Upskilling and Reskilling students for small-scale farming – Pilot Courses

Activity 4.1. was done by organizing learning events, lectures and courses that included the target groups of this project, or better graduate, postgraduate and Lifelong Learning students in the area of small-scale farming (SMF: small and medium farm enterprises) and other stakeholders in the agricultural sector. Learning activities were tailored to necessities of the agricultural sector in the South Tyrolian context, reflecting on most required tasks.

Agroforestry Innovation Lab

• Realization of tractor implement prototypes for minimal preparatory tillage and stripper header harvesting
• Fieldyieldestimationthroughspectrographicsurveys
• Post-harvest facilities set up in historic buildings, with bread-making and direct marketing

Objectives 4.2. of laboratory exercises on specialized farming technologies for students and LLL were realized using specialized and unique technologies available in the AFI-Lab. The exercises related to particular instruments or laboratory set-ups:

• Exercise on machine safety and roll-over stability on mobile platform. For this particular
  task the AFI-Lab possess a unique mobile platform where vehicles up to 10t can be tilted
  in 360°.
• Exercise on spraying machinery and components under laboratory and simulated real
  world conditions. AFI-Lab disposes of a wind channel to simulate field conditions.
• Exercise on crop monitoring in orchards and vineyards, using a prototype vehicle
  equipped with scanning devices and optical sensors to monitor orchards (ByeLab).
• Exercise to measure performance and efficiency of agricultural machines and processes
  (e.g. cereal production, machine performance) using a set of instruments and sensors.

Syllabus of the course “Introduction to Smart Agriculture Technologies for Mountain Ecosystems”

“Introduction to Smart Agriculture Technologies for Mountain Ecosystems” – Slides from the course

Lab experience

• Task 1: determination of length and measures.
• Task 2: experimental determination of the CoG position.
• Task 3: calculation of lateral rollover instability angle.
• Task 4: experimental determination of lateral rollover instability angle.

The test evaluates the uniformity and symmetry of airflow produced by agricultural sprayers, in line with ISO 16119, with the aim of adapting air velocity and flow rates to the canopy structure of vineyard crops. Using a WP5000 test bench equipped with five ultrasonic anemometers, airflow distribution was precisely measured and analysed, supported by custom post-processing software to identify areas of over- or under-supply.

Results show a highly consistent airflow profile across five repeated tests, with limited spatial variability except at the canopy margins. Iterative adjustments of the vineyard sprayer (targeting a 2.2 m canopy height) significantly improved distribution uniformity, indicating that only minor further refinements are needed to optimise sprayer performance and canopy-adapted application efficiency.

Activity 4.3 concentrated on piloting, testing, and refining innovative learning modules that integrate digital agriculture, intercultural collaboration, and entrepreneurial thinking. A major achievement was the development and delivery of the module Intercultural Communication & Academic Entrepreneurship in Agricultural Engineering, which combined Smart Farming concepts with innovation management, communication skills and business development practices. Students participated in international learning experiences in the United States and Japan, engaged with universities, companies and innovation hubs, and produced comparative analyses of agricultural innovation ecosystems. These experiences deepened their understanding of how Smart Farming technologies evolve within different socio-technical environments.

Electromobility has a long yet often overlooked history in agricultural engineering and is increasingly regaining relevance in the context of sustainable and smart crop production. Early concepts such as electric cable ploughs and diesel-electric tractors already demonstrated the technical feasibility of electrically driven agricultural machinery, even though limitations in energy efficiency, weight, and energy storage hindered widespread adoption. Modern approaches build on these foundations, combining electric drives with advanced power electronics, generators, and hybrid systems. Diesel-electric drivetrains, in particular, allow a combustion engine to operate at optimal efficiency while supplying electrical power to wheel hub motors, power take-offs, and auxiliary systems.

Today, electromobility in agriculture extends beyond full electric tractors to include electric auxiliary drives and implements. Tractors capable of supplying electrical power (e.g., 230/400 V or high-voltage connections) enable electrically driven implements such as fertilizer spreaders, forage mixers, or loaders, reducing mechanical complexity and improving control precision. Battery-electric and cable-powered autonomous tractors represent forward-looking concepts that aim to lower emissions, reduce machine weight, and enable new operational models in precision agriculture. While challenges remain—especially regarding energy storage, infrastructure, and standardization—electromobility is a key technological pathway toward more resource-efficient, low-emission, and digitally integrated plant production systems.

Efficient fertilization is a key factor in sustainable crop production, as plants require nutrients in addition to natural growth factors such as light, water, and heat. When fertilization approaches the yield optimum, accuracy becomes critical: even small deviations can lead to yield depression or unnecessary environmental impact. Therefore, modern agriculture places strong emphasis on precise nutrient dosage and timing rather than on maximizing fertilizer input.

Modern fertilizer application technology aims to distribute nutrients as evenly and accurately as possible across the field. This requires the reliable interaction of two core functions: dosing and distribution. Advanced spreading systems—such as centrifugal, pneumatic, or precision applicators—are designed to improve uniformity, reduce losses at field boundaries, and adapt application rates to site-specific conditions, supporting both economic efficiency and environmental protection.

