Forestry methods and applications

1. Forestry Methods and Applications
Objective Timber Management: The
Key to Higher Returns
Barry D. Shiver
President, Smarter Forestry
Bshiver@SmarterForestry.com

2. Objective?
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3. What I mean by Objective Decisions
• Decisions based on knowledge AND Data
• As a forestry student learned “symptoms” of when a stand
needed to be thinned
• A Dr examines us when we are sick and makes prescriptions
based on “symptoms” AND if possible test results (Data)
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4. Objectives Change with Owners so Decision
may Change given same Data
• A 75 year old private landowner likely views regeneration
expenses differently than an institutional investor looking for
situations in which to invest available capital
• Landowners have different primary objectives
• Keep Land in Family
• Aesthestics & Recreation
• Wildlife Habitat
• Financial Returns
4

5. Management Decisions
Affect Stand Development
5
Age 6
Age 50

6. Only talking Plantations
Management Decisions That are Made:
• Site Preparation
• Genetics
• Planting Density
• Herbaceous Weed Control
• Fertilization
• Woody Release
• Thinning
• Number, Timing, Intensity, Which Trees
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7. Factors that Impact Objective Decisions
• Markets
• Remember ours is a long term investment and current markets are
not as important as what markets will be in 15-30 years
• High pulp stumpage prices may dictate no thins
• Low pulp stumpage prices may dictate thinning to produce solid
wood
• No CNS or small log market (premium) may dictate 1 thin vs 2 thins
• Site Quality
• Some sites are not high probability risks for responses
• Treatment Costs
• These vary. Cost of bedding is different depending on location
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8. What are returns if we invest in these treatments?
8
Many new
Treatments in
the 1980’s

9. 1980’s
• Imazapyr herbicide as Aresenal AC and later Chopper labeled for
use
• Used for Site Preparation, Woody Release, and HWC
• 2,4-5 T Banning led to other herbicides (Oust, Escort, Velpar, Glyphosate, Triclopyr)
• Enough 1st Gen seedlings available to plant widely with other
Gens following closely
• Research on silviculture at university cooperatives throughout the
South emphasized responses
• Growth and Yield models available to help quantify gains
• Important for objective decision making
• Prediction models for what is expected from different management
• Projection models from an existing condition to future under different
management
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10. Foresters are Slow to Change
• We have long term investments
• Mistakes made in Management Choices are usually slow to show
up if they become apparent at all - Blame it on weather; poor
site, etc.
• Especially if money is to be spent, we want proof
• Insurance companies mine Data to determine how we will age
and ultimately when we will die
• On some of us they are wrong
• On the majority of us though, they get it right and in doing so
make money
• Data and models in forestry are similar
10

11. Growth and Yield Systems Should
• Incorporate silvicultural responses into yield prediction and
projection
• Work up inventories of individual trees to provide tons by
product. Change of product specs simply requires running data
through again
• Project future product tons keeping inventory data by tree
detail and keeping consistency with whole stand yield models
incorporating silvicultural responses
• Be able to update old inventory data to any future date(s)
keeping tree detail such as TQI and stopper heights
• I have built SMART to do these things (and more!)
• SMART provides the yields in examples in this talk 11

12. General Types of Silvicultural Responses
0
1
2
3
4
5
6
0 5 10 15 20 25 30
Years since treatment
Response
A Resp
B Resp
C Resp
An A response keeps getting
larger and larger from time
of treatment forward
A B response gets larger for
awhile and then levels out
and maintains an absolute
realized gain
A C response gains quickly,
peaks, and the response falls
back to or below (some call
this a D) the level of the
untreated stand

13. Stand Characteristics Impacted by Treatments
• The two stand characteristics most often impacted by silvicultural
treatments are dominant height and basal area per acre
• Of course dominant height development influences estimation of site
index and is partially why foresters change site index to estimate
response
• This can cause real issues, especially if the silvicultural response is a C
type response and height gets measured at or near the peak of the
response.
• Can result in an overestimate of eventual site index obtained

14. 14
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
DominantHeight(ft)
Age
Height Development for 1st Gen Site 68 and 605 TPA Planted with Different
Silvicultural Treatments
Base 2nd Gen 2nd Gen+HWC 2ndGen+ChemSP 2ndGen+ChemSP+HWC

15. Adding Height Automatically Adds Basal Area per Acre
• Stands that grow taller faster (higher site index) also develop dbh
faster and therefore have a higher basal area
• For many silvicultural treatments adding height increment, even
correctly, does not add enough basal area per acre increment
• So, a good treatment response estimate must take that into account

