Optimization of early cleaning and precommercial thinning methods in juvenile stand management of Norway

The purpose of the work done for this thesis was to construct and 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 (FMP) data. effects of soil preparation and Early Cleaning (EC) on further development of the stands were studied in intensive field experiments. Extensive survey data from juvenile stands were used to develop methods applicable for efficient decision making in JSM. The survey data were used to model the effects of site and stand attributes on the need for EC and labor time consumption of PreCommercial Thinning (PCT). 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 the need for PCT arose. 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. The soil preparation method had a major effect on the number of trees to be removed in EC, and spot mounding led to a less labor consuming JSM program than disc trenching. Thus, spot mounding appeared to be a less costly activity overall even though it initially appeared a more expensive method than disc trenching. results showed that interactions in regeneration chain activities are important for cost-effective wood production. Furthermore, a priori information can have practical implications in decision making for JSM. Several site or stand attributes were found to explain the need for EC or for labor consumption of PCT. However, the models developed in this study are rather inaccurate for reliable a priori estimation. The modeling data in further research should go beyond the data of traditional FMP. Big data offers promising opportunities, but data collection and storage need to include data with the relevant attributes.