SlideShare a Scribd company logo
1 of 5
Download to read offline
1
Key Concepts
James N. Long
Department of Wildland Resources & Ecology
Center
Utah State University
Objectives
• Review several key concepts and tools;
• These are useful in designing reasonable
and effective density management regimes
(thinning);
• Design a density management regime for a
particular set of objectives.
Concepts and tools
• Site index
• Stand dynamics
• Size-density relationships and relative
density
• Designing a density management regime
Site index
From Vacchiano et al. (2008), after Wiedemann (1949)
Site index
• Average height of dominant trees is:
– A function of potential productivity
– Little influenced by stand history
• Used as a species-specific index of site
quality
• SI = ave. dominant height at the base age
(e.g., 50, 100, etc.)
• Used to estimate age in future
Site index
From Vacchiano et al. (2008), after Wiedemann (1949)
2
Stand dynamics
Stages of stand development, with different levels
of:
relative density
competition
site occupancy
Stand dynamics
Pinus contorta var. latifolia
Stand dynamics
Pseudotsuga menziesii
Stand dynamics
Picea engelmannii
Stand dynamics
Pinus ponderosa
Size-density relations
• A way to quantify relative density
• A way to characterize DFC
• Basis for designing density management
regimes
DFC = Desired future condition
3
Relative density
• Absolute density and relative density
• The predictable relationship between mean
size and density in crowded, self-thinning
populations
Powell
Relative density
Insectanddiseasesusceptibility
Crowningindex
Relative density
Stand dynamics
Picea engelmannii
4
D
A
B
C
60%
25%
35%
A C D EB
Density management
• Relative density is basic tool for translating
qualitative objectives into a quantitative
density management regime
• We use an index of relative density to:
– assess current condition;
– characterize desired future condition; and
– develop a tactical plan for achieving DFC
Some key relative densities
• Crown closure/on-set of competition
25% SDImax
• Lower limit of full site occupancy
35% SDImax
• Lower limit of self-thinning
60% SDImax
Density management
• Deciding on appropriate upper and lower
limits of relative density
• Choices depend on management objectives
Examples of (situational)
appropriate limits
• Maintain vigor, avoid self-thinning < 60%
• Delay self-pruning < 25%
• Promote self-pruning > 25%
• Full site occupancy > 35%
D
A
B
C
60%
25%
35%
5

More Related Content

Similar to FVS Training Bolzano 2/9

Using Advanced Analytics for Data-Driven Decision Making
Using Advanced Analytics for Data-Driven Decision MakingUsing Advanced Analytics for Data-Driven Decision Making
Using Advanced Analytics for Data-Driven Decision MakingBooz Allen Hamilton
 
Gribb integration of planning documents into a spatial decision
Gribb integration of planning documents into a spatial decisionGribb integration of planning documents into a spatial decision
Gribb integration of planning documents into a spatial decisionGeCo in the Rockies
 
Decision Support Systems
Decision Support SystemsDecision Support Systems
Decision Support SystemsHadi Fadlallah
 
Introduction to machine learning
Introduction to machine learningIntroduction to machine learning
Introduction to machine learningSanghamitra Deb
 
Crisis information management framework for regional disaster resiliency (Joe...
Crisis information management framework for regional disaster resiliency (Joe...Crisis information management framework for regional disaster resiliency (Joe...
Crisis information management framework for regional disaster resiliency (Joe...Learning Manager
 
Using case-based methods to assess scalability and sustainability: Lessons fr...
Using case-based methods to assess scalability and sustainability: Lessons fr...Using case-based methods to assess scalability and sustainability: Lessons fr...
Using case-based methods to assess scalability and sustainability: Lessons fr...Barb Knittel
 
Introduction to Data Management Maturity Models
Introduction to Data Management Maturity ModelsIntroduction to Data Management Maturity Models
Introduction to Data Management Maturity ModelsKingland
 
SimCLIM Booth presentation
SimCLIM Booth presentationSimCLIM Booth presentation
SimCLIM Booth presentationclimsys
 
Module_1___Analytical_Thinking___Problem_Solving.ppt.pptx
Module_1___Analytical_Thinking___Problem_Solving.ppt.pptxModule_1___Analytical_Thinking___Problem_Solving.ppt.pptx
Module_1___Analytical_Thinking___Problem_Solving.ppt.pptxsolomonvijayanand2
 
pattern recogintion learning and adaption
pattern recogintion learning and adaptionpattern recogintion learning and adaption
pattern recogintion learning and adaptionMOHDNADEEM971008
 
