FIRST AMENDMENT TO
PURCHASE AND SALE AGREEMENT

This First Amendment to Purchase and Sale Agreement (this “Amendment”) is
entered into and effective as of October 4, 2018, by and among CATCHMARK HBU,
LLC, a Delaware limited liability company (“HBU”), CATCHMARK SOUTHERN
TIMBERLANDS II, L.P., a Delaware limited partnership (“Southern Timberlands”),
CATCHMARK TRS HARVESTING OPERATIONS, LLC, a Delaware limited liability company
(“TRS”), and CATCHMARK TEXAS TIMBERLANDS, L.P., a Texas limited partnership
(“Texas Timberlands”, and collectively with HBU, TRS and Southern Timberlands,
“Seller”), and FOREST INVESTMENT ASSOCIATES L.P., a Delaware limited partnership
(hereinafter referred to as “Purchaser”).

W I T N E S S E T H:

WHEREAS, Seller and Purchaser entered into that certain Purchase and Sale
Agreement dated effective August 20, 2018 (with all exhibits, addenda and
amendments thereto, the “Contract”), covering certain real property located in
Hardin, Jasper, Liberty, Newton, Orange, Polk and Tyler Counties, Texas, and
Allen, Beauregard, Calcasieu, DeSoto, Natchitoches, Rapides, Sabine and Vernon
Parishes, Louisiana containing approximately 55,702 acres, (the “Property”), as
more particularly described in the Contract; and

WHEREAS, Seller and Purchaser have agreed to Cruise Specifications as set forth
on Exhibit J attached hereto and hereby made a part hereof; and

WHEREAS, pursuant to Section 35(c) of the Contract, Seller has exercised its one
(1) time right to elect to remove certain entire stands from the list of
Reserved Harvest Stands, and Exhibit K-2 attached hereto and hereby made a part
hereof reflects the so updated Reserved Clearcut Harvest Stands, and there was
no change in the Reserved Thinning Harvest Stands resulting from such removal;
and

WHEREAS, pursuant to the last paragraph of Section 35 of the Contract, as a
result of the update of the list of Reserved Clearcut Harvest Stands, the Value
Table has been updated to Exhibit F attached hereto and hereby made a part
hereof and the Purchase Price has been updated to $80,401,422.88; and

WHEREAS, Seller and Purchaser have agreed to enter into this Amendment on the
terms and conditions set forth herein.

NOW, THEREFORE, for good and valuable consideration, the receipt and sufficiency
of which are hereby acknowledged, the undersigned parties hereby agree to amend
the Contract as follows:

--------------------------------------------------------------------------------

1.The phrase “SEVENTY EIGHT MILLION FIVE HUNDRED THOUSAND AND NO/100 DOLLARS
($78,500,000.00)” in Section 2 of the Contract is hereby deleted in its entirety
and the following is inserted in lieu thereof:

“EIGHTY MILLION FOUR HUNDRED ONE THOUSAND FOUR HUNDRED TWENTY-TWO AND 88/100
DOLLARS ($80,401,422.88)”

2.Exhibit F of the Contract is hereby deleted in its entirety and Exhibit F
attached hereto is inserted in lieu thereof.

3.Exhibit J of the Contract is hereby deleted in its entirety and Exhibit J
attached hereto is inserted in lieu thereof.

4.Exhibit K-2 of the Contract is hereby deleted in its entirety and Exhibit K-2
attached hereto is inserted in lieu thereof.

5.The parties acknowledge and agree that Seller has used its one (1) time right
to remove entire stands from the list of Reserved Harvest Stands and Section
35(c) and the last paragraph of Section 35 of the Contract are of no further
force or effect. Exhibit L of the Contract is hereby deleted in its entirety.

6.In the event of any conflict between the terms of this Amendment and the terms
of the Contract, the terms of this Amendment shall control.

7.    Except as otherwise amended hereby, all of the other terms and provisions
of the Contract are and shall remain in full force and effect and are hereby
ratified by the parties hereto. All capitalized but undefined terms used in this
Amendment shall have the meanings ascribed in the Contract.

8.    This Amendment may be executed in any number of counterparts, each of
which shall be an original and all of which together shall constitute but one
and the same instrument. The delivery of counterpart signatures by facsimile
transmission or e-mail of PDF format electronic copy shall have the same force
and effect as the delivery of a signed hard copy.

[Signatures begin on following page]

--------------------------------------------------------------------------------

WITNESS THE EXECUTION HEREOF as of the date first set forth above.

SELLER:

CATCHMARK HBU, LLC, a Delaware limited liability company

By: /s/ Don Warden                
Name: Don Warden            
Title: Vice President            
Real Estate and Alternative Income
(SEAL)

CATCHMARK SOUTHERN TIMBERLANDS II, L.P., a Delaware limited partnership

By: /s/ Don Warden                
Name: Don Warden            
Title: Vice President            
Real Estate and Alternative Income
(SEAL)

CATCHMARK TRS HARVESTING OPERATIONS, LLC, a Delaware limited liability company

By: /s/ Don Warden                
Name: Don Warden            
Title: Vice President            
Real Estate and Alternative Income
(SEAL)

CATCHMARK TEXAS TIMBERLANDS, L.P., a Texas limited partnership

By: /s/ Don Warden                
Name: Don Warden            
Title: Vice President            
Real Estate and Alternative Income
(SEAL)

PURCHASER:

FOREST INVESTMENT ASSOCIATES L.P.
By:  Forest Investment Associates, LLC
            Its General Partner

--------------------------------------------------------------------------------

By: /s/ Charles L. VanOver          (SEAL)
Name: Charles L. VanOver        
Title: Vice President             

--------------------------------------------------------------------------------

Portions of the exhibit, indicated by the mark “[***],” were omitted and have
been filed separately with the Securities and Exchange Commission pursuant to
the Registrant’s application requesting confidential treatment pursuant to Rule
24b-2 of the Securities Exchange Act of 1934, as amended.

Exhibit F
Land
Acres
Per Acre Value
Total Value
Total
                [***]
 $ [***]
 $ [***]
 
 
 
 
Merchantable Timber (Planted Stands)
 
 
Product
Volume (tons)
Unit Value
Total Value
Pine Pulpwood
                [***]
 $ [***]
 $ [***]
Pine Chip-n-saw
                [***]
 $ [***]
 $ [***]
Pine Sawtimber
                [***]
 $ [***]
 $ [***]
Hardwood Pulpwood
                [***]
 $ [***]
 $ [***]
Hardwood Sawtimber
                [***]
 $ [***]
 $ [***]
Total
                [***]
 
 $ [***]
 
 
 
 
Merchantable Timber (Natural Stands)
 
 
Product
Volume (tons)
Unit Value
Total Value
Pine Pulpwood
                [***]
 $ [***]
 $ [***]
Pine Chip-n-saw
                [***]
 $ [***]
 $ [***]
Pine Sawtimber
                [***]
 $ [***]
 $ [***]
Hardwood Pulpwood
                [***]
 $ [***]
 $ [***]
Hardwood Sawtimber
                [***]
 $ [***]
 $ [***]
Total
                [***]
 
 $ [***]
 
 
 
 
Premerchantable Timber (Age 0-14)
 
 
Age
Acres
Per Acre Value
Total Value
CC
                [***]
 $ [***]
 $ [***]
0
                [***]
 $ [***]
 $ [***]
1
                [***]
 $ [***]
 $ [***]
2
                [***]
 $ [***]
 $ [***]
3
                [***]
 $ [***]
 $ [***]
4
                [***]
 $ [***]
 $ [***]
5
                [***]
 $ [***]
 $ [***]
6
                [***]
 $ [***]
 $ [***]
7
                [***]
 $ [***]
 $ [***]
8
                [***]
 $ [***]
 $ [***]
9
                [***]
 $ [***]
 $ [***]
10
                [***]
 $ [***]
 $ [***]
11
                [***]
 $ [***]
 $ [***]
12
                [***]
 $ [***]
 $ [***]
13
                [***]
 $ [***]
 $ [***]
14
                [***]
 $ [***]
 $ [***]
Total
                [***]
 
 $ [***]
 
 
 
 
Total Value
 
 
 $ [***]
Total Value Per Acre
 
 
 $ [***]

--------------------------------------------------------------------------------

Exhibit J
(see attached)

--------------------------------------------------------------------------------

INVENTORY VERIFICATION OF THE CTT SOUTHWEST PROPERTY
LOCATED IN LOUISIANA AND TEXAS

SEPTEMBER 6, 2018

PREPARED BY:
FORESTECH INTERNATIONAL, LLC

INTRODUCTION

ForesTech International (FTI) was retained by Forest Investment Associates (FIA)
to design and conduct a verification inventory on the CTT Southwest property
located in Louisiana and Texas. The CTT Southwest property is located centrally
between Beaumont, TX and Natchitoches, LA and consists of +/- 55,702 acres
(Figure 1). FTI was tasked with designing an inventory to target the property
total value within the allowable error (AE) target of 5%. The approach that FTI
felt would achieve the desired statistics targets and time constraints motivated
our decision to perform a two-stage list sample. A total of 8 strata were
created to accurately quantify attributes such as origin, age class, thin
classification, and species group. Overall, the inventory project will sample
242 stands (5,496 plots over 13,706 acres).

STRATIFIED TWO-STAGE LIST SAMPLING

Stratified two-stage list sampling is a cost and time efficient sampling scheme
that has application for forest level inventories, particularly when time or
budget constraints are important. Before discussing stratified two-stage
sampling, we will first discuss conventional inventory and then how two-stage
sampling differs from conventional inventory.

In a conventional stand inventory, sample plots are established, typically on a
grid across the stand area, and tree attributes (i.e. dbh, species, tqi, stopper
heights, total heights, etc.) are measured on the plots. The volume or other
attributes needed for the trees measured at those plots are used to estimate the
volume per acre represented at that location in the stand. The average of those
volumes per acre at the sample plots represents the estimated volume per acre in
the stand. The assumption, often not explicitly stated, is that the plots
selected represent the average conditions found throughout the stand and
therefore their average is an accurate estimate of the average volume per acre
in the stand. For a conventional forest inventory, each stand receives an
inventory from which an average volume per acre and total volume based on the
measured plot values within the stand are calculated. To get an estimate of the
forest total, the stand total volume estimates are simply added since every
stand has an estimated total volume. It should be noted that there is no
requirement that each stand be inventoried the same way.

Two-stage sampling involves selecting stands first (“primaries”) and then within
the selected stands a conventional inventory is conducted by putting in a grid
of sample plots and measuring tree attributes of interest on these plots. The
plots are called “secondaries” in the sampling literature since they are only
chosen and installed in the selected primaries. The estimated average
volume/value per acre and total volume for the stand are obtained in exactly the
same way as for conventional inventory. The only problem is that it is not
possible to add all the stand estimates to obtain the forest level estimate
because every stand does not have an estimate (not every stand had plot/points
installed in it). The assumption being made is that the stands selected for the
inventory as primaries represent the average condition of stands in the forest.
Thinking back to conventional inventory, this is exactly the same intuitive
logic used to estimate stand averages from plots. The assumption there is that
the plots chosen within a stand represent the average condition of the stand.
With two-stage sampling the assumption is that the stands chosen within the
forest represent the average condition of the forest.

exhibit103redactedfir_image2.gif [exhibit103redactedfir_image2.gif]

While all of this sounds logical in theory, how well has it worked in practice?
It has actually worked very well. Borders et al. (2005) provided a framework and
example from actual inventory comparing conventional and two-stage sampling.
This Southern Journal of Applied Forestry article was peer reviewed and provided
added detail to the textbook discussion on two-stage sampling in Shiver and
Borders (1996). Their discussion was a refinement of two-stage sampling
published by Cochran (1977) and DeVries (1986). The idea of inventorying in the
manner being described here has been thoroughly reviewed in the forestry
sampling literature and found to be sound.

The CTT Southwest property of roughly +/- 55,702 acres, is located in southern
Louisiana and Texas with a geographic distribution across fifteen counties. In
this case, an accurate forest level estimate is needed in a short time and it
would be difficult to physically go to every stand and sample it within the
desired time constraints. In regular two-stage sampling, primaries are selected
at random. This means that large stands and small stands have the same chance of
appearing in the sample. While it is possible to obtain a sample of stands that
is representative of the forest using random sampling, it takes a larger sample
of stands to ensure a representative sample. Borders et al. (2005) discuss the
disadvantages of such a sampling scheme on the first page of their article.
Fortunately most forest landowners keep an information database for their
properties in which they have relatively recent acreage, stand type, product
volume estimates, etc. This data, which exists for the CTT Southwest property,
allows for an inventory design that takes advantage of this information.

