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A system for quantification of
smallholder agriculture GHGs
Marja-Liisa Tapio-Bistrom
Mitigation of climate change in
Agriculture programme (MICCA)
FAO
Elements and tools for mitigation
planning in agriculture
• Data on emissions and projections for a baseline
• Mitigation options – LCA as a tool
• Knowledge on farming practices
• Emission factors
• A vision and means for landscape level options
for increasing the carbon content
• Greetings from GHG quantification workshop
• Food for thought
FAOSTAT Emissions from Agriculture and
Land Use Database
+IPCC Guidelines
=
& geo-referenced information
Tier 1, all sources of emissions from agriculture and LU, time
series from 1990, all countries, projections to 2030 and 2050
Life Cycle Analysis – identifying
mitigation options
• 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)
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
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
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
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
More analysis of farming practices
- We need rigorous analysis of farming practices combining the
science and farmers experiences to develop climate-smart
practices
- What works , where or why not
Emission factors
• Better emission factors for tropical and sub-
tropical areas, major farming systems and
farming practices
• A global plan, identifying priority systems and
gaps
• Longer term measurements – calibration of
models
• Network of research partners – spearheaded by
CCAFS?
Maximizing carbon content –
landscape approach
• Tap the mitigation potential at landscape level
trough holistic participatory land use planning
• CSA sourcebook gives ideas how
• Aboveground biomass as a proxy? –stable or
increasing 
• Opportunities for remote sensing – land
degradation in grasslands
Greetings from GHG quantification
workshop
The current systems are complex and expensive, not
appropriate for most low-income countries.
 We must invest in creative, low-cost systems for data
collection and analysis, such as
1. targeting global mitigation priorities 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 existing activity data from other sources
4. using consistent, comparable methods and data sharing
networks that enable robust estimates for different systems
Food for thought
• How exact do we need to know the net
emission reductions?
• Depends on the funding source – climate
funding vs. agricultural investments
• We need to transform the way we produce
food – more efficient, more resilient, with
mitigation co-benefit.

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Current state of agriculture and mitigation: NAMAs, quantifying emissions and links to adaptation

  • 1. A system for quantification of smallholder agriculture GHGs Marja-Liisa Tapio-Bistrom Mitigation of climate change in Agriculture programme (MICCA) FAO
  • 2. Elements and tools for mitigation planning in agriculture • Data on emissions and projections for a baseline • Mitigation options – LCA as a tool • Knowledge on farming practices • Emission factors • A vision and means for landscape level options for increasing the carbon content • Greetings from GHG quantification workshop • Food for thought
  • 3. FAOSTAT Emissions from Agriculture and Land Use Database +IPCC Guidelines = & geo-referenced information Tier 1, all sources of emissions from agriculture and LU, time series from 1990, all countries, projections to 2030 and 2050
  • 4. Life Cycle Analysis – identifying mitigation options • 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)
  • 5. 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
  • 6. 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
  • 7. 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
  • 8. 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
  • 9. More analysis of farming practices - We need rigorous analysis of farming practices combining the science and farmers experiences to develop climate-smart practices - What works , where or why not
  • 10. Emission factors • Better emission factors for tropical and sub- tropical areas, major farming systems and farming practices • A global plan, identifying priority systems and gaps • Longer term measurements – calibration of models • Network of research partners – spearheaded by CCAFS?
  • 11. Maximizing carbon content – landscape approach • Tap the mitigation potential at landscape level trough holistic participatory land use planning • CSA sourcebook gives ideas how • Aboveground biomass as a proxy? –stable or increasing  • Opportunities for remote sensing – land degradation in grasslands
  • 12. Greetings from GHG quantification workshop The current systems are complex and expensive, not appropriate for most low-income countries.  We must invest in creative, low-cost systems for data collection and analysis, such as 1. targeting global mitigation priorities 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 existing activity data from other sources 4. using consistent, comparable methods and data sharing networks that enable robust estimates for different systems
  • 13. Food for thought • How exact do we need to know the net emission reductions? • Depends on the funding source – climate funding vs. agricultural investments • We need to transform the way we produce food – more efficient, more resilient, with mitigation co-benefit.