Browsing by Author "Ugo-Mbonu, Nnemaka Amelia"
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Item Tree slenderness coefficients and crown ratio models for Gmelina arborea (Roxb) stand in Afi River Forest Reserve, Cross River State, Nigeria(Faculty of Agriculture, University of Uyo, Nigeria, 2017-03-24) Adeyemi, Adesoji Akinwumi; Ugo-Mbonu, Nnemaka AmeliaTree slenderness coefficient (SC) and crown ratio (CR) have been viewed as major attributes considered most efficient in determining health status of a forest. Hitherto, there is dearth of information on growth characteristics of Gmelina arborea plantation in Afi River Forest Reserve (ARFR), and there was no documented information on health status of the plantation. Therefore, we assessed health and stability of Bateriko Gmelina Plantation within ARFR in Cross River State. Simple random sampling technique was adopted for plot location. Thirty-four (34) sample plots of size 25 × 25 m were used. Tree growth variables including diameter at breast height (Dbh); total height (THT); merchantable height (MHT); stem quality (SQ), crown length (CL) and crown diameter (CD) were measured on all trees with Dbh ≥10 cm to compute CR, SC, basal area (BA) and stem volume (SV). Data were analysed using descriptive statistics and regression analysis. The fitted models were evaluated using significance of regression, co-efficient of determination (R2 ) and root mean square error (RMSE). Model validation was achieved using t-test and mean bias. The results revealed that the mean Dbh (39.35±8.91 cm) was lower than the stipulated minimum for timber in Nigeria. However, the mean BA (74.61±12.74 m2 /ha) was far more than 24 m2 /ha suggested for a well-stocked forest in Nigeria. Most (78.8%) of the stems/ha had low to moderate SC, indicating that most of trees in the plantation were less susceptibility to damage. The best CR model was lnCR = -1.81 - 0.708lnSQ2 + 0.993lnSC + 3.497BA having R2; RMSE and bias values of 0.59, 0.3138, and -0.01±0.08, respectively. For this model, the mean observed and the predicted CR values were not significantly different (P>0.05). Therefore, it is recommended for CR prediction in the study area.