Modelling Environmental Tree Species’ Volume using some selected Skewed Distributions

  • Authors

    • F. A. Hassan OGUN STATE INSTITUTE OF TECHNOLOGY IGBESE OGUN STATE NIGERIA
    • H. O. Ilo Department of Statistics, Ogun State Institute of Technology
    • N. O. Afolabi Department of Statistics, Ogun State Institute of Technology
    • K. A. Akintola Department of Statistics, Oyo State College of Agriculture and Technology, Igbo-ora, Oyo State
    2024-09-30
    https://doi.org/10.14419/8cp3kg21
  • Ecology; Environmental Statistics; Tree Species; Distribution; Goodness of Fit (GOF).

  • Abstract

    Ecological requirements' knowledge in determining tree species' distributions is a precondition for sustainable forest management. Tree species in all regions are threatened by climate change but some are more vulnerable than others. Rightly skewed distributions were used to take care of the environmental data set obtained from FRIN using five species which are: Beech Wood, White Afara, Opepe, Afon and Teak.

    Appropriate statistical tools distributions like descriptive analysis, Akaike Information Criterion (AIC), Goodness of Fit, Probability Functions, Kolmogorov Smirnovt-test, Gamma, Weibull, Log-normal and Beta-weibull were carried out to determine the best distribution for each selected specie in this research.

    It was established from the distributional pattern of the tree species' volume used in this research that the Gamma distribution was a better fit for the Beech wood with the AIC of 617.21, Beta-weibull distribution was a better _t for the White Afara, Opepe and Teak Species with the AIC values of 580.772, 630.84 and 733.60 respectively while the Weibull distribution was a better fit for the Afon specie with the AIC value of 752.07.

    Conclusively, the implication of the analysis is that the Beta-weibull distribution described the tree species' volume best among the four distributions used. In line with the findings, it is recommended that Beta-weibull should be the appropriate distribution to model forest specie when it comes to modelling because of its four (4) parameters and its goodness when fitting.

     

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  • Received date: 2022-08-06

    Accepted date: 2022-08-27

    Published date: 2024-09-30