Relating estimates of wood properties of birch to stem form, age and species  

作　　者：Grace Jones Maria Ulan Mateusz Liziniewicz Johan Lindeberg Stergios Adamopoulos 

机构地区：[1]Teagasc Forestry Development Department,Ashtown Research Centre,Dublin 15 D15DY05,Ireland [2]Research Institutes of Sweden(RISE),Goteborg,Sweden [3]The Forest Research Institute of Sweden(Skogforsk),Ekebo 2250,26890Svalov,Sweden [4]Department of Forestry and Wood Technology,Linnaeus University,35195Vaxjo,Sweden [5]Department of Forest Biomaterials and Technology,Swedish University of Agricultural Sciences,Box 7008,75007 Uppsala,Sweden

出　　处：《Journal of Forestry Research》2024年第3期1-11,共11页林业研究（英文版）

基　　金：financed by the research program FRAS-The Future Silviculture in Southern Sweden

摘　　要：Birch has long suffered from a lack of active forest management,leading many researchers to use mate-rial without a detailed management history.Data collected from three birch(Betula pendula Roth,B.pubescens Ehrh.)sites in southern Sweden were analyzed using regression analysis to detect any trends or differences in wood proper-ties that could be explained by stand history,tree age and stem form.All sites were genetics trials established in the same way.Estimates of acoustic velocity(AV)from non-destructive testing(NDT)and predicted AV had a higher correlation if data was pooled across sites and other stem form factors were considered.A subsample of stems had radial profiles of X-ray wood density and ring width by year created,and wood density was related to ring number from the pith and ring width.It seemed likely that wood density was negatively related to ring width for both birch species.Linear models had slight improvements if site and species were included,but only the youngest site with trees at age 15 had both birch species.This paper indicated that NDT values need to be considered separately,and any predictive models will likely be improved if they are specific to the site and birch species measured.

关 键 词：Acoustic velocity Non-destructive testing Predictive models Regression analysis Wood density 

分 类 号：S792.153[农业科学—林木遗传育种]