PhD students in forest sciences are frequently analyzing individual tree- and stand level growth. Response to treatments, natural dynamics, growth trends in response to climate change are examples of topics to be studied. Given that the studied system is far from a controlled experiment and that experimental and sampling design only can reduce some of the variation, multiple factors need to be accounted for when analyzing the data. This requires intensive training and experience in tree and stand growth analyses and extensive understanding of factors affecting these processes. The course will bring PhD students in touch with active researchers in forest growth and yield. Teachers will help students to get a broader understanding of the field and how to solve their specific problems.
The course will cover the following subjects:
- Experimental design and establishment of survey plots
- Response variables, e.g. volume, biomass, carbon, wood density, wood quality
- Allocation of growth
- Establishment of seedlings
- Density, competition and mortality
- Site characteristics and its variation on different scales
- Stand structure, its representation and effects on growth
- Inclusion of weather and climate in analysis of experiments and in growth and yield models
- Mechanistic vs. empirical models
Before the course week, students will get a literature list that should be studied. In addition, each student will get an assignment (designing or analyzing an experiment) that should be solved under supervision of one of the teachers. During the course week, the first two days will be filled with lectures and exercises. During the third day, students will present their PhD-project and present a problem that can be solved during and after the course week. The fourth day will be a field trip to forest experiments with a focus on experimental design and analysis of data. The fifth day will again be classes and exercises. Before leaving, students should also prepare an assignment that should be solved during the coming three-four weeks. The course ends with one or two Skype-meetings when the problem solving assignment is presented.
Students will complete two assignments, one pre- and one post-campus. Both assignments should be approved by supervisors.
After the course, students should have knowledge about forest experimental design and how to choose dependent and independent variables. Students should develop their skills in analyzing experimental data of various complexity and size as well as getting a basic understanding of different types of models used in forest growth and yield studies. Students should know how to use and where to find different data such as climate, site characteristics and national forest inventory data. Lastly, students should understand and be able to include stand dynamics (regeneration, competition, mortality, stand
structure development, etc.) in models and analysis of experimental data.
Students will complete two assignments, one pre- and one post-campus. Both assignments should be approved by supervisors. Students need to be present at lectures and the field trip during the course week. The evaluation is pass/fail.
The course will combine traditional classes with exercises and a field trip. An important part of the course is the two assignments that are done together with one of the teachers. This is where students will develop their generic skills in analyzing complex data.
- 40 hours reading before the course week
- 55 hours lectures, exercises and excursions during the course week
- 55 hours hours independent work and seminar after the course week
Students should be enlisted as PhD-students and should have a master in forest science (or similar).
Admission for NOVA courses is handled by the course organiser/ the NOVA member institution organising the course. Please see the links in the margin for more information.
Application to the course should be sent to Urban Nilsson urban.nilsson[at]slu.se before 15 Jun. 2018. The application should, beside name, department and major professor, shortly describe your thesis project and a motivation why you are interested in the course.