Courses open for registration

This course covers concepts and theory related to phenotypic plasticity and discusses quantitative genetic and molecular approaches. The course is composed of a series of lectures, discussions sessions, working group activities, and a final presentation and debate session on the groupwork output.

Human induced soil degradation is a very actual and increasing problem all over the world. Population growth, unsustainable management of natural resources as well as increasing amounts of harmful chemicals in the environment lead to severe land and soil degradation. This phenomenon has to be considered in a socio-ecological context with respect to economic, cultural and social conditions. This course will be focused on an interdisciplinary approach to learn about the different syndromes of soil degradation using Portugal as a case-study. Furthermore, we will look for solutions and sustainable management strategies.

This is a refresher course. The level is that of a second course in Statistics. We will refresh basic knowledge of Probability, Statistical Inference (Estimation and Testing), t-tests, simple cases of Regression and ANOVA, Experimental Design, Nonparametric Tests, and Chi-square Tests. Some time is reserved to discuss statistical problems of the participants.

In this course, we will approach Resource Use Efficiency at three different levels: the soil and plant level, the field level and the farm level. We will focus on how to improve RUE at these different levels and explore how changes at one level influence RUE at the other levels. We will concentrate on nitrogen and water use efficiency as these resources are key drivers of agricultural productivity and their use needs to be improved to ensure sustainability.

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.

In this module we continue with Regression, ANOVA, and ANCOVA, set in the general framework of Linear Models. We look at topics like parameter estimation and interpretation, checking model assumptions, regression diagnostics, analysis of unbalanced designs and multiple comparisons.

In this module we discuss how to analyse data for which the assumption of independence is violated. So: Do you have a nested experimental set-up? Or repeated measurements? Or weight of the same animal over time? Or pseudo-replication? Then, you are likely to need Mixed Models. In this course, you will learn all about it!

This postgraduate course presents the state-of-the-art methods for various farming applications such as irrigation scheduling, food processing, greenhouse management, and plant harvesting and inspection. The course will consist of lectures from worldwide experts, and hands-on practicals with real life examples.

In this module we study how to analyse data that are not normally distributed. We look at fractions (logistic regression), counts (Poisson regression, log-linear models), ordinal data (threshold models), and overdispersion. We discuss (quasi-) maximum likelihood estimation and the deviance.

Classical statistics offers a powerful toolbox for data analysis. This toolbox, however, may not always be sufficiently flexible for modern data situations. The Bayesian framework allows for the integration and inclusion of information from many sources as well as a natural quantification of uncertainty in subsequent analysis.

This summer school is in the form of a hackathon with the concept of learn-by-doing. PhD candidates, postdocs, and young researchers from both institutes will be mixed into small teams to actively work together on a research challenge of your preference, ranging from implementing AI methods to process images of plants and fields to programming robots to operate in agricultural environments. The teams will be supervised by experts from WUR and AgriForwards.

The Soil Biology Group of Wageningen University is organising a laboratory skills course to define which measurements of soil biology are relevant for which soil functions. This course will provide the participants with an overview of a range of methods related to the five soil functions and will provide detailed practical training in a subset of measures. The training will be a combination of lectures, laboratory and field sessions (interactive lectures and practical sessions each day). Assessing a range of measurement types, from simple visual assessments in the field, to training in microscope identification techniques for nematodes and earthworms, and functional measures in the lab such as MicroResp. All methods described in the course will be made available to participants as well as advice on how to analyse the data.

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.

Intercropping is the next frontier for diversifying agriculture and increasing its productivity, resource use efficiency and resilience to abiotic and biotic stresses in a sustainable manner. Given the challenges of biodiversity decline and climate change it might well be that intercropping is one of the solutions to strengthen the resilience of cropping systems to these environmental threats and sustain food security.

This course focuses on theoretical concepts, such as autocatalytic loops and positive and negative feedbacks between organisms in ecological networks as well as the importance of non-trophic interactions by ecosystem engineers. The course will address how these principles can be used to link communities to ecosystems enabling a better understanding of how environmental changes affect community and ecosystem dynamics. Students will construct ecological networks of their own study system or based on literature data and analyse these using structural equation modelling.

This course is for participants who want to deepen their knowledge of R programming and be able to use R to deal efficiently with computational problems and programming tasks. Participants will also gain more knowledge on working with R data structures and solving common problems, like working with and manipulating factors, extracting information from statistical models, working with other types of data (textual, time and dates) and handling multiple data files.

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 main goal of the course will be achieved if the participants acquire novel ideas and techniques for their own research. The course is primarily aimed at PhD level students, but is also open to advanced Master level students with interest in microbial ecology.

In this two day workshop you will learn about two most common long read sequencing technologies: Pacific Bioscience an Oxford Nanopore Technology. You will learn how to work with the data, perform assembly, mapping, and compare the results of both technologies.