MODELS FOR VOLUME ESTIMATION OF Tectona grandis STANDS AT OKE-ERI PLANTATION IN IJEBU-ODE, OGUN STATE, NIGERIA

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1 MODELS FOR VOLUME ESTIMATION OF Tectona grandis STANDS AT OKEERI PLANTATION IN IJEBUODE, OGUN STATE, NIGERIA Ureigho, U. N. and Akpobome, W. E. Department of Forestry and Wildlife, Delta State University, Asaba Campus ABSTRACT Forest models have been used as a very successful research and management tools. Yield is an important component in the development and implementation of sustainable forest management practices. The development of effective and accurate model to predict forest yield is essential for forest managers and planners. The main objective of this research work is to predict yield in stands of Tectona grandis at Okeeri plantation in IjebuOde, Ogun state. Twentyfive (25) temporary sample plots of 20 20m were laid in the plantations with age ranges from 11, 12 to 13years. Complete enumeration and measurement of all the trees were carried out in each sample plot. The growth data collected include diameter at breast height (Dbh), diameter at the base (Db), diameter at the middle (Dm), diameter at the top (Dt) and tree height. These were measured to estimate the tree volume using Newton s formula and also to estimate the tree basal area using the universal basal area formula. Six volume and six basal area models were developed and fitted into the data collected from the plantation. Based on the comparison of the statistical criteria such as the lowest Standard error of estimate and highest R 2 value, the volume models developed show that models 1 and 8 were found more suitable for yield prediction and very adequate for recommendation to be used for yield prediction in stands of Tectona grandis at Okeeri plantation, IjebuOde, Nigeria. Keywords: Volume, Basal area, Models, Tectona grandis Key words: Models, volume, Tectona grandis, plantation, Ogun State, Nigeria INTRODUCTION In recent decades there has been a growing interest towards Tectona grandis (L.F) plantations. Tectona grandis is better known with the common name teak. T. grandis is considered as a highly valuable timber material in furniture production, ship building industry and constructions (Malmstrom 2013). T. grandis species have been seen as an interesting investment target in Nigeria and other African countries due to its fast growth rate and its high demand in the wood global market. This result to it s over exploitation in the natural forest and also paved a way for the recent cultivation of teak in forest reserves and private plantations due to the uncontrollable illegal logging practices. (Kellert and Cherubini, 2012). According to Amaranthus (1997), forest management is difficult to define and when defined, it s hard to measure; this makes the picture very clear that sustainable management of the forest resources requires a large amount of supporting information from inventory especially when managing a forest for production of commercially valuable materials and estimation of some variables which cannot be measured directly from the fields. Stand growth models are abstractions of the natural dynamics of a forest stand, which may encompass growth, mortality and other changes in stand composition and structure (Vanclay, 2002). Therefore, forest models can be used as a very successful research and management tools. The models designed for research requires many complicated and not readily available data, whereas the models designed for management uses simpler and more readily accessible data (Nurudeen, 2011). Forest manager s monitors stand growth and prescribe silvicultural treatments due to the potential value of the plantations. Basal area plays a high degree of exactness in measuring and predicting concept. It is applicable to a wide range of conditions and at times point to the true causes and effects of underlying superficial appearances. Basal area is of interest for forest inventories because it is highly correlated with volume and growth of forest stand. Yield prediction is important for decision making and sustainable management of forest resources. Bole volume estimates are useful in forest inventory because the volume of timber is the basic management unit of forest (Chaidez, 2009). Forest volume dictates the allocation of forest