Plant Breeding: Experimental Design and Data Analysis of Breeding Trials

If you are a working professional in the plant breeding sector, this course is the perfect opportunity for you to expand your knowledge and sharpen your skills. This comprehensive course will provide you with the tools you need to succeed and advance in your career, especially in combination with other advanced Online Master's Courses Plant Breeding.

Prerequisite knowledge
You should have sufficient knowledge on concepts and methodologies related to plant biology, such as genetics, plant breeding, plant physiology and molecular biology. Moreover, a solid basis in research methodology and statistics are necessary. It's important to realise that, if you do not comply with these recommendations, you cannot claim extra support from the instructor and cannot claim a refund of the registration fee, if you decide to discontinue the course.

• Explain and apply statistical principles underlying experimental designs for breeding trials with respect to randomisation, replication, blocking, experimental units
• Explain, distinguish and characterise the following experimental designs: completely randomised design (CRD), randomised complete block design (RCB), incomplete block designs (including resolvable designs: lattice designs and alpha designs, row-column designs) and split-plot designs
• Explain and apply linear models, different kinds of generalised linear models (GLM) and mixed models and know the similarities and differences between these
• Explain genotype by environment interaction in multi-environment trials and quantify, test and characterise such interactions using analysis of variance, mixed models, Finlay-Wilkinson regression, AMMI and GGE biplot

In this course, participants are taught principles of experimental design of trials and statistical analysis of trial data with a special emphasis on linear and generalised linear methods, mixed models, analysis of multi-environment trials using different statistical methods.

The course includes knowledge clips, individual and group exercises or discussions, and e-learning modules. Literature will be available through the online learning environment (included in course fee).

This course is quite time-intensive and requires approximately 20 hours per week for the average participant. There are assignments with deadlines.

Software used in this course:
R including R studio.

Self-Paced Online Course Getting Started with R
You need to have a good basic understanding of statistics, and you need to have experience with software 'R'. If the latter is not the case, you can follow the Self-Paced Online Course Getting Started with R first. For more information and registration, please check the document linked below.

This course fits logically after the online master's course Advanced Statistics.

Upon successful completion - passing the exam -, a digital Micro-credentials certificate (EduBadge) with 3 study credits (ECTS) is issued. The EduBadge certifies the learning outcomes of short-term learning experiences, marking the quality of a course.

Participation in the remotely proctored exam is optional. If you decide not to participate in the exam, you do not qualify for a certificate and/or Micro-credentials.

The date of examination is 4 March 2027. The duration of an exam is 3 hours. The resit will be scheduled on 5, 6 or 7 May 2027.