Agrivoltaics, the integration of photovoltaic (PV) systems with agricultural activities, is gaining attention as an innovative solution to improve land use efficiency and address climate challenges. This study investigates the potential and challenges of the Agri-PV in the Italian context using a bottom-up SWOT–AHP methodology, incorporating data from stakeholders across various sectors. Key findings highlight significant strengths, such as increased land use efficiency and technological innovation, as well as opportunities such as renewable energy production and local economic growth. However, barriers such as high installation costs, regulatory ambiguity, and potential impacts on biodiversity remain crucial issues. SWOT–AHP analysis reveals balanced global priorities, with leading opportunities (26.8%) and stakeholder-specific differences that offer valuable insights for inclusive strategies. The research also estimates the technical potential of Agri-PV in Italy, showing that using a fraction (1% or 5%) of “Unused Agricultural Land” could triple the energy targets outlined in the National Integrated Energy and Climate Plan (PNIEC).

Climate changes and their negative effects on the environment and society are now well known, also due to the increasingly frequent news episodes reporting the occurrence of extreme events with disastrous consequences in economic terms and in terms of human lives. Fires have and will have a major impact on agricultural resources and urban settlements, with critical consequences for the safety and health of citizens, the safeguarding of economic assets and the provision of essential services from fire-damaged ecosystems. Nowadays, satellite monitoring platforms are largely diffused. Therefore, environmental data start to be broadly available. This very preliminary work aims at proposing an effective workflow for wildfire prevention using the leaf area index (LAI) as precursor. By looking at the time evolution of three indices (mean Green, NDV and LAI) on an area interested by wildfire, we show that LAI has maximum before the fire and can be used as an indicator in defining a fire-risk category map. Though many more data is necessary to validate the method and ground truth LAI is lacking, the workflow presented could be the first step of an effective towards a fire-prevention system aiming at reducing the reaction time of safeguards and fire-damage.

Tractor rollover caused by physical contact with static obstacles or harsh terrain is a major hazard in agriculture. In addition, obstacle detection systems based on cameras and lidars struggle to perform well in agricultural working environments while their success rates might also be affected by a high number of false alarms. On the other hand, modern tractors are increasingly getting equipped with embedded GNSS and real-time kinematics (RTK) technologies that allow the driver to know its position on Earth with little error: this capability, combined with precise speed estimation which is a common feature of such systems, can be used to improve the safety of agricultural vehicles if static obstacles are georeferenced and adaptive buffer zones are created to serve as danger areas. In this study, which is part of a project called “Obstacle detection and tracking system for fixed and moving obstacles in agriculture” (SIRTRAck) funded by the Italian National Institute for Insurance against Accidents at Work (INAIL), a Keyhole Markup Language (KML) file containing georeferenced information on static obstacles in an experimental field has been created and passed to an algorithm on board of a tractor equipped with RTK, which integrated the dataset with the vehicle's real-time position, direction and speed. As the tractor entered the field, risk zones have been calculated by the algorithm from real-time motion information and have been used to alert the driver if the tractor entered one of them. The possibility of relying on ground truth and on adaptive shapes for danger zones built on speed, driver’s reaction time and expected slippage proved to be a valid and low-cost method for preventing tractor rollover hazards and might be easily used to improve existing geofencing features in both manned and autonomous agricultural vehicles.

Accidents and deaths at work are a persistent problem, with numbers still worrying. The agricultural and forestry sector is among the most exposed to work risks, with particular attention to noise risk from the use of agricultural machinery and operators. This study aims to analyze the exposure to noise risk during use of wheeled and tracked tractors, with or without a cab, as well as other operating machines. The analysis takes into account the parameters Lpeak (peak sound pressure values), LAeq.T (time-weighted equivalent noise exposure levels) and LAS (maximum and minimum values weighted according to the Slow time constant) in order to assess the noise impact and define strategies for improving the safety and health of workers. This study demonstrates that in multiple cases, the regulatory thresholds for the examined variables are exceeded, regardless of the presence of a cabin. Specifically, Lpeak values approach 140 dB, dangerous to human health, while LAeq.T levels are close to or, in some instances, exceed 87 dB. It is also verified that agricultural and forestry operators who mainly use crawler tractors have greater and constant exposure to noise compared to those who use tractors with a cabin.

