Standard assessment of mitigation potentials
and livelihoods in smallholder systems
Klaus Butterbach-Bahl, Mariana Rufino,...
Outline
• Agriculture and GHG emissions
• Why we need a GHG lab at ILRI
• What have I done before?
• What do we want to do...
Biosphere as source for atmospheric
trace gases
CH4
CO2
VOC
NOx
N2O
60-70%
60-70%
Isoprenoid-
production
90%
Nitrification...
Atmospheric composition change and
sources of GHG‘s
IPCC, 2007
66.7%
33.3%
Biogen
Anthropogen
Fossil fuel burning
Land use...
Atmospheric composition change and
sources of GHG‘s
IPCC, 2007
66.7%
33.3%
Biogen
Anthropogen
Fossil fuel burning
Land use...
Why do we need a GHG lab at ILRI?
• In developing countries GHG emissions from
agricultural activities are the dominant so...
Why do we need a GHG lab at ILRI?
• No measurements available. Countries need to rely on
EF obtained from other climate zo...
What I have done before?
• PhD on strategies to mitigate CH4 emissions
from rice paddies
Rice varieties significantly affe...
• Postdoc: N deposition effects on forest
functions and GHG fluxes
What I have done before?
Atmospheric N deposition due t...
What I have done before
• Scientist: Global source strength of forests for N2O
• Combining measurements and modeling
Ident...
• Running a number of projects worldwide on
GHG emissions from various ecosystems,
identifying involved processes, estimat...
What do we want to do?
• Enable ILRI to develop capacity for quantifying
GHG emissions from agricultural sources
• Make IL...
On-going projects
SAMPLES
Identifying pro-poor mitigation options for
smallholder agriculture in the developing world
-
a ...
• Mitigation not linked to livelihoods
• Fragmented and diverse landscapes
• No data on mitigation
• Multi-criteria approa...
Develop a low-cost protocol to quantify greenhouse gas
emissions and to identify mitigation options for
smallholders at wh...
How to identify mitigation options at farm and
landscape level?
Landscape analysis
and targeting
Landscape
implementation
Multi-dimensional evaluation of
mitigation options
Scalable and ...
Complex landscape: f (m, n, o, p, q)
m Landscape units
n Farm types
Land
Livestock
Other assets
Sources of
incomes
p Field...
Nyando,
western
Kenya
Landscape
structure
Landscape units and
land users
Sampling intensity
(sites: area)
In terms of a 250 m
square grid
class sites area (km2) sit...
Targeting and upscaling: from
landscape to fields and back…
Step 1. Landscape analysis
Step 2. Installing measurement
stations
Targeting:
- Landscape units, farm types,
field types, ...
Step 3. Measurements applying
gas pooling
Step 4. Lab analysis and flux
calculations
Field work:
- Overcoming spatial vari...
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
-...
0
5
10
15
20
CumulativeN2
O-fluxes
[mgNm
-2
] Highland Control Highland NPK Lowland Control Lowland NPK
-60
-40
-20
0
Cumu...
Complex landscape: f (m, n, o, p, q)
m Landscape units
n Farm types
Land
Livestock
Other assets
Sources of
incomes
p Field...
Farm
type
Field
type
Profit
($/ha)
Production
(kg/ha)
Emissions
(t CO2eq
per ha)
Emissions
(kg CO2 per
kg product)
Social ...
Landscape analysis
and targeting
Landscape
implementation
Multi-dimensional evaluation of
mitigation options
Scalable and ...
• Why multiple scales? -> landscape redesign
• Why multi-criteria? -> landusers are (often) poor
On-going projects - manure
The source of manure matters…
Summary and Outlook
• Agriculture is a key source for atmospheric GHG
• Little is known for developing countries
• Little ...
Thanks for your attention
K.Butterbach-Bahl@cgiar.org
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Can smallholders mitigate global warming: Standard assessment of mitigation potentials and livelihoods in smallholder systems

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

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Can smallholders mitigate global warming: Standard assessment of mitigation potentials and livelihoods in smallholder systems

  1. 1. Standard assessment of mitigation potentials and livelihoods in smallholder systems Klaus Butterbach-Bahl, Mariana Rufino, David Pelster, Todd Rosenstock, Lini Wollenberg,
  2. 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. 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. 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. 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. 6. Why do we need a GHG lab at ILRI? • In developing countries GHG emissions from agricultural activities are the dominant source
  7. 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. 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. 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. 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. 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. 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. 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. 14. • Mitigation not linked to livelihoods • Fragmented and diverse landscapes • No data on mitigation • Multi-criteria approaches missing The concerns
  15. 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. 16. How to identify mitigation options at farm and landscape level?
  17. 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. 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. 19. Nyando, western Kenya Landscape structure
  20. 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. 21. Targeting and upscaling: from landscape to fields and back…
  22. 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. 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. 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. 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. 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. 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. 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. 29. • Why multiple scales? -> landscape redesign • Why multi-criteria? -> landusers are (often) poor
  30. 30. On-going projects - manure
  31. 31. The source of manure matters…
  32. 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. 33. Thanks for your attention K.Butterbach-Bahl@cgiar.org

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