Earticle

초록

영어

Stem diameter variability is an essential inventory result that provides useful information in forest management decisions. Little has been done to explore the modelling potentials of standard deviation (SDD) and coefficient of variation (CVD) of diameter at breast height (dbh). This study, therefore, was aimed at developing and testing models for predicting SDD and CVD in stands of Pinus caribaea Morelet (pine) in south west Nigeria. Sixty temporary sample plots of size 20 m×20 m, ranging between 15 and 37 years were sampled, covering the entire range of pine in south west Nigeria. The dbh (cm), total and merchantable heights (m), number of stems and age of trees were measured within each plot. Basal area (m2), site index (m), relative spacing and percentile positions of dbh at 24th, 63rd, 76th and 93rd (i.e. P24, P63, P76 and P93) were computed from measured variables for each plot. Linear mixed model (LMM) was used to test the effects of locations (fixed) and plots (random). Six candidate models (3 for SDD and 3 for CVD), using three categories of explanatory variables (i.e. (i) only stand size measures, (ii) distribution measures, and (iii) combination of i and ii). The best model was chosen based on smaller relative standard error (RSE), prediction residual sum of squares (PRESS), corrected Akaike Information Criterion (AICc) and larger coefficient of determination (R2). The results of the LMM indicated that location and plot effects were not significant. The CVD and SDD models having only measures of percentiles (i.e. P24 and P93) as predictors produced better predictions than others. However, CVD model produced the overall best predictions, because of the lower RSE and stability in measuring variability across different stand developments. The results demonstrate the potentials of CVD in modelling stem diameter variability in relationship with percentiles variables.

목차

Abstract
 Introduction
 Materials and Methods
  The Data
  Model Formulation
  Goodness-of-fit Criteria
 Results
  Data Summary
  Models for Predicting SDD and CVD
  Prediction Equation for P24 and P93
 Discussion
 Conclusion
 References

키워드

저자

• Peter Oluremi Adesoye [ Department of Forest Resources Management, University of Ibadan, Ibadan 200005, Nigeria ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  Journal of Forest and Environmental Science [산림과학연구]

• 간기

  계간

• pISSN

  2288-9744

• eISSN

  2288-9752

• 수록기간

  1981~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 526 DDC 634