Laboratory of Precision Agronomy

· Advances in sensing technologies, data analytics, and modern agricultural mechanization now enable the implementation of management strategies aimed at the efficient and targeted use of agricultural inputs within cropping systems. These technologies also support multi-scale monitoring and adaptive management of agro-silvo-pastoral systems, contributing to improved environmental, economic, and productive sustainability.
· The primary objective of the course is to provide students, through an integrated theoretical and practical approach, with the knowledge and skills required to monitor, analyze, and interpret the status of crops and agro-silvo-pastoral environments. This will enable them to design site-specific management plans based on the ability to intervene at the right place and time using the most appropriate rates and techniques.
· Specifically, the course aims to:
· Develop a solid understanding of methods for analyzing and managing spatial variability in different agroecosystems, including cropping and agro-silvo-pastoral systems.
· Provide tools for identifying the most appropriate scales of analysis, key variables, and sensing technologies according to specific operational contexts.
· Develop competencies in the generation and interpretation of thematic and prescription maps, as well as in the design of optimized management plans based on precision agriculture principles.

· Upon successful completion of the course, students will be able to:
1. Analyze and interpret site-specific data related to soil and agroecosystem conditions and apply this information to agronomic management within precision agriculture frameworks.
2. Understand the operating principles, limitations, and conditions of use of major remote and proximal sensing technologies, integrating them into different agronomic management practices.
3. Acquire, process, and modify multispectral imagery collected through remote (satellite and UAV) and proximal sensors in order to extract information useful for describing crop and pasture conditions and supporting management decisions.
4. Assess the spatial and temporal variability of vegetation and crops through the use and interpretation of proxy variables (e.g., vegetation indices, biophysical parameters, multispectral signals) derived from remote and proximal sensing systems.
5. Understand, apply, and develop basic algorithms for prescription map generation, with particular emphasis on nitrogen fertilization management.
6. Understand, apply, and develop basic algorithms for thematic mapping to support the monitoring of agro-silvo-pastoral environments and grazing management.