Experimental Design and Data Analysis of Breeding Trials


About this course

Note 1: This course can not be combined in an individual programme with ABG-30806 Modern Statistics for the Life Sciences (MSLS)
Note 2: The period mentioned below is the period in which this course starts. For the exact academic weeks see the courseplanning on
Note 3: This course is offered online and it takes about 20 hours to complete the weekly task. There are assignments with deadlines and non-synchronous interaction with teachers and fellow students. An online exam is offered in the last week.
Note 4: This is an online course, but it can also be followed by on-campus students after consultation of the course coordinator.
Note 5: Because of overlap between this online course and on-campus courses, it is not possible to combine this course with ABG30806 - Modern Statistics for the Life Sciences in your study program to obtain a minimum amount of credits.
In this course, students are taught principles of experimental design of trials and statistical analysis of trial data with a special emphasis to linear and generalized linear methods, mixed models, analysis of multi-environment trials using different statistical methods

Learning outcomes

After successful completion of this course students are expected to be able to:

  • Comprehend statistical principles underlying experimental designs for breeding trials with respect to randomization, replication (including types of replicates and pseudo-replication), blocking, experimental units, the use of controls, orthogonality, balance and efficiency, power
  • Comprehend the connections between these design principles and the models and model assumptions underlying statistical analyses, most importantly linear regression and analysis of variance (distributional assumptions, independence, equal variance, additivity or linearity of effects, single or multiple random error terms)
  • Apply these concepts when designing an experiment
  • Explain, distinguish and characterize the following experimental designs: completely randomized design (CRD), randomized complete block design (RCB), incomplete block designs (including resolvable designs: lattice designs and alpha designs, row-column designs) and split-plot designs
  • 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 when generalized 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
  • Understand how and why distribution and link functions need to be specified in GLMs
  • Understand the difference between fixed terms and random terms in a mixed model analysis, both conceptually and in applications
  • Specify a linear mixed model in fixed and random terms for a data analysis with unbalanced designs
  • Specify a linear mixed model in fixed and random terms for a data analysis with 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
  • Quantify, test and characterize genotype-by-environment interaction using different evaluation methods: analysis of variance, mixed models, Finlay-Wilkinson regression, AMMI and GGE biplot
  • Comprehend and discuss the concepts of stability, adaptability and (wide/specific) adaptation in plant breeding in the context of Finlay-Wilkinson regression
  • Estimate heritability of traits from estimates of variance components obtained from Anova and mixed models in genotype trials

Required prior knowledge

Assumed Knowledge:
MAT25303 Advanced Statistics (online) Furthermore: students should have followed an online course at Wageningen University before, or the special Onboarding course for distance learning. To get access to the Onboarding course, send an email to:
YPS60315 Plant Breeding Design Cluster (online) - The R module of this course: knowledge of and experience with R and R Studio is needed. The student is expected to be able to modify existing R scripts and to write short scripts to perform analyses.

Link to more information

If anything remains unclear, please check the FAQ of Wageningen University.


  • Start date

    10 February 2025

    • Ends
      7 March 2025
    • Term *
      Period 4
    • Location
    • Instruction language
    • Register between
      1 Jun, 00:00 - 12 Jan 2025
These offerings are valid for students of Utrecht University