Conference Agenda

Understanding soil–tire interaction under deformable ground conditions is important for evaluating the mobility and traction performance of off-road vehicles in agricultural and construction environments. This study developed and validated a discrete element method-multi body dynamics(DEM-MBD) coupled simulation model through comparison with indoor soil-bin experiments under varying vertical loads. Experiments were conducted using dry sandy soil (particle diameter 0.18–0.20 mm) at a constant theoretical tire speed of 0.5 m/s with five vertical load levels (0–40 kg). The tire traveled 3 m in straight-line motion with five repetitions per condition. Soil thrust was estimated from the driving motor torque, slip ratio was calculated from the difference between theoretical and actual travel distances, and slip efficiency was derived from these measurements. Average soil thrust increased from 27.073 N to 47.194 N (1.74-fold increase). The slip ratio exhibited nonlinear behavior with a minimum at 20 kg. Slip efficiency was highest at 20 kg and lowest at 40 kg. The DEM soil model was calibrated using measured bulk density (1631.75 kg/m³) and angle of repose (27.38°), yielding errors below 0.5%. The DEM-MBD simulation reproduced similar trends. Further refinement of DEM calibration and coupling parameters is planned.

Understanding soil resistance under varying soil conditions is essential for predicting soil–tool interactions and improving the design and energy efficiency of agricultural implements. However, systematic experimental data quantifying the combined effects of soil texture and water content, and their integration into discrete element method (DEM) simulations, remain limited. In this study, resistance tests were conducted for three representative soil textures—sandy loam, sandy clay loam, and loam—under multiple water content conditions using a controlled soil bin system. Soil resistance was measured during tool penetration and horizontal motion under identical operating conditions. Results showed that resistance increased with water content, with distinct trends depending on soil texture. Soils with higher clay content exhibited greater moisture sensitivity, leading to a pronounced increase in resistance at higher water contents. DEM soil models were developed and calibrated according to texture and moisture conditions. Simulated results agreed well with experimental measurements, demonstrating the model’s ability to capture moisture-dependent soil resistance behavior. This framework provides a systematic approach for incorporating moisture effects into DEM simulations and supports improved prediction and optimization of soil–tool interactions.

Agro-forestry operations in mountainous regions face significant safety challenges, particularly due to tractor rollover risks. Despite technological progress, over 120 fatal tractor rollover accidents per year were recorded in Italy between 2008 and 2018, causing severe human and economic losses. Although machine safety has improved, it remains difficult to reliably assess stability risk during transit across fields characterized by highly variable slopes. Farmers often identify operational limitations only after acquiring equipment. This study addresses this gap through GIS-based stability feasibility assessment that classifies a mountainous farm into safety zones for selected machine. A pilot assessment was conducted in Bolzano province (Italy) using an orchard tractor previously characterized by stability maps obtained from tests on a rotating and tilting test rig at the Agroforestry Innovation Laboratory of the Free University of Bolzano. Slope and exposition were derived from high-resolution DTM, and site-specific slope conditions were combined with expected machine trajectories and compared with orientation-dependent rollover thresholds from the predefined stability map. The approach generates spatial stability maps classifying terrain as Safe, On-Alert, Dangerous, or Not-Transitable. Results reveal heterogeneous risk patterns, with critical zones mainly associated with lateral slopes. The method supports machine-specific operability assessment and future route-planning applications in mountainous farms.

Drivers of tractors and other self-propelled agricultural machinery are exposed to significant vibration due to operation on uneven terrain. In agricultural tractors, seat suspension systems represent the primary mitigation measure: that are typically mechanical or pneumatic, with stiffness adjustable according to the driver’s body mass (50-140 kg). However, this adjustment is frequently neglected or improperly set, thereby reducing its protective effectiveness.

This study investigates the effects of incorrect seat suspension adjustment on driver vibration exposure. Two tractors of comparable size and mass, equipped with different seat models, were tested under identical operating conditions to evaluate differences in perceived discomfort. Experimental trials were conducted on an unpaved compacted inter-farm road characterized by discrete surface profile conditions, at varying travel speeds and tire inflation pressures. An 84 kg driver performed the tests using both correctly adjusted suspension settings and intentionally misadjusted configurations (i.e., excessively stiff and excessively soft).

The measured vibration data were compared with established regulatory exposure limits, to assess the adverse effects associated with improper seat adjustment. The results emphasize the critical importance of accurate seat suspension calibration in reducing driver discomfort and mitigating potential long-term health risks associated with vibration exposure.

Traction efficiency of agricultural machinery varies depending on different factors like tire pressure, tractive power, soil moisture and soil type. Most of the factors vary both in space and time domain.

We have investigated the effect of soil heterogeneity, tire pressure (0.55 and 1.50 bar) and slope by mapping the electric conductivity (EC), retrieving a publicly available high-resolution DEM and logging RTK-positions and CAN-Bus data during a tillage operation at 1Hz interval. The trial setup follows a design with alternating tire pressures in each tram line using a predefined GIS map and an automatic RTK-steering system.

The results reveal substantial differences with respect to slip, fuel consumption area efficiency, fuel efficiency and tractive power depending on slope and electric conductivity of the soil. The most remarkable result was the close relation between electric conductivity (EC) at shallow depths (0 to 30 cm) and the slip at high tire pressures.Slip being correlated to EC has the potential of serving as an input for varying the seed density online without requiring data processing and the creation of offline maps.