Watershed Management_Social,ecology,economic
Watershed Management_Social,ecology,economicWatershed Management_Social,ecology,economic
Watershed Management_Social,ecology,economicshrutik57
 
Justifying Qualitative Factors - 2015 Risk Management Summit
Justifying Qualitative Factors - 2015 Risk Management SummitJustifying Qualitative Factors - 2015 Risk Management Summit
Justifying Qualitative Factors - 2015 Risk Management SummitLibby Bierman
 

Similar to FVS Training Bolzano 2/9 (20)

Decision making systems
Decision making systemsDecision making systems
Decision making systems
 
Intro to ml_2021
Intro to ml_2021Intro to ml_2021
Intro to ml_2021
 
Sampling
SamplingSampling
Sampling
 
Zero defect
Zero defectZero defect
Zero defect
 
Using Advanced Analytics for Data-Driven Decision Making
Using Advanced Analytics for Data-Driven Decision MakingUsing Advanced Analytics for Data-Driven Decision Making
Using Advanced Analytics for Data-Driven Decision Making
 
Gribb integration of planning documents into a spatial decision
Gribb integration of planning documents into a spatial decisionGribb integration of planning documents into a spatial decision
Gribb integration of planning documents into a spatial decision
 
Decision Support Systems
Decision Support SystemsDecision Support Systems
Decision Support Systems
 
Introduction to machine learning
Introduction to machine learningIntroduction to machine learning
Introduction to machine learning
 
Crisis information management framework for regional disaster resiliency (Joe...
Crisis information management framework for regional disaster resiliency (Joe...Crisis information management framework for regional disaster resiliency (Joe...
Crisis information management framework for regional disaster resiliency (Joe...
 
Using case-based methods to assess scalability and sustainability: Lessons fr...
Using case-based methods to assess scalability and sustainability: Lessons fr...Using case-based methods to assess scalability and sustainability: Lessons fr...
Using case-based methods to assess scalability and sustainability: Lessons fr...
 
Chapter9
Chapter9Chapter9
Chapter9
 
Introduction to Data Management Maturity Models
Introduction to Data Management Maturity ModelsIntroduction to Data Management Maturity Models
Introduction to Data Management Maturity Models
 
SimCLIM Booth presentation
SimCLIM Booth presentationSimCLIM Booth presentation
SimCLIM Booth presentation
 
2.2 Mesure Phase (1).pptx
2.2 Mesure Phase (1).pptx2.2 Mesure Phase (1).pptx
2.2 Mesure Phase (1).pptx
 
Module_1___Analytical_Thinking___Problem_Solving.ppt.pptx
Module_1___Analytical_Thinking___Problem_Solving.ppt.pptxModule_1___Analytical_Thinking___Problem_Solving.ppt.pptx
Module_1___Analytical_Thinking___Problem_Solving.ppt.pptx
 
pattern recogintion learning and adaption
pattern recogintion learning and adaptionpattern recogintion learning and adaption
pattern recogintion learning and adaption
 
sudarshan_reservation
sudarshan_reservationsudarshan_reservation
sudarshan_reservation
 
Watershed Management_Social,ecology,economic
Watershed Management_Social,ecology,economicWatershed Management_Social,ecology,economic
Watershed Management_Social,ecology,economic
 
Salafsky web enabled wildlife action plans
Salafsky web enabled wildlife action plansSalafsky web enabled wildlife action plans
Salafsky web enabled wildlife action plans
 
Justifying Qualitative Factors - 2015 Risk Management Summit
Justifying Qualitative Factors - 2015 Risk Management SummitJustifying Qualitative Factors - 2015 Risk Management Summit
Justifying Qualitative Factors - 2015 Risk Management Summit
 

More from Giorgio Vacchiano

2020-12-02_Cislaghi_Seminario_Invarianza.pptx
2020-12-02_Cislaghi_Seminario_Invarianza.pptx2020-12-02_Cislaghi_Seminario_Invarianza.pptx
2020-12-02_Cislaghi_Seminario_Invarianza.pptxGiorgio Vacchiano
 
