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Staż naukowy w Katedrze Leśnych Biomateriałów i Techniki Leśnej w Szwedzkim Uniwersytecie Nauk Rolniczych (Sveriges Lantbruksuniversitet)
Influence of cutting attachment on work efficiency, fuel consumption and environmental pollution from plastic wire during tending of young forests with brush cutters
Abstract Petrol brush cutters are among the most widely used devices for tending young forests. During this work, environmental pollution is generated by the combustion of fuel and by the discarding of pieces of the plastic cutting line. The aim of this study was to compare operating parameters and the degree of plastic pollution from the cutting line, and to determine fuel consumption during tending of young forest with the use of a petrol brush cutter equipped with different cutting attachments: a plastic wire head, and 2-, 3-, and 24-tooth cutting blades. Measurements were made in the course of work on 2–3-year-old oak plantations containing two vegetation types (herbaceous and mixed). It was found that the most efficient cutting attachment was the wire head, but its use was associated with significant wire and fuel consumption. In the mixed vegetation case, wire consumption was 575.89 g*ha−1, which is comparable to eighteen polyethylene terephthalate (PET) bottles. Similar performance and significantly lower fuel consumption were obtained with the 2-tooth blade. In addition, when using cutting blades, there was no wire consumption and thus no plastic pollution of the environment. It was concluded that, in order to eliminate plastic pollution and reduce fuel consumption while achieving satisfactory working efficiency, the use of wire heads should be abandoned in favor of metal cutting blades.
Learning Curves of Harvester Operators in a Simulator Environment
Simulator training helps provide safe and cost-effective training for operators of modern forestry machines that require high motor skills, constant concentration, and proper planning. The aim of the study was to analyze the learning curves of the trainees in order to determine the period during which most development takes place. In this study, 11 trainees were trained on a John Deere harvester simulator for approximately 15 h each. In each case, a clear learning curve could be identified, despite high inter- and intra-person variability. Effective time showed a steady decrease during training, with a group minimum at the end of training (1.25 min). Crane tip distance per tree dropped rapidly in the first 3–4 h, followed by a more gradual decrease to reach a minimum of 23.8 m. Crane control showed a significant increase from an initial 0.63 to a maximum of 0.8 by the 9th hour of training. A number of crane functions used simultaneously increased more rapidly to almost a maximum value (1.8) already in the 5th hour. The individual curves for each trainee were highly variable, showing a wide range of values and shapes. In conclusion, most personal development occurs during the first phase of simulator training, which typically takes approximately 9–10 h. It is important to consider significant inter-personal variability and tailor the duration of simulator training to individual needs.
Effect of logging residue removal and mechanical site preparation on productivity of the subsequent Scots pine (Pinus sylvestris L.) stands
Abstract Key message Removal of logging residue negatively affected tree diameter and height, but had no significant effect on the basal area of the subsequent stand (in the mid-term). On the other hand, different methods of mechanical site preparation (bedding, plowing furrows, and trenching) had no effect on tree growth 1 year after planting, but had a significant effect on tree diameter, tree height, and basal area in the mid-term. Bedding treatments could have a significant positive impact on the productivity of the subsequent Scots pine stands, even when planted on sandy, free-draining soils. Context Increased use of logging residues in forests may address the growing demand for renewable energy. However, concerns have arisen regarding the depletion of the forest soil, resulting in a decrease in the productivity of the next forest generation. Identifying the drivers of forest growth may be the key to understanding the relationship between logging residue removal and stand productivity. Aims Quantifying the effect of three mechanical site preparation methods (bedding, plowing furrows, and trenching) combined with five methods of logging residue management (complete removal, comminution, incineration, leaving whole, comminution with, and without mixing with topsoil) on growth of subsequent Scots pine stands, 1 year and 12 years after planting. Methods The experiment was set up as a randomized complete block design of 45 plots with three replications of combinations of three mechanical site preparation methods and five logging residue treatment methods. Results The effects of the different methods of mechanical site preparation were not significant 1 year after planting but bedding treatment caused increase in DBH, tree height, and basal area after 12 years. Various methods of logging residue management did not cause any differences in the survival rate nor the basal area of the next-generation stands; however, there was a significant influence on tree sizes. Moreover, the effects changed with time; in plots with a complete removal of logging residues, the trees were the highest 1 year after planting, but after 12 years, their height and DBH were the lowest. Conclusions It can be concluded that bedding treatments could have a significant positive impact on the productivity of the subsequent Scots pine stands. No effect found of different logging residue treatments on the productivity of Scots pine stands further confirms that the increased removal of biomass from the forest environment does not necessarily result in its rapid degradation. Observations at longer term are however needed to obtain the full spectrum of responses to logging residue removal.
Data Mining in the Analysis of Tree Harvester Performance Based on Automatically Collected Data
Data recorded automatically by harvesters are a promising and potentially very useful source of information for scientific analyses. Most researchers have used StanForD files for this purpose, but these are troublesome to obtain and require some pre-processing. This study utilized a new source of similar data: JDLink, a cloud-based service, run by the machine manufacturer, that stores data from sensors in real time. The vast amount of such data makes it hard to comprehend and handle efficiently. Data mining techniques assist in finding trends and patterns in such databases. Records from two mid-sized harvesters working in north-eastern Poland were analyzed using classical regression (linear and logarithmic), cluster analysis (dendrograms and k-means) and Principal Component Analysis (PCA). Linear regression showed that average tree size was the variable having the greatest effect on fuel consumption per cubic meter and productivity, whereas fuel consumption per hour was also dependent, e.g., on distance driven in a low gear or share of time with high engine load. Results of clustering and PCA were harder to interpret. Dendrograms showed most dissimilar variables: total volume harvested per day, total fuel consumption per day and share of work time on high revolutions per minute (RPMs). K-means clustering allowed us to identify periods when specific clusters of variables were more prominent. PCA results, despite explaining almost 90% of variance, were inconclusive between machines, and, therefore, need to be scrutinized in follow-up studies. Productivity values (avg. around 10 m3/h) and fuel consumption rates (13.21 L/h, 1.335 L/m3 on average) were similar to the results reported by other authors under comparable conditions. Some new measures obtained in this study include, e.g., distance driven in a low gear (around 7 km per day) or proportion of time when the engine was running on low, medium or high load (34%, 39% and 7%, respectively). The assumption of this study was to use data without supplementing from external sources, and with as little processing as possible, which limited the analytic methods to unsupervised learning. Extending the database in follow-up studies will facilitate the application of supervised learning techniques for modeling and prediction.