| Abstract |
- The change in emphasis from tractor and highlead logging systems to the advanced logging systems of skyline, balloon, and helicopter has stirred researchers to study the operations of the advanced systems. The extra work involved in preparing an area for contract timber removal using advanced logging systems, however, has not been studied. The impacts of the extra time and extra manpower required for this process are large and they are important impacts which need to be taken into consideration during crew work planning. This is difficult to do since the factors which affect crew manhours have not been defined. The objectives of this paper are to determine which factors affect the number of manhours spent on the presale fieldwork portion of preparing a U.S.D.A. Forest Service timber sale for contract; to compare presale field work time on conventional and special-design sales; and to develop a methodology for predicting presale fieldwork time. Previous studies suggest some factors which affect forestry work, but the quantitative changes in production caused by each factor have not been measured. Logging operation research and human engineering studies report that the following factors affect production: underbrush, topography, weather, heat, cold, muscle fatigue, motivation, the work-rest period, tobacco, and alcohol. Motivation is often an overriding factor which outweighs all other considerations. Motivation was not measured in this study, but it was expected to have some effect on the results. Topography, underbrush, and weather were expected to be the
most important variables for predicting manhours. Data or presale fieldwork time were collected from 16 districts
on eight National Forests in Oregon and Washington, using a form
designed to simplify data collection and coding for computer input.
The form was a daily work record which could be easily filled out in
a few minutes by the field crew. District personnel on each of the 16 districts were personally
contacted about cooperating in the study, but they were not pressured
to collect data even if they agreed to cooperate. The sample was designed to collect a distribution of data from
the coast, eastside, and Cascade areas respectively, but most data
collected came from the Cascade area. Activity categories for data collection included layout, traverse,
marking,cruising, skyline profile, wildlife related activity, land line
retracement, spur road traverse, spur road location, and timber stand
examination. The data were separated by activity and by complete units,
and were analyzed using a stepwise multiple regression program. Variables found to increase the number of manhours required for
presale fieldwork were acres, boundary sinuosity, and silvicultural
treatment. The significant variables which reduced manhours were landscape
management designation and the distance walked from the vehicle
to the unit. These two variables had been expected to increase manhours,
but the effect of motivation may have been the cause for the
unexpected reverse of sign on the regression coefficients. Special
design designation showed no significant effect on manhours except
through boundary sinuosity. The regression equations recommended for use in predicting manhours
are for the activities of layout using on-the-ground aerial photo-interpretation; traverse using a hand compass, clinometer, and chain;
marking of leave trees; cruising by the variable plot method; and for
the Cascade area complete units. Nomographs are provided for the
solution of these regression equations.
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