedlots and reduce the growth, yield, and

degrade losses due to fusiform rust when these seedlings are

planted in high-hazard fusiform rust areas.

Hyde and others (1992) and Newman (199 1) used a statistical

model to estimate research-driven improvements in forest

productivity across the South between 1952 and 1985. Using

FIA data aggregated to the State level as units of observation,

they were able to run regressions on a set of explanatory

variables including dummy variables for different time

periods. After accounting for the variation explained by other

biological and ownership variables, the variation in the

aggregate inventory variable explained by the dummy

variables was attributed to productivity gains from research

expenditures. Because the dummy variable approach simply

2
captures temporal variation that is not explained by other

explanatory variables, attribution to a single causal factor is

somewhat tenuous.

None of these studies satisfactorily estimated the regional

economic benefits and costs of fusiform rust research itself,

tracking the effects of such research through seedling

production, planting, growth, harvesting, merchandising, and

sale. The lack of such a comprehensive analysis of this

research’s effectiveness led to the evaluation reported here.

Methods

We chose a simulation approach over empirical alternatives

for this evaluation for several reasons. Simulation offers

unambiguous assignment of causality, ability to estimate

market effects yet to occur, and discrimination of marginal

effects despite fluctuations in the market. The availability of

growth and yield models for slash and loblolly pine that

incorporate effects of fusiform rust infection was also

important to our decision. Using our own routines and data

from other sources, we were able to design a simulation

model that projects the impacts of changing fusiform rust

resistance on regional timber supplies.

The Simulation System

To evaluate the benefits of genetically improved resistance,

we must contrast the values of southern plantations without

rust-resistant technologies against the values of these

plantations with the improved rust resistance. The difference

between plantation values for these two scenarios provides

our measure of the benefit of the fusiform rust research

program.

The modeling scheme we developed simulates the

establishment of new plantations and their growth to rotation

age, harvesting, merchandising into products, and valuation.

The sequence is simulated separately for loblolly and slash

and across a range of initial site qualities and early fusiform

rust infection levels to reflect the diversity of plantation

conditions across the region. Southwide production is

calculated by multiplying the simulated per-acre yields and

values for each initial condition with that condition’s

corresponding area in the region and then summing across

the South.

This sequence provides the basis for the without scenario,

The with scenario is simulated by modifying the without

scenario to reflect the deployment of genetically resistant

seedlings. Resistant seedlings are assigned to particular site

conditions and their resulting infection rates reduced to

reflect their higher resistance. The deployment of seedlings

and the establishment and growth of plantations are repeated

across a range of years for the with and without scenarios to

reflect changing planting rates and the improving availability

and resistance of genetically resistant seedlings over time.

The simulation encompasses loblolly and slash pine

plantations established throughout the South between 1970

and 2020.

We can illustrate the simulation system as a mathematical

formula. Let Yiik represent the yield per acre and let Vllk

represent the economic value per acre from a site planted

with species i, on site qualityj, with infection level k. Then

the aggregate production functions for the South can be

represented by multiplying the number of acres planted to

species i on site quality i with infection level k in year t, AC&

by the appropriate yield values Ytik, and summing across

historical and anticipated values for the arguments

represented by the subscripted variables:

VdlUtE wirhout = c c c c AU&Y,,,

i j k 1

where

Volumetib = aggregate production from timber stocks

without genetic improvement.

(1)

Aggregate values for the South can be computed by

substituting Vtikfor Yijk in equation (1).

value with = cc cc C4jktrvfjk- i j k t r

(2)

To reflect the introduction of resistant seedlings, equation

(2) is modified to include a resistance factor r. The subscript

only appears on A because the increased resistance is

simulated as a shift in the distribution of acres toward lower

infection rates.

The benefits attributable to introduction of rust-resistant

planting stock can be computed as the difference between

values with and without the use of genetically improved

stock:

Volume Benefits = Volume with - Volume withour, (3)

V&e Bene.ts = Value”‘” - Value lvifhvut. (4)

3
Stand-Level Simulations

Impacts of infection at early ages were projected through to

yields at various ages for a range of initial site conditions. The

range of initial site conditions for loblolly and slash pine was

represented by 3 levels of site quality, termed “site classes,”

and 11 levels of rust infection at age 5, termed “infection

classes.” The combination of 3 site classes and 11 infection

classes resulted in 33 initial conditions for each species.

Growth and yield-To generate yield tables for the 33 initial

conditions for slash pine, we used the University of Georgia

GAPPS model, developed from extensive growth and yield

research by their Plantation Management Research

Cooperative (Burgan and others 1989, Pienaar and others

1988). We used a second model developed by the North

Carolina State University (NCSU) Loblolly Yield Model

(Hafley and Smith 1989) to produce a similar set of yield

tables for loblolly pine. Both models project fusiform rustassociated mortality over time and infection rates of surviving

stems through the life of the stand.

For the simulations, we specified no thinning and an initial

planting density of 700 seedlings per acre. We assumed early

survivorship varied with site quality: 75 percent survival for

low-quality sites, 80 percent for medium, and 85 percent for

high. Infection classes for the simulations were indicated as 0,

10,20, . . . 90, 100 percent infection at age 5. The three site

classes were termed low, medium, and high, defined for

loblolly as site index 50,65, and 80 ft at base age 25.

Corresponding site indices for slash pine were 50, 60 and 70.

We estimated even-aged pine plantation yield tables for each

species and each of the 33 initial conditions, calculating yield

at 5-year increments starting with stand age 10 and ending

with stand age 35. For each age, the model reported volume

yield in cubic feet by breast height diameter class, as well as

an overall percentage of rust-infected trees. We applied this

overall stand infection rate to the number of stems in each of

the diameter classes to distribute infected stems to the different

diameter classes.

Model outputs were less reliable for extreme input values.

Volume predictions were more tenuous at the oldest ages than

for younger ages where empirical data were more readily

available, and prediction of yield at 100 percent infection rates

was also problematic. Because the model used for loblolly

pine calculations did not permit 100 percent initial infection as

an input, we substituted 99 percent. For slash pine, the model’s

results proved unstable at 100 percent infection so we used

quadratic regression to estimate outputs under these

conditions. In a few cases, this approach yielded either

negative product volumes or implausibly high pulp yields.

When this occurred, negative volumes were reset to zero and

each of the three product volumes was rescaled so their sum

matched a similarly extrapolated total stand volume.

Product merchandising-While both yield models

incorporated mortality effects of rust, additional

merchandising simulations were needed to capture how galls

affected wood utilization on the surviving stems. Because

empirical information was unavailable on logger responses to

galls, we used four timber utilization scenarios to account for a

range of possible merchandising intensities. Case I (poor

utilization) assumed any tree with a stem gall is left in the

woods. Case II (pulpwood utilization) assumed any tree with a

stem gall is pulped. Case TII (sawtimber utilization) assumed

the tree was pulped unless at least a 16-q gall-free log is

present. Case IV (full utilization) assumed optimal utilization

of all trees, even those with galls.

Within these constraints, we used the following rules to

merchandise logs. Sawtimber required at least an 8-ft, gallfree log with a small end diameter outside bark (sedob) 18

inches (in.) or a small end diameter inside bark (sedib) 2 6 in.

Sawtimber log lengths ranged from 8 ft to 16 ft in 2-ft

increments. Longer log lengths were preferred. Chip-n-saw

required at least an 8-ft, gall-free log with a sedob L 4 in. or a

sedib 13 in. The log lengths were the same as sawtimber.

There were no length restrictions for pulp. All trees were

merchandised to a 4-in. top sedob or 3-in. top sedib. In

loblolly, we assumed the gall was 2 ft in length; in slash, 4 ft in

length. In all but Case I, galls were pulped. We based the

small end diameters on personal communications with

knowledgeable loggers from the area of concern and

developed and programmed our own merchandising routines

to do these analyses because existing software was inadequate.

The loblolly merchandiser incorporated the model 4 plantation

taper equations used by the NCSU Loblolly Yield Model

(Max and Burkhart 1976). Loblolly yields were estimated

using Newton’s volume formula for a paraboloid We

assumed the only stem defect was fusiform rust galls.

Merchandising infected stems required information on where

the stem galls occurred and how many galls occur on an

individual stem. Geron and Hafley (1988) defined the

probability of a gall occurring at a given height on the stem.

To account for occurrences of more than one gall segment on a

stem, we used a 0.75 probability of one segment, 0.15 for two

I Personal communication. 1993. William D. Smith, Assessment

Coordinator, Southern Research Station, 3041 Cornwallis Road, Research

Triangle Park, NC 27709

4
segments, and 0.10 for three segments on any given infected

We used a uniform variate random number generator

combined with these frequency distributions to determine the

number of galls and the height of the segments on any given

stem. Therefore, merchandised yields of infected stands reflect

some degree of unreplicated stochasticity. Following

allocation of gall segments, each infected tree within a standage class and diameter at breast height (d.b.h.) class was

merchandised individually.

The slash merchandiser incorporated the yield and taper

equations used by GAPPS (Pienaar and others 1988). We

used the same assumptions as in the loblolly merchandiser,

with two exceptions. The number of gall segments on a slash

stem was based on research by Belanger and others (1985),

and their height on the stem was based on research by Webb

and Patterson ( 1984).

Product prices-Because fusiform rust infection greatly

affects timber utilization, detailed product price information

was critical in the valuation process. We recognized three

product classes: pulpwood, chip-n-saw, and sawtimber. For

sawtimber, we included the effect of log length on price,

Base product prices were computed using 1992 average prices

(Norris 1992) for the Southern States and sub-State regions

where fusiform rust is prevalent: Alabama, Arkansas (region

2), Florida, Georgia, Louisiana, Mississippi, North Carolina

(region 2), South Carolina, and Texas. State product prices

were weighted by removal rates to compute a weighted

average price per cubic foot. Using this method, the following

base prices were used in the economic analysis: (1) pulpwood

= $0.32 per cubic foot, (2) chip-n-saw = $0.66 per cubic foot,

and (3) sawtimber = $0.94 per cubic foot.

To account for the effect of galls on sawtimber log length and

therefore value, it was necessary to compute sawtimber values

for various log lengths. We assumed that lumber price

relations by length adequately represented log price relations

by length. Relations were calculated using reported prices

(Random Lengths Price Report 1993) for kiln-dried southern

yellow pine (P. echinata Mill.) averaged over the west,

central, and east reporting regions for dimensions ranging

from 2 by 6 to 2 by 12 in. and lengths ranging from 8 ft to 24

ft.

Linear models were estimated by regressing lumber prices on

lumber length. The estimated regression line was: price =

$302.91 + $6.45 x length. The t-statistics on the intercept and

2 Personal communication. 1993. Charles Walkinshaw, Plant Pathologist,

Southern Forest Experiment Station, Alexandria Forestry Center, 2500

Shreveport Highway, Fineville, LA 71360

length variable were 15.333 and 4.025, respectively, and the

adjusted r-square was 0.128. This regression can be

interpreted to mean that the base lumber price is $302.91 and

increases $6.45 with each foot increment in length. Therefore,

we used this relationship to estimate lumber prices for 8-, 10-,

12-, 14-, and 16-ft lengths. A relative price index was then

computed using 12 ft as the standard length. The index ranged

from 0.932 for 8-ft boards to 1.068 for 16-ft boards.

Finally, the relative lumber prices were applied to the base

stumpage prices to estimate log prices by length. We assumed

that the average prices were computed for 12-A logs (i.e., the

relative price index = 1). Multiplying the relative price indices

by the base timber price yielded stumpage prices in terms of

log lengths. Assuming a conversion rate of 200 per

thousand board feet, the following log values were computed

and used in the analysis:

Log length Value/ft3

8 ft $0.88

10 ft $0.91

12 ft $0.94

14 ft $0.97

16 ft $1.00

This pricing scheme provided a higher price per cubic foot for

longer logs than for shorter logs. No similar price premium

was provided for log diameter. For a given length sawtimber

log, larger diameter stems were valued at the same per-cubicfoot price as those with smaller diameters.

Plantation establishment costs-Regeneration costs were

based on cost trends published by Belli and others (1993). We

assumed the costs of establishing lobloly and slash pines were

equal; however, better quality sites required more intensive

site preparation because they supported greater vegetative

competition. Initial site preparation consisted of combinations

of shearing, chopping, raking, disking, and piling, with all site

classes receiving a prescribed bum. A herbicide treatment was

included on high-quality sites. Seedlings were hand planted on

low-quality sites, hand and machine planted on mediumquality sites, and machine planted on high-quality sites. With

seedling costs included, these activities led to regeneration

costs of $139, $155, and $197 for the low-, medium-, and

high-quality sites, respectively.

Economic value calculations-We chose two financial

measures of impact at both the stand and regional levels: net

present value at time "t" (PVt ) and soil expectation value

(SEV). Present value (PV) describes the value of a single

rotation and consists of the gross revenue at harvest less the

costs of stand establishment t-years earlier, shown in equation

(5). To keep revenues and costs commensurate, establishment
costs were compounded forward to harvest date. Harvest date

was chosen over planting date because harvest is when

management decisions finally influence timber markets. In

this evaluation, PV's from individual stands were summed

across the region and then over time to calculate the regional

benefits of improved resistance, following the approach used

by Liifgren (1988) to value genetic improvements. As with

other calculations in this evaluation, we assumed a discount

rate of 4 percent, per Row and others (1981).

where

W = v, - C(1 +r)‘, (5)

V, = the value (gross revenue) of the stand at stand age t,

r = the discount rate, and

C = the costs of regenerating the stand.

Soil expectation value represents the combined PV of an

infinite sequence of rotations, all discounted back to a

common stand establishment date and summed. Soil

expectation value is useful for comparing the long-term,

“bare-laud” value of a given system of management at time of

plantation establishment, the time genetically resistant

seedlings would be produced and deployed. We used SEV to

compare stand-level conditions and to compare the “with”

and “without” scenarios at selected points in time, an

approach previously used to evaluate impacts of fire and

insect outbreaks (Holmes 1991, Matte.11 1980, Reed 1983,

Routledge 1980). Because SEV includes future rotations as

well as the present rotation, it is not useful for aggregating

across changing technology. Therefore, in this evaluation it

described aggregate conditions at individual points in time.

Present value was used to aggregate regional benefits across

time.

SEV, = vt ( 1 +r )-’ - c

1 -( 1+r)-’ ’ (6)

Two rotation standards were evaluated in this study. The first

system assumed a fixed rotation length of 35 years, referred

to as the sawtimber rotation. Product yields at 35 years were

used as input to the PV and SEV equations. The results of

fixed rotations are relatively easy to interpret but imply an

uncommonly long rotation and simplistic stand management.

The fixed rotation standard is most useful for illuminating

simulation processes in the absence of shifts in rotation

length.

The second rotation standard maximizes SEV, referred to as

the optimal economic rotation. Under this standard, each

combination of species and site condition was evaluated

separately to determine its own year of maximum SEV.

Because the generated yield tables were computed using 5-

year intervals, SEV values were interpolated to determine the

maximum, using a three-step process, First, the SEV's were

calculated for stand ages 10 through 35 in 5-year increments,

using the yield data. Second, the six calculated SEV' were

used to estimate the coefficients of a fourth order polynomial:

SEV, = 0, + Pit + &t2 + &t3 + P4t4 + E,, (7)

where

t = stand age.

Third, coefficients of the regressions were used to predict

SEV annually between the stand ages 10 to 35. These were

compared to find the maximum SEV and corresponding

optimal rotation age T:

SEV, = Max SEV, (8)

where

t=10,.,,35.

Simple linear interpolation was used to estimate volumes of

pulp, chip-n-saw, and sawtimber for optimal rotation ages

that fell between calculated ages, Present value was

calculated from SEV:

PV = SEV[l - (1.04-‘]. (9)

Aggregate Benefit Simulations

We estimated regional harvest volumes and plantation values

over time by extrapolating from the stand-level yields

previously described. Cross-scale extrapolations are subject

to various forms of extrapolation error (Rastetter and others

1992). which we minimized by partitioning conditions into

classes and calibrating selected parameters to regional scale

measures.

For each of the 66 initial stand conditions, the fine-scaled

processes of stand establishment, growth, yield, and

harvesting were simulated. We then used region-wide

information on historical and projected planting and the

diffusion of rust resistance through the industry to aggregate

these simulations to regional measures of timber supply and

value.

Region-wide information needed to perform aggregate

simulations included information on acreages planted and

species mix over time, dissemination of rust-resistant

planting stock, amount of reduction in infection in that stock,

and information on baseline infection rates in the absence of

genetic selection. Several assumptions about fusiform rust

resistance and technology helped structure the simulations.

6
Evidence from field trials indicated that genetic gains in

resistance tend to be proportional across sites of differing risk

(Hodge and others 1993), i.e., genetic resistance conveys a

proportional reduction in infection rates. We assumed this

proportional reduction in infection in early years is the sole

effect of selection for genetic resistance to fusiform rust.

