PERFORMANCE OF GRAPPLE SKIDDER IN DIFFERENT GROUND INCLINATIONS

Author:

Cavassin Diniz Carlos CezarORCID,Yoshihiro Nakajima Nelson,Gonçalves Robert Renato Cesar,Fonseca Dolácio Cícero Jorge,Alba da Silva Franciele,Balensiefer Daniel Francisco

Abstract

Land slope contributes to decrease the productivity in the forestry sector activities, including skidding operations. Thus, it is important to study it in order to improve the forest operations planning. Based on this hypothesis, this study aims to analyze the times of the operational cycle and the productivity of the skidder in slope terrain. The study was conducted in Pinus taeda plantations of a forest company located in the CentralWest region of the state of Paraná, Brazil, in three slope classes: flat to moderate, steep and very steep. The data were obtained by the continuous timing method in a time study. Productivity and mean effective cycle time were determined for the three slope classes. The results show that the search and load and the unloading slopes are the ones that consume less time between the activities evaluated in the operational cycle. Considering the slope classes evaluated, flat to moderate and steep require less time to perform all activities of the operational cycle, and their productivities are higher, when compared to the very steep slope class. The productivity of the very steep slope class was 35.3% and 45.0% lower than the flat to moderate and steep classes, respectively. Skidding with skidder on slopes over 26.1º should be avoided because the productivity is negatively influenced in this condition.

Publisher

Universidade Federal do Parana

Subject

Nature and Landscape Conservation,Ecology,Forestry

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3