The document outlines strategies for low emission development in the agricultural sector, emphasizing the need for countries to adopt technologies that minimize greenhouse gas emissions while promoting economic growth. It discusses the importance of integrating national characteristics with global economic factors, and details a technical approach for modeling land use changes that affect emissions. The potential benefits include enhanced food security and sustainable practices, alongside challenges such as the permanence of emission reductions and the economic implications for farmers.

1. Technical Training for Modeling
Scenarios for
Low Emission Development
Strategies
Alex De Pinto - Senior Research Fellow
Dr. Tim Thomas - Research Fellow
Dr. Man Li - Research Fellow
Dr. Ho-Young Kwon - Research Fellow
Ms. Akiko Haruna - Senior Research Assistant
Ms. Shahnila Islam - Senior Research Assistant
Mr. Daniel Mason – D’Croz - Research Analyst
Ms. Subhashini Mesipam - Administrative Coordinator
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

2. FROM GLOBAL TO LOCAL:
AN INTEGRATED APPROACH TO
LOW EMISSIONS
DEVELOPMENT STRATEGIES
(LEDS)
Alex De Pinto - Senior Research Fellow
Dr. Tim Thomas - Research Fellow
Dr. Man Li - Research Fellow
Dr. Ho-Young Kwon - Research Fellow
Ms. Akiko Haruna - Senior Research Assistant
Ms. Shahnila Islam - Senior Research Assistant
Mr. Daniel Mason – D’Croz - Research Analyst
Ms. Subhashini Mesipam - Administrative
Coordinator
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

3. Introduction
 Globally, agriculture is responsible for 10 – 14%
of GHG emissions and largest source of no-CO2
GHG emissions.
 Countries can choose among a portfolio of
growth-inducing technologies with different
emission characteristics.
 We believe that is less costly to avoid highemissions lock-in than replace high-emissions
technologies. NEED TO ENCOURAGE Low
Emission Development Strategies.
 Since countries are part of a global economic
system, it is critical that LEDS are devised based
both on national characteristics and needs, and
with a recognition of the role of the international
economic environment.

4. SCOPE OF WORK
 Main objective: Identification and quantification
of LEDS for agriculture/forestry/natural resources
use.
 Analysis and modeling based on IFPRI expertise
and in-country knowledge coming from existing
country programs in the CGIAR system and other
local institutions
 Output
• Simulations that show the long term effect on emissions
and sequestration trends of policy reforms, infrastructure
investments and/or new technologies that affect the
drivers of land use-related emissions and sequestration.
• Consistent with global outcomes (global markets, trade).

5. Technical Approach
 Combines and reconciles
• Limited spatial resolution of macro-level economic models that
operate through equilibrium-driven relationships at a subnational
or national level with
• Detailed models of biophysical processes at high spatial
resolution.
 Essential components are:
• a spatially-explicit model of land use choices which captures the
main drivers of land use change
• IMPACT model: a global partial equilibrium agriculture model that
allows policy and agricultural productivity investment simulations;
• DNDC crop modeling tool to determine GHG emissions from crop
production
Output: spatially explicit country-level
results that are embedded in a framework
that enforces consistency with global
outcomes.

6. Climate Change
Mitigation in the
Agricultural Sector
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

7. Agriculture is net emitter
of GHG
 Agriculture emits about 14% of total GHG
emission
•
•
•
•
•
Fertilizers (nitrous oxide or N2O)
Livestock (methane/CH4)
Rice production (methane/CH4)
Soil „mining‟ (depleting soil C)/Land degradation
Drying of peat and wetlands for agriculture

8. Agriculture and
Mitigation Potential
 Agriculture can also help reducing GHG
emission
MITIGATION:
Climate change mitigation refers to actions that reduce the
potentially harmful effects of global warming by reducing
the atmospheric concentration of GHG.

9. Different ideas about Agriculture
and Mitigation Potential


10. A brief review of key
terminology






Adaptation
Additionality
Baseline
Leakage
Monitoring Reporting and Verification
Permanence

11. A brief Review of key
terminology
 Adaptation: Climate change adaptation
refers to a set of actions, strategies,
processes, and policies that respond to actual
or expected climate changes so that the
consequences for individuals, communities,
and economy are minimized.
 Additionality: A project is said to meet the
additionality criterion if the carbon
sequestration or emission reductions achieved
by the project would not have been
obtained in absence of the project.
Pag
e
11

12. A brief Review of key
terminology
 Baseline: The baseline scenario describes
GHG emissions in the absence of a project or
a policy.
 Leakage: The adoption of certain
agricultural practices may reduce emissions in
a given area or region; however, these
emission savings could be negated if the type
of agricultural production a project is trying to
prevent shifts to other regions.
Pag
e
12

13. A brief Review of key
terminology
 Permanence: This concept is related to the length of
time the carbon sequestration or reduction of
emissions last. The objective is to assure that the
mitigation service is provided for the entire length of a
project. Permanence raises significant issues related
to opportunity costs.
 Measurement Reporting and Verification (MRV):
• Measurement: “The process of data collection over time,
providing basic datasets, including associated accuracy and
precision, for the range of relevant variables. Possible data
sources are field measurements, field observations, detection
through remote sensing and interviews.”
• Reporting: “The process of formal reporting of assessment results
according to predetermined formats and according to established
standards”
• Verification: “The process of formal verification of reports, for
example, the established approach to verify national
communications and national inventory reports to the UNFCCC.”
Pag
e
13

14. Global mitigation potential in
agriculture
Total technical mitigation potentials (all practices, all GHGs: MtCO2-eq/yr) for each region by 2030.
Note: based on the B2 scenario though the pattern is similar for all SRES scenarios.
Source: Smith et al. (2007)