Having accounted for this genetic impact on early infection,

we assumed that plantations of similar infection rate and site

quality would all grow at the same rate and suffer the same

dynamics of new infections and mortality regardless of their

genetic source.

Assuming that resistance only affects infection rates implies

that selection for resistance can be attained without sacrifice of

growth. Conversations with tree improvement researchers

indicated no consensus on this issue. Some reported that their

resistant families showed growth equal to their other improved

lines, while others indicated that tradeoffs between resistance

and growth have been necessary. Because no definitive

scientific studies on such tradeoffs could be found, we chose to

assume that no tradeoff exists.

We also assumed that once resistant genotypes are identified,

their production and planting technologies are no different

than those of improved, nonresistant seedlings, Seed orchards

for resistant and nonresistant seedlings should cost the same

and be equally productive. We assumed nursery and planting

costs for both types of seedlings were also equal. Any

additional costs incurred in genetic selection for resistance

were included as costs for research, including the study of

fusiform rust biology and risk, the development of methods for

screening, and the evaluation of fusiform rust resistance.

Distribution of conditions across the region-we obtained

data on the distribution of stand conditions from the

southeastern FIA (SEFIA) unit in Asheville, NC, and from the

southern FIA (SOFIA) unit in Starkville, MS. Plot-level

information with appropriate area expansion factors was

obtained for all FIA loblolly- and slash pine-dominated plots

in the South collected since 1968 (southeastern) and 1974

(southern) and described the three most recent cycles of

measurement of the six completed to date. Dates of data

collection for the fourth survey were 1968 to 1977; for the

fifth, 1978 to 1986; for the sixth, 1986 to 1993.

Standard FIA variables from the 33,036 plot records describe

forest type, survey cycle, expansion area, site class, stand

origin, and age. Forest Inventory and Analysis calculated two

additional measures for this study: quadratic mean d.b.h. and

percentage of stems infected with fusiform rust. These

calculations used only a plot’s dominant (pine) species to

simplify interpretation of species differences and projections

of yield. Thus, infection rates for a loblolly pine stand refer

only to infection rates among the loblolly stems. Although we

obtained records for all ages of stands and for both planted and

natural pine stands, the extrapolation process focused on

young plantations, those less than 10 years old, forming a

subset of 6,117 records over the three survey cycles.

Plot records were used to generate area distributions for each

survey cycle, with groupings corresponding to those used in

the yield simulations and separate tables for loblolly and slash

pines. Infection rates were rounded into 11 classes for

tabulation: O-4,5-14, 15-19, . . .95-100. For site-quality

classes, SEFIA and SOFIA recognize five and six classes,

respectively, but some of these are rarely reported. Those

categories with the least acres were collapsed into adjoining

categories to form the three final site-quality classes used in

the evaluation and defined in table 1.

We attempted to maximize comparability both between the

two FIA collecting units and across the three survey cycles.

However, two discrepancies remain in the fourth cycle:

While fifth and sixth cycle data span the entire South,

fourth cycle data were not available for three

States-Arkansas, Mississippi, and Tennessee.

Survey coding changed during the fourth cycle from the

general “disease” category to the more specific “fusiform

rust stem gall.” Only North Carolina and Virginia used the

more specific category in this cycle,

The potential impact of omitting three States was examined by

temporarily dropping the three States from the fifth and sixth

Table l-Correspondence between site classes reported in

Forest Inventory and Analysis (FIA) surveys by southern

FIA (SOFIA) and southeastern FIA (SEFIA) and site

indices (SI) used in the stand-level yield simulations

Site quality

FIA surveys

SOFIA SEFIA

Simulation

Loblolly Slash

Site class codes SI base age 2 5

High l-3 l-2 80 70

Medium 4 3 65 60

Low 5-6 4-5 50 50

7
To separate the “with” from the “without” conditions, the

“without” selection scenario used data only from the fourth and

fifth surveys, before substantial deployment of genetically

resistant seedlings, which might have reduced the infection

rates observed in the FIA data. However, empirical analyses

were conducted for all three surveys to provide insight into

actual patterns of infection and their variability over time.

Evaluating the causes of empirical disease patterns is

simplified when incidence is stratified by principal causal

factors. For fusiform rust, these factors include species, age,

stand origin, site quality, alternative host availability, climatic

suitability, and genetic resistance of the host, although not all

can he addressed using FIA data To minimize impacts of age,

our empirical analyses were confined to stands less than 10

years in age at the time of survey, referred to here as "young."3

All analyses were stratified by dominant species and stand

origin, and most analyses were also stratified by site quality.

We analyzed infection patterns by survey cycle, although this

stratification inevitably reflects a number of causal factors,

including availability of oak hosts and year-to-year variations

in weather. At least for plantations, changes over time also

included genetic changes in seedlings.

400

0

1~81198219831~841985198619871988198919901991.

Year

Figure I-Southern pine seedling production estimates (Carey and Kelley

1993). Diamonds indicate southern seedling production estimates (all species)

derived from annual USDA Forest Service Planting Reports.

that young, planted loblolly and slash area increased 75

percent from fifth to sixth survey cycles (roughly 1982 to

1990). However, our extrapolation process is based on acres

planted per year, by species and over time, rather than on total

plantation area.

Accounting for total pine plantation area-Recent decades

have seen dramatic increases in area of southern pine

plantations. Sources indicate a doubling from 1970 to 1990,

with projections suggesting perhaps a threefold to fourfold

increase by the year 2020.’ This historic increase was

corroborated by the FIA inventory records, which indicated

For the years 1970, 1975, 1980 and 1985, we estimated

planted area using Forest Service tree planting reports (e.g.,

Mangold and others 199 1; USDA Forest Service 198 1, 1982;

Williston 1980), summing the acres reported planted in the 13

Southern States: Alabama, Arkansas, Florida, Georgia,

Kentucky, Louisiana, Mississippi, North Carolina, Oklahoma,

South Carolina, Tennessee, Texas, and Virginia. Figures did

not include seeded acres. For the years from 1990 to 2020, we

used unpublished ATL.AS/TAMM projections provided by

John Mills.5

3 Infection rates typically change with stand age due to the interacting

processes of new infection and mortality. operational surveys of rust

incidence are ideally conducted at age 5, and measures taken at this age are

typically used as inputs for yield projections. Because FIA data do not

consistently support this more precise age measure across all three cycles, we

assumed that infection rates reported over the broader age category (0 to 9

years) are equivalent to infection at age 5. Note that extremely young stands

do not contribute to FIA calculations of infection rates, because only stems of

1 in. d.b.h. or greater are tallied. Analysis shows that among those young

plantations for which infection rates could be calculated, only 10 percent were

coded as younger than 5 years of age.

Because both planting sources described aggregate softwood

plantation acres, acres had to be apportioned by species before

use. For the years from 1980 through 2020, we used the

relative frequencies from a survey of seedling producers

reported in Carey and Kelley (1993) to apportion aggregate

planted acres to species (fig. 1). Early survey reports indicated

an annual average of 74 percent loblolly and 2 1 percent slash

for the years 198 1-9 1; we applied these seedling frequencies

to total planted pine acres. However, analyses of FL4 data

4 Personal communication. 1994. David N. Wear, Project Leader, 5 Personal communication. 1993. John Mills, Research Forester, Pacific

Southeastern Forest Experiment Station, 3041 CornwallisCornwallis Cornwallis R. Northwest Station, 1221 Southwest Yamhill, S uite 200, Portland, OR 97208-3890

Research Triangle Park, NC 27709

cycle tallies and recalculating plantation acres by site-quality

and infection rates. Relative shifts in distributions were small,

indicating that relative frequencies were fairly insensitive to

the omission. For the change in disease coding, we followed

the advice of FIA staff and assumed that all of the early fourth

cycle “disease” cases were, in fact, fusiform rust. This

overstates actual fusiform rust incidence but apparent rates of

infection in the fourth cycle were still lower than those

observed in the fifth and sixth cycles.

Seedling production (millions)

2,000

1,600

1,200

800

8
indicated that this distribution had not always prevailed.

Therefore, for the years 1970 and 1975, we calculated the

relative shares of newly established loblolly and slash

plantations from fourth and fifth cycle FIA plot data.

Loblolly's share was 63 percent; slash’s was 37 percent.

Plantations of other softwood species were assumed to be

negligible for these years.

Estimation of rust resistance adoption-Forest Inventory

and Analysis data from the fourth and fifth forest survey cycles

provided the infection rates for the baseline “without”

scenario, Simulating the changes in these infection rates under

the “with” scenario required information on technology

diffusion and selection gain. We needed to know how quickly

industry adopted rust resistant selection into their seedling

production activities, and how effective that genetic selection

has been. To obtain this information, we collaborated with the

major southern tree improvement cooperatives in surveying

the principal producers of loblolly and slash seedlings, both

State and industry.

We surveyed producers of loblolly and slash seeds and

seedlings for estimates of their historical and planned

production of resistant and total pine seedlings for selected

years from 1970 to 2020. We also requested an estimate of the

gain in resistance for those resistant seedlings produced in the

same years. Gain was defined in the questionnaire as “the

relative reduction in infection in your resistant seedlings when

compared with infection rates which would have occurred had

nonresistant seedlings been planted instead.“6 We also asked

for estimates of past expenditures for fusiform rust resistance

research and development. Survey questions were developed

cooperatively and reviewed by additional Forest Service and

university specialists for clarity and relevance, Appendix A

shows the questionnaire for slash pine,

The Western Gulf Forest Tree Cooperative and the Florida

State Tree Improvement Cooperative sent survey forms for

slash pine to their entire memberships. Their combined

memberships comprise virtually all slash pine seedling

producers in the South. The NCSU-Industry Cooperative Tree

Improvement Program sent out questionnaires to loblolly

seedling producers. Because industry is more fragmented

among loblolly producers, this cooperative surveyed only

those members thought to produce fusiform rust-resistant

6 Gains in resistance are expressed in percentage terms, where 0 percent

gain indicates no improvement in resistance over nonresistance-selected stock,

and 100 percent indicates total immunity to infection. Thus, if genetically

resistant seedlings with a 50 percent gain in resistance are planted in an area

that normally experiences 30 percent infection rates, 15 percent of the resistant

seedlings should become infected. The terms “gains in resistance” and

"resistance gains” are synonymous

stock. We also contacted two additional nonmember loblolly

producers. We made followup phone calls to improve

response rate, answer questions, and minimize double

counting that could arise from contract production of

seedlings. Tree improvement cooperatives provided additional

information on gain, diffusion, and research costs.

The slash pine survey provided information that allowed us to

calculate the Southwide weighted average resistance gain by

year and the proportion of resistant slash seedlings. Because

the loblolly pine sampling strategy did not provide complete

coverage of total loblolly pine seedling production, we derived

estimates of total loblolly seedling production from other

sources. For the years 1980,’ 1985, and 1990, we used direct

estimates of regional loblolly seedling production from the

Auburn University nursery survey (Carey and Kelley 1993).

For the remaining years, estimates of loblolly planting acres

were converted to seedlings assuming 768 seedlings per acre,

an average derived from a sample of USDA Forest Service

Planting Reports from that period.

We obtained estimates of gain for a given species and year by

calculating the average gain reported by the different

producers, weighted by the number of resistant seedlings they

reported for a given year. Thus, the number represents an

average across all resistant seedlings for that species. For this

survey, resistant seedlings are seedlings where (1) resistance

to fusiform rust was an important factor in their genetic

selection, and (2) their perceived resistance probably resulted

in planting them in areas at risk to fusiforrn rust infection.

Rust-resistant seedling deployment--The benefit of using

resistant seedlings depends on the degree to which those

seedlings are planted in locations where their resistance will

do the most good, in short, on their deployment. In lieu of

explicit empirical information on deployment, we chose to

simulate three scenarios: (1) uniform-assumed no

information on areas at risk, (2) optimal---assumed perfect

knowledge of areas at risk, and (3) total resistance--assumed

all plantations were free of infection. The uniform and optimal

scenarios were chosen to bracket industry abilities to target

resistant seedlings effectively. The total resistance scenario

was added to help delimit the maximum benefits possible from

increased fusiforrn rust resistance in these species.

7The earliest year for which seedling production estimates were available was

1981. The survey’s 1981 count wasused for 1980 in the absence of a more

timely estimate.

9
Under uniform deployment, resistant seedlings were assigned

equally to all combinations of site quality and infection.

Uniform deployment simulated the distribution of seedlings

that might be expected if seedlings were distributed without

regard to their rust resistance, or without any knowledge of

which sites were more at risk to fusiform rust infection.

Under optimal deployment, resistant seedlings were assigned

to those combinations of site quality and initial infection that

benefit the most from gains in resistance. To accomplish this,

we calculated for each combination of site quality and

infection (“cell”) the change in SEV that would result from a

hypothetical 50-percent gain in resistance and ranked the cells

from most to least improvement in SEV. The resistant

seedlings reported in the producer survey were allocated first

to those cells with the greatest benefit and continued through

the ranking until the resistant seedlings ran out. Remaining

cells received nonresistant seedlings.

Under total resistance, all plantations were assumed to have

zero infection levels. This assumption was imposed over all

simulation years-past, present, and future--to allow

flexibility in constructing hybrid scenarios. For example, the

consequences of deploying a breakthrough discovery in the

year 2005 might be evaluated by combining the benefits

under the optimal deployment scenario for years before 2005

with those from the total resistance scenario for subsequent

years.

For the total resistance scenario, adjusting for increased

resistance was straightforward-initial infection levels were

set to 0 percent on all acres, simulating a 100~percent gain in

resistance. For both the uniform and optimal deployment

scenarios, adjusting for gains in resistance was only slightly

more involved. For those acres planted with resistant

seedlings. initial infection rates were adjusted downward

based on the average gains in resistance for that species and

year. That is, the original infection rates were proportionally

reduced by an amount corresponding to the reported

resistance gain for that species and year. The resulting

infection level was then rounded to the nearest lo-percent

infection level, allowing resistant acres to be merged with

yield numbers based on their site quality and revised infection

level, the same approach used with nonresistant acres.

The aggregation process-To recap, we first defined the

base case (“without” selection) by distributing planting acres

for each species and each simulation year across the 66 site

conditions, using the distributions from the FIA data. We then

modified the base-case acres three different ways to represent

alternative “with” resistance scenarios {uniform, optimal, and

total resistance). Each modification consisted of dividing the

acres in each site condition into resistant and nonresistant

acres.’ For the resistant acres, we adjusted their resulting

infection level downward based on the gains in resistance

reported for that year. We repeated this process for each

combination of four utilization and two rotation standards.

The next step in the aggregation process was to assign yields

to those acres. We merged the per-acre outputs from the

stand-level simulations with the corresponding acre figures to

yield output numbers suitable for aggregation. For the

baseline case, product outputs can be summarized:

where

i = loblolly or slash;

.j = high, middle, or low site quality;

k = fusiform rust infection percentage at age 5;

I= poor, pulpwood, sawtimber, or full utilization scenario;

m = economically optimal or fixed 35-year rotation;

t = simulation year of 1970, 1975, . . .2020.

This equation is modified to include resistant seedlings,

representing the various ‘With” resistance scenarios:

where

II = uniform, optimal, or total resistance deployment scenario,

r = planted with genetically resistant seedlings or not.

Calculations for SEV and PV used the Same formulas except

that the dependent variable represents values rather than

volumes.9

8For the total resistance scenario, the amount of nonresistant acres was

always zero. Only under uniform allocation did every cell have both resistant

and nonresistant acres. For the optimal scenario, some cells had resistant

acres and some nonresistant, with only one cell having both types of acres.

That cell indicated where resistant seedlings ran out with the remainder

receiving nonresistant seedlings.

9

 For PV, some timing adjustments were required to account for

differences between planting and harvest dates. The regional benefits

evaluation simulates planting and harvesting activities every 5th year. For

PV calculations, planted acres must be "aged" to harvest year by adding

rotation length to their planting year. This addition places some harvests

between simulation years. Such years were rounded to the nearest S-year

interval (2000,2005,2010. . .) to ensure a complete population of acres for

each simulation year.

10
Summing values over time-To sum SEV’s and PV's over

time, annual values must be interpolated between simulation

years, and future values discounted to a common year. To fill

in the 4 intervening years between each simulation year,

SEV’s and PV's are interpolated to generate annual values.

These are then discounted back to 1992 using our standard 4-

percent discount rate before aggregating. For SEV's, we

discounted back from planting date; for PV's, from harvest

date.

Timber prices were assumed constant over time, a reasonable

assumption when supply effects are small enough to have

modest impacts on regional prices. The aggregate volume

estimates were used later to check the validity of this

assumption. Discounting to a common year adjusted for the

future value of money and is necessary when comparing costs

and benefits that occur at different times.

