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Developing Inventory Projection Models Using Empirical Net Forest Growth and Growing-Stock Density Relationships Across US Regions and Species GroupAuthor(s): Prakash Nepal; Peter J. Ince; Kenneth E. Skog; Sun J. Chang
Source: USDA Forest Service, Forest Products Laboratory, Research Paper, FPL-RP-668, 2012
Publication Series: Research Paper (RP)
Station: Forest Products Laboratory
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DescriptionThis paper describes a set of empirical net forest growth models based on forest growing-stock density relationships for three U.S. regions (North, South, and West) and two species groups (softwoods and hardwoods) at the regional aggregate level. The growth models accurately predict historical U.S. timber inventory trends when we incorporate historical timber harvests. The models also project future timber inventory trends when linked to a model of regional timber harvest, forest product markets, and trade, specifically the U.S. Forest Products Module (USFPM) within the Global Forest Products Model (GFPM). The market model also takes into account the timber supply and market impacts of projected trends in U.S. timber inventory, and results show the sensitivity of U.S. regional timber inventory projections to alternative timber market scenarios. Given the parsimonious nature of the model and its simplicity, the developed net forest growth models can be very useful in providing projections of growing-stock inventory trends across U.S. regions and species groups for alternative U.S. and global timber market scenarios.
CitationNepal, Prakash; Ince, Peter J.; Skog, Kenneth E.; Chang, Sun J. 2012. Developing inventory projection Models using empirical net forest growth and growing-stock density relationships across U.S. regions and species group. Research Paper FPL-RP-668. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory. 20 p.
KeywordsForest Inventory and Analysis (FIA), Forest and Rangeland Resource Planning Act (RPA), growing-stock density, growing-stock inventory projection, net forest growth, nonlinear regression, prediction error
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