Investigation of Exposure to Occupational Noise among Forestry Machine Operators: A Case Study in Brazil

Camargo, Diego Aparecido; Munis, Rafaele Almeida; Simões, Danilo

doi:10.3390/f12030299

Cited by 11 publications

(6 citation statements)

References 67 publications

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“…Forwarders, harvesters, and farm tractors make up a bulk of the machines used in partially or fully mechanized harvesting systems. While forwarders and harvesters are considered the pinnacle of fully mechanized harvesting systems [73,74], mechanized harvesting of wood carried out by cut-to-length processing can present ergonomic risks, causing the emergence of occupational diseases [4]. It is considered an unhealthy environment that exposes operators to physical and psychological disorders [19,75].…”

Section: Discussionmentioning

confidence: 99%

“…The exposure of harvester and forwarder operators to noise in karst areas does not exceed the limit for daily noise exposure defined in the EU legislation [34] but is higher than exposure in fully mechanized CTL harvesting on flat terrain [23]. Carmago et al [4] observed that 36.4% of harvester operators were exposed to values above the exposure limit of 85 dB(A) and 63.6% to the action level of 80 dB(A). Regarding the forwarder operators, 100% were exposed to values that exceeded the action level.…”

Section: Noise and Vibrationmentioning

confidence: 99%

“…Risk factors included in forestry ergonomics publications are substantial and their dynamic appearance is observed in this review. Regardless of time period or harvesting systems, "traditional" risk factors like noise [3][4][5][6][7], vibration [8][9][10][11], and workload [12][13][14]…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Forestry Ergonomics Publications in the Last Decade: A Review

Bačić,

Landekić,

Pandur

et al. 2024

Forests

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Compiling the research on forestry ergonomics, which is still a marginal field in terms of the sheer volume of published forestry-related articles, gives a good foundation and guidance for future research and publishing. This review aims to compile, classify, and analyze forestry ergonomics publications in JIF (Journal Impact Factor) journals regarding their spatial and temporal distribution, observed operations, machines and tools, and risk factors. A reference period from 2014 to 2023 was observed in this study. The Web of Science Core Collection database was used to filter publications in the field of forestry and ergonomics. A total number of 102 articles were selected. After selection, data regarding publishing year, journal name, main field, country of origin, forest operation, machine/tool, and risk factor were noted. The number of articles is ever-increasing with the last four years having above average numbers of articles. Countries from Europe and South America (Brazil) have the most publications. Most of the journals are ranked in the top 50%. Harvesting, wood extraction, and pre-harvesting operations have the highest number of records. Chainsaw, skidder, and pre-harvesting tools are the most observed means of work. The risk factors with the highest percentage of records are workload (23%), noise (20%), vibration (20%), postural load (16%), and MSD (Musculoskeletal Disorder) occurrence (7%).

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Section: Discussionmentioning

confidence: 99%

Section: Noise and Vibrationmentioning

confidence: 99%

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Forestry Ergonomics Publications in the Last Decade: A Review

Bačić,

Landekić,

Pandur

et al. 2024

Forests

View full text Add to dashboard Cite

show abstract

“…Due to the use of a harvester in the configuration of the CTL system, the trees are cut and sectioned into processed logs. Additionally, the timber without harvest residues, suitable for industrial processes, is arranged for extraction by means of a forwarder [9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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

Munis

Almeida

Camargo

et al. 2022

Forests

Self Cite

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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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“…In Brazil, the cut-to-length system is the most used in harvesting the Eucalyptus for the pulp industry (Camargo et al, 2021). In this system, the tree is processed at the harvest site by the harvester and extracted to the roadside by the forwarder (Bont et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Forest Extraction Management With the Indicator of Overall Efficiency of Forest Machines (Oefm)

et al. 2022

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Performance indicators are tools capable of exposing measurable characteristics and generating relevant information on forest operations, thus being considered pillars for managers to make agile and assertive decisions. Forest extraction with a forwarder must be improved, understanding the factors that affect the costs of this machine, such as productivity (PR), fuel consumption (FC), operational efficiency (OE), and quality of operation. Thus, the objective of this study was to evaluate the implementation of the Overall Efficiency of Forest Machines (OEFM) indicator in the management of forest extraction data using forwarders. Data were collected during forest harvesting from five operating fleets, in commercial eucalypt plantations, in full-tree and coppice regimes, in the states of Bahia and Espírito Santo. The indicator was expressed as a percentage calculated by OEFM = ( ( 4 ∗ PR ) + ( 3 ∗ FC ) + ( 3 ∗ OE ) ) / 10. The performance of the machines was evaluated by a stochastic model of dynamic simulation of systems in eight scenarios, proposing improvement for the average individual volume harvested, fuel consumption, and mechanical or operational stops. Analyzes were performed using PowerSim Studio 9 software. The OEFM of two fleets was higher than the established target of 95.17%, with 95.72% and 97.44%. The OEFM indicator proved to be useful in the management of forest extraction with adequate and easy-to-understand information from a large amount and variety of data. The stochastic simulation model was efficient to study the impact on the global efficiency and the flow of wood extraction by the forwarder.

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Investigation of Exposure to Occupational Noise among Forestry Machine Operators: A Case Study in Brazil

Cited by 11 publications

References 67 publications

Forestry Ergonomics Publications in the Last Decade: A Review

Forestry Ergonomics Publications in the Last Decade: A Review

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

Forest Extraction Management With the Indicator of Overall Efficiency of Forest Machines (Oefm)

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