Implementation details--Our simulation scheme resulted in

726 separate base-case acreage figures, corresponding to 2

species by 3 site-quality classes by 11 infection levels by 11

simulation years. We replicated each base-case acreage record

eight times to provide for the different “with resistance”

scenarios evaluated, corresponding to the different

combinations of utilization and rotation standards employed.10

We merged these acreage records with the corresponding

stand-level yield records and multiplied the per-acre volumes

and values by the acres in that class, then summed the

resulting values across site classes and infection classes to

provide totals by species and simulation year for each

simulation scenario.

 We computed aggregate volume and value for each

combination of utilization (poor, pulpwood, sawtimber, and

full) and rotation standard (economic, optimum, and fixed).

Within each of these combinations, separate aggregate values

were calculated for each deployment option, permitting

comparison of timber supplies and values “without” rust

protection (the base case) with those of different scenarios of

increased rust resistance (the cases “with” fusiform rust

research improvements).

10The base-case acres are the same between the different utilization and

rotation standards, and the total number of acres per species is the same across

all the deployment scenarios. Thus, reproducing the base-case acres across all

these scenarios would seem unnecessary. However, each utilization and

rotation standard produces different yields for the different site conditions and

hence, different marginal benefits of resistance. The different site conditions

might, therefore, rank differently and result in different allocations of resistant

seedlings under the optimal deployment scenario. Therefore, we used separate

base-case acre estimates for each scenario.

Benefits calculations-Benefits were. calculated for each

simulation year by subtracting SEV for a “without” base case

from the SEV for one of its corresponding “with” deployment

scenarios. These comparisons were possible for each of the

three deployment scenarios in each of the eight utilization-byrotation standards, and for each of the 11 simulation years.

Soil expectation value provided a useful comparison for such

“point-in-time” comparisons.

To compare benefits with costs, both benefits and costs must

be summed over time. We used PV to prevent the double

counting of future rotations that occurs when aggregating

SEV’s over time. Summing PV over time requires some

adjustments in time. To simplify data and processing

requirements, we simulated planting activity every 5th year,

from 1970 through the year 2020, To aggregate PV by harvest

year, we first computed the harvest year for each set of acres,

adding rotation length to their planting year. Because such

years could fall between the 5-year simulation intervals, we

reassigned each to the nearest simulation year, a rounding

process which ensured compatible totals of PV for each

simulation year.” Once aggregated to these simulation years,

annual benefits can be calculated by subtracting the PV's of

the base case from those of the different deployment scenarios.

For aggregation over time, these annual benefits were

interpolated to fill in the intervening years between simulation

years before discounting and summing.

Note that the various utilization and rotation standards

represent alternative assumptions of prevailing forestry

practices in the South. Each combination of utilization and

rotation standards has one “without” case (base) plus three

“with” improved resistance cases (uniform, optimal, and total

resistance). The choice of uniform or optimal deployment

represents a third set of alternatives about prevailing practices,

which together bracket real world conditions. The total

resistance scenario is an entirely hypothetical condition where

fusiform rust never existed. Summing the values of this

unattainable condition enables estimation of total fusiform rust

damages Southwide over the simulation period.

11The 15-year spread in rotation ages that occurs across conditions in the

economic rotation scenario has implications for the aggregated numbers in the

beginning and ending years of the simulation period. For example, some of the

stands planted in 1970 were harvested as early as 1990, while those with

longer rotations were not harvested until 2005. For the same reason, harvests

reported in the year 1990 did not include harvests from plantations with long

rotations that were planted before 1970. Similar processes affected the

aggregate values for the ending harvest years. Because this evaluation

involves differences from baseline, where this process also occurs, the

“priming effect” does not affect conclusions over the differences from baseline

(benefits) but should be kept in mind when examining the detailed information

from which these differences were derived.

11
Research Costs

Research costs for the fusiform rust program evaluation were

collected from seedling producers, research cooperatives, the

Forest Service, and other unpublished data sources. The

survey canvassing the university cooperatives and principal

producers of loblolly and slash pine seedlings included

questions on past annual resources they expended on fusiform

rust research or development in 1970, 1975, 1980, 1985, and

1990 (Appendix A). To simplify this task, respondents could

specify resources in either dollars or scientist-years. We used

other sources to obtain expenditures for Forest Service

research and to translate scientist-years into dollars.

The Forest Service provided a precise estimate of the full-time

equivalents (FTE's) and total budgets spent annually for

fusiform rust research from 1976 to 1993, based on an

historical tally of the research work units and scientists in the

South.” In addition, we obtained estimates of university

forestry research expenditures and research FTE's from the

Southern National Association of Professional Forestry

Schools and Colleges (NAPFSC) summaries.” We used the

Southern NAPFSC data to calculate an average annual cost

per forestry research FTE (or scientist-year). Similarly, the

Forest Service data were used to calculate an average annual

fusiform rust research cost per FTE. Because neither the

Forest Service nor the NAPFSC data covered all the relevant

years for which fusiform rust research was performed, simple

linear regressions of the average research cost per FTE as a

function of year were performed to fill in data for missing

years. To calculate the average research costs per FTE for the

industry, we computed a simple average of the (lower) Forest

Service costs and (higher) university costs.

Simple linear interpolation was used to estimate the annual

research expenditures for both university cooperatives and

seedling producers. For each sector, we used costs per FTE

times the number of FTE's, plus research expenditures, to

calculate the total research costs for each sector and for the

South per year.

Benefit:Cost Computations

Once we had estimated all the stand-level financial impacts,

regional aggregate economic benefits, and regional research

costs of fusiform rust research, we were able to compute the

12 Personal communication. 1994. Richard S. Smith, retired, USDA Forest

Service, P.O. Box 96090, Washington, DC 20090-6090.

13 Personal communication. 1994. Arnett Mace, Dean, D.B. Warnell School

of Forest Resources, University of Georgia, Athens, GA 30602

regional cost and benefits of fusiform rust research. The

benefit:cost (B/C) ratios and net benefits were calculated with

all benefits and costs indexed to a common year. We

compounded the research costs for the region to 1992, the

base year used for the financial analyses, at a 4 percent real

interest rate. Similarly, we discounted all the benefits of

improved fusiform rust protection back to 1992 using a 4-

percent real discount rate,

Calculating the B/C ratios and the net benefits was

computationally simple. For the B/C ratio, we divided the

aggregated research benefits by the single regional research

cost term for the various scenarios. For the net benefits, we

subtracted the single aggregated regional research cost term

from the aggregated research benefits for the various

scenarios. Given the detailed analyses made to calculate the

biological, financial, and regional impacts of increased

fusiform rust resistance in southern pines, the estimated total

research benefits should be reasonably accurate. The cost

estimates, while simpler to obtain, may not be as precise.

Simple sensitivity analyses of costs could be performed by

merely multiplying the base costs by some factor to examine

the effects of their variation on the net benefits associated with

fusiform rust research.

Results and Discussion

The results of this research are summarized in five sections:

(1) adoption of rust resistance, based on the data provided by

southern tree improvement cooperatives and forest industry;

(2) regional fusiform rust infection patterns, based on analysis

of the FIA data; (3) stand-level fusiform rust physical and

financial impacts; (4) aggregate regional. impacts of fusiform

rust protection research; and (5) overall B/C analyses for rust

resistance research.

Adoption of Rust Resistance

The response rate from the questionnaire sent to seedling

producers was generally good. Over four-fifths of the

organizations contacted provided at least some quantitative

information on seedling production or resistance gain, with

only one nonrespondent. For slash, comparisons with other

estimates of production indicate the responses covered about

half of total slash seedling production. Responses to questions

addressing recent accomplishments were the most complete,

with somewhat fewer responses for the earliest period (1970)

and even fewer for projections far in the future. Questionnaires

where estimates for all dates were provided indicated a pattern

of rising gains in resistance and production of resistant and

total seedlings in the 1970’s and 1980’s. These increases
Total seedling production (millions)

1200 I

600

j”“e OL I

1970 1975 1980 1985 1990 1995 2000 2005 2010 20155 2020

Year

‘0 197k5 1980 1985 1990 1995 2000 ZOOS 2010 20155 2020

Year

Figure 2--Southwide loblolly and slash pine seedling production,

reconstructed using responses from a survey of seedling producers and other

Figure 3--Southwide production of loblolly and slash pine seedlings

data.

genetically selected for their resistance to fusiform rust, based on a survey of

seedling producers,

leveled off in later years, We used this general pattern to avoid

problems caused by missing values in incomplete survey

responses. In cases where organizations declined to project

future production levels, we assumed the last production rates

provided would be maintained. No substitutions were made

for missing estimates of resistance gain; calculation of average

gain was based solely on actual responses.

Total seedling production-Figure 2 shows the

reconstructed estimate of total loblolly and slash seedling

production over time, based on survey results and external

estimates of seedling production. They show a dramatic rise in

production for loblolly from 1970-85, rising to 1 billion

seedlings per year. This rise is followed by modest declines.

Responses from slash seedling producers indicate gradually

declining production through the period.

Resistant seedling production-Figure 3 shows the reported

production of resistant loblolly and slash seedlings over time,

showing steadily increasing production of resistant seedlings.

Starting from virtually zero in 1970, production of resistant

seedlings exceeded 210 million seedlings Southwide by 1990.

Production of resistant slash began more quickly than for

resistant loblolly but was numerically outstripped in the early

1980’s as loblolly increased in popularity.

Adoption rates appear somewhat different when adjusted for

each species’ total production (fig. 4). Using this measure,

adoption of genetic resistance in slash pine has been more

rapid than in loblolly. In slash, resistant lines now apparently

Resistant seedling production (millions)

200

150

100

50

account for more than 40 percent of production and are

expected to plateau at between 70 percent and 80 percent by

the year 2010. In loblolly, the diffusion of resistance has been

much less complete. Currently, just over 15 percent of loblolly

seedling production is identified as fusiform rust resistant, and

resistant seedlings are not expected to account for more than

25 percent in the foreseeable future. Note, however, that

respondents expressed difficulty projecting future production

decisions, and their responses represent their best judgments.

For loblolly, the adjusted percentage is a ratio of two

independently derived measures: survey responses and

planting acreage projections. Its accuracy should, therefore, be

viewed as the more tenuous.

Resistance gain-Equally important to total production of

resistant seedlings is the increased resistance in those

seedlings. Producers thought resistance increased continually

over the survey period (fig. 5). The expected gains in

resistance for both species began at low levels in the early

1970’s. However beginning in the late 1980’s, gains in slash

pine resistance surpassed loblolly and were expected to

maintain greater gains in the foreseeable future. Industry

experts expected gains in resistance in slash to eventually

exceed 60 percent, compared to just above 40 percent for

loblolly. For seedlings currently being produced, average

gains in resistance were estimated to be around 45 percent for

slash and 30 percent for loblolly.

These gain numbers were averaged across seedling lines

designated as fusiform rust resistant. Many respondents,

1 3
1 0 No infection w 5% or greater 1

q No infection n 5% or greater ]

Slash low Slash medium Slash high

Lablolly low Loblolly medium Lablolly high

I Slash low Slash medium Slash high

1 Loblolly low Loblolly medium Loblolly high

Figure &Proportion of young planted pine acres with 5 percent or more of

stems infected in FL4 survey cycle 4 (1968-1977), broken out by site quality.

Figure 9-Proportion of young planted pine acres with S percent or more of

sterns infected with fusiform rust in FIA survey cycle 5 (1978-1986), broken

out by site quality.

1 0 no infection m 5% or greater 1

Slash low Slash medium Slash high

Loblolly low Loblolly medium Lablolly high

Figure I&-Proportion of young planted pine acres with 5 percent or more of

stems infected with fusiform rust in FIA survey cycle 6 (1986-1993), broken

out by site quality.

6th with

Without

5th with

Without

4th with

Without

012345

_ -

6 7 8;

Million acres

Figure 1 l-Acres of young slash pine plantations by survey cycle and site

quality class. The “with” indicates Southwide tallies, “Without” indicates that

AR, MS, and TN are omitted.

1 6
for each of the three survey cycles. To remove population

differences from the comparisons, the table contains two sets

of numbers for the fifth cycle, The “Without AR, MS, and TN”

set contains the same population of States available for the

fourth cycle. The “With all States” set matches the complete

sample reported in the sixth cycle.

Table 3 shows that in the fourth cycle young, natural slash

stands were located farther south than young slash plantations,

but this difference was eliminated by the fifth cycle. As

infection rates for slash are higher at more northern latitudes,

the location difference would have acted to reduce infection

rates in natural slash in the fourth cycle relative to those in

plantations. It is not clear, however, whether the l-degree

latitude shift sufficiently explains the twofold infection

difference.

Site class-When infection patterns in plantations are broken

out by site quality as shown in figures 8,9, and 10, three

patterns emerge:

1. Infection rates increased consistently with increasing site

quality for both slash and loblolly plantations in both the

fourth and fifth cycles. This agrees with the widespread belief

that better quality sites are generally at greater risk to fusiform

rust.

2. In young plantations, infection rates in slash and loblolly

were quite similar once site quality was taken into account.

These are, however, aggregate statistics based on only one of

various ways of defining site classes. The aggregation process

obscures differing distributions of the species across the

South, differing patterns of development of the disease as

stands age, and differences that might occur for infection

levels higher than 5 percent.

3. Similarity in infection rates between species, and increasing

infection with improving site quality, held for the fourth and

fifth cycles but disappeared in the sixth. In the sixth cycle,

infection rates for slash were twice those for loblolly, and

infection rates for both species were nearly constant across

different quality sites.

The loss of site-quality differences in cycle 6 might have

resulted from increased deployment of resistant seedlings to

the highest quality sites. Given the past correlations between

site quality and infection and the patterns of economic impacts

explored later in this paper, such a strategy makes economic

sense. However, shifts in the genetic makeup of planting stock

do not explain the more recent difference in infection rates

between species. Seedling producers thought genetic selection

and production of resistant seedlings were more successful in

slash than in loblolly, but infection rates are substantially

higher in slash plantations.

Class and location--Shifts in latitude can be tested for their

role in the divergence of infection rates between species. Such

a divergence would be consistent with a northward shift in

either loblolly or slash plantations. However, table 3 shows

that plantations of both species shifted southward rather than

northward over the three cycles.

Shifts in longitude are somewhat more promising. Both

species have shifted westward over the period of interest. As

was shown earlier, the statistical correlation between longitude

and infection was marginal in the simple model used,

However, the sign of the correlations implies that westward

shifts would be associated with increased infection for slash

and reduced infection for loblolly, as has been observed.

Because this is a qualitative comparison, it is not clear

whether the spatial shift in distributions quantitatively

accounts for the divergence between species’ infection rates.

Shifts in site class and species-The different species and

site classes described here are not equally common in the

South, nor has their relative importance remained constant

over time. Figures 11 and 12 show the acres of young slash

and loblolly plantations for each survey cycle subdivided by

site class. The graphs also illustrate the impact of omitting

Arkansas, Mississippi, and Tennessee from the Southwide

tallies. Including both "with" and “without” totals for cycles 5

and 6 allowed us to compare populations across all three

survey cycles and estimate the probable impacts of excluding

these States from fourth cycle totals.

6th with

Without

5th with p1

Without

II

l.__L Site class:

High

@ Mediun:

n Low

I I I

0123456789

Million acres

Figure 12--Acres of young loblolly pine plantations by survey cycle and site

quality class. The "with" indicates Southwide tallies, “Without” indicates that

AR, MS, and TN are omitted.

17
Forest Inventory and Analysis data (figs. 11 and 12) on young

plantations (aged less than 10 years) support several

conclusions:

1. Slash pine has enjoyed varying popularity over the period

surveyed, with slash plantation acreage dropping from 3.5

million acres in cycle 4 to 1.9 million acres in cycle 5 but

rebounding to 2.6 million acres by cycle 6. These figures

understate the early decline in slash plantations because the

fourth cycle figures omitted Mississippi, a State where later

surveys reported significant amounts of slash pine.

2. Young loblolly plantation acreage has climbed steadily,

almost tripling over the three survey cycles.

3. Combined acreage of young plantations dropped slightly

from fourth to fifth cycles (5.7 percent) but increased by threequarters by the sixth.

4. Shifting preferences moved loblolly from a minority 40-

percent share of combined plantation acreage to a dominant

three-quarters share by the sixth cycle.

5. Most plantations occurred in the lowest site-quality class,

especially for slash. However, this dominance weakens over

time as more new plantations appeared in the better site

quality classes. The shift over time may be due to afforestation

on better quality sites or improvements in management.

Because FIA crews base site-quality estimates on the early

growth of seedlings, any of a number of factors may be

responsible for the apparent improvement, including changes

in genetic stock, fertilization, or weed control.