A two-stage list sample is a specific application of two-stage sampling that
replaces the random sampling with sampling in which the larger “size” stands
have a greater probability of being included in the first stage (where primaries
or stands are chosen) than smaller “size” stands. Size can refer to acres or if
another stand characteristic that is correlated with size may be calculated, the
probability may be in proportion to those values. In the case of the CTT
Southwest property, the estimated total value of each stand was calculated by
applying realistic stumpage rates (Table 1) to the product volumes per acre to
get an estimated value per acre and then multiplying by the acres to obtain an
estimate of total stand value. With list sampling, the stands are placed in a
list and are selected from the list with probability proportional to their total
value.

Table 1. FIA Market Inventory Product Specifications and Stumpage Prices

Planted Stands
Product
Min DBH
Max DBH
Min. Length
Top Dob
Height Meas.
Topwood
Stumpage
PTOP
-
-
-
3.0
Total
No
$ [***]
PPWD
3.6
21.6
21.0
3.0
Total
No
$ [***]
PCNS
8.6
11.6
20.0
5.0
Total
Yes
$ [***]
PSAW
12.6
-
17.0
8.0
Total
Yes
$ [***]
HTOP
-
 
-
-
Total
No
$ [***]
HPWD
4.6
21.6
20.0
4.0
Total
No
$ [***]
HSAW
12.6
 
17.0
9.0
Total
No
$ [***]
 
 
 
 
 
 
 
 
Natural Stands
Product
Min DBH
Max DBH
Min. Length
Top Dob
Height Meas.
Topwood
Stumpage
PTOP
-
-
-
3.0
Total
No
$ [***]
PPWD
3.6
21.6
21.0
3.0
Total
No
$ [***]
PCNS
8.6
11.6
20.0
5.0
Total
Yes
$ [***]
PSAW
12.6
-
17.0
8.0
Total
Yes
$ [***]
HTOP
-
 
-
-
Total
No
$ [***]
HPWD
4.6
21.6
20.0
4.0
Total
No
$ [***]
HSAW
12.6
 
17.0
9.0
Total
No
$ [***]

It should be noted that even though more valuable stands have a higher
probability of selection to be inventoried, this does not imply that the
estimate of total value will be biased upward. In fact, the estimator used in
this sampling procedure produces unbiased estimates of value. De Vries (1986)
and Cochran (1977) both show proof that the estimator is unbiased. This is
similar to another estimator that foresters commonly use. Within a stand,
foresters often use point sampling. Point sampling selects trees with
probability proportional to their basal areas. Larger trees have a higher
probability of selection than smaller trees. This allows foresters to focus
their time on measuring mostly larger, more valuable trees. In stands with a
wide diameter distribution this results in a more efficient inventory. The
resulting point sampling estimators also produce unbiased estimates.

The ability of a two-stage sample to produce accurate estimates depends on
choosing stands that represent the average condition of the forest. Partitioning
the forest into more uniform strata to select stands that represent the average
of the stratum should logically allow for more efficient sampling. Stratifying
also allows for different sampling intensities within strata that can later be
combined into an overall forest level estimate. Stratification with two-stage
list sampling is discussed in Borders et al. (2005) and in Shiver and Borders
(1996).

When conducting a two-stage stratified list sample, the first operation is to
remove any stands that will not be part of the inventory. This may include
pre-merchantable stands. For the CTT Southwest property, removal of stands that
did not have significant merchantable volume was the first step. This resulted
in 638 stands covering 26,519 acres. This defines the “forest” for this sampling
design.

--------------------------------------------------------------------------------

Page 8 of 8

The second step when conducting a two-stage stratified list sample is to define
strata. While strata definitions are somewhat arbitrary, they should be chosen
with an objective of forming more consistent sets of stands than what would be
present in the forest as a whole. For the CTT Southwest property 8 strata were
defined (Table 2).

Table 2. Strata Descriptions for inventory of CTT Southwest property

StrataOrder
Description
1
PPN_UT_15-19 consisted of all unthinned pine plantations age 15 to 19
2
PPN_TH_15-19 consisted of all thinned pine plantations age 15 to 19
3
PPN_UT_20-24 consisted of all unthinned pine plantations age 20 to 24
4
PPN_TH_20-24 consisted of all thinned pine plantations age 20 to 24
5
PPN_UT_25+ consisted of all unthinned pine plantations age 25+
6
PPN_TH_25+ consisted of all thinned pine plantations age 25+
7
NPN_20+ consisted of all natural pine stands age 20+
8
NHD_20+ consisted of all natural hardwood stands age 20+

Table 3 shows the general characteristics of these eight strata. Table 4 shows
the total value of stands in each stratum as estimated using the CTT Southwest
product tons provided in each stand multiplied by the following product prices
according to stand origin. Planted pine – pine pulpwood $9/ton, hardwood
pulpwood $7.5/ton, pine chip-n-saw $15/ton, pine sawtimber $25/ton, and hardwood
sawtimber $21/ton. Natural stands – pine pulpwood $6/ton, hardwood pulpwood
$5/ton, pine chip-n-saw $10/ton, pine sawtimber $16.67/ton, and hardwood
sawtimber $14/ton. The variance (S^2) for each stratum was estimated from the
value per stratum data using equations (3) and (4) of the Borders et al. (2005).

Table 3. General characteristics of the 8 strata on the CTT Southwest property.

Strata
N_Stands
Avg_StandSize
Sum_NetAcres
Avg_ValuePerAcre
PPN_UT_15-19
92
48
4,398

$1,043

PPN_TH_15-19
47
51
2,408

$690

PPN_UT_20-24
14
24
338

$1,512

PPN_TH_20-24
31
47
1,447

$1,087

PPN_UT_25+
14
21
288

$1,704

PPN_TH_25+
88
41
3,569

$1,396

NPN_20+
50
28
1,421

$756

NHD_20+
302
42
12,649

$688

 
638
 
26,519

$1,109

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Page 9 of 9

Table 4. Estimated total values by stratum and overall and variances of value
(S^2) by stratum.

Strata
N_Stands
Sum_TotalValue
S^2 WithinStrata
S^2 OfTotal
PPN_UT_15-19
92

$4,272,948

$174,880

$36,770,607,716

PPN_TH_15-19
47

$1,554,735

$65,965

$8,141,137,114

PPN_UT_20-24
14

$393,900

$832,031

$6,805,691,025

PPN_TH_20-24
31

$1,541,574

$88,087

$5,947,790,363

PPN_UT_25+
14

$510,491

$654,467

$3,881,474,995

PPN_TH_25+
88

$4,874,546

$316,989

$45,881,290,043

NPN_20+
50

$1,246,468

$228,331

$9,227,466,805

NHD_20+
302

$8,193,714

$183,442

$97,178,934,867

 
638

$22,588,376

 

$213,834,392,928

The third step in conducting a stratified two-stage list sample is to select
primaries in each of the strata. A two-stage list sample was carried out for
each of the eight strata. Simulated results were made using the values
constructed from the product values assumed and the acreage and product tons in
the CTT Southwest database (Table 5). For field inventory sampling efficiency,
all stands that fell below the 15 acre minimum threshold were excluded from
being selected but will be included in the overall strata totals. Sample sizes
were changed as needed to obtain a stratum AE around 20%, if achievable. Some of
the smaller sized strata naturally had higher variability within the strata and
thus were harder to reduce the AE. For some of the less variable strata, the AE
is considerably lower than 20%. It should be noted that the variances used in
this inventory design depend entirely on the product values and acreages in the
CTT Southwest database. Deviations from those stand product volumes or from the
acreages by stand in the actual inventory once conducted will result in
deviations from these projected AEs.

Table 5. Simulated results of a two-stage list sample assuming perfect
reproduction of acres and product volumes in the CTT Southwest database.

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Page 10 of 10

Strata
N_Stands
Sum_NetAcres
SE OfTotal
AE
Sample Value
PPN_UT_15-19
54
2,907
227,549
11
%

$4,245,155.64

PPN_TH_15-19
26
1,532
124,007
15
%

$1,645,365.75

PPN_UT_20-24
4
247
220,206
82
%

$535,948.54

PPN_TH_20-24
27
1,403
76,557
10
%

$1,535,004.76

PPN_UT_25+
5
240
89,570
35
%

$513,743.06

PPN_TH_25+
50
3,358
239,103
9
%

$5,057,323.04

NPN_20+
18
986
104,718
18
%

$1,195,646.57

NHD_20+
58
3,034
562,722
13
%

$8,446,548.04

 
242
13,706
 
 

$23,174,735

Note the advantages for obtaining an efficient estimate. Even though there are
638 stands in the forest, only 242 of them (about 38% of stands but 52% of the
area in acres) will be inventoried. Though only one third of all stands will be
inventoried, over half the acreage (13,706 out of 26,519) will be inventoried.
The stands chosen for selection contain a total estimated value of $13,206,632
of the estimated $22,588,376 total in all stands. This emphasizes the efficiency
of list sampling in placing plots in stands that are more important to obtaining
a good estimate of the total value by sampling higher value stands with higher
probability. These stratum totals are not of particular interest in and of
themselves. They were rather arbitrarily chosen and the AE for the totals only
serves as a barometer of how well the inventory within each stratum estimates
the stratum total. The variance of the total for each stratum is added to obtain
the variance of the forest total. The square root of this value is the standard
error of the estimate of the forest total and is used in estimating the realized
allowable error (RAE). For this simulation the RAE was about 6%. Again, it
cannot be overemphasized that changes in product tons within the selected stands
to be inventoried in the actual inventory versus the values in the database
along with changes in acres can change the RAE in the actual inventory
significantly. However, in the absence of other data from which to design the
inventory, the data provided must be assumed to be reasonably accurate until
proven otherwise.

The fourth step in conducting a stratified two-stage list sample is to assign
plots (secondaries) in each of the chosen stands (primaries). The actual
inventory intensity within each selected primary for the CTT Southwest project
varies by stratum. All strata will be inventoried using a predetermined basal
area factor to target a minimum 6 tree average per plot. Planted pine stands age
25 and greater (PN_TH_25+ and PPN_UT_25+) will have a cruise intensity of 1 plot
per 2 acres with a minimum of 20 plots per stand and a maximum of 40 plots per
stand. Planted pine stands 15 to 20 years of age (PPN_UT_15-19, PPN_TH_15-19,
PPN_UT_20-24 and PPN_TH_20-24) will have a cruise intensity of 1 plot per 3
acres with a minimum of 20 plots per stand and a maximum of 40 plots per stand.
Natural pine and hardwood stands will have a cruise intensity of 1 plot per 5
acres with a minimum of 15 plots per stand and a maximum of 40 plots per stand.
The result is 5,496 points (Table 6). Points will be installed by FTI according
to the

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Page 11 of 11

inventory cruise specifications and instructions
(FIA_CTT_Southwest_Inventory_Specifications_20180906.pdf).

A subsample of points will be audited by FTI personnel to ensure inventory
foresters adhere to the cruise specifications and quality expectations. The
audit parameters are also included in the inventory cruise specifications. Data
will be compiled and estimates calculated by estimating product volumes through
FTI’s SiMS system. It will be possible to evaluate pre-inventory database values
versus inventory estimates for each of the 242 stands selected if that is
desired.

Table 6. Inventory selected stands summary by Strata.

Strata
N_Stands
N_Plots
N_NetAcres
PPN_UT_15-19
54
1,272
2,907
PPN_TH_15-19
26
607
1,532
PPN_UT_20-24
4
100
247
PPN_TH_20-24
27
616
1,403
PPN_UT_25+
5
124
240
PPN_TH_25+
50
1,480
3,358
NPN_20+
18
316
986
NHD_20+
58
981
3,034
 
242
5,496
13,706

Two-stage stratified list sampling is an efficient inventory method when the
objective is an estimate of forest level value. It does not provide an estimate
of every stand, but in many acquisition situations the overall value is the
value needed. This efficient sampling design should be more widely used in
acquisition inventories.

INVENTORY VERIFICATION CRITERIA

The inventory verification results will be compiled as follows:

Step 1: Estimate the Stand Level Volume for the Cruised Stands:

The raw data from the field will be imported into FTI’s SiMS system for the
cruise workup (Appendix 1. SiMS2012_SystemInfo_20180906.pdf). The inventory
design product specifications for pine and hardwood will be utilized from
earlier in document (Table 1). Tons by product will be estimated for each tree
in the raw data and expanded to a per acre basis for each point. Estimated
product tons per acre for each stand will be calculated using the standard
methodology for point samples.  The tons per acre will be multiplied by the
acres to determine the total stand level volumes.

Step 2: Within Each Strata, Compare the Total Value of Cruised Stands to the
Total Estimated Value of Cruised Stands based on Seller Data

For each stand the total volumes for the cruised stands will be multiplied by
the unit values from the value table to determine the verification value of
cruised stands.  Within each strata, this value will be summed for all cruised
stands and this value will be compared to the estimated value of cruised stands
based on seller’s volume data to determine a percent difference for each strata.
Baseline values were estimated from the seller provided volumes expanded by
acres and stumpage prices for a property level value of $22,588,376 (Table 7).

Table 7. Property level summary values by products and values for the CTT
Southwest Property.

