While obtaining more detailed information on operational harvest units can be costly, it can lead to significant improvements in the financial performance of an integrated forest products firm as log mix can be optimally matched with processing facilities (Wagner et al., 1996 Uusitalo, 1997). To reduce this variation, forest products companies have sought to optimize the sample size for a desired level of precision (Oderwald and Jones, 1992 Brooks and Wiant, 2004 Zeide, 1980 Gambill et al., 1985), increase the level of detail in sampling procedures by measuring additional attributes (Mandallez and Ye, 1999) and by including additional data sources in the inventory (Holmström, 2002 Kilkki and Päivinen, 1987 Korhonen and Kangas, 1997). The quality of and quantity of the raw material within a given order may vary within certain predefined levels such as diameter ranges, lengths or surface characteristics and unlike finished panel products or boards, which have a minimal variation, the production of the raw material is subject to variation within the stem, stand and season. However, their importance to the precision was not as clear. Subsample tree measurement strategies need further studies, as they were an important cost factor. While the optimal radius of plots and other parameters were sensitive to the measurement times and other cost factors, the concentric plot type was optimal in almost all studied cases. In cluster level, the most important factor is the transfer time between plots. The subsample tree selection strategy was more important in selecting optimal plot than many other factors. The concentric plot seems to be a good compromise between these two in many cases. Weselect the optimal type and size first at plot level using a cost-plus-loss approach and then at cluster level byminimizing the weighted standard error with fixed budget.Īs relascope plots are very efficient at the plot level for volume and basal area, and fixed-radius plots for stems per ha, the optimal plot type strongly depends on the relative importance of these variables. The plot types used are fixed-radius, concentric and relascope plots. We simulate different plot types and sizes and subsample tree selection strategies on measuredtest areas from North Lapland.

The factors include time used to lay out the plot and to make the tree measurements within the plot, the between-plot variation of each of the variables of interest in the area, and the measurement and model errors for the different variables.

We explore the factors affecting the optimal plot design (size and type as well as the subsample tree selection strategies within a plot) and their relative importance in defining the optimal plot design in amultipurpose forest inventory.

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