Projection of future availability of teak wood from forest plantations and its prices in Kerala State, India

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1 Projection of future availability of teak wood from forest plantations and its prices in Kerala State, India M.Sivaram Statistics Discipline Division of Forest Information Management System Kerala Forest Research Institute Peechi Kerala

2 Introduction Teak plantations was first raised at Nilambur in Kerala, which dates back to Teak plantations were brought under scientific working with the introduction of Working plans in Teak was the most preferred species after the Second World War as an afforestation effort During the early 1960 s liberal approach was considered due to preference over even poor quality teak. Area (ha) Others Eucalypt Teak Five Year Plans accelerated the plantation activity in Kerala Year

3 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.

4 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.

5 Objectives To assess the present status of extent of teak plantations in Kerala To project the future availability of teak wood from forest plantations based on age structure under different scenarios and assess how far forest plantations will meet the future teak wood demand To analyze the trends in the real prices of teak wood To make short term forecasts of current prices of teak wood

6 Database used for the study The tasks involved in this study require a good database.

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10 Data sets used Data with respect to first two objectives are: Currently available forest plantations including details such as location 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 was originally from Krishnankutty (1998) and data for was from Krishnankutty and Sivaram (2003). Data for the period was collected and compiled during the development of the database software.

11 PART I Present Status of Extent of Natural Forests and Forest Plantations in Kerala (2005) Total forest area 1.12 million ha 17% Natural Forests Forest Plantations 83%

12 Extent of Forest Plantations under different agencies in Kerala Total forest plantation area 191,000 ha 5% Kerala Forest Department Kerala Forest Development Corporation 95%

13 Extent of Forest Plantations under different management category in Kerala % Territorial Divisions/KFDC Protected area 88%

14 Species wise distribution of forest plantations in Kerala Area (ha) Teak Eucalyptus Rosewood Mahogany Acacia Cane 1481 Reeds 175 Bamboo 3843 Others Total -167, 983 ha

15 Spatial distribution of forest plantations in Kerala Area (ha) Southern HighRange Central Eastern Northern KFDC Others Bamboo Reeds Cane Acacia Mahogany Rosewood Eucalyptus Teak

16 Projection of future availability of teak wood from forest plantations Key factors involved in projections Age structure of teak plantations in Kerala, Area (ha) >59 Age (years)

17 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 m 3 per ha.

18 Modeling the future availability of teak wood from forest plantations a i be the area of the i th individual plantation in ha (i =1,2, N). c i be the year of planting of the i th plantation. Projected availability of teak wood (P tr ) in a given projection year t is a sum of the quantum of yield that is obtained from thinning (T tr ) and felling (F tr ) for the given rotation age r and thinning year j =1,2,... k. P = T + tr tr F tr = N Atj ai i= 1 for all i satisfying the condition c i +j = t k T tr = A tj j = 1 t j = N Atr ai i= 1 for all i satisfying the condition c i + r = t F tr = Atr y r t j - thinning yield (cu m/ha) for the given thinning year y r - felling yield (cu m/ha) for the given rotation age

19 Formula used for projecting the future demand D ( 1 r ) n n 0 D = + D n = demand in ending year D 0 = demand in the beginning year n = number of years between beginning and ending year r = compound growth rate

20 Options involved in future projection of availability of teak wood Felling yield used for projection Felling year Felling yield (cu m/ha)* * Productivity 2.1 cum/ha

21 Thinning yield used for projection of teak wood Thinning year 4 Potential Yield (cu m/ ha) Thinning year 7 Estimated Yield (cu m/ha)

22 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 m 3 in and 96,000 m 3 in 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.

23 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.

24 Future trends in the gap between demand and availability of teak wood from forest plantations (rotation: 50 years) Estimated yield Potential yield % demand Volume (cu m) 3% demand 2% demand Year

25 Future trends in the gap between demand and availability of teak wood from forest plantations (rotation: 55 years) Estimated yield Potential yield % demand Volume (cu m) 3% demand 2% demand Year

26 Future trends in the gap between demand and availability of teak wood from forest plantations (rotation: 60 years) Estimated yield Potential yield 4% demand Volume (cu m) % demand 2% demand Year

27 Production of Teak wood from forests of Kerala ( ) Other species Teak Production (Cu m) Year

28 Conclusions It appears that the existing level of teak plantations in Kerala are potential enough to meet the demand up to However, the average production for the past 5 years shows that about only 50 per cent of the demand is met by the teak plantations. Therefore, activities in promoting teak outside the forest plantations such as home gardens, farm lands will bridge the gap between the demand and supply from forest plantations.

29 PART II Trends in current and real prices of teak wood 5 different girth classes viz., Export class (185 cm and above), Girth Class I ( cm), Girth Class II ( 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 duly accounting for the quantity of timber sold. Real Price = Current Price Whole Sale Price The base year for the calculation of real prices is (=100).

30 Percentage annual increase in current prices of teak wood ( ) % Annual increase in price Export Girth Class I Girth Class II Girth Class III Girth Class IV Time period

31 Current and real prices of teak wood in Kerala Teak Pr i ces ( Rs) Current Prices Real Prices Teak Pr i ces ( Rs) Current Prices Real Pr i ces Year Year Girth Class Exp Girth Class I

32 Teak Pr i ces ( Rs) Current Prices Real Prices Teak Pr i ces ( Rs) Current Prices Real Prices Year Girth Class II Year Girth Class III Teak Pr i ces ( Rs) Current Prices Real Prices The real prices were almost same during the period 1993 to Year Girth Class IV

33 Forecasting of current prices of teak wood A 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)

34 ARIMA Model ARIMA model is usually denoted as ARIMA (p,d,q), which can be expressed mathematically as q t p t t t p t p t t a a a a z z z = θ θ θ φ φ where t z = t d y p = Order of the autoregressive process d = Degree of differencing involved q = Order of the moving average process

35 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.

