A call to improve methods for estimating tree biomass for regional and national assessments

Aaron R. Weiskittel, David W. MacFarlane, Philip J. Radtke, David L.R. Affleck, Hailemariam Temesgen, Christopher W. Woodall, James A. Westfall, John W. Coulston

Research output: Contribution to journalArticlepeer-review

71 Scopus citations


Tree biomass is typically estimated using statistical models. This review highlights five limitations of most tree biomass models, which include the following: (1) biomass data are costly to collect and alternative sampling methods are used; (2) belowground data and models are generally lacking; (3) models are often developed from small and geographically limited data sets; (4) simplistic model forms and predictor variables are used; and (5) variation is commonly averaged or grouped rather than accounted for. The consequences of these limitations are highlighted and discussed. Several recommendations for future efforts are presented including the following: (1) collection of field measurements of tree biomass using consistent protocols; (2) compilation of existing data; (3) continued evaluation and improvement of existing models; (4) exploration of new models; and (5) adoption of state-of-the-art analytical and statistical techniques. Given the increasing importance of accurately estimating forest biomass, there is a critical need to understand, evaluate, and improve current tree biomass prediction methods.

Original languageEnglish
Pages (from-to)414-424
Number of pages11
JournalJournal of Forestry
Issue number4
StatePublished - Jul 6 2015


  • Forest biomass
  • Statistical equations
  • Tree measurements


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