Data Analysis for Agriculture
A.Y. 2026/2027
Learning objectives
To provide the methodological and practical basis for a rigorous management of quantitative data in agriculture, the design of sample surveys, field surveys and experimental tests in agriculture.
Develop advanced skills in the use of spreadsheets and dedicated software for the collection and statistical analysis of the increasingly significant mass of management and biological data in agriculture.
Provide a solid theoretical and practical foundation for the reading, analysis, interpretation and presentation of data from industry databases, sample surveys, field measurements and experimental test results.
Develop advanced skills in the use of spreadsheets and dedicated software for the collection and statistical analysis of the increasingly significant mass of management and biological data in agriculture.
Provide a solid theoretical and practical foundation for the reading, analysis, interpretation and presentation of data from industry databases, sample surveys, field measurements and experimental test results.
Expected learning outcomes
Ability to manage data from national and international databases of the sector, and / or from sampling surveys in agriculture.
Ability to carry out graphic and quantitative descriptive analyzes, to analyze trends, variation factors and confounding factors through the use of appropriate graphic and statistical techniques with spreadsheets and statistical processing software.
Ability to present, read and interpret data from both the field and the laboratory.
Ability to carry out graphic and quantitative descriptive analyzes, to analyze trends, variation factors and confounding factors through the use of appropriate graphic and statistical techniques with spreadsheets and statistical processing software.
Ability to present, read and interpret data from both the field and the laboratory.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
SHORT TEACHING OVERVIEW
The teaching provides the methodological and operational foundations for the management, analysis, and interpretation of quantitative and qualitative data in agriculture. After reviewing the principles of descriptive statistics and graphical data representation, the teaching introduces the main tools of statistical inference, with particular emphasis on theoretical and sampling distributions, parameter estimation, and hypothesis testing. Special attention is devoted to understanding statistical reasoning, selecting appropriate methods, and critically interpreting results.
The teaching also covers relationships among variables through correlation and regression analyses, techniques for comparing means, linear models, and analysis of variance. Finally, students are introduced to the main experimental designs, multiple regression, multivariate analysis techniques, and the role of regression in modern predictive approaches and artificial intelligence applications in agriculture.
DETAILED PROGRAMME - LECTURES ( 4 ECTS, 32 HOURS)
INTRODUCTION TO DATA ANALYSIS IN AGRICULTURE AND DATASET MANAGEMENT. Types of variables, data organization and preparation for statistical analysis, 2 h.
FREQUENCY TABLES AND GRAPHICAL REPRESENTATION OF QUALITATIVE AND QUANTITATIVE DATA. Bar charts, histograms, box plots and scatter plots, 4 h.
DESCRIPTIVE STATISTICS. Measures of central tendency (mean, median, mode, percentiles) and dispersion (variance, standard deviation, coefficient of variation). Skewness, kurtosis and data standardization, 4 h.
RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES. Covariance, correlation and simple linear regression. Least-squares estimation and interpretation of model parameters, 4 h.
POPULATIONS, SAMPLES AND PRINCIPLES OF STATISTICAL ESTIMATION. Point and interval estimation. Properties of estimators, 2 h.
THEORETICAL AND SAMPLING DISTRIBUTIONS. Normal, standard normal, binomial, Poisson, chi-square and F distributions. Relationship between statistical distributions and inference, 4 h.
STATISTICAL HYPOTHESIS TESTING. Null and alternative hypotheses, Type I and Type II errors, significance level and statistical power. z-tests and t-tests for one and two means, 2 h.
ANALYSIS OF QUALITATIVE DATA. Contingency and tetracoric tables. Chi-square tests for goodness-of-fit and independence between categorical variables. Bayes' theorem. Sensitivity, specificity, predictive values, diagnostic likelihood ratios and odds ratios, 2 h.
ANALYSIS OF VARIANCE AND LINEAR MODELS. One-way and factorial ANOVA, interactions, ANCOVA, fixed and random effects, and multiple comparison procedures, 3 h.
LINEAR REGRESSION AND MODEL EVALUATION. Regression coefficients, coefficient of determination, residual analysis, assessment of model assumptions and interpretation of results, 3 h.
MULTIPLE REGRESSION, INTRODUCTION TO EXPERIMENTAL DESIGNS, NON-PARAMETRIC TESTS, MULTIVARIATE ANALYSIS AND PREDICTIVE MODELS. The role of regression in modern data analysis and artificial intelligence applications in agriculture, 2 h.
