A Tool for Estimating Nutrient Fluxes in Harvest Biomass Products for 30 Canadian Tree Species

1. A Tool for Estimating Nutrient Fluxes in Harvest Biomass Products for 30 Canadian Tree Species David Paré, Pierre Bernier, Evelyne Thiffault, Brian D. Titus, and Benoît Lafleur Natural Resources Canada CONTEXT: With a growing interest in using forest biomass for bioproducts including bioenergy, there is a need to better quantify the nutrient budget implications of extracting more biomass. Published information on nutrient concentrations in four tree biomass components (stem bark, stem bole, branches, foliage) were compiled from existing databases and form the literature on the 30 most important Canadian tree species. This database was linked to a series of allometric biomass equations that have been validated at the national scale to generate estimates of biomass and nutrient export upon stem-only or whole-tree harvesting at the tree or stand level. Approximations of error terms are provided at each step. 3-Including nutrient concentrations to predict nutrient content at the stand level Using existing databases (see references) and literature, we compiled nutrient concentrations for four biomass compartments and five elements (N, P, K, Ca, Mg). A total of 330 studies were compiled yielding 2922 entry lines times 5 elements. We used the standard deviation of nutrient concentrations (per species and compartment) and combined it with the error terms of biomass estimates using rules for propagating errors in products. SD of concentrations was generally high (often around 40%) making the error terms of nutrient contents much greater than that of biomass. 1-Biomass equations at the tree level (user: research) http://www.cfl.scf.rncan.gc.ca/calculateurs-calculators/biomasse-eng.asp Equations:(1) Mi= βi1Dβi2+e or (2) Mi= βi1Dβi2Hβi3+e Where: M = Mass (kg), D = DBH (cm), H = Height (m), β = mode parameters, i = biomass component (Branch, Bark, Foliage, Bole), e = error term INPUT 1-Select tree species 2-Enter DBH or DBH and height Conclusion: SD for biomass estimates is low while that for nutrient concentrations per species / compartment is high. This generates large error terms for nutrient pools It is possible that this variability could be reduced through a better understanding of soil, climate, age, or size effects. OUTPUT: Example: jack pine, DBH = 30cm • References: • Lambert, M.-C.; Ung, C.-H.; Raulier, F. 2005. Canadian national tree aboveground biomass équations. Can. J. For. Res. 35:1996-2018. • 2. Ung, C.-H.; Bernier, P.; Guo, X.-J. 2008. Canadian national biomass equations: new parameter estimates that include British Columbia data. Can. J. For. Res. 38:1123-1132. 4-Can we group species into classes according to nutrient concentrations? Cedar Conifers-----Deciduous Conclusion: Equations to predict biomass compartments at the tree level are available and robust Larch 2-Biomass equations at the site level (users: research, forest management) Conifers and deciduous species are clustered into two distinct groups with foliar concentration. N and Mg play an important role (one exception: larch). Conifers are tightly clustered (two exceptions: larch and cedar). Clustering is not efficient for other biomass components (see graphs below). R2 of biomass model (Black spruce) INPUT 1-Select tree species 2-Enter basal area of the species as well as total stand basal area Equations: (3) M = aiGibiGtci Where Gi is basal area of species i and Gt is total plot basal area Wood .94 Bark .98 Species clustering from foliage nutrient concentrations Cedar Stem .95 We built equations for predicting biomass for each compartment (4) and each species (30) at the stand level using basal area (m2/ha) of the species (Gi) and total stand basal area (Gt). 1000 plots were considered (CANFI database). Error was propagated throughout the database by reintroducing a random error term and running estimates of the 1000 plots 1000 times using a Monte Carlo procedure (Yanai et al. 2010). The biomass of the components of each individual tree was calculated using the tree-level equations with a random error term (reintroduced at the tree level (red) or at the stand level (black) – see graphs). Equations showed high R2. We also modelled standard deviation of the biomass estimates from BA (Gi) from the generated database. Again R2 values were high >0.99. Branch .97 Foliage .88 Crown .95 R2 of models estimating the SD of biomass components (Black spruce) Conclusion: Nutrient concentrations in trunk, bark and branches is not well correlated to that of foliage and exhibit wide distribution ranges suggesting that individual species-compartments estimates are needed. Wood .998 Acknowledgements: We thank Ariane Béchard for data compilation and XiaoJing Guo for statistical programming References:Nutrient concentration data were taken from the literature as well as from the following databases: -Pardo, Linda H.; Robin-Abbott, Molly; Duarte, Natasha; Miller, Eric K. 2005. Tree chemistry database (version 1.0).Gen. Tech. Rep. NE-324. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northeastern Research Station. 45 p. -Kimmins, J.P., Binkley, D., Chatarpaul, L. and De Catanzaro, J. 1985. Biogeochemistry of temperate forest ecosystems: literature on inventories and dynamics of biomass and nutrients. Petawawa National Forestry Institute, Canadian Forestry Service PI-X-47/F -Compilation from the Georgia Basin project -Compilation from the Sustainable Forest Management Network (J.W. Fyles) -Yanai et al. 2010. Estimating uncertainty in ecosystem budget calculations. Ecosystems 13: 239-248. Branch .998 Bark .995 Foliage .998 Conclusion: It is possible to estimate biomass compartment mass per area using basal area (total and species).