%0 Articles %T Optimization of early cleaning and precommercial thinning methods in juvenile stand management of Norway %A Uotila, Karri %D 2017 %J Dissertationes Forestales %V 2017 %N 231 %R doi:10.14214/df.231 %U http://dissertationesforestales.fi/article/2014 %X The purpose of this thesis was to develop the concept of cost-efficient Juvenile Stand Management (JSM) for planted Norway spruce (Picea abies L. Karst) stands. The principles of time based management were followed, by integrating regeneration activities as a cost-efficient value chain and by minimizing non-value-adding work with straightforward decision making based on forest management plan data. The effects of soil preparation and Early Cleaning (EC) on further development of the stands were studied in intensive field experiments. Extensive survey data were used to develop methods applicable for efficient decision making in JSM, such as estimating need for EC or labor time consumption of PreCommercial Thinning (PCT). Timing of JSM had major effect on its costs; a delay in PCT increased the labor time needed to manage a stand by 8.3% annually. Moreover, 61–70% of the saplings in a typical Norway spruce stand were considered to need EC years before PCT was appropriate to be done. EC was also found to be an effective release treatment as it subsequently increased the diameter growth of crop trees by 21–32%. However, a two-stage management regimen, which included EC and PCT, appeared to be somewhat more labor consuming than the PCT only option. Soil preparation method had a major effect on emergence and growth of non-crop trees, and thus, on overall costs of JSM-program. The results showed that understanding the interactions in regeneration chain activities is important for productive forestry. Furthermore, a priori information can have practical implications in decision making for JSM. Several site or stand attributes were found to explain labor consumption of PCT or the need for EC. However, decision making in daily forestry requires more reliable models. The modelling data should go beyond the data of traditional forest management planning in further research. Big data offers promising opportunities.