Products
Tons
Value
PTOP_T + PPWD_T
[***]
$ [***]
PCNS_T
[***]
$ [***]
PSAW_T
[***]
$ [***]
ALL PINE
[***]
$ [***]
 
 
 
HPWD_T
[***]
$ [***]
HSAW_T
[***]
$ [***]
ALL HARDWOOD
[***]
$ [***]
 
 
 
ALL TONS
      [***]
 $ [***] 

Step 3: Expand the Value Comparison for the Cruised Stands within Each Strata to
the Entire Strata:

For each strata, the Percent Difference for the cruised stands within the strata
will be applied to all stands included within the strata.  For example, if the
verification value of cruised stands for the 50 stands cruised in the PPN_TH_25+
is found to be 108% of the

estimated value for the same 50 stands using seller data, then a 108% value
adjustment will be applied to all of the 88 stands within this strata.  (At the
strata level, these adjustments will be applied without regards to the 3%
threshold that will be used for the overall comparison).

Step 4:  Sum the Strata Level Results to get the Timber Verification Inventory
Value of the Property:

All eight strata will be compiled using the same methodology for adjustment. 
The sum of these adjusted values for the eight strata will be the Timber
Verification Inventory Value.  The Timber Verification Inventory Value for all
stands will be compared to the Total Estimated Merchantable Timber Value from
the value table to determine the purchase price adjustment, if required.

Borders, B. E., B. D. Shiver, and M. L. Clutter. 2005. Timber Inventory of Large
Acreages
Using Stratified Two-Stage List Sampling. South. J. Appl. For. 29(3): 152-157.

Cochran, W. G. 1977. Sampling techniques, 3rd Edition. John Wiley & Sons, New
York, 428 p.

DeVries, P. G. 1986. Sampling theory for forest inventory. Springer-Verlag, New
York. 399 p.

Shiver, B. D. and B. E. Borders. 1996. Sampling techniques for forest inventory.
John Wiley &
Sons, New York, 356 p.

exhibit103redactedfir_image3.gif [exhibit103redactedfir_image3.gif]

FIA INVENTORY VERIFICATION
CRUISE INSTRUCTIONS
SEPTEMBER 6, 2018

PREPARED BY:
FORESTECH INTERNATIONAL
P.O. BOX 1819
WATKINSVILLE, GEORGIA 30677

Table of Contents

INVENTORY1
Cruise Design1
DATA DELIVERY3
GENERAL INSTRUCTIONS4
Navigation4
Plot Monument4
Plot Type and Plot Size4
Stand Level Data6
Stand Level Attributes recorded in Microsoft Excel template7
DATA COLLECTION WITH TCRUISE 9
Plot Level Data9
Plot Identification collected with TCruise (Screen 1)9
Plot Level Attributes collected with TCruise (Screen 2)10
Tree Level Data11
Tree Level Attributes collected with TCruise (Screen 3)12
Stopper Heights14
Site Tree Designation15
Plot Irregularities16
Different Forest Type16
Unmapped SMZ16
Plot Falls outside Stand Boundary16
Unmapped Utility Corridors / Roads16
Non-Stocked Areas16
Edge Plots16
Dropped Plots17
Mistyped Stands17
Do not cruise17
Cruise17
TCruise Error Checks Setting17
QUALITY CONTROL19
Appendix A - BAF 10 Limiting Distance Table21
Appendix B - BAF 15 Limiting Distance Table22
Appendix C - BAF 20 Limiting Distance Table23
Appendix D - DBH Measurements24
Appendix E - Crown Classes25
Appendix F - Estimating 4 Inch Top in Hardwood Trees with Decurrent Branching26
Appendix G - Hardwood Groupings27
Noncommercial species to be considered in woody competition assessment27

--------------------------------------------------------------------------------

--------------------------------------------------------------------------------

INVENTORY
Overall objectives are to provide stand level assessments and to collect
accurate plot and tree level data. ForesTech (FTI) provides digital copies of
location maps (e.g. overview map, tract maps, and stand maps) and shapefiles.
Inventory foresters are expected to record plot and tree level data using
TCruise software. FTI provides the TCruise templates (TCC files). Stand level
assessments will be recorded using the provided Microsoft Excel template
(FTI_Stand_Template.xlsx). Contractors must use a GPS device to navigate to
pre-numbered plots using the coordinates provided by FTI.
The document defines cruise design, stand level data to summarize, plot
attributes and tree measurements to capture at each sample point, and how to
handle plot irregularities. Screenshots of the TCruise templates, limiting
distance tables, tree measurement diagrams, and audit expectations are provided.

Cruise Design
•
Sampling Intensity - Excel document, StandLister.xlsx, provides detail for
individual stands.

•
Sampling Protocol

a.
All stands within a strata must use the same prism throughout strata, no
exceptions.

Strata
StrataOrder
PlotType
PlotSize
PPN_UT_15-19
1
BAF
20
PPN_TH_15-19
2
BAF
10
PPN_UT_20-24
3
BAF
20
PPN_TH_20-24
4
BAF
15
PPN_UT_25+
5
BAF
20
PPN_TH_25+
6
BAF
15
NPN_20+
7
BAF
15
NHD_20+
8
BAF
15

•
DBH

a.
Tally ALL PINE 3.6” (4” class) and greater in 1” DBH classes

b.
Tally ALL HARDWOOD 4.6” (5” class) and greater in 1” DBH classes

•
Tree Quality Index (TQI) - TQI (1, 3, or 4) will be utilized to determine a
stem’s future potential.

•
Height Measurements

a.
PINE - Total height to nearest foot. Minimum of 30 total heights (minimum of two
per plot) per stand to be measured. This includes one site index tree per plot.

b.
HARDWOOD - Heights measurements to 4” top to nearest foot. All hardwood stems
tallied require a 4” top height. No hardwood site trees to be measured.

•
Stopper Heights (sawtimber and pulpwood) - Each stem should be assessed for
stoppers. Measure stoppers to nearest foot interval for all trees.

a.
Minimum Lengths

i.
Sawtimber potential stems

1.
Pine - Each tallied tree should have a minimum of 16’ of clear log in the first
24’ and have sawtimber potential.

--------------------------------------------------------------------------------

2.
Hardwood - Each tallied tree should have a minimum of 16’ of clear log (above
butt swell).

ii.
Always pulpwood stems - No minimum length required. No sawtimber stopper
allowed.

--------------------------------------------------------------------------------

DATA DELIVERY
Completed data to be uploaded daily. FTI will only accept completed stands. A
“Dropbox” account will be available to upload completed data sets.
File naming: StandName_PlotType&PlotSize_CruiserInitials_PlotCount.tce
Example: BD7061-0338_BAF 15_RDC_48.tce
Data submissions to include:
•
TCD and/or TCE files

•
Stand Template Excel spreadsheet (FTI_Stand_Template.xlsx) - Professional
comments for each stand are required for payment.

•
Completed stand maps (only ones with annotations).

•
Waypoint file.

From time to time cruisers are given options on sample sizes (e.g. BAF 10, 15 or
20) for different stand types; therefore, FTI has applied three (3) ways to
assure that the correct sample size is being identified for processing.
1.
The cruiser selects the appropriate TCruise TCC file. The parameters are set to
the BAF selection that the cruiser chooses.

2.
The TCE file name includes the plot type and plot size in its title.

3.
The Microsoft Excel Template is completed by the cruiser after the stand is
inventoried and the cruiser fills in the plot type and plot size used.

A work order will be provided that details zip folder names for uploaded files.
If maps cannot be sent electronically, please mail them to:
ForesTech International
1800 Hog Mountain Road
Building 600, Suite 101
Watkinsville, GA 30677

GENERAL INSTRUCTIONS

--------------------------------------------------------------------------------

Navigation
Inventory foresters are required to use GPS navigation to locate sample plots. A
33 foot tolerance from desired plot location is allowed. For sample points
outside tolerance, cruisers are required to record and provide new coordinates
for purposes of relocation.
With different navigation software (SOLO Forest) and hardware available, FTI
require inventory foresters to use available versions that log exact coordinates
where the sample plot is taken.

Plot Monument
Orange or pink vinyl flagging (fluorescent) is requested to be used in this
cruise. Identify the exact plot center with a ground flag by exposing bare soils
and staking a flag in the ground leaving 12”-18” left visible (pin flags are the
preferred marking method for centers but ground flags are acceptable if
installed appropriately). Tie a vertical flag at eye-level or higher as close to
plot center as possible. Write cruiser initials (3 letters) and point number on
the vertical flag. (Ex: RDC 100) Failing to correctly monument point (i.e.
missing or inadequate flagging) can result in deductions during an audit.
•
Plantations:

a.
Double-band one dominant/co-dominant tree on the plot to represent a site index
tree. Tree selected should be the dominant species in the stand and represent
the stand age. Use this tree as the first tally tree.

b.
Single-band additional pine trees to represent height trees. These trees should
also be the dominant species and represent the stand age.

•
Natural Stands:

a.
Double-band one dominant/co-dominant pine tree on the plot to represent a site
index tree. Tree selected should be the dominant pine species in the stand and
representative of the stand age. If no pine is available then double-band a
dominant/co-dominant crown class tree as close to plot center as possible. Use
the double banded tree as the first tally tree on the plot.

b.
Identify additional pine height trees in natural stands with a single band of
flagging. No need to identify hardwood trees with flagging (unless tally begins
with a hardwood tree).

•
Clockwise Tally:

a.
After recording the first measured tree (identified with double flagging), move
in a clockwise manner around the plot recording tree data.

b.
If a tree is tallied out of order, please note in the TNote box with “ooo” to
signal that the tree was tallied out of order.

Plot Type and Plot Size
Cruisers should load the appropriate TCC file for the intended plot type and
size of the stand to be inventoried. Note, TCC parameters are reflected in their
respective names (origin, plot type, plot size) and should be loaded for each
stand individually (i.e. each stand should have its own TCC file loaded). Once a
TCC file is loaded, cruisers should rename and save the file to reflect the
stand that is being inventoried. Refer to page 2 of this document for file
naming.
FTI stand maps provide history detail with the intended plot type and plot size;
however, stand history may not be current. For example, an expected unthinned
plantation has been recently thinned. Furthermore, the unexpected thinned
plantation needs to be updated in the Excel template that accompanies the TCE
file submitted.
Both fixed radius and prism plots are utilized.
Table 1. Sampling protocol for intended strata type.

Strata
StrataOrder
PlotType
PlotSize
PPN_UT_15-19
1
BAF
20
PPN_TH_15-19
2
BAF
10
PPN_UT_20-24
3
BAF
20
PPN_TH_20-24
4
BAF
15
PPN_UT_25+
5
BAF
20
PPN_TH_25+
6
BAF
15
NPN_20+
7
BAF
15
NHD_20+
8
BAF
15

*No minimum tree requirement for BAF 10.
For a variable radius plot, the limiting distance is calculated using the tree’s
DBH and a plot radius factor (PRF). This measured distance is compared to the
calculated limiting distance (DBH*PRF) for that tree. If the measured distance
is less than or equal to the limiting distance, then the tree is counted. Refer
to Appendix for limiting distance tables. To determine if a candidate tree is in
or out of a both plot types, the cruiser measures the horizontal distance from
the plot center to the tree’s pith.
Table 2. Basal area factor and corresponding plot radius factor to determine
limiting distance calculations.

Basal Area Factor
Plot Radius Factor
10
2.750
15
2.245
20
1.944

Limiting distance (feet) = Diameter Breast Height (DBH inches) * PRF

If slope is encountered and influences plot sampling then refer to Table 3 for
critical distance measurements.
Table 3. Slope Correction Table

% Slope
Correction Factor
% Slope
Correction Factor
% Slope
Correction Factor
10
1.005
45
1.097
80
1.281
15
1.011
50
1.118
85
1.312
20
1.020
55
1.141
90
1.345
25
1.031
60
1.166
95
1.379
30
1.044
65
1.193
100
1.414
35
1.059
70
1.221
 
 
40
1.077
75
1.250
 
 

Example:
BAF     =    10
DBH    =    20.0
Slope    =    20%
Slope Corrected Plot Radius Factor
= Slope Correction Factor * Plot Radius Factor = 1.020 * 2.750
= 2.805
Limited Distance
= Slope Corrected Plot Radius Factor * DBH
= 2.805 * 20.0 = 56.1 feet

Stand Level Data
Stand level data will be recorded in a separate Microsoft Excel template
provided in the cruiser’s project folder. The template is to summarize the
stand, post-cruise. Cruisers should verify a stand’s cover type, stand lines,
non-productive areas, age, etc. Any discrepancies or issues should be notated on
the stand maps as well as noted in the Stand Template ‘Comments’ field
(FTI_Stand_Template.xlsx). Stand maps should be submitted if edits are
necessary.
The Stand Template is a required submission and is needed for processing. DATA
SUBMISSIONS ARE INCOMPLETE WITHOUT STAND TEMPLATE AND ARE REQUIRED FOR PAYMENT.
Most cells can be filled using the drop down selections. FTI requires stand
comments. The ‘Comments’ field is intended for cruisers to expand on what was
seen in the field. Even if the stand is “normal”, commenting about a stand’s
overall health and tree form within the stand is good information to pass along.
Noting crown development, site conditions, access roads, insect damage, thinning
potential, understory species, logging damage, site cleanup, SMZ’s, etc. are
beneficial to forest management. Each column heading in the Stand Template is
further explained below. (Figure 1)

Stand Level Attributes recorded in Microsoft Excel template

ID
StandID
CruiseDate
Cruiser
Stand Type
PlotType
PlotSize
N_Plots
Comments
GISEdits
1
T20120-0050
2/27/2017
FTI-MAA
Natural - Unthinned
BAF
15
25
The stand was totally flooded. Coul
No
2
T20120-0973
2/27/2017
FTI-MAA
Planted - Thinned
BAF
10
11
Trees look good, however, the stand
No
3
T20120-0969
2/27/2017
FTI-MAA
Planted - Thinned
BAF
20
12
Trees look good, however, the stand
No
4
T11210-0402
3/1/2017
FTI-MAA
Natural - Unthinned
BAF
15
5
Stand is very steep, hard to access,
No
5
T11210-0503
3/1/2017
FTI-MAA
Natural - Unthinned
BAF
15
5
Beautiful stand, park like, very tall
No
6
T11456-0202
3/1/2017
FTI-MAA
Planted - Thinned
BAF
15
3
Small stand, harvested really close
No

Figure 1. Example stand data entry in Stand Template.
•
ID - Denotes entry number.

