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Summary

Published in Northern Journal of Applied Forestry 29(4): 173-181

Motor-manual brushcutting is commonly used throughout North America for regeneration release. Companies pay for this silvicultural treatment according to a piece-rate wage system that does not consider stand and site characteristics, though both have been shown to affect worker productivity. This study aimed to establish a formal regression model relating stand and site conditions to brushcutter productivity for regeneration release operations.
Time studies were conducted in 0.12 ha observation areas, with the participation of 91 brushcutters representing 22 silvicultural contractors (32 sites). The best fitted regression model (theoretical model) to predict effective time consumption (ETC) (R2 = 0.61, RMSE = 4.3 hours per hectare, n = 91) contained three independent variables: (1) percent cover (%) of three brush species (raspberry, ferns, fireweed), (2) density of young trees higher than 1 m (number per hectare) and (3) density of obstacles hindering worker progression in the field (woody residue, stumps left after harvest, boulders and terrain roughness). As workers indicated that hindering obstacles significantly affected their productivity, principal component analysis was used to discriminate between obstacle types. Two components representing woody residue and stumps bypassed by the worker were significant predictors of ETC (R2 = 0.14, RMSE = 6.4 hours per hectare, n = 91). The findings could be useful for piece-rates adjustment. Further research is needed to better understand how obstacles impact worker productivity.

Sector(s): 

Forests

Categorie(s): 

Scientific Article

Theme(s): 

Forestry Research, Forestry Work, Forests

Departmental author(s): 

Author(s)

DUBEAU, Denise, Luc G. LEBEL, Daniel IMBEAU et Isabelle AUGER

Year of publication :

2012

Keywords :

traitement sylvicole, facteur de station, étude du travail, régression, taux forfaitaires, travail forestier, forestry work, silviculture treatments, site factors, work study, regression, piece-rates

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