Transcript Diapositive 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
INPUT
1-Select tree species
2-Enter DBH or DBH
and height
Equations: (1) Mi= βi1D βi2+e or (2) Mi= βi1D βi2 H βi3+e
Where:
M = Mass (kg), D = DBH (cm), H = Height (m), β = mode parameters,
i = biomass component (Branch, Bark, Foliage, Bole), e = error term
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:
1. 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?
foliar
Conifers-----Deciduous
foliar
Cedar
1.6
0.35
1.4
0.3
1.2
1
Ca
Mg
0.25
0.2
0.15
0.6
0.1
0.4
0.2
0.05
Conclusion: Equations to predict biomass compartments at the tree level are available and robust
0.8
0
0
0
0
0.5
1
1.5
2
2.5
0.5
1
1.5
Ca-coni
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.
biomass
model
(Black spruce)
Wood
.94
Bark
.98
Stem
.95
Branch .97
Foliage .88
Crown
.95
Trunk
Trunk
Species clustering from foliage
nutrient concentrations
Cedar
0.8
0.7
0.04
0.035
0.03
0.025
0.02
0.015
0.01
0.005
0
0.6
0.5
models
estimating the SD
of biomass
components
(Black spruce)
Wood
0.4
0.3
0.2
0.1
0
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
N
R2 of
Larch
Ca-dec
Mg-dec
Ca
INPUT
1-Select tree species
2-Enter basal area of the species
as well as total stand basal area
Equations: (3) M = ai Gibi Gtci
Where Gi is basal area of species i
and Gt is total plot basal area
Mg-coni
0.16
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
Mg-dec
Ca-coni
Ca-dec
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.
Acknowledgements: We thank Ariane Béchard for data compilation and XiaoJing Guo for statistical programming
Bark
.995
Foliage .998
Conclusion: It is possible to estimate biomass compartment mass per area using basal area (total and species).
0.16
N
.998
Branch .998
3
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
Mg
2-Biomass equations at the site level (users: research, forest management)
2.5
N
N
Mg-coni
2
3
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.