productions such as pole for fences, sawn wood, pulp and paper and plywood. Estimation of wood volume enables calculation of monetary value of commodities and services the forest provides to society for forest management and planning purposes at natural and plantation level. It is vital to know the volume of the wood resources and their rate of growth (Atriell et al., 2010). Diamantopolou and Ball (2006) saw tree volume prediction as an integral part of forest growth and yields forecasting. Ability to estimate tree growth and volume can provide forests owners understanding actions and policies (Philip 1997). Modeling is an essential tool for forest management and planners as it aids in the decision NJAFE VOL. 14 No. 1,

2 making process. On the other hand, volume equations which are mathematical expressions help to relate tree volume to tree measurable attributes (Avery and Burkhart, 2002). However, in order to do this, management goals must be formulated, effective treatment options capable of producing the desired results must be found and outcomes of treatment in the productive system must be described (Lund, 1985). Growth and yield models are generally used to predict the temporal development of forest stands. Growth and yield are important component in the development and implementation of sustainable forest management practices. Models are particularly useful for testing longterm management strategies such as the management of current stands for mid and long term timber supply. Although models use variety of different approaches to predict forest growth, most are constructed by linking together submodels that correspond to specific biological process. To be useful, a model must include the full range of identified process within the system being modeled (EK et al., 1997). In unmanaged forest system, a process that constitutes a major challenge to modeling is natural regeneration. Volume and basal area which are functions of yield models are obtained from measurement data. They are tools that have mainly been used to provide information for decision making that meets basic operational needs for evaluating various forest management scenarios (Avery and Burkhart, 2002). The prediction of Tectona grandis stand development in the west has overtime relied on accurate volumebasal area data collection (Nurudeen, 2014) as basic input variables for present estimation and future prediction. But for the Okeeri plantation, there hasn t been any documented work of this nature done by pundits in forestry which therefore justified the need to present a competent model for volume and basal area prediction of its teak stands. This will in no doubt play a predominant role in achieving a better management scheme for the teak plantation in the stands of Tectona grandis at Okeeri plantation. Furthermore, this research work will aim at isolating the most appropriate model amongst other explored models which will then form a consistent methodology for modeling relative tree volume and basal area of Tectona grandis in the stands of Tectona grandis at Okeeri plantation. MATERIALS AND METHODS The study area is the Okeeri Teak Plantation in IjebuOde north, Ogun state, Nigeria. Located along the equator on latitude of 11 0 N 23 1 E and longitude 12 0 N 23 1 E.The vegetation is a tropical rain forest zone with a mean annual rainfall of 1581mm. The driest month is December which has 14mm of precipitation and the most precipitation falls in June with an average of 268mm. Notable timber species that are prominent in this area includes; Tectona grandis, Gmelina arborea, Terminalia species, Parkia biglobosa and Mansonia altissima. Data collection Sampling was done within the area where there are existing stands of Tectona grandis in the study area. Sampling designs The sampling design is determined by the kind of sampling units employed, the manner of selecting the sampling units and distributing them over the study area as well as the procedures and analysis of the results. Stratified random sampling also known as restrictive random sampling was adopted for this study. This is because stratified random sampling is applicable to small or medium size forest area in which strata can be recognized due to heterogeneity and the stratification criterion was based on ages distributed. Each subject was numbered within