African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6853

Full Length Research Paper

Path models-approach to the study of the effect of climatic factors and tree age on radial growth of juvenile Eucalyptus hybrid clones

Sileshi F. Melesse* and Temesgen Zewotir
School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa
Email: [email protected]

  •  Accepted: 30 May 2013
  •  Published: 13 June 2013

Abstract

Due to increasing wood consumption and pulp and paper demands, plantations of fast growing tree species,have a growing importance for the sustainability of industrial wood raw material. Consequently, the efficient utilization of fast growing plantations can have a large impact on productivity. Adequate management requires good understanding of factors affecting tree growth. This study aimed to determine the factors that influence stem radial growth of juvenile Eucalyptus hybrids grown in the east coast of South Africa. Measurement of stem radius was conducted using dendrometers on sampled trees of two Eucalyptus hybrid clones(Eucalyptus grandis × Eucalyptus urophylla, GU and E. grandis × Eucalyptus camaldulensis, GC). Daily averages of climatic data (temperature, solar radiation, relative humidity and wind speed) were simultaneously collected with total rainfall from the site. In this study, path analysis was employed. The joint effect of the climatic variables as well as the direct effect of each climatic variable was studied. Bootstrap estimation procedures, which relax the distributional assumption of the maximum likelihood estimation method, were used. It is found that all variables had a positive effect on stem radial growth. The study showed that tree age is the most important determinant of radial measure.

 

Key words: Bootstrap, cross-validation, dendrometer, maximum likelihood, path analysis, standardized regression weights.