A comparison of two methods of data collection for modelling productivity of harvesters: manual time study and follow-up study using on-board-computer stem records
Journal article, Peer reviewed
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Date
2018-04-13Metadata
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Abstract
Productivity of a mechanized P. patula cut-to-length harvesting operation was estimated and modelled using two methods of data collection: manual time study and follow-up study using StanForD stem files. The objective of the study was to compare the productivity models derived using these two methods to test for equivalence. Manual time studies were completed on four different machines and their operators. Two Ponsse Bear harvesters fitted with H8 heads, and two Ponsse Beaver harvesters, fitted with H6 heads, were included. All machines were equipped with Ponsse Opti2 information system. All four operators had approximately 1 year of experience working with their respective machines. The four machines worked in separate four-tree-wide harvesting corridors, and they each harvested 200 trees. Individual tree diameter at breast height (DBH), and height measurements were made manually. Subsequently, data on the trees in each study were extracted from the StanForD stem reports from each of the harvesters. Cycle times in the stem reports were determined based on the difference between consecutive harvest timestamps. The two methods were compared in terms of their abilities to estimate equivalent measures for tree DBH, volume, and productivity. In all four cases, significant differences were found between the DBH and volume measures derived using the two methods. Subsequently, the volume measures from the manual methods were used as the basis for productivity calculations. Results of the productivity comparisons found no significant differences between the models developed from the two methods. These results suggest that equivalent productivity models can be developed in terms of time using either method, however volume discrepancies indicate a need to reconcile bark and volume functions with the high variability experienced in the country.