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Master in

Plant breeding

Next edition: 1st part: September 2020 – June 2021 / 2nd part: September 2021 – June 2022

Master in

Plant breeding

General information on the Unit

Contact hours: 70 (30 lectures, 40 practicals)
Personal work hours: 80
Character: Compulsory
Venue: Mediterranean Agronomic Institute of Zaragoza
- Developed during the first academic year of the Master, during the first semester.
- The assessment of this Unit consists in a theoretical and practical exam and the assessment of the work in groups during the first semester.
Requisites and permanence
There are no previous requisites
Learning methods
Combination of theoretical and practical classes, and group study and work.
Lecturers may deliver the topics in Spanish or in English. In the second case, simultaneous interpretation into Spanish is provided. The documents supplied by the lecturers may also be written in Spanish or in English.


Presentation of the Unit and context within the syllabus

The first part of this unit deals with the design and analysis of individual experiments, while the second part is related with multienvironmental trials. After reviewing the main statistical concepts necessary for the understanding of Genotype x Environment interaction (GxE) and phenotype adaptation, the statistical principles, objectives and methods for such an analysis are introduced. Tutorials and practicals on statistical assessment of GxE interaction and adaptation are carried out to illustrate the theory, as well as a work in groups. To complement this, the issues of how to manage a set of data and how to characterize the environment are explained. Genstat Discovery software is used as a computer tool which offers open access to professionals in developing countries and to all types of university learning.



Specific competences

  • SC4 Understanding and using quantitative tools to solve biological, mathematical and statistical problems.
  • SC5 Designing, planning and analyzing statistical and agricultural experiments with methodological thoroughness, assuming the limitations of the experimental approach.

General competences

  • GC5 Learning and working autonomously, responding to unforeseen situations and re-aiming a strategy if necessary.
  • GC6 Team-working and promoting exchange and collaboration attitudes with other students, researchers and professionals.
  • GC7 Communicating reasoning and conclusions both to a general audience and to a specialized public.
  • GC8 Writing presentations and synthesis, preparing and presenting oral communications, and defending them in public.


Learning outcomes

At the end of Unit 2, the student:

  • 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.



  • Design and analysis of individual experiments
  • Design and analysis of multienvironmental trials
    • Data management
    • Environmental characterization
    • Genotype x Environment interaction

Learning activities

Learning activity 1: Lectures combined with examples
Hours: 75
Percentage of contact: 40%

Learning activity 2: Practical sessions with solving of exercises and problems in the computer room.
The first objective of these practices is to learn how to use the statistical package JMP. Likewise, the use of Excel as a tool for some basic statistical analysis is also shown. Throughout these practices students working in pairs solve all the exercises presented in the theoretical classes, relating to basic statistical analyses and of uni - and multienvironmental experiment designs.
ECTS: 2.4
Hours: 60
Percentage of contact: 60%

Learning activity 3: Work in groups of four-five students who will 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: 0.6
Hours: 15
Percentage of contact: 25%


Assessment methods

Assessment system 1: Theoretical and practical exam on the PC, in which examples of concrete data given must be solved, complemented with theoretical questions requiring a short development. The exam assesses both the content of the theoretical part and the ability to solve exercises individually.
In the written exams, the questions are marked according to the technical and conceptual precision of the answer, and to the reasoning approach, and the exercises according to the understanding of the methodology and the validity of the results.
Weighting: 80% of the final score of the Unit.

Assessment system 2: Assessment of the work in groups. Each group will present and defend in public the work performed in front of the unit lecturers and the rest of the groups. Both the written document and the public presentation and defence made by each group member will be assessed.
Each group will get a global score. In this score, the understanding of the methodology, the results obtained and the quality of the conclusions will be assessed.
75% of each member's score is the global score of the group, and 25% corresponds to the individual score awarded by the lecturers. Participation in the group work, clarity in the presentation and soundness of the defence will be assessed.
Weighting: 20% of the final score of the Unit



Ricardo BLANCO, UdL, Lleida (Spain)
Daniela BUSTOS, Wageningen UR (The Netherlands)
Gemma RODRIGO, Lab-Ferrer, Lleida (Spain)
Ignacio ROMAGOSA, IAMZ-CIHEAM, Zaragoza (Spain)
Fred VAN EEUWIJK, Wageningen UR (The Netherlands)
Jordi VOLTAS, UdL, Lleida (Spain)