Antti Mäkinen , Annika Kangas, Mikko Nurmi

Using cost-plus-loss analysis to define optimal forest inventory interval and forest inventory accuracy

Mäkinen A., Kangas A., Nurmi M. (2012). Using cost-plus-loss analysis to define optimal forest inventory interval and forest inventory accuracy. Silva Fennica vol. 46 no. 2 article id 55. https://doi.org/10.14214/sf.55

Abstract

In recent years, optimal inventory accuracy has been analyzed with a cost-plus-loss methodology, where the total costs of inventory include both the measurement costs and the losses from the decisions based on the collected information. Losses occur, when the inaccuracies in the data lead to sub-optimal decisions. In almost all cases, it has been assumed that the accuracy of the once collected data remains the same throughout the planning period, and the period has been from 10 up to 100 years. In reality, the quality of the data deteriorates in time, due to errors in the predicted growth. In this study, we carried out a cost-plus-loss analysis accounting for the errors in (stand-level) growth predictions of basal area and dominant height. In addition, we included the inventory errors of these two variables with several different levels of accuracy, and costs of inventory with several different assumptions of cost structure. Using the methodology presented in this study, we could calculate the optimal inventory interval (life-span of data) minimizing the total costs of inventory and losses through the 30-year planning period. When the inventory costs only to a small extent depended on the accuracy, the optimal inventory period was 5 years and optimal accuracy RMSE 0%. When the costs more and more heavily depended on the accuracy, the optimal interval turned out to be either 10 or 15 years, and the optimal accuracy reduced from RMSE 0% to RMSE 20%. By increasing the accuracy of the growth models, it was possible to reduce the inventory accuracy or lengthen the interval, i.e. obtain clear savings in inventory costs.

Keywords
value of information; prediction error; inventory error

Received 8 December 2010 Accepted 20 January 2012 Published 31 December 2012

Views 3569

Available at https://doi.org/10.14214/sf.55 | Download PDF