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Construction of Reducible Stochastic Differential Equation Systems for Tree Height–Diameter Connections

Author

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  • Martynas Narmontas

    (Agriculture Academy, Vytautas Magnus University, 53361 Kaunas, Lithuania)

  • Petras Rupšys

    (Agriculture Academy, Vytautas Magnus University, 53361 Kaunas, Lithuania
    Department of Mathematics and Statistics, Vytautas Magnus University, 53361 Kaunas, Lithuania)

  • Edmundas Petrauskas

    (Agriculture Academy, Vytautas Magnus University, 53361 Kaunas, Lithuania)

Abstract

This study proposes a general bivariate stochastic differential equation model of population growth which includes random forces governing the dynamics of the bivariate distribution of size variables. The dynamics of the bivariate probability density function of the size variables in a population are described by the mixed-effect parameters Vasicek, Gompertz, Bertalanffy, and the gamma-type bivariate stochastic differential equations (SDEs). The newly derived bivariate probability density function and its marginal univariate, as well as the conditional univariate function, can be applied for the modeling of population attributes such as the mean value, quantiles, and much more. The models presented here are the basis for further developments toward the tree diameter–height and height–diameter relationships for general purpose in forest management. The present study experimentally confirms the effectiveness of using bivariate SDEs to reconstruct diameter–height and height–diameter relationships by using measurements obtained from mountain pine tree ( Pinus mugo Turra) species dataset in Lithuania.

Suggested Citation

  • Martynas Narmontas & Petras Rupšys & Edmundas Petrauskas, 2020. "Construction of Reducible Stochastic Differential Equation Systems for Tree Height–Diameter Connections," Mathematics, MDPI, vol. 8(8), pages 1-21, August.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:8:p:1363-:d:399014
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    References listed on IDEAS

    as
    1. Joe, Harry, 2008. "Accuracy of Laplace approximation for discrete response mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5066-5074, August.
    2. Masae Iwamoto Ishihara & Yasuo Konno & Kiyoshi Umeki & Yasuyuki Ohno & Kihachiro Kikuzawa, 2016. "A New Model for Size-Dependent Tree Growth in Forests," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-18, April.
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