•
Stand ID - StandName.

•
CruiseDate (Cruise Date) - Enter the cruise date for the stand. If the stand is
cruised over multiple days, use the last date cruiser visited the stand in the
month/day/year format (Ex. 8/28/2018).

•
Cruiser (Cruiser) - Enter the “contractor-cruiser” abbreviation for the plot.
The format is 7 characters total in length. Three character abbreviation for the
contractor name and the cruiser’s three initials. Separate contractor and
cruiser with a hyphen (Ex: FTI-RDC). If multiple cruisers are within a single
stand, enter each cruiser’s 7 character abbreviations separated with a comma
(only one stand entry needed).

•
Stand Type - Select from the drop down menu the stand type cruised:

•
Planted - Thinned

•
Planted - Unthinned

•
Natural - Thinned

•
Natural - Unthinned

•
PlotType (Plot Type) - Enter the three letter abbreviation plot type selected
for the inventory cruise. (BAF)

•
PlotSize (Plot Size) -BAF used (10, 15 or 20).

•
N Plots (Number of Plots) - Enter the total number of inventory plots collected
during the cruise. If multiple cruisers are within a single stand, enter only
one stand record in spreadsheet and provide the total plot count for the stand.

•
Comments (Comments) - Enter comments for each stand to provide information for
future management. Notes about beetles, access, stand boundaries, dropped plots,
poor logging, etc. are all topics worthy of comment. One to two sentences.

•
GIS Edits - Select ‘Yes’ or ‘No’ to indicate if edits to the shapefiles are
needed due to stand delineation issues encountered. Completed stand map is
required with data submission if edits are required.

•

DATA COLLECTION WITH TCRUISE 

Plot Level Data
Cruisers will navigate to a predetermined plot location with a GPS device and
locate the plot center. There are two levels of plot data collection in TCruise:
plot identification (screen 1) and plot assessment attributes (screen 2).

Plot Identification collected with TCruise (Screen 1)
For inventory foresters using Real Time Inventory, the Plot Info fields will
populate automatically once the plot is located within the tolerance desired.
For cruisers navigating with alternative GPS hardware, they will need to fill
the plot identification fields manually. Template examples of the plot
identification are shown in Figure 2 below.
TCruise Plot Info Screen (Screen 1)
exhibit103redactedfir_image4.gif [exhibit103redactedfir_image4.gif]
Figure 2. TCruise plot information screen (screen 1).
•
Stratum (StandName) - Input the stand number provided in the GIS database from
the StandName field for the stand. The StandName is used to map the stand to the
GIS database.

•
Plot ID (Plot identification number) - Enter the appropriate plot identification
provided by PCH personnel. The value is this field should only be an integer
number, no text or characters are allowed.

•
Cruiser (Cruiser) - Enter the “contractor-cruiser” abbreviation for the plot.
The format is seven (7) characters total in length, including the dash. Three
character abbreviation for the contractor name and the cruiser’s three initials.
Separate contractor and cruiser with a hyphen (Ex: FTI-RDC).

•
Custom Info (Plot Attributes) - Opens Screen 2 (Plot Attributes).

•
OK - Opens Screen 3 (Tree Attributes).

Plot Level Attributes collected with TCruise (Screen 2)
After the waypoint is logged and identification recorded, the cruiser will
assess and select the attributes for each sample point. Refer to Figure 3 for
example data entry required. Explanations for each attribute follow. These must
be recorded for each sample point before logging tree data.
TCruise Plot Info Screen (Screen 2)
exhibit103redactedfir_image5.gif [exhibit103redactedfir_image5.gif]
Figure 3. Custom plot info screen. (Screen 2)
•
Plot Status - Indicates plot status/location. Selections other than “Normal” may
be necessary when the plot does not conform to normal stand conditions. Please
provide a comment in PNote that may help explain a selection that deviates from
the default setting “Normal”.

a.
Normal - (Default) - No attributes in the list apply to the plot. Normal plot
location identified and installed.

b.
Offset - Selection made if the plot had to be moved. For example, a plot may
need offsetting for safety reasons. If a plot is intentionally moved outside the
33 feet tolerance, then please provide the reason for the offset and the new
location as it relates to the original plot coordinates in PNote.

c.
Road - Selection for a plot that falls in an unmapped road. Describe road type
(e.g. gravel, ATV, etc.) and enter width (in feet) (Ex: “ATV 10FT”)

d.
Deck - Plot fell in an unmapped deck. Enter approximate rectangular dimensions
(in feet). (Ex: “150X200”)

e.
Cut - Plot fell in an unmapped cut area. Enter approximate rectangular
dimensions (in feet). (Ex: “150X200”)

f.
Beaver - Plot fell in an area affected by beaver activity.

g.
Sensitive - Plot fell in an unmapped SMZ or plot fell in an area where grades
are erodible.

h.
EndSpecies (Endangered Species) - Plot either included or was in view of an
endangered species (includes both flora and fauna). Please comment in PNote.

i.
Dropped - Plot was not sampled. Notate in PNote and provide a comment in the
Stands Excel Template.

j.
Other - Category used if none of the above apply. Please use PNote to explain.

•
NoTally - Answer the question, “Is this plot a ‘No Tally’?” (Yes or No) (Default
‘No’)

•
Woody (Woody Competition) - In pine plantations, an evaluation of the woody
competition growing on each plot will be assessed (Table 4). Include both pine
and hardwood, below the minimum diameter thresholds, in the woody assessment.
Note, all stems will be considered woody competition if they fall below the
lower diameter threshold for the stand, but certain noncommercial species will
always be considered woody competition regardless of diameter size (Appendix H).

Table 4. Woody competition level assessment.

Comp Level
Measurement
Low
0-1 woody competitors per planted tallied stem
Mod
2-3 woody competitors per planted tallied stem
High
4 or more woody competitors per planted tallied stem

•
PNote (Plot Note) - Plot comments.

Tree Level Data
Using the TCruise template (TCC file) selected at the stand level, tree data
will be captured. Several attributes will be recorded for each sample tree.
Cruisers will identify tree species, measure DBH’s, grade a tree’s future
potential using a tree quality index scale, and measure heights. Cruisers will
verify and record age for site index trees. Remnant trees are categorized and
tallied. Also, when applicable, the cruiser selects a defect from a dropdown
menu. Figure 4 shows example tree entries in TCruise. Further discussion about
each attribute follows the screenshot.

Tree Level Attributes collected with TCruise (Screen 3)
exhibit103redactedfir_image6.gif [exhibit103redactedfir_image6.gif]
Figure 4. TCruise tree information screen (screen 3).
•
Spec (Species) - Record species for every tree tallied. Default code is LB
(Loblolly Pine). There is no differentiation between Planted and Natural Pine.

a.
Pine Plantations - Hardwood tally is not species specific, rather tally by group
Hard Hardwood (HH) or Soft Hardwood (HS) (Appendix G).

b.
Natural Stands - There are several natural species selections available in Table
4. However, if a particular hardwood species is not available in the table,
please use a group code of Hard Hardwood (HH) or Soft Hardwood (HS) (Appendix
G). 

Table 4. Species List.
Pine Species List
Code
Species
LB
Loblolly Pine
SL
Slash Pine
LL
Longleaf Pine
SP
Sand Pine
VP
Virginia Pine
SF
Shortleaf Pine
OP
Other Pine

Hardwood Species List
Code
Species
 
Code
Species
 
Code
Species
HH
Hard Hardwood
 
EL
Elm
 
SG
Sweetgum
HS
Soft Hardwood
 
HB
Hackberry
 
SO
Shumard Oak
AS
Ash
 
HK
Hickory
 
SY
Sycamore
BE
Beech
 
NO
Nuttall Oak
 
WA
Water Oak
BG
Blackgum
 
OO
Overcup Oak
 
WI
Willow Oak
CD
E Red Cedar
 
OR
Other Red Oak
 
WL
Walnut
CH
Black Cherry
 
OW
Other White Oak
 
WO
White Oak
CO
Cherrybark Oak
 
PO
Post Oak
 
WT
Water Tupelo
CY
Cypress
 
QM
Chestnut Oak
 
YP
Yellow Poplar
EH
Hemlock
 
RO
Red Oak
 
 
 

•
DBH (Diameter Breast Height) - Enter DBH in 1” classes for each tree tallied.
Measure and record all pine 3.6” and greater, (4” class). Measure and record all
hardwood 4.6” and greater, (5” class). Encountering stem variability is not
uncommon when measuring a tree’s DBH. A diagram is included for ways to measure
DBH on trees with irregularities (Appendix D).

•
TQI (Tree Quality Index) - Enter tree TQI (1, 3, or 4) on each tree tallied.
(Default ‘1’). Tree Quality Index is assigned to all sample trees. There are
three different values which are independent of time, size, or position in the
canopy. The purpose of this designation is not only to assess current quality or
form but to also consider the future for growing volume as well. Tree Quality
refers to the form of the entire tree and is NOT to be confused with log
quality. TQI is assigned to each tree based on its potential use now or in the
future and is not based on its current size. This is very important to keep in
mind as you are determining the appropriate TQI value for a given tree. For
example, a very high quality tree that is currently a 6” DBH tree should be
assigned TQI = 1 even though it does not currently qualify as sawtimber in the
market place. The three different categories used are as follows:

a.
Tree Quality 1: Sawtimber potential. Tree has good form and quality consistent
with producing a sawlog now or within the rotation cycle of the stand. These
trees need not be perfect and can contain some minor defects including sweep,
crook, knots, etc. A pine stem should have the potential to produce at least one
16 foot log within the first 24 feet of the stem. A hardwood stem should have at
minimum 16 feet of clear log above the butt swell. A sawtimber stopper height
(SST) should be determined by the cruiser when the sawlog portion of the stem is
‘stopped’ by any single or combination of defects that preclude the stem from
being used for sawn material above that point.

b.
Tree Quality 3: Always pulpwood. These trees have poor quality and/or some type
of defect that will limit them to always being a pulpwood product. Using this
code will flag the tree as always pulpwood to be utilized for fiber volume only.
TQI should not be confused with pulpwood stopper. A TQI 3 code quantifies the
entire stem as pulpwood. A pulpwood stopper height (PST) should be determined by
the cruiser when one or more defects will preclude the stem from being used for
fiber above that defect (e.g. broken top).

c.
Tree Quality 4: Cull, trees that are not capable of producing any product. These
trees usually have extremely poor form or some type of major defect. Cull trees
are not dead trees. Do NOT tally dead trees.

•
HGT (Height) - Enter tree height to the nearest foot. The ‘HGT’ field in the
TCruise template will be used to record the height of candidate trees. ‘HGT’
measurements vary based on species (pine or hardwood) (Table 5). The ‘HGT’ field
in the TCruise templates will be utilized to accept both the total height
measurements for pine and the 4” height measurements for hardwood trees.

Table 5. Site Tree and Height Tree (HGT) measurements by species and DBH.

