Allometry for estimating aboveground tree biomass in tropical and subtropical eucalypt woodlands: towards general predictive equations
Richard J. Williams A B H , Ayalsew Zerihun C D , Kelvin D. Montagu C D , Madonna Hoffman E D , Lindsay B. Hutley F B and Xiaoyong Chen F GA CSIRO Sustainable Ecosystems, PMB 44 Winnellie, NT 0810, Australia.
B Cooperative Research Centre for Tropical Savanna Management, Charles Darwin University, Darwin, NT 0909, Australia.
C NSW Department of Primary Industries, Science & Research, PO Box 100, Beecroft, NSW 2119, Australia.
D Cooperative Research Centre for Greenhouse Accounting, GPO Box 475, ACT 2601, Australia.
E Queensland Department of Primary Industries and Fisheries, PO Box 6014, Rockhampton, Qld 4702, Australia.
F School of Science and Primary Industries, Charles Darwin University, Darwin, NT 0909, Australia.
G Current address: Department of Earth and Environmental Science, Okanagan University College, 3333 College Way Kelowna, British Columbia V1V 1V7, Canada.
H Corresponding author. Email: dick.williams@csiro.au
Australian Journal of Botany 53(7) 607-619 https://doi.org/10.1071/BT04149
Submitted: 17 Sept 2004 Accepted: 4 July 2005 Published: 29 November 2005
Abstract
A fundamental tool in carbon accounting is tree-based allometry, whereby easily measured variables can be used to estimate aboveground biomass (AGB). To explore the potential of general allometry we combined raw datasets from 14 different woodland species, mainly eucalypts, from 11 sites across the Northern Territory, Queensland and New South Wales. Access to the raw data allowed two predictor variables, tree diameter (at 1.3-m height; D) and tree height (H), to be used singly or in various combinations to produce eight candidate models. Following natural log (ln) transformation, the data, consisting of 220 individual trees, were re-analysed in two steps: first as 20 species–site-specific AGB equations and, second, as a single general AGB equation. For each of the eight models, a comparison of the species–site-specific with the general equations was made with the Akaike information criterion (AIC). Further model evaluation was undertaken by a leave-one-out cross-validation technique. For each of the model forms, the species–site-specific equations performed better than the general equation. However, the best performing general equation, ln(AGB) = –2.0596 + 2.1561 ln(D) + 0.1362 (ln(H))2, was only marginally inferior to the species–site-specific equations. For the best general equation, back-transformed predicted v. observed values (on a linear scale) were highly concordant, with a slope of 0.99. The only major deviation from this relationship was due to seven large, hollow trees (more than 35% loss of cross-sectional stem area at 1.3 m) at a single species–site combination. Our best-performing general model exhibited remarkable stability across species and sites, when compared with the species–site equations. We conclude that there is encouraging evidence that general predictive equations can be developed across sites and species for Australia’s woodlands. This simplifies the conversion of long-term inventory measurements into AGB estimates and allows more resources to be focused on the extension of such inventories.
Acknowledgments
The Tropical Savanna Management CRC and the Greenhouse Accounting CRC, the Australian Greenhouse Office and the Bureau of Rural Resources all supported the work financially. There were many helpers in the field. We thank all landholders whose support enabled the study. NT Department of Business Industry and Rural Development, in particular Don and Judy Cherry of Kidman Springs Research Station, provided resources and support in the NT. We thank Garry Cook, Harold Hopman and two anonymous referees for their comments on the manuscript.
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