Synopsis
Use technology efficiently to reduce the environmental impact of farming activities through combined strategies, such as minimising inputs (water, fertilisers, energy) to lower costs and emissions, reducing the presence of agrochemicals in surface and underground waters, and avoiding soil degradation.
Foster the adoption of technological and agronomic advances (precision agriculture, remote sensing, digital models and scenarios, data mining and data science, artificial intelligence, and decision support systems) to enhance agricultural production.
Preventive and reactive strategies in precision agriculture
Synopsis
The human population on Earth is growing exponentially and is expected to reach 9 billion by 2025.
On the other hand, the expansion of urban areas over land previously dedicated to agriculture is affecting agricultural production. In addition, phenomena due to climate change are causing problems in the management of crops and favouring the development of pests.
Environmental protection, conservation of natural resources, maintenance and improvement of soil fertility and efficient use of water are the main objectives of sustainable agricultural practices.
Synopsis
The machines used in precision agriculture are equipped with a computer that integrates a geographic information system (GIS) and various information reception systems such as satellite navigation systems (GNSS), position error correction systems (RTK), digital cameras, sensors (RGB, NIR, Thermal) and Internet of Things (IoT).
This set of technologies allows for the automation of procedures and allows for the optimisation of agricultural operations and improved resource efficiency.
Precision agriculture uses the technology of variable rate application of fertilisers, pesticides and water, in real time and based on georeferenced field data previously recorded and processed in a GIS environment
Synopsis
A reactive management strategy in agriculture identifies and resolves issues and problems as they arise at a given time.
It is a short-term approach to solving unexpected problems that result from pests, extreme and unusual weather events, prolonged drought, or disease reaching levels that could cause economic losses.
In some situations, reactive measures might be necessary when preventative approaches are not practical or available
Synopsis
Soil variability can be classified according to texture and soil type (sandy, clay, silty, peaty, limestone), soil origin (granite, limestone, shale, metamorphic, anthropic).
Crop variability can be evaluated through productivity values, production uniformity and response to climatic conditions such as precipitation, temperature and evapotranspiration.
Agricultural productivity is intrinsically dependent on soil properties, which vary spatially according to the area of the parcels and their locations.
This variation results from the combination of intrinsic factors, such as the soil’s origin, age and structure, and extrinsic factors such as human activities, such as management, fertilisation and irrigation practices.
Synopsis
The precision agriculture classes aim to transmit to students concepts that allow them to develop production and agricultural management models based on the use of technology.
This technology should be applied to various crops, and, depending on the site's characteristics, to optimise crop management, focusing on principles such as variable-rate application and precision mapping.
They often incorporate concepts such as GPS, GIS, Drones, IoT, Sensors, and aim to increase efficiency in using seeds, fertilisers, and energy, while reducing waste in agricultural operations.
The classes also address the cost/benefit ratio and environmental protection