Species Group
DBH (in.)
Tree Classification
Num of Height Trees per Plot
Site Tree Measurement
Height Tree Measurement
Pine
> 3.6
Total
Total
See Stand Map
Hardwood
> 4.6
NA
4” DOB*
ALL

*Refer to Appendix F for 4” top diagram
Stand maps give reference to a number of ‘Height Trees’ to be measured. This
number applies to only PINE and informs a cruiser of the number of sample pine
heights to measure on each plot. For example, the map for a planted pine stand
may state that two (2) ‘Height Trees’ should be collected on each plot. This
means, that in addition to the ‘Site Tree’ height measurement, one more height
will need to be collected on each plot for a total of two (2) height
measurements for each plot. Cruisers should target trees that are relatively
straight and free of major defect (i.e. forks and broken tops). Also, Pine
‘Height Trees’ should represent the varying diameter classes represented in the
stand.
Note, ‘Height Trees’ does not apply to hardwood trees. All hardwood trees,
whether they are in plantations or natural stands, require height measurements.
The only time a hardwood tree would not have a height measurement is if it has a
TQI value 4, cull.
If a pine tree other than the dominant species in the stand (both plantation and
natural stands) is encountered in the plot then the first occurrence of this
“Other” pine species requires a height measurement. For example, when cruising a
planted loblolly stand a natural shortleaf pine is tallied then a height
measurement is required (only the first occurrence).

Stopper Heights
Each sample tree should be assessed for stoppers (sawtimber and/or pulpwood).
For example, a pine height tree may require only one height which will be a
total height measurement, but a hardwood height tree may have a sawtimber
stopper height, a pulpwood stopper height, and a 4” DOB height measured because
of a forked bole and excessive branching in the crown (Appendix F).
•
SST (Sawtimber Stopper Height) - Enter height to nearest foot where sawtimber
merchantability ends. The material above this point will not be used for solid
wood material. The minimum length is 16 feet of usable sawtimber potential in
the first 24 feet of the tree. TQI 1 trees may have an SST measurement; however,
TQI 3 trees should not have an SST measurement because these trees will never
have sawtimber potential.

•
PST (Pulpwood Stopper Height) - Enter height to nearest foot where pulpwood
merchantability ends. The pulpwood stopper height is the height to the nearest
foot where material above this point cannot be used for pulpwood (e.g. broken
top pines or decurrent branching in hardwood crowns (Appendix F)). TQI 1 trees
and TQI 3 trees can both have a PST.

Site Tree Designation
One PINE site index tree per plot is required in both pine plantations and
natural stands. The site index tree chosen should be a dominant or co-dominant
crown class sample and represent both the dominant pine species in the stand as
well as represent the stand’s age (i.e. not a remnant tree). The tree selected
should represent the “site” and preferably free of disease or any major defects
(i.e. forks, broken tops, excessive branching, etc.). For all site index trees,
record its AGE and its TOTAL HEIGHT in the TCruise template.
Note: The dominant pine species selected for the site tree should be used
throughout the stand (i.e. do not mix species for site index trees).
•
Age - Enter age for ONLY the “Site Index Tree” in years. Stand age is
represented from the site tree’s age. Planted pine ages will be on the
respective stand map and site trees do not need to be cored unless the cruiser
disagrees with the age printed. Verify stand age in natural stands as well.

If the cruiser disagrees with the stand age then coring the site tree at breast
height and recording the new age is needed. Please do not replace core in the
bore hole, but rather tie the core to the site tree flagging on the trunk.
Record site tree ages in the Age field of the TCruise template. A minimum of
five (5) site trees will need to be cored for age. Notate in the TNote field of
the TCruise template that the tree was cored. Site tree ages on other plots can
be estimated.
Note: Recording an age is the only identifier for the Site Index Tree in the
template. Please do not omit.
•
Remn - Remnant refers to a pine tree left from a previous harvest. This tree
represents an older age class in a planted stand and needs identifying. If
tallied, the cruiser classifies this stem in the ‘Remn’ field of the TCruise
template with a “Yes” indicator. Indicator is applicable to only pine
plantations.

•
Defect - Select from the list the tree defect that represents the downgrade of
the stem or influences a ‘HGT’ measurement (Table 6). Note, for any tree that is
“jump/junk butted” record the measurement, in feet, in the TNote column.

Table 6. Abbreviations for tree defects.

Code
Tree Note
Code
Tree Note
CR
crook
BT
broken top
SW
sweep
JB
jump butt
RU
rust
WH
whorl
FK
fork
OT
other

•
TNote - Tree level comment.

Plot Irregularities
Due to inconsistencies with shapefile stand line edits, cruisers may encounter
plots that do not coincide with map layers. Plots may fall entirely out of stand
boundaries, in unmapped SMZs, cut areas, etc. We have tried to be proactive and
alleviate as much as possible before the inventory but undoubtedly situations
will arise. The following examples will answer ways best to handle such
encounters; however, if a cruiser gets to an area and cannot determine the best
action on how to handle the plot, then please contact ForesTech personnel. With
any of these situations, please use your stand maps to notate edits, record new
GPS coordinates and use the “Offset” selection in the PlotStatus field of the
TCruise template.

Different Forest Type
If sample plots fall in an unmapped area within the stand that is a different
forest type than the main stand being cruised, then measure the plots. Use the
PNote field to comment as well as make stand map edits. Use the same plot
type/size as the main stand.

Unmapped SMZ
Collect the plot where it falls as it relates to an unmapped or mistyped SMZ.
These riparian sample areas will be included as valid plots and should not be
offset. Use the PNote field to comment as well as make stand map edits. Use the
same plot type/size as the main stand.

Plot Falls outside Stand Boundary
If a plot falls clearly outside actual stand boundaries move the plot into the
appropriate stand type. Use ‘Offset’ in the PlotStatus field of the TCruise
template and record new coordinates if location exceeds the 33 feet tolerance.
Move far enough in the stand to not take an edge plot.

Unmapped Utility Corridors / Roads
If a plot falls on the edge or entirely within unmapped transmission lines,
ROWs, etc. that are encountered within a stand, then consider these areas as
parts of the stand and take the sample plot. Please add a Plot Comment that the
plot fell in a utility corridor, etc.

Non-Stocked Areas
If sample plots falls in an area that is non-stocked (including clearcuts),
record the plots in the area as ‘No Tally’ plots. Measure all remaining forested
plots in the stand. Use the No Tally indicator and the PNote field in the
TCruise template to explain. Please monument plot center.

Edge Plots
If a plot falls at the edge of a stand boundary and prevents a plot from being
sampled in its entirety, then move the plot to be able to capture a full sample.
If the plot needs to be offset by more than the 33 feet allowable tolerance
please capture the new GPS coordinates.

Dropped Plots
Drop plots only for safety concerns. Please note why the plot was dropped.

Mistyped Stands
If the entire stand is mistyped, then use the following protocol. Cruisers need
to make stand map edits and contact ForesTech with questions if they are
uncertain on how to proceed.

Do not cruise
1.
Clearcuts

2.
If the stand was intended to be a planted stand and turns out to be a natural
stand.

Cruise
1.
If a pine plantation was noted as an unthinned (UT) stand and intended to be
cruised but was discovered to be thinned (TH). Document in the Stand Template
Excel document.

2.
If the stand was expected to be a natural stand and turns out to be a planted
stand - use cruise protocol for forest type. Consult with FTI for sampling
intensity.

3.
If the stand was a mistyped natural stand. Ex: stand was intended to be a mature
Pine Hardwood stand and turns out be a mature Bottomland.

TCruise Error Checks Setting
In order for TCruise to accept sawtimber stopper height measurements on smaller
diameter stems, the error check setting for merchantable height - top should be
unchecked. After loading the TCruise template at the stand level, select ‘Opts’
then ‘Error Checks’. Next, deselect ‘Merchantable height - top’ (Figure 5).

TCruise Error Check
exhibit103redactedfir_image7.gif [exhibit103redactedfir_image7.gif]
Figure 5. TCruise Error Checks setting.

QUALITY CONTROL
Check cruising will follow the scoring system in Table 7. A cruiser’s
understanding of the job control document will be scrutinized. Check cruising is
to be used as a teaching tool to ensure that the cruise instructions and
measurement standards are understood and the data collection is consistent from
cruiser to cruiser.
Table 7. Check cruising tolerances for accepting/rejecting a cruiser’s work.

Stand Penalties
Penalty1
Penalty2
Tolerance
Measurement
Additional Penalty Comments
Plot Type and Size Selection1
10
-
-
N/A
None

Stand Penalties
Penalty1
Penalty2
Tolerance
Measurement
Additional Penalty Comments
Plot Type and Size Selection1
10
-
-
N/A
None
Plot Flagged Correctly
5
-
-
N/A
None
Woody Comp
2
-
1
N/A
None

Stand Penalties
Penalty1
Penalty2
Tolerance
Measurement
Additional Penalty Comments
MissedAdded Tree
8
-
0.3
feet
None
SpeciesPenalty
3
-
-
species
None
DBH
2
-
0.4
inches
None
TQIPenalty
1
-
-
N/A
None
Missed/Add Saw Stop
2
-
5
feet
None
Missed/Add Pulp Stop
2
-
5
feet
None
Heights (Pine Site Trees)
2
0.5
3
feet
0.5*ft outside tolerance
Heights (All Pine)
1
0.25
5
feet
0.25*ft outside tolerance
Heights (Hardwoods)
1
0.25
8
feet
0.25*ft outside tolerance
SiteTreeAge
1
-
2
years
None
Remn Penalty (Remnant)
1
-
-
Indicator variable
None

1Plot Type and Size Selection - cruiser will be penalized if auditor disagrees
with BAF selection. For example, cruiser selected a BAF20 in a thinned pine
plantation and not able to average a minimum of 6 trees per plots. The check
cruiser felt that a BAF10 should have been selected. Please use the comments in
TCRUISE (PNote) to explain your decision process.
2Penalty 2 - this is an additional point penalty that applies if the recorded
measurement is out of tolerance. For example, if the check cruise determines
that a pine tree height is 10 feet off, the penalty would be 2.25 points. One
point for being > 5 feet different plus 1.25 points (10’ - 5’ = 5’ x 0.25 points
= 1.25 points).
Stands will be assessed based on a batch of three plots per stand. The total
point deduction will be calculated on each plot. Then, the simple average of the
point deduction is calculated across the three plot batch. This yields the
average point deduction per plot. If there was a stand deduction, it is added to
the average point deduction per plot. This yields the total point deduction for
the stand. Using a scale of 100, if the total point value in the stand is > 80,
the stand passes. Table 8 shows an example calculation. In the example, the
average point deduction per plot = 17.2. There was no stand level penalty added,
so the total point deduction for the stand = 17.2. The stand would pass since
100 - 17.2 = 82.8 which is > 80.
Table 8. Example Check Cruising Analysis

Stand
Plot
Plot and Tree Penalties
1
1
19.3
1
2
24.3
1
3
8.0
Average Penalty
17.2
Stand Level Penalty
-
Total Penalty
17.2

Check cruising is part of a larger quality control effort, which also includes
the following:
1.
Sufficient training of all cruisers at the start of a project to ensure that the
cruise instructions, measurement standards, and other protocols are understood,
and that work will be consistent from cruiser to cruiser.

2.
Actions in the event of substandard work by any cruiser, including:

a.
On-site training of cruisers and review of the instructions and protocols.

b.
Return to cruised plots in the audited stand to redo some or all measurements.

c.
Removal of a cruiser from the project.

Work that is deemed as unacceptable will require the check-cruiser to decide how
much of the cruiser’s work must be discarded and redone. To minimize the
likelihood of rejecting a large amount of work, check cruising will be done
frequently, and results will be shared with the cruiser in a timely manner.
In addition to the check cruising, we will also be reviewing the cruise data to
determine the following:
1.
Total height measurement distribution across pine tree diameter classes.