PRACTICAL SESSIONS (2 ECTS ,32 HOURS), IN PRESENCE 26 HOURS
Dataset preparation and management using spreadsheets and statistical software, 2 h. Construction and interpretation of tables and graphs, 3 h. Calculation and interpretation of descriptive statistics, 3h. Application of the main statistical distributions 3h. Correlation and regression analyses, 3h. Application of statistical tests for quantitative and qualitative data, including chi-square tests, contingency tables and diagnostic indices, 3 h. Analysis of variance and interpretation of results,3h. Assessment of model assumptions and residual analysis, 3h. Critical interpretation of results through case studies drawn from the different disciplines of agricultural sciences 3h.
SYNCHRONOUS ONLINE ACTIVITIES (6 HOURS)
Integrated review and descriptive and inferential statistics, 2 h. Selection of statistical methods, assessment of assumptions and critical interpretation of results, 2h. Review and clarification session on teaching topics, 2 h
The teaching provides the methodological and operational foundations for the management, analysis, and interpretation of quantitative and qualitative data in agriculture. After reviewing the principles of descriptive statistics and graphical data representation, the teaching introduces the main tools of statistical inference, with particular emphasis on theoretical and sampling distributions, parameter estimation, and hypothesis testing. Special attention is devoted to understanding statistical reasoning, selecting appropriate methods, and critically interpreting results.
The teaching also covers relationships among variables through correlation and regression analyses, techniques for comparing means, linear models, and analysis of variance. Finally, students are introduced to the main experimental designs, multiple regression, multivariate analysis techniques, and the role of regression in modern predictive approaches and artificial intelligence applications in agriculture.
DETAILED PROGRAMME - LECTURES ( 4 ECTS, 32 HOURS)
INTRODUCTION TO DATA ANALYSIS IN AGRICULTURE AND DATASET MANAGEMENT. Types of variables, data organization and preparation for statistical analysis, 2 h.
FREQUENCY TABLES AND GRAPHICAL REPRESENTATION OF QUALITATIVE AND QUANTITATIVE DATA. Bar charts, histograms, box plots and scatter plots, 4 h.
DESCRIPTIVE STATISTICS. Measures of central tendency (mean, median, mode, percentiles) and dispersion (variance, standard deviation, coefficient of variation). Skewness, kurtosis and data standardization, 4 h.
RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES. Covariance, correlation and simple linear regression. Least-squares estimation and interpretation of model parameters, 4 h.
POPULATIONS, SAMPLES AND PRINCIPLES OF STATISTICAL ESTIMATION. Point and interval estimation. Properties of estimators, 2 h.
THEORETICAL AND SAMPLING DISTRIBUTIONS. Normal, standard normal, binomial, Poisson, chi-square and F distributions. Relationship between statistical distributions and inference, 4 h.
STATISTICAL HYPOTHESIS TESTING. Null and alternative hypotheses, Type I and Type II errors, significance level and statistical power. z-tests and t-tests for one and two means, 2 h.
ANALYSIS OF QUALITATIVE DATA. Contingency and tetracoric tables. Chi-square tests for goodness-of-fit and independence between categorical variables. Bayes' theorem. Sensitivity, specificity, predictive values, diagnostic likelihood ratios and odds ratios, 2 h.
ANALYSIS OF VARIANCE AND LINEAR MODELS. One-way and factorial ANOVA, interactions, ANCOVA, fixed and random effects, and multiple comparison procedures, 3 h.
LINEAR REGRESSION AND MODEL EVALUATION. Regression coefficients, coefficient of determination, residual analysis, assessment of model assumptions and interpretation of results, 3 h.
MULTIPLE REGRESSION, INTRODUCTION TO EXPERIMENTAL DESIGNS, NON-PARAMETRIC TESTS, MULTIVARIATE ANALYSIS AND PREDICTIVE MODELS. The role of regression in modern data analysis and artificial intelligence applications in agriculture, 2 h.
PRACTICAL SESSIONS (2 ECTS ,32 HOURS), IN PRESENCE 26 HOURS
Dataset preparation and management using spreadsheets and statistical software, 2 h. Construction and interpretation of tables and graphs, 3 h. Calculation and interpretation of descriptive statistics, 3h. Application of the main statistical distributions 3h. Correlation and regression analyses, 3h. Application of statistical tests for quantitative and qualitative data, including chi-square tests, contingency tables and diagnostic indices, 3 h. Analysis of variance and interpretation of results,3h. Assessment of model assumptions and residual analysis, 3h. Critical interpretation of results through case studies drawn from the different disciplines of agricultural sciences 3h.
