Courses open for registration

The purpose of this course is to familiarize participants with statistical methods to analyse uncertainty propagation in spatial environmental modelling, such that they can apply these methods to their own models and data. It also teaches methods that quantify the contribution of individual uncertainty sources and statistical validation methods to assess the accuracy of spatial model outputs with independently sampled data. Quantification of model parameter uncertainty is covered using Bayesian calibration techniques.

The aim of this course is to provide an understanding of the statistical principles underlying experimentation. A proper set-up of an experiment is of utmost importance to be able to draw statistically sound conclusions. The role of sample size, randomization and the reduction of unwanted noise factors will be highlighted. The way errors propagate will be discussed. The difference between experimental unit and measurement units and consequences for statistical analysis will be discussed.

The aim of this course is to provide an understanding of the statistical principles underlying experimentation. A proper set-up of an experiment is of utmost importance to be able to draw statistically sound conclusions.

Soil ecology, from global patterns to mechanisms that matter. This is the theme for the 9th edition of the international PhD course on Soil Ecology.

This course is intended for those just embarking on genomics within an ecological setting and teaches the fundamentals of the discipline, while concentrating on ecological questions. The course focuses on four topics: population genomics, comparative genomics, metagenomics and functional genomics. Each of the topics is treated by lectures, case studies and participant presentations. Also, participants are encouraged to apply the principles of the course to their own research.

This course focuses on the transition to sustainable agriculture which requires action on the farm, in the food chain, in policy and legislation and on consumer behavioural level.

This on site course in Zambia aims to provide skills and methods for developing transdisciplinary research to promote advancement of Sustainable development Goals in Low and Middle Income countries by mobilizing local traditional foods. Participants will experience how this requires both in-depth expertise and knowledge from various disciplines as well as synthesis of disciplines and rooting with local stakeholders.

This course will make you familiar with a modern toolbox of analysis techniques at the interface of statistics and machine learning. You will develop the skills to build and evaluate modeling strategies for complex (big, high-dimensional, hierarchically-structured) data as occurring in the areas relevant to WIAS and PE&RC. Moreover, the course will give you insights in the connections between modern modeling strategies and will teach you to ask the right questions in order to choose the best method for the data at hand. Illustrations will come from relevant case studies.

This course is intended for everyone who is working on a modelling project and wants to learn more about mathematical and computational modelling.

This course offers the basic theories in the field of remote sensing, starting from the information needs of various land applications. Hands-on skills for handling remote sensing data are central in the practical exercises.

This postgraduate course focuses on root ecology, and how roots grow, function and interact with the surrounding environment. We will discuss differences and similarities between roots in natural ecosystems and roots of agricultural plants.

In this workshop, participants will learn the principle of tidy data, how to transform and combine datasets using the tools from the tidyverse and how to generate advanced visualization with the ggplot2 package.

The science of conservation is an interdisciplinary field that focuses on the study and application of principles, methods, and practices aimed at strategies to preserve natural resources, biodiversity and ecosystems. Before we can develop these strategies, we need to understand the complexity of human-nature interactions using natural and social sciences. The question how can we better understand interactions between humans and nature to support conservation strategies? is therefore central to the 2024 course on the science of conservation.

The aim of this course is to provide an introduction to R and R Studio. It introduces the participants to R language syntax, to enable them to write their own R code. They will also learn about R data-types and data-structures, and they will be taught how to explore the data and produce plots. The course will be a combination of lectures and practicals.

This course is the follow-up of and will build on the 2023 Participatory Plant Breeding and Resilient Seed Systems postgraduate course that also focused on smallholder engagement in plant breeding and seed system development.