Above-ground Biomass Models of 40-year-old Norway Spruce in Latvia

The importance of wood biomass as a source of bioenergy at wider scales, i.e. for a centralized production of heat and electricity, is increasing. Hence, precise estimates of biomass are necessary for the calculation of available resources and carbon budget. Mathematical models based on easily measurable variables can notably facilitate the estimation of biomass of trees. However, such models are usually locally (regionally) applicable. The biomass of the aboveground parts (stems and branches and total) and the dimensions were measured for 29 Norway spruce (Picea abies (L.) Karst.) trees at the age of 40 years, representing the eastern part of Latvia. Different linear models were fitted to the biomass data; tree height (H), stem diameter at 1.3 m height (D) and their combinations were used as the independent variables. The estimates of stem biomass and their mean relative errors were compared between the best of the developed models, traditionally used derivatives of wood volume tables and models developed for Finland by J. Repola (2009). A linear model with D and H as two independent variables showed the best fit to the biomass of stem; the power model with 1341 as the independent variable performed best for the total aboveground biomass; the branch biomass was best predicted by the power model based on D. The mean relative errors of the stem and total aboveground biomass was ca 8%, while the model of branch biomass had higher relative error, ca 20%. The estimates of stem biomass by the traditionally used volume tables and the developed models did not differ significantly, but the relative errors were lower for the developed model. However, the relative errors of the stem biomass estimated by the volume tables were correlated with D, suggesting systematic bias. The developed models performed better than those established in Finland, which underestimated stem and branch biomass, but overestimated the total aboveground biomass and had significantly higher relative errors of the estimates.