Productivity Standards for Harvesters and Processors in Italy

Abstract

The authors developed a general productivity model for the harvesters and processors currently used in Italy. The model consists of a set of mathematical relationships that can estimate the productivity of these machines under the whole range of specific work conditions faced in Italy. Such relationships can provide general directions to prospective users and can contribute to the development of scenario predictions. The original data pool contained more than 15,000 individual timestudy records, each representing a single harvesting cycle (most often one tree). The records were extracted from 38 studies conducted with the same methods and by the same principal investigators between 1998 and 2008. Statistically significant models were developed for all cyclic work phases, such as moving, brushing, felling, and processing. Accessory time and delay time were added as percent factors, also estimated from the same studies. Model development aimed at achieving the best compromise solution between accuracy and easy use, avoiding the introduction of an excessively large number of input variables. Selected independent variables were tree volume, tree species, task type (harvesting or processing), machine power and type, density of residual stand and of harvest trees, stand type, and slope gradient. These models could predict a large proportion of the variability in the data and were successfully validated using reserved cycle records, extracted from the same data pool and not used for model development. Comparison with similar Nordic and German standards confirmed the sound structure of the Italian models while highlighting the need for specific productivity norms due to the different work conditions faced by Italian operators.

Use, Utilization, Productivity and Fuel Consumption of Purpose-Built and Excavator-Based Harvesters and Processors in Italy

“…Given the limited detail included in a long-term study, it is difficult to thoroughly discuss the similarities and the differences between the figures obtained from this study and those reported in the general literature on the subject. However, the figures in this study are generally compatible with those indicated in the literature, and they match quite well the Italian productivity standards for harvesters and processors [26]. This said, readers must be aware that direct comparison can be deceiving, because most of the existing productivity figures are obtained from short-term time study sessions, not from long-term company records.…”

Evaluation of Salvage Logging Productivity and Costs in Windthrown Norway Spruce-Dominated Forests

“…For example, the clear-cutting data in the study by Nurminen et al [76] included 636 stems, and the data in the study by Dvořák [30] included 631 processed trees. There are some other larger sets of cutting data of clear cuts but they are mainly long-term follow-up studies, such as the study by Spinelli et al [77] in which the data included 15,366 trees. Further examples include the research by Gerasimov et al [78] in which 4.3 million stems were cut, and the research by Eriksson and Lindroos [62] which had a dataset of more than 12,000 clear-cut stands.…”

Applicability of International Harvesting Equipment Productivity Studies in Maine, USA: A Literature Review

“…Due to a large area of forestland with small diameter trees [13], the diameter range of interest in Maine and this region is from 10 cm to 33 cm. The majority of the reviewed studies fall within the dbh range of interest, however, only few cover the entire diameter range [3,23,29]. Stem size also affects skidding and forwarding productivity.…”

“…Spinelli et al [3] reported that in addition to significantly limiting accessibility of a stand, slope affects the moving time within the stand. A wide range of slopes have been studied previously, ranging from level ground to slopes of 45% (Figure 4).…”

“…Further, accuracy of machine rate calculations have been and will be improved in Europe to meet the needs of the logging industry [1,2]. Research studies in Italy [3,4], France and Finland [5], and some parts of the United States [6][7][8][9] use similar approaches in methods and analysis of their data to develop machine rates, and productivity and cost functions for their logging equipment with respect to site and forest conditions. This type of research allows stakeholders in the logging industry to accurately predict logging costs and benchmark their productivity to regional averages.…”