The document provides an overview of options for greenhouse gas mitigation in agriculture. It discusses: 1) Agriculture contributes significantly to global emissions and reductions are necessary to meet climate targets. Many mitigation practices are compatible with sustainable development goals. 2) Key greenhouse gases from agriculture include methane, nitrous oxide, and carbon dioxide. Soils can also store carbon. 3) Common mitigation practices discussed include alternate wetting and drying of rice fields, livestock management improvements, efficient fertilizer use, agroforestry, and reducing food loss and waste. 4) The EX-ACT tool is introduced as a way to estimate and compare emissions between baseline and project scenarios to identify mitigation opportunities in agriculture

1. ADB Training 9-11 October, 2018,Tokyo
Options for Mitigation in
Agriculture
Lini Wollenberg, CCAFS Low emissions agriculture

2. Why mitigation in agriculture?
1. Significant
• 10-12% of global emissions
• Agriculture contributes on
average 35% of developing
countries’ total emissions
2. Necessary
Reductions in other sectors will
not be enough to achieve 2 °C
and 1.5 °C targets
3. Possible
Many practices are compatible
with SDGs, hence the possibility
of “low emissions development”
Mercator Institute
Roe et al. 2017

3. (1) Agriculture emits 3 key greenhouse gases (GHGs)
Methane (CH4)
Nitrous oxide (N2O)
Carbon dioxide (CO2)

4. And can store carbon in plants
and soil
Above-ground
and below-
ground biomass
Grassland
Soil organic
carbon
Organic carbon

5. Units: tons CO2equivalent/yr
One ton of carbon equals
3.67 tons of carbon dioxide
CO2e
Global warming
potential over 100
years
• CO2: 1
• CH4: 25x
• N2O: 298X
2) Global net CO2e is what matters to the climate

6. 3) Two key metrics for emissions
• CO2e per ha
• CO2e per kg
- “Emissions Intensity”
- “Yield-scaled emissions”
Used for adding up or comparing emissions
for a given area of land
Used to show the GHG efficiency of a food
product or supply chain.
Also called:

7. 4) Mitigation is a reduction in emissions
Reductions are measured relative to the previous land use or supply chain
emissions
The reference can be a base year or baseline. A baseline is a projection into
the future showing business-as-usual (BAU) emissions.
0
5
10
15
20
25
2015 2016 2017 2018 2019
BAU
emissions
Reduced
emissions
GHGemissionstCO2e
Reduction
in emissions
using
baseline =
15 tCO2e
Base
year Increase in emissions of 5 tCO2e

8. AFOLU Emissions: agriculture, forestry & land use
Agriculture produces GHG emissions of
5.4 GtCO2e/yr (IPCC)

9. • Paddy rice - alternate wetting and drying (AWD)
• Livestock systems - improving feeding, animal and herd management;
pasture management
• Cereal crops- building soil organic matter, e.g. through integrated soil
fertility management; nutrient efficiency through technologies such as urea
deep placement; BNI in crops
• Perennial crops- transitioning from annual crops or degraded land to
agroforestry, forestry or grassland
Existing mitigation options in agriculture
• Avoided conversion of high carbon landscapes
(forests, peatlands, mangroves, grasslands)
• Reduced food loss and waste- storage, packaging,
waste recycling
• Supply chain energy use – fertilizer production, cooling,
transportation
• Dietary shifts- shift to low emissions food products, e.g.
beef to chicken

10. Water management in paddy rice:
Alternate wetting and drying
• Reduces CH4 emissions up to 38%. Also reduces fossil fuel use,
lodging and pests
• Issues: requires farmer control over irrigation, uneven incentives for
water-level management, increased weeding, N2O, difficult to verif
• IRRI information hub: ghgmitigation.irri.org/

11. • Livestock intensification
reduces emissions intensity up
to 20X for beef, 300X for dairy
• Improve digestibility of feed
• Reduce numbers of animals
0.00
50.00
100.00
150.00
200.00
250.00
300.00
350.00
400.00
450.00
7.50 8.50 9.50 10.50 11.50
methane-kgCO2/kgproteinproduced
metabolisable energy (MJ/kg DM)
developed
developing
BRICS
Pastoralist farmers in
Chad
Herrero et al. 2013, PNAS
Livestock intensification
US, EU
intensive
cattle
production
• Issues: absolute emissions
increase, cost of improved
feed, cultural barriers,
emissions from land use
change and feed production,
other environmental impacts
• Resource: Tackling Climate Change
through Livestock
www.fao.org/docrep/018/i3437e/i3437e00.htm

12. Efficient use of nitrogen fertilizer
• Increasing NUE from 19 to 75%, decreases
emissions intensity by 56% (12.7 to 7.1 g
N2O-N/kg N uptake)
• Increase efficiency of N fertilizer uptake by
plants, e.g. timing, rates, deep placement,
microdosing
• Issues: most smallholder farmers only use
small amounts of N, so absolute emissions
will increase.
• Resource: Site-specific nutrient management
https://ccafs.cgiar.org/publications/site-specific-nutrient-management-
implementation-guidance-policymakers-and-investors#.W7ZgSC-ZPEY

13. Soil carbon sequestration
• Agriculture is the major driver of soil carbon loss
• But soil C can be managed: e.g. reduced burning, legume
intercropping, agroforestry, compost, manure, deep-rooted plants.
• Issues: finite, reversible, ambitious potentials, competition for
biomass inputs, insufficient other nutrients, MRV costs and
detectable changes only after ~20 years.

