111 Comparison of a forest process model (3-PG) with growth and yield models to predict productivity at Bago State Forest, NSW P.K. Tickle 1 , N.C. Coops 2 and S.D. Hafner 1 1 Bureau of Rural Sciences (BRS), PO Box E11, Kingston, ACT 2604. Australia Present address: Raytheon Australia. Level 2 15 National Ct Barton 2600, Email: ptickle@raytheon com.au 2 CSIRO Forestry and Forest Products, Private Bag 10, Clayton South 3169, Melbourne Australia Revised manuscript received 15 February 2001 Summary In this paper predictions from a process model, based on the Physiological Principel Predicting Growth (3-PG) model, are compared with those of two conventional growth and yield models. A number of forest growth variables are compared including the standing volume, mean diameter at breast height (DBH), and stocking over 50 000 ha of native eucalypt forest in south-eastern Australia. Stand variable predictions at 22 permanent plot locations, using a locally calibrated empirical growth model and 3-PG were highly correlated with field estimates derived from plot data. 3-PG predictions of standing volume, diameter at breast height (DBH) and stocking explained 86%, 59% and 89% of the variance respectively, compared to the local empirical model which explained 84%, 59% and 78% of the variance in predictions of the same variables. A generic forest growth model explained only 6% of the variance in standing volume predictions. A number of methods of estimating maximum potential standing volume across the landscape were also compared. The estimates over an 80-year rotation varied by as much as 24% for a forest stratum, and by as much as 40% at the stand level. Results suggest that significant improvements in local and regional prediction of forest growth may be gained by augmenting information derived from aerial photography and limited field inventory, with predictions made from process models such as 3-PG. The utility of process models to predict forest growth variables at specific stand ages, and their capacity to be extrapolated across the landscape using geographic information system (GIS) technology, now offer operational potential for use in routine forest management and planning. Keywords: Forest growth model; physiological processes; forest productivity; site index; spatial; GIS; landscape scale Introduction A key variable in the sustainable management of both native and plantation forests is an accurate projection of growth and timber yield at a range of spatial and temporal scales. Landsberg and Coops (1999) suggest three main types of models have been developed to deal with different aspects of, and approaches to, forest productivity. These are conventional growth and yield models, based on statistical relationships derived from long-term measurements on trees (Ek and Monserud 1979; Campbell et al. 1979; West and Mattay 1993); gap models (Shugart 1984; Bugmann et al. 1996) concerned with species succession and dynamics; and carbon balance or biomass models (Landsberg and Gower 1997) which predict net primary productivity (PN) using climatic and edaphic variables. Growth and yield models, which are statistical descriptions of patterns of tree growth, determined by measurements made in forests over time have been the conventional tools used to predict forest production. In Australia, these estimates are traditionally extrapolated across the forest estate using aerial photo mapping of appropriate strata (Skidmore et al. 1987; Black 1996; Victorian Department of Natural Resources and Environment 1999). The past two decades have seen considerable progress in developing process-based models to predict current and potential forest productivity. These process-based models aim to simulate the growth of stands in terms of the underlying physiological processes and the way stands are affected by the physical conditions to which trees are subject and with which they interact. Process-based models have the potential to be far more flexible than empirical relationships and can be used in a heuristic sense to evaluate the consequences of change and the likely effects of stimuli (Landsberg and Gower 1997). In general, these models have proved useful for integrating different processes and scales of knowledge, for honing research hypotheses, and for making broad predictions of relative productivity regionally or under different environmental change scenarios (Coops and Waring 2000). Despite the potential for process-based models to contribute to forest management goals, there has been little operational adoption by forest management agencies. This may be largely attributed to the fact that process- models have focused on producing estimates of total biomass production, rather than variables of interest to forest managers such as basal area (BA), stem volume and stocking (Landsberg and Waring 1997). In addition, until recently, the detailed information and powerful computing systems required to run complex process models has not been commonly available at the forest management level, and the models have not generally been available in user-friendly forms. In this paper, we detail the use of 3PG-SPATIAL, a Geographic Information System (GIS) based implementation of the 3-PG model (Landsberg and Waring 1997) to make fine-scale, spatially explicit predictions of standing volume, mean diameter at breast height (DBH) and stocking over 50 000 ha of native eucalypt forest in south-eastern Australia. These predictions are then compared, at a series of plots (Ryan et al. 2000), to conventional forest growth and yield predictions. These conventional approaches utilise yield prediction curves developed either by Lindsay (1939) from regional yield and volume data for Eucalyptus delegatensis (alpine ash) or a set of equations developed by West and Mattay (1993) from national datasets for a number of species including E. delegatensis. In addition, comparisons are made using regional forest type information derived from 1:25 000 scale mapping to extrapolate empirical yield prediction curves.