A Regression Modelling Approach for Stem Volume Estimation of Two Exotic Plantations within Dogo-Kétou Forest Reserve, Benin Republic

Abstract

: Stem volume models play an important role in forest management, evaluating the economic value of a forest stand and assisting forest managers and other interested parties in determining the optimal strategies for the utilization and conservation of forest resources. Little attention is given to the use of multivariate regression models for plantation species in the study area. This study involved the development of a multivariate regression equation with continuous and categorical independent variables for simultaneous prediction of merchantable volume for Gmelina arborea and Tectona grandis in Dogo-Ketou Forest Reserve. Simple random sampling technique was adopted for plot location from the selected two plantations. Thirty-one temporary plots of dimension 25m by 25m were selected for complete enumeration in all the two plantations of the same age. Tree growth variables measured included diameter at breast height (Dbh) and merchantable height. All data obtained were analyzed using descriptive statistics and multivariate regression analysis. The predictors for the equation were Dbh, merchantable height and tree species type. The results of the analysis revealed that Gmelina arborea exhibited higher average Dbh and height, wider Dbh and height range, more pronounced positive skewness in Dbh distribution, and more negative skewness in height distribution compared to Tectona grandis . Kurtosis values indicated relatively flatter Dbh and height distributions for both species, with Gmelina arborea showing a more peaked height distribution. Gmelina arborea also showed higher mean volume than Tectona grandis . The multivariate regression model developed is: Volume (m 3 ) = -0.467 + 0.024*(Height) + 2.683*(Dbh) + 0.016 (Tree species) with R 2 of 91.3%. The diameter at breast height (Dbh), height, and tree species were found to be statistically significant predictors for stem volume estimation. The developed model for both plantation species will provide useful basis for yield prediction in the study area.