A model to predict productivity of different chipping operations

The chipping operation is an important component of harvesting systems producing biomass and pulp chips. This paper aimed to develop a valid model to predict the productivity of chipping as part of these operations. Over a number of years more than 200 different time studies were conducted on chipping operations in Italy and Australia. Multiple regressions and backward stepwise data analysis methods were applied to develop a productivity prediction equation, considering the following variables: machine power (kW), piece size (m3), crew size, harvesting method, species, tree part, wood condition, wood lay-out, chipping type, propulsion, feeding method, point of chipping, season, location of chip discharge, country (Italy or Australia) and type of operation (biomass chip operation or pulp chip operation). The final productivity model included machine power, average piece size, location of chip discharge and type of operation as significant variables. The internal validation test was conducted using five witness samples from Italy and Australia, which confirmed the validity at α=0.05. Additional international case studies from North America, South America, and central and northern Europe were used to test the accuracy of the model, in which 15 studies confirmed the model's validity and two failed to pass the test.

[1]  M. R. Ghaffariyan,et al.  Review of European biomass harvesting technologies. , 2010 .

[2]  Bruce R. Hartsough,et al.  A survey of Italian chipping operations , 2001 .

[3]  S. Chatterjee,et al.  Regression Analysis by Example , 1979 .

[4]  Michael Chad Bolding,et al.  FOREST FUEL REDUCTION AND ENERGYWOOD PRODUCTION USING A CTL / SMALL CHIPPER HARVESTING SYSTEM , 2002 .

[5]  Dominik Röser,et al.  Chipping operations and efficiency in different operational environments. , 2012 .

[6]  B. Stokes,et al.  Harvesting costs and utilization of hardwood plantations , 1994 .

[7]  Wim Turkenburg,et al.  Technological learning and cost reductions in wood fuel supply chains in Sweden , 2005 .

[8]  Bruce R. Hartsough,et al.  Harvesting SRF poplar for pulpwood: Experience in the Pacific Northwest , 2006 .

[9]  Recovery of small tree biomass as a function of harvesting system and forest type - Nordic approach , 1994 .

[10]  P. Mohana Shankar,et al.  Concepts of Probability and Statistics , 2012 .

[11]  Bruce Talbot,et al.  Analysis of Two Simulated In-field Chipping and Extraction Systems in Spruce Thinnings , 2005 .

[12]  M R Ghaffariyan,et al.  Biomass harvesting in Eucalyptus plantations in Western Australia , 2011 .

[13]  Natascia Magagnotti,et al.  A tool for productivity and cost forecasting of decentralised wood chipping , 2010 .

[14]  Karl Stampfer,et al.  Current state and development possibilities of wood chip supply chains in Austria , 2006 .

[15]  Tapio Ranta,et al.  The profitability of transporting uncomminuted raw materials in Finland , 2006 .

[16]  B. Talbot,et al.  Good practice guidelines for biomass production studies , 2012 .

[17]  Terri L. Moore,et al.  Regression Analysis by Example , 2001, Technometrics.

[18]  John Sessions,et al.  Production Equations for Tower Yarders in Austria , 2009 .

[19]  Rien Visser,et al.  Analyzing and estimating delays in wood chipping operations. , 2009 .

[20]  Bryce J. Stokes,et al.  Productivity of in-woods chippers processing understory biomass , 1986 .