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Factors Affecting Operational Cost and Productivity of Ground-Based Timber Harvesting Machines: a Meta-analysis

Factors Affecting Operational Cost and Productivity of Ground-Based Timber Harvesting Machines: a... Purpose of ReviewOptimization of cost and productivity is an important aspect of sustainable timber harvesting which have global level implications on renewable energy, climate change, carbon sequestration, and biodiversity. Major operational level managerial decisions associated with stump-to-truck timber harvesting activities are made at the stand-level. The primary goal of this study was to identify and estimate the relationship between cost and productivity of ground-based mechanical harvesting with key variables and evaluate the variance composition at the stand, country, and continental scales. We followed a meta-analysis approach to gather data for 439 individual machines from 53 scientific studies conducted in 19 countries. Boosted regression trees and hierarchical mixed-effects regression were used to identify and determine the effect of the major variables.Recent FindingsAverage stem size (cm), harvest unit size (ha), and harvesting process were important variables that influenced both harvesting cost and productivity. In addition, harvesting cost (US$ m−3) varied significantly with tree height and country, while harvesting productivity (m3 PMH−1) was mostly influenced by machine rate (US$ PMH−1) and utilization (%). Higher tree height reduced harvesting cost. High stem size and machine rate both increased harvesting productivity even after accounting for the geographical variations.SummaryThe variability in harvesting cost decreased with changing geographical scale from stand to continent, whereas the productivity variation was highest at the continent-level and least at the country-level. This study provides insights for forestry stakeholders and future research indicating that the variance structure and stand-level characteristics of harvesting cost and productivity should be considered for comparisons and decision-making in timber harvesting operations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Current Forestry Reports Springer Journals

Factors Affecting Operational Cost and Productivity of Ground-Based Timber Harvesting Machines: a Meta-analysis

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References (161)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021
eISSN
2198-6436
DOI
10.1007/s40725-021-00156-5
Publisher site
See Article on Publisher Site

Abstract

Purpose of ReviewOptimization of cost and productivity is an important aspect of sustainable timber harvesting which have global level implications on renewable energy, climate change, carbon sequestration, and biodiversity. Major operational level managerial decisions associated with stump-to-truck timber harvesting activities are made at the stand-level. The primary goal of this study was to identify and estimate the relationship between cost and productivity of ground-based mechanical harvesting with key variables and evaluate the variance composition at the stand, country, and continental scales. We followed a meta-analysis approach to gather data for 439 individual machines from 53 scientific studies conducted in 19 countries. Boosted regression trees and hierarchical mixed-effects regression were used to identify and determine the effect of the major variables.Recent FindingsAverage stem size (cm), harvest unit size (ha), and harvesting process were important variables that influenced both harvesting cost and productivity. In addition, harvesting cost (US$ m−3) varied significantly with tree height and country, while harvesting productivity (m3 PMH−1) was mostly influenced by machine rate (US$ PMH−1) and utilization (%). Higher tree height reduced harvesting cost. High stem size and machine rate both increased harvesting productivity even after accounting for the geographical variations.SummaryThe variability in harvesting cost decreased with changing geographical scale from stand to continent, whereas the productivity variation was highest at the continent-level and least at the country-level. This study provides insights for forestry stakeholders and future research indicating that the variance structure and stand-level characteristics of harvesting cost and productivity should be considered for comparisons and decision-making in timber harvesting operations.

Journal

Current Forestry Reports – Springer Journals

Published: Mar 1, 2022

Keywords: Forest operations; Boosted regression model; Hierarchical model; Logging cost; Machine learning; Spatial scale

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