Stochastic economic analyses of even-aged timber growing

Abstract

Background: StochPV is a computer programme enabling the risks and uncertainties inherent in even-aged timber growing to be recognised through stochastic analysis. Most financial analyses of even-aged plantation or forest stands have used deterministic analyses, such that a single set of volumes, costs and prices yields a single answer for the present value. Risks and uncertainties are ignored, even though well-recognised by practitioners. StochPV provides a probability distribution, not just a single value, for the present value of an even-aged stand, allowing practitioners to better gauge their decisions regarding the impact of risks and uncertainties on silvicultural options and stand valuation. More generally, it will enable analysts to explore whether or when deterministic analyses are a reliable guide for such decisions. Methods: Means and coefficients of variation, together with maximum and minimum values and correlation coefficients of prices and costs, are read in via a spreadsheet and used to estimate beta distribution parameters for each data cell. The resulting distributions can be reviewed visually and amended, if necessary. Fire frequency and fire salvage data are also read in via spreadsheet. Univariate or multivariate pseudo-random values are simulated for each variable involved, enabling the present value to be calculated for each iteration. Present value is calculated for the stochastic analogues of Faustmann perpetual rotations and a single rotation, the latter taking account of the initial land cost and revenue at the end of that rotation. The present values of the many iterations are then used to calculate mean, standard deviation and other summary statistics. Results: The programme integrates all known risks relating to stand management in an analytical framework that is relatively simple and communicable. The use of beta distributions provides flexibility in representing the nature of the empirical distributions involved. Inter-relationships between kindred variables are taken into account using multivariate distributions based on copulas and the marginal distributions involved. Conclusions: The programme results are suitable for evaluation of first-degree stochastic dominance and could also be applied to second-degree stochastic dominance if the decision-maker had a material aversion to risk, all necessary data being available as outputs of the programme.