Roll Over Protection Structures (ROPS) are conceived to provide passive protection for operators of heavy, self-propelled machinery in the event of a rollover. The verification of ROPS requires the application of a sequence of loads and the subsequent analysis of any resulting permanent deformations, that must guarantee access by the operator to an adequate living space (clearance zone). In many instances, the construction of structural components necessitates the iterative development of multiple physical prototypes. The utilization of computer-aided design (CAD) and finite element analysis (FEA) enables the visualization of the product and the evaluation of the mechanical resistance of the structure, even prior to the manufacturing and testing. This results in a significant reduction in the costs associated with the certification of the structure and the creation of physical prototypes. The aim of this study is to develop, by means of virtual prototyping tools, a driver’s cab for a self-propelled hazelnut harvester that complies with the OECD Code 4 standard for the approval of protective structures. Design for manufacturing and assembly criteria have been used for the 3D CAD modeling of the sheet metal assembly. Non-linear structural analysis was set up to simulate crushing tests, considering the elastic-plastic behavior of the material. Results and visualization of stresses and displacements show that the protection structure passes all acceptance conditions according to regulations. The designed driver’s cab has become an optional feature that can be installed on these vehicles, at the customer’s choice in place of the ROPS.

Noise and vibration are important risk factors for agricultural operators. In fact, musculoskeletal disorders of occupational origin linked to exposure to vibrations in the workplace have continuously increased in recent years and are followed, in terms of reported cases, by pathologies of the auditory system linked to noise exposure. In agriculture, the use of machinery for various cultivation operations involves exposure to these risk factors at levels that are often higher than the action values defined by the laws transposing European directives. In almond cultivation, as in other crops, harvesting is one of the most mechanized operations. The objective of this research is to carry out a comparison on workers’ exposure to noise and vibrations during almond harvesting organized in three different harvest yards: 1. Harvesting with a FACMA Semek 1000 self-propelled harvester; 2. Harvesting with Vimar MK2 shaker operated by John Deere 5105 GF tractor; 3. Harvest with Berardinucci Yellow Devil Vini 260 P self-propelled harvester. The average equivalent noise levels detected in the field, during harvesting in farms in central Italy, were respectively 84.6, 79.2 and 83.2 dB(A) with very similar maximum peak levels (137.7, 137.3 and 137.9 dB(C) respectively). The weighted accelerations transmitted to the whole body, detected on the most stressed axis (mainly y in yard 1 and x in 3, while in 2 the two axes are equally stressed) were, respectively, 0.29, 0.40 and 0.24 ms−2. The results show how the factor that most penalizes workers’ exposure is noise and, in particular, the peak values that exceed the upper action value of 137 dB(C) and which entail the application of specific prevention obligations towards exposed workers. Even with regards to exposures for prolonged periods, it is the noise which, in the case of harvest yards 1 and 3, can cause the action values to be exceeded, in the case of use of the machines for daily times exceeding 435 and 305 min respectively.

This work presents the design and implementation of a novel hybrid GNSS-RFID circuit architecture aimed at enhancing positioning accuracy in environments where satellite navigation performance is degraded by signal obstruction and multipath effects. The RFID subsystem enhances global GNSS positioning by leveraging a network of local passive resonant circuits, i.e., UHF RFID tags, which act as local anchors. These RFID tags provide position refinement information that is used by the control circuit to improve localization performance. The theoretical model and the circuit-level implementation for the adaptive and dynamic power control over the RFID interface are presented, enabling the estimation of distance from local tags based on backscattered signal strength and controlled RF excitation levels. The circuit provides RFID-assisted dynamic reconfiguration of GNSS receiver parameters, including carrier-to-noise ratio thresholds (CN0), satellite angle-of-arrival filtering, and elevation-based 2D/3D mode switching. To validate the proposed circuit, experimental tests were conducted in three obstructed agricultural and forestry environments using U-blox Zed-F9P GNSS modules as the hardware platform. Positioning performance was compared between a standalone GNSS configuration and the same module augmented with the RFID-assisted circuit. A third Zed-F9P receiver, operating in real-time kinematic (RTK) fix mode, served as a ground-truth reference for accuracy evaluation. Positioning errors were evaluated using metrics such as mean error, median error, and error variance. Results demonstrate that the RFID-assisted architecture enables up to 43% reduction in positioning error and a significant decrease in error variance, confirming the robustness and adaptability of the proposed circuit under challenging conditions.

