Changes in Environmental

Variables on Site Index of Teak (Tectona

grandis) in West AfricaStephen Adu-Bredu

CSIR-FORESTRY RESEARCH INSTITUTE OF GHANA

IntroductionTeak (Tectona grandis) is native to South

Asia.Introduced into West Africa in the early

1900s.First Teak plantation in Ghana in 1905Teak has gained a world wide reputation on

account of attractiveness and wood durability.

Teak does well in all the ecological zones in Ghana. Moist/Wet Evergreen, Moist Semi-deciduous, Dry Semi-deciduous Forests and Savannah.

Export of Teak Products (m3)2006 (4th) 2007 (1st)

Lumber (KD) 206.869 328.340Lumber (AD) 46,099.14 44,981.272Lumber (Overland) 5.663Sliced Veneer 388.870Poles 15,103.39 75,364.257Pegs 23.88Billet 7,756.95 7,106.477Boules (AD) 93.057 57.454Furniture Parts 83.399 9.985Flooring 19.83 1.036Broomstick 54.517Total 69,441.038 128,269.357

Introduction: Cont.There is the need to;

Analyse the effects of site and silvicultural regimes on growth, stem form and wood quality

Provide growth, stem form and wood quality prediction models

Determine ecoclimatic suitable zones for economically and ecologically suitable teak plantations

Introduction: Cont.Site Productive Capacity

Total stand biomass produced by stand when all resource available for tree growth from a site have been fully utilised, up to any stage in development

It indicates maximum amount of woodStand dominant height reflects productive capacityDominant height is not affected by stocking density

Stand dominant height at a particular age is termed “Site Index”.

ObjectivesTo develop height growth model for TeakTo determine Site Index of TeakTo predict Site Index from environmental

variables

Sampling:Requires Data from permanent sample

plots Ring analysis was carried out to retrieve

height growth

Two main definitions

SIage_ref = Dominant height at a given reference age

SIage-ref

Ageref

Dominant Height (m)

AgeSIage_infinity = Maximum Dominant height that can be reached

SIage_infinity

At the same reference age, SI of stand 2 is higher than SI of stand 1. Stand 2 will provide a higher MAI than stand 1 if appropriate silviculture are applied

SIstand1

Ageref

Dominant Height (m)

Age

SIstand2

Two stands of different Site Productive Capacity (combination of climate and soil properties)

Candidate growth functionsFunction Equation

Chapman-Richard Ht = a(1 - exp (-

bt))c

Gompertz Ht = a(exp-b exp(-ct))

Logistic Ht = a/(1 + c exp –

bt) Korf Ht = a(exp -bt-c)

Hossfeld Ht = tc (b + tc/a)

Distribution of the sampled trees (Ghana)

Ecozone Age Class (Years)

0-9 10-19 20-29 30-39 40-49 50-59 Total

MEF 9 9 - - - - 18

MSDF - 12 21 6 - - 39

DSDF 8 18 15 12 - 3 56

Savannah - 12 6 6 3 3 30

Total 17 51 42 24 3 6 143

Distribution of sampled trees (Cote d’Ivoire)

Ecozone

Age Class (Years)

0-9 10-19 20-29 30-39 40-49 50-59 Total

MEF - - 12 - - - 12

MSDF - 12 24 - - - 36

DSDF 6 - - 6 - - 12

Savannah - - - - - - -

Total 6 12 36 6 60

Distribution of the sample plots in Ghana

!(!(

!(

!(

!(!(

!(!(

!(!(!(

!(!(!(

!(

!(!(!(!(

!( !(!(

!(!(!(!(

!(!(

!(

!(

!(!(!(!( !(

!(!(!(!(!(

!( !(!(!(!(

!(!(!(

!(

!(

!(

Northern

Ashanti

Western

Eastern

Central

Volta

Brong Ahafo

Upper West

Upper East

Greater Accra

P3-53

P3-52

P3-51

P3-50P3-49P3-48

P3-47P3-46

P3-42P3-41P3-40P3-39

P3-37P3-36 P3-35P3-34

P3-33P3-31

P3-29

P3-28

P3-27P3-26

P3-24

P3-23

P3-21

P3-20P3-19P3-18

P3-17

P3-11P3-10P3-09

P3-08P3-07

P3-04

P3-03P3-02 P3-01

Data collected from each TSP

• Geographical Position• Elevation

• Slope• Current stocking (Trees ha-1)

• Canopy Closure• Individual Tree height

• Individual Stem diameter at breast height (1.3m)

• Crown base height• Soil samples collected up to 20cm depth

• Rainfall Amount• Rainy Days

• Maximum and Minimum Temperatures

Destructive sampling

H = HS1 + {(HS2 – HS1)/(Nb1 – Nb2 +1)}

0 5 10 15 20 25 30 35 40 45 500

5

10

15

20

25

30

Age-Rings (years)

Heig

ht

(m)

Performance of the candidate functions with respect to SE and RMSE

Function

a b c RMSE

Chapman-Richard

13.57(0.5247)

0.1495(0.0240)

0.9953(0.1121)

4.2467

Gompertz

12.72(0.3335)

2.1881(0.1026)

0.2609(0.0223)

4.2570

Logistic 12.33(0.2852)

0.3795(0.0308)

5.2571(0.6439)

4.2707

Korf 20.34(2.7097)

2.6273(0.1260)

0.5875(0.0918)

4.2484

Hossfeld 15.63(1.0524)

0.5215(0.0745)

1.2164(0.1200)