6. The distribution of site qualities for a given species and

time period was fairly insensitive to inclusion of Arkansas,

Mississippi, and Tennessee. However, the three States

together accounted for 6.4 and 4.8 percent of young slash pine

plantation acreage in the fifth and sixth cycles and 10.5 and

17.8 percent of young loblolly plantation acreage.

Regional distributions-We generated distributions of young

plantation acreage by site quality and infection level from FIA

data for slash and loblolly for each survey cycle. Tables 4 and

5 show the relative distributions. The tables reiterate the

correlation between lower quality sites and lower infection

rates, and show distributions skewed toward, the lower

infection levels, Stands with greater than 50 percent infections

generally make up less than 10 percent of acreage for a given

species, cycle, or site quality. Table 6 shows the distribution

we used for the regional extrapolation, calculated from the

combined FL4 records for cycles 4 and 5. The distribution was

calculated by pooling all young plantation plot records from

the fifth and sixth cycles and calculating a distribution

weighted by each plot’s expansion factor. This approach

produced an area-weighted frequency distribution somewhat

biased toward the more recent survey because planting activity

increased over that period.

Stand-Level Impacts

The tables in Appendix B show product yields and SEV's for

stands with different initial conditions. In the absence of rust,

the simulations showed markedly increasing yield for the

higher site qualities, particularly for sawtimber volumes. The

merchandising routines placed most of the timber in the chipn-saw and sawtimber products, with relatively little volume

going directly to pulp in uninfected stands. Economically

optimal rotations were always less than the 35 years assumed

for the fixed rotation. Consequently, the economic rotation

tables all have smaller yields but larger SEV’s than in the

corresponding fixed rotation tables. Volumes and SEV's at

zero infection are identical across the different utilization

standards because these standards only affect merchandising

of infected stems.

Increasing levels of rust infection shifted volumes away from

sawtimber and into pulp, illustrated in figure 13 for loblolly,

Under the highest utilization standard, total loblolly stand

volumes may actually remain fairly constant across different

infection levels. However, for loblolly’s three lesser

utilizations and for all of the cases for slash, high levels of rust

brought lower total stand yields and product quality (figs. 13

and 14). Generally, the decline in yield and product quality at

higher infection levels resulted in greater drops in value at the

lesser utilizations (fig. 15). Although high infection levels

proved damaging, infection levels of around 10 to 20 percent

sometimes resulted in increases in value compared with the

no-rust case. This generally occurred in slash plantations and

particularly on high-quality sites at high utilizations.

Total volume per acre increased with infection on slash pine

stands up to about 30 percent infection on high-quality sites

and up to about 20 percent infection on low-quality sites. This

increase was apparently due to a competitive release of

infected and uninfected trees, allowing more volume accretion

on fewer stems. The stand-level economic benefits of

reductions in fusiform rust infection rates can be computed

from these tables by subtracting the SEV at the initial

infection rate from the SEV at the subsequent infection rate.

For example, in table Bl-Loblolly pine plantation yields for

differing site qualities and age-5 fusiform infection rates under

the full utilization and economic rotation assumptions-the

economic benefits of reducing fusifonn rust infection rates

from 60 to 30 percent in loblolly pine stands (i.e., rcprcsenting

a 50-percent gain) with a high site index and full timber

18
Table 4--Percentage of O- to 9-year-old loblolly pine plantations Southwide by site quality and

infection class for fourth through sixth survey cycles

Infection (percent)

Cycle/site quality 0 10 20 30 40 50 60 70 80 90 100

Acres in cycle (percent)

Fourth cycle”

High

Middle

Low

Fifth cycle

High

Middle

Low

Sixth cycle

High

Middle

LOW

1.7 0.6 0.8 0 0 0

18.5 2.5 2.9 2.3 .8 0

52.2 4.5 4.2 2.7 .7 1.0

8.8 1.7 1.8 1.6 .6 .5

21.0 3.4 2.6 1.9 1.2 1.3

35.7 3.5 3.4 3.1 2.2 2.3

16.2 1.3 1.3 .7 .1 .3

28.1 4.5 2.4 1.4 .3 . 1

32.5 3.9 1.4 2.0 1.2 .5

0 0 0 0 0

.5 .5 0 0 .2

.6 1.3 .5 .2 .8

.2 0 .2 0 .2

.3 .4 .2 0 .3

.3 .5 0 0 . 4

.3 .1 .1 0 .2

0 .2 0 0 .1

.2 .1 .2 .1 .2

a Fourth cycle data omits Arkansas, Mississippi, and Tennessee and for some States includes infections other than fusiform rust.

Table 5--Percentage of 0- to 9-year-old slash pine plantations Southwide by site quality and

infection class for fourth through sixth survey cycles

Infection (percent)

Cycle/site quality 0 10 20 30 40 50 60 70 80 90 100

Fourth cycle

High

Middle

Low

Fifth cycle

High

Middle

Low

Sixth cycle

High

Middle

Low

Acres in cycle (percent)

0.4 0.2 0.2 0 0 0 0 0 0 0 0

9.6 2.2 2.2 .8 1.4 .7 .2 .2 .3 0 .1

60.5 3.6 4.7 5.4 2.1 2.1 .8 .4 .8 .3 .7

2.70 .5 1.50 0 0 .5 .5 0 0

13.8 .5 .5 1.4 0 .5 0 0 1.0 .5 .5

54.4 6.3 4.6 3.4 1.8 1.4 1.4 1.0 .2 .3 1.1

4.0 0 1.9 0 .8 0 .4 .4 0 0 0

11.4 1.7 3.3 2.2 1.5 2.0 .8 0 .2 0 .8

39.9 6.8 6.1 6.0 2.6 2.4 2.1 1.4 .2 .5 .6

a Fourth cycle data omits Arkansas, Mississippi, and Tennessee and for some States includes infections other than fusiform rust.

19
Table 6--Percentage of 0- to 9-year-old slash pine and loblolly plantations Southwide by site

quality and infection class for combined survey cycles 4 and 5 _ _

Infection (percent)

Cycle/site quality 0 10 20 30 40 50 60 70 80 90 100

Acres in cycle (percent)

Slash

High

Middle

LOW

Loblolly

High

Middle

LOW

1.1 0.1 0.3 0.5 0 0 0 0.1 0.1 0 0

10.9 1.7 1.7 1.0 1.0 .6 .1 .2 .5 .1 .3

58.7 4.4 4.6 4.8 2.0 1.9 1.0 .6 .6 .3 .8

6.1 1.3 1.4 1.0 .4 .3 .1 0 .1 0 .2

20.1 3.1 2.7 2.1 1.0 .8 .4 . 5 .1 0 .3

41.9 3.9 3.7 3.0 1.7 1.8 .4 .8 .2 .1 .5

Cubic feet per acre

4,000 , I

3,500

3,000

2,500

2,000

i ,500

1,000

500

0

0 10 20 30 40 50 60 70 80 90 100

Percent infection at age 5

3,500

3,000

2,500

2,000

1,500

1,000

500

0

0 10 20 30 40 50 60 70 80 90 100

Percent infection at age 5

Figure 13--Pulpwood, chip-n-saw, and sawtimber volumes harvested from Figure 14--Pulpwood, chip-n-saw, and sawtimber volumes harvested from

loblolly pine plantations under different levels of early fusiform rust infection. slash pine plantations under different levels of early fusiform rust infection.

Data were obtained from simulations of growth, yield, and merchandizing that Data were obtained from simulations of growth, yield, and merchandizing that

assumed economically optimal rotation age, full utilization of infected stems, assumed economicaIly optimal rotation age, full utilization of infected stems,

and a medium-quality site (SI 65). and a medium-quality site (SI 60).

utilization are $90 per acre ($2,243-$2,153). In loblolly

plantations, reducing rust virtually always yielded a positive

economic benefit.

In slash plantations, the economic benefits to reduced rust

infection were often negative at the low initial levels of

infection. Managers predicting infection rates that fall in this

range have little economic incentive to reduce infection. On

the other hand, the incremental benefits of reducing fusiform

rust infection increase with site index over the high range of

Cubic feet per acre

4,000 j

infection levels. Therefore, managers must be able to predict

infection rates to determine their best planting strategy.

As expected, utilization standard had a large effect on the

magnitude of physical and economic damages from fusiform

rust infection. The absolute level of damage was generally

higher for stands managed on short rotations than on long

rotations for poor utilization standards; the opposite was true

for full utilization.

20
SEV in dollars per acre

-c)_ Slash -full utilization

-+-Loblolly - poor utilization

0 10 20 30 40 50 60 70 80 90 10

Percent infection at age 5

Figure 15--Soil expeetation values (SEV) for loblolly and slash plantations

assuming either full or poor utilization of infected stems across a range of

early fusiform rust infection rates. Results shown are for economically optimal

rotations on mediumquality sites.

The marginal benefits of reducing infection rates on loblolly

and slash pine stands managed on short rotations with high

utilization standards generally increased with site index.

Marginal benefits of reductions in infection rates increased

with site index for loblolly stands with poor utilization

standards, regardless of rotation length. The marginal benefits

of reductions in infection rate were higher on short rotation

stands than on long rotation stands, regardless of site index.

Two cautions are warranted regarding these tables. First,

simulation results at the highest infection levels and for the 35-

year rotations push the limits of the yield models and their

underlying data and should be viewed as less reliable than

those for conditions more commonly encountered in the field.

Second, stochasticity in the merchandising routines may be

responsible for modest anomalies in individual numbers but

do not alter the general patterns discussed.

Aggregate Benefits

Aggregating the volumes and SEV's in tables B 1-B 16 using

the acres in the various base-case scenarios produced eight

sets of regional totals that varied over time, one for each

combination of utilization and rotation standards. While the

volume estimates do not directly contribute to a financial

evaluation of benefits and costs, they highlight the importance

of valuing product shifts and permit reappraisal of constant

price assumptions.

Volume benefits by year-The regional plantation harvest

volumes show patterns that echo those from the stand-level

evaluation. First, rust resistance had a negligible effect on total

harvest volume. Even complete resistance to rust failed to

change total plantation harvests by more than 1 percent under

all scenarios except one. Under the poor utilization standard,

where infected stems were assumed left in the woods, harvests

increased in the final simulation years by as much as 5 percent

under the optimal allocation scenario and 9 percent for the

total resistance case.

Second, relatively little harvest volume went to the pulp

product class. Pulp represented about 3 percent of baseline

harvest volumes for most scenarios and years simulated,

jumping to 9 percent in the pulpwood utilization case.

Although generally small in absolute terms, pulp volumes

were sensitive to rust resistance, dropping to even lower

shares of harvest under the uniform, optimal, and especially

the total resistance scenarios compared with the no-resistance,

baseline case. Pulpwood harvests dropped to near zero in the

total resistance scenarios.

Third, chip-n-saw and sawtimber products were important

products under all scenarios investigated, but their relative

dominance depended on rotation assumptions. Chip-n-saw

accounted for roughly 55 to 60 percent of harvest volume

under economically optimal rotations but under the fixed 35-

year rotation, sawtimber volumes accounted for the dominant

share.

Fourth, rust resistance shifted harvests toward higher valued

products, but these shifts were generally small. Except in the

pulpwood utilization scenario, shifts were limited to only a few

percentage points of overall volume, moving from pulpwood

into the higher valued products.

Because chip-n-saw constitutes an intermediate product class,

it ultimately contributes wood to both sawtimber and pulp chip

markets. Accounting for this contribution alters the apparent

effects of resistance on overall pulp and sawtimber supplies. If

we assume that chip-n-saw supplies equal volumes to pulp

and sawtimber markets (Koch 1972), we can evaluate the

relative pulp and sawtimber shifts from the baseline case

implied by the genetic resistance scenarios.

Under the higher utilization scenarios, projected increases in

resistance resulted in shifts in pulp and dimension wood

supplies of less than 3 percent assuming optimal deployment,

and even less under uniform deployment. Total eradication of

infection would reduce pulp supplies to the market by 6 to 9

percent under these assumptions and increase sawtimber

supplies 2 to 3 percent above those in the baseline.

If the industry pulps all infected stems, supply shifts would be

more responsive to resistance. Pulp supplies could drop 19

2 1
percent under economic and 26 percent under 35-year

rotations, balanced by increases in sawtimber supplies of 9 to

10 percent. Should the industry leave infected stems in the

woods, both pulp and sawtimber supplies would be increased

by improved resistance. Projected improvements in resistance

would ultimately increase supplies by 1 to 5 percent, while

eradication would increase them by 9 percent.

These supply shifts apply only to outputs from loblolly and

slash plantations. Southern pine plantations accounted for only

14 percent of southern softwood supplies in 1984, although

this share is projected to increase to 65 percent by 2030

(USDA Forest Service 1988). Even so, the modest supply

shifts projected to result from genetic resistance to fusiform

rust are generally small enough to support our assumption of

constant prices.

Financial benefits by year-Variations over time for the

different base cases were driven primarily by changes in

planting activity. Figure 16 illustrates this finding for the full

utilization, economic rotation base case. Aggregate SEV’s and

planted acres more than doubled between 1970 and 1985 but

then dropped sporatically back to intermediate levels. Figure

16 also shows that loblolly made up most of these aggregate

values, in keeping with its much larger share of plantation

acres, Aggregate base-case values under fixed rotations and

less intense utilization were somewhat less, but followed

similar dynamics.

The simulations of improved genetic resistance produced three

additional trend lines for each base case. Figure 17 repeats the

base-case aggregate value from figure 16 and adds lines for

the corresponding uniform deployment, optimal deployment,

and total resistance scenarios. Several general patterns were

apparent. First, regardless of variations over time, the

deployment scenarios consistently ranked the same, with base

case the least valuable followed by uniform and optimal, with

total resistance the most valuable. Second, the uniform and

optimal cases started out the same as the base case in 1970,

but diverged upward over time as resistance technology was

increasingly adopted by industry. Third, total resistance SEV’s

roughly paralleled those of the base case and provided a cap

that the uniform and optimal values approached but never

reached. The differences between these scenarios were small

relative to the overall value of plantations. For the full

utilization and economic rotation scenario, resistant seedlings

ultimately increased aggregate plantation values by only 0.6

percent under uniform deployment and 1.3 percent if

deployment was optimal. Under these assumptions, even total

eradication of rust would increase plantation values by only

2.1 percent, although under poor utilization, total resistance

could increase SEV’s by as much as 12.7 percent.

While the relative differences between the scenarios may be

small, their absolute differences are more central to this “with

versus without” comparison, specifically the differences

between the three resistant seedling scenarios and the base

case. The differences for two scenarios are shown graphically

in figures 18 and 19. Benefits were much higher under poor

utilization (fig. 18), but in both scenarios increasing resistance

over time resulted in a gradual rise in benefits under the

uniform case, a more rapid rise under optima! deployment, but

in neither case attaining the maximum, represented by the total

resistance line.

Table 7 reports the base-case values plus the differences

above base for four of the eight scenarios. Patterns for the four

cases not shown were always intermediate between these four

extremes. Not surprisingly, aggregate base-case values were

larger under more intensive management, represented by full

utilization and/or economic rotation. Base-case SEV’s varied

over time but paralleled each other.

The improvements in SEV shown under total resistance

measure the economic damages fusiform rust causes across

the region and represent the maximum annual benefit

achievable from genetic resistance to fusiform rust. Such

benefits differed substantially depending on the utilization and

rotation standards evaluated, but year-to-year fluctuations

were much smaller. This occurred because under the

assumptions we used, year-to-year fluctuations were caused by

changes in species mix and Southwide planting area; genetic

resistance and fusiform rust infection rates remained constant

for both the base case and total resistance standards.

For the uniform and optimal standards, changes in genetic

resistance and resulting infection levels led to longer-term

shifts in plantation value and benefits over the base case.

Under optimal deployment, these benefits climbed steadily

toward the total resistance benefits, eventually reaching full

potential benefits only under the assumptions of full utilization

and fixed rotations. Under other conditions, benefits from

optimal deployment remained below those from total

resistance, indicating that under most sets of assumptions,

substantial improvements were still unrealized. When

seedlings were distributed randomly rather than optimally,-

benefits remained substantially lower, generally less than half

those achieved under optimal deployment.

Present values were also aggregated by year (not shown), but

their aggregation was by harvest date. Thus, while both PV

and SEV followed the same dynamics and trends, patterns for

aggregate PV were shifted roughly 20 to 35 years later,

depending on the rotation assumption.

22
SEV in million dollars (1992)

2,500 , II

Million acres planted

2,000 1.8

1.5

1,500 1.2

1,000 0.9

0.6

500 0.3

0 0

1970 1980 1990 2000 2010 2020

Planting year

Million dollars (1992)

2,000

~ ~ Uniform

-Baseltne

1970 1980 1990 2000

Planting year

Figure 16--Southwide aggregate soil expectation values (SEV) for

loblolly and slash pine plantations under the baseline scenario of no

genetic selection for resistance to fusiform rust. The line indicates

Southwide planting activity assumed in the simulation, the major

determinant of the year-to-year variations shown.

