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ABSTRACT. Estimates of national-scale forest carbon (C) stocks and fluxes are typically based on allometric regression equations developed using dimensional analysis techniques. However, the literature is inconsistent and incomplete with respect to large-scale forest C estimation. We compiled all available diameter-based allometric regression equations for estimating total aboveground and component biomass, defined in dry weight terms, for trees in the United States. We then implemented a modified meta-analysis based on the published equations to develop a set of consistent, national-scale aboveground biomass regression equations for U.S. species. Equations for predicting biomass of tree components were developed as proportions of total aboveground biomass for hardwood and softwood groups. A comparison with recent equations used to develop large-scale biomass estimates from U.S. forest inventory data for eastern U.S. species suggests general agreement (+ or -30%) between biomass estimates. The comparison also shows that differences in equation forms and species groupings may cause differences at small scales depending on tree size and forest species composition. This analysis represents the first major effort to compile and analyze all available biomass literature in a consistent national-scale framework. The equations developed here are used to compute the biomass estimates used by the model FORCARB to develop the U.S. C budget. FOR. Sci. 49(1):12-35.
Key Words: Allometric equations, forest biomass, forest inventory, global carbon cycle.
RESEARCHERS IN VARIOUS COUNTRIES have developed national-scale forest carbon (C) budgets to increase understanding of forest-atmosphere C exchange at large scales and to support policy analysis regarding greenhouse gas reductions (Birdsey and Heath 1995, Turner et al. 1995, Kauppi et al. 1997, Nabuurs et al. 1997, Kurz and Apps 1999, Nilsson et al. 2000). These C budgets have been based primarily on regional forest inventory data, which provide a good representation of forest conditions and trends when the data are based on extensive networks of sample plots that are remeasured periodically. In the United States, the USDA Forest Service's Forest Inventory and Analysis (FIA) sampling design includes a network of plots chosen to represent conditions across the landscape. In the past, the plots were periodically measured; however, an annualized design was recently adopted. In either design, plot-level information is computed directly from individual tree characteristics, such as diameter at breast height (dbh) and species, which are...