each stratum with unique identification number, by dividing it into sample units (plots) of 20m x20m (0.04ha). The plots sizes (20m x 20m) were randomly laid in each stratum with all the trees measured in each plot. Variables measured Measurements were limited to Tectona grandis plantation established at Okeeri Bisrod plantation in Ogun state and the following were the variables measured during this work. 1. Diameter at breast height over bark, diameter breast at height is the stem diameter at the standard position of 1.3m about the ground level using a diameter girth tape. 2. Diameter at the base (cm): This will be measured with the aid of the diameter girth tape (DGT). 3. Diameter at the top and middle (cm): This will be measured with the aid of a Spiegel relaskop. 4. Total Height (m): This is the height of all trees in the stand using Spiegel relaskop. 5. Merchantable Height: This is the height of the last usable portion of the tree and it will be measured using Spiegel relaskop and a 50cm distance tape. 6. Mean Dominant Height: this is the height of four largest trees in a plot representing the largest trees per hectare. Age and size of plantation The Tectona grandis plantation at Okeeri Teak Plantation will be divided into strata based on age criterion. The ages were 2006, 2007 and The various ages of the plantation constitute the strata and each stratum will be divided into a plot of 20m x 20m respectively. Complete enumeration of all trees will be carried out in each of the plot selected. The corresponding details of the plantation are presented in the table below: NJAFE VOL. 14 No. 1,

3 Table 1: Age of hectare and sample plots Variables Stratum 1 Stratum 2 Stratum 3 Ages No of Sample plot Hectares Area of Plantation (Plot) = 20m x 20m The data collected was subjected to regression analysis in order to select the best, more suitable and fitted model (s) with the highest R 2 values (coefficient of determination) and lowest MSE (Mean Square Error) values for volume and basal area respectively. Basal area estimation The basal area of each tree was estimated using the formula Where BA = Basal area (m 2 ), D=diameter at Breast Height The total BA for each plot was obtained by adding all trees BA in the plot. BAP = BA tree The mean BA for the plot was calculated with the formula, Where BAP =mean basal area per plot n = number of plots or sampling unit. Basal area per hectare will be obtained by multiplying mean basal area per plot with number of plots per hectare (25 plots) BA ha = BAP x 25 Where BAha = Basal area of hectare; BAP =mean basal area per plot. Volume estimation The volumes for each tree in each sample plot were estimated using Newton s formula developed by Hutsch et al. (2003) Where v = volume (m 3 ) h = height (m) Db = diameter at the base Dm = diameter at the middle Dt = diameter at the top and The plot volume was obtained by adding the volumes of all the trees in the plot (VP) while mean plot value was obtained by dividing the total plot volume by number of sample plots. Volume of trees per hectare (Vha) was estimated by multiplying the number of plots in a hectare Vha = VP x 25 Models tried The following regression models were tried in the course of this work. Volume Models generated V= b 0 +b 1B a+b 2B a 2 +b 3B a 3 equation 1. V= b o +b 1InB a+b 2 (InB a) 2 = b 3(InB a) 3 equation 2. V = b 0 + b 1Dbh + b 2Dbh 2 + b 2Dbh 3 equation 3. V = b 0 + b 1(InDbh) + b 2(InDbh) 2 + b 3(InDbh) 3 equation 4. V = b 0 + b 1H + b 2H 2 + b 3H 3 equation 5. V= b 0 +b 1(InH) + b 2(InH) 2 + b 3(InH 3 ) equation 6. Nurudeen, (2014) Where v = volume (m) 3, Ba = Basal area (m) 2, Dbh = Diameter at breast height (m), h = tree height (m), In = natural logarithm Basal Area models generated BA= b 0+ b 1D b+b 2D m+b 3D t equation 7. LogBA= b 0+ b 1LogD b+b 2LogDmb3LogD tequation 8. B = b o + b 1 (In 1/A) + b 2 (In 1/H) + b 3 (In1/N) equation 9. B = b o + b 1log (ln 1 / A) + b 2log (ln 1 / 4) + b 3log (ln 1 / N) equation 10. BA=b o+b 1 (In1/A)b 3 (In1/H) equation 11 BA=b o+b 2 (LogIn1/A)b 3 (LogIn1/H) equation 12 Where; B = Basal Area, A = Ages of the different sampling units consisting the strata H = Height of trees in meters, N = number of trees per hectare b o, b 1, and b 3 = parameters in the equation. NJAFE VOL. 14 No. 1,