14. Agroforestry
• Global review shows maximum carbon increases in
-Plant biomass in improved fallows: 11 tC/ha/yr
-Soil C in silvopastoral systems: 4 tC/ha/yr
• Issues: finite, reversible, can conflict with crops,
classification as forestry or agriculture,
trees on farms often not counted due to scale
Feliciano et al. 2018 https://doi.org/10.1016/j.agee.2017.11.032

15. Reduce food loss and waste in supply chains
Nash et al. 2017
• 10-20% reduction of
emissions based on
assessment of Feed the
Future projects in Asia
• Issues: Reduces emissions
intensity, but not necessarily
total emissions; lack of
available data
Product Country % Food loss and waste
Without
project
With
project Reduction
Dairy (cattle) Bangladesh 17 7 10
Maize Cambodia 30 10 20
Bitter gourd " 30 10 20
Cucumber " 30 10 20
Eggplant " 30 10 20
Long bean " 30 10 20
Rice (irrigated) " 20 5 15

16. ADB projects and some mitigation options
ADB project examples
Location Activity GHG mitigation opportunities
HP, India Horticulture LUC
C storage
in above-
ground
biomass
FLW
reduction
Storage and
transportation?
Mongolia
Vegetable
production LUC
N-fertilizer
efficiency
FLW
reduction
Cold storage and
transportation
Tajikistan Dairy LUC Livestock
FLW
reduction
Cold storage and
transportation

17. 10/23/2018 17
Landscape
transitions
Crop
transitions
Rice
crops
Crops
(non rice) Fertilizer Livestock
- 4.7M
TotalAnnualtCO2e
Land use
change
Agricultural practice
improvements
Increased
emissions
Reduced
emissions/
increased C
sequestration
(1,865,626)
(905,776)
(433,447)
(616,320)
(32,068)
(819,848
)
435,313
1,723,672
2.1 M
Mitigation benefits of USAID’s agricultural development portfolio
https://ccafs.cgiar.org/blog/greenhouse-gas-emission-analyses-nine-agricultural-development-projects-reveal-
mitigation#.WqrhAGbMzEY
25developmentprojects,15countries,3continents.

18. Feasibility Case 1: Developing an investment
plan for AWD in Vietnam
Comprehensive, comparative analysis of potentially viable LED practices and
their supporting interventions within the rice supply chain
Geographic suitability Barriers
Incentives, enabling
conditions
Costs, benefits and risk
analysis
1. Domestic investment plan for
AWD and mid-season drainage
2. Outline for international
investment proposal for AWD
Policy gap analysis
Identification of policy
levers to incentivize
adoption
Quantification of
investment needed
Identification of international funding sources
Investment plan for AWD
Prioritize
interventions,
identify
instruments to
encourage large-
scale adoption
Slides courtesy of Tran Van The

19. Business case for AWD in Vietnam
-
5,000.00
10,000.00
15,000.00
20,000.00
An Giang Kien Giang Soc Trang Average
Costs (1000 VND/ha/season)
AWD None
-
10,000.00
20,000.00
30,000.00
An Giang Kien Giang Soc Trang Average
Net benefit (1000 VND/ha/season)
AWD None
30,000.00
35,000.00
40,000.00
45,000.00
An Giang Kien Giang Soc Trang Average
Revenue (1000 VND/ha/season)
AWD None

20. Suitability maps for mitigation
practices and planning finance

21. Key points
1. Many options: Opportunities for mitigation occur via agricultural
practices, land use change, supply chain or shifts in diet
preferences
Best practices for mitigation:
- Seek absolute reductions where possible (e.g. AWD)
- Elsewhere at least improve GHG efficiency (emission
intensity)
- Offset emissions with C sequestration
- Work at all scales
2. Agricultural development can yield significant climate change
mitigation co-benefits
3. Actions are already being taken to plan and implement mitigation
at large scales where net benefits occur

22. CCAFS Flagship Low Emission Development
https://ccafs.cgiar.org/flagships/low-emissions-development
Thank you!
Lini.wollenberg@uvm.edu
ccafs.cgiar.org

