Effect of Stand Structure and Number of Sample Trees on Optimal Management for Scots Pine: A Model-Based Study

Abstract

This study presents an attempt to discover the effect of sample size on the financial outcome derived by stand-level optimization with individual tree modeling. The initial stand structure was altered to reflect sparse, average, and dense Scots pine (Pinus sylvestris L.) stands. The stands had varying numbers of stems but identical weighted median diameters and stand basal areas. The hypothetical Weibull diameter distributions were solved according to the parameter recovery method. The trees were systematically sampled with respect to the tree basal area corresponding to sample sizes of 10, 20, or 40 trees. We optimized the stand management with varying numbers of sample trees and with varying stand structures and compared the optimal solutions with respect to the objective function value (maximum net present value) and underlying management schedule. The results for the pine stands in southern and central Finland indicated that the variations in the objective function value relating to sample size were minor (<2.6%) in the sparse and average stand densities but exceeded 3% in the dense stands. Generally, the stand density is not always known, and thus, we may need to generalize the average density for all cases in question. This assumption, however, resulted in overestimations with respect to the optimal rotation period and financial performance in this study. The overestimations in the net present value decreased along with the increasing sample size, from 22% to 14% in the sample sizes of 10 and 40 trees, respectively.

Field sampling methods on seismic lines: a comparison between circular plots and belt transects

“…To determine if sampling a smaller area, and subsequently fewer trees, would have been sufficient in establishing an accurate dataset, we calculated the relative standard error (RSE). In forested habitats, recommended maximum RSEs of 10–20% have been considered as sample size standards (Strimbu 2014; Nowak et al 2015; Ahtikoski et al 2018), and datasets with a greater RSE could benefit from an increased sample size. The RSE for the average total tree density measured across the three Site Types was 18%.…”

Evaluation of operating cost management models for selection cutting in Scandinavian continuous cover forestry

“…Since there were differences between the models regarding total costs, this may have a consequence for stand-level optimisation, as even small changes in endogenous variable values could have a profound effect on optimal solutions (Ahtikoski et al 2018, Pyy et al 2020.…”