Modeling stump biomass of stands using harvester measurements for adaptive energy wood procurement systems
Lauri Vesa and
Teijo Palander
Energy, 2010, vol. 35, issue 9, 3717-3721
Abstract:
The value and volumes of industrial stump fuel supply are increasing for energy production. Accurate estimates of aboveground and belowground biomass of trees are important when estimating the potential of stumps as a bioenergy source. In this study two stump biomass equations were adapted and tested using them as calibrated stump biomass models computed as the cumulative sum by a local stand. In addition, variables derived from stem measurements of the forest harvester data were examined to predict stump biomass of a stand by applying regression analysis. The true stump yield (dry weight) was used as the reference data in the study. Both biomass models performed well (adjusted R2 ˜ 0.84) and no advance was found in using other stem dimensions as independent variables in the model. The stand-level model can be used in innovative stump biomass prediction tools for increasing efficiency of energy wood procurement planning to stands within a certain area. In practice, wood procurement managers would need to adapt developed system and decide whether the degree of accuracy/precision provided by the models is acceptable in their local stand harvesting conditions.
Keywords: Energy wood; Planning system; Forest harvester; Adaptive methods (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544210002872
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:35:y:2010:i:9:p:3717-3721
DOI: 10.1016/j.energy.2010.05.017
Access Statistics for this article
Energy is currently edited by Henrik Lund and Mark J. Kaiser
More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().