Understanding and modeling masting in European tree species
Understanding and modeling masting in European tree speciesUnderstanding and modeling masting in European tree species
Understanding and modeling masting in European tree speciesGiorgio Vacchiano
 
Prescribed burning for forest management in Italy
Prescribed burning for forest management in ItalyPrescribed burning for forest management in Italy
Prescribed burning for forest management in ItalyGiorgio Vacchiano
 
European forests and the bioeconomy
European forests and the bioeconomyEuropean forests and the bioeconomy
European forests and the bioeconomyGiorgio Vacchiano
 
Esercitazioni di statistica 8/10
Esercitazioni di statistica 8/10Esercitazioni di statistica 8/10
Esercitazioni di statistica 8/10Giorgio Vacchiano
 
Esercitazioni di statistica 7/10
Esercitazioni di statistica 7/10Esercitazioni di statistica 7/10
Esercitazioni di statistica 7/10Giorgio Vacchiano
 
Esercitazioni di statistica 10/10
Esercitazioni di statistica 10/10Esercitazioni di statistica 10/10
Esercitazioni di statistica 10/10Giorgio Vacchiano
 
Esercitazioni di statistica 6/10
Esercitazioni di statistica 6/10Esercitazioni di statistica 6/10
Esercitazioni di statistica 6/10Giorgio Vacchiano
 
Esercitazioni di statistica 9/10
Esercitazioni di statistica 9/10Esercitazioni di statistica 9/10
Esercitazioni di statistica 9/10Giorgio Vacchiano
 
Esercitazioni di statistica 5/10
Esercitazioni di statistica 5/10Esercitazioni di statistica 5/10
Esercitazioni di statistica 5/10Giorgio Vacchiano
 
Esercitazioni di statistica 1/10
Esercitazioni di statistica 1/10Esercitazioni di statistica 1/10
Esercitazioni di statistica 1/10Giorgio Vacchiano
 

More from Giorgio Vacchiano (20)

2020-12-02_Cislaghi_Seminario_Invarianza.pptx
2020-12-02_Cislaghi_Seminario_Invarianza.pptx2020-12-02_Cislaghi_Seminario_Invarianza.pptx
2020-12-02_Cislaghi_Seminario_Invarianza.pptx
 
Understanding and modeling masting in European tree species
Understanding and modeling masting in European tree speciesUnderstanding and modeling masting in European tree species
Understanding and modeling masting in European tree species
 
Prescribed burning for forest management in Italy
Prescribed burning for forest management in ItalyPrescribed burning for forest management in Italy
Prescribed burning for forest management in Italy
 
European forests and the bioeconomy
European forests and the bioeconomyEuropean forests and the bioeconomy
European forests and the bioeconomy
 
FVS Training Bolzano 10/9
FVS Training Bolzano 10/9FVS Training Bolzano 10/9
FVS Training Bolzano 10/9
 
FVS Training Bolzano 5/9
FVS Training Bolzano 5/9FVS Training Bolzano 5/9
FVS Training Bolzano 5/9
 
FVS Training Bolzano 7/9
FVS Training Bolzano 7/9FVS Training Bolzano 7/9
FVS Training Bolzano 7/9
 
FVS Training Bolzano 3/9
FVS Training Bolzano 3/9FVS Training Bolzano 3/9
FVS Training Bolzano 3/9
 
FVS Training Bolzano 1/9
FVS Training Bolzano 1/9FVS Training Bolzano 1/9
FVS Training Bolzano 1/9
 
FVS Training Bolzano 6/9
FVS Training Bolzano 6/9FVS Training Bolzano 6/9
FVS Training Bolzano 6/9
 
FVS Training Bolzano 4/9
FVS Training Bolzano 4/9FVS Training Bolzano 4/9
FVS Training Bolzano 4/9
 
FVS Training Bolzano 8/9
FVS Training Bolzano 8/9FVS Training Bolzano 8/9
FVS Training Bolzano 8/9
 
FVS Training Bolzano 9/9
FVS Training Bolzano 9/9FVS Training Bolzano 9/9
FVS Training Bolzano 9/9
 
Esercitazioni di statistica 8/10
Esercitazioni di statistica 8/10Esercitazioni di statistica 8/10
Esercitazioni di statistica 8/10
 