4 Assessment of the models (Statistical test used) The basal area and volume models were assessed based on Significance of regression (f. ratio), Coefficient of determination (R 2 ), Standard Error of estimate (SEE) and Percentage Bias Estimation with view to recommending those with good fit for further uses. For this work, 10% of the data was used for validation set while the rest of the data will be used in calibrating the models. RESULTS The summary the growth data is shown in table 2. Table 3 gives the volume model of best fit for yield prediction. This was based on the comparison of lowest standard error (SE) and highest Coefficient of Determination (R 2 ). The results of the Models analyzed are also presented. Result of volume models The result showed that Standard Error values ranged from to and R 2 values ranged from to (Tables 3 and 4. The quality of the model was judged by the highest R 2 value and the lowest standard Error. Based on these criteria, Equation 1 had the highest R 2 value of and lowest Standard Error of as shown in Table 5 Table 2: Summary of tree growth data for Tectona grandis in Okeeri plantation, IjebuOde, Ogun State. Age (Years) Number of Mean Diameter Basal Areal Volume/Hectare Volume/stem M 3 stems per at breast Height Hectare (M 3 /Ha) hectare (N) (cm) (M 2 /Ha) Table 3: Parameter estimates from volume models Models R 2 SEE b 0 b 1 b 2 b 3 Model Model Model Model Model Model Table: 4 Developed models for volume estimation in Tectona grandis Model 1 V= Ba+18.40Ba Ba 3 ( R=0.915, R 2 = 0.838) Model 2 V= (InBa)+5.72(InBa) (InBa) 3 (R=0.915, R 2 = 0.835) Model 3 V= Dbh+29.43Dbh Dbh 3 (R=0.913, R 2 = 0.834) Model 4 V= (InDbh)+(InDbh) 2 *+(InDbh) 3 * (R=0.825, R 2 = 0.680) Model 5 V= H11.03H 2 * H 3 (R=0.552, R 2 = 0.305) Model 6 V= (InH)*+(InH) 2 *+4.00(InH) 3 (R=0.525, R 2 = 0.274) N.B:* implies that the component has no relative significance on the model itself. Prediction Table 5 shows that the mean volume value observed at the field is cm 3 and the present value is cm 3. This result shows that the observed and present values are not significantly different which implies that the model is accurate. The result shows that in the next ten (10) years, it is expected that the yield in volume would have increased from cm 3 to cm 3 and that after the next ten (10) years and ten (10) years above, the yield in volume must have increased from cm 3 to cm 3 and cm 3 consecutively. Table: 5 Observed and predicted volume of trees Models Observed(m 3 ) Present Predicted(m 3 ) Model NJAFE VOL. 14 No. 1,

5 Result of volume model validation The result of the validation test showed that models 1 gave the best fit with a % bias 0f 24.04% which is less than 30%. Hence is recommended for yield prediction of Tectona grandis stands in Okeeri plantation, IjebuOde, Nigeria. Table 6: Model validation Models TTest Value Significance Bias (%) Remarks Model Significant Basal area models Table 7 below gives the basal area model of best fit for yield prediction. This was based on the comparison of lowest standard error (SE) and highest Coefficient of Determination (R 2 ). The results of the Models analyzed are presented below: Table 7: Basal area model assessment Models R 2 SEE b 0 b 1 b 2 b3 Model Model Model Model Model Model Basal area models The result showed that Standard Error values ranged from to and R 2 values ranged from to The quality of the model was judged by the highest R 2 value and the lowest standard Error. Based on these criteria, Equation 1 gave the highest R 2 value of and lowest Standard Error of as shown in Table 8 below. Table 8: Models for basal area estimation of Tectona grandis stands Model 7 BA= D b d m2.546d t (R=0.950, R 2 = 0.903) Model 8 LogBA= LogD b+0.307logd m0.012d t (R=0.967, R 2 = 0.935) Model 9 BA= A+0.02H (R=0.467, R 2 = 0.218) Model 10 BA= (1/A)12.08(1/H) (R=0.448, R 2 = 0.201) Model 11 BA= (In1/A)0.43(In1/H) (R=0.460, R 2 = 0.212) Model 12 BA= (LogIn1/A)3.34(LogIn1/H) (R=0.457, R 2 = 0.209) Basal area prediction Table 9 below shows that the mean basal area value observed at the field is 3.328m 3 and the present value is m 3. This result shows that the observed and present values are not significantly different which implies that the model is accurate. The result shows that in the next twenty (20) years, it is expected that the yield in volume would have increased from m 3 to 4.06m 3 and that after the next twenty (20) years and twenty (20) years above, the yield in basal area must have increased from 4.06m 3 to 4.31m 3 and 4.38m 3 consecutively. Table 9: Observed and predicted basal area Predicted LogBA by Years Model 1 Observed Present Model *Observed BA= 10^3.328=21.281(m 2 ) *Predicted BA=10^3.2544=21.281(m 2 ) Basal area model validation. The result of the validation test showed that models 8 gave the best fit with a % bias of 2.24% which is less than 30%, also there is no significant difference between the result of model prediction and model validation at a T. value of and a P value NJAFE VOL. 14 No. 1,

