New Zealand wood availability forecasts indicate that increases in the future wood availability significantly relies on small-scale forest owners' resources. This "small-scale" resource is poorly understood and comprises a large number of owners. It is questionable how many of these forests are established with consideration of the cost and practicality of harvesting. An improved understanding of the likelihood of this resource ever being harvested is important for understanding future wood supply. The main objective of this study is to answer a fundamental question on how much small scale forest area is economic to harvest. The study aims to estimate the basic stumpage value of the forests at modelled costs and different log price levels, and to analyse the profitability of the small scale forests by looking at the historic rate of return, as well as the net present value (NPV) and internal rate of return on existing and future forest land. The emission trading scheme (ETS) was also taken into account during the analyses and the effects of the ETS on the profitability, optimum age and future wood availability were investigated. The methodology developed for this study uses a forest growth model (Radiata Pine Calculator), Geographic Information Systems, the Visser harvest cost model, and Microsoft Excel. The growth model enables the analysis to be customised to a specific region of interest, while spatial characteristics such as slope and transportation distance of individual forests were taken into account by using GIS. The cost model allows the analysis to be customised to individual forests to some extent although a number of assumptions are made generalising the forests as whole. Developing the overall framework within Excel allows easy analysis of the results and changes to the underlying assumptions. Harvesting and transportation costs are the main drivers in determining the profitability of small scale forests. A significant increase in log prices is required for the existing forests to obtain substantial profit from log production. At current log prices 90% of small-scale forests in the Wanganui District are economically available. The other 10% small blocks on steep sites, have negative stumpage revenues because of high harvesting costs. Additional cashflows from entering the ETS have the potential to generate significant revenue for post-89 forests. However the substantial increases in optimal rotation age are likely to delay the increase in harvest volumes forecast from the small-scale estate.

Most estimates of wood supply do not reflect the true availability of wood resources. The availability of wood resources ultimately depends on collective wood harvesting decisions across the landscape. Both social and biophysical constraints impact harvesting decisions and thus the availability of wood resources. While most constraints do not completely inhibit harvesting, they may significantly reduce the probability of harvest. Realistic assessments of woody availability and distribution are needed for effective forest management and planning. This study focuses on predicting the probability of harvest at forested FIA plot locations in Virginia. Classification and regression trees, conditional inferences trees, random forest, balanced random forest, conditional random forest, and logistic regression models were built to predict harvest as a function of social and biophysical availability constraints. All of the models were evaluated and compared to identify important variables constraining harvest, predict future harvests, and estimate the available wood supply. Variables related to population and resource quality seem to be the best predictors of future harvest. The balanced random forest and logistic regressions models are recommended for predicting future harvests. The balanced random forest model is the best predictor, while the logistic regression model can be most easily shared and replicated. Both models were applied to predict harvest at recently measured FIA plots. Based on the probability of harvest, we estimate that between 2012 and 2017, 10 – 21 percent of total wood volume on timberland will be available for harvesting. / Master of Science