ESTIMATION OF ROOT BIOMASS AND DYNAMICS FOR THE CARBON BUDGET MODEL OF THE CANADIAN FOREST SECTOR

Citation
Wa. Kurz et al., ESTIMATION OF ROOT BIOMASS AND DYNAMICS FOR THE CARBON BUDGET MODEL OF THE CANADIAN FOREST SECTOR, Canadian journal of forest research, 26(11), 1996, pp. 1973-1979
Citations number
48
Categorie Soggetti
Forestry
ISSN journal
00455067
Volume
26
Issue
11
Year of publication
1996
Pages
1973 - 1979
Database
ISI
SICI code
0045-5067(1996)26:11<1973:EORBAD>2.0.ZU;2-Z
Abstract
Root biomass is expected to contribute significantly to total ecosyste m carbon (C) pools and their dynamics. A method for estimating belowgr ound biomass pools and their dynamics was developed for application in the carbon budget model of the Canadian forest sector (CBM-CFS2). Roo t biomass data for temperate and boreal softwood and hardwood species were compiled from the literature. Total root biomass for softwood and hardwood species was estimated using regression models that incorpora te total aboveground biomass as the independent variable. Fine root bi omass was estimated as a proportion of total root biomass using a sing le regression model for softwood and hardwood species combined. A regr ession model to estimate annual fine root production was derived for s oftwood and hardwood species. In the CBM-CFS2, net increments of total biomass were estimated using empirical growth functions to predict ab oveground biomass. The regression models developed in this study were then used to predict the corresponding root biomass. Total root produc tion was calculated as the sum of net increments, i.e., the change in root biomass per hectare plus annual turnover. The application of this approach to estimate root biomass pools and their dynamics in the CBM -CFS2 is demonstrated. As with all regression models that are develope d from regional databases, this approach should not be used to predict root biomass and dynamics of an individual forest ecosystem, because the influence of species, site, and stand characteristics may lead to significant deviations from the regional averages.