Determining the shape of the productivity function for mechanized felling and felling-processing

Rien Visser; Raffaele Spinelli

doi:10.1007/s10310-011-0313-2

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Determining the shape of the productivity function for mechanized felling and felling-processing

Journal article

Peer reviewed

Determining the shape of the productivity function for mechanized felling and felling-processing

Rien Visser and Raffaele Spinelli

Journal of Forest Research, Vol.17(5), pp.397-402

2012

DOI: https://doi.org/10.1007/s10310-011-0313-2

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Abstract

Forestry Sciences

economics

harvester

logging

mechanized felling

productivity functions

Productivity studies in forest operations are often carried out on new equipment, or on equipment being used in new conditions. Understanding how stand and terrain parameters impact the productivity of harvesting machines is important for determining their optimum use. Such information is normally presented as a productivity or efficiency function; that is, a regression equation that best represents the data. Most studies establish that piece size is the dominant predictor that impacts overall productivity. A common concept known as the "piece-size law" is that productivity increases at a decreasing rate with increasing piece size. What is not well understood is the upper limit to this piece-size law. That is, as the trees get "too" large, we can expect the machine to start to struggle, resulting in a decrease in productivity. Four different data sets-two based in New Zealand and two in Italy-are presented that clearly show an "optimum" piece size for maximum productivity. On average, productivity tended to decrease gradually, not drop off suddenly beyond the optimum. Using more complex statistical functions, it was possible to correctly correlate piece size to productivity.

Details

Title: Determining the shape of the productivity function for mechanized felling and felling-processing
Authors: Rien Visser (Author) - Canterbury University, New Zealand
Raffaele Spinelli (Author) - CNR IVALSA, Italy
Publication details: Journal of Forest Research, Vol.17(5), pp.397-402
Publisher: Springer Japan KK
Date published: 2012
DOI: 10.1007/s10310-011-0313-2
ISSN: 1341-6979
Organisation Unit: University of the Sunshine Coast, Queensland; Forest Industries Research Centre; Forest Research Institute
Language: English
Record Identifier: 99447761502621
Output Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Web Of Science research areas: Forestry

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