Esercitazioni di statistica 7/10
Esercitazioni di statistica 7/10Esercitazioni di statistica 7/10
Esercitazioni di statistica 7/10
 
Esercitazioni di statistica 10/10
Esercitazioni di statistica 10/10Esercitazioni di statistica 10/10
Esercitazioni di statistica 10/10
 
Esercitazioni di statistica 6/10
Esercitazioni di statistica 6/10Esercitazioni di statistica 6/10
Esercitazioni di statistica 6/10
 
Esercitazioni di statistica 9/10
Esercitazioni di statistica 9/10Esercitazioni di statistica 9/10
Esercitazioni di statistica 9/10
 
Esercitazioni di statistica 5/10
Esercitazioni di statistica 5/10Esercitazioni di statistica 5/10
Esercitazioni di statistica 5/10
 
Esercitazioni di statistica 1/10
Esercitazioni di statistica 1/10Esercitazioni di statistica 1/10
Esercitazioni di statistica 1/10
 

Recently uploaded

GBSN - Biochemistry (Unit 1)
GBSN - Biochemistry (Unit 1)GBSN - Biochemistry (Unit 1)
GBSN - Biochemistry (Unit 1)Areesha Ahmad
 
TEST BANK For Radiologic Science for Technologists, 12th Edition by Stewart C...
TEST BANK For Radiologic Science for Technologists, 12th Edition by Stewart C...TEST BANK For Radiologic Science for Technologists, 12th Edition by Stewart C...
TEST BANK For Radiologic Science for Technologists, 12th Edition by Stewart C...ssifa0344
 
Botany 4th semester file By Sumit Kumar yadav.pdf
Botany 4th semester file By Sumit Kumar yadav.pdfBotany 4th semester file By Sumit Kumar yadav.pdf
Botany 4th semester file By Sumit Kumar yadav.pdfSumit Kumar yadav
 
Pests of cotton_Borer_Pests_Binomics_Dr.UPR.pdf
Pests of cotton_Borer_Pests_Binomics_Dr.UPR.pdfPests of cotton_Borer_Pests_Binomics_Dr.UPR.pdf
Pests of cotton_Borer_Pests_Binomics_Dr.UPR.pdfPirithiRaju
 
FAIRSpectra - Enabling the FAIRification of Spectroscopy and Spectrometry
FAIRSpectra - Enabling the FAIRification of Spectroscopy and SpectrometryFAIRSpectra - Enabling the FAIRification of Spectroscopy and Spectrometry
FAIRSpectra - Enabling the FAIRification of Spectroscopy and SpectrometryAlex Henderson
 
GBSN - Microbiology (Unit 3)
GBSN - Microbiology (Unit 3)GBSN - Microbiology (Unit 3)
GBSN - Microbiology (Unit 3)Areesha Ahmad
 
Formation of low mass protostars and their circumstellar disks
Formation of low mass protostars and their circumstellar disksFormation of low mass protostars and their circumstellar disks
Formation of low mass protostars and their circumstellar disksSérgio Sacani
 
Bacterial Identification and Classifications
Bacterial Identification and ClassificationsBacterial Identification and Classifications
Bacterial Identification and ClassificationsAreesha Ahmad
 
Chemistry 4th semester series (krishna).pdf
Chemistry 4th semester series (krishna).pdfChemistry 4th semester series (krishna).pdf
Chemistry 4th semester series (krishna).pdfSumit Kumar yadav
 
American Type Culture Collection (ATCC).pptx
American Type Culture Collection (ATCC).pptxAmerican Type Culture Collection (ATCC).pptx
American Type Culture Collection (ATCC).pptxabhishekdhamu51
 
Pests of cotton_Sucking_Pests_Dr.UPR.pdf
Pests of cotton_Sucking_Pests_Dr.UPR.pdfPests of cotton_Sucking_Pests_Dr.UPR.pdf
Pests of cotton_Sucking_Pests_Dr.UPR.pdfPirithiRaju
 
Forensic Biology & Its biological significance.pdf
Forensic Biology & Its biological significance.pdfForensic Biology & Its biological significance.pdf
Forensic Biology & Its biological significance.pdfrohankumarsinghrore1
 