Plant protection has always been a central element of arable farming, as agriculture inherently means selecting specific crops and safeguarding their growth against pests, diseases, and competing weeds. Historically, humanity was often helpless in the face of natural threats such as locust plagues, fungal diseases, or crop failures, which at times led to famine and mass migration. Only gradually did scientific understanding of plant diseases develop, replacing myths and misinterpretations with evidence-based explanations. This evolution laid the foundation for modern plant protection, which aims to reduce yield losses and secure global food production in a world where a significant share of potential harvests is still lost to biotic stressors.

Economic, Ecological, and Technical Perspectives

Today, plant protection operates at the intersection of economic efficiency and ecological responsibility. On the one hand, it seeks to stabilize yields, maintain crop quality, reduce labor demands, and lower production costs. On the other hand, it must minimize negative impacts on soil, water, air, non-target organisms, and human health. Modern approaches therefore emphasize integrated plant protection, combining chemical, mechanical, biological, and digital methods, guided by damage thresholds and predictive models. Advances in application technology—such as precise spraying systems, optimized nozzles, and electronic machine control—aim to ensure that active substances are applied in the smallest effective quantities and only where truly needed.

Activity 4.4. was more experimental as we used exercises, tests and algorithms to assist in the decision-making regarding agricultural technologies and machinery. The approach is promising and provided useful results. With a more elaborated database, testing and programming, the exercises may further increase the range of use.

The material presents applied examples of decision-making algorithms used in agricultural machinery management, focusing on energy consumption, work performance, and traction requirements. Through structured problem statements and calculation templates, it illustrates how variables such as operating time, field geometry, machine efficiency, soil conditions, and power demand interact in real farming scenarios. The exercises guide learners step by step from given input data to concrete technical decisions, making abstract engineering principles tangible and operational.

Overall, the examples emphasize a systems perspective on farm operations: machinery is not evaluated in isolation, but as part of an integrated workflow that balances productivity, energy efficiency, and feasibility. By comparing alternative machine configurations and operating conditions, the material supports evidence-based choices rather than rule-of-thumb decisions. This approach is particularly relevant for sustainable agriculture, where optimizing energy use and matching machinery to actual field requirements can significantly reduce costs and environmental impact.

Activity 4.5 of the USAGE-NG project focused on delivering GIS workshops at Turkana University College (Kenya) as part of a broader strategy to extend digital agricultural and lifelong-learning competencies beyond Europe. Through an eight-day study visit, a train-the-trainer course was implemented to equip local academic staff and community educators with foundational geospatial skills using open-source tools such as QGIS. The training combined theoretical inputs with hands-on field mapping exercises tailored to local realities, including limited infrastructure, intermittent electricity, and varying levels of digital literacy. This approach aligned with the project’s objective of testing micro-credential-style learning formats in low-resource environments while ensuring practical relevance for rural development and agricultural planning.

The workshops demonstrated both strong institutional interest and clear capacity-building potential, while also revealing structural challenges such as limited ICT infrastructure and the need for preparatory digital-skills training. Despite these constraints, participants successfully completed the course and produced basic GIS outputs, indicating the feasibility of scaling such initiatives through modular, learner-centred formats. The activity generated valuable insights for future follow-up actions, including the planned development of an advanced GIS module and the integration of mobile and offline learning components. Overall, Activity 4.5 confirmed the role of context-sensitive GIS education as a catalyst for sustainable lifelong learning and community empowerment in rural settings.

The first course introduces the fundamental principles of Geographic Information Systems (GIS) and frames them within the broader goal of using geodata to support social empowerment, particularly for women in Turkana County. It explains what GIS is, how geodata is defined through spatial reference, and which key questions GIS can answer (e.g., what is where, what has changed, what if scenarios). Core components such as data acquisition, management, analysis, and visualization are presented as an integrated workflow rather than isolated steps, emphasizing GIS as “the science of where.”

In addition, the course outlines essential data types and analytical methods. Vector and raster data models are explained, along with common spatial analyses such as overlay, proximity, hotspot, and surface analysis. The material also highlights the importance of cartographic visualization and map design to communicate complex spatial relationships effectively. Overall, Day 1 establishes a conceptual and methodological foundation for understanding how GIS supports evidence-based decision-making in environmental, social, and development contexts.

The second file focuses on practical field data acquisition using QGIS in combination with QField on mobile devices. It introduces the complete workflow, from preparing a QGIS project and defining data layers to exporting a QField package, collecting data in the field, and synchronizing results back to the desktop environment. A strong emphasis is placed on GNSS-based positioning, including its principles, accuracy limits, and typical sources of error in real-world conditions.

Furthermore, the file provides detailed, step-by-step guidance on collecting point, line, and polygon data, attaching photos and attributes, and working offline in remote areas. It also covers data synchronization, troubleshooting, and basic editing of collected features. This practical orientation makes the file especially relevant for applied GIS projects, where reliable and structured field data collection is a prerequisite for subsequent spatial analysis and decision support.

This file presents the Institute of Geomatics at BOKU University, outlining its academic profile, research fields, and teaching activities. It highlights core competencies such as remote sensing, GIS, land administration, surveying, time-series analysis, and machine learning, illustrating how geomatics integrates spatial data, technology, and applied research. Selected research topics—ranging from agriculture and forestry to wildlife corridors and land management—demonstrate the institute’s strong application-oriented focus.