Apart from learning methodologies and facts related to the agricultural and food sector, Agricultural Economics Master students should also train the skills of proper academic writing and presentation. Effective communication of ideas and research results is key for professional success at higher levels. This module provides an opportunity to improve such skills. Furthermore, it constitutes a forum for the discussion of topical issues in agricultural economics across sub-disciplines.
By taking part in this module, students will develop analytical thinking, their ability of scientific writing and various skills of scientific presentation (e.g., creating a powerpoint slides, giving a talk, discussing in question and answer sessions).

Students understand fundamental concepts of land use economics. They can model land use decision problems at various spatial scales. They have gained insights into advanced techniques such as bio-economic modeling and multi-agent systems. By developing their own simulation models, students apply analytical thinking and aquire various scientific skills (e.g., data handling, processing and analysis, oral presentation).

Students are familiar with the basic concepts of constrained optimization. They are able to analyze typical decision problems in farm systems and formulate them as mathematical programming models. They are able to implement and solve MP problems using spreadsheet software, assess the stability and sensitivity of the solution, and interpret the results in the context of the farm-system decision problem. By developing their own case study, students apply analytical thinking and aquire various scientific skills (e.g., literature search and reading, data handling, processing and analysis, oral presentation).

Exercise course - Modelling of Land Use Decisions with Mathematical Programming.
First half of the semester.

Exercise course - Introduction to Excel Spreadsheet Models.
First half of the semester.

Topics dealt with in this course are as follows:

• Agent-based land-use modeling
• Application of Mathematical Programming based Multi-Agent Systems (MPMAS)
• Combining MPMAS with econometrics
• Modeling innovation diffusion in MPMAS
• Coupling of MPMAS with external crop-growth models (AquaCrop, Lucia, Expert-N)
• Capturing climate variability in MPMAS (as part of Integrated Land Model Systems)

Each participating student will be assigned a seminar topic and a tutor at the beginning of the semester, whereby own fields of interest will be taken into account. Participants will have 6-8 weeks to prepare a short paper on the topic (maximum 15 pages) under the supervision of the tutor. Seminar topics can include literature reviews about current issues, book reviews, small empirical analyses, research proposals, and other tasks. In the second half of the semester, the papers will have to be presented orally (15-20 minutes), followed by a critical discussion.

After completing this module, students are able to design, implement and interpret models for farm-system optimization and simulation with sound consideration of uncertainty. They are able to extract farm decision-relevant information from farmer surveys and agroeconomic databases and process large datasets efficiently. They are able to apply advanced mixed integer modeling techniques to represent multiple farm objectives and discrete decisions. They are able to build a locally generic farm system model and initialize it for many farm agents, and design and conduct problem-oriented uncertainty and sensitivity analyses. They are able to apply appropriate techniques to model farm decisions under risk. They understand the basics of dynamic simulation of farm-system decisions over time.
Through the application of statistical scripting, model formulation and data processing for practical modeling tasks, students train their abilities in analytical problem-oriented thinking, specifcally abstraction and algorithmic thinking. In individual and group exercises they strengthen organizational and communication skills and their ability to work independently.

Topics dealt with in this course are as follows:

• Agent-based land-use modeling
• Application of Mathematical Programming based Multi-Agent Systems (MPMAS)
• Combining MPMAS with econometrics
• Modeling innovation diffusion in MPMAS
• Coupling of MPMAS with external crop-growth models (AquaCrop, Lucia, Expert-N)
• Capturing climate variability in MPMAS (as part of Integrated Land Model Systems)