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Master in

Plant genetics, genomics and breeding

Next edition: 1st part: September 2023 – June 2024 / 2nd part: September 2024 – June 2025

Master in

Plant genetics, genomics and breeding

General information on the Unit

ECTS: 7.5
Contact hours: 70 (36 lectures, 34 practicals)
Personal work hours: 105
Character: Compulsory
Venue: Mediterranean Agronomic Institute of Zaragoza (CIHEAM Zaragoza)
-- Developed during the first academic year of the Master, at the middle of the first semester.
- The assessment of this Unit consists of a written exam and the evaluation of practical exercise during the first semester.
Requisites and permanence
Knowledge on basic statistics and R software is required. Propaedeutic on basic statistics and R software are organized for students who need them.
Learning methods
Combination of theoretical and practical sessions consisting of lectures, computer work, and individual and group study and work..
Lectures are delivered in English and the documents supplied by the lecturers are also in English. The exams can be taken either in English, Spanish or French.


Presentation of the Unit and context within the syllabus

The Unit gathers the main statistical tools and methods used in the analysis of genotypic and phenotypic performance of plant material in breeding programmes.
The first part of this Unit presents an introduction to the scripting languages. Then the unit deals with the design and analysis of individual experiments, and linear regression and correlation analysis. After an introduction to multivariate methods, the Unit deals with the statistical principles, objectives and methods for Genotype x Environment interaction (GxE) analysis. Finally, main elements of the selection theory are presented. The different parts of the Unit combine tutorials and practicals. Open-access software R studio is used throughout the different subjects of the Unit.



Specific competences

  • SC1 Understanding and using molecular and quantitative tools to solve biological, genetic, mathematical and statistical problems.
  • SC2 Designing, planning and analyzing statistical and agricultural experiments with methodological thoroughness, assuming the limitations of the experimental approach.
  • SC3 Understands the concepts and methods behind the selection theory aimed at augmenting the efficiency of selection in breeding programmes

General competences

  • GC1 Learning and working autonomously, responding to unforeseen situations and re-aiming a strategy if necessary.
  • GC2 Team-working and promoting exchange and collaboration attitudes with other students, researchers and professionals.
  • GC3 Communicating reasoning and conclusions both to a general audience and to a specialized public.
  • GC4 Writing presentations and synthesis, preparing and presenting oral communications, and defending them in public.


Learning outcomes

The student, at the end of the learning of this Unit:

  • Knows the basic statistical principles relevant to data analysis in plant breeding programmes.
  • Uses the statistical methods, particularly those of experimental design and linear regression, to be able to interpret the results correctly.
  • Can utilize the computer software useful for statistical analyses.
  • Has practical experience in the management, analysis and interpretation of real data from experiments common to plant breeding.
  • Can assess the importance that genotype by environment (GE) interaction has as determinant of variety adaptation to be developed in a plant breeding programme, and knows the different GE analysis models, and how to interpret their results.
  • Is familiar with some of the tools used in environmental characterization, of potential usage in more complex GE analyses.
  • Understands the concepts and methods behind the selection theory aimed at augmenting the efficiency of selection in breeding programmes.


  • Scripting (BASH, R, Python)
  • Experimental design
  • Introduction to multivariate methods
  • Genotype x environment interaction: adaptation, stability and resilience
  • Selection theory


Learning activities

Learning activity 1: Lectures combined with applied examples
Hours: 100
Percentage of contact: 38%

Learning activity 2: Tutored individual work
(a) Solving exercises on scripting
(b) Analysis of regression and correlation using R and Excel
(c) Analysis of experimental designs using different R packages
(d) Solving exercises of selection theory using R
Students, guided by the corresponding tutor, work individually on the exercises proposed. Thereafter, students have to solve similar exercises as homework and present a brief document to the tutor with the results.
(e) Exercises for exemplification in multivariate methods using R
Students, guided by the tutor, work individually on the exercises proposed. Thereafter, students have to answer a series of multiple choice questions as homework and present the results to the tutor.
ECTS: 2.7
Hours: (a): 8; (b): 3; (c): 10; (d): 2; (e): 6
Percentage of contact: (a): 28%; (b): 10%; (c): 34%; (d): 6%; (e): 21%

Learning activity 3: Tutored group work
Students, guided by the tutor, work in groups of 5-6 persons. They receive a set of data from multienvironmental trials of different crops and geographical areas, including variables such as varieties, environments, years and yields, to be analyzed. Each group chooses one database and applies in practice all the statistical analyses required, extracting the conclusions of such analyses. Every group must draft a synthesis document for public presentation and joint discussion.
ECTS: 0.3
Hours: 8
Percentage of contact: 75%


Assessment method

Assessment system 1: Written exams, composed of exercises and questions provided by all lecturers of the Unit, except for the topic genotype x environment interaction.
The questions are either multiple choice or concrete questions and exercises requiring a short explanation or calculation with R. The exam assesses the content of lectures and, in some cases, the practical work, in addition to the home work exercises presented by the students.
In the written exam, the questions which are not multiple choice are marked according to the technical and conceptual precision of the answer, and to the reasoning approach.
Weighting: 50% of the final score of the Unit

Assessment system 2: Global assessment by the tutors of the individual work related to learning activities 2 (a, b, c, d and e) based on the results of the exercises submitted by each student. Understanding of the methodology and quality of the results will be assessed.
Weighting: 30% of the final score of the Unit

Assessment system 3: Global assessment by the tutor of the group work related to learning activity 3 based on the document prepared and the quality of the public presentation. The rate is the same for all group members.
Weighting: 20% of the final score of the Unit



Carlos CANTALAPIEDRA, CBGP, Madrid (Spain)
Julio DI RIENZO, Univ. Nacional de Córdoba (Argentina)
Marcos MALOSETTI, BASF Vegetable Seeds, Nunhems (The Netherlands)
Hans-Peter PIEPHO, Univ. Hohenheim (Germany)
Ignacio ROMAGOSA, Univ. Lleida (Spain)
Chris-Carolin SCHÖN, Technical Univ. Munich (Germany)
Paul SCHMIDT, Univ. Hohenheim (Germany)