2.
Average number of trees per plot. This will be used to determine if too many or
too few trees are being measured.

3.
Plantation age is consistent with inventory data.

Appendix A
BAF 10 Limiting Distance Table
Limiting Distance for Tree Inclusion w/ Point Cruising
(BAF 10 Prism in Feet -- Multiplier Value of 2.750)
DBH Tenths of an Inch

DBH
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0
0.0
0.3
0.6
0.8
1.1
1.4
1.7
1.9
2.2
2.5
1
2.8
3.0
3.3
3.6
3.9
4.1
4.4
4.7
5.0
5.2
2
5.5
5.8
6.1
6.3
6.6
6.9
7.2
7.4
7.7
8.0
3
8.3
8.5
8.8
9.1
9.4
9.6
9.9
10.2
10.5
10.7
4
11.0
11.3
11.6
11.8
12.1
12.4
12.7
12.9
13.2
13.5
5
13.8
14.0
14.3
14.6
14.9
15.1
15.4
15.7
16.0
16.2
6
16.5
16.8
17.1
17.3
17.6
17.9
18.2
18.4
18.7
19.0
7
19.3
19.5
19.8
20.1
20.4
20.6
20.9
21.2
21.5
21.7
8
22.0
22.3
22.6
22.8
23.1
23.4
23.7
23.9
24.2
24.5
9
24.8
25.0
25.3
25.6
25.9
26.1
26.4
26.7
27.0
27.2
10
27.5
27.8
28.1
28.3
28.6
28.9
29.2
29.4
29.7
30.0
11
30.3
30.5
30.8
31.1
31.4
31.6
31.9
32.2
32.5
32.7
12
33.0
33.3
33.6
33.8
34.1
34.4
34.7
34.9
35.2
35.5
13
35.8
36.0
36.3
36.6
36.9
37.1
37.4
37.7
38.0
38.2
14
38.5
38.8
39.1
39.3
39.6
39.9
40.2
40.4
40.7
41.0
15
41.3
41.5
41.8
42.1
42.4
42.6
42.9
43.2
43.5
43.7
16
44.0
44.3
44.6
44.8
45.1
45.4
45.7
45.9
46.2
46.5
17
46.8
47.0
47.3
47.6
47.9
48.1
48.4
48.7
49.0
49.2
18
49.5
49.8
50.1
50.3
50.6
50.9
51.2
51.4
51.7
52.0
19
52.3
52.5
52.8
53.1
53.4
53.6
53.9
54.2
54.5
54.7
20
55.0
55.3
55.6
55.8
56.1
56.4
56.7
56.9
57.2
57.5
21
57.8
58.0
58.3
58.6
58.9
59.1
59.4
59.7
60.0
60.2
22
60.5
60.8
61.1
61.3
61.6
61.9
62.2
62.4
62.7
63.0
23
63.3
63.5
63.8
64.1
64.4
64.6
64.9
65.2
65.5
65.7
24
66.0
66.3
66.6
66.8
67.1
67.4
67.7
67.9
68.2
68.5
25
68.8
69.0
69.3
69.6
69.9
70.1
70.4
70.7
71.0
71.2
26
71.5
71.8
72.1
72.3
72.6
72.9
73.2
73.4
73.7
74.0
27
74.3
74.5
74.8
75.1
75.4
75.6
75.9
76.2
76.5
76.7
28
77.0
77.3
77.6
77.8
78.1
78.4
78.7
78.9
79.2
79.5
29
79.8
80.0
80.3
80.6
80.9
81.1
81.4
81.7
82.0
82.2
30
82.5
82.8
83.1
83.3
83.6
83.9
84.2
84.4
84.7
85.0
31
85.3
85.5
85.8
86.1
86.4
86.6
86.9
87.2
87.5
87.7
32
88.0
88.3
88.6
88.8
89.1
89.4
89.7
89.9
90.2
90.5
33
90.8
91.0
91.3
91.6
91.9
92.1
92.4
92.7
93.0
93.2
34
93.5
93.8
94.1
94.3
94.6
94.9
95.2
95.4
95.7
96.0
35
96.3
96.5
96.8
97.1
97.4
97.6
97.9
98.2
98.5
98.7
36
99.0
99.3
99.6
99.8
100.1
100.4
100.7
100.9
101.2
101.5
37
101.8
102.0
102.3
102.6
102.9
103.1
103.4
103.7
104.0
104.2
38
104.5
104.8
105.1
105.3
105.6
105.9
106.2
106.4
106.7
107.0
39
107.3
107.5
107.8
108.1
108.4
108.6
108.9
109.2
109.5
109.7
40
110.0
110.3
110.6
110.8
111.1
111.4
111.7
111.9
112.2
112.5

Appendix B
BAF 15 Limiting Distance Table
Limiting Distance for Tree Inclusion w/ Point Cruising
(BAF 15 Prism in Feet -- Multiplier Value of 2.245)
DBH Tenths of an Inch

DBH
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0
0.0
0.2
0.4
0.7
0.9
1.1
1.3
1.6
1.8
2.0
1
2.2
2.5
2.7
2.9
3.1
3.4
3.6
3.8
4.0
4.3
2
4.5
4.7
4.9
5.2
5.4
5.6
5.8
6.1
6.3
6.5
3
6.7
7.0
7.2
7.4
7.6
7.9
8.1
8.3
8.5
8.8
4
9.0
9.2
9.4
9.7
9.9
10.1
10.3
10.6
10.8
11.0
5
11.2
11.4
11.7
11.9
12.1
12.3
12.6
12.8
13.0
13.2
6
13.5
13.7
13.9
14.1
14.4
14.6
14.8
15.0
15.3
15.5
7
15.7
15.9
16.2
16.4
16.6
16.8
17.1
17.3
17.5
17.7
8
18.0
18.2
18.4
18.6
18.9
19.1
19.3
19.5
19.8
20.0
9
20.2
20.4
20.7
20.9
21.1
21.3
21.6
21.8
22.0
22.2
10
22.5
22.7
22.9
23.1
23.3
23.6
23.8
24.0
24.2
24.5
11
24.7
24.9
25.1
25.4
25.6
25.8
26.0
26.3
26.5
26.7
12
26.9
27.2
27.4
27.6
27.8
28.1
28.3
28.5
28.7
29.0
13
29.2
29.4
29.6
29.9
30.1
30.3
30.5
30.8
31.0
31.2
14
31.4
31.7
31.9
32.1
32.3
32.6
32.8
33.0
33.2
33.5
15
33.7
33.9
34.1
34.3
34.6
34.8
35.0
35.2
35.5
35.7
16
35.9
36.1
36.4
36.6
36.8
37.0
37.3
37.5
37.7
37.9
17
38.2
38.4
38.6
38.8
39.1
39.3
39.5
39.7
40.0
40.2
18
40.4
40.6
40.9
41.1
41.3
41.5
41.8
42.0
42.2
42.4
19
42.7
42.9
43.1
43.3
43.6
43.8
44.0
44.2
44.5
44.7
20
44.9
45.1
45.3
45.6
45.8
46.0
46.2
46.5
46.7
46.9
21
47.1
47.4
47.6
47.8
48.0
48.3
48.5
48.7
48.9
49.2
22
49.4
49.6
49.8
50.1
50.3
50.5
50.7
51.0
51.2
51.4
23
51.6
51.9
52.1
52.3
52.5
52.8
53.0
53.2
53.4
53.7
24
53.9
54.1
54.3
54.6
54.8
55.0
55.2
55.5
55.7
55.9
25
56.1
56.3
56.6
56.8
57.0
57.2
57.5
57.7
57.9
58.1
26
58.4
58.6
58.8
59.0
59.3
59.5
59.7
59.9
60.2
60.4
27
60.6
60.8
61.1
61.3
61.5
61.7
62.0
62.2
62.4
62.6
28
62.9
63.1
63.3
63.5
63.8
64.0
64.2
64.4
64.7
64.9
29
65.1
65.3
65.6
65.8
66.0
66.2
66.5
66.7
66.9
67.1
30
67.4
67.6
67.8
68.0
68.2
68.5
68.7
68.9
69.1
69.4
31
69.6
69.8
70.0
70.3
70.5
70.7
70.9
71.2
71.4
71.6
32
71.8
72.1
72.3
72.5
72.7
73.0
73.2
73.4
73.6
73.9
33
74.1
74.3
74.5
74.8
75.0
75.2
75.4
75.7
75.9
76.1
34
76.3
76.6
76.8
77.0
77.2
77.5
77.7
77.9
78.1
78.4
35
78.6
78.8
79.0
79.2
79.5
79.7
79.9
80.1
80.4
80.6
36
80.8
81.0
81.3
81.5
81.7
81.9
82.2
82.4
82.6
82.8
37
83.1
83.3
83.5
83.7
84.0
84.2
84.4
84.6
84.9
85.1
38
85.3
85.5
85.8
86.0
86.2
86.4
86.7
86.9
87.1
87.3
39
87.6
87.8
88.0
88.2
88.5
88.7
88.9
89.1
89.4
89.6
40
89.8
90.0
90.2
90.5
90.7
90.9
91.1
91.4
91.6
91.8

Appendix C
BAF 20 Limiting Distance Table
Limiting Distance for Tree Inclusion w/ Point Cruising
(BAF 20 Prism in Feet -- Multiplier Value of 1.944)
DBH Tenths of an Inch

DBH
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.7
1
1.9
2.1
2.3
2.5
2.7
2.9
3.1
3.3
3.5
3.7
2
3.9
4.1
4.3
4.5
4.7
4.9
5.1
5.2
5.4
5.6
3
5.8
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
4
7.8
8.0
8.2
8.4
8.6
8.7
8.9
9.1
9.3
9.5
5
9.7
9.9
10.1
10.3
10.5
10.7
10.9
11.1
11.3
11.5
6
11.7
11.9
12.1
12.2
12.4
12.6
12.8
13.0
13.2
13.4
7
13.6
13.8
14.0
14.2
14.4
14.6
14.8
15.0
15.2
15.4
8
15.6
15.7
15.9
16.1
16.3
16.5
16.7
16.9
17.1
17.3
9
17.5
17.7
17.9
18.1
18.3
18.5
18.7
18.9
19.1
19.2
10
19.4
19.6
19.8
20.0
20.2
20.4
20.6
20.8
21.0
21.2
11
21.4
21.6
21.8
22.0
22.2
22.4
22.6
22.7
22.9
23.1
12
23.3
23.5
23.7
23.9
24.1
24.3
24.5
24.7
24.9
25.1
13
25.3
25.5
25.7
25.9
26.0
26.2
26.4
26.6
26.8
27.0
14
27.2
27.4
27.6
27.8
28.0
28.2
28.4
28.6
28.8
29.0
15
29.2
29.4
29.5
29.7
29.9
30.1
30.3
30.5
30.7
30.9
16
31.1
31.3
31.5
31.7
31.9
32.1
32.3
32.5
32.7
32.9
17
33.0
33.2
33.4
33.6
33.8
34.0
34.2
34.4
34.6
34.8
18
35.0
35.2
35.4
35.6
35.8
36.0
36.2
36.4
36.5
36.7
19
36.9
37.1
37.3
37.5
37.7
37.9
38.1
38.3
38.5
38.7
20
38.9
39.1
39.3
39.5
39.7
39.9
40.0
40.2
40.4
40.6
21
40.8
41.0
41.2
41.4
41.6
41.8
42.0
42.2
42.4
42.6
22
42.8
43.0
43.2
43.4
43.5
43.7
43.9
44.1
44.3
44.5
23
44.7
44.9
45.1
45.3
45.5
45.7
45.9
46.1
46.3
46.5
24
46.7
46.9
47.0
47.2
47.4
47.6
47.8
48.0
48.2
48.4
25
48.6
48.8
49.0
49.2
49.4
49.6
49.8
50.0
50.2
50.3
26
50.5
50.7
50.9
51.1
51.3
51.5
51.7
51.9
52.1
52.3
27
52.5
52.7
52.9
53.1
53.3
53.5
53.7
53.8
54.0
54.2
28
54.4
54.6
54.8
55.0
55.2
55.4
55.6
55.8
56.0
56.2
29
56.4
56.6
56.8
57.0
57.2
57.3
57.5
57.7
57.9
58.1
30
58.3
58.5
58.7
58.9
59.1
59.3
59.5
59.7
59.9
60.1
31
60.3
60.5
60.7
60.8
61.0
61.2
61.4
61.6
61.8
62.0
32
62.2
62.4
62.6
62.8
63.0
63.2
63.4
63.6
63.8
64.0
33
64.2
64.3
64.5
64.7
64.9
65.1
65.3
65.5
65.7
65.9
34
66.1
66.3
66.5
66.7
66.9
67.1
67.3
67.5
67.7
67.8
35
68.0
68.2
68.4
68.6
68.8
69.0
69.2
69.4
69.6
69.8
36
70.0
70.2
70.4
70.6
70.8
71.0
71.2
71.3
71.5
71.7
37
71.9
72.1
72.3
72.5
72.7
72.9
73.1
73.3
73.5
73.7
38
73.9
74.1
74.3
74.5
74.6
74.8
75.0
75.2
75.4
75.6
39
75.8
76.0
76.2
76.4
76.6
76.8
77.0
77.2
77.4
77.6
40
77.8
78.0
78.1
78.3
78.5
78.7
78.9
79.1
79.3
79.5

Appendix D
DBH Measurements
exhibit103redactedfir_image8.gif [exhibit103redactedfir_image8.gif]

Appendix E
Crown Classes

exhibit103redactedfir_image9.gif [exhibit103redactedfir_image9.gif]

Appendix F
Estimating 4 Inch Top in Hardwood Trees with Decurrent Branching

exhibit103redactedfi_image10.gif [exhibit103redactedfi_image10.gif]

Appendix G
Hardwood Groupings

Species
General Species
Type
 
Species
General Species
Type
American Holly
Hard Hardwoods, Other
Hard Hardwood
 
American Elm
Soft Hardwoods, Other
Soft Hardwood
Birch Sp.
Hard Hardwoods, Other
Hard Hardwood
 
Black Cherry
Soft Hardwoods, Other
Soft Hardwood
Black Locust
Hard Hardwoods, Other
Hard Hardwood
 
Black Willow
Soft Hardwoods, Other
Soft Hardwood
Black Oak
Red Oaks, Other
Hard Hardwood
 
Boxelder
Soft Hardwoods, Other
Soft Hardwood
Blackjack Oak
Red Oaks, Other
Hard Hardwood
 
