On 4 June the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) convened a side event on "Agriculture and Mitigation: Towards low emissions development" featuring speakers from FAO (Marja-Liisa Tapio Biström), Ugandan Delegation (Moses Tenywa), University of Abderdeeen (Jon Hillier), Unique Forestry and Land Use (Timm Tennigkeit), KIT Germany (Eugenio Diaz-Pines) and University of Edinburgh (Nicholas Berry). The session was chaired by James Kinyangi, Regional Program Leader for CCAFS East Africa. Read more about the event: http://ow.ly/lIQ2c
ICT role in 21st century education and it's challenges.
Bonn Climate Conference Side Event: 4 June 2013
1. 14 June 2013
Official
UNFCCC
side
event:
Agriculture
and
Mi8ga8on:
Towards
low
emissions
development
2. Na2onal
integrated
mi2ga2on
planning
in
agriculture
Timm
Tennigkeit,
Bonn,
04.06.2013
SBSTA
side
event
on:
Current
state
of
agriculture
and
mi2ga2on:
NAMAs,
quan2fying
emissions
and
links
to
adapta2on
13. KONTAKT
CONTACT
CONTACTO
UNIQUE
forestry
and
land
use
GmbH
Schnewlinstr.
10
79098
Freiburg,
Germany
Tel:
+49
-‐
761
20
85
34
-‐
0
Fax:
+49
-‐
761
20
85
34
-‐
10
eduard.merger@unique-‐landuse.de
www.unique-‐landuse.de
Financed
&
edited
by:
14. How
to
determine
which
site-‐
specific
GHG
mi2ga2on
op2ons
give
the
greatest
benefits?
Jon
Hillier
SBSTA,
Bonn,
5th
June
2013
15. • Key
sources
(sinks)
for
carbon
(arable
crops):
– Biomass
–
above
and
below
ground.
Depends
on
soil
and
climate.
– Soil
carbon
flux
-‐
depends
on
soil
and
climate.
– Nitrous
oxide
-‐
depends
on
soil
and
climate.
• One
size
does
not
fit
all!
– Effec%veness
of
mi%ga%on
op%ons
varies
with
loca%on
• Can
we
provide
site/region
specific
decision
support?
16. COMBINE
FOUR
SIMPLE
MODELS
– Soil
carbon
flux
• No-‐2ll
(IPCC,
Tier
1
method)
• Increased
carbon
inputs
(IPCC,
Tier
1
method)
*
– Soil
N2O
• Depends
on
soil
clay
content,
drainage,
carbon
stock,
climate.
Bouwman
et
al
2002
• Impact
of
nitrifica2on
inhibitors
– Emissions
from
fer8liser
produc8on
• Newer
have
technologies
substan2ally
lower
emissions
(EFMA,
older
and
abated
fer2liser
produc2on
values)
No
factors
for
tropical
climates.
Assumed
effect
as
in
temperate
climates
17. • Provide
simple
screening
method
for
iden2fica2on
of
promising
op2ons
– If
this
is
my
loca2on
and
produc2on
system
what
is
my
most
effec2ve
op2on
in
terms
of
SOC
or
fer2liser
management?
Drainage Climate Soil C
N
application
rate
Emissions
(kg CO2-
equiv)
reduce N
application
rate*
use low
emissions
fertiliser
employ
no-till
increase
C inputs
use
NIs
. .
. .
. .
Good Tropical 3-4% 100-150 1343 46% 16% 65% 44% 13%
Good Tropical 4-5% 100-150 1469 43% 18% 77% 54% 16%
Poor Temperate 0-1% 150-200 1426 29% 30% 9% 11% 12%
Poor Temperate 1-2% 150-200 1485 27% 31% 21% 25% 15%
. .
. .