6 Table 10: Model validation result Models TTest Value Significance Bias (%) Remarks P 0.05 Model Not Significant DISSCUSSION The efficiency of models for estimating and predicting yield in stands of Tectona grandis at Okeeri Bisrod teak plantation, IjebuOde was obtained in this study. The tree yield variables measured in the study area shows that the total basal area observed per hectare was m 2 while the total volume were found to be m 3 per hectare. The value obtained for basal area is an indication of a wellstocked forest which is in line with the findings of (Alder and Abayomi 1994). The mean Dbh and height observed (1.26m & 30.56m) is an indication that most of the trees encountered in the study area are above minimum merchantable size of 48cm stipulated by logging policy of south western Nigeria. The skewness and kurtosis (Table 2) for DBH and height were found to be very low. This agreed with the findings of (Nurudeen 2011) who reported low skewness and kurtosis as an indication of right tailed distribution and also the evidence of a good stock of a stand. In this study coefficient of determination (R 2 ), significant of regression and standard error of estimate was computed in order to select the best models. The assessment criteria revealed that for Tectona grandis plantation model 1 was discovered best to have good fit for the volume models and model 8 for the basal area models and are very suitable for yield estimation and prediction in the study area due to their highest R 2 value (0.838)/ (0.935) and with the lowest SEE value (1.996)/ (0.069) respectively. Model 2, 3 and 4 were found to be averagely good with their coefficient of determination (R 2 ) to be 0.835, and respectively (Table 2). The good fit exhibited by model 1, 2 and 3 were similar to those used by Adegbehin (1985) for Pinus carribea and Eucalyptus cloeziana stands and Nokoe (1980) for some plantations species. The parameter estimates from the model (Table 6) shows that model 1 and 6 have negative intercept. This is in accordance with the findings of Avery and Burkhart (2002) and (Nurudeen, 2014) who agreeably reported that volume predictions usually give negative intercept. Similar findings were observed by Adekunle (2007) who reported that index of fit, R and R 2 values were high and significant fratio at P<_=0.05 was obtained for nonlinear models for volume estimation in natural forest and thus found to have good fit and therefore suitable for use within the context of the field data used. The model 5 and 6 were found not suitable even though basal area was used as independent variable. This is contrast with the findings of Osho 1998 and Daniel et al (1979) who replace age with diameter during model generation in their study. Model 1 was found to be more suitable despite the fact that height was used as independent variable Laiho et al (1995) noted that the height structure is highly heterogeneous and so its determination in practical forestry is not meaningful and also very rare on research plot. The validation result for selected models using T test and percentage bias shows that most of the models are significant and have a low percentage bias varying from % (Table 5). Similar findings were observed by Adekunle (2007) and Nurudeen (2014) who reported that the percentage bias less than 30% is an indication of good fit models. This result suggests that model 1 which gave the best fit is most suitable for yield estimation in the study area. CONCLUSION Models 1 and 8 were found to be more suitable and fit for yield prediction based on the models evaluated or examined in this study. However, Models 2 to 7 were not too suitable in the study because of the low R 2 values and high standard