Blue-stain development on Norway spruce logs under alpine conditions

被引:1
|
作者
Bohm, Stephan [1 ]
Baier, Peter [2 ]
Kirisits, Thomas [2 ]
Kanzian, Christian [1 ]
机构
[1] Univ Nat Resources & Life Sci, Dept Forest & Soil Sci, Inst Forest Ecol, Vienna BOKU, Peter Jordan Str 82, A-1190 Vienna, Austria
[2] Univ Nat Resources & Life Sci, Inst Forest Entomol Forest Pathol & Forest Protect, Dept Forest & Soil Sci, Vienna BOKU, Peter Jordan Str 82, A-1190 Vienna, Austria
关键词
Picea abies; bark beetle; moisture content; predictive model; sap-stain; temperature; weather data; LODGEPOLE PINE; MECHANICAL-PROPERTIES; STORM DAMAGE; PICEA-ABIES; FUNGI; SAPSTAIN; ROUNDWOOD; DEGRADE; GROWTH; XYLEM;
D O I
10.14214/sf.23054
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
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.
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页数:23
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