Climate change significantly intensifies agroforestry workers’ exposure to atmospheric particulate matter (PM), raising occupational health concerns. This review, based on the analysis of 174 technical and scientific sources including articles, standards and guidelines published between 1974 and 2025, systematically analyses the main sources of PM in agricultural and forestry activities (including tillage, pesticide use, harvesting, sowing of treated seeds and mechanized wood processing) and focuses on the substantial contribution of agricultural and forestry machinery to PM emissions, both quantitatively and qualitatively. It highlights how changing climatic conditions, such as increased drought, wind and temperature, amplify PM generation and dispersion. The associated health risks, especially respiratory, dermatological and reproductive, are exacerbated by the presence of toxicants (such as heavy metals, volatile organic compounds and pesticide residues toxic for reproduction) in PM. Despite existing regulatory frameworks, significant gaps remain regarding PM exposure limits in the agroforestry sector. Emerging technologies, such as environmental sensors, AI-based predictive models and drone-assisted monitoring, are proposed for real-time risk detection and mitigation. A multidisciplinary and proactive approach integrating innovation, policies and occupational safety is essential to safeguard workers’ health in the context of increasing climate stress.

Recent years have seen a growing interest in hazelnut cultivation, justified by a doubling of the area cultivated with this crop since the year 2000. As a result, mechanization in this field, particularly with regards to harvesting machines, has also evolved significantly. Nevertheless, spread of electronics and telemetry systems only now has taken place, and there is a need to production mapping system. The aim of this work is to prototype and test a low-cost system for measuring the volume of harvested fruits using distance sensors. In detail, ultrasonic sensors have been employed connected to an Arduino microcontroller. Measured data have been acquired by serial communication and processed later using Matlab software. Several processing algorithms were tested in order to obtain more precise information about the volume occupied by fruits from the measured distances. Several tests were conducted by discharging hazelnut samples inside a sheet metal container that simulates on a small scale, the harvesting trailer. The container was provided with a lid, featuring holes for ultrasonic sensors fixing, and has been placed on 4 load cells, to monitor the weight of the hazelnuts during the tests. Measurements with ultrasonic systems show that the spherical shape of hazelnuts does not allow for high accuracy. However, the system allows adequate tracking of the filling status and distribution of hazelnuts in the trailer, and with the elimination of outlier data, it is possible to reduce the volume measurement error.

Mechanised agricultural operations are often performed individually, under minimal supervision and across a wide range of unfavourable working conditions, resulting in a complex mixture of hazards and external stressors that severely affect safety conditions. Socio-technical and environmental constraints significantly affect safety culture and require continuous performance adjustments to overcome timing pressures, resource limitations, and unstable weather conditions. This study introduces a FRAM-based safety culture model that embeds the thoroughness-efficiency trade-off (ETTO) in four distinct operational modes that adhere to specific safety cultures, namely, thoroughness, risk awareness, compliance, and efficiency. This model has been instantiated for mechanised ploughing: foreground task functions were coupled with background functions that represent socio-technical constraints and environmental variability, while severity classes for potential incidents were derived from the US OSHA accident database. The framework was also supported by a semi-quantitative Resonance Index based on severity and coupling strength, the Total Resonance Index (TRI), to assess how variability propagates in foreground functions and to identify hot-spot functions where small adjustments can escalate into high resonance and hazardous conditions. Results showed that the negative effects on functional resonance generated by safety detriment on TRI observed between compliance and effective working modes were three times larger than the drift between risk awareness and compliance, demonstrating that efficiency comes with a much higher cost than keeping safety at compliance levels. Extending the proposed approach with quantitative assessments could further support the management of socio-technical and environmental drivers in mechanised farming, strengthening the role of safety as a competitive asset for enhancing resilience and service quality.

