Volume 39 No. 1
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Productivity of Cut-to-Length Harvesting by Operators’ Age and Experience

Copyright © 2017 by Croatian Journal of Forest Engineering
volume: 39, issue: 1
pp: 8
  • Malinen Jukka
  • Taskinen Janne
  • Tolppa Timo
Article category:
Original scientific paper
human influence, harvester operator, learning, ageing, experience




In the study, the relationship between operators’ age, experience and mechanized cut-to-length
(CTL) harvesting productivity was examined. The data were five-year follow-up data from 28
operators and 38 CTL harvesters collected from southern Finland. Productivities were converted
to relative productivities and average productivity models were created. Case specific
productivities were compared to modelled values, and productivity ratio models including
separate lower and upper quartile models were produced.
The relative productivity of operators at the age of 45 years in clear cuttings was 17.8%
higher and in thinnings 14.9% higher than that of operators at the age of 25 years. The relative
lower quartile productivity increased from operators aged 25 to operators aged 45 years by
38.6% in clear cuttings and 29.4% in thinnings. The relative productivity of operators having
experience of 20 years was 23.6% higher in clear cuttings and 16.2% higher in thinnings than
that of operators having experience of 3 years. Operators’ experience of 20 years produced
43.1% better lower quartile relative productivity in clear cuttings and 29.1% in thinnings
compared to 3 years’ experience. The relative upper quartile productivity was 5.7% higher in
clear cuttings for operators aged 45 years than for operators aged 25 years, but otherwise, there
was no statistical correlation between upper quartile productivity and age or experience.
As a conclusion, CTL harvester operators’ average productivity increases slowly after the
initial learning phase up to 15 years of experience. The peak productivity was uncorrelated to
age or experience, but the experience raised the bottom productivity values.


Web of Science Impact factor (2019): 2.500
Five-years impact factor: 2.077

Quartile: Q1 - Forestry

Subject area

Agricultural and Biological Sciences