23. Feasibility Case 2: Private drone companies help
scaling out of technologies for better N fertilizer
management
• 3 drone companies delivering N
recommendations to maize farmers
in Mexico using NDVI from their
drones and an algorithm.
• Farmers are willing to pay for this
service (approx. 3 USD / ha per
flight).
• N saving of ca. 60 – 70 kgN/ha

24. ADB Training 9-11 October, 2018,Tokyo
An introduction to the EX-ACT
Tool
Lini Wollenberg, CCAFS Low emissions agriculture

25. What is the EX-ACT Tool?
Ex-Ante Carbon Balance Tool developed by FAO
• Compares net emissions with and without a project
• Intended for planning, not assessment or verification
How does it work?
• A set of linked Microsoft Excel sheets
• Structured as 8 modules reflecting land use and management
practices
• Compares baseline, project and BAU scenarios
• Covers emissions and C sequestration, in CO2e/ha/yr
• Uses IPCC default (Tier 1) or user can specify region-specific
emission values (Tier 2)
• Available in 4 languages
• Simple supply chain calculation possible

26. On-line course available on World Bank Site
• https://olc.worldbank.org/content/estimating-ghg-emissions-and-
carbon-sequestration-agriculture-forestry-and-other-land-use-ex

27. Estimating emissions
Emissions = Activity x Emissions Factor
IPCC default EF’s are based on global or
regional average values
Ex. Nitrous Oxide =
Annual amount of synthetic fertilizer N
applied to soils, kg N yr-1 x 1%
Guidance from IPCC 1996 and 2006 guidelines:

28. Tier 1 Emission Factors
Default emission factor provided by the IPCC. Usually regional or global.
Tier 2 Emission Factors
More complex and country-specific emission factor. Not usually available in low
income countries.
Tier 3 Emission Factors
Most complex, often modeled.
See also the IPCC
Emissions Factor
Database
https://www.ipcc-
nggip.iges.or.jp/EFDB/
Example of IPCC Tier 1 defaults in Good
Practice Guidelines
Tiers: Specificity of estimates

29. Poor accuracy of GHG calculators
using Tier 1 emissions
Comparison of Cool Farm Tool and
Ex-Act Tool results against
measurements in tropical agriculture.
Default emission factors lead to
over-prediction of emissions.
• Dashed line is a 1:2 line; data
points above this line represent an
overestimation by a factor of 2 or
more.
• Solid line is a 1:1 line; data points
above this line represent an over-
estimation of GHG emissions by
the calculator.
Richards et al. 2016 https://dx.doi.org/10.1038/srep26279

30. Calculators using Tier I emissions don’t even
predict well whether emissions have increased
or decreased
30
Richards et al. 2016 https://dx.doi.org/10.1038/srep26279
Change in GHG emissions due to an intervention
Incorrect calculator
predictions (opposite
direction of change than
measured observations)
EX-ACT
underestimated
emissions in a significant
number of cases

31. 1. Complete “Description” module
Using EX-ACT
Download: www.fao.org/tc/exact/carbon-balance-tool-ex-act.
Total time period should not be less than 20 years if carbon
sequestration is expected.

32. 2. Build scenarios
Three scenarios required
1. Start: Initial conditions
2. Without: Baseline showing expected
conditions without the project (business-as-
usual)
3. With: Conditions with project intervention

33. 2. Build scenarios (cont)
“With” scenario (with project)
 Identify expected changes
 Identify dynamic of changes over time
• “Without” scenario (baseline)
 Defined as would have happened without the project.
This is what EX-ACT compares project results against.
 Options:
− No change from initial conditions (simplest, and likely to be most common)
− Extrapolation of past trends
− Modeling of future trends

34. 3. Choose relevant modules to complete

35. 4. Collect activity data for each land use
and practice, for each scenario
Estimate areas (ha) of
land use change
and changes in management practices
• Can use expert judgement, project planning documents, national statistics,
focus groups, primary data collection etc. Quality affects results.
• Most time consuming stage, especially to produce relevant scenarios

36. Example of module: Land use change (LUC)
Navigation bar for modules
Data entry required in light blue cells; not all cells must be completed

37. Land use change (cont): Deforestation

38. Example of module: Annual crops
Areas continuously managed as annuals. Enter
names of additional cropping systems.
Areas converted to
annuals: Prefilled based
on Land Use module
Dynamics of intervention:
immediate (default),
linear or exponential

39. Remember
• Complete Description and then only relevant modules
• Provide data for all three scenarios (start, without, with project),
remembering that some land uses will be relevant to only one
scenario (e.g. baseline or project)
• Normal for many cells or modules to be left blank
• Scroll down and across so you don’t miss modules, links, etc.
See user guidelines: http://www.fao.org/tc/exact/user-guidelines/en/
 Quick, complete and technical guidance available

