Master's course Plant Breeding - Experimental Design and Data Analysis of Plant Breeding Trials
Attention working professionals in the plant breeding sector! Enhance your statistical analysis skills with our course on experimental design and data analysis of plant breeding trails. You will learn the principles of experimental design for breeding trials and how to apply statistical methods such as linear and generalised linear methods, mixed models, and analysis of multi-environment trials. By completing this online academic-level course, you will be able to design efficient experiments, understand the connections between design principles and statistical models, and perform different analyses using GLMs. You will also gain knowledge in linear mixed models, genotype-by-environment interaction, heritability estimation, and more. Don't miss the opportunity to advance your career with our comprehensive and practical course.
Registration deadline: 14 January 2024
Exam date: 7 March 2024
Why follow this online course?
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.
Is this online course for you?
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 the more advanced plant breeding courses in our Online Master's course series Plant Breeding.
This course is also part of the Online Master's course series - Plant Breeding Experimental Design, Data & Statistics: experimental design and quantitative analysis of breeding trials.
What you'll learn
After successful completion of this online course, you will be able to:
- 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;
- 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);
- apply these concepts when designing an experiment;
- 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;
- 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 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;
- 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 characterise 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.
Knowledge clips, individual and group exercises or discussions, E-learning modules.
When enrolling in this course, you may apply for the use of the STAP budget. Check if you are eligible for the STAP-budget.
Available through the course website (included in course fee).
Software used in this course
R including R studio.
This course is quite time-intensive and requires approximately 20 hours per week for the average participant. There are assignments with deadlines.
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.
Yo ushould 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. On top of that, knowledge of and some experience with R and R Studio is required to design experiments and perform statistical analyses. You are expected to be able to modify existing R scripts and to write short scripts to perform analyses. If the latter is not the case, you can follow the introductory online course Getting Started with R. For more information and registration, please check this document.
Participation in the remote 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 07-03-2024 (7 March 2024) from 08:30 - 22:00 (Amsterdam Time Zone). The duration of an exam is 3 hours. The resit will be scheduled in the week of 6 May 2024.
Upon successful completion - passing the exam -, a digital certificate with 3 study credits (ECTS) is issued. This certificate offers no immediate rights to apply for a formal degree programme at a university, but might support your request for admission. In case you've also completed the Master's Course Advanced Statistics successfully, you can obtain a Micro-credentials certificate.
More information & Registration
You can register for this course. Have any questions? Contact Wageningen Academy.