Emerging techniques for soil carbon measurements

D. Milori, A. Segnini, W. Da Silva, A. Posadas, V. Mares, R. Quiroz, & L. Martin-Neto& contributions from L. Claessens & K. Shepherd

OUTLINE

•Emerging techniques for…

•Quick overview of selected emerging techniques

•Examples of field measurements

•Data input for SC modeling

•A “synthetic” scenario

•Summary

Emerging SC measuring techniques for...

•Geospatial baseline

•Quantity and quality of SC stocks

•Field-base measurements

•Better input for models

•Assessing tradeoffs

Mostly used SOM techniques in developing countries

&/or

Examples of emerging techniques for SOC measurements

460 480 500 520 540 560 580 600 620 640 660

5

10

15

20

25

30

35

40

45

LIF

inte

nsity

(a.u

.) / C

(g k

g-1)

λ (nm)

0-2.5 cm 2.5-5 cm 5-10 cm 10-20 cm 20-30 cm

Infrared Spectroscopy for rapid soil characterization

• Rapid, Low cost

• Reproducible

• Predicts many soil functional properties

Parameter R2 PCs

Total N 0.9 8

Total C 0.92 6

Organic C 0.92 6

pH 0.89 10

Ca 0.95 9

K 0.81 10

Mg 0.92 10

Source: K. Shepherd (ICRAF)

LIBS System

Source: Da Silva et al., 2008

LIF Emission spectrum

400 450 500 550 600 650 700

0

1

2

3

soil calcinate and treated soil

Inte

nsity

(a.u

.)

λ (nm)

Milori et

al., SSSAJ, 2006.; González-Pérez et

al., Geoderma, 2007

λexcitation

= 458 nm

Humification Degree:HLIF

= LIF Area

/total carbon

Bench and portable LIF correlate well with EPR findings

Electron Paramagnetic Resonance (EPR)EMBRAPA Lab.

2 3 4 5 6 7 8 9

2.0

2.5

3.0

3.5

4.0

4.5

5.0R=0.93; P<0.0001

LIF

benc

h sy

stem

: HLI

F (a.

u.)

EPR [(spins g-1C) x 1017]

SOM characterization with 13C-NMR

Nuclear Magnetic ResonanceEMBRAPA Lab

Source: Segnini et al., 2011

A B2

3

4

5

6

7

8

9

spin

s (x

1017

) g-1 C

Bofedales (wetlands)

0-2.5 cm 2.5-5 cm 5-10 cm 10-20 cm 20-30 cm

Forest Tea Degradedvegetation Native veg.

On-going analysis in Kenya

CARBON STOCKS# (kg m-2)

Area 1 Area 2 Area 3

sites depth (cm)

Forest Tea Coffee + eucalyptus

Coffee Native vegetation

Rotationcrops

Native vegetation

Rotationcrops

0-2.5 1.8 ±0.1 0.6 ±0.0 0.6 ±0.0 0.5 ±0.0 0.3 ±0.0 0.7 ±0.1 1.0 ±0.0 0.5 ±0.1

2.5-5 1.3 ±0.1 0.3 ±0.0 0.6 ±0.1 0.5 ±0.0 0.2 ±0.0 0.7 ±0.1 0.8 ±0.0 0.5 ±0.1

5-10 2.4 ±0.1 1.2 ±0.1 1.3 ±0.3 1.0 ±00 0.5 ±0.0 1.3 ±0.1 1.4 ±0.0 0.9 ±0.1

10-20 4.1 ±0.6 2.1 ±0.0 2.1 ±0.1 1.8 ±0.2 0.8 ±0.1 2.1 ±0.4 2.8 ±0.1 2.0 ±0.3

20-30 3.1 ±0.3 2.1 ±0.0 1.9 ±0.1 1.8 ±0.2 0.8 ±0.2 1.7 ±0.2 1.8 ±0.1 1.3 ±0.1

Total (0-30) 12.7 ±1.2 6.3 ±0.1 6.4 ±0.5 5.6 ±0.4 2.6 ±0.4 6.5 ±0.9 7.8 ±0.3 5.1 ±0.6

Results from EMBU-Kenya

LIF results:Kenya

#

HLIF

can be estimated through the ratio area under fluorescence emission (excitation range 350 -

480 nm) / total organic carbon content.

