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International Journal of Forestry Research
Volume 2015 (2015), Article ID 878402, 17 pages
Research Article

Biomass Modelling of Androstachys johnsonii Prain: A Comparison of Three Methods to Enforce Additivity

1Departamento de Engenharia Florestal, Universidade Eduardo Mondlane, Campus Universitário, Edifício No. 1, 257 Maputo, Mozambique
2Department of Forest and Wood Science, University of Stellenbosch, Stellenbosch 7602, South Africa

Received 26 January 2015; Revised 23 March 2015; Accepted 5 April 2015

Academic Editor: Piermaria Corona

Copyright © 2015 Tarquinio Mateus Magalhães and Thomas Seifert. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Three methods of enforcing additivity of tree component biomass estimates into total tree biomass estimates for Androstachys johnsonii Prain were studied and compared, namely, the conventional (CON) method (a method that consists of using the same independent variables for all tree component models, and for total tree model, and the same weights to enforce additivity), seemingly unrelated regression (SUR) with parameter restriction, and nonlinear seemingly unrelated regression (NSUR) with parameter restriction. The CON method was found to be statistically superior to any other method of enforcing additivity, yielding excellent fit statistics and unbiased biomass estimates. The NSUR method ranked second best but was found to be biased. The SUR method was found to be the worst; it exhibited large bias and had a poor fit for the biomass. Therefore, we recommend that only the CON and NSUR methods should be used for further estimates, provided that their limitations are considered, that is, exclusion of contemporaneous correlations for the CON method and consideration of the significant bias of the NSUR method.