Comparison of various sources of uncertainty in stand-level net present value estimates

The objective of this study was to compare the relative importance of various sources of uncertainties in determining the net present value of forest stands and forested property. This was achieved by performing stand-level simulations that took into account: i) input data errors (airborne laser-scanning data vs. ocular standwise field inventory data), ii) stochastic future development of timber assortment prices and iii) errors in stand-level growth projection models. The starting point of the study was a simulated forest estate comprising 40 stands of various types sufficiently represented (e.g. with respect to species composition, development class distribution, and site quality). Stochastic timber price models were formulated, employing geometric mean-reverting principles. The results showed that sources of uncertainty all had significant effects on the probability distribution of the net present value of the stand. The relative standard deviations of stand net present values averaged 8% for stochastic timber price, 29% for errors in standwise field inventory data, 26% for errors in airborne laser-scanning data and 33% for errors in growth projection models when applying a 3% discount rate. When all three sources of uncertainty were analysed simultaneously, the highest average standard deviation was 47.4%. Interestingly, errors in the growth projections and the quality of inventory data contributed more to the variation in stand net present value than fluctuation in timber price did, although this result was based on the assumption that the forestry industry maintains its competitiveness in the long run. Our modeling approach made it possible to compare various sources of uncertainty and to set confidence intervals for net present value estimates. This approach can also result in information on which sources of uncertainty are focused.Forest estate valuation Net present value Uncertainty Forest management planning Simulation Growth and yield prediction