18. Reduce
tillage
Increase
C
inputs
Use
BAT
Reduce
N
rate
Use
soil
inhibitors
N rate > 200 kg/ha/yr
N <= 100 kg/ha/yr
19. Reduce
tillage
Increase
C
inputs
Use
BAT
Reduce
N
rate
Use
soil
inhibitors
150 < N <- 200
(kg/ha/yr)
100 < N <= 150
(kg/ha/yr)
21. Reduce
tillage
Increase
C
inputs
Use
BAT
Reduce
N
rate
Use
soil
inhibitors
150
<
N
<-‐
200
(kg/ha/yr)
100
<
N
<=
150
(kg/ha/yr)
With
yield
penalty
applied
22. Conclusions
1
• Effec2veness
of
prac2ces
depends
on
loca2on
• Good
natural
C
stocks,
or
low
input
system
–
soil
carbon
management
is
best
• Abated
fer2lisers
is
low
risk,
effec2ve
op2on,
as
are
inhibitors
• Mi2ga2on
prac2ces
must
consider
the
impact
on
produc2on
– Best
op2ons
may
be
those
which
increase
produc2on,
e.g.
increased/improved
inputs
or
water
management
23. Conclusions
2
• Other
high
poten2al
mi2ga2on
op2ons
not
included
– Agroforestry
– Residue
management
• Accurate
region
specific
– N-‐response
curves
for
a
range
of
crops
to
iden2fy
op2mal
N
for
both
yield
and
GHG
impacts
– Empirical
emissions
data/meta-‐models
for
tropical
climates
– Consistent
datasets
comparing
a
range
of
management
prac2ces,
e.g.
no-‐2ll
,
cover
cropping,
agroforestry,
residue
management,
N2O
emissions
24. A
system
for
quan2fica2on
of
smallholder
agriculture
GHGs
Marja-‐Liisa
Tapio-‐Bistrom
Mi2ga2on
of
climate
change
in
Agriculture
programme
(MICCA)
FAO
25. Elements
and
tools
for
mi8ga8on
planning
in
agriculture
• Data
on
emissions
and
projec2ons
for
a
baseline
• Mi2ga2on
op2ons
–
LCA
as
a
tool
• Knowledge
on
farming
prac2ces
• Emission
factors
• A
vision
and
means
for
landscape
level
op2ons
for
increasing
the
carbon
content
• Gree2ngs
from
GHG
quan2fica2on
workshop
• Food
for
thought
26. FAOSTAT
Emissions
from
Agriculture
and
Land
Use
Database
+IPCC
Guidelines
=
&
geo-‐referenced
informa8on
Tier
1,
all
sources
of
emissions
from
agriculture
and
LU,
8me
series
from
1990,
all
countries,
projec8ons
to
2030
and
2050
27. Life
Cycle
Analysis
–
iden8fying
mi8ga8on
op8ons
• LCA
is
an
approach
to
emissions
analysis
which
makes
sense
to
policy
makers,
investors
farmers
since
it
describes
the
system
• Global
LCA
on
all
livestock
systems
coming
out
soon
(different
intensity
levels,
different
agroecological
zones)
28. Emission
intensity
of
milk
in
East
Africa
FAO,
2013
Source:
Global
Environmental
Assessment
Model
(GLEAM)
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
Kenya Uganda United Republic
of Tanzania
KgCO2eq/kgFPCM
CO2, Post-farm
gate
CO2, Direct and
embedded energy
Feed CO2
Feed N20
Manure N20
Manure methane
Enteric
fermentation
29. 0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
Temperate Arid Humid
KgCO2e/kgFPCM
Kenya: Grazing
systems
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
Temperate Arid Humid
Kenya: Mixed systems
CO2, Direct and
embedded energy
Feed CO2
Feed N20
Manure N20
Manure methane
Enteric fermentation
Source:
Global
Environmental
Assessment
Model
(GLEAM),
FAO,
2013
60%
2%
6%
28%
2% 1% 1%
Emission
intensity
of
milk
in
Kenya
30. Enteric
methane
emissions
at
farm
scale
-‐
Kaptumo,
Kenya
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
500 1000 1500 2000 2500 3000 3500 4000
KgEntericCH4perlitremilk
Liter of milk per cow per lactation
Source:
Based
on
Global
Environmental
Assessment
Model
(GLEAM),
farm
scale
LCA
based
on
Household
data,
Opio
et
al.,
2013
31. Enteric
methane
-‐
improving
feed
use
efficiency
-‐
Kaptumo,
Kenya
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80
KgentericCH4perlitre
milk
Feed efficiency (litre milk/kg DM intake)
Source:
Based
on
Global
Environmental
Assessment
Model
(GLEAM),
farm
scale
LCA
based
on
Household
data,
Opio
et
al.,
2013
32. More
analysis
of
farming
prac8ces
-‐
We
need
rigorous
analysis
of
farming
prac2ces
combining
the
science
and
farmers
experiences
to
develop
climate-‐smart
prac2ces
-‐
What
works
,
where
or
why
not
33. Emission
factors
• Bewer
emission
factors
for
tropical
and
sub-‐
tropical
areas,
major
farming
systems
and
farming
prac2ces
• A
global
plan,
iden2fying
priority
systems
and
gaps
• Longer
term
measurements
–
calibra2on
of
models
• Network
of
research
partners
–
spearheaded
by
CCAFS?
