Prediction of timber harvesting productivity for semi-mechanised systems in Viphya forest plantations, Malawi

Show simple item record

dc.contributor.advisor Brink, Michal
dc.contributor.coadvisor Chirwa, Paxie W.
dc.contributor.postgraduate Ngulube, Elisha Stephen
dc.date.accessioned 2013-09-09T07:51:50Z
dc.date.available 2013-06-28 en
dc.date.available 2013-09-09T07:51:50Z
dc.date.created 2013-04-12 en
dc.date.issued 2012 en
dc.date.submitted 2013-06-21 en
dc.description Dissertation (MSc)--University of Pretoria, 2012. en
dc.description.abstract At least 200,000 m3 of timber are harvested annually using semi-mechanised harvesting systems (SMS) on the Viphya forest plantations in Malawi. Although these systems have long been used on the Viphya, no investigation on their productivity has so far been reported. The absence of local productivity models created uncertainty about the importance of sitebased factors that influence timber harvesting productivity of these systems on the Viphya. Secondly, there is paucity of information regarding the appropriate timber harvesting systems for production maximisation and cost minimisation. This study aimed to develop prediction models for estimating the productivity and costs of semi-mechanised and simulated mechanised timber harvesting systems on the Viphya forest plantations. The study was conducted in Pinus kesiya stands at Kalungulu and Champhoyo forest stations of the Viphya forest plantations. A work study approach was followed to capture harvesting time and volume data. Stepwise multiple regressions were used to develop felling time models for a chainsaw over tree size, inter-tree distance, slope, ground condition, brush density, and ground roughness; and skidding time models over distance, slope, ground condition, ground roughness and volume skidded per cycle for a grapple skidder. Models were statistically validated. Secondary work study data for semi-mechanised systems were simulated for mechanised productivity based on local site factors. The study had shown that diameter at breast height and inter-tree distance were important factors that best explained felling time prediction models in Pinus kesiya stands on the Viphya forest plantations. Similarly, distance from stump to the roadside landing was the most important factor in addition to volume load, slope and ground conditions that determined grapple skidding time. Mechanised systems appear to be more advantageous than semi-mechanised systems. The former are associated with lower operating costs and inventories with relatively high production rates. Therefore, mechanised systems could help to optimise timber harvesting productivity on the Viphya. Further studies should be conducted to determine the effect of different ground conditions and roughness on skidding productivity. en
dc.description.availability Unrestricted en
dc.description.degree MSc
dc.description.department Plant Production and Soil Science en
dc.identifier.citation Ngulube, ES 2012, Prediction of timber harvesting productivity for semi-mechanised systems in Viphya forest plantations, Malawi, MSc Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/30939> en
dc.identifier.other E13/4/465/gm en
dc.identifier.upetdurl http://upetd.up.ac.za/thesis/available/etd-06212013-123721/ en
dc.identifier.uri http://hdl.handle.net/2263/30939
dc.language.iso en
dc.publisher University of Pretoria en_ZA
dc.rights © 2012 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria E13/4/465/ en
dc.subject Timber harvesting en
dc.subject Extraction
dc.subject Time study
dc.subject Viphya forest
dc.subject UCTD
dc.title Prediction of timber harvesting productivity for semi-mechanised systems in Viphya forest plantations, Malawi en
dc.type Dissertation en


Files in this item

This item appears in the following Collection(s)

Show simple item record