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 time-study 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.

Productivity Standards for Harvesters and Processors in Italy

Spinelli R;Magagnotti N
2010

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 time-study 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.
2010
Istituto per la Valorizzazione del Legno e delle Specie Arboree - IVALSA - Sede Sesto Fiorentino
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/31455
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