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Understanding the influence of tree size distribution on the CTL harvesting productivity of two different size harvesting machines

Simon Ackerman; James Bekker; Rasmus Astrup; Bruce Talbot
European Journal of Forest Research · Vol. 143, Issue 4 · pp. 1199-1211 · 2024

Abstract

Tree size is one of the major factors that determines harvester productivity and is heavily influenced by forest managerial activities. Stand silvicultural management can lead to managing tree size, the distribution of tree size, and tree height amongst others. Understanding the effect of tree size distribution on harvesting productivity is central for optimizing management of operations. To investigate the effects of tree size distribution on harvester productivity, productivity functions for a medium and larger-sized harvester were applied to harvester derived tree size distributions from 35 clearfelled pine stands. These functions were applied to a normal distribution of trees covering the same tree size ranges. Productivity differences were analysed on a stand-by-stand basis. Results showed that for the larger harvester, productivity rates remained constant (67.1 vs. 67.6 m 3 ·PMH − 1 ) indicating relatively little sensitivity to variations in tree size distributions. Although the standard deviation (SD) halved from 11.6 to 5.6 in the case of the uniform tree distribution. The smaller harvester productivity decreased by 15% from 47.3 to 40.1 m 3 ·PMH − 1 and the coefficient of variation (CV) by 6% in the same transition to a uniform distribution. Further investigation was done on more skewed tree size distributions, a family of nine Weibull distributions was generated, representing combinations of three mean DBH classes (25 cm, 30 cm, and 35 cm) and three levels of CV (15%, 20%, 25%), for each DBH class. Results clearly indicate that different distribution shapes have different effects on different machine sizes, and that a low CV correlates to a higher productivity in larger tree sizes. A more uniform tree size distribution also provides more predictable results (lower CV), which would promote machine scheduling and result in fewer discrepancies on production rates.

Bibliographic Information

JournalEuropean Journal of Forest Research
PublisherSpringer
Publication Date2024-08-01
Publication Year2024
Volume143
Issue4
Pages1199-1211
Document TypeJournal Article
Print ISSN1612-4669
eISSN1612-4677
DOI10.1007/s10342-024-01680-2

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NARA Access Coverage1857-01-01~Current
Journal Homepagehttps://www.springer.com/journal/10342
Publisher PageOpen Publisher Page
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