Projection of future availability of teak wood from forest plantations and its prices in Kerala State, India
M.SivaramM.SivaramStatistics DisciplineStatistics Discipline
Division of Forest Information Management SystemDivision of Forest Information Management SystemKerala Forest Research InstituteKerala Forest Research Institute
PeechiPeechi –– 680 653 Kerala680 653 Kerala
IntroductionIntroductionTeak plantations was first raised at Nilambur in Kerala, which dates back to 1842.
Teak plantations were brought under scientific working with the introduction of Working plans in 1895.
Teak was the most preferred species after the Second World War as an afforestation effort.
During the early 1960’s liberal approach wasconsidered due topreference over even poor quality teak.
Five Year Plans accelerated the plantation activity in Kerala.
0
50000
100000
150000
200000
250000
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2005
Year
Are
a (h
a)
Others
Eucalypt
Teak
Teak plantations form the major source of teak wood supply.
Home gardens are also major source for the teak wood supply.
Most of the teak wood produced is consumed within the state.
Other important timber species are
anjily (Artocarpus hirsutus), jack, mango, coconut and
rose wood (Dalbergia latifolia) and
imported timbers such as pynkado.
The rubber wood has been used heavily in industries sector as Kerala has large tracts of rubber plantations.
Future projection of supply, demand and prices is an important activity in any business enterprise.
In forestry such exercise will aid developing forest policies for the sustainable forest management.
ObjectivesObjectives
To assess the present status of extent of teak plantations To assess the present status of extent of teak plantations in Keralain Kerala
To project the future availability of teak wood from To project the future availability of teak wood from forest plantations based on age structure under different forest plantations based on age structure under different scenarios and assess how far forest plantations will meet scenarios and assess how far forest plantations will meet the future teak wood demandthe future teak wood demand
To analyze the trends in the real prices of teak wood To analyze the trends in the real prices of teak wood
To make short term forecasts of current prices of teak To make short term forecasts of current prices of teak wood wood
Database used for the studyDatabase used for the studyThe tasks involved in this study require a good database.
Data sets usedData sets usedData with respect to first two objectives are:
Currently available forest plantations including details such aslocation and year of planting and production of teak wood from forest plantations etc. (Kerala Forest Department).
Volume estimates (productivity), thinning and rotation age etc. (Research reports and all India yield tables).
Data with respect to last two objectives are:
Current prices of teak wood belonging to different girth classes were collected from different Timber Sales Divisions of the Kerala Forest Department.
Data relating to 1943-1994 was originally from Krishnankutty (1998) and data for 1994-98 was from Krishnankutty and Sivaram (2003). Data for the period 1998-2006 was collected and compiled during the development of the database software.
PART IPART IPresent Status of Extent of Natural Forests and
Forest Plantations in Kerala (2005)
Total forest area – 1.12 million ha
83%
17%
Natural Forests
Forest Plantations
Extent of Forest Plantations under different agencies in Kerala - 2005
Total forest plantation area – 191,000 ha
95%
5%
Kerala Forest Department
Kerala ForestDevelopment Corporation
Extent of Forest Plantations under different management category in Kerala - 2005
88%
12%
TerritorialDivisions/KFDC
Protected area
Species wise distribution of forest plantations in Kerala- 2005
69021
14632
458 243
12247
1481 1753843
65884
0
10000
20000
30000
40000
50000
60000
70000
80000
Teak
Eucaly
ptus
Rosew
ood
Mah
ogan
y
Acacia
Cane
Reeds
Bambo
o
Others
Area
(ha)
Total -167, 983 ha
Spatial distribution of forest plantations in Kerala- 2005
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5000
10000
15000
20000
25000
30000
35000
40000
45000
Southern HighRange Central Eastern Northern KFDC
Area
(ha)
Others
Bamboo
Reeds
Cane
Acacia
Mahogany
Rosewood
Eucalyptus
Teak
Key factors involved in projections
Projection of future availability of teak wood from forest plantations
0
2000
4000
6000
8000
10000
12000
14000
0-4 5-9 9-10 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 >59
Age (years)
Are
a (h
a)
Age structure of teak plantations in Kerala, 2005
Site quality and Stocking
86 per cent of the teak plantation area in Kerala belonged to Site quality II/III.
Under stocked/over stocked plantations were 74 per cent based on basal area and
81 per cent based on number of trees per ha (Jayaraman and Chacko, 1997) ..
Thinning and rotation age
The prescribed thinning years are 4, 8, 12, 18, 28 and 40 years.
