Presentation at workshop: Reducing the costs of GHG estimates in agriculture to inform low emissions development November 10-12, 2014 Sponsored by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the Food and Agriculture Organization of the United Nations (FAO)

1. Challenges
for
agricultural
GHG
quan4fica4on
Fahmuddin
Agus
Indonesian
Soil
Research
Ins5tute
Jl.
Tentara
Pelajar
No
12,
Cimanggu,
Bogor
16114,
Indonesia
F_agus@litbang.pertanian.go.id
Interna'onal
Workshop
Reducing
the
costs
of
GHG
es4mates
In
agriculture
to
inform
low
emissions
development
www.litbang.deptan.go.id
Opening
Panel,
Rome,
Italy,
10-­‐12
Nov.
2014

2. 3
Ques4ons
1. What
approaches
are
currently
used
to
es5mate
GHG
emissions
from
agriculture?
2. Have
you
developed
any
innova5ons
to
reduce
the
cost
of
greenhouse
gas
es5mates?
3. What
are
the
major
challenges
and
priori5es
for
improving
es5mates
of
agricultural
greenhouse
gases?

3. Na4onal
Communica4ons,
BUR
REDD+
NAMAs
Land-­‐based
(Forestry
+
Agric.)
Transporta5on
Energy
Industry
Wastes
LAMAs
(Provincial)
1. CO2 from LUC
2. CO2 from drained
peat oxidation
3. CO2 and CH4
emissions from
forest and peat
fire
4. CH4 from rice
field
5. N2O from N
fertilizer and
animal manure
6. CH4 from enteric
fermentation

4. Rela4ve
importance
of
land-­‐based
GHG
emissions
?
Land
use
change
and
peat
decomposi5on
contributed
about
87%
of
the
total
land
based
emissions
and
thus
the
26%
na5onal
emission
reduc5on
target
can
only
be
achieved
if
emissions
from
these
sources
can
be
reduced
significantly

5. 1-­‐2.
Current
approach
and
Innova4on
to
reduce
the
cost
Aspect
Current
approach
Cost
reduc4on
Innovtn.
1.
Land
use
change
and
peat
emissions
Ac5vity
data
o 23
x
23
LU/Lcover
change
matrix
from
landsat
TM
1:250,000
scale;
3-­‐5
yearly
o Overlay
of
the
23
land
cover
classes
with
peat
soil
map
Emission
factors:
o IPCC
(2006),
IPCC
(2013)
for
peat
oxida5on
and
na5onally
generated
data
Adapt
the
available
data
to
the
23
Land
cover
types
2.
CH4
emissions
from
rice
field
Ac5vity
data
o Areal
of
lowland
rice,
harvest
index
o Harvest
area
o Area
by
rice
variety
o Area
by
irriga5on
system
(con5nuous
flooding
vs
intermifent)
Priori5ze
on
area
by
irriga5on
system
Emission
factor
o IPCC
(2006),
local
research
data

