Forest modelling for PhD students
Course description
The overall goal of the course is to give students an in-depth understanding of forest models with a focus on sustainable forestry and equip them for qualified use of models in research and as decision support in forestry.
The course gives students an overview of approaches in forest modelling including empirical and process-based (mechanistic models) and insight into what data are needed to develop different types of models. Students learn the areas of use of different models, their strength and limitations.
Study topics include analysis of important variables such as basal area, volume, biomass, and carbon based on field trial data. In addition, key processes such as regeneration, growth and mortality and their relation to stand structure and various site variables are considered from the modelling perspective.
To further student learning and promote discussion, a variety of methods are used: lectures, literature studies, seminars, individual assignments, group work, and a field trip.
Individual and group assignments consist of literature studies and analysis of theoretical and practical questions with the help of statistical data processing and simulation tools.
The course focuses on the following generic competencies: Information competence, critical thinking and reflection, problem solving, scientific methods, digital competence, use of technology, oral and written communication, teamwork.
Course facts
P000176, Forest modelling for PhD students, 4.5 Hp
Print syllabus
Syllabus
Level
Third cycle
Subject
Forest Management
Grading Scale
The grade requirements within the course grading system are set out in specific criteria. These criteria must be available by the course start at the latest.
Course language
English
Entry Requirements
Objectives
Upon successful completion of the course, students should be able to:
- Design field trials based on statistical principles to ensure valid and reliable results.
- Discuss and critically examine the characteristics and limitations of main types of forest growth models.
- Use models to analyze forest dynamics under different environmental conditions and management scenarios.
Content
The course covers field trial design, data analysis and forest growth models. The course is carried out with lectures, assignments, seminars and a field trip. Except for the field trip most of the course content can be provided through online channels.
The course aims to contribute to sustainable development through efficient multiple use forestry by enabling development and use of advanced digital decision support for forest management. Furthermore, we strongly focus on the challenges for the development of predictive forest models posed by climate change.
The course is driven by an international teacher team with experiences from different climatic zones and cultural and institutional environments (Brazil, Poland, Latvia, and Sweden). The connection to the MSc program Euroforester provides students with additional opportunities for international knowledge exchange.
The teachers team includes both male and female teachers who strive for an inclusive, respectful and safe learning environment.
Examination Formats and Requirements for Passing the Course
Examination will be in the format of an individual assignment.
Responsible Department/Equivalent
Department of Southern Swedish Forest Research Centre
Supplementary information
Other Information
Basic knowledge of silviculture and forest management is desirable. Prior familiarity with coding is an advantage but not mandatory.