The average thinning years worked out by Jayaraman and Chacko (1997) are 7, 10, 16, 24, 31 and 35 years.
In general, teak plantations in Kerala are managed on a rotation age of 50 to 60 years.
Volume estimates
MAI varies from 1 to 5 m3 per ha.
Modeling the future availability of teak wood from forest plantations
ai be the area of the ith individual plantation in ha (i =1,2, …N).
ci be the year of planting of the ith plantation.
Projected availability of teak wood (Ptr) in a given projection year t is a sum of the quantum of yield that is obtained from thinning (Ttr) and felling (Ftr) for the given rotation age r and thinning year j =1,2, ... k.
trtrtr FTP +=
rtrtr yAF ×=
∑=
=N
1iitj aA
∑=
=N
1iitr aA
for all i satisfying the condition ci +j = t
for all i satisfying the condition ci + r = t
tj - thinning yield (cu m/ha) for the given thinning year
yr- felling yield (cu m/ha) for the given rotation age
j
k
1jtjtr tAT ×= ∑
=
Formula used for projecting the future demand
n0n )r1(DD +=
Dn= demand in ending year
D0= demand in the beginning year
n = number of years between beginning and ending year
r = compound growth rate
Options involved in future projection of availability of teak wood
Felling yield used for projectionFelling year Felling yield (cu m/ha)*
50
55
60
109.5
116.5
125.2
* Productivity 2.1 cum/ha
Thinning yield used for projection of teak wood
Potential EstimatedThinning year
Yield (cu m/ ha) Thinning year
Yield (cu m/ha)
4
8
12
18
28
40
10.92
14.27
14.83
14.41
12.45
9.45
7
10
16
24
31
35
4.439
6.029
4.095
5.660
12.706
8.159
Options involved in future projection of demand for teak wood
According to studies by Krishnankutty (1997 and 2004)the total demand for teak wood was
64,000 m3 in 1987-88 and
96,000 m3 in 2000-2001
Annual compound growth rate of nearly 3.2 per cent over a period of 13 years.
The different annual growth rates considered for the projection were 2 per cent, 3 per cent and 4 per cent respectively.
Key assumptions involved in projections
1) Plantations that are felled will be replanted in the subsequent year.
2) Addition of new teak plantations during the projection period would be negligible. This assumption seemed plausible because there was no land available for extending teak plantations.
Future trends in the gap between demand and availability of teak wood from forest plantations
(rotation: 50 years)
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100000
200000
300000
400000
500000
600000
700000
800000
90000020
05
2008
2011
2014
2017
2020
2023
2026
2029
2032
2035
2038
2041
2044
2047
2050
2053
Year
Volu
me
(cu
m)
Estimated yield
Potential yield
2% demand2% demand
3% demand3% demand
4% demand4% demand
Future trends in the gap between demand and availability of teak wood from forest plantations
(rotation: 55 years)
0
200000
400000
600000
800000
1000000
120000020
05
2008
2011
2014
2017
2020
2023
2026
2029
2032
2035
2038
2041
2044
2047
2050
2053
2056
2059
Year
Volu
me
(cu
m)
Estimated yield
Potential yield
2% demand2% demand
3% demand3% demand
4% demand4% demand
Future trends in the gap between demand and availability of teak wood from forest plantations
(rotation: 60 years)
0
200000
400000
600000
800000
1000000
1200000
140000020
05
2009
2013
2017
2021
2025
2029
2033
2037
2041
2045
2049
2053
2057
2061
2065
Year
Volu
me
(cu
m)
Estimated yieldPotential yield
2% demand2% demand
3% demand3% demand
4% demand4% demand
Production of Teak wood from forests of Kerala (1981-2005)
0
100000
200000
300000
400000
500000
600000
700000
1980
-81
1981
-82
1982
-83
1983
-84
1984
-85
1985
-86
1986
-87
1987
-88
1988
-89
1989
-90
1990
-91
1991
-92
1992
-93
1993
-94
1994
-95
1995
-96
1996
-97
1997
-98
1998
-99
1999
-00
2000
-01
2001
-02
2002
-03
2004
-05
Year
Prod
uctio
n (C
u m
)
Other species
Teak
ConclusionsConclusions
It appears that the existing level of teak plantations in It appears that the existing level of teak plantations in Kerala are potential enough to meet the demand up to Kerala are potential enough to meet the demand up to 2030 2030 –– 2040.2040.