Cypress
Soft Hardwoods, Other
Soft Hardwood
Cherrybark Oak
Red Oaks, Select
Hard Hardwood
 
Elm
Soft Hardwoods, Other
Soft Hardwood
Chestnut Oak
White Oaks, Other
Hard Hardwood
 
Gray Birch
Soft Hardwoods, Other
Soft Hardwood
Chinkapin Oak
White Oaks, Select
Hard Hardwood
 
Hackberry
Soft Hardwoods, Other
Soft Hardwood
Common Persimmon
Hard Hardwoods, Other
Hard Hardwood
 
Hackberry Sp.
Soft Hardwoods, Other
Soft Hardwood
Flowering Dogwood
Hard Hardwoods, Other
Hard Hardwood
 
Magnolia Sp.
Soft Hardwoods, Other
Soft Hardwood
Honeylocust
Hard Hardwoods, Other
Hard Hardwood
 
Paper Birch
Soft Hardwoods, Other
Soft Hardwood
Laurel Oak
Red Oaks, Other
Hard Hardwood
 
Redbay
Soft Hardwoods, Other
Soft Hardwood
Live Oak
White Oaks, Other
Hard Hardwood
 
River Birch
Soft Hardwoods, Other
Soft Hardwood
Northern Red Oak
Red Oaks, Select
Hard Hardwood
 
Sassafras
Soft Hardwoods, Other
Soft Hardwood
Nuttall Oak
Red Oaks, Other
Hard Hardwood
 
Slippery Elm
Soft Hardwoods, Other
Soft Hardwood
Overcup Oak
White Oaks, Other
Hard Hardwood
 
Southern Magnolia
Soft Hardwoods, Other
Soft Hardwood
Pin Oak
Red Oaks, Other
Hard Hardwood
 
Sugarberry
Soft Hardwoods, Other
Soft Hardwood
Post Oak
White Oaks, Other
Hard Hardwood
 
Sweetbay
Soft Hardwoods, Other
Soft Hardwood
Red Mulberry
Hard Hardwoods, Other
Hard Hardwood
 
Sycamore
Soft Hardwoods, Other
Soft Hardwood
Rock Elm
Hard Hardwoods, Other
Hard Hardwood
 
Willow
Soft Hardwoods, Other
Soft Hardwood
Scarlet Oak
Red Oaks, Other
Hard Hardwood
 
Winged Elm
Soft Hardwoods, Other
Soft Hardwood
Shumard Oak
Red Oaks, Select
Hard Hardwood
 
Yellow Buckeye
Soft Hardwoods, Other
Soft Hardwood
Southern Red Oak
Red Oaks, Other
Hard Hardwood
 
 
 
 
Swamp Chestnut Oak
White Oaks, Select
Hard Hardwood
 
 
 
 
Swamp White Oak
White Oaks, Select
Hard Hardwood
 
 
 
 
Sweet Birch
Hard Hardwoods, Other
Hard Hardwood
 
 
 
 
Water Oak
Red Oaks, Other
Hard Hardwood
 
 
 
 
White Mulberry
Hard Hardwoods, Other
Hard Hardwood
 
 
 
 
White Oak
White Oaks, Select
Hard Hardwood
 
 
 
 
Willow Oak
Red Oaks, Other
Hard Hardwood
 
 
 
 

--------------------------------------------------------------------------------

Noncommercial species to be considered in woody competition assessment.
(Not to be tallied as Hardwood)

Species
General Species
 
Species
General Species
Bamboo
noncommercial
 
Sparkleberry
noncommercial
Gallberry
noncommercial
 
Tallow Tree
noncommercial
Palmetto spp.
noncommercial
 
Titi
noncommercial
Privet
noncommercial
 
Wax myrtle
noncommercial
Redbud
noncommercial
 
Yaupon
noncommercial
Sabal palm
noncommercial
 
 
 

--------------------------------------------------------------------------------

--------------------------------------------------------------------------------

SiMS 2012 Suite of Software Products - Growth Model Documentation

Stand Type Growth Modules Currently Available Within SiMS_2012
Cutover Loblolly Pine (Pinus taeda L.) Plantations (Southeast)
Cutover Loblolly Pine (Pinus taeda L.) Plantations (West Gulf)
Oldfield Loblolly Pine (Pinus taeda L.) Plantations (CRP type stands)
Cutover Slash Pine (Pinus elliottii Engelm) Plantations (Southeast)
Cutover Slash Pine (Pinus elliottii Engelm) Plantations (West Gulf)
Oldfield Slash Pine (Pinus elliottii Engelm) Plantations (CRP type stands)
Longleaf Pine (Pinus palustris Mill.) Plantations
Natural Loblolly Pine (Pinus taeda L.) Stands
Natural Slash Pine (Pinus elliottii Engelm) Stands
Natural Longleaf Pine (Pinus palustris Mill.) Stands
Natural Mixed Pine-Hardwood Stands
Natural Mixed Hardwood Stands
Sand Pine (Pinus clausa) Plantations

Detailed discussion of model architecture along with references is given below
for each of the growth modules.

Modeling Architecture

The underlying modeling architecture used in SiMS_2012 products is described as
a hybrid modeling system that makes use of the best traits of both whole-stand
and individual-tree models. Whole-stand models are used to predict and project
estimates of surviving stems/acre, stand dominant height (ft) and stand basal
area (ft2/acre). If the user does not have a current estimate of the associated
stand table then a stand table is predicted using a Weibull (Bailey and Dell
1973) probability distribution function or percentile-based system developed for
the stand type in question. See individual stand type model descriptions below
for specific citations. If a stand table is available from a cruise then this
stand table is input into the SiMS_2012 system and a projection is made from
this beginning stand table using generalized stand table projection methodology
of Pienaar and Harrison (1988). This procedure projects from the current stand
table condition to the expected future stand table condition such that the
projected stand table is compatible with the projected whole stand estimates of
stems/acre and basal area/acre. Once a predicted and/or projected stand table is
available, a height – diameter function is used to obtain heights for each
diameter class. Diameter class volume and/or weight per acre is then obtained
using individual tree volume and/or weight equations appropriate for the stand
type.

Growth projections for thinned stands are accomplished using the concept of a
competition index originally presented by O’Connor (1935) modified by Pienaar
(1979) and discussed by Harrison and Borders (1996). This methodology makes use
a an “unthinned counterpart” to project growth for thinned stands and assures
that projections are logical and reasonable relative to expected development of
unthinned stands (the case which is best understood).

Growth responses to cultural treatments such as chemical site preparation (based
on expected residual hardwood levels), mechanical site preparation treatments
that improve tillage (e.g. ripping, bedding, etc.), herbaceous weed control,
woody understory release, level of genetic improvement in seedlings used for
plantation establishment, and fertilization are obtained using the approach of
Pienaar and Rheney (1995).

--------------------------------------------------------------------------------

This approach makes use of an additive growth response term that is used with
whole-stand models for dominant height and stand basal area. This approach is
very flexible and, based on ForesTech’s proprietary modifications of Pienaar and
Rheney’s (1995) published approach, can be used to model expected stand response
for treatments that exhibit different long-term response patterns (e.g. 1:
treatments that are expected to show increased growth relative to untreated
conditions for some number of years after which the cumulated difference between
treated and untreated stands is expected to dissipate, 2: treatments that are
expected to show an increased growth rate relative to untreated conditions for
some number of years and then maintain the cumulated difference through the
rotation). Note that ForesTech personnel have been instrumental in the
development of this modeling architecture (which is used in many proprietary
models developed by individual forest products companies and organizations) and
have modified the approach to be consistent with the best understanding
available concerning expected responses to silvicultural treatments used singly
and in various combinations. Proprietary modifications that adjust expected
treatment responses relative to underlying site quality and expected understory
competition levels are also built into the system. All silvicultural treatment
adjustments are based on the latest information from studies across the
southeastern U.S. (e.g. Martin et al. 1999, Amateis et al. 2000, NCSFNC 2000,
Borders and Bailey 2001, Lauer and Zutter, 2001, Martin and Shiver 2002, Martin
and Shiver 2002, NCSFNC 2002, Miller et al. 2003).

Individual Stand Type Models

When searching the scientific literature for growth and yield systems for
various stand types that are managed in the southern U.S. it is rare to find
complete systems that allow for the capability and flexibility to make
projections under various types of management. Typically, pieces of complete
growth and yield systems are developed and published separately by various
individuals and organizations. Such individual growth and yield model components
are often times not only published separately by different individuals and
organizations but they are calibrated with different data. This “piece meal”
model development and publication makes for many problems in implementation and
logical growth and yield prediction and projection capability. For example, many
models have been developed for dominant height development of loblolly pine
plantations in the southern U.S. However, they all exhibit different
characteristics and can lead to very different projections of dominant height
development for the same stand. Furthermore, when growth projections of various
species are made using models taken “as is” from the literature many illogical
results can be obtained if one chooses to compare projections for the various
stand types. For example, for a given set of stand conditions a model for a
longleaf pine plantation may project a stand characteristic such as dominant
height or basal area/acre that is totally illogical when compared to what
actually happens on the ground in longleaf pine stands and/or when compared to
what happens in a loblolly pine stand on a similar site with similar management.
This is because both the model form and associated parameter estimates (and
hence model behavior) are unique to the data used for model development and
calibration. Since data available for model development and calibration are
limited to the specific set of conditions represented by the data then many
situations exist for which growth projections represent extrapolations of both
the model form and the data used to calibrate the model.

Prior to the development of the SiMS_2012 system the problem of illogical
projections of individual stands as well as illogical properties of how
projections of one stand type relate to other stand types have been ignored.
However, with the expertise of ForesTech personnel we have modified base model
forms taken from the literature and built a modeling architecture that produces
growth and yield projections of individual stands that are logical across a wide
array of stand conditions and cultural treatments as well as among

--------------------------------------------------------------------------------

stand types. These proprietary modifications and modeling approaches make the
SiMS_2012 system the most powerful and realistic growth and yield simulator
available for southern pine stand types.

--------------------------------------------------------------------------------

Literature Cited

Amateis, R.L., J. Liu, M.J. Ducey, and H. Lee Allen. 2000. Modeling response to
midrotation nitrogen and phosphorous fertilization in loblolly pine plantations.
Southern Journal of Applied Forestry 24(4):207-212.
Bailey, R.L. and T.R. Dell. 1973. Quantifying diameter distributions with the
Weibull function. Forest Science19:97-104.
Borders, B.E. and R.L. Bailey. 2001. Loblolly pine – pushing the limits of
growth. Southern Journal of Applied Forestry 25(2): 69-74.
Harrison, W.M. and B.E. Borders. 1996. Yield prediction and growth projection
for site-prepared loblolly pine plantations in the Carolinas, Georgia, Alabama,
and Florida. PMRC Technical Report 1996-1. D.B. Warnell School of Forest
Resources. The University of Georgia. Athens, GA. 59pp.
Lauer, D.K. and B.R. Zutter. 2001. Vegetation cover response and second-year
loblolly and slash pine response following bedding and pre- and post-plant
herbicide applications in Florida. Southern Journal of Applied Forestry
25(2):75-83.
O’Connor, A.J. 1935. Forest research with special reference to planting
densities and thinning. Br. Emp. For. Conf. South Africa. 30pp.
Martin, S.W., R.L. Bailey, and E.J. Jokela. 1999. Growth and yield predictions
for lower coastal plain slash pine plantations fertilized at mid-rotation.
Southern Journal of Applied Forestry 23(1):39-45.
Martin, S.W. and B.D. Shiver. 2002. Impacts of vegetation control, genetic
improvement and their interaction on loblolly pine growth in the southern United
States – age 12 results. Southern Journal of Applied Forestry 26(1):37-42.
Martin, S.W. and B.D. Shiver, B.D. 2002. Twelve-year results of a loblolly site
preparation study in the piedmont and upper coastal plain of South Carolina,
Georgia, and Alabama. Southern Journal of Applied Forestry 26(1):32-36.
Miller, J.H., B.R. Zutter, S.M. Zedaker, M.B. Edwards, and R.A. Newbold. 2003.
Growth and yield relative to competition for loblolly pine plantations to
midrotation – a southeastern United States regional study. Southern Journal of
Applied Forestry 27(4): 237-252.
NCSFNC. 2000. Eight-Year Responses of Young Loblolly Pine to Nitrogen,
Phosphorus, and Potassium Fertilization. NCSFNC Report No. 45. Dept. of
Forestry. North Carolina State Univ., Raleigh, NC.
NCSFNC. 2002 Responses to Nutrient Additions in Young Loblolly Pine Plantations:
Regionwide 18 Third Report. NCSFNC Report No. 49. Dept. of Forestry. North
Carolina State Univ., Raleigh, NC.
Pienaar, L.V. 1979. An approximation of basal area growth after thinning based
on growth in unthinned plantations. Forest Science 25(2): 223-232.

