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Forest Mensuration II. Lecture 13 Growth and Yield Models Avery and Burkhart, Chapter 16. A few thoughts. Forest management decisions-making relies on accurate information about both current and future forest resource conditions - PowerPoint PPT Presentation
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Lecture 13FORE 3218
Forest Mensuration IIForest Mensuration II
Lecture 13Lecture 13
Growth and Yield ModelsGrowth and Yield Models
Avery and Burkhart,Avery and Burkhart,
Chapter 16Chapter 16
Lecture 13FORE 3218
A few thoughtsA few thoughts
Forest management decisions-making relies on Forest management decisions-making relies on accurate information about both current and accurate information about both current and futurefuture forest resource conditionsforest resource conditions
Stand-table projections for future growth based on Stand-table projections for future growth based on the past may be most accurate for a limited period of the past may be most accurate for a limited period of time, but time, but – Not reliable for long periodsNot reliable for long periods– Not necessarily useful for evaluate treatment alternatives Not necessarily useful for evaluate treatment alternatives
because …because …
Therefore, many models for forecasting stand Therefore, many models for forecasting stand dynamics are developed, ranging from tables, dynamics are developed, ranging from tables, equations, to computer simulation modelsequations, to computer simulation models
Lecture 13FORE 3218
Definition of yieldDefinition of yield
The total amount The total amount available at a given point available at a given point of time of time – gross volumegross volume– merchantable volume merchantable volume – biomassbiomass
Lecture 13FORE 3218
Definition of yield (Definition of yield (cont.cont.) ) – Merchantability– Merchantability
B Potential usable growth under present technology and policy
C Growth actually removed and utilized
A Total biological growth
Gro
wth
30 cm
OMNR (1995) Scaling Manual
Top D
Merchantable
10 cm: Pj, Sb, Sw, Bf, Ce16 cm: Pw, Pr, He, Po, Bw20 cm: other hardwoods
Lecture 13FORE 3218
Jack pine site class 1
0
100
200
300
400
500
0 20 40 60 80 100
Stand age (years)
Vo
um
e (
m3 /h
a)
Definition of yield Definition of yield ((cont.cont.))
Gross
Plonski 1974
Merch
Jack pine site class 1
0
20
40
60
80
0 20 40 60 80 100
Stand age (years)
Mec
han
tab
ilit
y (%
)
100(%) gross
merch
Y
YM
Merchantability
Lecture 13FORE 3218
Definition of growthDefinition of growth
The increase (increment) The increase (increment) over a given period of timeover a given period of time
– Mean annual increment (MAI)Mean annual increment (MAI)– Periodic annual increment Periodic annual increment
(PAI) (PAI) dt
dYG
Lecture 13FORE 3218
Definition of growth (Definition of growth (cont.cont.))
0
100
200
300
400
500
0 20 40 60 80 100
Stand age (years)
Vo
um
e (m
3 /ha)
Gross
Merch
0
2
4
6
8
0 20 40 60 80 100
Stand age (years)
Gro
wth
(m
3 ha
-1 y
ear-1
)
Gross MAI
Merch MAI
0
0
TT
YY
dt
dYG
i
i
MAI
Lecture 13FORE 3218
0
2
4
6
8
0 20 40 60 80 100
Stand age (years)
Gro
wth
(m
3 ha
-1 y
ear-1
)
Gross PAI
Merch PAI
Definition of growth (Definition of growth (cont.cont.))
12
12
TT
YY
dt
dYG
PAI
Lecture 13FORE 3218
Relationships Relationships between yield between yield and growthand growth 0
100
200
300
400
500
0 20 40 60 80 100
Stand age (years)
Vo
um
e (m
3 /ha)
Gross
Merch
0
2
4
6
8
0 20 40 60 80 100
Stand age (years)
Gro
wth
(m
3 ha
-1 y
ear
-1)
Gross MAI
Merch MAI
0
2
4
6
8
0 20 40 60 80 100
Stand age (years)
Gro
wth
(m
3 ha
-1 y
ear-1
)
Gross PAI
Merch PAI
Lecture 13FORE 3218
Types of ModelsTypes of Models
Whole stand modelsWhole stand models
Diameter class modelsDiameter class models
Individual tree modelsIndividual tree models
Simple
Sophisticated
Development
Even-aged
Uneven-aged
Application
Empirical
Process
Nature
Lecture 13FORE 3218
Whole stand modelsWhole stand models
Density-free modelsDensity-free models– Normal yield tablesNormal yield tables– Empirical yield tables for average Empirical yield tables for average
standsstands
Variable density modelsVariable density models– Predict yieldPredict yield
– Predict growth (Predict growth (gg1212) and yield () and yield (VV11) )
),( SAfVA
),( SAfVA
EquationsModel type
),,( DSAfVA
1212 gVV
),,(12 DSAfg
Lecture 13FORE 3218
Diameter class modelsDiameter class models
Empirical stand table Empirical stand table projectionsprojections
EquationsModel type
Measurement 1
0
100
200
300
400
500
600
<5 5-9 10-14 15-19 20-24 25-29 30-34
DBH class (cm)
Nu
mb
er o
f tr
ees
per
ha
Measurement 2
0
100
200
300
400
500
600
<5 5-9 10-14 15-19 20-24 25-29 30-34
DBH class (cm)
Nu
mb
er o
f tr
ees
per
ha
],)[()( 12 INCRndfnd ii
1212
122 )(
VVg
ndvVn
iii
Lecture 13FORE 3218
Individual tree modelsIndividual tree models
EquationsModel type
],,,),,[( 11 kkkkk DISTSDchdfCCI
],,,,),,[(),,( 12112 PSDCCIchdfchd kkkkkkk
1212
122 )(
),(
VVg
vV
hdfv
n
kk
kkk
Distance-dependentDistance-dependent
Distance-independentDistance-independent
Lecture 13FORE 3218
Which type of models is best suited for the Which type of models is best suited for the following situations? Why? following situations? Why?
Depends on purposes of modeling…, but if Depends on purposes of modeling…, but if ……– Even-aged, single species stands?Even-aged, single species stands?– Uneven-aged single species stands?Uneven-aged single species stands?– Even- and uneven-aged, mixed-species stands?Even- and uneven-aged, mixed-species stands?
Lecture 13FORE 3218
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