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Applied Research

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What do we look for? Does a treatment work? How well does it work? ... Forest manager's point of view. Make decisions daily based on imperfect knowledge coin flip ... – PowerPoint PPT presentation

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Title: Applied Research


1
Applied Research Forest Management
  • Dan Miller
  • Silviculture Manager
  • Potlatch Corporation
  • Lewiston, Idaho

2
Research Types
  • Basic research
  • Applied research
  • (Research application)

3
Basic Research
  • Discovers new things makes the bricks
  • Often lab coat stuff
  • Rewards
  • Fame
  • Fortune
  • Promotions

4
Applied Research(Calibration Research)
  • Based on basic research findings builds with
    the bricks
  • Woods work, dirt under the fingernails
  • Rewards
  • None of the above
  • Frost bite
  • Get to see the application

5
Research Application
  • Extension forestry
  • Using applied research findings to improve
    management decisions
  • Not really considered real researchers
  • Rewards
  • Knowing you helped someone make better decisions
  • (They get the reward for making the decision)

6
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7
Research DataWhat do we look for?
  • Does a treatment work?
  • How well does it work?
  • When should I use it (thresholds)?

8
Vegetation Control
9
Vegetation Control
10
Hardpan Soils
  • Problem High GF mortality
  • Know What species to manage
  • Need What density (threshold)?
  • By species?
  • By rooting depth?

11
How do we get data?
  • Brain picking find out who knows and ask
  • Buy it
  • Consultants
  • Fund research

12
Funding Research
  • Find researcher with expertise
  • Expensive Benefits of coop projects
  • Share cost
  • More can buy more
  • Control objectives get what we want (we hope)

13
How does Potlatch use applied research
information?
  • Applied Research
  • Management
    Guidelines
  • Research Application

14
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15
Fertilizer Guidelines
  • IFTNC early data preliminary N response
  • Potlatch early guidelines
  • N only
  • Apply to healthy stands
  • High of DF

16
Management Need
  • Fertilize more acres make more
  • Research data needed
  • Expand to other species
  • Include more sites

17
IFTNC
  • Increased plot installation more species, more
    sites
  • North Idaho Nutrition Guidelines by Rock Type
  • Fertilizer prescriptions by rock types
  • Based on mineralogy and research data
  • Tied to geology maps

18
IFTNC - Potlatch Interaction
  • PC sent IFTNC a list of our rock types
  • IFTNC grouped our rock types into categories
    based on mineralogy
  • IFTNC developed fertilizer prescriptions based on
    mineralogy and expected response
  • About 50 of PC land fell into the dont
    fertilize category.

19
Fertilizer Screening Tests
  • Potlatch contracted with IFTNC to test several
    fertilizer mixes on several species on the dont
    fertilize rock types.
  • Preliminary results NKSB blend produced
    preliminary growth response.
  • Potlatch uses preliminary data to expand
    fertilizer program.

20
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21
Preliminary Data Issues
  • Researchers point of view
  • Seeking The answer
  • Statistical and professional certainty
  • Research must pass peer review
  • Results should hold up in the real world

22
Preliminary Data Issues
  • Forest managers point of view
  • Make decisions daily based on imperfect knowledge
    coin flip
  • Cant wait until we know everything to do
    anything
  • Forest management moves on daily Do nothing
    is a management decision with implications as
    serious as Do something.

23
Preliminary Data Risks
  • To the Researcher
  • Additional data can result in a vastly different
    answer than the preliminary data indicated.
  • Armillaria and potassium research
  • Blister rust control
  • If the answer changes your credibility may take a
    hit

24
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25
Preliminary Data Risks To the
Forest Manager
  • Desirable
  • Things work as planned
  • Projects are started sooner and make more
  • Undesirable
  • Poor or negative response - Square death
  • Unhappy boss, etc.
  • Waste lots of

26
The Solution
  • Risk analysis
  • How confident am I in the data?
  • What am I risking if it is wrong?
  • Is it worth the risk?
  • Managers realize imperfect data make for
    imperfect decisions results
  • Imperfect decisions may be better than decisions
    made without the data.

27
Preliminary DataThe Solution
  • Risk analysis
  • How confident am I in the data?
  • What am I risking if it is wrong?
  • Is it worth the risk?
  • Managers realize imperfect data make for
    imperfect decisions results
  • Imperfect decisions may be better than decisions
    made without the data.
  • The sun comes up and life goes on in the woods
    and do nothing is indeed a management decision
    with

28
The Result
  • The sun comes up and life goes on in the woods.
  • And Do nothing is indeed a management decision
    with results every bit as serious as Do
    something.

29
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30
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