Figure 17-Aggregate soil expectation values of southern Ioblolly and slash

plantations for the four deployment scenarios investigated. Values shown

assume economically optimal rotations and poor utilization of infected stems.

Million dollars (1992)

- . - _ _ . 50 - __--. - . - _ .

o _/__i--‘_r --- --,---,

1970 1980 1990 2000 2010 2020

Planting year

Figure 18--The annual benefits of genetic resistance to fusiform rust

assuming uniform or optimal deployment of genetically resistant

seedlings or total resistance to fusiform rust Poor utilization and

economically optimal rotations are assumed, and benefits are calculated

as the difference in aggregate soil expectation values from those in the

baseline scenario.

Million dollars (1992)

300

2500 - pi

150 -

100 -

50 -

o - _ CL_- --‘_ _,-L--_-L‘ -

1970 1980 1990 2000 2010 2020

Planting year

Figure 19--The annual benefits of genetic resistance to fusiform rust

assuming uniform or optimal deployment of genetically resistant seedlings or

total resistance to fusiform rust. Full utilization and economically optimal

rotations are assumed, and benefits are calculated as the difference in

aggregate soil expectation values from those in the baseline scenario.

23
Table 7-Annual soil expectation values (SEV) for Southwide loblolly and slash pine plantations

Economic rotation Fixed rotation

Planting Base Total Base Total

year case’ Uniform Optimal resistance case Uniform Optimal resistance

Million constant dollars

Difference from baseb Difference from baseb

Poor utilization

1970 733 0 0

1975 1064 1 7

1980 1210 3 13

1985 1710 9 32

1990 1372 13 55

1995 1513 17 64

2000 1064 16 51

2005 1340 23 70

2010 1242 25 70

2015 1216 25 69

2020 1175 24 67

1970 804 0 0 18 601 0 0 12

1975 1166 0 3 25 871 0 2 18

1980 1335 1 4 28 999 1 4 14

1985 1888 3 10 40 1411 2 7 20

1990 1514 4 14 32 1132 4 12 15

1995 1670 5 17 35 1248 5 13 17

2000 1175 5 12 25 878 5 11 12

2005 1479 8 17 32 1107 8 15 15

2010 1370 8 17 29 1025 8 14 14

2015 1341 8 17 28 1004 8 14 14

2020 1298 8 17 27 971 8 15 14

89 549

128 796

154 902

217 1275

174 1022

193 1128

135 794

170 1000

158 926

155 906

149 877

Full utilization

0 0 64

0 5 92

2 10 110

7 27 156

10 42 125

13 48 138

12 39 97

17 53 122

19 53 113

19 52 111

18 51 107

a

 Base case values assume no genetic selection for fusiform rust resistance

b“Difference frombase” columns indicate increases in SEV from the base case given genetic selection assuming uniform or optimal

targeting of resistant seedlings or complete resistance (‘Total resistance”) in all seedlings.

The data in table 7 can alternatively be viewed on a per-acre

basis, removing the effects of changing planting activity over

time (table 8). Dividing the base case SEV values from table 7

by total loblolly and slash planting acres gives an average of

the yields from Appendix B, weighted by their relative

frequency of occurrence. Repeating this process with the total

resistance SEV’s shows how much higher the average per-acre

value would be without any fusiform rust infections. Given the

assumptions of the simulation, these two measures are

virtually constant over time except for the shift in species

allocations between 1975 and 1980. For the uniform and

optimal deployment cases, the aggregate improvements in

SEV were divided by those acres where resistant seedlings

were planted. This measure indicates the per-acre marginal

value of resistant seedlings under uniform and optimal

deployment. These two measures vary over time because of

changes in resistant seedling supply and resistance.

The per-acre SEV’s for the base case ranged from $626 to

$93 1 depending on utilization and rotation assumptions. Total

resistance SEV’s were anywhere from $9 to $108 higher, but

these values were spread across all plantations. Under the

optimal allocation regime, the added benefits of resistant

seedlings were about $20 to $25 per acre under full utilization

and $70 to $100 under poor utilization for plantations

established over the next several decades. Per-acre benefits

24
,

Table 8--Annual per-acre soil expectation values (SEV) for Southwide loblolly and slash pine

plantations in constant dollars per acre

Planting

Year

Base

case”

Economic rotation Fixed rotation

Total Base Total

resistance Uniformb Optimal case resistance Uniform Optimal

Poor utilization

1970 836 937 0 0 626 699 0 0

1975 836 937 15 103 626 698 0 74

1980 843 950 12 53 628 705 8 41

1985 843 950 13 45 628 705 10 38

1990 843 950 24 100 628 705 18 76

1995 843 950 24 92 628 705 19 69

2000 842 949 34 109 629 705 26 83

2005 843 950 31 94 629 706 23 71

2010 843 950 36 100 629 705 27 75

2015 843 951 36 100 628 705 28 75

2020 842 949 38 107 629 705 29 81

Full utilization

1970 917 937 0 0 685 699 0 0

1975 917 936 0 44 685 699 0 29

1980 930 949 4 16 696 705 4 16

1985 931 950 4 14 695 705 3 10

1990 931 950 7 25 696 705 7 22

1995 930 950 7 24 695 705 7 19

2000 930 950 11 26 695 705 11 23

2005 930 950 I1 23 696 706 11 20

2010 930 950 11 24 696 705 11 20

2015 930 949 12 25 696 706 12 20

2020 930 950 13 27 696 706 13 24

aNumbers under the base case and total resistance columns are weighted average SEV's across all loblolly and slash plantations. bNumbers under the uniform and optimal columns represent increascd SEVs above the base case for the two deployment regimes

indicated, but spread only across those plantations that received rust-resistant seedlings

under uniform allocation were substantially lower than under

optimal allocation. Their difference indicates the value of

targeting resistant seedlings, a difference that was highest in

the early years but declined somewhat in later years as

resistant seedling supplies increased.

Benefits summed over time-Tables 9 to 11 show the

Southwide PV’s, discounted to 1992 and summed across

years, for loblolly and slash plantations separately and for both

combined. As anticipated, PV’s were greater for economic

rotations than for fixed rotations within each merchandising

scenario. Also, PV increased with improvements in the

utilization and deployment standards. In general, marginal

efficiency gains were more difficult to attain as utilization

efficiency increased. For example, the difference in PV

between pulpwood and poor utilization standards was greater

than the difference in PV between full and sawtimber

utilization standards.

The benefits of improved resistance to fusiform rust were

obtained by subtracting the PV of a particular base ease from

the PV of one of the three ‘With resistance” scenarios on the

same row, shown in tables 12, 13, and 14. For example, the

total benefit of improved resistance assuming uniform

deployment, poor utilization, and a fixed rotation age for

loblolly pine was (29,683 ~ 29,547 =) $137 million (table

12). For a different combination of utilization and rotation type

(i.e., full utilization and an economic rotation age), the benefit

was (36,887 - 36,872 =) $14 million. Computed research

benefits were greater for slash pine than loblolly. Although

various aspects of host-pathogen relations may have

contributed to the species difference, greater adoption of

resistance technology by slash producers doubtless played a

major role. Table 14 gives the research benefits for the two

species combined. These benefits ranged from $108 to $999

million depending on rotation length and utilization and

25
Table 13--Present value of the research benefits for

slash pine, planted from 1970 to 2020

Deployment

Utilization/

rotation type

Poor

Economic

Fixed

Pulp wood

Economic

Fixed

Sawtimber

Economic

Fixed

Full

Economic

Fixed

Total

Uniform Optimal resistance

Million constant dollars (1992)

128 249 342

145 286 562

125 251 381

126 247 500

95 187 308

110 212 446

91 184 273

105 203 432

Table 14--Present value of the combined research

benefits for slash and loblolly pine, planted from 1970

to 2020

Deployment

Utilization/

rotation type

Total

Uniform Optimal resistance

Million constant dollars (1992)

Poor

Economic

Fixed

Pulpwood

Economic

Fixed

Sawtimber

Economic

Fixed

Full

Economic

Fixed

269 991 4,619

282 999 4,394

214 708 3,037

211 687 2,728

114 281 973

134 337 835

108 261 850

123 294 614

deployment standards. In most cases, economic damages were

higher for economic rotations than under the longer fixed case.

The one exception was for slash stands with high utilization

standards-here damages were higher for long rotations.

The values in table 14 represent benefits relative to the base

case, and caution should be exercised when making

comparisons across scenarios. For example, a comparison of

the combined research benefit for the poor utilization standard

with a fixed rotation age and uniform deployment ($282

million) with the research benefit for the full utilization

standard with an economic rotation age given optimal

deployment ($261 million) does not suggest that the former

scenario is more desirable from an economic perspective,

because hvo different base cases were used to calculate the

research benefits ($40.7 and $45.1 billion, respectively).

However, our results indicate that efficiency gains associated

with the introduction of genetically improved planting stock

are greatest for producers with the lowest utilization standard

and lowest for producers with the highest utilization standards.

Research Costs

Table 15 shows the costs of fusiform rust research and

development of resistant genotypes, with scientist-years and

expenditures by the Forest Service, university-industry

research cooperatives, and forest industry and other seedling

producers.

Forest Service research costs per FTE were greater than the

universities in the early to middle 1970’s. By the late 1970’s to

the middle 1980’s, university research costs per FTE had

increased as university scientists expanded their overall

research efforts. Forest Service total costs on fusiform rust

research increased until the middle 1980’s then started to

decline. The estimated university total fusiform rust research

effort increased until the early 1980’s then started to decline,

but by the late 1980’s and early 1990’s research costs

increased again, primarily from increased costs per FTE. The

estimated industry total costs on fusiform rust research have

increased steadily.

Benefit:Cost Analysis

Table 16 gives a summary of the B/C analysis. The

calculations of the B/C ratios and the net present values were

straightforward. The PV of the research costs was

$48.8 million (table 15). The B/C ratios for each scenario

27
Table 15--Fusiform rust research costs by sector from 1970 to 1992

Forest Service University (coops only)a Industry Total

PV total

Total Expen- Total Expen- Total Expen- Total research costs

Yea r FTE's $/FTE ($,000's) FTE's $/FTE diture ($1,000's) FTE's $/FTE diture ($1,000’s) FTE's $/FTE diture ($1,000's) ($1,000’s 1992)

1970 6 $ 50,292 $318

1971 6 57,089 347

1972 6 63,887 376

1973 6 70,685 405

1974 6 77,482 435

1975

1976

1977

1978

1979

g 1980’

1981

1982

1983

1984

6 84,280 464

7 98,923 643

5 86,415 458

6 88,103 511

5 96,875 465

1985

1986

1987

1988

1989

5

3

4

5

4

5

7

7

5

5

5

4

4

104,167 500

164,118 558

121,190 509

122,667 552

133,182 586

158,235 807

196,567 1,317

173,636 1,146

2 15,600 1,078

195,769 1,018

1990

1991

1992

192,308 1,000

179,524 754

176,905 743

3

3

3

3

3

3

3

3

3

3

3

3

2

2

2

1

1

1

1

I

1

1

2

$ 29,300 $ 6,663 $102 0.3 $ 39,796 $120,000

37,535 7,398 129 .3 47,3 12 126,400

45,770 8,133 157 .3 54,829 132,800

54,005 8,868 184 .3 62,345 139,200

62,240 9,603 212 .3 69.861 145,600

70,476 10,338 239

78,711 12,742 269

86,946 15,146 298

95,181 17,550 327

103,416 19,954 356

77,378 152,000

88,817 162,600

86,680 173,200

91,642 183,800

100,146 194,400

111,651 22,358 329

115,444 22,977 340

128,121 23,596 248

126,070 24,215 245

144,592 24,834 278

107,909 205,000

139,781 273,264

124,656 341,528

124,368 409,792

138,887 478,056

152,827 25,453 216

153,253 26,896 218

177,332 28,339 250

212,581 29,783 296

185,767 31,226 263

155,53 1 546,320

174,910 543,056

175,484 539,792

214,091 536,528

140,768 533,264

194,003 32,669 275

202,238 32,669 285

197,857 32,669 428

193,155 530,000 1,129 10 579,465 562,669

390,881 530,000 1,122 9 572,642 562,669

187,381 530,000 1,111 9 562,143 542,669

$ 132 10

141 10

149 9

158 9

167 9

198 9

216 10

225 9

239 10

254 9

453 10

595 8

628 8

696 9

797 8

951 9

998 11

996 10

1,093 9

1,029 9

$119,387 5126,663

141,937 133,798

164,486 140,933

187,035 148,068

209,584 155,203

232,133 162,338

266,45 1 175,342

260,04 1 188,346

274,927 201,350

300,437 2 14,354

323,727 227,358

419,342 296,24 1

373,968 365,124

373,105 434,007

4 16,660 502,890

466,593 571,773

524,730 569,952

526,453 568,131

642,272 566,3 11

572,305 564,490

$ 552 $ 1,307

617 1,406

682 1,495

748 1,575

813 1,647

902 1,756

1,127 2,112

981 1,767

1,077 1,864

1,076 1,791

1,283 2,053

1,493 2,299

1,385 2,050

1,493 2,125

1,661 2,274

1,974 2,598

2,533 3,205

2,392 2,910

2,467 2,886

2,311 2,599

2,404 2,600

2,161 2,248

2,282 2,282

Present value of research cost = $48,851

FTE = full-time employee; PV = present value

aUniversity and industry totals calculated as (FTE's x $/FTE) + direct expenditures.
Table 16--Benefit:cost ratio and net present value of

fusiform rust research in loblolly pine and slash pine

Deployment

Utilization/ Benefit

rotation type measure Uniform Optimal

Poor

Economic

Fixed

Pulpwood

Economic

Fixed

Saw-timber

Economic

Fixed

Full

Economic

Fixed

B/Ca 5.51 20.29

PVb 220.36 942.47

B/C 5.77 20.44

PV 232.80 949.68

B/C 4.37 14.49

PV 164.79 659.07

B/C 4.31 14.06

PV 161.74 638.18

B/C 2.33 5.75

PV 64.95 232.13

B/C 2.74 6.89

PV 85.03 287.93

B/C 2.21 5.33

PV 58.93 211.68

B/C 2.53 6.02

PV 74.55 245.03

a

 B/C is defined as the benefit:cost ratio. b PV is defined as the net present value, in million constant dollars

(1992).

were calculated as the aggregate research benefit (table 14)

divided by $48.8 million. For example, the B/C ratio under

poor utilization with an economic rotation age and uniform

deployment was (269 / 48.8 -) 5.5 1. If the B/C ratio is greater

than 1, then the benefits were greater than the costs, implying

the research program was economically efficient. In table 16,

the B/C ratios were ail greater than 1.

The net benefits for each scenario were calculated as the PV of

the aggregate research benefits (table 14) minus the PV of the

research cost ($48.8 million). For example, the net benefit

under poor utilization with an economic rotation age and

uniform deployment was (269 - 48.85 1 =) $220 million. If the

net benefit was greater than zero, then the PV of the benefits

was greater than the PV of the costs, implying the research

program was economically efficient. Research costs could

double and, in some scenarios, increase by a factor of 10, and

net benefits would remain positive.

Conclusions

The above results provide information on five aspects of

fusiform rust: (1) the speed of adoption of rust-resistant

technology across the South; (2) the historical distribution of

fusiform rust infection across sites of different quality and

location; (3) fusiform rust’s probable financial effect on

individual landowners across stands of differing infection and

site quality, and for different utilization and rotation standards;

(4) the aggregate timber supply effects of improved fusiform

rust resistance in southern pines; and (5) B/C ratios for the

fusiform rust research. Several implications of our analyses

and findings bear discussion.

Resistance Gains and Regional Conditions

By 1980, results of research into selection methods for

resistance to fusiform rust began to be implemented on the

ground as producers of loblolly and slash seedlings began

producing and planting resistant stocks in steadily increasing

numbers. These first resistant seedlings yielded minor gains in

resistance but these improved over time. The biggest gains in

resistance and most rapid changeover to resistant lines

occurred in slash, where mortality effects are more severe and

where a greater share of the planting range is at risk of

infection. Today, resistant lines account for almost half of

slash seedling production and a sixth of loblolly, with

resistance gains around 40 and 30 percent, respectively. Both

production of resistant lines and the resistance achieved in

those lines are expected to increase through the next several

decades, increasing by half again before leveling off, Thus, the

benefits of past research are by no means fully realized in

current seedling nurseries.

The production of resistant seedlings (fig. 5) can be compared

with the historical measures of risk shown (fig. 7 or tables 4

through 6) as a rough measure of production success relative

to plantations at risk. This comparison suggests that the slash

pine seedling industry adopted resistance technology so

rapidly in the 1970's, that by 1980 they were essentially

producing enough resistant seedlings to cover all areas of even

minor (5 percent) risk to infection. The projections for loblolly

indicate that nurseries will not produce enough resistant

seedlings for areas at risk even by the year 2020.

