Applied Statistics for Mountain Agri-Environmental Analyses

The course will cover the various data sources available in agriculture, from which sensors they originate, and what they can be used for. The main descriptive and software tools for processing, visualizing, and analyzing data will be provided. Tabular data, time series, and spatially distributed data will be considered. In particular, the following topics will be addressed: Introduction to precision agriculture and forestry and the role of data in the description and management of the agri-environmental models. Data sources: field sensors and remote sensing Integration, cleaning, and encoding of tabular, temporal, and spatial data. Basic models for regression, classification, and clustering.
Lectures will be organized as a combination of theory and software laboratory exercises (using the R and Python programming languages) to allow students to acquire operational skills on the topics of the course. Real world use cases will be also presented through seminars given by experts in the field.