4.2444

Prediction of stand dominant height

Ht1 = a(1 - exp(-bt1))c

Ht2 = a(1 - exp(-bt2))c

Ht2 = Ht1 {(1 - exp(-bt2))/(1 - exp (-bt1))}c

Phytocentric Site IndexN = (sampled area / 100) – 1

SI = H0 /(1 - exp(-bt))c

Relationship between SI and Stand age

0 10 20 30 40 50 6005

10152025303540

f(x) = − 0.0253636444740913 x + 15.4624140722976R² = 0.008077677453176

f(x) = 0.214486143054786 x + 19.0566681470919R² = 0.0858440036875728

f(x) = 0.047673900104156 x + 19.93453072267R² = 0.0246580482464971

f(x) = 0.102857504371397 x + 21.7213839163127R² = 0.0428893213343461f(x) = − 0.00152611556091372 x + 21.6339870138707

R² = 1.12535834144722E-05

Stand age (years)

Site

Ind

ex (

m)

Chapman-Richards

0 10 20 30 40 50 600

20

40

60

80

100

f(x) = − 0.182868155390104 x + 22.7130490698504R² = 0.11649730967245

f(x) = − 1.58615717892884 x + 58.0039339204129R² = 0.246702155787174

f(x) = − 0.303926749542682 x + 35.5718690701208R² = 0.141448375064356

f(x) = − 0.930102941512017 x + 55.6809911529239R² = 0.19182158467969f(x) = − 0.64558527977898 x + 45.8257543195216

R² = 0.179871518608215

Stand age (years)

Site

Ind

ex (

m)

Hossfeldt

Relationship between SI and Stand Density

0 500 1000 1500 2000 25000

5

10

15

20

25

30

35

40

f(x) = − 0.0057410523399273 x + 20.2342347730539R² = 0.22498745203106

f(x) = 0.00383235097935338 x + 17.2656670066642R² = 0.145097858305501

f(x) = − 0.0021445698689337 x + 22.4062985538544R² = 0.0619720834494698

f(x) = 0.00364492611045226 x + 20.5213902717857R² = 0.193246538074463f(x) = 0.00132595124508467 x + 20.4543374996606R² = 0.0165030925919853

Stand Density (trees ha-1)

Sit

e In

dex

(m)

Chapman-Richards

Correlation of Site Index with climatic and soil variables

Climatic variables   Edaphic variables  

Variable Relationship R2   Variable Relationship R2

DSRF Power 0.5070 Nitrogen Power 0.4028

Latitude Power 0.4170 Organic Matter Power 0.3802

Max Temp Exponential 0.3684 Organic Carbon Power 0.3785

Rainfall Logarithmic 0.3160 Clay Power 0.3385

Slope Polynomial 0.3054 C-N Ratio Exponential 0.2664

Canopy Power 0.2800 CEC Power 0.2358

WSRF Polynomial 0.1960 Ca Power 0.2087

R. Days Polynomial 0.1619   TEB Power 0.2001

Geocentric Site Index: Principal Component Analysis

29.5 30 30.5 31 31.5 32 32.5 33 33.5 34 34.50

5

10

15

20

25

30

35

40

f(x) = 1373.13966150484 exp( − 0.13061897600978 x )R² = 0.368400202521051

Mean Maximum Temperature (oC)

Site

Ind

ex (

m)

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.00

5

10

15

20

25

30

35

40

f(x) = 6.13514256451607 x^0.364471900945391R² = 0.506986895184564

Dry Season Rainfall (mm month-1)

Site

Ind

ex (

m)

Stepwise Multiple Regression Models of Site Index on environmental variables

Variables Model Multiple Regression Equation R2 RMSE

DSRF, MTEMP GLM SI = 68.33 + 0.1940DSRF + 1.660MTEMP 0.5325 3.3265

NLINSI = (7.69x10-8DSRF4.6175) + (706.4exp(-

0.1118MTEMP )) 0.5443 3.2841

DSRF, MTEMP, Ca GLM

SI = 51.18 + 0.2128DSRF + 1.2209MTEMP + 0.3438Ca 0.6366 2.9596

NLINSI = (4.795x10-8DSRF4.7758) + (605.4exp(-

0.1164MTEMP )) + (1.5121Ca0.6175) 0.6846 2.7576

DSRF, MTEMP, Ca, CNRatio GLM

SI = 57.68 + 0.1982DSRF + 1.1125MTEMP + 0.3121Ca + 0.7920CNRatio 0.6448 2.9262

  NLIN

SI = (1.257x10-6DSRF3.9483) + (2500exp(-

0.1793MTEMP )) + (0.2001Ca1.1706) + (53.36exp(-

0.1524CNRatio )) 0.7065 2.6598

MEF MSDF DSDF Savannah-10-8-6-4-202468

10

Ecological zone

Bia

s (m

)

-10

-5

0

5

10

Soil Texture

Bia

s (m

)

0 to 9 10 to 19 20 to 29 30 to 39 40 to 49-10

-8-6-4-202468

10

Age Class (years)

Bia

s (m

)

10 15 20 25 30 35-10

-8-6-4-202468

10

Residual analysis for the prediction of site index

Estimated Site Index (m)

Resi

du

s (m

)

5 10 15 20 25 30 35 400

5

10

15

20

25

30

35

40

f(x) = 0.984600279273241 x

Measured Site Index, M (m)

Pre

dic

ted

Sit

e In

dex

, P (

m)

NLIN

ConclusionsHeight growth curve have been developed

for teak in West AfricaHeight growth can thus be predicted for

teakPhytocentric SI has been developedSI can be predicted from climatic and

edaphic variables even if teak plantation has not be established at that site

What needs to be done is to verify the height growth curve prediction from independent data set.