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Consequences of different sample drying temperatures for accuracy of biomass inventories in forest ecosystems

Biomass estimation is one of the crucial tasks of forest ecology. Drying tree material is a crucial stage of preparing biomass estimation tools. However, at this step researchers use different drying temperatures, but we do not know how this influences accuracy of models. We aimed to assess differen...

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Detalles Bibliográficos
Autores principales: Jagodziński, Andrzej M., Dyderski, Marcin K., Gęsikiewicz, Kamil, Horodecki, Paweł
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7525458/
https://www.ncbi.nlm.nih.gov/pubmed/32994525
http://dx.doi.org/10.1038/s41598-020-73053-9
Descripción
Sumario:Biomass estimation is one of the crucial tasks of forest ecology. Drying tree material is a crucial stage of preparing biomass estimation tools. However, at this step researchers use different drying temperatures, but we do not know how this influences accuracy of models. We aimed to assess differences in dry biomass between two drying temperatures (75 °C and 105 °C) in tree biomass components and to provide coefficients allowing for recalculation between the given temperatures. We used a set of 1440 samples from bark, branches, foliage and wood of eight European tree species: Abies alba Mill., Alnus glutinosa (L.) Gaertn., Betula pendula Roth., Fagus sylvatica L., Larix decidua Mill., Picea abies (L.) H. Karst., Pinus sylvestris L. and Quercus robur L. The differences between drying temperatures were 1.67%, 1.76%, 2.20% and 0.96% of sample dry masses of bark, branches, foliage and stem wood, respectively. Tree species influenced these differences. Our study provided coefficients allowing for recalculation of masses between the two temperatures, to unify results from different studies. However, the difference in dry mass between the two temperatures studied is lower than the range of uncertainty of biomass models, thus its influence on results of large-scale biomass assessments is low.