--------------------------------------------------------------------------------

Pienaar, L.V. and W.M. Harrison. 1988. A stand table projection approach to
yield prediction in unthinned even-aged stands. Forest Science 34(3): 804-808.
Pienaar, L.V. and J.W. Rheney. 1995. Modeling stand level growth and yield
response to silvicultural treatments. Forest Science 41(3):629-638.

--------------------------------------------------------------------------------

Literature for Individual Stand Type Models

Pertinent literature for each stand type available in the SiMS_2012 system is
cited below. A short description of the data used for model development is also
given. As discussed in detail above, the publications cited below provided
individual model components that were combined as necessary and adjusted to
develop full growth and yield functionality for each stand type.

Cutover Loblolly Pine (Pinus taeda L.) Plantations (Southeast)
Harrison, W.M. and B.E. Borders. 1996. Yield prediction and growth projection
for site-prepared loblolly pine plantations in the Carolinas, Georgia, Alabama
and Florida. PMRC Technical Report 1996-1. D.B. Warnell School of Forest
Resources. The University of Georgia. Athens, GA. 59pp.
Data are from a total of 805 temporary and permanent sample plots located
throughout the lower coastal plain, upper coastal plain and piedmont of North
Carolina, South Carolina, Georgia, Florida and Alabama.
Cutover Slash Pine (Pinus elliottii Engelm) Plantations (Southeast)
Pienaar, L.V., B.D. Shiver and J.W. Rheney. 1996. Yield prediction for
mechanically site-prepared slash pine plantations in the Southeastern Coastal
Plain. PMRC Technical Report 1996-3. D.B. Warnell School of Forest Resources.
The University of Georgia. Athens, GA. 57pp.
Data are from a total of 691 temporary plots and 254 permanent plots located in
the lower coastal plain of South Carolina, Georgia and north Florida. Additional
data come from a regionwide (lower coastal plain) planting density study with
114 installations that has planting densities of 100 to 900 trees/acre that were
measured up to 8 times at 3 year intervals beginning at age 5 years.
Old-Field Loblolly Pine (Pinus taeda L.) Plantations (CRP-type stands)
Lenhart, J.D. and J.L. Clutter. 1971. Cubic-foot yield tables for oldfield
loblolly pine plantations in the Georgia piedmont. GA For. Res. Council Rep. No.
22. 12pp.
Data are from 226 sample plots distributed throughout the piedmont of Georgia.
Pienaar, L.V. and J.W. Rheney. 1998. An evaluation of the potential productivity
of intensively managed pine plantations in Georgia. Final Report of a study
funded by The Georgia Consortium for Technological Competitiveness in Pulp and
Paper. 41pp.
Data obtained from 37 randomly chosen loblolly pine plantations throughout
Georgia established under the Conservation Reserve Program as well as from
designed studies associated with the Plantation Management Research Cooperative
at the D.B. Warnell School of Forest Resources at the University of Georgia.
Old-Field Slash Pine (Pinus elliottii Engelm) Plantations (CRP-type stands)
Bennett, F.A. 1970. Yields and stand structure patterns for old-field
plantations of slash pine. USDA For. Serv. Res. Paper SE-60. 81pp.

--------------------------------------------------------------------------------

Clutter, J.L. and E.P. Jones. 1980. Prediction of growth after thinning in
oldfield slash pine plantations. USDA For. Serv. Res. Paper SE-217. Southeast
For. Exp. Stn. Asheville, NC. 14pp.
Data for the above two publications are from 212 permanent sample plots
distributed through north Florida, south Georgia, southern Alabama and
Mississippi.
Pienaar, L.V. and J.W. Rheney. 1998. An evaluation of the potential productivity
of intensively managed pine plantations in Georgia. Final Report of a study
funded by The Georgia Consortium for Technological Competitiveness in Pulp and
Paper. 41pp.
Data from 23 oldfield slash pine plantations established under the Conservation
Reserve Program as well as from designed studies associated with the Plantation
Management Research Cooperative at the D.B. Warnell School of Forest Resources
at the University of Georgia.
Cutover Loblolly Pine (Pinus taeda L.) Plantations (West Gulf)
Baldwin, V.C., Jr. and D.P. Feduccia. 1987. Loblolly pine growth and yield
prediction for managed west gulf plantations. USDA For. Serv. Res. Paper SO-236.
Southern For. Exp. Station. New Orleans, LA. 27pp.
Data are from 85 unthinned and 167 thinned permanent plots distributed through
Louisiana, a single county in southwestern Mississippi and two counties in
eastern Texas.
Cutover Slash Pine (Pinus elliottii Engelm) Plantations (West Gulf)
Zarnoch, S.J., D.P. Feduccia, V.C. Baldwin, Jr, and T.R. Dell. 1991. Growth and
yield model predictions for thinned and unthinned slash pine plantations on
cutover sites in the west gulf region. USDA For. Serv. Res. Paper SO-264.
Southern For. Exp. Station. New Orleans, LA. 32 pp.
Data consist of 507 unthinned stand yield observations and 543 thinned stand
growth period observations from permanent sample plots distributed in the West
Gulf region as well as 530 residual stand observations of post-thinning
conditions from permanent sample plots distributed in the West Gulf region.
Longleaf Pine (Pinus palustris Mill.) Plantations
In the SiMS_2012 system a single model is used for longleaf pine plantation
growth and yield across the entire southern U.S. Models available in the
published literature for planted longleaf pine stands are produce illogical and
biologically unreasonable long-term growth projections outside of the limited
range of data used to develop available models. Therefore, a study of how
longleaf pine growth and yield relates to the growth and yield of slash pine
plantations was carried out. Based on the results of this internal study
ForesTech has developed a growth and yield system for longleaf pine plantations
that are a variation of the slash pine plantation models discussed above. This
model produces biologically reasonable growth and yield projections for this
stand type. Estimates of per acre yield in terms of volume and/or weight is
obtained using the individual tree volume and weight equations cited below.
Baldwin, V.C., Jr., and J.R. Saucier. 1983. Aboveground weight and volume of
unthinned, planted longleaf pine on West Gulf Forest Sites. USDA For. Serv. Res.
Paper SO-191. Southern For. Exp. Station. New Orleans, LA. 8pp.

--------------------------------------------------------------------------------

Natural Loblolly Pine (Pinus taeda L.) Stands
In the SiMS_2012 system a single model is used for natural loblolly pine stands
across the entire southern U.S. Several published and unpublished models were
evaluated for extrapolative properties and biological reasonableness. The best
model available was developed by Souter (1986) and is cited below.
Souter, Ray Albert. 1986. Dynamic Stand Structure in Thinned Stands of
Naturally-Regenerated Loblolly Pine in the Georgia Piedmont. PhD Dissertation.
School of Forest Resources, University of Georgia. Athens, GA.
Data come from 71 permanent sample plots located on the Hitchiti Experimental
Forest in the Piedmont of Georgia (the Growing Space Study). These research
plots were established in 1948 and remeasured six times through 1982. Even
though the data used in the development of this model are limited
geographically, the models behave very well and produce reasonable growth
projections for naturally regenerated loblolly pine stands.
Natural Slash Pine (Pinus elliottii Engelm) Stands
In the SiMS_2012 system a single model is used for natural slash pine stands
across the entire southern U.S. The models are based on the publications by
Frank Bennett.
Bennett, Frank A. 1970. Variable-density yield tables for managed stands of
natural slash pine. USDA For. Serv. Res. Note SE-141. Southeastern For. Exp.
Station. Asheville, NC. 7pp.
Data are from 82 permanent sample plots that had initial measurement and one
five year re-measurement. Plots were distributed throughout north Florida and
southeastern Georgia.
Bennett, Frank A. 1981. Growth and yield in natural stands of slash pine and
suggested management alternatives. USDA For. Serv. Res. Paper SE-211.
Southeastern For. Exp. Station. Asheville, NC. 8pp.
Data are the same data as in the 1970 paper with additional sample plots for a
total of 176 permanent sample plots distributed throughout north Florida and
southeastern Georgia.
Natural Longleaf Pine (Pinus palustris Mill.) Stands
In the SiMS_2012 system a single model is used for natural longleaf pine stand
growth and yield across the entire southern U.S. Models available in the
published literature for natural longleaf pine stands are produce illogical and
biologically unreasonable growth projections outside of the limited range of
data used to develop available models. Therefore, a study of how natural
longleaf pine growth and yield relates to the growth and yield of natural slash
pine stands was carried out. Based on the results of this internal study
ForesTech has developed a growth and yield system for natural longleaf pine
plantations that are a variation of the natural slash pine models discussed
above. This model produces biologically reasonable growth and yield projections
for this stand type. Individual tree volume and weight equations are those used
for planted longleaf stands (see citation for Longleaf Pine Plantations above).
Natural Mixed Pine-Hardwood Stands
There are very few models available for growth and yield of mixed pine-hardwood
stands that provide estimates of how pine growth and yield changes relative to
the amount of hardwood present in the stand.

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The SiMS_2012 system uses models from a recent study in Georgia that provides
such capability. All models were evaluated and adjusted as needed to obtain
logical and biologically reasonable growth patterns.
Martin, S.W. and G.H. Brister. 1999. A growth and yield model incorporating
hardwood competition for natural loblolly pine stands in the Georgia piedmont.
Southern Journal of Applied Forestry 23(3):179-185.
Data are from 75 permanent sample plots located in the Piedmont of Georgia.
Natural Hardwood Stands
Very few models exist for growth and yield of natural hardwood stands. Most
hardwood projections are made using a simplifying assumption such as hardwood
yield is expected to grow at 2 to 3% per year. To obtain more flexibility and
perhaps more realistic hardwood stand projections ForesTech has evaluated models
from the scientific literature and put them together in a workable and complete
growth and yield system.
Bowling, E.H., H.E. Burkhart, T.E. Burk and D.E. Beck. 1989. A stand level
growth model for Appalachian hardwoods. Canadian Journal of Forest Research
19:405-412.
Graney, D.L. and P.A. Murphy. 1994. Growth and yield of thinned upland oak
stands in the Boston Mountains of Arkansas. Southern Journal of Applied Forestry
18(1):10-14.
Zahner, R. and M.K. Myers. 1984. Productivity of young Piedmont oak stands of
sprout origin. Southern Journal of Applied Forestry 8(2):102-108.
Olson, D. J., Jr. 1959. Site index curves for upland oak in the Southeast. USDA
Forest Service Research Note SE-125. 2p.
Data are varied. Please see publications for data descriptions.
Sand Pine (Pinus clausa) Plantations
Brendemuehl, R. H. 1981. Options for Management of Sandhill Forest Land.
Southern Journal Of Applied Forestry 5(4): 216-222.
Burns, Russell M., and R. H. Brendemuehl. 1969. Yield of a Choctawhatchee Sand
Pine Plantation at Age 28. USDA Forest Service Research Note SE-103. 4pp.
Outcalt, Kenneth W. 1986. Stand Density Affects Growth of Choctawhatchee Sand
Pine. Southern Journal of Applied Forestry 10(3): 128-131.
McNab, W.H., K.W. Outcalt, R.H. Brendemuehl. 1985. Weight and Volume of
Plantation-Grown Choctawhatchee Sand Pine. USDA Forest Service Research Paper
SE-252. 44pp.
Data are varied. Please see publications for data descriptions.
Individual-Tree Taper, Volume and Weight

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Individual-tree stem profiles and cubic-foot volumes are estimated using a form
class based, segmented taper equation described in Clark et.al, 1991.
Coefficients are available for most commercial and non-commercial tree species
encountered in the southeastern U.S. The coefficients were updated using FIA
data in 2001 (Souter and Clark, 2001).
To calculate total-stem and merchantable green weights for individual trees,
pound-per-cubic-foot conversion factors are calculated using associated weight
and volume tables in Clark et al., 1986 and Clark et al., 1990.
Clark Alexander; Saucier, Joseph R.; McNab, W. Henry. 1986. Total-tree weight,
stem weight, and volume tables for hardwood species in the southeast. Georgia
Forest Research Paper GF-RP-60. Georgia Forestry Commission. 44 p.
Clark, Alexander; Saucier, Joseph R. 1990. Tables for estimating total-tree
weights, stem weights, and volumes of planted and natural southern pine in the
southeast. Georgia Forest Research Paper GF-RP-79. Georgia Forestry Commission.
23 p.
Clark, Alexander, III; Souter, Ray A.; Schlaegel, Bryce E. 1991. Stem Profile
for Southern Equations for Southern Tree Species. Res. Pap. SE-282. Asheville,
NC: U.S. Department of Agriculture, Forest Service, Southeastern Forest
Experiment Station. 117 p.
Souter, R.A. and A. Clark III. 2001. Taper and volume prediction in southern
tree species. USDA For. Serv. Southern Research Station. FIA Work Unit
Administrative Report.

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Portions of the exhibit, indicated by the mark “[***],” were omitted and have
been filed separately with the Securities and Exchange Commission pursuant to
the Registrant’s application requesting confidential treatment pursuant to Rule
24b-2 of the Securities Exchange Act of 1934, as amended.

Exhibit K-2

[***]