In practical forestry stands are not managed in isolation. Instead, foresters need to think of nature conservation, social and economic goals at the overall property, district or enterprise level. Management decisions need to be taken despite imperfect information and in the face of a future of many unknowns. This course is about making management decisions in practical forestry.
Your decisions can be only as good as the information that you use. The development of remote sensing technologies should lead to cost saving by reducing the time that has to be spent on field-inventories. But what is the state-of-the-art actually? Are we already there or do we still need to survey the forest in field, stand after stand? You will get an overview of the latest advances from one of Sweden's top experts in the area. In field, you will have an opportunity to compare the accuracy of objective and subjective forest inventory methods which are commonly used to collect data for management planning.
Many forest owners and most forest companies are interested in obtaining income from forest. But how do we measure the economy of the biological wood-production in forestry? How do we say apart a "good" economy from a "bad" economy? Financial analysis provides the means of comparing economic performance of forest management alternatives and helps us make informed choices between them. Our experience is that many students, even though familiar with the necessary economic concepts, lack the skill to use them in forestry decision making. In this course we review the most important concepts of financial analysis in forestry and learn how to use them to support management decisions.
Predicting forest growth has always been central to all forest management decisions. For a long time, tables and the foresters gut feeling where the main tools to make such predictions. Today, in many countries there are advanced computer based decision support systems allowing virtual experimentation and comparison of forest management alternatives with unprecedented level of detail. Increasingly, the systems include features helping to assess other than economic aspects of the forest, such as models of deadwood accumulation and decomposition, habitat mapping for selected species and soil carbon models. Many systems also include algorithms for finding best alternatives based on the user's specification of goals. In this course we use the Swedish forest decision support system Heureka (Wikström et al. 2011) to explore possible long term futures of an actual forest estate in Southern Sweden as a case.
The highlight of the course is a 1.5-week long fieldtrip to a large forest estate in Southern Sweden (Östad) where we learn how the owners deal with the strategic questions of land property management that include not just forests but diverse land-uses and the shorter term tactical questions of forest management. Students use part of the property placed under an imaginary independent owner as the object for developing a detailed forest management plan for a 10-year period.
The teachers core-team from the department consists of Renats Trubins, PM Ekö, Eric Agestam, Emma Holmström and Narayanan Subramanian. In addition, we are visited by several guest lecturers from other departments at SLU and other forest sector organizations.
Welcome to the course!
Renats Trubins and the teachers team.
Wikström, P., L. Edenius, B. Elfving, L. O. Eriksson, T. Lämås, J. Sonesson, K. Öhman, J. Wallerman, C. Waller and F. Klintebäck (2011). "The Heureka forestry decision support system: an overview." Mathematical and Computational Forestry & Natural Resource Sciences 3(2): 87