Accurate detection of machinery-induced strip roads after forest operations is fundamental for assessing soil disturbance and supporting sustainable forest management. However, in Mediterranean pine forests where canopy openings after boom-corridor thinning are moderate, the effectiveness of different remote sensing techniques remains uncertain. Previous studies have shown that LiDAR-based methods can reliably detect logging trails in different forest stands, but their direct transfer to structurally simpler, even-aged Mediterranean stands has not been validated. This study addresses this gap by testing whether UAV-derived RGB imagery can achieve comparable accuracy to LiDAR-based methods under the canopy conditions of boom-corridor thinning. We compared four approaches for detecting strip roads in a black pine (Pinus nigra Arn.) plantation on Mount Amiata (Tuscany, Italy): one based on high-resolution UAV RGB imagery and three based on LiDAR data, namely Hillshading (Hill), Local Relief Model (LRM), and Relative Density Model (RDM). The RDM method was specifically adapted to Mediterranean conditions by redefining its return-density height interval (1–30 cm) to better capture areas of bare soil typical of recently trafficked strip roads. Accuracy was evaluated against a GNSS-derived control map using nine performance metrics and a balanced subsampling framework with bootstrapped confidence intervals and ANOVA-based statistical comparisons. Results confirmed that UAV-RGB imagery provides reliable detection of strip roads under moderate canopy openings (accuracy = 0.64, Kappa = 0.27), while the parameter-tuned RDM achieved the highest accuracy and recall (accuracy = 0.75, Kappa = 0.49). This study demonstrates that RGB-based mapping can serve as a cost-effective solution for operational monitoring, while a properly tuned RDM provides the most robust performance when computational resources are sufficient to work on large point clouds. By adapting the RDM to Mediterranean forest conditions and validating the effectiveness of low-cost UAV-RGB surveys, this study bridges a key methodological gap in post-harvest disturbance mapping, offering forest managers practical, scalable tools to monitor soil impacts and support sustainable mechanized harvesting.

An overview is presented of the progress since 2021 in the construction and scientific programme preparation of the Divertor Tokamak Test (DTT) facility. Licensing for building construction has been granted at the end of 2021. Licensing for Cat. A radiologic source has been also granted in 2022. The construction of the toroidal field magnet system is progressing. The prototype of the 170 GHz gyrotron has been produced and it is now under test on the FALCON facility. The design of the vacuum vessel, the poloidal field coils and the civil infrastructures has been completed. The shape of the first DTT divertor has been agreed with EUROfusion to test different plasma and exhaust scenarios: single null, double null, X-divertor and negative triangularity plasmas. A detailed research plan is being elaborated with the involvement of the EUROfusion laboratories.

Overturning is one of the main causes of fatal accidents in agriculture. Farming activities that are affected the most by this type of hazard often involve trailers, balers, and sprayers: a tractor and any kind of towed equipment. This is a well-known issue, described by a great number of accident reports. But despite efforts in accident prevention the occurrence is still high and most of the assessments focus on addressing responsibilities and reconstruct the accidents by cause-effect relations. Accidents, in reality, are never the same because actually the working environment, social constraints, deadlines and seasonality play a major role in accident dynamics and therefore should be tracked by any safety assessment in agriculture as safety conditions gradually degrade with time or even in a matter of a single activity. The analysis of safety conditions through new methodologies and the realization of possible work scenarios are the objectives of this work.

This paper explores the correlation between climate change (CC) and chemical risk in agriculture. Agriculture is vulnerable to CC due to its dependence on weather conditions, with extreme events impacting productivity. Crop protection products help mitigate losses, but CC affects their distribution and persistence, increasing pollution risks. The study employs a bibliometric analysis using VOSviewer to identify keywords. It follows a two-part methodology: systematic literature searching and in-depth analysis. The search yields 133 relevant articles from 2019 to 2023. Findings indicate a connection between CC and chemical risk in agriculture. Rising temperatures and altered pest pressures may necessitate increased pesticide use, climate-induced factors affect chemical persistence, runoff and volatilization, technological advancements and organic practices offer mitigation strategies, precision agriculture and nanopesticides reduce emissions and, finally, organic farming and irrigation methods also impact chemical transport. The analysis reveals an evolving thematic research area with publications spanning various disciplines. The paper concludes that a holistic approach involving stakeholders is necessary to address the dual challenges of agricultural sustainability and climate change adaptation. Promoting eco-friendly practices can contribute to a toxic-free environment and a sustainable, circular economy.

Obstacle detection in mechanised agricultural activities is a key feature in safety management systems which prevents accidents involving workers on foot. Presence of obstacles in blind spot areas utterly represent a major issue for tractor drivers. This paper illustrates the model of a safety management system that aims to handle obstacles by using depth cameras and LIDARs to detect the obstacles and estimate their distances, risk maps that provide information obtained by Real Time Kinematics (RTK) georeferencing of fixed obstacles such as trees or buildings, assisted by an additional system based on Ultra-Wide Band (UWB) wireless communication devices for real-time localization of workers on foot and notifications through Bluetooth Low Energy (BLE) devices. The model is part of a project called SIRTRAck and also provides an assessment of a tractor's blind spots obtained by both field tests and simulations. The preliminary part of research activities showed that prototyping a low-cost aftermarket system for obstacle detection is possible by adapting different data acquisition methods for safety purposes and by making use of the information accordingly. Further steps of the project include a prototyping phase of a demo kit based on the model.