Connaught Place, Delhi Call girls :8448380779 Model Escorts | 100% verified
Connaught Place, Delhi Call girls :8448380779 Model Escorts | 100% verifiedConnaught Place, Delhi Call girls :8448380779 Model Escorts | 100% verified
Connaught Place, Delhi Call girls :8448380779 Model Escorts | 100% verifiedDelhi Call girls
 
Biogenic Sulfur Gases as Biosignatures on Temperate Sub-Neptune Waterworlds
Biogenic Sulfur Gases as Biosignatures on Temperate Sub-Neptune WaterworldsBiogenic Sulfur Gases as Biosignatures on Temperate Sub-Neptune Waterworlds
Biogenic Sulfur Gases as Biosignatures on Temperate Sub-Neptune WaterworldsSérgio Sacani
 
Chemical Tests; flame test, positive and negative ions test Edexcel Internati...
Chemical Tests; flame test, positive and negative ions test Edexcel Internati...Chemical Tests; flame test, positive and negative ions test Edexcel Internati...
Chemical Tests; flame test, positive and negative ions test Edexcel Internati...ssuser79fe74
 
COST ESTIMATION FOR A RESEARCH PROJECT.pptx
COST ESTIMATION FOR A RESEARCH PROJECT.pptxCOST ESTIMATION FOR A RESEARCH PROJECT.pptx
COST ESTIMATION FOR A RESEARCH PROJECT.pptxFarihaAbdulRasheed
 
Nanoparticles synthesis and characterization​ ​
Nanoparticles synthesis and characterization​  ​Nanoparticles synthesis and characterization​  ​
Nanoparticles synthesis and characterization​ ​kaibalyasahoo82800
 
Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)
Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)
Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)Joonhun Lee
 

Recently uploaded (20)

GBSN - Biochemistry (Unit 1)
GBSN - Biochemistry (Unit 1)GBSN - Biochemistry (Unit 1)
GBSN - Biochemistry (Unit 1)
 
TEST BANK For Radiologic Science for Technologists, 12th Edition by Stewart C...
TEST BANK For Radiologic Science for Technologists, 12th Edition by Stewart C...TEST BANK For Radiologic Science for Technologists, 12th Edition by Stewart C...
TEST BANK For Radiologic Science for Technologists, 12th Edition by Stewart C...
 
Botany 4th semester file By Sumit Kumar yadav.pdf
Botany 4th semester file By Sumit Kumar yadav.pdfBotany 4th semester file By Sumit Kumar yadav.pdf
Botany 4th semester file By Sumit Kumar yadav.pdf
 
Pests of cotton_Borer_Pests_Binomics_Dr.UPR.pdf
Pests of cotton_Borer_Pests_Binomics_Dr.UPR.pdfPests of cotton_Borer_Pests_Binomics_Dr.UPR.pdf
Pests of cotton_Borer_Pests_Binomics_Dr.UPR.pdf
 
FAIRSpectra - Enabling the FAIRification of Spectroscopy and Spectrometry
FAIRSpectra - Enabling the FAIRification of Spectroscopy and SpectrometryFAIRSpectra - Enabling the FAIRification of Spectroscopy and Spectrometry
FAIRSpectra - Enabling the FAIRification of Spectroscopy and Spectrometry
 
GBSN - Microbiology (Unit 3)
GBSN - Microbiology (Unit 3)GBSN - Microbiology (Unit 3)
GBSN - Microbiology (Unit 3)
 
Formation of low mass protostars and their circumstellar disks
Formation of low mass protostars and their circumstellar disksFormation of low mass protostars and their circumstellar disks
Formation of low mass protostars and their circumstellar disks
 
Clean In Place(CIP).pptx .
Clean In Place(CIP).pptx                 .Clean In Place(CIP).pptx                 .
Clean In Place(CIP).pptx .
 
Site Acceptance Test .
Site Acceptance Test                    .Site Acceptance Test                    .
Site Acceptance Test .
 
