Additive biomass equations for slash pine trees: comparing three modeling approaches

  • Authors: Zhao, Dehai; Westfall, James; Coulston, John W.; Lynch, Thomas B.; Bullock, Bronson P.; Montes, Cristian R.
  • Publication Year: 2019
  • Publication Series: Scientific Journal (JRNL)
  • Source: Canadian Journal of Forest Research
  • DOI: 10.1139/cjfr-2018-0246

Abstract

Both aggregative and disaggregative strategies were used to develop additive nonlinear biomass equations for slash pine (Pinus elliottii Engelm. var. elliottii) trees in the southeastern United States. In the aggregative approach, the total tree biomass equation was specified by aggregating the expectations of component biomass models, and their parameters were estimated by jointly fitting all component and total biomass equations using weighted nonlinear seemingly unrelated regression (NSUR) (SUR1) or by jointly fitting component biomass equations using weighted NSUR (SUR2). In an alternative disaggregative approach (DRM), the biomass component proportions were modeled using Dirichlet regression, and the estimated total tree biomass was disaggregated into biomass components based on their estimated proportions. There was no single system to predict biomass that was best for all components and total tree biomass. The ranking of the three systems based on an array of fit statistics followed the order of SUR2 > SUR1 > DRM. All three systems provided more accurate biomass predictions than previously published equations.

  • Citation: Zhao, Dehai; Westfall, James; Coulston, John W.; Lynch, Thomas B.; Bullock, Bronson P.; Montes, Cristian R. 2019. Additive biomass equations for slash pine trees: comparing three modeling approaches. Canadian Journal of Forest Research. 49(1): 27-40. https://doi.org/10.1139/cjfr-2018-0246.
  • Keywords: nonlinear seemingly unrelated regression, Dirichlet regression, biomass additivity, heteroscedasticity
  • Posted Date: June 21, 2022
  • Modified Date: June 21, 2022
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