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Articles containing the keyword 'green density'

Category: Research article

article id 10539, category Research article
Jaakko Repola, Juha Heikkinen, Jari Lindblad. (2021). Pulpwood green density prediction models and sampling-based calibration. Silva Fennica vol. 55 no. 4 article id 10539. https://doi.org/10.14214/sf.10539
Keywords: pulpwood; conversion factor; green density
Highlights: The developed models provided a realistic description of the observed seasonal variation in pulpwood green density; The model predictions were more reliable than those obtained with current practices.
Abstract | Full text in HTML | Full text in PDF | Author Info

Pulpwood arriving at the mills is mainly measured by weighing. In the loading phase of forwarding and trucking, timber is weighed using scales mounted in the grapple loader. The measured weight of timber is converted into volume using a conversion factor defined as green density (kg m–3). At the mill, the green density factor is determined by sampling measurements, while in connection with weighing with grapple-mounted scales during transportation, fixed green density factors are used. In this study, we developed predictive regression models for the green density of pulpwood. The models were constructed separately by pulpwood assortments: pine (contains mainly Pinus sylvestris L); spruce (mainly Picea abies (L.) Karst.); decayed spruce; birch (mainly Betula pubescens Ehrh. and Betula pendula Roth); and aspen (mainly Populus tremula L.). Study material was composed of the sampling-based measurements at the mills between 2013–2019. The models were specified as linear mixed models with both fixed and random parameters. The fixed effect produced the expected value of green density as a function of delivery week, storage time, and meteorological conditions during storage. The random effects allowed the model calibration by utilizing the previous sampling weight measurements. The model validation showed that the model predictions faithfully reproduced the observed seasonal variation in green density. They were more reliable than those obtained with the current practices. Even the uncalibrated (fixed) predictions had lower relative root mean squared prediction errors than those obtained with the current practices.

  • Repola, Natural Resources Institute Finland (Luke), Natural resources, Ounasjoentie 6, 96200 Rovaniemi, Finland E-mail: jaakko.repola@luke.fi (email)
  • Heikkinen, Natural Resources Institute Finland (Luke), Production systems, Yliopistokatu 6, FI-80100 Joensuu, Finland E-mail: juha.heikkinen@luke.fi
  • Lindblad, Natural Resources Institute Finland (Luke), Natural resources, Latokartanonkaari 9, 00790 Helsinki, Finland E-mail: jari.lindblad@luke.fi

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