However, the average production for the past 5 years shows However, the average production for the past 5 years shows that about only 50 per cent of the demand is met by the teak that about only 50 per cent of the demand is met by the teak plantations. plantations.
Therefore, activities in promoting teak outside the forest Therefore, activities in promoting teak outside the forest plantations such as home gardens, farm lands will bridge the plantations such as home gardens, farm lands will bridge the gap between the demand and supply from forest plantations.gap between the demand and supply from forest plantations.
PART IIPART II
Trends in current and real prices of teak wood
5 different girth classes viz., Export class (185 cm and above), Girth Class I (150-184 cm), Girth Class II (100-149 cm), Girth Class III (75-99 cm) and Girth Class IV (60-74 cm) were considered.
The weighted average prices were used for analysis after dulyaccounting for the quantity of timber sold.
Price Sale WholePriceCurrent Price Real =
The base year for the calculation of real prices is 1993The base year for the calculation of real prices is 1993--94 (=100).94 (=100).
Percentage annual increase in current prices of teak wood (1981-2005)
0
5
10
15
20
25
30
1981-1990 1991-2000 2001-2005
Time period
% A
nnua
l inc
reas
e in
pric
e
ExportGirth Class IGirth Class IIGirth Class IIIGirth Class IV
Current and real prices of teak wood in Kerala
Current Pr i ces Real Pr i ces
Teak Pr i ces (Rs)
20000
30000
40000
50000
60000
70000
Year
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Current Pr i ces Real Pr i ces
Teak Pr i ces (Rs)
20000
30000
40000
50000
60000
Year
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Girth Class IGirth Class Exp
Current Pr i ces Real Pr i ces
Teak Pr i ces (Rs)
10000
20000
30000
40000
50000
Year
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Girth Class II
Current Pr i ces Real Pr i ces
Teak Pr i ces (Rs)
8000
9000
10000
11000
12000
13000
14000
15000
16000
17000
18000
19000
20000
Year
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Girth Class IV
Current Pr i ces Real Pr i ces
Teak Pr i ces (Rs)
11000
12000
13000
14000
15000
16000
17000
18000
19000
20000
21000
22000
23000
24000
25000
26000
27000
28000
29000
Year
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Girth Class III
The real prices were almost same during the period 1993 to 2004
Forecasting of current prices of teak woodA time series is a sequence of observations taken sequentially in time.
The succession of price values in a time series is usually influenced by some external information. If this information is not known, only the past price values of the time series itself can be used to build a mathematical model for forecasting future price values.
ARIMA Model (Auto Regressive Integrated Moving Average)
Artificial Neural Network (ANN)
ARIMA Model
ARIMA model is usually denoted as ARIMA (p,d,q) , which can be expressed mathematically as
qtptttptptt aaaazzz −−−−− −−−−+++= θθθφφ ...... 221111
where tz
=
td y∇
p
=
Order of the autoregressive process
d =
Degree of differencing involved
q =
Order of the moving average process
Artificial Neural Network (ANN)
ANN is a powerful data modeling tool that is able to capture and represent complex input/output relationships (linear/ non-linear).
The motivation for the neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain.
ANN acquires knowledge through learning and knowledge is stored within inter-neuron connection strengths known as synaptic weights.
Input layer Xi
Hidden
Artificial Neural Network
Output layer Yk
Layer Hj
x1 b1j
b2j
bnxj
ϕ (.) Outputyk
Inputsignals
Summing junction
Activationfunction
∑
x2
gj
xnx
Synapticweights
Nonlinear model of a neuron
Identification of ARIMA AutocorrelationIdentification of NN structure
The appropriate MLP network was identified by trial and error method.
In the neural network model NN the number of inputs , lag period and number of neurons were variedbased on Autocorrelation co-efficient.
)k,j,i( )( i)( j )k(
The models were applied to raw price data, log transformed pricedata, and to prices obtained after linear detrending.
The synaptic weights were optimized using back propagation method in which Levenberg algorithm was used for minimizingthe error sum of square
aa11
Diagrammatic representation of ANN (3,3,3)
yytt--11H1H1
H2H2
H3H3
bb1212
bb1313
bb2121
bb2222
bb2323
bb3131
bb3232
bb3333
dd1111
dd2121
dd3131
bb1111
yytt--22
yytt--22
OutputOutput
yytt
aa2cc112
aa33
Performance Evaluation of ARIMA and ANN models
2t
n
1tt )yy(
n1MSE −= ∑
=
Mean Square Error (MSE)
Root Mean Square Error (RMSE) RMSE MSE=
Mean Absolute Percent Error (MAPE) ( )∑+
−=
n
1tt
tt
yyy
n100MAPE
∑=
−=n
1ttt yy
n1MAEMean Absolute Error (MAE)
Akaike's Information Criterion (AIC)
k2)MSEln(nAIC +=
n is the number of non-missing observations and k is the number of fitted parameters in the model.
