Quantification of above- and belowground biomass carbon
in agricultural landscapes
The significance ofempirically validated allometries
Kuyah Shem
and
Dietz J, Jamnadass R, Muthuri C, Mwangi P
ICRAF Seminar Series - 03 May 2011
Measurement of Biomass Carbon• Trees in agricultural landscapes are sinks for
carbon
• Biomass carbon can be measured by direct or indirect methods (e.g. Allometric Equations)
• Allometric equations relate biomass to measureable parameters
e.g. diameter at breast height (dbh)
• Power function was used:
– It has a more natural scaling than polynomials, quadratic and cubic
bdbhaBiomass
Allometric equations have advantagesOnce developed:
• Are non-destructive, less laborious
• Allow ‘follow-up measurements’
• Can be applied on a large area e.g. forest inventories
Do we need new allometries?• What exists:
1. Species specific equations
2. Global equations (e.g. Chave et al. 2005)
• Their limitations:
1. Agricultural mosaics are heterogeneous
2. Global equations have not been validated
Diverse species Varied management
Where we worked
In three 100 km2 Sentinel sitesElevation: 1200 – 2200 m a.s.l.
A landscape approach
Random sampling
Stratified by size class;6 dbh classes used
In western Kenya
30 x 30 m plotsLDSF (Walsh and Vȧgen, 2006)
What was measured• GPS coordinates
• Diameters
• Tree height
• Crown dimensions
• Crown conditions
• Tree species name
• Cores for wood density
• 72 trees sampled
• 879 trees measured toestimate representative biomass
Ab
ove
gro
un
d b
iom
ass
(AG
B)
Also belowground biomass (BGB)
Biomass of missing roots determined by extrapolation
• Root collar diameter (RCD)
• Diameters of main roots
• Length of main roots
• Depth excavated
l1 = total root length; l2 = excavated section; l3 = missing portion
2 m
The equations: development and validation• Diameter (dbh) as lone predictor for AGB
–AGB, dbh and RCD as lone predictor for BGB
• Height, wood density, crown area as additional explanatory variables
• Multiple sample holdouts for cross-validation
– Equations = Average of parameters in 12 holdouts
• Model fit and accuracy determined
• Suitability of using published models assessed
Cross validationHoldout a b R2 Error (%)
1 0.081 2.497 0.96 -252 0.090 2.470 0.98 -43 0.089 2.478 0.98 134 0.090 2.474 0.98 35 0.091 2.472 0.98 126 0.097 2.448 0.98 -357 0.095 2.458 0.98 -128 0.090 2.471 0.98 -149 0.096 2.455 0.97 -4
10 0.087 2.488 0.99 4011 0.091 2.471 0.98 1012 0.089 2.478 0.98 26
Average 0.090 2.742 0.98 -5
Published equations testedAuthor Site Equation
Chave et al. 2005
Global dry forest
0.112*(dbh2Hρ)0.916
Brown, 1997 Global wet forest
21.297-6.953*dbh+0.74*dbh2
Henry et al. 2009
western Kenya
0.051*(dbh2H)0.930
Cairns et al. 1997
Tropical dry forests
0.347*AGB0.884
Mokany et al. 2006
Tropical dry forest
0.489*AGB0.890
Diameter is a reliable proxy for estimation of aboveground biomass
09040 7422.dbh.AGB
• Strong correlation with AGB, R2 = 0.98
• Error = 5 %
Global equations overestimated AGB
• Agricultural landscapes resemble a hybrid of dry and wet forest type
• Henry et al. 2009 underestimated AGB
0
3
6
9
12
15
0 30 60 90 120
AG
B (
Mg)
dbh (cm)
Our Equation Chave et al. 2005
Brown, 1997 Henry et al. 2009
Performance of equation depends on tree size
-20
-10
0
10
20
30
40
Erro
r (%
)
Our equationWestern KenyadbhTotal error = -5 %
-20
-10
0
10
20
30
40 Chave et al. 2005Global dry forestdbh, H, ρTotal error = 4 %
-20
-10
0
10
20
30
40
<10 20 30 40 60 >60
Erro
r (%
)
Diameter at breast height (cm)
Brown, 1997Global wet forestdbhTotal error = 7 %
-20
-10
0
10
20
30
40
<10 20 30 40 60 >60
Diameter at breast height (cm)
Henry et al. 2009Western Kenyadbh, HTotal error = -11 %
H = heightρ = wood density
Diameter best predictor of BGB
0340 4142.dbh.BGB
• Error for BGB models
—dbh = -4 %
—AGB = 3 %
—RCD = -1 %
• dbh, AGB and RCD showed strong correlation with BGB, R2 >0.90
Root:Shoot ratios (RS)
• Decreased with increase in dbh, and AGB
• Was greatly influenced by management (black)
• Varied across the three sites investigated
• Mean = 0.33; Median = 0.29
0.0
0.3
0.6
0.9
1.2
0 30 60 90 120
Ro
ot:
Sho
ot r
atio
diameter at breast height (cm)
Global equations underestimated BGB
-60
-30
0
30
60
90
<10 10 20 30 40 >60
Erro
r %
Diametter at breast height (cm)
Cairns et al. 1997
Mokany et al. 2006
IPCC RS
<10 10 20 30 40 >60
Diameter at breast height (cm)
AGB based equation
dbh based equation
RCD based equation
Mean RS
Performance of RS was inconsistent:• Overall error (3 blocks) = 1 %;• Lower Yala = -35 %, Mid. Yala = 11 % Upper Yala = 17 %
It is also possible to estimate whole tree biomass using diameter
R² = 0.986
0
3
6
9
12
15
0 20 40 60 80 100 120
Tree
bio
mas
s (M
g)
Diameter at breast height (cm)
Crown area models can be a useful link between ground data and remotely sensed imagery
R² = 0.841
0
3
6
9
12
15
0 60 120 180 240 300
Tre
e b
iom
ass
(Mg)
Crown area (m2)
• Greater variability exists compared to dbh-biomass relationship
• Management and interplant competition have a significant influence
Representative landscape biomass
Size does matter
• <20 cm diameter = 20 % biomass
• 5 % largest trees = 60 % biomass
0
15
30
45
60
75
10 20 30 40 60 >60
Shar
e (
%)
dbh (cm)
No. of trees measured (n = 879)
Estimated biomass (91.16 Mg)
The potential of agricultural mosaics
• Average carbon content was 0.48
• Aboveground biomass carbon = 17.36 Mg C ha-1
– Foliage = 4 %; branches = 39 %; stem = 57 %
• Belowground biomass carbon = 5.27 Mg C ha-1
–BGB account for 23 % of the total tree biomass
• Biomass of roots not excavated was 23 % of the total BGB
Conclusions• Diameter was confirmed as a robust proxy
even complex agricultural landscape
• Management significantly affect biomass and contribute to the heterogeneity of the landscape
• Root:shoot ratios should be used with great care depending on soil and management conditions
Outlook
• Testing the performance of equations developed at national level
– Tested in Uganda on coffee trees
• Validation of Non-destructive approaches
• Fractal branch Analysis (van Noordwijk)
• Relate Root:Shoot ratios to soil properties
Potentials
• Guidelines for establishing regional allometric equations for biomass estimation through destructive sampling
• Validation of non-destructive methods
–Remote sensing
– Fractal branch analysis
• Up-scaling of biomass
• Use for national greenhouse national inventory
Acknowledgement
• ICRAF for the fellowship
• Supervisors
• Anja and Team (Research Methods)
• Kisumu Field crew
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