Bacterial Identification and Classifications
Bacterial Identification and ClassificationsBacterial Identification and Classifications
Bacterial Identification and Classifications
 
Chemistry 4th semester series (krishna).pdf
Chemistry 4th semester series (krishna).pdfChemistry 4th semester series (krishna).pdf
Chemistry 4th semester series (krishna).pdf
 
American Type Culture Collection (ATCC).pptx
American Type Culture Collection (ATCC).pptxAmerican Type Culture Collection (ATCC).pptx
American Type Culture Collection (ATCC).pptx
 
Pests of cotton_Sucking_Pests_Dr.UPR.pdf
Pests of cotton_Sucking_Pests_Dr.UPR.pdfPests of cotton_Sucking_Pests_Dr.UPR.pdf
Pests of cotton_Sucking_Pests_Dr.UPR.pdf
 
Forensic Biology & Its biological significance.pdf
Forensic Biology & Its biological significance.pdfForensic Biology & Its biological significance.pdf
Forensic Biology & Its biological significance.pdf
 
Connaught Place, Delhi Call girls :8448380779 Model Escorts | 100% verified
Connaught Place, Delhi Call girls :8448380779 Model Escorts | 100% verifiedConnaught Place, Delhi Call girls :8448380779 Model Escorts | 100% verified
Connaught Place, Delhi Call girls :8448380779 Model Escorts | 100% verified
 
Biogenic Sulfur Gases as Biosignatures on Temperate Sub-Neptune Waterworlds
Biogenic Sulfur Gases as Biosignatures on Temperate Sub-Neptune WaterworldsBiogenic Sulfur Gases as Biosignatures on Temperate Sub-Neptune Waterworlds
Biogenic Sulfur Gases as Biosignatures on Temperate Sub-Neptune Waterworlds
 
Chemical Tests; flame test, positive and negative ions test Edexcel Internati...
Chemical Tests; flame test, positive and negative ions test Edexcel Internati...Chemical Tests; flame test, positive and negative ions test Edexcel Internati...
Chemical Tests; flame test, positive and negative ions test Edexcel Internati...
 
COST ESTIMATION FOR A RESEARCH PROJECT.pptx
COST ESTIMATION FOR A RESEARCH PROJECT.pptxCOST ESTIMATION FOR A RESEARCH PROJECT.pptx
COST ESTIMATION FOR A RESEARCH PROJECT.pptx
 
Nanoparticles synthesis and characterization​ ​
Nanoparticles synthesis and characterization​  ​Nanoparticles synthesis and characterization​  ​
Nanoparticles synthesis and characterization​ ​
 
Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)
Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)
Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)
 

FVS Training Bolzano 2/9

  • 1. 1 Key Concepts James N. Long Department of Wildland Resources & Ecology Center Utah State University Objectives • Review several key concepts and tools; • These are useful in designing reasonable and effective density management regimes (thinning); • Design a density management regime for a particular set of objectives. Concepts and tools • Site index • Stand dynamics • Size-density relationships and relative density • Designing a density management regime Site index From Vacchiano et al. (2008), after Wiedemann (1949) Site index • Average height of dominant trees is: – A function of potential productivity – Little influenced by stand history • Used as a species-specific index of site quality • SI = ave. dominant height at the base age (e.g., 50, 100, etc.) • Used to estimate age in future Site index From Vacchiano et al. (2008), after Wiedemann (1949)
  • 2. 2 Stand dynamics Stages of stand development, with different levels of: relative density competition site occupancy Stand dynamics Pinus contorta var. latifolia Stand dynamics Pseudotsuga menziesii Stand dynamics Picea engelmannii Stand dynamics Pinus ponderosa Size-density relations • A way to quantify relative density • A way to characterize DFC • Basis for designing density management regimes DFC = Desired future condition
  • 3. 3 Relative density • Absolute density and relative density • The predictable relationship between mean size and density in crowded, self-thinning populations Powell Relative density Insectanddiseasesusceptibility Crowningindex Relative density Stand dynamics Picea engelmannii
  • 4. 4 D A B C 60% 25% 35% A C D EB Density management • Relative density is basic tool for translating qualitative objectives into a quantitative density management regime • We use an index of relative density to: – assess current condition; – characterize desired future condition; and – develop a tactical plan for achieving DFC Some key relative densities • Crown closure/on-set of competition 25% SDImax • Lower limit of full site occupancy 35% SDImax • Lower limit of self-thinning 60% SDImax Density management • Deciding on appropriate upper and lower limits of relative density • Choices depend on management objectives Examples of (situational) appropriate limits • Maintain vigor, avoid self-thinning < 60% • Delay self-pruning < 25% • Promote self-pruning > 25% • Full site occupancy > 35% D A B C 60% 25% 35%
  • 5. 5