Registration deadline: 23 January 2025

Target audience

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.

Learning outcomes

After successful completion of this programme, you will be able to:

• 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.
• Estimate heritability of traits from estimates of variance components obtained from Anova and mixed models in genotype trials.
• Understand effects of missing values, data errors, outliers, uneven replication, confounding of effects, and violations of distributional assumptions and assumptions of equal variance and independence.
• Understand how and why distribution and link functions need to be specified in GLMs.
• Quantify, test and characterise genotype-by-environment interaction using different evaluation methods: analysis of variance, mixed models, Finlay-Wilkinson regression, AMMI and GGE biplot.
• Understand the difference between fixed terms and random terms in a mixed model analysis, both conceptually and in applications.
• Comprehend the connections between these design principles and the models and model assumptions underlying statistical analyses, primarily linear regression and analysis of variance (distributional assumptions, independence, equal variance; additivity or linearity of effects, single or multiple random error terms).
• Comprehend statistical principles underlying experimental designs for breeding trials with respect to randomisation, replication (including types of replicates and pseudo-replication), blocking, experimental units, the use of controls, orthogonality, balance and efficiency, power.
• Apply these concepts when designing an experiment.
• Understand when generalised linear models (GLM) are more appropriate for data analysis than linear regression or Anova.
• Understand when linear mixed models (LMM) are more appropriate for data analysis than linear regression or Anova.
• Perform different analyses using GLMs: logistic regression for binary data, threshold models for multinomial or ordinal data, loglinear regression for counts.
• Specify a linear mixed model in fixed and random terms for a data analysis with unbalanced designs or dependent observations.
• Comprehend and apply linear mixed models in different contexts: estimation of variance components (e.g. for heritability estimation), or quantify the relative importance of environmental and genetic contributions to the variation in multi-environment trials; analysis of split-plot trials.
• Use a linear mixed model for the estimation of variance components.
• Explain genotype by environment interaction as a concept in multi-environment trials in plant breeding and in statistical terms.
• Comprehend and discuss the concepts of stability, adaptability and (wide/specific) adaptation in plant breeding in the context of Finlay-Wilkinson regression.

Programme

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 advanced knowledge of statistics and some experience with R and R Studio in order to perform statistical analyses. 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 in the right-hand column.

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

Examination

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 6 March 2025. The duration of an exam is 3 hours. The resit will be scheduled on 7, 8 or 9 May 2025.

Certification

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.