changes in environmental variables on site index of teak (tectona grandis) in west africa stephen...
TRANSCRIPT
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
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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.