SYNCHRONOUS ONLINE ACTIVITIES (6 HOURS)
Integrated review and descriptive and inferential statistics, 2 h. Selection of statistical methods, assessment of assumptions and critical interpretation of results, 2h. Review and clarification session on teaching topics, 2 h
Prerequisites for admission
Basic knowledge of descriptive statistics. Ability to use electronic spreadsheets.
Prerequisites for non-attending students are the same as for attending students.
Prerequisites for non-attending students are the same as for attending students.
Teaching methods
The teaching consists of 32 hours of lectures (4 ECTS credits), 26 hours of computer-based practical sessions, and 6 hours of synchronous online teaching activities (2 ECTS credits of practical training overall). Lectures combine theoretical and applied aspects with the aim of developing students' understanding of statistical reasoning and their ability to critically apply the main methods used in agricultural data analysis.
Practical sessions involve the use of spreadsheets and statistical software, with particular emphasis on the interpretation of results and the informed selection of appropriate statistical procedures.
The synchronous online activities, scheduled at the end of the teaching, are devoted to the review and integration of the main topics covered through examples and case studies. These activities support the consolidation of acquired knowledge and provide additional opportunities for discussion and further learning for all students.
Regular attendance at lectures, practical sessions, and online activities is strongly recommended.
Practical sessions involve the use of spreadsheets and statistical software, with particular emphasis on the interpretation of results and the informed selection of appropriate statistical procedures.
The synchronous online activities, scheduled at the end of the teaching, are devoted to the review and integration of the main topics covered through examples and case studies. These activities support the consolidation of acquired knowledge and provide additional opportunities for discussion and further learning for all students.
Regular attendance at lectures, practical sessions, and online activities is strongly recommended.
Teaching Resources
ecture slides and supporting material for the course will be made available to students on the course Ariel site: datasets, links to scientific articles and web pages of interest. Reference texts will be given during the course and in the slides of the first lecture.
Assessment methods and Criteria
The exam consists of a written and a practical examination. The written part consists of 6 questions (1 multiple choice question, 2 open theoretical questions; 2 practical problems; 1 statistical analysis to comment on (points 0-4 for each question), and a computer session with 1 dataset to analyze. The biostatistical test will assess the ability to organize, summarize, and represent biotechnological data by choosing the appropriate experimental methodologies and statistical tests. The final score will be the summation of the points obtained in the different parts (written part , points 0-4 for each questions, computer session points 0-6), aimed at ascertaining:
(a) your knowledge and ability to understand the topics of the course as well as mastery of the specific language related to the use of statistical techniques and the ability to present topics in a clear and orderly manner
(b) your ability to apply knowledge and understanding through the analysis of a dataset or the execution and discussion of a statistical problem using excel
(c) your ability to understand and interpret the results of statistical analysis by commenting on an analytical output of statistical software used during the course and your ability in organize an experimental plan.
The final evaluation is based on a total of 30 points.
Activities carried out by students during the course, such as commentaries on analyses, completion of exercises, a compilation of a dictionary of terms, etc. will also be taken into account when determining the final grade.
Students with SLD or disability certifications are kindly requested to contact the teacher at least 15 days before the date of the exam session to agree on individual exam requirements. In the email please make sure to add in cc the competent offices: [email protected] (for students with SLD) o [email protected] (for students with disability).
The learning verification methods for non-attending students are the same as for attending students.
(a) your knowledge and ability to understand the topics of the course as well as mastery of the specific language related to the use of statistical techniques and the ability to present topics in a clear and orderly manner
(b) your ability to apply knowledge and understanding through the analysis of a dataset or the execution and discussion of a statistical problem using excel
(c) your ability to understand and interpret the results of statistical analysis by commenting on an analytical output of statistical software used during the course and your ability in organize an experimental plan.
The final evaluation is based on a total of 30 points.
Activities carried out by students during the course, such as commentaries on analyses, completion of exercises, a compilation of a dictionary of terms, etc. will also be taken into account when determining the final grade.
Students with SLD or disability certifications are kindly requested to contact the teacher at least 15 days before the date of the exam session to agree on individual exam requirements. In the email please make sure to add in cc the competent offices: [email protected] (for students with SLD) o [email protected] (for students with disability).
The learning verification methods for non-attending students are the same as for attending students.
AGRI-09/A - Livestock Systems, Animal Breeding and Genetics - University credits: 6
Computer classroom exercises : 32 hours
Lessons: 32 hours
Lessons: 32 hours
Professor:
Crepaldi Paola
Shifts:
Turno
Professor:
Crepaldi PaolaProfessor(s)
Reception:
keeping an appointment by e-mail
Sezione di Zootecnica Agraria, 1st floor, Via Celoria 2