Background and Objectives: The continuous increase in the amount of atmospheric CO2 is a factor that significantly contributes to global warming. Forests can be used to mitigate climate change by absorbing carbon and storing it. Scots pine (Pinus sylvestris L.) is the most abundant tree species in Polish forests and can substantially aid carbon accumulation. The aim of the study was to determine the carbon content in the dry mass of various parts of Scots pine trees and to evaluate the relationship between the accumulation of carbon in aboveground tree biomass and some stand parameters. Materials and Methods: The research was carried out in 20 even-aged (81–90 years old) Scots pine stands in northwestern Poland (Drawno Forest District). The densities of these stands ranged from 476 to 836 trees per hectare. The aboveground biomass was calculated as the sum of the following tree compartments: stem (wood and bark), dead branches, thick branches, thin branches and needles. The carbon content and storage in these compartments was determined. Results: The mean carbon content was lowest in stem wood (47.0%) and highest in needles (50.3%). No correlation between the stand density and the level of carbon stored in the aboveground biomass of Scots pines was found.
A brush cutter is the most frequently used equipment for tending young forests. When cutting unwanted vegetation, the operator is exposed to various harmful factors, such as: a forced body position, noise, vibrations and exhaust emissions. In this study, the impact of cutting attachment type on the noise level during tending of young pine stands was examined. The attachments used during the tests included: a wire head and cutting blades with 2, 3 and 24 cutting teeth. The research was carried out on 2–3 year old Scots pine plantations covered with three types of vegetation: herbaceous, mixed and woody. It was proven that the the wire head was the device that generated the highest level of noise. In the case of cutting blades, the number of cutting teeth was the important factor. The greater the number of teeth in the cutting blades, the lower the noise level the device produced. There was no significant influence of vegetation type on noise emission level. Based on the results, in order to minimize operators’ exposure to noise, the use of wire cutting attachment should be limited.
Harvesting large quantities of timber requires the use of various technical means, including harvesters. The introduction of machine logging has greatly improved safety and reduced accident rates but has also resulted in the risk of musculoskeletal disorders and increased psychological strain. The aim of this study was to determine the level of the mental workload of harvester operators in wind-damaged stands, during daytime and nighttime clearfelling, and during late thinning using the technique of eye-tracking (analysis of saccades and pupil dilation). The highest number of saccades for both felling and processing operations was recorded during daytime and nighttime clearcutting, while the lowest number was recorded in late thinning. For both operations, the highest mean saccade duration was found in late thinning (felling 38.7 ms, processing 36.0 ms) and the lowest in nighttime cutting (felling 33.1 ms, processing 35.5 ms). The highest frequency of saccades in both operations was recorded in clearcut areas during both daytime and nighttime operations. The largest mean pupil diameters during saccades were recorded in night clearfelling plots (felling 5.57 mm, processing 5.52 mm), while the smallest were recorded in plots with windbreaks (felling 2.91 mm, processing 2.89 mm). Comparison of the number, duration, frequency, and time proportion of saccades as well as pupil diameter provided a quantifiable assessment of mental workload in clearcut, wind-damaged, and thinning stands. The indicators analyzed showed that the cutting category can significantly affect the level of mental workload and thus fatigue of harvester operators.
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 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.
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