The functional form and coefficients of the ARIMA models chosen for forecastingprices of teak wood of different girth classes
Values of the CoefficientValues of the Coefficient
Girth Girth ClassClass
ARIMA ARIMA ((p,d,qp,d,q))
Functional form of the chosen prediction Functional form of the chosen prediction equationequation
φφ11 θθ11 θθ22
ExportExport(1,2,2)(1,2,2)
yytt = (2 + = (2 + φφ11) ) yytt--11-- ( 1 + 2( 1 + 2φφ1 1 ) ) yytt--22
+ + φφ 1 1 yytt--33 + + aatt -- θθ11 aatt--1 1 -- θθ2 2 aatt--22
--.90812.90812(.3032)(.3032)
.38726.38726(.3802)(.3802)
.40601.40601(.3903)(.3903)
II (1,2,1)(1,2,1)yytt = (2 + = (2 + φφ11) y) ytt--11-- ( 1 + 2( 1 + 2φφ11 ) y) ytt--22
+ + φφ1 1 yytt--3 3 + + aatt -- θθ11 aatt--11
--.49757.49757(.16)(.16)
.85056.85056(.0859)(.0859)
IIII (1,2,1)(1,2,1)yytt = (2 + = (2 + φφ11) y) ytt--11-- ( 1 + 2( 1 + 2φφ11 ) y) ytt--22
+ + φφ1 1 yytt--3 3 + + aatt -- θθ11 aatt--11
.03254.03254(.1529)(.1529)
.90156.90156(.0887)(.0887)
IIIIII (1,2,2)(1,2,2)yytt = (2 + = (2 + φφ11) ) yytt--11-- ( 1 + 2( 1 + 2φφ1 1 ) ) yytt--22
+ + φφ 1 1 yytt--33 + + aatt -- θθ11 aatt--1 1 -- θθ2 2 aatt--22
--.62596.62596(.1539)(.1539)
--.05393.05393(.1446)(.1446)
.89158.89158(.1115)(.1115)
IVIV (1,2,1)(1,2,1)yytt = (2 + = (2 + φφ11) y) ytt--11-- ( 1 + 2( 1 + 2φφ11 ) y) ytt--22
+ + φφ1 1 yytt--3 3 + + aatt -- θθ11 aatt--11
.02265.02265(.2363)(.2363)
.89004.89004(.1797)(.1797)
The synaptic weights of the Feed Forward Neural Network models chosen for forecasting prices of teakwood of different girth classes
Synaptic weightsSynaptic weights
Export Export ClassClass
GirthGirthClass IClass I
GirthGirthClass IIClass II
GirthGirthClass IIIClass III
GirthGirthClass IVClass IV
ANN ANN (3,3,3)(3,3,3)
ANN ANN (3,3,3)(3,3,3)
ANN ANN (3,3,3)(3,3,3)
ANN ANN (3,3,3)(3,3,3)
ANN ANN (2,2,2)(2,2,2)
yytt--11 H1H1 bb1111 --3.9573.957 --0.5830.583 --1.0651.065 --0.9370.937 6.3976.397
yytt--22 H1H1 bb2121 3.3063.306 0.6190.619 1.4351.435 1.3471.347 --4.7094.709
yytt--33 H1H1 bb3131 --0.2410.241 --2.0682.068 --2.4412.441 --2.4312.431 --
yytt--11 H2H2 bb1212 --5.7465.746 0.5560.556 4.2794.279 --0.0720.072 0.1360.136
yytt--22 H2H2 bb2222 4.2554.255 --0.8040.804 --13.01113.011 --64.6064.60 --0.3950.395
yytt--33 H2H2 bb3232 2.3892.389 5.8235.823 36.47536.475 92.94892.948 --
yytt--11 H3H3 bb1313 --32.10732.107 356.149356.149 --3.1553.155 --2.6782.678 --
yytt--22 H3H3 bb2323 11.52111.521 --588.280588.280 --4.0384.038 --2.7482.748 --
FromFrom ToTo NotationNotation
Cont.
