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Biogeochemical Modeling of Agroecosystems
Last Updated: 2026-06-01 11:32:51
Abstract
This class provides an introduction to biogeochemical modeling in the context of agricultural ecosystems. It covers the general background and principles of modeling agricultural biogeochemistry in a theoretical part (e.g., plant growth and soil C dynamics), while the focus is on learning how to code biogeochemical models in the R software environment.
Objective
The aim of the course is to (i) introduce students to a range of concepts applied in biogeochemical modelling of agroecosystems, with an emphasis on soil biogeochemistry (carbon cycling) and (ii) teach students the basics of coding biogeochemical models in a free and open-source software environment (R). The focus of the course is on hands-on coding. At the end of the course, students will: - Be able to critically evaluate different concepts applied in widely used biogeochemical models in an agricultural context - Be familiar with the basic concepts of programming that are specifically applicable to modelling biogeochemical cycles in an agricultural context - Be able to come up with basic conceptual models to evaluate crop growth and carbon cycling in agricultural soils, given certain constraints and - Be able to write computer codes to convert their conceptual models to numerical models, and evaluate model outcomes.
Content
The class consists of (i) a limited theoretical part, in which students learn the basics of biogeochemical models in an agricultural context and (ii) a more extensive part, in which students learn to program plant growth models and soil biogeochemical models in the R software environment. Throughout the course, the R skills necessary to code biogeochemical models are explained using examples related to soil biogeochemical cycling or crop growth (e.g., different data structures, loops, functions, if/else etc.). Aspect of biogeochemical modeling that are covered include constructing sets of coupled differential equations that form the core of numerical models, solving differential equations analytically and numerically, by writing solvers and using existing solvers, model calibration techniques and using different modelling aspects to code flexible models. These principles are applied to code published and widely-used biogeochemical models to solve research questions. The course encourages the use of large language models as coding aid.
Resources
Literature
The following handbooks serve as the basis for the concepts and skills the students will learn in the course: - Soltani and Sinclair, 2012, Modeling physiology of crop development, growth and yield. - Soetaert and Herman, 2009, A Practical Guide to Ecological Modelling, Springer Netherlands. - Wallach et al., 2018, Working with Dynamic Crop Models, 3rd Edition, Academic Press. - van Oijen, M., 2020, Bayesian Compendium. Springer International Publishing.
General Information
- Language
- English
- Levels
- MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
- Digital
- The examination takes place on your own device. No installation of SEB required.
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise |
Biogeochemical Modeling of Agroecosystems
This course takes place in the meeting room of the LFH-building (LFH B2) on Wednesday 14:15-17:00.
This room has 18 seats.
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3 h weekly |
Offered In
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Crop- and Grassland Science (Dieser Minor ist neu und gilt ab dem Studienjahr 22/23. Das Gesamtangebot des Minors wird im Sommer 2022 auf der Website des Studienganges veröffentlicht.)
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