40. CO2e
Reminder: Global net CO2e is what matters
Be careful about project “leakage,” increases in emissions elsewhere

41. Tips
• “Help” tab provides soil, climate and ecological zone classifications;
see additional resources here by scrolling to the right
• Additional information and options are located throughout the tool
in the form of orange buttons: e.g. look-up tables, value chain
calculations, uncertainty information, land use change matrix
• All calculations are based on IPCC guidance, so you can look up
assumptions if not available in Ex-Act documents.
 Example of assumption: Changes in carbon sequestration are calculated
over 20-year time period and annualized.
• In some cases FAO can “unlock” pages for you if you need to
customize calculations

42. EX-ACT Exercise (20 minutes)
• Working in teams of 2-3 people, complete description module and
one to two other modules
• Can make up data, but try to be realistic
Identify
1. Net change in emissions/ha/yr as a result of your project
2. Uncertainty of your estimate
3. Largest contributing:
Source of emissions (“component”):
Type of GHG:
4. Resulting emissions intensity (or at least where the calculation
option appears)
5. Value chain results (or at least where calculation option appears)

43. Relevant EX-ACT links
• EX-ACT home page: http://www.fao.org/tc/exact/ex-act-home/en/
• EX-ACT tool Version 7:
http://www.fao.org/fileadmin/templates/ex_act/excel/EX-ACT-
v7.2.xlsx
• Quick guide: http://www.fao.org/3/a-i8075e.pdf
• Comprehensive user guide:
http://www.fao.org/fileadmin/templates/ex_act/pdf/Technical_guideli
nes/EX-ACTUserManuaFinal_WB_FAO_IRD.pdf
• Translations and other guidance: http://www.fao.org/tc/exact/user-
guidelines/en/
• World Bank on-line course
https://olc.worldbank.org/content/estimating-ghg-emissions-and-
carbon-sequestration-agriculture-forestry-and-other-land-use-ex

44. CCAFS Flagship Low Emission Development
https://ccafs.cgiar.org/flagships/low-emissions-development
Thank you!
Lini.wollenberg@uvm.edu
ccafs.cgiar.org

45. Negative emissions needed to limit warming
below 1.5 or 2 °C
Roe et al. 2017 How Improved Land Use Can Contribute to the 1.5°C Goal of the Paris Agreement

46. IPCC emissions factors and uncertainty levels
Emissions source
Range of emissions
factors for developing
country (warm wet/warm
moist conditions) Unit
Uncertainty/error for
Tier 1 emissions factors
Biomass C storage 12 to 228 t C/ha/yr 6-126%
Biomass C loss with harvest 9 to 50 t C/ha every 5 - 8 years 75%
Relative stock change in soil C 0.48 to 144 t C/ha/yr 26% (7 - 61%)
Land conversion 1.8 to 10 t C/ha/yr 75%
N2O fertilizer 0.01 kg N2O-N/kg N .003 - .30
CH4 Paddy rice 1.3* kg CH4/ha/day 62-169%
CH4 Enteric fermentation - dairy
cattle 46 to 72 kg CH4/ha/day 30-50%
CH4 Enteric fermentation -other
cattle 27 to 56 kg CH4/ha/day 30-50%
CH4 Enteric fermentation -other 1 to 55 kg CH4/ha/day 30-50%
CH4 manure 1 to 2 kg CH4/head/year 30%
N2O urine 0.32 to 1.57
kg N/1000Kg animal
mass/day 50%
*if scaling factors used, ranges from 0.35 to 1.34

47. Agricultural greenhouse gas sources and
sinks
Percent of agricultural emissions (IPCC AR4 2007)

48. Sources: Bernoux et al. 2012
Scale: + indicates <1 day, ++++ indicates >1 month
Geographi
c scope
Intended
scale of
calculation
Time and
data
requiremen
ts
User
input
EFs
Scenarios Uncertain
ty
estimates
EX-ACT World Landscape + Yes Initial,
BAU,
project
Yes, for
EFs
Cool Farm
Tool
World Farm ++ In
excel
versio
n
Yes, with
different
tool runs
No
USAID
AFOLU
Calculator
Developing
countries
Landscape + No In different
runs
No
ALU World Landscape ++++ Yes No Entered by
user
Carbon
benefits
project
World Landscape +++ Yes Initial,
BAU,
project
Yes

49. Precision example:
N2O calculation methodology
EX-ACT IPCC default 1% of applied N or user-defined
Cool Farm Tool Bouwman model: accounts for N-rate*Fertilizer type interaction,
crop type, soil texture, SOC, soil drainage, soil pH, climate type
USAID AFOLU
Calculator
IPCC default EFs, specific to fertilizer type
ALU IPCC default EFs
Carbon benefits project Range: IPCC Tier 1 defaults or user-defined

50. Ex-Act calculates through capitalization phases.
Build scenarios (cont)