36 Hidden Layer Hj Input layer X i Output layer Y k Artificial Neural Network

37 I n p u t s i g n a l s x 1 x 2 x nx b 1j b 2j b nxj Synaptic weights Summing junction g j Activation function ϕ(.) Output y k Nonlinear model of a neuron

38 Identification of ARIMA Identification of NN structure Autocorrelation ( i, j,k ) ( i ) In the neural network model NN the number of inputs, lag period ( j ) and number of neurons ( k ) were varied based on Autocorrelation co-efficient. The appropriate MLP network was identified by trial and error method. The models were applied to raw price data, log transformed price data, and to prices obtained after linear detrending. The synaptic weights were optimized using back propagation method in which Levenberg algorithm was used for minimizing the error sum of square

39 a 1 y t-1 y t-2 y t-2 b 12 b 13 b 21 b22 b 11 b 23 b31 b 32 b 33 H1 H2 H3 d 11 a 2 c 1 2 d 21 d 31 Output y t a 3 Diagrammatic representation of ANN (3,3,3)

40 Performance Evaluation of ARIMA and ANN models Mean Square Error (MSE) MSE n 1 = n t = 1 ( y 2 ŷ ) t t Root Mean Square Error (RMSE) RMSE = MSE n Mean Absolute Percent Error (MAPE) 100 ( y ŷ ) t t MAPE = n t + 1 y t Mean Absolute Error (MAE) Akaike's Information Criterion (AIC) MAE = 1 n n t= 1 y t ŷ t AIC = n ln( MSE ) + 2k n is the number of non-missing observations and k is the number of fitted parameters in the model.

41 The functional form and coefficients of the ARIMA models chosen for forecasting prices of teak wood of different girth classes Girth Class ARIMA (p,d,q) Functional form of the chosen prediction equation Values of the Coefficient φ 1 θ 1 θ 2 Export (1,2,2) y t = (2 + φ 1 ) y t-1 - ( φ 1 ) y t-2 + φ 1 y t-3 + a t - θ 1 a - θ t-1 2 a t (.3032) (.3802) (.3903) I (1,2,1) y t = (2 + φ 1 ) y t-1 - ( φ 1 ) y t-2 + φ 1 y t-3 + a t - θ 1 a t (.16) (.0859) II (1,2,1) y t = (2 + φ 1 ) y t-1 - ( φ 1 ) y t-2 + φ 1 y t-3 + a t - θ 1 a t (.1529) (.0887) III (1,2,2) y t = (2 + φ 1 ) y t-1 - ( φ 1 ) y t-2 + φ 1 y t-3 + a t - θ 1 a - θ t-1 2 a t (.1539) (.1446) (.1115) IV (1,2,1) y t = (2 + φ 1 ) y t-1 - ( φ 1 ) y t-2 + φ 1 y t-3 + a t - θ 1 a t (.2363) (.1797)

42 The synaptic weights of the Feed Forward Neural Network models chosen for forecasting prices of teakwood of different girth classes From y t-1 y t-2 y t-3 y t-1 y t-2 y t-3 y t-1 y t-2 To Notation Export Class ANN (3,3,3) Synaptic weights Girth Class I ANN (3,3,3) Girth Class II ANN (3,3,3) Girth Class III ANN (3,3,3) Girth Class IV ANN (2,2,2) H1 b H1 b H1 b H2 b H2 b H2 b H3 b H3 b Cont.

43 Synaptic weights From To Notation Export Class Girth Class I Girth Class I Girth Class I Girth Class I ANN (3,3,3) ANN (3,3,3) ANN (3,3,3) ANN (3,3,3) ANN (2,2,2) y t-3 Bias Bias Bias H1 H2 H3 Bias H1 b H1 a H2 a H3 a Output d Output d Output d Output c

44 Comparison of fit statistics for ARIMA and ANN Models 18 Mean Absolute Percentage Error (MAPE) Export I II III IV Girth Class of Teak wood ARIMA ANN

45 Forecasting of prices of teak wood using ARIMA and ANN models Teak Pr i ces ( Rs) Act ual ARIMA ( 122) NN (333) Teak Pr i ces ( Rs) Act ual ARIMA ( 121) NN ( 333) Year Year Export Class Class I Teak Pr i ces ( Rs) Act ual ARIMA ( 121) NN (333) Year Class II

46 Teak Pr i ces ( Rs) Act ual ARI MA ( 122) NN ( 333) Teak Pr i ces ( Rs) Act ual ARI MA ( 121) NN ( 222) Year Year Class III Class IV

47 Forecasted percentage increase in Teak wood prices in Kerala using ARIMA model Girth Class Current Price (Rs/Cu m) Forecasted Current Price (Rs( Rs/ / Cu m) Percentage increase Export 57,270 (1437) 69,830 (1753) 21.9 Girth Class I 48,937 (1228) 56,834 (1426) 16.1 Girth Class II 44,295 (1112) 46,231 (1160) 4.4 Girth Class III 33,174 (833) 34,783 (873) 4.9 Girth Class IV 24,638 (618) 25,949 (651) 5.3 US dollar equivalent is provided in parentheses (1 US $ = INR)

48 Conclusions 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.

49 Thank you for your kind attention