Harvesting System Suitability as Decision Support in Selection Cutting Forest Management in Northwest Bosnia and Herzegovina

Marčeta, Dane; Petković, Vladimir; Ljubojević, Darko; Potočnik, Igor

doi:10.5552/crojfe.2020.744

Cited by 11 publications

(11 citation statements)

References 20 publications

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“…Slovenia was involved in the Quality Forest project (2017-2019), which addressed the challenge of elaborating new professional qualifications, focusing mainly on the European Chainsaw Certificate, but also on carrying out tree felling and processing with harvesters. According to Marčeta et al [97], the use of a chainsaw and skidder is the most common harvesting system in Bosnia and Herzegovina. The authors promoted the planning of harvesting operations and investments with new CTL technologies and improved working methods using GIS and the Analytic Hierarchy Process (AHP) method.…”

Section: South-east Europementioning

confidence: 99%

Mechanised Harvesting of Broadleaved Tree Species in Europe

et al. 2022

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Purpose of the Review The application of cut-to-length (CTL) technology for harvesting and processing timber from broadleaved tree species is a challenging process, due to thick branches, stem sweep, forks and high wood density. The objective of this review was to describe the current state of harvester use and characterise the effectiveness of mechanised forest operations for broadleaved and mixed forest stands in Europe, paying particular attention to productivity and product quality aspects. An additional aim was to identify the greatest difficulties associated with harvesting broadleaved tree species today and to indicate future challenges and areas for improvement. Recent Findings An interest in wider harvester use for broadleaved tree species started after the storm Lothar in France in 1999. Early experiences demonstrated that lower productivity is common when harvesting broadleaved tree species, due to limitations in delimbing and the high frequency of sweep. Initially, modifications were made to harvester heads, while a newly designed head (CTL 40 HW) was later developed within a European project in 2005–2007. Using various heads, very high productivity was achieved in some cases, but log recovery was unsatisfactory, with large tree tops left without delimbing. The delimbing was of lower quality and log length inaccuracy was observed, as well as economic losses due to damage to plywood caused by feed rollers. These issues resulted in a change in harvester head construction, including work on a patent for a head designed specifically for harvesting broadleaved tree species. Summary The harvester heads that have been developed to date for broadleaved tree species have mainly been used within Central Europe, where the high incidence of broadleaved tree species and their particular morphological features (thick branches and sweep) are a major challenge for CTL technology. There have been many research projects related to this issue: in the last 15 years, there were a few larger initiatives leading to the design and development of harvester heads for broadleaved tree species, with participation from institutions and universities in France, Germany and Poland. There are currently no ongoing initiatives, but the formation of new ones is strongly recommended because today’s forests have a growing share of broadleaved tree species and are being managed to an increasing extent with CTL technology.

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Section: South-east Europementioning

confidence: 99%

Mechanised Harvesting of Broadleaved Tree Species in Europe

et al. 2022

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“…In fact, with improvements in this phase of the supply chain, the environmental impact of wood products can be significantly reduced while creating better working conditions for employees [18]. Physical characteristics of the soil, accessibility, infrastructures, and purpose of the forestry interventions are the main driving factors to consider in this kind of Multi-Criteria Decision Analysis (MCDA) [19]. In fact, this tool holistically evaluates different alternatives that may result from the combinations of these factors [8,20,21].…”

Section: Introductionmentioning

confidence: 99%

“…Objectivity in the selection of a given harvesting system is necessary for the context of forest operations planning, currently considering that this selection is often based on the experience of the forest engineer working in the field rather than on an objective evaluation. The application of GIS-AHP for the selection of the harvesting system at the cutting block level has been proven to be an effective tool to increase objectivity in the choice [19]. However, it is important to highlight that the efficacy of the GIS-AHP method is strictly related to the resolution of entry data; that is, the higher the resolution, the more trustable the output is [28].…”

Section: Introductionmentioning

confidence: 99%

A Preliminary Validation and Assessment of a GIS Approach Related to Precision Forest Harvesting in Central Italy

Stefanoni

Tocci

Latterini

et al. 2023

Forests

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There has been a growing awareness of the need for sustainable forest management among forest engineers during the last decades. The selection of the best harvesting system for logging operations plays a fundamental role in this effort. Nowadays, in the decision-making process, forest engineers rely on their own experience, and it is to be expected that their evaluation of the level of appropriateness may be biased. There may be a potential solution to this problem in the implementation of AHP (Analytic Hierarchy Process) in the GIS (Geographic Information System) environment. The application of AHP gives objective information and thus reduces the level of bias in deciding which logging harvest system is best to use. The aim of this study was to evaluate the reliability of a GIS-AHP approach applied to the development of the harvesting plan of a public forest estate in Central Italy. Three extraction methods were considered: forwarder, cable skidder, and all-terrain cable yarder. Thus, the GIS-AHP model was run by relying on the basic data attainable from the local forest management plan and then on detailed information provided by field data collected from 10 harvested blocks. Among these, in three blocks, the logging harvest method was changed from forwarder to cable skidder. Statistically significant differences were found between the suitability values obtained from the forest management plan and those retrieved from the field surveys for the forwarder and cable skidders but not for all-terrain cable yarders.

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“…Forestry process automation has been proven to increase productivity and work quality. However, the mechanized timber harvesting operation is challenging and complex [52][53][54][55].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Methods to Estimate Productivity of Harvesters: Mechanized Timber Harvesting in Brazil

Munis

Almeida

Camargo

et al. 2022

Forests

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The correct capture of forest operations information carried out in forest plantations can help in the management of mechanized harvesting timber. Proper management must be able to dimension resources and tools necessary for the fulfillment of operations and helping in strategic, tactical, and operational planning. In order to facilitate the decision making of forest managers, this work aimed to analyze the performance of machine learning algorithms in estimating the productivity of timber harvesters. As predictors of productivity, we used the availability of hours of machine use, individual mean volumes of trees, and terrain slopes. The dataset was composed of 144,973 records, carried out over a period of 28 months. We tested the predictive performance of 24 machine learning algorithms in default mode. In addition, we tested the performance of blending and stacking joint learning methods. We evaluated the model’s fit using the root mean squared error, mean absolute error, mean absolute percentage error, and determination coefficient. After cleaning the initial database, we used only 1.12% to build the model. Learning by blending ensemble stood out with a determination coefficient of 0.71 and a mean absolute percentage error of 15%. From the use of data from machine learning algorithms, it became possible to predict the productivity of timber harvesters. Testing a variety of machine learning algorithms with different dynamics contributed to the machine learning technique that helped us reach our goal: maximizing the model’s performance by conducting experimentation.

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Harvesting System Suitability as Decision Support in Selection Cutting Forest Management in Northwest Bosnia and Herzegovina

Cited by 11 publications

References 20 publications

Mechanised Harvesting of Broadleaved Tree Species in Europe

Mechanised Harvesting of Broadleaved Tree Species in Europe

A Preliminary Validation and Assessment of a GIS Approach Related to Precision Forest Harvesting in Central Italy

Machine Learning Methods to Estimate Productivity of Harvesters: Mechanized Timber Harvesting in Brazil

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