34. Maximizing
carbon
content
–
landscape
approach
• Tap
the
mi2ga2on
poten2al
at
landscape
level
trough
holis2c
par2cipatory
land
use
planning
• CSA
sourcebook
gives
ideas
how
• Aboveground
biomass
as
a
proxy?
–stable
or
increasing
J
• Opportuni2es
for
remote
sensing
–
land
degrada2on
in
grasslands
35. Gree8ngs
from
GHG
quan8fica8on
workshop
The
current
systems
are
complex
and
expensive,
not
appropriate
for
most
low-‐income
countries.
à
We
must
invest
in
crea8ve,
low-‐cost
systems
for
data
collec8on
and
analysis,
such
as
1.
targe2ng
global
mi2ga2on
priori2es
and
hotspots
('or
key
categories')
in
landscapes
and
farming
systems
2.
combining
modeling,
remote
sensing
and
field
measurements
(crowd-‐sourcing
and
mobile
technology)
3.
building
on
exis2ng
ac2vity
data
from
other
sources
4.
using
consistent,
comparable
methods
and
data
sharing
networks
that
enable
robust
es2mates
for
different
systems
36. Food
for
thought
• How
exact
do
we
need
to
know
the
net
emission
reduc2ons?
• Depends
on
the
funding
source
–
climate
funding
vs.
agricultural
investments
• We
need
to
transform
the
way
we
produce
food
–
more
efficient,
more
resilient,
with
mi2ga2on
co-‐benefit.
37. K. Butterbach-Bahl | IMK-IFU | March 2008
KIT – die Kooperation von
Forschungszentrum Karlsruhe GmbH
und Universität Karlsruhe (TH)
Standard Assessment of Mitigation Potential and
Livelihoods in Smallholder Systems (SAMPLES)
Eugenio Díaz-Pinés, Mariana Rufino, Todd Rosenstock, Klaus Butterbach-Bahl,
Lini Wollenberg et al.
Current state of agriculture and mitigation:
NAMAs, quantifying emissions and links to adaptation.
June 2013, Bonn, Germany
38. Institute for Meteorology and Climate Research,
IMK-IFU
38 6/5/13
" Very few data on mitigation
" Mitigation not linked to livelihoods
" Fragmented and diverse landscapes
" Multi-criteria approaches missing
The concerns
Develop a low-cost protocol to quantify
greenhouse gas emissions and to identify
mitigation options for smallholders at whole-
farm and landscape levels
The goal
39. Institute for Meteorology and Climate Research,
IMK-IFU
39 6/5/13
Landscape analysis
and targeting
Landscape
implementation
Multi-dimensional evaluation
of mitigation options
Scalable and social
acceptable mitigation options
System-level estimation
of mitigation potential
Set-up of state-of-the-art
laboratory facilities
Training of laboratory
and field staff
Phase III:
Development of systems-level
mitigation options
Phase I: Targeting, priority setting and infrastructure
Phase II: Data acquisition
Capacitybuilding
Phase IV:
Implementation with
development partners
(UPCOMING)
Productivity
assessment
GHG
measurements
Profitability
evaluation
Social acceptability
assessment
Joint
scientific &
stakeholder
evaluation
40. Institute for Meteorology and Climate Research,
IMK-IFU
40 6/5/13
How to identify mitigation options at farm and
landscape level?