However, comparison of "sufficiency" is rather limited

because landowners have limited ability to determine which

planting sites will be at risk to fusiform rust. Many landowners

may plant sites with resistant stock anticipating conditions

favorable to fusiform rust, but such conditions may not occur.

Thus, planting resistant seedlings can provide insurance

against risk especially when resistant seedlings are abundant.

29
These are rough physical indicators of sufficiency of

production. Financial indicators provide a more relevant test,

but these would need to consider the predictability of infection

for individual locations in a risk framework.

Tree improvement specialists anticipate limits to gains in

resistance based on current research. For slash this limit is

thought to be just over 60 percent but for loblolly closer to 40

percent. The reasons for such anticipated limits are unclear,

but could involve genetic tradeoffs or limitations, financial

considerations, or limited benefits to be gained by much

higher resistance levels.

Extrapolation of historical infection patterns to future conditions

must recognize the limitations of our measurement period.

While the empirical infection rates represent the longest

consistent record obtainable, the rates are inevitably subject to

various edaphic conditions. Thus, there is no guarantee that

the infection patterns reflected in the three FIA survey cycles

will prevail in the next decade, much less in any particular

year. We did not forecast alternate host availability or spring

periods of high humidity, conditions that favor fusiform rust

infection in pine hosts (Froelich and Snow 1986).

Empirical analyses confirmed the importance of site quality

and regional location to fusiform rust incidence, although

stand origin appeared less important once differences in site

quality were considered. In the 1970’s and early 1980’s, slash

and loblolly plantations appeared to experience similar

incidences of fusiform rust infection once site quality was

taken into account. However, slash plantations experienced

high infection rates more often, and site correlations dissolved

in the most recent survey. Over the two decades for which data

are available, plantations shifted toward higher quality sites

and to the south and west, factors that should influence the

risks of infection. However, accounting for shifts in risk

factors was beyond the scope of the present evaluation.

Financial Analyses and Management Implications

The volume and SEV projections represent the first consistent

examination of fusiform rust effects on merchandised yield and

return on investment in slash and loblolly under optimal

economic and fixed rotations, By allowing a range of site

conditions and harvesting-utilization intensities, owners of

young plantations can compare the likely future returns for

their stand under a wide range of conditions.

The financial returns show that stand compensation for

mortality at lower levels of infection can significantly

ameliorate financial damage from fusiform rust, primarily for

slash. The dispersed nature of fusiform rust mortality in stands

is particularly well suited to such compensation, creating

patches of mortality, which under light infection rates might be

isolated individuals. It is, of course, also advantageous for the

mortality to occur early in the stand’s life. Should other

stresses create additional dispersed mortality, the

compensation seen in our simulations would not be possible,

and financial losses from fusiform rust would be

correspondingly greater. Similarly, planting at substantially

lower densities would increase the financial loss if moderate

infections developed.

The financial results show that the absolute levels of economic

damage are greater for slash than loblolly pine at all but the

lowest infection levels. Generally, economic damage increases

with site index, Because most pine acres in the South

experience low to moderate infection, we conclude that the

greatest economic damages are occurring on the most

common sites (i.e., low to moderate site index).

Thinning represents an alternative means of stocking control

and can be a useful strategy for alleviating damages in infected

stands (Nance and others 1983, Powers and Brender 1977).

Including the option of thinning could increase financial

returns and total harvested volumes but not uniformly across

stand conditions or species. Increases would probably be

greater on higher quality sites, and because infections in

loblolly are less likely to kill the stem than in slash, thinning

would increase yields more in infected loblolly than slash

stands, Such differential shifts affect the relative benefits of

reduced infection in complex ways and could not be evaluated

in this study.

Our results show that under some conditions of extremely high

infection, planting and harvesting pine trees promises financial

losses, not gains. Under these cases, landowners might

reasonably choose to replant their stands, although the returns

in such cases have not been evaluated in this study. No

empirical information is available on how often stands are

replanted, but Williston (1980) cites one 30-year program in

Mississippi where 18 percent of the stands had to be

replanted. To what degree fusiform rust may have been

involved in some of those planting failures is unknown.

Regional Research Benefits

We found that the aggregate economic impacts of genetic

improvements in rust resistance vary widely depending on

utilization and deployment assumptions. In general, the

economic gains from improved resistance are less when

infected stems are utilized efficiently. Likewise, the marginal

economic benefits of improved rust resistance were generally

higher for long rotations than economic rotations. While the

30
total value of rotations that maximize economic value were

higher than long rotations, our results indicate that gains from

improved biotechnology are more valuable on economically

“inefficient” stands.

More generally, the alternative assumptions used in the

analysis of rotation and utilization standards and seedling

deployment show the sensitivity of Southwide plantation

revenues to these factors. Comparing the PV totals under

different sets of assumptions provides dollar estimates of their

aggregate importance to plantation owners across the region.

The study found that moving from the least intensive to most

intensive utilization of infected stems increases aggregate net

revenues by roughly $3.8 to $4.4 billion over the study period,

representing about $76 to $88 million per year over the 50-

year simulation period (table 11). Unfortunately, there are no

data to tell how close industry currently comes to full

utilization. We can say, however, that aggregate revenues of

southern plantations depend strongly on how efficiently rust

infected stems are utilized.

Rotation standards also exert a strong effect on net revenue for

southern pine producers. For example, a shift from uniform

35-year rotations to economically optimal rotations for the

variety of site indices and rust infection levels found

throughout the South would increase net revenues by about

$5.1 billion (table 1 l), representing about $102 million per

year. However, based on FIA data, 35-year-old pine

plantations are rare in the South.

We found that the research benefits of improved rust

resistance depend on how accurately resistant seedlings can be

deployed (table 14). For example, the returns with optimal

deployment over uniform deployment range from about $ 153

to $722 million over the study period. This represents an

average annual gain from about $3.1 to $14.4 million per year.

The sensitivity of results to utilization, deployment, and

rotation practices demonstrates the importance of research and

technology transfer in areas such as milling and logging

practices, fusiform rust hazard assessment, and economic

guidelines to landowners. We know that these factors are

economically important to the industry but cannot say where

current practices lie within the range of conditions explored.

Only with information on current practices can we quantify the

potential benefits available from improvements in logging

practices, hazard assessment, and harvest timing.

We found that, in general, the investments made in fusiform

rust research have been efficient when gauged by computed

B/C ratios. If the sawtimber utilization standard is “typical” in

the South, B/C ratios range from 2.33 to 6.89, depending on

rotation length and seedling deployment. Likewise, the total

net PV (research benefits minus research costs) for this

utilization standard range from about $65 million to $288

million. Other utilization standards yielded different measures

of efficiency, but all scenarios led to the same conclusionfusiform rust research in the South has been a good investment

from an economic efftciency standpoint.

Our conclusion about the positive returns of fusiform rust

research differ from those of Hyde and others (1992) for all

research investments in southern pine productivity. They state

that “The net PV results are negative throughout most of the

range of feasible benefit and cost estimates” (p. 190) and

“timber growth and management has not been an outstanding

historic achievement” (p. 192). The different conclusions may

result from differences in approach (simulation versus

empirical), scope of the research evaluated, and quality of the

data on costs.

First, our analysis is forward looking and uses simulation

analysis to gauge productivity changes, while their analysis is

backward looking and uses statistical estimates of changes in

productivity. Using statistical analysis on historical data is

difficult because the data available to estimate changes in

timber productivity are of poor quality. Because productivity

was measured as a residual effect after the inclusion of various

biologic and ownership variables, the residual approach

simply detects “other” changes in the biologic production

function occurring over time. If the production function is

changing for reasons not captured in the model, such as

changes in land or atmospheric quality, the residual approach

cannot isolate the effects of research versus these other factors

on timber growth. Simulation avoids this problem by imposing

a true ceteris paribus condition-everything is held constant

except for the productivity modifying factor under

consideration.

Second, we consider the efficiency of a single timber research

program in the South, whereas Hyde and others (1992)

consider all timber research programs. The particular case we

studied is probably one of the more promising timber research

programs for traditional market analysis because

improvements in genetic resistance to fusiform rust translate

directly into increased tree survival, wood quality, and

therefore economic value. Furthermore, we assume

productivity gains will not influence per unit price, whereas

the model by Hyde and others includes both price and quantity

impacts.

Third, the data we used for computing research costs, while

imperfect, was specific to the fusiform rust research program

31
and could be considered generally reliable. Hyde and others

(1992) noted that the quality of the research cost data they

used was of questionable quality and could not be correlated

with a specific timber research program.

Maximum Potential Gains

The uniform and optimal deployment scenarios in our analyses

measured the potential benefits that could be achieved from

implementation of existing and anticipated fusiform rust

protection research. Fully targeting all rust-resistant seedlings

to the highest risk sites could generate research benefits

ranging from $261 million if all trees were fully merchandised

with economic rotation to $999 million if poor utilization and

fixed rotation prevailed (table 14). These numbers translate

into annual benefits of about $5 million to $20 million per

year.

Table 14 can be used to compute the maximum potential

economic gains that could be achieved if fusiform rust were

eliminated-as represented by the total column. Again,

depending on the existing utilization standards, the net PV of

total benefits could range from a low of $0.6 billion to a high

of $4.6 billion. These values translate into annual figures of

about $ 12 million to $92 million-and represent the imputed

annual loss to fusiform rust infection in southern pines. If we

assume that the economic sawtimber rotation represents the

most typical combination of forest management and timber

utilization regimes, the maximum potential net research

benefits (or total fusiform rust losses) are about $1 billion in

net PV, or $20 million per year. This sum compares

reasonably well with the annual loss estimates of $28 million

per year from Powers and others (1974), $35 million per year

from Anderson and others (1986b), and tens of millions from

Holley and Veal (1977). The poorer utilization pulpwood

scenarios would roughly triple our loss estimates, making

them somewhat higher than those in previous studies.

The potential annual benefits of completely eliminating

fusiform rust-perhaps $20 to $40 million per year-could be

used when considering further investments in fusiform rust

research. The existing tree breeding and selection programs

were implicitly considered by our survey respondents when

they made estimates of future gain and rust protection. To

achieve something closer to the maximum potential benefits,

significant breakthroughs in rust prevention research and

implementation must occur, such as identifying genetic

markers of resistance (Wilcox and others 1994). Will we be

able to support this research in the future and who will pay,

public organizations or private firms, are the key questions for

the future.

Acknowledgments

This study relied heavily on the work of a number of

individuals and institutions and would not have been possible

without their contributions. Jim Stewart, Richard Smith, and

Fred Kaiser from the USDA Forest Service Research Staff

made suggestions on the suitability of the subject for research,

made contributions during the work, and remained patient

throughout its execution. George Kuhlman, Warren Nance,

Charles Walkinshaw, and the many other fusiform rust

researchers patiently shared their knowledge of how fusiform

rust operates. Joe Glover and John Vissage from the Forest

Inventory and Analysis units of the Southern Research Station

provided consistent data on rust incidence across the South.

Robert Anderson and Dale Starkey from Region 8, Forest

Health Protection consulted with us on fusiform rust

incidence, Judy Binns remained with us through the

interminable stages of manuscript preparation. Lastly, but

most importantly, both the leadership and members of the

North Carolina State University-Industry Tree Improvement

Cooperative, the Florida State Tree Improvement Cooperative,

and the Western Gulf Forest Tree Cooperative generously

provided information on the costs and practical results of

fusiform rust research.

Literature Cited

Anderson, R.L.; Cost, N.D.; McClure, J.P.; Ryan, 6. 1986a Predicting

severity of fusiform rust in young Ioblolly and slash pine stands in Florida,

Georgia, and the Carolinas. Southern Journal of Applied Forestry. IO:

38-41.

Anderson, R.L.; McClure, J.P.; Cost, N.D.; Uhlcr, R.J. 1986b. Estimating

fusiform rust losses in five Southeastern States. Southern Journal of

Applied Forestry. 10: 237-240.

Anderson, R.L.; McCartney, T.C.; Cost, N.D. [and others]. 1988.

Fusiform-rust- hazard maps for loblolly and slash pines. Res. Note 35 1.

Asheville, NC: United States Department of Agriculture, Forest Service,

Southeastern Forest Experiment Station. 7 p.

Belanger, RP.; Miller, T.; Godbee, J.F.; Webb, R.s. 1985. Rust in slash

pine plantations In: Barrows-Broaddus, J.; Powers, H.R. eds. Proceedings

of the msts of hard pines working party conference. Athens, GA: University

of Georgia: 43-5 1.

Belli, M.L.; Straka, T.J.; DuBois, M.; Watson, W.F. 1993. Cost and cost

trends for forestry practices in the South. Forest Farmer. 52(3): 25-3 1.

Borders, B.E.; Bailey, R.L. 1986. Fusiform rust prediction models for

site-prepared slash and loblolly pine plantations in the southeast. Southern

Journal of Applied Forestry. 10: 145-151.

Burgan, Thomas M.; Bailey, R.L.; Brooks, J.R. 1989. Georgia pine

plantation simulator, ver. 3.0. Athens, GA: University of Georgia, School

of Forest Resources.

32
Busby, R.L.;Haines, T.K. 1989. Determining the value of a fusiform rust

infected stand. Res. Pap. SO-245. New Orleans, LA: U.S. Department of

Agriculture, Forest Service, Southern Forest Experiment Station, 5 p.

Norris, F.W. 1992. Timber Mar-South. Stumpage price mart, southwide

summary. Highlands, NC. Norris, F.W.

Carey, W.A.;Kclley, W.D. 1993. Seedling production trends and fusiform

rust control practices at Southern nurseries, 198 1- 199 1. Southern Journal

of Applied Forestry. 17(4): 207-2 11.

Phelps, W.R 1977 Incidence and distribution of fusiform rust. In: R.J.

Dinus; Schmidt, R.A. eds. Management of fusiform rust in southern pines.

Gainesville, FL; University of Florida, Southern Forest Disease and Insect

Research Council: 25-3 I.

Froelicb, R.C.; Snow, G.A. 1986. Predicting site hazard to fusiform rust.

Forest Science. 32: 21-35.

Geron, C.D.; Hafley, W.L 1988. Impact of fusiform rust on product yields

of loblolly pine plantations. Southern Journal of Applied Forestry. 12(4):

226-23 1.

Pieoaar, L.V.; Harrison, W.M.; Burgan, T.; Rheaey, J.W. 1988. Yield

prediction for site-prepared slash pine plantations in the coastal plain. Tech.

Rep. NO. 1988-I. Athens, GA: University of Georgia, School of Forest

Resources, Plantation Management Research Cooperative.

Hafley, W.L.; Smith, W.D. 1989. North Carolina State University managed

pine plantation growth and yield simulator. Raleigh, NC: N.C. State

Ilniversity, School of Forest Resources.

Powers, H.R., Jr.; Brender, E.V. 1977. Fusiform rust management

strategies in concept: plantation management. In: Dinus, R.J.; Schmidt,

R.A. eds. Management of fusiform rust in southern pines. Gainesville, FL:

University of Florida, Southern Forest Disease and Insect Research

Council: 122-127.

Haynes, RW. 1990. An analysis of the timber situation in the United Slates:

1989-2040. Gen. Tech. Rep. RM-199. Fort Collins, CO: U.S. Department

of Agriculture, Forest Service, Rocky Mountain Forest and Range

Experiment Station, 269 p.

Powers, H.R., Jr.; McClure, J.P.; Knight, H.A.; Dutrow, G-F. 1974.

Incidence and financial impacts of fusiform rust in the Southern Journal of

Forestry. 72(7):398-401.

Hodgc, G.R.; White, T.L; Schmidt, RA.; Allen, J.E. 1993. Stability of

rust infection ratios for resistant and susceptible slash and loblolly pine

across rust hazard levels. Southern Journal of Applied Forestry. 17(4):

188-192.

Random Lengths Price Report. 1993. January 8 report. Eugene, OR: Jon

P. Anderson.

Holley, D.L.; Veal, M.A. 1977. Economic impact of fusiform rust. In: Dinus,

R.J.; Schmidt, R.A. eds. Management of fusiform rust in southern pines.

Gainesville, FL: University of Florida, Southern Forest Disease and Insect

Research Council: 39-50.

Rastetter, E.B.; King, A.W.; Cosby, B.J. [and others]. 1992. Aggregating

fine-scale ecological knowledge to model coarser-scale attributes of

ecosystems. Ecological Applications. 2: 55-70.

Redmond, C.H.; Anderson, RL 1986. Economic benefits of using the

resistance screening center to assess relative resistance to fusiform rust

Southern Journal of Applied Forestry. 10: 34-37.

Holmes, T.P. 1991. Price and welfare effects of catastrophic forest damage

from southern pine beetle epidemics. Forest Science. 37: 500-516.