16. 16
0.0
50.0
100.0
150.0
200.0
250.0
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
BasalArea/Acre(ft2)
Age
Basal Area Development for 1st Gen Site 68 and 605 TPA Planted
with Different Silvicultural Treatments
Base 2nd Gen 2nd Gen+HWC 2nd Gen+ChemSP 2ndGen+ChemSP+HWC

17. With these adjusted height and basal area inputs
• Can estimate product yields for different
• Site Preparation
• Mechanical and Chemical
• Genetics
• HWC
• These are all regeneration treatments (costs)
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18. What about Existing Stands?
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• More interesting since most acreage has existing
stands
• Before we can estimate what could be there
under different management regimes, we need
to know what is there now
• We get this information from an inventory
• Timber Inventory is different from retail
inventory
• Don’t have many items that are the same so we
cannot just count
• Stands change over time as trees grow and some die
• Geographic spread combined with numbers mean
100% inventory is usually not possible

19. Inventory
• When integrated forest products companies owned a
majority of the plantation acres, money spent on inventory
was considered a cost
• Inventory did not have to be exact. The organization who
owned the plantation also owned the pulp mill, sawmill, etc.
• Those organizations also bought wood and often had
procurement foresters
• Procurement inventory needed a snapshot of timber value at
current time – Future of stand not important
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20. Procurement Inventory
• Typically, tally of trees by
current product, dbh class (1
or 2 inch) and merchantable
height (tally cards)
• Often “worked up” by hand
after cumulative tally – no
stats
• No consideration for future
condition or value
• Often provided good current
value information
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21. Management Inventory (Inventory as an Investment)
• Depending on age stand may not have much current value
• Objective is to accurately assess stand characteristics for use in growth and
yield models
• Stand Characteristics
• Trees per Acre, Basal Area/Acre, Dominant Height, Woody Vegetation Level,
• Stand History (Past is important in accurately projecting the future)
• Want to quantify future stand value under different management
alternative treatments (we call these regimes)
• Important to assess current tree characteristics and keep these through
future projection
• Dbh to nearest 0.1 inch
• Total heights (these can be grown)
• Tree Product Potential even for smaller trees
• Stopper heights (height of forks, crook, etc. that stop merchandising of solid wood)
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22. Management Inventory Projection
• Important to keep tree information such as TQI, stopper heights, etc.
for each year going forward
• Want to determine value at each year going forward – need for dbh to
nearest 0.1 is so all trees do not cross threshold to next dbh class at
same time.
• Determine value to all future years for each potential management
regime (thin, unthin, thin and then release, etc.)
• When to harvest stand (set rotation age) depends partially on the
highest value among alternative management regimes
• Rest of decision depends on what we can replace the stand with
• Sell land for an alternative high value use
• Replace with a stand with better genetics and silviculture that will grow faster
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23. Existing Stand Valuation
• For an existing stand we calculate the NPV using
the following approach:
FE NPVNPVNPV 
NPV = overall NPV for the existing
stand
NPVE = NPV for all expected cash
flows for the existing stand
NPVF = NPV for all expected cash
flows for the land following harvest of
the existing stand

24. Existing Stand Valuation
• When the property is to stay in timber production
following harvest of the existing stand “n” years in
the future NPVF is the present value of the BLV of
the stand that will be put into the ground following
harvest:
n
F
i
BLV
FNPV )1( 

• When the land will be sold following harvest
of the existing stand “n” years in the future NPVF
is the present value of the land value that is
expected at this point in time:
n
F
i
Land
FNPV )1( 


25. Say we have the following stand information
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Age 12
Trees per Acre 506
Basal Area 115.8
Dominant HT 41
Predicted Stand Table
Dbh TPA
1 0.0
2 0.4
3 5.1
4 26.1
5 79.2
6 151.5
7 161.0
8 73.0
9 9.7
506.0
Actual Stand Table
Dbh TPA TQI
2 1.0 3
3 12.0 3
4 43.0 3
5 90.0 3
6 127.0 3
7 23.4 3
7 106.6 1
8 10.2 3
8 62.8 1
9 2.6 3
9 21.4 1
10 0.5 3
10 5.5 1
506.0
What is TQI? If a tree
will never make more
than pulpwood in the
opinion of the cruiser
it is given a TQI of 3.
This can be from form,
defect, or size. If the
tree will make solid
wood the tree is given
a TQI of 1.

