28877 - Laboratory 1

Learning outcomes

At the end of the educational path, the student has the knowledge and skills to plan and analyze experiments and to develop and interpret experimental data sets. It also has, through the examination of case studies, knowledge and expertise on the multifunctional role of agriculture.

Course contents

Module 1 - MULTIFUNCTIONAL AGRICULTURE

Teaching unit 1

• Presentation of the course;
• Multifunctionality: definitions and basic concepts;
• Farms and taxation;
• Legal and tax profiles of the multifunctional farms;

Teaching unit 2

• The management of protected areas;
• Natura 2000;
• Biodiversity, natural ecosystems and landscape;
• Agriculture and landscape;
• Landscape policies;
• Ways of representing the landscape;

Teaching unit 3

• Activities related to agriculture, types of related activities, types of multifunctional farms;
• The multifunctionality in European policies: support for multifunctionality and diversification;
• Agritouristic farm and rural tourism;

Teaching unit 4

• The multifunctionality in European policies: Rural Development Programme and Leader;
• The agritouristic and territorial marketing;

Teaching unit 5

• Multifunctional farms experiences: case studies presentation;
• Closing of the module.

 

Module 2 – STATISTICAL METHODOLOGY IN AGRICULTURE

Teaching unit 1

• Introduction to statistical inference, descriptive statistics (types of variables, statistical indices, confidence intervals), distribution of data and tests of normality.
• Introduction to R software: functions, programming language, packages, illustration of the main commands in R with instructions on download and use. Practical examples of script setting for the main statistical indexes and tests described above using experimental datasets.

Teaching unit 2

• Correlation, regression, chi-squared.
• Parametric and non-parametric tests for comparison between two samples (T-test, Wilcoxon, Mann-Whitney).
• Practical examples of setting up scripts on R for the tests described above using experimental datasets.

Teaching unit 3

• Parametric and non-parametric tests for comparison among more than two samples (One or two way ANOVA, Krusal-Wallis, statistical models LME e GLM); transformation of data that does not comply with ANOVA assumptions.
• Practical examples of setting up scripts on R for the tests described above using experimental datasets.

  Teaching unit 4

• Practical application of the main statistical tests and models seen in the previous lectures to different experimental datasets on R.
• Discussion of elaborations in scientific papers (also in English).

  Teaching unit 5

• Seminar on the practical application of a statistical analysis of data in the agronomic field.

 

Readings/Bibliography

The Lecturers will provide materials relating to the case studies covered and the relevant regulations, power point presentations, experimental datasets, and scientific papers discussed. Documents will be available online.

https:// https://virtuale.unibo.it/

Teaching methods

The teaching will be divided into units that include both frontal lectures and case study presentations and statistical analysis of experimental dataset.

The laboratory part of the setting up of experimental drawings and statistical inference will be made with the help of material prepared by the lecturer and made available to students before the lectures. Video tutorials related to the use of R software (freely downloadable at www.r-project.org ) will be made available for the personal in-depth study and practical use after the lectures.

The statistical analysis of experimental data and the critical analysis of scientific bibliography (also in English) will be led by the lecturer with the involvement of the student.

The objectives of the practical lectures are aimed at making students familiar with the setting of experimental designs in research field and activity, developing the ability to recognize the main practical problems in experimental designs and sampling and conducting the student to acquire the scientific method in statistics to verify experimental or field data. It also intends to stimulate the students’ propensity to autonomously acquire new information, using critical discussion of scientific papers, also in English.

Assessment methods

The student who has successfully participated in at least 70% of the activities (successful participation verified by carrying out and passing a short learning test at the end of each teaching unit) will be recognized as suitable without having to take other verification tests.

The short learning test consists of four questions (two for module 1 and two for module 2) with closed-ended answers on the topics covered during the lessons.

To pass the test, the student must correctly answer at least two closed-ended questions.

In other cases, the verification of learning takes place through a single final test, which consists of an individual written test lasting a maximum of 60 minutes.

To carry out the test, technical manuals or calculation or multimedia aids are not necessary, and therefore not allowed.

The student should demonstrate, in addition to the knowledge of the topics covered, link capability and problem-solving.

The dates, times and locations of the exams are published on the course website.

To register for the exams, students must use the web application AlmaEsami.

https://almaesami.unibo.it/

Teaching tools

Personal computers and projector for classroom activities.

Bibliography available at the University Library System (SBA).

Mailing list for teacher-student communications, accessible only to students of the course, password protected distributed during the lesson or requested to the teacher.

Teaching material: the teaching material presented in class is made available to the students in advance in electronic format via a virtual online platform. Access to the material is allowed only to students belonging to the mailing list.

Office hours

See the website of Rino Ghelfi

See the website of Rubina Sirri