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