1. Changes in Environmental Variables on Site Index of Teak (Tectona grandis) in West Africa Stephen Adu-Bredu CSIR-FORESTRY RESEARCH INSTITUTE OF GHANA

2. Introduction Teak (Tectona grandis) is native to South Asia. Introduced into West Africa in the early 1900s. First Teak plantation in Ghana in 1905 Teak 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.

3. Export of Teak Products (m 3 ) 2006 (4 th )2007 (1 st ) Lumber (KD)206.869328.340 Lumber (AD)46,099.1444,981.272 Lumber (Overland)5.663 Sliced Veneer388.870 Poles15,103.3975,364.257 Pegs23.88 Billet7,756.957,106.477 Boules (AD)93.05757.454 Furniture Parts83.3999.985 Flooring19.831.036 Broomstick54.517 Total69,441.038128,269.357

4. 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

5. 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 wood Stand dominant height reflects productive capacity Dominant height is not affected by stocking density Stand dominant height at a particular age is termed “Site Index”.

6. Objectives To develop height growth model for Teak To determine Site Index of Teak To predict Site Index from environmental variables Sampling: Requires Data from permanent sample plots Ring analysis was carried out to retrieve height growth

7. Two main definitions SI age_ref = Dominant height at a given reference age SI age-ref Age ref Dominant Height (m) Age SI age_infinity = Maximum Dominant height that can be reached SI age_infinity

8. 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 SI stand1 Age ref Dominant Height (m) Age SI stand2 Two stands of different Site Productive Capacity (combination of climate and soil properties)

9. Candidate growth functions FunctionEquation Chapman-RichardHt = a(1 - exp (-bt) ) c GompertzHt = a(exp -b exp(-ct) ) LogisticHt = a/(1 + c exp –bt ) KorfHt = a(exp -bt-c ) HossfeldHt = t c (b + t c/a )

10. Distribution of the sampled trees (Ghana) Ecozone Age Class (Years) 0-910-1920-2930-3940-4950-59Total MEF99----18 MSDF-12216--39 DSDF8181512-356 Savannah -12663330 Total1751422436143

11. Distribution of sampled trees (Cote d’Ivoire) EcozoneAge Class (Years) 0-910-1920-2930-3940-4950-59Total MEF--12--- MSDF-1224---36 DSDF6--6--12 Savannah ------- Total61236660

12. Distribution of the sample plots in Ghana

13. 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

14. Destructive sampling

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

16. Performance of the candidate functions with respect to SE and RMSE FunctionabcRMSE Chapman- Richard 13.57 (0.5247) 0.1495 (0.0240) 0.9953 (0.1121) 4.2467 Gompertz12.72 (0.3335) 2.1881 (0.1026) 0.2609 (0.0223) 4.2570 Logistic12.33 (0.2852) 0.3795 (0.0308) 5.2571 (0.6439) 4.2707 Korf20.34 (2.7097) 2.6273 (0.1260) 0.5875 (0.0918) 4.2484 Hossfeld15.63 (1.0524) 0.5215 (0.0745) 1.2164 (0.1200) 4.2444

17. 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 Index N = (sampled area / 100) – 1 SI = H0 /(1 - exp (-bt) ) c

18. Relationship between SI and Stand age

19. Relationship between SI and Stand Density

20. Correlation of Site Index with climatic and soil variables Climatic variables Edaphic variables VariableRelationshipR2R2 VariableRelationshipR2R2 DSRFPower0.5070NitrogenPower0.4028 LatitudePower0.4170Organic MatterPower0.3802 Max TempExponential0.3684Organic CarbonPower0.3785 RainfallLogarithmic0.3160ClayPower0.3385 SlopePolynomial0.3054C-N RatioExponential0.2664 CanopyPower0.2800CECPower0.2358 WSRFPolynomial0.1960CaPower0.2087 R. DaysPolynomial0.1619 TEBPower0.2001

21. Geocentric Site Index: Principal Component Analysis

22. Stepwise Multiple Regression Models of Site Index on environmental variables VariablesModelMultiple Regression EquationR2R2 RMSE DSRF, MTEMPGLMSI = 68.33 + 0.1940DSRF + 1.660MTEMP0.53253.3265 NLIN SI = (7.69x10 -8 DSRF 4.6175 ) + (706.4exp (- 0.1118MTEMP ) )0.54433.2841 DSRF, MTEMP, CaGLM SI = 51.18 + 0.2128DSRF + 1.2209MTEMP + 0.3438Ca0.63662.9596 NLIN SI = (4.795x10 -8 DSRF 4.7758 ) + (605.4exp (- 0.1164MTEMP ) ) + (1.5121Ca 0.6175 )0.68462.7576 DSRF, MTEMP, Ca, CNRatioGLM SI = 57.68 + 0.1982DSRF + 1.1125MTEMP + 0.3121Ca + 0.7920CNRatio0.64482.9262 NLIN SI = (1.257x10 -6 DSRF 3.9483 ) + (2500exp (- 0.1793MTEMP ) ) + (0.2001Ca 1.1706 ) + (53.36exp (- 0.1524CNRatio ) )0.70652.6598

25. Conclusions Height growth curve have been developed for teak in West Africa Height growth can thus be predicted for teak Phytocentric SI has been developed SI 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.