Humification degree or carbon stability (HLIF) of whole soils obtained through Laser Induced Fluorescence (LIF) spectroscopy.

fore

st (1

)

tea

(1)

coffe

e +

euca

lypt

us (1

)

coffe

e (1

)

natu

ral v

eget

atio

n (2

)

rota

tion

(2)

natu

ral v

eget

atio

n (3

)

rota

tion

(3)

0 - 2.55 - 10

20 - 300

102030405060708090

LIF index (a.u.) (x1000)

Land use

depth (cm)

0 - 2.5

2.5 - 5

5 - 10

10 - 20

20 - 30

Extraction of soil and climate parameters from agro-ecological cells or polygons for model parameterization

Modeling Carbon Dynamics in Soils:

Weather data used to run the model:

Rainfall: essential

Air temperature: essential

Temporal resolution of weather data:

Monthly: essential

Spatial resolution of weather data:

Local scale: essential

Source: FAO

HUANCANE

01020304050

1-Jan-99 20-Jul-99 5-Feb-00 23-Aug-00 11-Mar-01 27-Sep-01 15-Apr-02 1-Nov-02

Días

m.m

.

(ppm)

Space-time scaling weather/climate data

✓60 primary sentinel sites9,600 sampling plots19,200 “standard” soil samples~ 38,000 soil spectra3,000 infiltration tests~ 1,000 Landsat scenes~ 16 TB of remote sensing data to date

AfSIS

Sampling-transect to assess carbon contents and stocks in Southern Peru.Source: Segnini et al., 2010

Selected characteristics of the sampling sites.

loamclay loamloamloamloamSoil class

20°C8 °C14°C17°C19°CT mean ( °C)

2133690-83451155Precipitation (mm)

Coffee, potato, maize, coca,

citrus

Potato, oat, alfalfa,

grasslands, peat lands

Avocado, potato, maize, alfalfa, cassava

Maize, potato, grape, orange, alfalfa, onion,

beans

Maize, oliveCropping system

1,3503,8302,200960135Altitude (m)

humid

valleySemi-Arid

high

plateauArid high

valley

Arid low

valley

Arid CoastAgro eco zones

San Juan del Oro

PunoTorataMoqueguaIlosites

Carbon stocks in diverse Andean soils

Mai

ze

Oliv

e

Alfa

lfa I

Pot

ato

I

Gra

pe

Avo

cado

Alfa

lfa II

Cof

fee

Fore

st

Pot

ato

II

0 - 2.5

10 - 2005

1015

2025

30

35

40

LIF index (a.u.)

Land use

depth (cm)

0 - 2.52.5 - 55 - 1010 - 2020 - 30

LIF results: Andes

#

HLIF

can be estimated through the ratio area under fluorescence emission (excitation range 350 -

480 nm) / total organic carbon content.

Humification degree or carbon stability (HLIF) of whole soils obtained through Laser Induced Fluorescence (LIF) spectroscopy.

Changes in potential potato (improved and native) in Peru: 2000-2050

S. De Haan & H. Juarez, CIP (2008)

1975:(4000-4150msnm)

2005:(4150-4300msnm)

As temperature and presence of pest increase in the Andes Potatoes are planted in higher grounds

Peatlands and other land uses in the Andean high plateau

Peatlands to potato

050

100150200250300350

2000 2050Scenarios

Gig

agra

ms

(10x

9)

Bolivia Peru

Grasslands to potato

0

2000

4000

6000

8000

10000

12000

2000 2050Scenarios

Gig

agra

ms

(10x

9)

Bolivia Peru

Potential loss of soil carbon stocks due to cropping peatlands and grasslands in Peru & Bolivia

Summary•Emerging SC measuring techniques / tools & MRV

•Further field testing under different agroecological conditions & creation of spectral libraries needed (C-contents & stability)

•Better input for SC modeling

•Better assessment of tradeoffs

•Synergy with complementary tools e.g. remote sensing