%0 Research article
%T Using cost-plus-loss analysis to define optimal forest inventory interval and forest inventory accuracy
%A Mäkinen, Antti
%A Kangas, Annika
%A Nurmi, Mikko
%D 2012
%J Silva Fennica
%V 46
%N 2
%R doi:10.14214/sf.55
%U https://silvafennica.fi/article/55
%X 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.