Huang, Y.S.; Teeter, L 1990. An economic evaluation of research on

herbaceous weed control in southern pine plantations. Forest Science. 36:

3 13-329.

Reed, W.J. 1983. The effects of the risk of fire on the optimal rotation of a

forest. Journal of Environmental Economic Management. 1 1: 180-190.

Routledge, R.D. 1980. The effect of potential catastrophic mortality and

other unpredictable events on optimal forest rotation policy. Forest Science.

26: 389-399.

Hyde, W.F.; Newman, D.H.; Seldon, B.J. 1992. The economic benefits of Row, C.H.; Kaiser, F.; Sessions, J. 1981. Discount rates for long-term

forestry research. Ames, IA: Iowa State University Press: 249 p. Forest Service investments. Journal of Forestry. 79(6): 367-369,376.

Koch, P. 1972. Utilization of the southern pines. Volume II. Processing.

Agric. Handb. 420. Washington, DC: U.S. Department of Agriculture

LGfgrco, K-G. 1988. On the economic value of genetic progress in forestry.

Forest Science. 34: 708-723.

U.S. Department of Agriculture, Forest Service. 198 I Forest planting,

seedling, and silvical tieatments in the United States 1980 report. FS-368.

Washington, DC U.S. Department of Agriculture, Forest Service. 15 p.

Mangold, R.D.; Moulton, R.J.; Snellgrove, J,D, 1991. Tree planting in the

United States. 1990. Washington, LX. U.S. Department of Agriculture,

Forest Service. State and Private Forestry, Cooperative Forestry: 18 p.

U.S. Department of Agriculture, Forest Service. 1982. 1981 U.S. Forest

planting report. FS-379. Wwhington, DC: U.S. Department of Agriculture,

Forest Service. 15 p.

Martell, D.L 1980. The optimal rotation of a flammable forest stand.

Canadian Journal of Forest Research. IO: 30-34.

U.S. Department of Agriculture, Forest Service. 1988. The South’s fourth

forest: alternatives for the future. Forest Resource Rep. 24. Washington,

DC: U.S. Department of Agriculture, Forest Service. 512 p.

Max, T.A.; Burkhart, H.E. 1976. Segmented polynomial regression applied

to taper equations. Forest Science. 22(3): 283-289.

Webb, R.S.; Patterson, H.D. 1984. Effect of stem location of fusiform rust

symptoms on volume yields of loblolly and slash pine sawtimbcr.

Phytopathology. 74(8): 980-983.

Nancc, W.L.; Froelicb, R.C.; Dell, T.R.; Shoulders, E. 1983. A growth and

yield model for unthinned slash pine plantations infected with fusiform

rust. In: Proceedings of the second biennial southern silvicultural research

conference, 1982 November 4-5; Atlanta, GA. Asheville, NC: Gen. Tech.

Rep. SE-24. US. Department of Agriculture, Forest Service, Southeastern

Forest Experiment Station: 275-282.

Wilcox, P.L. Amcrsoo, H.V.; Kuhlman, E.G. [and others]. 1994. Genetic

dissection of fusiform rust resistance in loblolly pine. Contributed paper

and abstract. IUFRO Molecular Genetics Workshop; 1994 May 20-23;

Prouts Neck, ME.

Newman, D. 1991. Changes in southern softwood productivity. a modified

production function analysis. Canadian Journal of Forest Research 21(8):

1278-1287.

Willistoo, H.L. 1980. A statistical history of tree planting in the South: 1925-

1979 Misc. Rep. SE-MK 8. Atlanta, GA: U.S. Department of Agriculture,

Forest Service, State and Private Forestry. 36 p.

33

Appendix A

Sample Questionnaire

Sent by Tree Improvement Cooperatives

to Pine Seedling Producers:

Version for Slash Pine Producers
Benefits of Selecting for Fusiform Rust-Resistant Slash Pine

This survey is part of a study estimating the benefits and costs of selecting for resistance to fusiform rust. Benefits to

selection depend on when and to what extent resistant seedlings have been used in plantations, and how successful

genetic selection has been in reducing infection rates in those resistant plantations. Costs refer to the additional resources

needed to enable produetion of specific fusiform resistant lines over and above costs which would have been necessary

to produce nonresistant stock. Please answer as much of the survey as YOU can.

1. Please estimate selection effectiveness and annual seedling production figures for the selected years

shown. GAIN refers to the relative reduction in infection in your resistant seedlings when compared with

infection rates which would have occurred had nonresistant seedlings been planted instead.’ For the RESISTANT

and TOTAL seedling-production estimates as well as estimates of gain, include slash pine seedlings produced in

your own organization’s nurseries or produced under contract for your organization, regardless of who ultimately

plants the seedlings. Seedling production estimates are necessary for calculating the relative contribution of

resistant seedlings to total slash pine production for the region. Projecting production and gain several decades

into the future will enable estimation of anticipated benefits of genetic selections which have already occurred.

Year

Expected GAIN in

fusiform resistance in

your resistant slash

seedlings (%)’

Number of RESISTANT slash

seedlings produced by your

organization

TOTAL number of slash

seedlings produced by your

organization

[resistant + nonresistant)

1 For example, suppose infection rates are 80 percent for nonresistant stock, while infection in resistant stock in the

same area is only 20 percent. Gain in this case would be (80 - 20)/80 or 75 percent.

36
Please estimate for the selected years below the approximate annual resources your organization has devoted to

research into selection, breeding, or screening specifically for resistance to fusiform rust in loblolly or slash pine.

Resources can be expressed either as dollar expenditures or as scientist-years, whichever is most convenient. Costs of

research conducted by tree improvement cooperatives will be obtained directly from the cooperatives and should not be

included here.

Year

Expenditures

or

scientist-years

1975

1980

1985

1990

Thank you for your assistance.

37

Appendix B

Financial Analyses of Impacts of Fusiform Rust
Table B1--Loblolly pine plantation yields for differing site qualities and age-5 fusiform

infection rates under the full utilization and economic rotation assumptions

Infection Pulp Chip-n-saw Saw Rotation SEVa

Pct Ft 3/m Ft ~/LX Ft ‘/m Yr $(1992)

0 1 1 1,531 3,090 23 2,313

1 0 109 1,522 3,043 23 2,307

20 193 1,514 2,967 23 2,273

30 270 1,519 2,885 23 2,243

40 381 1,469 3,096 24 2,205

50 481 1,481 2,983 24 2,164

60 535 1,468 2,941 24 2,153

70 658 1,483 2,803 24 2,109

80 704 1,479 2,763 24 2,096

90 802 1,474 2,670 24 2,053

100 867 1,481 2,596 24 2,021

0 1 8

10 102

20 174

30 250

40 321

50 400

60 463

70 534’

80 615

90 662

100 724

0 7 1,388 998 29 687

10 82 1,374 927 29 662

20 135 1,370 879 29 646

30 188 1,361 835 29 631

40 195 1,354 716 28 626

50 320 1,351 711 29 591

60 372 1,326 681 29 578

70 456 1,307 616 29 555

80 493 1,318 568 29 541

90 562 1,300 516 29 524

100 575 1,314 490 29 516

Site classb (high)

Site classb (medium)

1,482 1,725

1,469 1,670

1,468 1,598

1,453 1,735

1,457 1,661

1,441 1,599

1,431 1,544

1,445 1,461

1,423 1,401

1,428 1,349

1,392 1,441

Site classb (low)

24 1,422

24 1,396

24 1,368

25 1,341

25 1,312

25 1,286

25 1,261

25 1,239

25 1,212

25 1,192

26 1,164

‘SEV (soil expectation value).

b Site class includes different assumptions of site index, site preparation, and early survivorship.

40
Table B2--Loblolly pine plantation yields for differing site qualities and age-5 fusiforru

infection rates under the full utilization and fixed rotation assumptions

Infection Pulp Chip-n-saw Saw Rotation SEV

Pet Ft “/ac

0 1

10 95

20 1x2

30 260

40 351

50 477

60 497

70 655

80 811

90 855

100 895

0 8

10 76

20 167

30 231

40 251

50 390

60 459

70 534

80 569

90 603

100 727

0 1 3 1,152 1,604

1 0 72 1,156 1,540

20 140 1,143 1,482

30 204 1,155 1,409

40 246 1,157 1,364

50 296 1,142 1,330

60 372 1,145 1,250

70 427 1,169 1,171

80 503 1,143 1,121

90 555 1,150 1,063

100 613 1,151 1,003

Ft 3/ac Ft ‘/ac

Site classb (high)

1,017 4,93 1

1,069 5,344

1,072 5,25 1

1,069 5,174

1,069 5,079

1,075 4,944

1,083 4,913

1,066 4,750

1,072 4,551

1,077 4,532

1,075 4,492

Site class” (medium)

1,030 3,197

1,017 3,178

1,020 3,086

1,024 3,018

1,029 2,994

1,033 2,849

1,025 2,784

1,026 2,707

1,010 2,689

1,007 2,658

1,019 2,52 l

Site elas$ (low)

Y/-

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

$(1992)

1,463

1,601

1,586

1,574

1,557

1,532

1,529

1,495

1,453

1,457

1,452

1,002

1,001

984

972

967

944

929

914

913

908

879

579

566

553

540

530

522

506

493

481

469

457

“SW (soil expectation value).

b Site class includes different assumptions of site index, site preparation, and early survivorship

41
Table B3--Loblolly pine plantation yields for differing site qualities and age-5 fusiform

infection rates under the sawtimber utilization and economic rotation assumptions

Infection Pulp

Pet Fl I’/ac

0 11

10 125

20 223

30 374

40 498

50 585

60 686

70 812

80 908

90 1,011

100 1,065

0 18

10 118

20 218

30 283

40 440

50 495

60 585

70 678

80 722

90 834

100 910

0 7 1,388 998 29 687

10 92 1,376 916 29 658

20 163 1,382 838 29 636

30 261 1,359 764 29 609

40 338 1,371 675 29 584

50 389 1,410 698 30 567

60 497 1,364 519 29 534

70 564 1,432 500 30 513

80 652 1,430 413 30 490

90 745 1,358 275 29 461

100 793 1,430 273 30 446

Chip-n-saw Saw Rotation SE\P

Ft 3/ac Ft 3/m

Site class’(high)

1,531 3,090

1,539 3,011

1,560 2,891

1,503 3,070

1,556 2,891

1,579 2,781

1,570 2,689

1,595 2,539

1,600 2,436

1,563 2,642

1,672 2,480

Site class’ (medium)

1,482 1,725

1,484 1,638

1,468 1,752

1,481 1,676

1,473 1,525

1,528 1,416

1,500 1,354

1,540 1,220

1,503 1,333

1,542 1,181

1,502 1,145

Site class6 (low)

Yr $(I9921

23 2,313

23 2,295

23 2,243

24 2,195

24 2,139

24 2,104

24 2,057

24 2,013

24 1,981

25 1,933

25 1,911

24 1,422

24 1,386

25 1,352

25 1,323

25 1,266

25 1,242

25 1,203

25 1,180

26 1,146

26 1,104

26 1,081

‘SEV (soil expectation value).

b Site class includes different assumptions of site index, site preparation, and early survivorship.

42
Table B4--Loblolly pine plantation yields for differing site qualities and age-5 fusiform

infection rates under the sawtimber utilization and fixed rotation assumptions

Infection Pulp Chip-n-saw Saw Rotation SEV”

PC1 Ft ‘/a

0 1

1 0 127

20 262

30 366

40 382

50 580

60 677

70 832

80 894

90 1,028

100 1,075

0 8

10 103

20 206

30 271

40 375

50 423

60 540

70 623

80 692

90 796

100 903

0 1 3 1,152 1,604

10 94 1,172 1,501

20 178 1,176 1,413

30 252 1,202 1,314

40 315 1,213 1,238

50 377 1,236 1,154

60 482 1,286 999

70 542 1,237 988

80 613 1,326 829

90 746 1,294 728

100 812 1,335 618

Ft ‘/ac Ft 3/‘~c

Site classb(bigh)

1,017 4,93 1

1,075 5,306

1,083 5,159

1,083 5,055

1,092 5,024

1,097 4,820

1,107 4,708

1,106 4,53 1

1,095 4,480

1,122 4,315

1,114 4,272

Site classb(medium)

1,030 3,197

1,034 3,136

1,027 3,038

1,046 2,956

1,055 2,841

1,062 2,789

1,078 2,649

1,073 2,572

1,111 2,465

1,111 2,359

1,125 2,239

Site classb(low)

Yr

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

%(1992)

1,463

1,595

1,568

1,549

1,545

1,508

1,488

1,452

1,444

1,414

1,405

1,002

994

975

961

941

931

906

891

874

855

834

539

560

543

525

510

496

469

461

439

414

397

a SEV (soil expectation value)

b Site class includes different assumptions of site index, site preparation, and early survivorship.

43
Table B5--Loblolly pine plantation yields for differing site qualities and age-5 fusiform

infection rates under the pulpwood utilization and economic rotation assumptions

Infection Pulp

Pet Ft :‘/a~

Chip-n-saw saw Rotation SW

Ft h Ft 3/ac

Site classb (high)

Yr W992)

0 11

10 479

20 945

30 1,360

40 1,839

50 2,238

60 2,720

70 3,140

80 3,546

90 4,023

100 4,393

1,531 3,090

1,371 2,824

1,216 2,513

1,079 2,234

924 1,909

792 1,643

637 1,316

499 1,035

366 761

215 436

90 190

Site classb {medium)

23 2,313

23 2,180

23 2,015

23 1,868

23 1,699

23 1,557

23 1,390

23 1,241

23 1,100

23 930

23 800

0 18

10 336

20 668

30 954

40 1,277

50 1,559

60 1,860

70 2,147

80 2,435

90 2,720

100 2,985

1,482 1,725

1,329 1,575

1,181 1,392

1,047 1,238

899 1,065

771 911

634 746

501 593

367 438

236 283

118 138

Site class’ (low)

24 1,422

24 1,328

24 1,225

24 1,134

24 1,032

24 943

24 849

24 759

24 667

24 578

24 495

0 7 1,388 998 29 687

10 248 1,245 890 29 630

20 461 1,122 801 29 583

30 696 984 703 29 531

40 908 860 616 29 485

50 1,118 732 531 29 437

60 1,363 594 423 29 382

70 1,578 467 333 29 334

80 1,703 344 214 28 288

90 1,903 219 139 28 243

100 2,084 108 69 28 201

E SEV (soil expectation value).

b Site class includes different assumptions of site index, site preparation, and early survivorship.

44
Table B6--Loblolly pine plantation yields for differing site qualities and age-5 fusiform

infection rates under the pulpwood utilization and fixed rotation assumptions

Infection Pulp Chip-n-saw Saw Rotation SW

Pet Ft ‘/ac

0 1

10 674

20 1,302

30 1,890

40 2,539

50 3,127

60 3,756

70 4,326

80 4,904

90 5,562

100 6,067

0 8

10 446

20 881

30 1,229

40 1,684

50 2,063

60 2,466

70 2,831

80 3,218

90 3,586

100 3,917

0 1 3 1,152 1,604

10 294 1,034 1,440

20 540 930 1,297

30 821 813 1,133

40 1,062 714 991

50 1,300 611 856

60 1,582 494 691

70 1,823 395 549

80 2,084 283 399

90 2,332 183 252

100 2,544 92 130

Ft ‘/ac Ft 3/ac

Site clad (high)

1,017 4,931

958 4,875

856 4,346

754 3,858

652 3,307

551 2,819

453 2,284

352 1,793

255 1,309

148 754

66 329

Site clas$ (medium)

1,030 3,197

911 2,916

811 2,581

726 2,318

618 1,971

525 1,685

427 1,374

339 1,096

247 802

164 517

81 269

Site class’ (low)

Yr

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

$(1992)

1,463

1,496

1,382

1,276

1,157

1,051

935

826

721

601

509

1,002

936

858

797

716

649

576

511

442

377

318

579

532

490

443

402

362

314

274

229

188

152

’ Site class includes different assumptions of site index, site preparation, and early survivorship.

’ SEV refers to soil expectation value.

45
Table B7--Loblolly pine plantation yields for differing site qualities and age-5 fusiform

infection rates under the poor utilization and economic rotation assumptions

Infection Pulp Chip-n-saw Saw Rotation SEV

Pet Ft 3/m

0 11

10 13

20 11

30 10

40 8

50 6

60 5

70 4

80 3

90 1

100 1

0 18

10 17

20 15

30 14

40 12

50 11

60 9

70 7

80 4

90 3

100 1

0 7 1,388 998 29

10 10 1,245 890 29

20 9 1,122 801 29

30 8 984 703 29

40 7 862 688 30

50 6 732 593 30

60 5 596 474 30

70 4 467 373 30

80 3 332 297 31

90 2 204 207 32

100 1 92 130 35

Ft -l/m Ft ‘/ac

Site classb(high)

1,531 3,090

1,371 2,824

1,216 2,513

1,079 2,234

924 1,909

771 1,812

619 1,447

484 1,140

355 838

197 554

68 299

Site class (medium)

1,482 1,725

1,329 1,575

1,181 1,392

1,030 1,395

884 1,200

758 1,027

623 840

492 668

351 534

224 343

100 219

Site class (low)

YP

23

23

23

23

23

24

24

24

24

26

31

24

24

24

25

25

25

25

25

26

26

30

$(I 992)

2,313

2,078

1,808

1,569

1,294

1,066

793

550

321

(147)

1,422

1,262

1,091

941

773

625

471

321

170

(loz:,

687’

594

514

427

348

269

177

96

(5;

(125)

‘SEV (soil expectation value).

