Presented by Klaus Butterbach-Bahl, Bjoern Ole Sander, David Pelster and Eugenio Diaz-Pines at the FAO‐CCAFS Workshop on Reducing the Costs of GHG Estimates in Agriculture to Inform Low Emissions Development, Rome, 10-12 November 2014

1. Quan&fying greenhouse gas emissions from
managed and natural soils
Klaus
Bu(erbach-­‐Bahl1,2,
Bjoern
Ole
Sander3,
David
Pelster1,
Eugenio
Díaz-­‐Pinés2
Rome, Reducing the costs of GHG es&mates in agriculture to inform low emissions
development, FAO-­‐CCAFS Workshop, November 10-­‐12, 2014
1Interna(onal Livestock Research Ins(tute, Kenya; 2Karlsruhe Ins(tute of Technology, Germany; 3Interna(onal Rice
Research Ins(tute, Phillipines; 4The University of Western Australia, Australia

2. Agricultural GHG emissions and developing
countries
• Agriculture
is
responsible
for
47
and
84%
of
anthropogenic
CH4
and
N2O
emission,
respec@vely
(Smith
et
al.
2007)
• But
these
es@mates
are
based
on
studies
in
Europe
/
N
America
/
Australia
• Importance
of
smallholder
farms
(e.g.
in
SSA)
• 75%
of
agricultural
produc@on
and
75%
of
job
produc@on
in
SSA
(Africa
Development
Bank,
2010)
• 80%
of
farms
in
SSA
<
2
ha
(FAO
2010)
• Yield
are
very
low
(~1
Mg
ha-­‐1)

3. GHG emissions and underlying mechanisms
Emission
=
produc@on
(microbial/
chemical)
–
consump@on
(microbial/
chemical)
BuZerbach-­‐Bahl
et
al,
2013,
Phil.
Trans.
R.
Soc.

4. GHG emissions processes and measuring
techniques
BuZerbach-­‐Bahl
et
al,
2013,
Phil.
Trans.
R.
Soc.

5. Drivers of soil GHG emissions
Turner
et
al.
2008,
Plant
&
Soil
Van
Beek
et
al.
2010,
Nutr.
Cycl
Agroecosys.
• Soil
proper@es
and
soil
environmental
condi@ons
• Agricultural
management
(e.g.
fer@liza@on,
irriga@on,
residue
management…)
• Microbe-­‐plant
interac@ons
and
microbial
diversity
• ……..

6. Advantages of chamber techniques
Plus
• Simple,
low
cost,
„easy“
to
apply
• Allows
studying
of
management
effects
• Can
be
established
elsewhere
• Existence
of
protocolls
(e.g.
USDA,
GRA)
Minus
• Change
in
soil
environmental
condi@ons
• Spa@al
and
temporal
variability
• Accuracy
of
measurements
• ….

7. Chamber techniques – general points

8. Chamber techniques – chamber placement

9. Chamber techniques – chamber placement

10. Chamber techniques – spa&al variability
Arias-­‐Navarro
et
al.,
2013,
Soil
Biol.
Biochem.

11. Chamber techniques – temporal variability
Barton
et
al.,
2014,
in
prep.
OVERALL OBJECTIVE
Investigate the effect of sample
frequency on estimates of annual N2O
fluxes, using published data collected:
• On a sub-daily
basis using
automated
chamber
systems
• From a variety
of climates and
land-usesMeasuring
soil
N2O
emissions
from
a
cropped
soil
using
chambers.
Photo:
Graeme
Schwenke,
NSW,
Australia

12. Chamber techniques – temporal variability
APPROACH
Daily
fluxes
by
averaging
sub-­‐daily
fluxes
(removed
diurnal
varia0on)
Annual
fluxes
at
different
sampling
frequencies
Propor@on
of
‘daily’
annual
flux
es@mated
by
each
sample
frequency
=
%
devia0on
of
‘daily’
annual
flux
For each data set, we calculated:
Barton
et
al.,
2014,
in
prep.

13. Chamber techniques – temporal variability
Measurement frequency
0 5 10 15 20 25 30
%Deviationofannualflux
-50
0
50
100
150
200
250
300
350
SAMPLING
FREQUENCY
&
ANNUAL
FLUX:
‘Highly’
episodic
Steppe
grassland,
semi-­‐arid
climate,
Inner
Mongolia
Barton
et
al.,
2014,
in
prep.

14. Chamber techniques – temporal variability
Barton
et
al.,
2014,
in
prep.
Measurement frequency
0 7 14 21 28
Numberofdata-sets
0
5
10
15
20
25
Within 10%
Within 20%
Within 30%
8%
RECOMMENDED
SAMPLING
FREQUENCY
Annual
flux
within
10%,
20%
and
30%

15. Chamber techniques – data processing

16. Chamber techniques – auxiliary
measurements and repor&ng

17. Summary
• Measurements
are
needed,
not
only
GHG
fluxes,
but
also
auxilliary
data
• Chamber
techniques
are
best
suited
to
address
the
diversity
of
systems
in
developing
countries,
but
• hierachical
approach
should
be
considered
(very
detailed,
detailed,
basic)
• Piralls
at
every
step,
QA/
QC
is
essen@al
• Targe@ng
is
needed,
to
close
gaps
in
knowledge