Methodological features of professional risk assessment of tractor drivers

Existing occupational risk assessment methodologies often fail to sufficiently account for objective factors that influence risk realization. Consequently, there is a critical need to revise the conceptual approach to assessing risks for tractor and agricultural machinery operators. The study aims to identify overlooked occupational and environmental factors to enhance the accuracy and quality of risk assessments. The methodology is based on a comprehensive synthesis of research regarding the impact of workplace conditions and human factors on safety. The study demonstrates that risk levels are significantly influenced by investments in occupational health and safety (OHS) measures, as well as the presence of environmental stressors – such as excessive noise and extreme cabin temperatures – that accelerate fatigue and impair attention and reaction speeds. Findings show that an increase in workplace temperature to 26-28°C reduces operator performance by 20-25%. Conversely, poorly regulated air conditioning (temperatures below 20°C) also increases fatigue and accident risk by 15-20%. Similarly, every 10 dB increase in noise level leads to a proportional rise in fatigue and a decline in productivity. To address these «risk intensification factors,» the authors propose the use of correction factors in hazard coefficient calculations. These factors account for the impact of environmental stressors, operator age, and other variables on fatigue. The proposed framework provides a scientific basis for refining occupational risk assessment methods and improving the reliability of safety evaluations for tractor and machinery operators.

1. Junkes V.H. et all. Occupational risks of work in the agricultural sector: a systematic literature review. Production. 2024;34(2): e20230042. https://doi.org/10.1590/0103-6513.20230042

2. Shirokov Yu.A. Analysis of opportunities to manage the cost of crop production. Agrarnaya Rossiya. 2020;2:32-39. (In Russ.) https://doi.org/10.30906/1999-5636-2020-2-32-39

3. Broder J.F., Tucker E. Risk analysis and the security survey. URL: https://booksite.elsevier.com/samplechapters/9780123822338/Front_Matter.pdf https://doi.org/10.1016/C2009-0-63855-1 (дата обращения: 22.02.2020).

4. Shirokov Y., Tikhnenko V. Analysis of environmental problems of crop production and ways to solve them. E3S Web of Conferences. 14th International Scientific and Practical Conference on State and Prospects for the Development of Agribusiness, INTERAGROMASH 2021. Rostov-on-Don, 2021. https://doi.org/10.1051/e3sconf/202127301025

5. Cordova-Pozo K., Rouwette E.A.J.A. Types of scenario planning and their effectiveness: A review of reviews. Futures. 2023;149:103153. 6. https://doi.org/10.1016/j.futures.2023.103153

6. Korol’ E.A., Degaev E.N. Cost-effectiveness analysis of labour protection measures. Vestnik MGSU. 2024;19(1):67-76. (In Russ.) https://doi.org/10.22227/1997-0935.2024.1.67-76

7. Silva J.M.N., Bispo L.G.M., Leite W.K.S. et all. Assessing the link between occupational risk factors, work-related musculoskeletal disorders and quality of work life: An analysis using PLS-SEM. International Journal of Industrial Ergonomics. 2024;104(4):103658. https://doi.org/10.1016/j.ergon.2024.103658

8. Wong I., Swanson N. Approaches to managing work-related fatigue to meet the needs of American workers and employers. Am J Ind Med. 2022;65(11):827-831. https://doi.org/10.1002/ajim.23402

9. Shirokov Yu.A. Risk assessment in the field of occupational safety in connection with a retirement-age increase. Occupational Safety in Industry. 2020;6:29-34. (In Russ.) https://doi.org/10.24000/0409-2961-2020-6-29-34

10. Hart J., Hartová V. Development of microclimate in the New Holland T6.165 tractor. Research in Agricultural Engineering 2023;69(4):206-210. https://doi.org/110.17221/27/2023-RAE

11. Ružić D., Homa D., Simikić M. et al. Comparative experimental study of microclimate conditions in farm tractor cabs. Sādhanā. 2023;258(48). https://doi.org/10.1007/s12046-023-02306-w

12. Ferguson J.M. et all. Distribution of working hour characteristics by race, age, gender, and shift schedule among U.S. manufacturing workers. Chronobiology International. 2023;40(3):310-323. https://doi.org/10.1080/07420528.2023.2168200

13. Barač Željko et all. Noise in the cabin of agricultural tractors. Tehnicki vjesnik. Technical Gazette. 2018;25(6):1611-1615. https://doi.org/10.17559/TV-20170223093448

14. Han S. et al. Ontology-based noise source identification and key feature selection: A case study on tractor cab. Shock and Vibration. 2019;1:1-14. https://doi.org/10.1155/2019/6572740