Forest operations, particularly ground-based skidding, are known to cause soil disturbances, yet their long-term impact on soil biological communities remains understudied. The goal of this study was to assess the effects of thinning and skidding on Mesostigmata mite communities in Polish beech forests, analyzing soil disturbance in skid trails, undisturbed harvested areas, and unharvested control sites. This study is especially needed in Polish beech forests, considering that recently both the shift towards continuous cover forestry and the increasing demand for beech timber have fostered the shortening of the re-entry intervals between two consecutive logging operations in the same forest management unit. Further to the above, the aim of this study was to leverage a well-recognized indicator of soil health, i.e. mite biodiversity, to check both the short-term consequences of ground-based forest operations and the related recovery times after harvesting. A total of 670 individual mites from 49 taxa were identified, with significant reductions in abundance and diversity in the disturbed soil. Mite abundance in recently established skid trails was approximately 80% lower than in unharvested control sites, while species richness and diversity were reduced fivefold. Even five years after logging, mite abundance and diversity remained three times lower than in unharvested stands, indicating prolonged recovery. Unexpectedly, uncompacted soil within harvested blocks also showed a 50–75% decrease in mite diversity, suggesting that thinning alone can significantly affect soil mites. Statistical analyses revealed strong correlations between mite abundance and soil penetration resistance and organic matter content, emphasizing the role of both compaction and organic matter depletion in shaping soil biodiversity. Given that the current re-entry interval is insufficient for biological recovery, forest management should consider increasing the re-entry interval, implementing further soil protection measures to skid trails, and assessing alternative extraction methods, e.g. forwarding. These findings highlight how integrating soil biological indicators into forestry operations planning can lead to lowered impacts on soil and soil biota.
Skidders used in forest operations are regarded as specialized machines optimized for effective and safe wood skidding across rugged terrain. Their dimensions, design, and various technical attributes are primarily influenced by practical considerations, such as productivity, durability, energy efficiency, environmental sustainability, and immediate worker safety. However, there is limited knowledge about the ergonomics of these machines, as negative ergonomic aspects often accumulate over time and become apparent much later, in contrast to more immediate features. Featured studies imply ergonomic inferiority of skidders in comparison to other extraction machinery. The aim of this study was to conduct a comprehensive assessment of the ergonomic features of skidders manufactured by the Croatian company Hittner Ltd, and to propose possible solutions for their improvement. For this task, ergonomic guidelines (checklist) for forest machines, established by several European countries, were used. Skidders Ecotrac 60V and 140V were assessed at the Hittner Ltd. facility. Results show significant drawbacks in Cab access, Working posture, and Winch sections. Workstation levelling, better seat, thicker steel rope, and a hydraulic spool-out device were offered as major potential means of improving ergonomics.
Driving over the same spot multiple times is often unavoidable in agriculture and forestry, yet leaving the same ruts in the soil leads to challenging outcomes. While soil compaction causes excessive damage, it simultaneously enhances vehicle trafficability and reduces resistance forces acting on both wheeled and tracked vehicles. In this study, the rolling resistance was measured during repeated passes of a BKT 280/70 R18 Agrimax RT 765 radial tyre with directional tread over loamy sand with a 30% moisture content. Results showed a nonlinear dependence of the rolling resistance on the number of passes made. The greatest reduction occurred after the first pass, up to 54% at an inflation pressure of 100 kPa. Subsequent passes caused smaller decreases in the tyre rolling resistance until stabilisation during the seventh and eighth passes. These findings provide practical guidance for optimising mechanised vehicle operations to mitigate soil compaction and environmental impact. In addition, they can provide practical solutions for optimal vehicle management, and for reducing environmental impacts and soil compaction. To reduce the harmful impact of, for example, heavy forestry machinery, such as forwarders or harvesters, on forest soil, it is further recommended to optimise operational planning according to weather and forest soil conditions. If it is necessary to eliminate soil damage and ensure specific values of rolling resistance of mechanised means, it is possible to recommend movement on frozen soils, soils less saturated with water, the use of wider tyres, bearing in mind that in this case the choice of tyre inflation pressure is not negligible, or ensure a lower load on these mechanisms. From this point of view, logistical and organizational measures are not negligible, both in mining and agrotechnical activities.
Logging operations in mountainous environments present unique challenges, particularly in terms of machine-soil interaction and energy demand. The study analyzes the rolling resistance coefficient (µRR) of an eight-wheeled forwarder during uphill salvage logging operations in an alpine forest, using high-frequency machine data collected through a CAN-bus system integrated with GNSS positioning. This approach allows for a detailed assessment of the machine’s rolling resistance under contrasting weather conditions (rainy vs. no rain) and operational settings (winch-assisted vs non-assisted). To reduce variability associated with payload, the analysis focused on uphill, unloaded travel segments. The rolling resistance coefficient was evaluated in relation to terrain features and soil moisture proxies, including slope, cumulative rainfall, and moisture-sensitive areas derived from Depth-To-Water (DTW) maps interpolated for the study area. The results reveal that the µRR is positively correlated with slope and rain, while inversely correlated with DTW. To account for strong temporal autocorrelation and non-linearity, a Generalized Additive Mixed Model (GAMM) was used for inference, while an Extreme Gradient Boosting (XGBoost) model with lag features was implemented for prediction. While raw comparisons showed a positive correlation of µRR with rainy conditions and steeper slopes, the GAMM results (Adjusted R2=0.991) revealed that such effects were largely explained by the mechanical and terrain covariates considered, specifically navigation speed, engine RPM and fuel rate. The XGBoost model achieved excellent predictive accuracy (R2=0.99), identifying recent µRR values (lagged variables) as the dominant predictors, followed by speed and slope. Overall, the findings suggest that while weather and winch use influence the operational context, µRRis primarily governed by short-term temporal dynamics and machine-terrain interactions. These insights provide a robust basis for using machine-sensor data to improve operational planning and reduce energy consumption in sensitive mountainous environments.
As the New Zealand forestry industry progresses toward the implementation of Forestry 4.0 and 5.0 concepts, the use of machine data for operational monitoring and analysis is becoming increasingly valuable. This study investigates the application of machine learning to automate time studies of forwarders by predicting machine activity using Controller Area Network data. Data were collected from two John Deere 1910E forwarders over three harvesting sites (295 cycles) in the South Island of New Zealand. Four machine learning models, Random Forest, Classification and Regression Tree, Long Short-Term Memory, and Gated Recurrent Unit, were trained on data of Forwarder One at an 80:20 (training:testing) split (185 cycles). The models were then further evaluated for generalisability using data of Forwarder Two (110 cycles). Initial trials revealed that machine learning models struggled to distinguish between machine activities such as Loading and Unloading or Driving Loaded and Unloaded, with the neural network models (Long Short-Term Memory and Gated Recurrent Unit) demonstrating superior performance in capturing temporal dependencies. After relabelling and reducing feature complexity, the Gated Recurrent Unit model achieved a validation accuracy of 95.51%, outperforming all other models. Comparison with existing methods from the literature showed that this model provided lower error rates for cycle time, with a percent error of 0.05% and a Root Mean Square Error of 0.02 minutes. At the machine activity level, the model was significantly better, with a decrease in percent error of up to approximately 130% and Root Mean Square Error of up to approximately 1.8 minutes, compared to previous literature. This research demonstrates the feasibility of using machine learning to automate forwarder time studies with high accuracy, providing a scalable and replicable framework that could extend to other forestry machinery. The findings support the integration of these models into Cyber-Physical Systems for real-time monitoring, though challenges such as data access and model generalisation must be addressed to fully realise the benefits of Forestry 4.0.
