Stephan Böhm, Peter Baier, Thomas Kirisits, Christian Kanzian. (2023). Blue-stain development on Norway spruce logs under alpine conditions. Silva Fennica vol. 57 no. 3 article id 23054. https://doi.org/10.14214/sf.23054

Keywords: Picea abies; moisture content; temperature; weather data; bark beetle; predictive model; sap-stain

Highlights: A nonlinear model was developed to predict the temperature-dependent spread rate of blue-stain in Norway spruce logs in alpine areas in Austria; The influence of temperature sum on the development of blue-stain was confirmed; The effect of harvesting season on the development and amount of sap-stain (faster and more extensive in summer than in spring) was observed.

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Discoloration of the sapwood caused by blue-stain fungi on conifer logs during interim storage causes significant loss to the forest industry. The fungal infection is often associated with bark beetle attacks because the spores are transmitted by the beetles. They can also be disseminated by rain-splash and moist air. While there are methods to protect logs from sap-stain in wood yards, this is often not possible in the forest for practical and regulatory reasons. Timing of harvesting and timely transportation are often the only ways to prevent blue-stain. To estimate the urgency of transportation, knowledge of the growth of blue-stain fungi and its dependence on weather conditions is of great interest. The proportion of discolored sapwood on Norway spruce logs was recorded along a time series, together with weather data in two field experiments conducted in spring and summer at two alpine sites in Austria. A predictive model was developed to estimate the proportion of blue-stained sapwood based on the temperature sum to which the logs were exposed. After harvest in March, there was a time lag of 82 and 97 days at the two respective sites, caused by initially low temperatures, before discoloration started. In contrast, sap-stain occurred 14 days after the harvest in June, when warm conditions prevailed from the start. The nonlinear least square regression model can help to estimate a window of opportunity to transport wood before it loses its value and serves as a sub model for lead time estimation within logistic decision support systems.

Böhm, Department of Forest and Soil Sciences, Institute of Forest Engineering, University of Natural Resources and Life Sciences, Vienna (BOKU), Peter-Jordan-Strasse 82, 1190 Vienna, Austria https://orcid.org/0000-0001-7803-6618 E-mail: stephan.boehm@boku.ac.at
Baier, Department of Forest and Soil Sciences, Institute of Forest Entomology, Forest Pathology and Forest Protection, University of Natural Resources and Life Sciences, Vienna (BOKU), Peter-Jordan-Strasse 82, 1190 Vienna, Austria https://orcid.org/0000-0002-1029-5637 E-mail: peter.baier@boku.ac.at
Kirisits, Department of Forest and Soil Sciences, Institute of Forest Entomology, Forest Pathology and Forest Protection, University of Natural Resources and Life Sciences, Vienna (BOKU), Peter-Jordan-Strasse 82, 1190 Vienna, Austria https://orcid.org/0000-0002-9918-3593 E-mail: thomas.kirisits@boku.ac.at
Kanzian, Department of Forest and Soil Sciences, Institute of Forest Engineering, University of Natural Resources and Life Sciences, Vienna (BOKU), Peter-Jordan-Strasse 82, 1190 Vienna, Austria https://orcid.org/0000-0002-1198-9788 E-mail: christian.kanzian@boku.ac.at

article id 10385, category Research article

Perttu Anttila, Tuomas Nummelin, Kari Väätäinen, Juha Laitila. (2020). The effect of winter weather on timber truck tare weights. Silva Fennica vol. 54 no. 4 article id 10385. https://doi.org/10.14214/sf.10385

Keywords: forestry logistics; payload; tare weight; weather data

Highlights: In a one-year follow-up study of eight timber trucks in Finland, up to 6–12% increase in tare weights was observed in wintertime; Compared with summertime tare weights, the maximum increase varied between 1560 kg and 3100 kg; Potential payload loss was explained by temperature, relative humidity and precipitation.

Abstract | Full text in HTML | Full text in PDF | Author Info

In wintertime, the payload capacity of a timber truck is reduced by snow that accumulates on the structures of the truck. The aim of this study was to quantify the potential payload loss due to snow and winter accessories and to predict the loss with weather variables. Tare weights of eight timber trucks were collected at mill receptions in Finland over a one-year period. Monthly and annual loss of potential payload was estimated using the tare measurements in summer months as a reference. Each load was also connected with weather data at the location and time of delivery and payload loss explained by the weather data with the aid of regression models. The maximum loss of payload varied between 1560 kg and 3100 kg. On a monthly basis, the highest losses occurred in January, when the median values varied between 760 kg and 2180 kg. Over the year, the payload loss ranged between the trucks from 0.5% to 1.5% (from 1.9% and 5.1% in January) of the total number of loads in the study. Payload loss was found to increase with decreasing temperature, increasing relative humidity and increasing precipitation. Although the average payload loss was not very high, the biggest losses occur just during the season of highest capacity utilization. Big differences were also found in the tare weights between the trucks. The results of the study give incentive to develop truck and trailer structures that reduce the adherence of snow.

Anttila, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland https://orcid.org/0000-0002-6131-392X E-mail: perttu.anttila@luke.fi
Nummelin, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: tuomas.nummelin@luke.fi
Väätäinen, Natural Resources Institute Finland (Luke), Yliopistokatu 6, FI-80100 Joensuu, Finland E-mail: kari.vaatainen@luke.fi
Laitila, Natural Resources Institute Finland (Luke), Yliopistokatu 6, FI-80100 Joensuu, Finland https://orcid.org/0000-0003-4431-3319 E-mail: juha.laitila@luke.fi

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