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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 allo