Testing a new component ratio method for predicting total tree aboveground and component biomass for widespread pine and hardwood species of eastern US

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dc.contributor.authorClough, Brian J.en
dc.contributor.authorDomke, Grant M.en
dc.contributor.authorMacFarlane, David W.en
dc.contributor.authorRadtke, Philip J.en
dc.contributor.authorRussell, Matthew B.en
dc.contributor.authorWeiskittel, Aaron R.en
dc.contributor.departmentForest Resources and Environmental Conservationen
dc.date.accessioned2020-03-09T14:18:11Zen
dc.date.available2020-03-09T14:18:11Zen
dc.date.issued2018-12en
dc.description.abstractThe US National Greenhouse Gas Inventory uses the component ratio method (CRM), a volume conversion approach that incorporates models for tree biomass components, for forest carbon assessments. However, the performance of the CRM relative to other methods, as well as influences on its accuracy and precision, must be evaluated. We constructed a data-driven CRM (n-CRM), used it to predict total tree and component biomass for six US tree species, and compared this approach to a reference allometric model. We also assessed the influence of size, crown dynamics, and stem growth on the performance of both methods. Results show that the n-CRM was more accurate for four species, resulting from the inclusion of more predictor variables. Both methods had high uncertainty, but the precision of n-CRM predictions was two to eight times higher for small diameter trees (< 10 cm) across all species. Accuracy and precision of the crown component models (i. e. branches and foliage) was low, though better for pines than for hardwoods. Species-level analysis suggests that poor precision is influenced by crown traits and the size distribution of fitting datasets. Our results highlight needed improvements to the n-CRM, and motivate further development of data that facilitate predictive evaluation of biomass models.en
dc.description.adminPublic domain – authored by a U.S. government employeeen
dc.description.notesData compilation for the legacy data and the independent validation datasets, as well as B. Clough's time, were funded by the USDA Forest Service Forest Inventory and Analysis Program, Northern Region. Additional funding and support was available from the Minnesota Agricultural Experiment Station. Part of D.W. MacFarlane's time was supported with funds from Michigan AgBioResearch through the USDA National Institute of Food and Agriculture.en
dc.description.sponsorshipUSDA Forest Service Forest Inventory and Analysis Program, Northern Region; Minnesota Agricultural Experiment Station; Michigan AgBioResearch through the USDA National Institute of Food and Agricultureen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1093/forestry/cpy016en
dc.identifier.eissn1464-3626en
dc.identifier.issn0015-752Xen
dc.identifier.issue5en
dc.identifier.urihttp://hdl.handle.net/10919/97263en
dc.identifier.volume91en
dc.language.isoenen
dc.rightsCreative Commons CC0 1.0 Universal Public Domain Dedicationen
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/en
dc.titleTesting a new component ratio method for predicting total tree aboveground and component biomass for widespread pine and hardwood species of eastern USen
dc.title.serialForestryen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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