26. Grow the stand to age 14
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3. 5.63 3
4. 28.30 3
5. 65.81 3
6. 102.42 3
7. 66.32 3
8. 16.05 3
9. 6.68 3
10. 1.78 3
11. 0.32 3
7. 52.79 1
8. 80.23 1
9. 42.98 1
10. 14.90 1
11. 3.70 1
Left hand diameter
distribution is from
predicted
Right hand is grown from
actual inventory detail at
age 12
Many organizations use
predicted to “update”
their inventory – Not ideal
Dbh TPA
3 1.9
4 12.4
5 45.2
6 107.5
7 159.5
8 122.7
9 35.9
10 2.5
Dbh TPA TQI

27. Thin by size (no TQI) – a Spatial Thinning
Thinned to Basal Area 63
Hardly an economic thinning with only about
17 tons removed.
Cash flow at thinning: $114.70
TQI added for information – would not normally
know this
Dbh TQI Residual
3. 3 1.25
4. 3 8.12
5. 3 23.15
6. 3 42.67
7. 3 31.93
8. 3 9.11
9. 3 4.37
10. 3 1.32
11. 3 0.26
7. 1 25.41
8. 1 45.56
9. 1 28.12
10. 1 11.04
11. 1 3.06
235.37

28. Another way would be to ignore the residual
BA and take out 90% of TQI=3 and 20% of “1”s
Dbh TQI Residual
3. 3 0.6
4. 3 2.8
5. 3 6.6
6. 3 10.2
7. 3 6.6
8. 3 1.6
9. 3 0.7
10. 3 0.2
11. 3 0.0
7. 1 42.2
8. 1 64.2
9. 1 34.4
10. 1 11.9
11. 1 3.0
185.0
The residual BA here is 63 which is why I
used it for the previous thinning.
Here, however, the tons removed is 35.6
tons/ac
The cash flow from the thinning is $284.75
AND, most importantly, the trees left have
fewer trees left to compete with over time
and with only 185 tpa dbh growth will be
larger as compared with 235
Only 29 TQI
3 Trees
Remaining

29. Do we do a good job of tree selection in thinning?
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30. Do we do a good job of tree selection in thinning?
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The average dbh tree in this stand is
about the size of the tree on the right.
Note the small and crooked tree to its
left and the tree in the background that
is two trees. This stand is not being
thinned to maximize future value

31. Stumpage Prices for Valuation
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Min Max Stumpage
Products Dbh Dbh Prices ($/ton)
Pulpwood 4.5 40.0 $8.00
Chip-n-Saw 8.5 11.5 $13.00
Sawtimber 11.5 40.0 $28.00
All first thin material valued as pulpwood at
$8.00/ton

32. Differences in Financial Return by Choosing
the Right Trees to Remove
Thin by TQI Thin by Size
Pulpwood (3) 17.6 tons 45.5
Pulpwood (1) 0.0 tons 0.0
CNS (1) 42.6 tons 24.3
Saw(1) 65.2 tons 48.1
Rotation Age 29 years 27
Harvest Value $2520 $2024
NPV $1289 $1094
The $1289 reflects the $284.75 cash flow at age 14 vs. $114.70

33. Would this make a difference in ranking
management regimes?
• Without a doubt
• Underscores the importance of management inventory and
using models to help make decisions
• Underscores the importance of growing inventory data while
keeping tree information
• Without going through all of the details, the following
management regimes results were obtained…..
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34. SMART Options for 12 Year Old Stand
Regime NPV Rot Age Final Hrvst $
Do Nothing $1069 19 $1170
Rel @ 12 $1196 26 $2533
T14, No Rel or Fert $1289 29 $2520
T14, Rel @ 15 $1503 29 $3265
Fert @12 $1100 20 $1477
T14, Fert 15 $1330 26 $2328
T14, Rel @ 15, Fert @16 $1474 27 $3199

35. Must remember that these are models!
• Sometimes what looks good on paper, does not look the
same way in the woods. We can thin by TQI in the computer.
How closely that is done in the woods impacts outcomes
• We still must remember the biology
• If crowns are gone it will take much longer, if ever, to get a thin
response
• There are differences in species; loblolly is more forgiving than
slash
• Thinning when there are scarce nutrients available from the site
may require fertilization to get a response
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36. 36
Poor Crowns

37. To Make Objective Decisions
• Invest in Management Inventory to Obtain Good Data
• Use the Data in Models to Provide more Information for
Decisions
• Alternative Management Regime Results
• Be like an insurance company!
• Make certain the biology is realistic, then
• Choose the management that optimizes financial returns
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38. Questions?