Volume 48 No. 1

This research assesses the feasibility of employing an electric battery as an energy storage medium in the parallel hybrid powertrain of a thinning harvester, proposing it as a more economical substitute for traditional supercapacitors. An AH6 thinning harvester incorporating a battery-hybrid system was observed during felling tasks across three combustion engine speeds: 1700 rpm, 1800 rpm, and 1900 rpm. These three modes were selected in an effort to get as close as possible to the optimal RPM of 1500. Battery energy accumulation rose by 0.06 kWh at both 1700 rpm and 1800 rpm; in contrast, it declined by 0.002 kWh at 1900 rpm. Multi-criteria evaluation of felling regimes substantiated the electric battery viability as an energy storage option for thinning harvesters. Peak efficacy emerged at reduced engine speeds, coinciding with the maximum torque output of the combustion engine. These outcomes bear substantial relevance for curbing emissions and operational expenses in thinning forestry practices.

The study presents five arrays of harvester head inclination angles. The measurements were taken using a MEMS inclinometer during harvester operation under the control of the same operator. The recording durations ranged from 2100 to 3600 seconds. MEMS inclinometers can record both the vertical and horizontal positions of the head. The study revealed that the head was in a vertical position 28% of the time and in a horizontal position 72% of the time. The average duration of a single episode was 12 seconds with the head positioned vertically and 33 seconds with the head positioned horizontally. Statistical analysis of all data arrays revealed the average inclination angles of the head: 76.0 degrees when operating in the vertical position and –4.2 degrees in the horizontal position. The positive direction of rotation is defined as the rotation of the head from the horizontal position towards its working vertical position. Data from inclinometers enabled a detailed analysis of the technological cycle of the harvester by operation type. Each operation was characterized by its own set of statistical parameters, including standard deviation, range, and interquartile range, which describe the dispersion of inclination angle values. These parameters enabled the distinction of individual operations within the complete data array. Based on the statistical processing of all data arrays, these parameters took the following values for each operation (standard deviation / range / interquartile range, in degrees): movement of the harvester with a vertically positioned head from one working position to another: 10.8/56.9/14.3; guidance of the head at the tree: 9.3/67.9/10.7; movement of the harvester with the grabbed tree during the bucking process: 4.9/28.4/5.5; delimbing and bucking the tree: 5.9/52.4/6.6; cutting the top part of the tree and collecting the branches onto the forwarding trail: 13.2/108.0/15.2; and movement of the harvester from one working position to another with a horizontally positioned head: 14.4/78.7/20.4. The average shares of these operations in the total structure of the technological cycle were as follows: 3.5%; 14.5%; 4.9%; 35.2%; 14.6%, and 5.6%, respectively. In addition: bringing the head to vertical –3.5%; tree felling –12.8%; other operations –3.0%; pauses –3.0%. The angle values, presented in the form of continuous data arrays, provide new data for the analysis of logging mechanics.

Wildfires are among the most significant natural disturbances affecting boreal forests, yet their operational impacts on mechanized harvesting remain poorly quantified. This study evaluated the effects of wildfire burn category on harvesting productivity and volume recovery under operational conditions in Quebec, Canada. Over 800 stems were harvested across four burn categories: carbonized, red, affected and green (unburned). Harvesting productivity was assessed through a time-and-motion study combined with harvester on-board computer (OBC) data, while volume recovery was calculated as the ratio of harvested volume to standing merchantable volume per stem. Non-parametric statistical tests and regression analyses were used to compare harvesting productivity and volume recovery across burn categories. Results showed that harvesting productivity did not differ significantly among burn categories within the examined stem size range (≤0.20 m³/stem), indicating that wildfire severity alone did not reduce operational productivity. In contrast, volume recovery rates were significantly lower in burned categories compared to unburned stands, with reductions increasing with burn severity. These results suggest that post-fire economic losses are more strongly linked to reduced wood recovery than to decreased harvesting productivity. Overall, this study provides empirical evidence supporting the operational feasibility of harvesting in burned boreal stands, while emphasizing the need to account for burn-related wood losses in planning following wildfires. Future research using large-scale OBC datasets is recommended to further refine the evaluations of harvesting productivity and volume recovery across burn categories.

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.

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, µRR is 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.

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.

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.

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.