Articles containing the keyword 'conversion factor'

The aim of the study was to explain the regression between the cubic content of the trunk of a tree and of logs cut from the trunk. The minimum size of trunk that can be used to saw logs has been 18 feet long and 5″ of top diameter in Finland (in 1930s). The average length of logs is 16‒18 feet. The data of this study was collected in seven areas around Finland. The results show that there is regression between the volume of the trunk, the volume and the logs cut from the trunk and the number of logs. Consequently, it is possible to determine the average regression between the trunk and log sizes for forest technical purposes. The regression varies in different parts of the country due to varying growth condition of the trees and different age classes. It is probable that the figures have to be re-examined in the future.

The PDF includes a summary in English.

• Vuoristo, E-mail: iv@mm.unknown

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
• 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

Harvesting residues collected from the final cuttings of boreal forests are an important source of solid biofuel for energy production in Finland and Sweden. In the Finnish supply chain, the measurement of residues is performed by scales integrated in forwarders. The mass of residues is converted to volume by conversion factors. In this study, weather based models for defining the moisture content of residues were developed and validated. Models were also compared with the currently used fixed tables of conversion factors. The change of the moisture content of residues is complex, and an exact estimation was challenging. However, the model predicting moisture change for three hour periods was found to be the most accurate. The main improvement compared to fixed tables was the lack of a systematic error. It can be assumed that weather based models will give more reliable estimates for the moisture in varying climate conditions and the further development of models should be focused on obtaining more appropriate data from varying drying conditions in different geographical and microclimatological locations.

• Lindblad, Natural Resources Institute Finland (Luke), Production systems, Yliopistokatu 6, FI-80100 Joensuu, Finland E-mail: jari.lindblad@luke.fi
• Routa, Natural Resources Institute Finland (Luke), Production systems, Yliopistokatu 6, FI-80100 Joensuu, Finland E-mail: johanna.routa@luke.fi
• Ruotsalainen, Finnish Meteorological Institute, Aviation and Military Weather Services, P.O. Box 1627, FI-70211 Kuopio, Finland E-mail: johanna.ruotsalainen@fmi.fi
• Kolström, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: marja.kolstrom@uef.fi
• Isokangas, University of Oulu, Control Engineering, P.O. Box 8000, FI-90014 University of Oulu, Finland E-mail: ari.isokangas@oulu.fi
• Sikanen, Natural Resources Institute Finland (Luke), Production systems, Yliopistokatu 6, FI-80100 Joensuu, Finland E-mail: lauri.sikanen@luke.fi