In addition to learning methods and facts related to the agriculture and food sector, students should also develop skills in proper academic writing and presentation, and especially in the use of generative AI. Effective communication of ideas and research findings is key to professional success at higher levels. This module provides an opportunity to improve these skills. It also provides a forum for discussion of current issues in agricultural economics across sub-disciplines.

By participating in this module, students will develop their analytical thinking, their scientific writing and various skills of scientific presentation (e.g., creating a PowerPoint slide, giving a talk, discussing in question and answer sessions).

This module develops knowledge of land use economics and the essential steps in the modeling process (conceptual modeling, model selection, parameterization, and validation). Hands-on computer exercises cover various aspects of agricultural land use systems, with emphasis on the design and analysis of simulation experiments for uncertainty and sensitivity assessment.

Students will understand basic concepts of land use economics. They will be able to model land use decision problems at different spatial scales. They will gain exposure to advanced techniques such as bio-economic modeling and multi-agent systems. By developing their own simulation models, students apply analytical thinking and acquire various scientific skills (e.g., data handling, processing and analysis, oral presentation).

Students will be familiar with the basic concepts of constrained optimization. They will be able to analyze typical farm system decision problems and formulate them as mathematical programming models. They will be able to implement and solve MP problems using spreadsheet software, evaluate 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 acquire various scientific skills (e.g., literature searching and reading, data handling, processing and analysis, oral presentation).

In this module, students will develop essential skills that are critical for success in their academic and professional careers. Organizational skills are emphasized, enabling students to effectively manage their time and resources in project work. The module encourages independent work and promotes self-motivation and responsibility in learning. Critical and analytical thinking is developed through engagement with spreadsheet models and real-world case studies, enabling students to evaluate and interpret data effectively.

Students work on an individual case study analyzing a decision problem in a farm system of their choice, solve this problem using mathematical programming, and document their case study in an electronic portfolio on ILIAS. Lectures will cover the following topics:

- Introduction to Farm System Modeling
- Mathematical programming
- Farm investment analysis

Students work on an individual case study analyzing a decision problem in a farm system of their choice, solve this problem using mathematical programming, and document their case study in an electronic portfolio on ILIAS. Tutorials will cover the following topics:

- Introduction to Farm System Modeling
- Mathematical programming
- Farm investment analysis

This module focuses on advanced agent-based land-use modeling techniques, providing students with a comprehensive understanding of Mathematical Programming based Multi-Agent Systems (MPMAS). Participants will explore the application of MPMAS in various contexts, including the integration of econometric methods to enhance model accuracy and validity. The course will also cover modeling innovation diffusion within the framework of MPMAS, allowing students to analyze how new practices spread across agricultural systems. Furthermore, students will learn to couple MPMAS with biophysical models such as AquaCrop, Lucia, and Expert-N, providing a holistic view of land-use dynamics.

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

This module focuses on essential skills for effective research planning and execution. Students will learn to extract and analyze information using R, using databases and surveys for data-driven insights. The course covers the generic formulation and automated initialization of static farm system decision problems, with an emphasis on advanced techniques using integers and multiple objectives in farm system models. In addition, students will design and implement uncertainty and sensitivity analyses to gain insights into decision making under risk. The module culminates in a dynamic farm simulation, allowing students to apply their knowledge to real-world scenarios and develop effective farm management strategies.

This module focuses on advanced agent-based land-use modeling techniques, providing students with a comprehensive understanding of Mathematical Programming based Multi-Agent Systems (MPMAS). Participants will explore the application of MPMAS in various contexts, including the integration of econometric methods to enhance model accuracy and validity. The course will also cover modeling innovation diffusion within the framework of MPMAS, allowing students to analyze how new practices spread across agricultural systems. Furthermore, students will learn to couple MPMAS with biophysical models such as AquaCrop, Lucia, and Expert-N, providing a holistic view of land-use dynamics.

In this part of the module, students will acquire practical skills in using the MPMAS software package to simulate land use change in agriculture and forestry. They will develop a comprehensive understanding of how to integrate economic models of farm household decision making with biophysical models that assess crop yield responses to variations in water supply and soil nutrients. Through hands-on computer exercises and practical case studies, students will gain valuable experience in applying MPMAS to real-world scenarios. By the end of the course, students will be equipped with the analytical skills necessary to evaluate land use dynamics, make informed decisions, and contribute to sustainable agricultural practices based on evidence-driven insights.