6. Land
cover
classes
and
emission
factors
No
.
Penggunaan
lahan
Time
averaged
C
stock
(t/ha)
Emisi
(t
CO2
ha-­‐1
th-­‐1)
Remarks
1
Primary
dryland
forest
195
0
Mineral
soil,
assumed
zero
2
Secondary
dryland
forest
169
0.
Mineral
soil,
assumed
zero
3
Primary
mangrove
170
0
Mineral
soil,
assumed
zero
4
Secondary
mangrove
forest
120
0
Mineral
soil,
assumed
zero
5
Primary
swamp
forest
196
0
IPCC
(2006)
6
Secondary
swap
forest
155
19
IPCC
(2013)
7
Timber
planta5on
64
73
IPCC
(2013)
8
Estate
planta5on
63/40
(OP)
40
IPCC
(2013)
9
Annual
upland
agriculture
10
51
IPCC
(2013)
10
Mixed
upland
Agriculture
30
51
IPCC
(2013)
11
Shrub
30
19
IPCC
(2013)
12
Swamp
Shrub
of
is
manual
30
19
IPCC
(2013)
13
Savanna/grassland
digi5zing
4
35
IPCC
(2013)
14
Paddy
Field
2
34
IPCC
(2013)
15
Swamp6)
0
0
Flooded,
assumed
zero
(IPCC
2013)
16
Ponds6)
0
0
Flooded,
assumed
zero
(IPCC
2013)
17
Transmigra5on
10
51
Assumed
similar
with
annual
crop
18
Seflement
4
35
Assumed
similar
with
savanna,
19
Airport
0
0
Assumed
zero
20
Mining
0
51
Assumed
same
as
bareland
21
Bareland
2.5
51
IPCC
(2013)
22
Water
body
0
0
Waterlogged,
assumesd
zero
23
Others
(cloud
cover)
?
?
Refer
to
the
previous
or
subsequent
LU
23
The
23
x
matrix
LUC
generated
landsat
from
TM
(mostly
screen)
on

7. 1-­‐2.
Current
approach
and
Innova4on
to
reduce
the
cost
(con4nued)
Current
approach
Cost
reduc4on
Invtn.
3.
N2O
Emission
from
fer4lizers
Ac5vity
data
o Amount
of
N
fer5lizers
Already
very
simple
method
Emission
factor
o IPCC
(2006)
and
na5onally
generated
data
for
AG
C
4.
Emissions
from
animal
husbandry
Ac5vity
data
CH4
from
enteric
fermenta5on
o Livestock
popula5on
o No
separa5on
between
conven5onal
and
befer
quality
feed
Emission
factor
o IPCC
(2006),
research
on-­‐going
for
country
specific

8. 3.
Challenges
and
priori4es
for
improvement
• MRV
and
assessment
of
GHG
emission
is
rela4vely
new
for
most
stakeholders,
especially
at
sub-­‐na4onal
level
• Despite
the
Presiden4al
Regula4on
No.
71/2011
on
MRV,
stakeholders
see
li]le
(short
term)
incen4ves
for
MRV,
no
market
whatsoever
for
carbon
emission
reduc4on
Need
to
look
at
the
synergy
between
adapta4on
and
mi4ga4on

9. Management
Adapta4on
Mi4ga4on
Intermifent
irriga5on
for
rice
Larger
plan5ng
area
with
the
same
volume
of
water
Reduced
CH4
emission
Balanced
and
efficient
fer5liza5on
Higher
yield
and
befer
plant
vigor
Lower
emissions
from
fer5lizers
Mul5strata
farming
on
drought
prone
areas
The
tree
crop
component
is
more
tolerant
to
and
can
s5ll
produce
during
long
dry
seson
Enhancement
of
C
by
the
tree
component
Improvement
of
livestock
feed
Increase
weight
gain
Decreased
CH4
emission
from
enteric
fermenta5on
• Treat
adapta4on
as
the
entry
point
for
GHG
quan4fica4on
• Quan4fy
mi4ga4on
as
the
extra
benefits

10. 3.
Challenges
and
priori4es
for
improvement
on
Ac4vity
data
and
Emission
Factors
Source
Challenges
and
priori4es
1.a.
Land
use
change
and
peat
emissions
o Development
of
sub-­‐na4onal
emission
factor
o Reducing
uncertainty
of
ac4vity
data
of
peat
and
forest
fire
emissions
2.
CH4
emissions
from
rice
field
3.
N2O
Emission
from
fer4lizers
o Improve
ac4vity
data
by
using
the
rate
of
N
fer4lizer
applica4on
by
cropping
system
4.
Emissions
from
animal
husbandry
o Development
of
emission
factors
by
feed
composi4on
o Assessment
of
average
animal
body
weigh
by
age
class
by
region

11. H
C
H
C
H
C
Thank
you