Dynamic Structural Stand Density Management Diagrams for even-aged natural stands and plantations

Abstract

A forest stand model that is able to account for individual stand characteristics and uses this information for state prediction, growth and yield projection and for management decisions at individual-stand level can be expected to possess the best properties and be of the highest utility. The aim of our study was to combine the advantages of the graphically presented whole-stand models called Stand Density Management Diagrams (SDMDs) with those of the state-space models to develop an improved, stand-specific density management model and to examine its performance with data from even-aged natural stands and plantations.A dynamic, structural Stand Density Management Diagram (DSSDMD) consisting of a whole-stand model and distribution sub-models was developed. The whole-stand model is composed of a state vector and transition functions and is presented diagrammatically by four sets of isolines on a density-total volume/biomass chart. The state, developmental scenarios and thinning schedules of any stand are simulated on its individual DSSDMD and are characterized by three principal local model parameters, two of which are stand-specific and the third is common to a range of stands of the species under the same environmental conditions.Dynamic Structural Stand Density Management Diagrams (DSSDMDs) were constructed for plantations of two pine species (Pinus radiata D. Don and P. sylvestris L.) and for natural even-aged stands of Quercus robur L. and Betula pubescens Ehrh. The goodness-of-fit tests revealed that, in most cases, regression equations explained more than 95% of the variation in the modelled variable and yielded a Root Mean Square Error <15% and bias <2% from the mean experimental variable values. When used for predicting or projecting total stand volume or biomass, the models performed acceptably well, in terms of the critical error estimate, within the observed range of ages and projection intervals. Management alternatives, according to specified objective, can be optimized for any particular stand using its DSSDMD, and the model can be incorporated into a simulator to ensure its most efficient usage.