error. Therefore, all categories of regression models generated in this study with good fit are recommended for yield prediction in plantation of Tectona grandis in Okeeri Bisrod teak plantation, IjebuOde South Western Nigeria. Many tropical afforestation program can be enhanced with these models which will eventually contribute to quality decision making with regards to silviculture and other aspects of plantation management. Finally, It is recommended that all categories of regression models generated in the study with good fit be used for yield prediction of Tectona grandis in the study area. REFERENCES Adegbehin J. O. 1985: Growth Prediction in some Plantation of exotic species in the savanna zone of Nigeria. Unpublished Ph.D. Thesis, University of Ibadan, Nigeria. 328p. Adekunle V.A.J, Non Linear Regression Model for Timber Volume Estimation in Natural Forest Ecosystem, Southwest Nigeria. Research Journal of Forestry, 1(2): Alder, D. and Abayomi, J.O Assessment of data requirement for sustained yield calculations. A consultancy Report prepared for the Nigerian Tropical Action Plan FORMECU, Federal Department of forestry, Ibadan. 26p. NJAFE VOL. 14 No. 1,

7 Amaranthus, M. R 1997; Forest Sustainability; and Approach to Definition and Assessment. Gen. Tech. Rep. PNW GTR416. Portland or; USDA Forest Service, Pacific Northwest Research Station. 14. Atriell, O. N., Philip, N. M. and Shater, M. O Validation of Regression Models: Methods and examples. Technimetrics, 19: Avery, E. T and Burkhart, H. E. 2002: Forest Measurements. 5 th edition. McGraw Hill New York, U.S.A. 456p. Chaidez, R. E Bole Volume Estimation using Regression Models on teak in Tanzania Forest Reserve. Tanka. pp Daniel, V., Helms, J. A. and Baker F. S. (1979). Principles of Silviculture. 2nd Edn., McGrawHill Book Company, New York, 500. Diamantopolou, E.A. and Ball, R. D., (2006): A generalized height diameter, model including random component for radiate pine plantation in northwestern Spain. 229: EK, A. R. and Monserud, R. A Trials with programme Forest: Growth and Reproduction Simulation for mixed species even or unevenaged forest stand. Proceedings of the IUFRO, IEEE Xplore pp Evans, Plantation forestry in the tropics: Tree planting for industrial, social, environmental, and Agroforestry purposes. Oxford University Press. New York. 403p. Hutsch, B., Beers T. W. and Kershaw, J. A. Jr., Forest Mensuration. 4 th edn; John Wiley and Sons, Inc., New Jersey, U.S.A. 949p. Kellert, S. F and Cherubini, T. N Forest Sustainability; and Approach to Definition and Assessment. Gen. Tech. Rep. PNWGTR416. Portland or; USDA Forest Service, Pacific Northwest Research Station. 14. Laiho, O., Lahde, E. A., Norokporpi, Y. and Saksa, T., Stand structure and the associated Terminologies: Recent Advances in Forest Mensuration and Growth and Yield Research. Proceedings of the 20 th World Congress of IUFRO, Finland, pp Lund, H. G. 1985: Designing Inventories to Support Multiple Decisions in Forest Resource. Inventories Appraisal tool for decision making proceedings, Venice, Italy. pp Malmstrom, M Optimal harvesting of Tectona grandis plantation stands in Costa Rica. University of Helsinki, Department of forest Science pp Nokoe, E. 1980: Demonstrating the Flexibility of Gompertz function as a yield. Man and Biosphere Series, 6: Nurudeen, T. A 2014: Regression Models for Tree Volume Prediction in Stands of Tectona grandis (Linn) at Federal College of Forestry, Jericho, Ibadan, Oyo state, Nigeria. 233: 19 Nurudeen, T.A 2011: Non Linear Regression Models for Volume Estimation in Gmelina arborea (Roxb) Stands at Oluwa Forest Reserve South Western Nigeria. Unpublished M.Sc., Thesis, Department of Forest Resource Management, University of Ibadan, Nigeria. 78p. Osho, J. S. A.1998: Tree population dynamics in a tropical Moist Forest in SouthWestern Nigeria. Unpublished Ph.D. Thesis, Department of Forestry Resource Management University of Ibadan, Ibadan, Nigeria. pp Palanisamy, K., Gireesan K., Nagarajav V. and Hedge M Clonal forestry for improvement of productivity in teak. pp Philip, G. B Growth functions for Tectona grandis (Linn. F) Plantation in Sri Lanka common W. Forest Rev., 74(4): Vanclay, J. K Sustainable timber harvesting: simulation studies in the tropical rainforests of north Queensland. Forest Ecology and Management, 69: NJAFE VOL. 14 No. 1,