General information on the Unit

ECTS: 5
Contact hours: 70 (36 lectures, 34 practicals)
Personal work hours: 55
Character: Compulsory
Venue: Mediterranean Agronomic Institute of Zaragoza
Scheduling:
- Developed in the first academic year of the Master, during the second semester.
- The assessment of this Unit consists of a continuous evaluation of the practical exercises and a written exam during the second semester.
Requisites and permanence
There are no previous requisites.
Learning methods
Combination of theoretical and practical sessions consisting of the analysis of case studies ans computer practicals.
Language
Lecturers may deliver the course in Spanish or in French. In the second case, simultaneous interpretation into Spanish is provided. The documents supplied by the lecturers may also be written in Spanish, English or French.

Presentation of the Unit

This Unit provides the student with knowledge and skills to analyse information available in experimental databases related to the speciality, to design experiments and to make statistical analyses of the data from such experiments. Furthermore it introduces the student to the application of models and decision support systems for prediction, evaluation and optimization of the physiological and nutritional processes and for ration calculation. The practical activities provide the student with skills in statistical analysis, meta-analytical studies and in the use of relevant software for statistical analysis and modelling.

Context within the syllabus

This Unit will be essential for the development of the units constituting the second part of the Master, where in both “Introduction to research” and “Master Thesis” the student will need to apply and deepen the acquired knowledge to be able to carry out the experimental design for the research project and the analysis of its results.

Competences

Specific competences

• SC8 Understanding the principles of statistics and experimental design in animal nutrition programmes and conducting statistical analysis of experimental results using the relevant computer analysis programs.
• SC9 Reaching conclusions from the results of previous experimentations and knowing and using models and decision support systems of interest in the speciality.

General competences

• ­GC1 Integrating scientific and technical knowledge and applying them discerningly.
• GC2 Performing scientific and/or technical information searches and processing them selectively.
• ­GC3 Analyzing results or strategies and elaborating conclusions which contribute to clarify the problems and to find possible solutions.
• ­GC6 Team-working and promoting exchange and collaboration attitudes with other students, researchers and professionals.

Objectives of learning

This unit has three main objectives. The first one is to provide the student with the statistical bases needed for designing an experiment and performing the statistical analysis of its results, knowing how to use the relevant software. The second objective is for the student to know how to carry out a meta-analysis study to reach conclusions from results obtained from experimental databases. The third one is for the student to be aware of the utility of models and decision support systems as tools to understand and predict processes.

Learning outcomes

Importance of the learning outcomes acquired in this unit

The professional in animal nutrition must know how to design and perform the data analysis of experiments related with this specialty, whether going to be involved in research or in the business professional practice. On the other hand, there is an increasing use of modelling tools that help the professional to understand and predict the nutrition related processes and to therefore attain further evidence supporting decision making.

Learning outcomes

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

• Knows the statistical principles relevant to data analysis and to the experimental design of animal nutrition programmes.
• Is familiar with the use of the appropriate computer software for such programmes.
• Has experience in data introduction and analysis by means of the appropriate software.
• Knows how to integrate and statistically analyse results from numerous experimental data bases in animal nutrition.
• Is aware of the potential use of models and aid-to-decision systems in animal nutrition, and has some experience in their practical use.

Contents

• Data analysis and experimental design
  • Basic statistical principles
  • Data mining and analysis: variance and covariance, regression, repeated measures and non parametric methods
  • Experimental design in animal nutrition studies
• Meta-analysis of experimental data
• The use of modelling as a tool in animal nutrition
  • Basics for empirical and mechanistic models
  • Modelling digestion processes
  • Growth modelling
  • Decision support models and software for rationing
• Case study
• Practicals

Learning activities

Learning activity 1: Lectures combined with applied examples
ECTS: 2.7
Hours: 68
Percentage of contact: 53%

Learning activity 2: Case studies to learn how to perform a meta-analysis research by searching the relevant bibliography, building and analysing a database of at least three scientific papers, interpreting the results, and preparing a short report.
ECTS: 0.4
Hours: 10
Percentage of contact: 60%

Learning activity 3: Computer practicals to:
(1) Solve exercises on analysis of variance and covariance, comparison of means, simple and multiple regression and experimental design using the relevant software.
(2) Apply models.
Students carry out the practicals working individually.
ECTS: 1.9
Hours: 47
Percentage of contact: 60%

Assessment method

Assessment system 1: Written exams, composed by questions provided by the different lecturers of the Unit. The exam is made up of concrete questions requiring a short answer, being possible also multiple-choice tests. The exam assesses both the content of the theoretical part and the understanding of case studies and the practical sessions on models with the computers.
In the written exams, the short-development questions are marked according to the technical and conceptual precision of the answer, and to the reasoning approach. The multiple-choice tests are marked according to the number of correct answers, rating negatively the wrong answers chosen within the same question.
Weighting: 30% of the final score of the Unit

Assessment system 2: Direct assessment by the lecturers of the results of the exercises carried out in the practical computer sessions in the subject "Data analysis and experimental design".
Understanding of the methodology and the validity of the results will be assessed.
Weighting: 40% of the final score of the Unit

Assessment system 3: Direct assessment by the lecturers of the degree of participation in the practical computer sessions in the subject "Data analysis and experimental design".
Weighting: 30% of the final score of the Unit

Lecturers

Lecturers from the University of Zaragoza

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Lecturers from other institutions

Emilio CARBONELL, Instituto Valenciano de Investigaciones Agrarias, Valencia, Spain
Jhonny R. DEMEY, Instituto de Estudios Avanzados, Caracas, Venezuela
Javier GARCÍA, Univ. Politécnica Madrid, Spain
Florence GARCIA-LAUNAY, INRA, Saint-Gilles L’Hermitage, France
Raúl E. MACCHIAVELLI, Univ. Puerto Rico, Mayagüez, Puerto Rico
Cándido POMAR, Agriculture and Agri-Food Canada, Sherbrooke, Canada