* Site class includes different assumptions of site index, site preparation, and early survivorship.

46
Table B8--Loblolly pine plantation yields for differing site qualities and age-5 fusiform

infection rates under the poor utilization and fixed rotation assumptions

Infection Pulp Chip-n-saw saw Rotation SEV

Pet Ft 3/m

0 1 1,017 4,93 1

10 3 958 4,875

20 3 856 4,346

30 2 754 3,858

40 2 652 3,307

50 2 551 2,819

60 1 453 2,284

70 1 352 1,793

80 1 255 1,309

90 0 148 754

100 0 66 329

0 8

10 8

20 7

30 6

40 5

50 4

60 4

70 3

80 2

90 1

100 1

0 13 1,152 1,604

10 1 2 1,034 1,440

20 11 930 1,297

30 9 813 1,133

40 8 714 991

50 7 611 856

60 6 494 691

70 4 395 549

80 3 283 399

90 2 183 252

100 1 92 130

Ft ‘/UC Ft ‘/ac

Site class’(high)

Site class (medium)

1,030 3,197

911 2,916

811 2,581

726 2,318

618 1,971

525 1,685

427 1,374

339 1,096

247 802

164 517

81 269

Site class (low)

Yr %(1992)

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

1,463

1,423

1,241

1,071

881

711

528

357

189

(3)

(150)

1,002

888

763

664

534

425

308

203

(1;

(107)

579

501

433

354

287

222

143

76

(65;

(124)

a SW (soil expectation value).

b Site class includes different assumptions of site index, site preparation, and early survivorship,

47
Table B9--Slash pine plantation yields for differing site qualities and age-5 fusiform

infection rates under the full utilization and economic rotation assumptions

Infection Pulp Chip-n-saw saw Rotation SEV”

0 20

10 34

20 8 1

30 137

40 194

50 286

60 354

70 462

80 524

90 613

100 732

0 19

10 50

20 83

30 111

40 161

50 200

60 285

70 367

80 416

90 521

100 614

Pet Ft kx Ft h Ft ‘he

Site class* (high)

Yr %{1992)

2,694 1,489

3,317 1,726

3,031 1,461

2,815 1,401

2,632 1,324

2,395 1,087

2,234 999

2,072 892

1,885 851

1,747 651

1,362 489

Site clask (medium)

23 1,830

24 2,104

23 1,988

23 1,867

23 1,744

22 1,629

22 1,514

22 1,392

22 1,295

21 1,193

21 964

2,238 666

2,518 655

2,358 638

2,216 604

2,049 568

1,839 478

1,689 423

1,490 322

1,378 282

1,235 218

923 103

Site class (low)

24 1,098

24 1,216

24 1,145

24 1,074

24 996

23 932

23 848

22 772

22 706

22 618

21 486

0 8 1,651 192 25 543

1 0 32 1,760 187 25 589

20 61 1,645 174 25 544

30 71 1,540 170 25 502

40 115 1,428 156 25 458

50 148 1,247 135 24 417

60 188 1,144 124 24 376

70 238 1,031 111 24 331

80 313 865 70 23 281

90 375 756 59 23 240

100 445 537 29 22 168

a SEV (soil expectation value).

b Site class includes different assumptions of site index, site preparation, and early survivorship.

48
Table B10--Slash pine plantation yields for differing site qualities and age-5 fusiform

infection rates under the full utilization and fixed rotation assumptions

Infection Pulp Chip-n-saw Saw Rotation SW

0 23

10 10

20 22

30 12

40 9

50 1 5

60 39

70 134

80 314

90 518

100 641

0 10

1 0 23

20 9

30 10

40 13

50 4

60 36

70 145

80 252

90 433

100 536

Pet Ft 3/m Ft 3/ac Ft 3/m Yr qruuz#I

Site classb (high)

2,754 3,267

3,473 3,372

2,957 3,254

2,544 3,078

2,119 2,961

1,686 2,856

1,315 2,738

1,001 2,529

797 2,172

627 1,788

41 1,496

Site cla&(medium)

2,486 1,561

2,843 1,571

2,498 1,530

2,151 1,505

1,886 1,416

1,581 1,398

1,267 1,373

966 1,299

754 1,157

603 883

142 800

Site class* (low)

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

3s

35

35

35

35

35

35

35

35

3s

1,358

1,549

1,399

1,248

1,118

991

874

753

619

490

286

825

909

818

732

649

574

502

425

349

253

139

0 32 1,921 555

1 0 27 2,042 532

20 29 1,849 505

30 13 1,641 515

40 0 1,438 533

50 10 1,244 523

60 21 1,083 492

70 93 916 419

80 160 702 423

90 285 554 312

100 353 288 278

4io

440

388

343

302

257

213

162

122

70

8

a SEV (soil expectation value).

* Site class includes different assumptions of site index, site preparation, and early survivorship.

49
Table B11-- Slash pine plantation yields for differing site qualities and age-5 fusiform

infection rates under the sawtimber utilization and economic rotation assumptions

Infection Pulp Chip-n-saw Saw Rotation SEV

Pet Ft ‘/ac

0 20

10 58

20 152

30 212

40 329

50 445

60 537

70 711

80 828

90 1,027

100 1,197

0 1 9

10 63

20 105

30 42

40 269

50 315

60 442

70 565

80 594

90 833

100 976

0 8 1,651 192 25 543

10 42 1,750 187 25 587

20 87 1,624 171 25 539

30 125 1,493 164 25 489

40 162 1,383 154 25 443

50 211 1,274 134 25 403

60 338 1,083 114 25 339

70 370 978 100 25 292

80 536 761 71 25 225

90 610 599 35 24 171

100 742 358 9 24 83

Ft 3/m Ft “/ac

Site slassb (high)

2,694 1,489

3,305 1,713

2,998 1,421

2,785 1,358

2,595 1,226

2,334 1,169

2,213 995

2,018 698

1,818 614

1,647 335

1,265 78

Site classb (medium)

2,238 666

2,506 654

2,355 619

2,211 520

1,984 525

1,841 488

1,583 373

1,416 293

1,227 336

1,014 127

658 78

Site classb (low)

Yr $(199&l

23 1,830

24 2,097

23 1,962

23 1,839

23 1,693

23 1,560

23 1,426

22 1,287

22 1,171

21 1,019

21 768

24 1,098

24 1,213

24 1,140

23 1,092

24 970

24 891

23 801

23 706

23 657

22 519

22 374

‘SEV (soil expectation value).

h Site class includes different assumptions of site index, site preparation, and early survivorship.
Table B12--Slash pine plantation yields for differing site qualities and age-5 fusiform

infection rates under the sawtimber utilization and fixed rotation assumptions

Infection Pulp

Pcf

0 23

10 10

20 22

30 1 2

40 9

50 15

60 43

70 231

80 121

90 731

100 754

0 10

10 23

20 9

30 10

40 1 3

50 4

60 52

70 165

80 365

90 606

100 751

0 32 1,921 555

10 27 2,042 532

20 29 1,849 505

30 13 1,641 515

40 0 1,438 533

50 10 1,244 523

60 35 1,082 479

70 149 890 389

8 0 285 693 308

90 426 526 197

100 552 253 112

Chip-n-saw Saw Rotation WV

Ff ‘/a Ft ‘/ac

Site classb(high)

2,754 3,267

3,473 3,372

2,957 3,254

2,544 3,078

2,119 2,961

1,686 2,856

1,320 2,731

1,048 2,384

804 2,356

720 1,481

136 1,286

Site classssb (medium)

2,486 1,561

2,343 1,571

2,498 1,530

2,151 1,505

1,886 1,416

1,581 1,398

1,262 1,362

999 1,245

790 1,008

674 639

231 497

Site classb (low)

Yf

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

a SEV (soil expectation value).

’ Site class includes different assumptions of site index, site preparation, and early survivorship.

$(I 992)

1,358

1,549

1,399

1,248

1,118

991

874

727

649

434

245

825

909

818

732

649

574

499

416

323

209

84

426

440

388

343

302

257

210

153

97

51
Table B13--Slash pine plantation yields for differing site qualities and age-5 fusiform

infection rates under the pulpwood utilization and economic rotation assumptions

Infection Pulp Chip-n-saw Saw Rotation SEV”

PGt Ft ‘/a

0 20

1 0 143

20 341

30 526

40 821

50 1,104

60 1,457

70 1,859

80 2,274

90 2,580

100 2,269

0 19

1 0 105

20 206

30 341

40 508

50 716

60 950

70 1,196

80 1,459

90 1,671

100 1,467

0 8 1,651 192 25

10 50 1,742 187 25

20 123 1,587 171 25

30 169 1,474 196 26

40 260 1,303 183 26

50 376 1,108 170 26

60 519 894 148 26

70 706 642 100 25

80 888 366 53 24

90 1,046 129 1 7 23

100 997 0 0 22

Ft h Ft 3/a~

Site classb (high)

2,694 1,489

3,235 1,698

2,895 1,594

2,561 1,495

2,203 1,333

1,840 1,184

1,438 850

1,032 536

632 355

252 85

0 0

Site class” (medium)

2,238 666

2,572 740

2,341 708

2,096 654

1,820 588

1,463 464

1,166 393

816 262

489 128

188 45

0 0

Site classb (low)

Yr S(1 PP2)

23 1,830

24 2,073

24 1,907

24 1,746

24 1,555

24 1,371

23 1,169

22 960

22 748

20 539

19 183

24 1,098

25 1,202

25 1,111

25 1,012

25 906

24 791

24 672

23 545

22 406

21 281

19 72

543

583

531

474

418

357

292

225

143

‘SEV (soil expectation value).

b Site class includes different assumptions of site index, site preparation, and early survivorship.

52
Table B14--Slash pine plantation yields for differing site qualities and age-5 fusiform

infection rates under the pulpwood utilization and fixed rotation assumptions

Infection Pulp

Pet Ft ‘hc Ft 3/ac Ft 3/ac

Site class’(high)

0 23 2,754 3,267

10 10 3,473 3,372

20 22 2,957 3,254

30 12 2,544 3,078

40 9 2,119 2,961

50 15 1,686 2,856

60 173 1,259 2,661

70 848 766 2,049

8 0 1,662 381 1,238

90 2,801 28 104

100 2,177 0 0

Site class’(medium)

0 10

10 23

20 9

30 10

40 13

50 4

60 116

70 555

80 1,127

90 1,849

100 1,479

2,486 1,561

2,843 1,571

2,498 1,530

2,151 1,505

1,886 1,416

1,581 1,398

1,207 1,352

766 1,089

378 658

27 43

0 0

Site class” (low)

0 32 1,921 555

10 27 2,042 532

20 29 1,849 505

30 13 1,641 515

40 0 1,438 533

50 10 1,244 523

60 88 1,037 471

70 324 734 369

80 669 361 255

90 1,124 15 10

100 917 0 0

Chip-n-saw Saw Rotation SW

YP

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35 13

35

35

35

35

35

35

35

35

35

35

35

35

$(I9921 -

1,358

1,549

1,399

1,248

1,118

991

853

628

382

(22;

825

909

818

732

649

574

490

358

203

(162)

420

440

388

343

302

257

203

131

(5;

(149)

‘SW (soil expectation value).

’ Site class includes different assumptions of site index, site preparation, and early survivorship

53
Table B15-- Slash pine plantation yields for differing site qualities and age-5 fusiform

infection rates under the poor utilization and economic rotation assumptions

Infection

Pet

Pulp

Ft -I/ix

0 20

10 15

20 18

30 14

40 20

50 1 4

60 10

70 9

80 3

90 2

100 0

0 19

10 25

20 25

30 1 5

40 7

50 11

60 12

70 4

80 2

90 2

100 0

0 8 1,651 192 25 543

10 19 1,794 219 26 577

20 17 1,641 205 26 515

30 15 1,532 22s 27 449

40 1 6 1,394 254 28 380

50 8 1,219 277 29 301

60 6 1,023 323 31 219

70 2 736 225 30 110

80 3 415 166 31 (12)

90 1 115 33 28 (152)

100 0 0 0 29 (303)

Chip-n-saw Saw Rotation SEV”

Ft ‘/ac Ft 9x

Site classb (high)

2,694 1,489

3,235 1,698

2,974 1,796

2,673 1,858

2,325 1,852

1,923 1,660

1,486 1,562

1,020 1,203

575 782

219 195

0 0

Site class’(medium)

2,238 666

2,572 740

2,341 708

2,146 744

1,902 779

1,619 794

1,277 673

905 608

513 456

167 98

0 0

Site ckd (low)

Yf $(1992)

23 1,830

24 2,046

25 1,844

26 1,655

27 1,421

27 1,179

28 907

28 589

28 252

24 (121)

24 (608)

24 1,098

25 1,186

25 1,077

26 956

27 824

28 673

28 501

29 317

30 113

26 (126)

27 (410)

0 SEV (soil expectation value).

‘Site class includes different assumptions of site index site oreoaration. and early survivorshin.

54
Table B16-- Slash pine plantation yields for differing site qualities and age-5 fusiform

infection rates under the poor utilization and fixed rotation assumptions

Infection

-

Pulp Chip-n-saw Saw Rotation

PGt

0 23

10 10

20 22

30 12

40 9

50 15

60 4.

70 1 1

80 0

90 0

100 0

0 10

10 23

20 9

30 10

40 13

50 4

60 11

70 6

80 2

90 0

100 0

0 32 1,921 555

10 27 2,042 532

20 29 1,849 505

30 13 1,641 515

40 0 1,438 533

50 10 1,244 523

60 8 1,037 471

70 1 734 369

80 3 361 255

90 0 15 10

100 0 0 0

Ft ‘he Ft ‘/a~

Site class* (high)

2,754 3,267

3,473 3,372

2,957 3,254

2,544 3,078

2,119 2,961

1,686 2,856

1,259 2,661

766 2,049

381 1,238

28 104

0 0

Site classb(medium)

2,486 1,561

2,843 1,571

2,498 1,530

2,151 1,505

1,886 1,416

1,581 1,398

i ,207 1,352

766 1,089

378 658

27 43

0 0

Site class* (low)

Yr

35

35

3s

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

35

‘SW (soil expectation value).

’ Site class includes different assumptions of site index, site preparation, and early survivorship.

SEV

$0 992)

1,358

1,549

1,399

1,248

1,1 18

991

834

537

201

(226)

(605)

825

909

818

732

649

574

479

299

(1;

(414)

420

440

388

343

302

257

195

(2;

(180)

(301)

Pye, John M.; Wagner, John E.; Holmes, ‘l’homas P.; Cubbage, Frederick W. 1997. Positive returns

from investment in fusiform rust research. Res Pap. SRS-4 Asheville, N C U.S. Department of

Agriculture, Forest Service, Southern Research Station 55 p.

Fusiforrn rust [Cronartium quuercuum (Berk.( Miy. ex Shirai f sp. fusiforme Burdsall et Snow] is a

widespread and damaging disease of loblolly and slash pine in the South. Research has identified families of

these pines with improved genetic resistance to the disease, allowing production and planting of resistant

seedlings in areas at risk. This study compared the cost of fusiform rust research to the simulated benefits of

rust-resistant seedlings in plantations established Southwide between 1970 and 2020. Seedling producers

provided estimates of resistant seedling production and gains in resistance over the period. Stand-level

simulations evaluated the impact of various infection rates on financial yield on low-, medium-, and highquality sites of each species, taking into account both mortality and product degrade effects of the disease.

Two rotation regimes and four levels of infected stem utilization were explored. Stand-level yields were

extrapolated to regional values using long-term distributions of plantation conditions from Forest Inventory

and Analysis surveys. Simulation results showed that past investments in fusiform rust research of S49 million

will return benefits to plantation owners of between $108 and $999 million in I992 constant dollars. Expected

improvements in resistance will not climtnate all financial damages from the disease; simulation results

indicate substantial financial benefits yet remain for additional research and devclopmcnt.

Keywords: Cronartium fusiforme, disease control, disease resistance, economic damages, fusiform rust,

loblolly pine, plantation valuation, research benefits, slash pine.
The Forest Service, U.S. Department of

Agriculture, 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.

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equal employment opportunity employer.