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

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