Synaptic weightsSynaptic weights
Export Export ClassClass
GirthGirthClass IClass I
GirthGirthClass IClass I
GirthGirthClass IClass I
GirthGirthClass IClass I
ANN ANN (3,3,3)(3,3,3)
ANN ANN (3,3,3)(3,3,3)
ANN ANN (3,3,3)(3,3,3)
ANN ANN (3,3,3)(3,3,3)
ANN ANN (2,2,2)(2,2,2)
yytt--33 H1H1 bb3333 16.36816.368 --424.530424.530 0.0050.005 1.6431.643 --
BiasBias H1H1 aa11 --1.6771.677 --0.8770.877 --0.8520.852 --0.9700.970 --0.0780.078
BiasBias H2H2 aa11 --0.0460.046 --7.6297.629 --41.10441.104 --39.65939.659 --0.4650.465
BiasBias H3H3 aa11 3.5293.529 508.997508.997 6.8766.876 3.7113.711 --
H1H1 OutputOutput dd2121 --3.7503.750 --4.7374.737 --4.6554.655 --4.6704.670 0.7240.724
H2H2 OutputOutput dd2121 3.2263.226 1.0701.070 0.4420.442 0.4130.413 --15.88215.882
H3H3 OutputOutput dd2121 --0.7280.728 --0.5780.578 --1.0951.095 --1.4451.445 --
BiasBias OutputOutput cc11 8.9388.938 9.9839.983 10.32610.326 10.22610.226 14.31314.313
FromFrom ToTo NotationNotation
Comparison of fit statistics for ARIMA and ANN Models
0
2
4
6
8
10
12
14
16
18
Export I II III IV
Girth Class of Teak wood
Mea
n A
bsol
ute
Perc
enta
ge E
rror
(M
APE
)
ARIMAANN
Forecasting of prices of teak wood using ARIMA and ANNForecasting of prices of teak wood using ARIMA and ANN modelsmodels
Act ual ARI MA (122) NN (333)
Teak Pr i ces (Rs)
0
10000
20000
30000
40000
50000
60000
70000
80000
Year
1970 1975 1980 1985 1990 1995 2000 2005 2007
Act ual ARI MA (121) NN (333)
Teak Pr i ces (Rs)
0
10000
20000
30000
40000
50000
60000
Year
1942 1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
Export ClassExport Class Class IClass I
Act ual ARI MA (121) NN (333)
Teak Pr i ces (Rs)
0
10000
20000
30000
40000
50000
Year
1942 1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
Class IIClass II
Act ual ARI MA (122) NN (333)
Teak Pr i ces (Rs)
0
10000
20000
30000
40000
Year
1942 1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
Act ual ARI MA (121) NN (222)
Teak Pr i ces (Rs)
0
5000
10000
15000
20000
25000
30000
Year
1970 1975 1980 1985 1990 1995 2000 2005 2007
Class IVClass IVClass IIIClass III
Forecasted percentage increase in Teak wood prices in Kerala Forecasted percentage increase in Teak wood prices in Kerala using ARIMA modelusing ARIMA model
Girth ClassGirth Class Current Price Current Price ((RsRs/Cu m) /Cu m) --20062006
Forecasted Current Forecasted Current Price (Price (RsRs/ Cu m) / Cu m) --20072007
Percentage Percentage increaseincrease
Girth Class IGirth Class I 48,937 48,937 (1228)(1228) 56,834 56,834 (1426)(1426) 16.116.1
Girth Class IIGirth Class II 44,295 44,295 (1112)(1112) 46,231 46,231 (1160)(1160) 4.44.4
Girth Class IIIGirth Class III 33,174 33,174 (833)(833) 34,783 34,783 (873)(873) 4.94.9
ExportExport 57,270 57,270 (1437)(1437) 69,830 69,830 (1753)(1753) 21.921.9
Girth Class IVGirth Class IV 24,638 24,638 (618)(618) 25,949 25,949 (651)(651) 5.35.3
US dollar equivalent is provided in parentheses (1 US $ = 39.845US dollar equivalent is provided in parentheses (1 US $ = 39.845 INR) INR)
ConclusionsConclusions
The analysis of trends in current and real prices indicate that the price increase during 1990’s was low probably due to availability of substitute materials and increased timber import during the period. However, of late market for teak wood is picking up.
Application of ANN model for forecasting timber prices requires further studies.
Our ARIMA forecasts indicate that the high quality teak wood would fetch high prices in the year 2007.
Thank you for your kind attention
