Presented by Klaus Butterbach-Bahl, Mariana Rufino, David Pelster, Todd Rosenstock and Lini Wollenberg at the ILRI 'Livestock Live Talk', Nairobi, 14 August 2013

1. Standard assessment of mitigation potentials
and livelihoods in smallholder systems
Klaus Butterbach-Bahl, Mariana Rufino, David Pelster, Todd Rosenstock,
Lini Wollenberg,

2. Outline
• Agriculture and GHG emissions
• Why we need a GHG lab at ILRI
• What have I done before?
• What do we want to do?
• On-going projects
• Outlook

3. Biosphere as source for atmospheric
trace gases
CH4
CO2
VOC
NOx
N2O
60-70%
60-70%
Isoprenoid-
production
90%
Nitrification Denitrification
MethanogenesisCH4-Oxidation
Photosynthesis
The Biosphere
• major source/ sink for trace
substances (N2O, CH4, NOx,
CO2, VOC)
• dynamic exchange with
atmosphere
• effects chemical
composition of the
atmosphere
• and, thus, environmental
conditions on earth (e.g.
climate and air pollution)

4. Atmospheric composition change and
sources of GHG‘s
IPCC, 2007
66.7%
33.3%
Biogen
Anthropogen
Fossil fuel burning
Land use change
Biogen
test
Industrial sources
Livestock, rice paddies, wetlands
Biogen
test
Industrial sources
Agriculture, forests, oceans

5. Atmospheric composition change and
sources of GHG‘s
IPCC, 2007
66.7%
33.3%
Biogen
Anthropogen
Fossil fuel burning
Land use change
Biogen
test
Industrial sources
Livestock, rice paddies, wetlands
Biogen
test
Industrial sources
Agriculture, forests, oceans
Food systems contribute 19%–29% of global
anthropogenic greenhouse gas (GHG) emissions,
releasing 9,800–16,900 megatonnes of carbon dioxide
equivalent (MtCO2e) in 2008.
Agricultural production, including indirect emissions
associated with land-cover change, contributes 80%–
86% of total food system emissions, with significant
regional variation.
(Vermeulen et al. 2012, Annu. Rev. Environ. Res.)

6. Why do we need a GHG lab at ILRI?
• In developing countries GHG emissions from
agricultural activities are the dominant source

7. Why do we need a GHG lab at ILRI?
• No measurements available. Countries need to rely on
EF obtained from other climate zones.
• Without data, countries have no chance to move from
Tier 1, to Tier 2 or 3 more accurate, better targeting
• Verification of agricultural intensification: produce
more with less emissions (or environmental impacts)
• Verification of climate smart agriculture: how can this
be demonstrated
• No expertise in Sub-Saharan Africa capacity building
• Plenty of project opportunities, e.g. World Bank has a
focus on agricultural production at lower GHG emission
costs. Should this be done only by desktop studies?

8. What I have done before?
• PhD on strategies to mitigate CH4 emissions
from rice paddies
Rice varieties significantly affect the CH4 emission
strength. Thus, choosing a high yielding variety with low
emission potential would significantly reduce CH4
emissions from rice paddies

9. • Postdoc: N deposition effects on forest
functions and GHG fluxes
What I have done before?
Atmospheric N deposition due to agricultural activities
has significantly enhanced N trace gas fluxes from forests
and leaching of NO3 from forest soils.

10. What I have done before
• Scientist: Global source strength of forests for N2O
• Combining measurements and modeling
Identifying regional and global hotpsots of GHG emissions
and improving global estimates

11. • Running a number of projects worldwide on
GHG emissions from various ecosystems,
identifying involved processes, estimating
GHG emissions at regional and global scales
and identifying possible mitigation options
What I have done before?
Measurements are needed for improving models (even
simple EF models), regional and global estimates. Process
studies allow necessary insights to improve mechanistic
models, which are the most promising tools for
developing mitigation strategies in view of global
environmental changes

12. What do we want to do?
• Enable ILRI to develop capacity for quantifying
GHG emissions from agricultural sources
• Make ILRI a competence centre for GHG
measurements in Africa
• Build a network of GHG labs across Africa and
elsewhere to allow developing countries to
obtain country specific information about
their agricultural GHG emissions
• ……

13. On-going projects
SAMPLES
Identifying pro-poor mitigation options for
smallholder agriculture in the developing world
-
a multi-criteria and across-scales assessment

14. • Mitigation not linked to livelihoods
• Fragmented and diverse landscapes
• No data on mitigation
• Multi-criteria approaches missing
The concerns

15. 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

16. How to identify mitigation options at farm and
landscape level?

17. 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

18. 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

19. Nyando,
western
Kenya
Landscape
structure

20. Landscape units and
land users
Sampling intensity
(sites: area)
In terms of a 250 m
square grid
class sites area (km2) sites:area
cultivated (cash and subsistence) 28 2.74 10.23
cultivated (cash) 47 5.94 7.91
cultivated (grasslands and pastures) 47 12.69 3.70
cultivated (subsistence) 141 41.54 3.39
mixed 93 34.69 2.68
uncultivated vegetation 4 2.39 1.67

21. Targeting and upscaling: from
landscape to fields and back…

22. Step 1. Landscape analysis
Step 2. Installing measurement
stations
Targeting:
- Landscape units, farm types,
field types, soils
- Site selection
Site characterization:
- Soils, crops, biomass
Installation of
chamber frames
Informing and
interviewing farmers

23. Step 3. Measurements applying
gas pooling
Step 4. Lab analysis and flux
calculations
Field work:
- Overcoming spatial variability
by gas pooling method
Gas sampling(closed
chamber method)
Storage of gas
samples in vials
Determination of trace
gas concentrations via
gas chromatography
Lab work:
- Analyzing gas samples
- Calculating concentrations and
fluxes
9
6
10**
10*60***
mCh
Ch
VA
VMwb
F
Flux
calculation
formula
Arias-Navarro et al., Soil Biol. Biochem. submitted

24. Step 5. Interpretation and
upscaling
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.
Synthesis of GHG measurements: information useful to derive emission factors,
empirical models, calibrating and validating of detailed models
Upscaling: using the targeting approach (assigning emissions to landscape elements)
and/or of GIS coupled biogeochemical models
Arias-Navarro et al., Soil Biol. Biochem. submitted

25. 0
5
10
15
20
CumulativeN2
O-fluxes
[mgNm
-2
] Highland Control Highland NPK Lowland Control Lowland NPK
-60
-40
-20
0
CumulativeCH4
-fluxes
[mgCm
-2
]
23 Apr 7 May 21 May 4 Jun 18 Jun 2 Jul 16 Jul
0
50
100
150
CumulativeCO2
-fluxes
[gCm
-2
]
23 Apr 7 May 21 May 4 Jun 18 Jun 2 Jul 16 Jul
0
20
40
60
CumulativeGHGfluxes
[CH4
+N2
O:CO2
eqha
-1
]

26. 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

27. 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

28. 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

29. • Why multiple scales? -> landscape redesign
• Why multi-criteria? -> landusers are (often) poor

30. On-going projects - manure

31. The source of manure matters…

32. Summary and Outlook
• Agriculture is a key source for atmospheric GHG
• Little is known for developing countries
• Little competence in Sub-Saharan Africa
• … the chance for ILRI, since this topic has a huge
importance for funding organizations
(„sustainable intensification“)
ILRI becomes a competence centre for GHG

33. Thanks for your attention
K.Butterbach-Bahl@cgiar.org