41. Institute for Meteorology and Climate Research,
IMK-IFU
41 6/5/13
Complex landscape: f (m, n, o, p, q)
m Landscape units
n Farm types
Land
Livestock
Other assets
Sources of
incomes
p Field types
Characterise
fertility x
management
Physical
environment
GIS analysis,
remote
sensing,
landuse
trends
Food
security,
poverty
levels
Productivity,
GHG
emissions,
crop
preferences
o Common lands
q Land types
42. Institute for Meteorology and Climate Research,
IMK-IFU
42 6/5/13
Landscape units and landusers Nyando, Kenya
Landscape analysis and targeting
43. Institute for Meteorology and Climate Research,
IMK-IFU
43 6/5/13
Targeting and upscaling: from
landscape to fields and back…
44. Institute for Meteorology and Climate Research,
IMK-IFU
44 6/5/13
Taking gas samples from
chambers
Step 1. Landscape analysis
Targeting:
- Landscape units, farm types,
field types, soils
- Site selection
Site characterization:
- Soils, crops, biomass
Installation of chamber
frames
Informing and
interviewing farmers
Step 2. Installing measurement stations
Step 3. Measurements applying
gas pooling
Field work:
- Overcoming spatial variability
by gas pooling
30 Oct 4 Nov 9 Nov 14 Nov 19 Nov 24 Nov 29 Nov
0
25
50
75
100
250
500
N2
Oflux[µgNm
-2
h
-1
]
2012
0
25
50
75
100
250
500
0
25
50
75
100
250
500
Cropland
Grassland
individual chambers
gas pooling
Forest
Temporal variability of N2O
fluxes at three sites differing
in land use at Maseno,
Kenya.
Arias-Navarro et al., Soil Biol. Biochem., in revision
45. Institute for Meteorology and Climate Research,
IMK-IFU
45 6/5/13
Lab work:
- Analyzing gas samples (GC)
- Calculating concentrations and fluxes
Step 5. Intepretation and upscaling
Step 4. Lab analysis and flux calculations
Synthesis of GHG measurements:
emission factors, empirical models, calibrating and
validating of detailed models
Upscaling: assigning emissions to landscape
elements and/or of GIS coupled biogeochemical
models
46. Institute for Meteorology and Climate Research,
IMK-IFU
46 6/5/13
Farm
type
Field
type
Profit ($/
ha)
Production
(kg/ha)
Emissions
(t CO2eq
per ha)
Emissions
(kg CO2 per
kg product)
Social
acceptability
(ranking)
1 1 50 500 0.6 1.2 1
1 2 140 5000 3 0.6 2
1 3 120 2000 2 1.0 2
1 4 40 4500 3 0.7 1
2 1 30 800 0.7 0.9 3
2 3 180 8000 3 0.4 2
2 4 250 300 0.5 1.7 1
n m Vn,m Wn,m Xn,m Yn,m Zn,m
Multi-dimensional assessment of mitigation
options
Trade-off analysis on multiple dimensions
47. K. Butterbach-Bahl | IMK-IFU | March 2008
KIT – die Kooperation von
Forschungszentrum Karlsruhe GmbH
und Universität Karlsruhe (TH)
Thanks for your attention
eugenio.diaz-pines@kit.edu
49. Greenhouse
gas
accoun2ng
for
different
purposes
Requirements
Data
Carbon
offsets
Precise-‐or-‐conserva2ve
es2mate
of
mi2ga2on
Local
measurements
and/or
modelling
Performance-‐based
finance
Evidence
that
mi2ga2on
targets
have
been
met
Regional
default
values
and
emission
factors
Planning
and
evalua8on
Comparison
between
projects
or
areas
Na2onal
default
values
and
emission
factors
51. An
example
for
Malawi
Baseline
• Conven2onal
maize
Conserva8on
agriculture
• With
and
without
reduced
2llage
Agroforestry
• Alley
cropping
• Intercropping
hwp://shamba.ourecosystem.com
52. SHAMBA
tool
(Malawi
demo)
hwp://shamba.ourecosystem.com
SHAMBA
methodology
hwp://www.2nyurl.com/shambatool
Berry
N.J.
and
Ryan
C.M.
(2013)
Overcoming
the
risk
of
inac2on
from
emissions
uncertainty
in
smallholder
agriculture.
Environmental
Research
Le8ers
8
011003
doi:10.1088/1748-‐9326/8/1/011003
nicholas.berry@ed.ac.uk
53. 534 June 2013
Official
UNFCCC
side
event:
Agriculture
and
Mi8ga8on:
Towards
low
emissions
development
www.ccafs.cgiar.org