15. We can do much better now
Pag
e
15

16. Agriculture can play a role in
mitigating climate change
 Modifying and introducing agricultural practices
and crop patterns so that:
• Sequester CO2 from atmosphere and store it soils (or trees –
agroforestry)
• Reduce GHG emissions
 It is obvious that most if not all modifications to
agricultural practices will have effects, positive
and negative, on output and will have
effects, positive and negative, of the economics of
these activities (i.e. profit)

17. Challenges and Opportunities
 Potential Opportunities (????)
• Provide farmers with an additional source of income –
carbon markets
• Help small poor farmers dealing with the effects of
climate change (co-benefit)
• Food security and resilience (co-benefit)
 Challenges
• Uncertainty in the amounts of GHG that can be
reduced, but also pemanence, MRV, lekeage
• Uncertainty in the economic effects that mitigation
policies might have

18. Co-benefits of mitigation
 Mitigation practices overlap considerably with
sustainable use of resources. Could be
interpreted as an increase of overall efficiency of
the production system.
 Positive correlation between soil C and crop
yield. Some agricultural practices improve soil
fertility and induce C sequestration
 More efficient water use (reduces CO2 from
fuel/electricity) and methane from rice paddy
 Agricultural R&D, advisory services, and
information systems

19. Consideration about mitigation and
adaptation
 Many people make a clear distinction
between mitigation and adaptation.
 In many instances mitigation
practices are good adaptation
practices.
 Mitigation as a path towards
adaptation.

20. Constraints to climate change
mitigation using agriculture
 Growing literature on the difficulties for agriculture
to contribute to mitigation:
•
•
•
•
•
•
•
Defining the baseline
Biophysical Modeling
Evidence of additionality
Profitability
High transaction costs SocioeconomicAnalysis
Property rights
Leakage
Wider Scale Modeling
Permanence

21. Constraints to climate change
mitigation using agriculture
Biophysical Modeling
• Defining the baseline
• Evidence of additionality

22. Emissions – CO2 eq
Constraints to climate change mitigation
using agriculture: Biophysical
BASELINE
Emissions with business as usual
Mitigation potential
Additionality
Emissions with mitigation option
Time
Pag
e
22

23. Defining the Baseline
 Forestry - one has to be able to “reliably”
project the future of a forest in absence of a
project.
 Agriculture - most important cropping
systems, production practices, need to be
identified:
• Easy for an American farmer who grows soybeansmaize year after year
• Easy for a Vietnamese farmer who only grows rice year
after year
• Difficult for a smallholder who utilizes complex cropping
systems that change in time: an example from some
African countries where cropping systems like maizecocoyam-cassava-plantain-fallow are the norm

24. Defining the Baseline
Geophysical
characteristics
Most
common/important
crops
Climate
scenarios
A
Crop
model
Climate
scenarios
B
Geophysical
characteristics
Mitigation Ag.
practices/input
package
A-B
Mitigation
potential
Current Ag.
practices
Most
common/important
crops
Carbon
baseline
Crop
model
Carbon profile
input package #1
Carbon profile
input package #2
.
.
Carbon profile
input package #n

25. Emissions and Carbon Stock (CO2 eq.)
Carbon stock and
GHG emissions computed
from simulations for
baseline
Growth in carbon stock and
GHG emissions extrapolated
from 2009 and 2030 values
Increase in Carbon stock
2009 – 2030
Carbon stock baseline
Emissions baseline
Increase in emissions
2009 – 2030 area ABC
C
A
B
2009
2030
Time

26. Assessing climate change mitigation
potential in agriculture
Key Modeling Issues
 How good are crop models?

Knowledge / quantification of how different agronomic
practices and different crops affect GHG emissions
(DSSAT/Century, DNDC, CropSys, EPIC, APSIM).
 How good is our ability to generate plausible scenarios of
future land-use choices, crop choices, agronomic practices
(surveys, models of land-use change).
 Major obstacle: creating a baseline. Predicting
smallholders behavior is challenging.

27. Constraints to climate change
mitigation using agriculture
Socioeconomic
Analysis
• Profitability – Will farmers be better off?
• Society-wide benefits – Will society be
better off?

28. Some Economic Results - Vietnam
1450
CH4 & N2O Emissions
(CO2eq/ha)
1400
1350
1300
1250
1200
1150
1100
1050
Conventional
New variety
Biochar
Composting
AWD irrigation
New variety
Biochar
Composting
AWD irrigation
200
150
50
0
Conventional
-50
-100
-150
-200
-250
High labor cost
to produce and
spread
Net profit (US$/ha)
compared to
Conventional
100

29. Some Economic Results - Ghana
Manure/Nitrogen
Carbon Accumulation,
Total Payment/Implicit
Yield Gains
applications
end of 20 yr period
Cost of Carbon
Maize/Cassava
Risk-neutral agent
Dry Weight
(USD/ha)
(Kg/ha)
(Kg/ha)
(Ton CO2 eq/ha)
250/0
0.31
207/670
40/162
500/0
0.45
456/278
58/290
750/0
0.81
732/247
74/406
1,000/0
1.16
1,000/235
88/509
250/60
2.16
73/34
215/950
500/60
3.40
220/65
215/1,14
750/60
4.63
462/100
214/1,071
1,000/60
5.85
719/123
215/1,134

30. Some Economic Results - Morocco
Method
Traditional
Soft
Wheat
NPV
1,000 US $
(r = 8%)
5.9
Necessary
Investment
(1,000 US$)
Yearly
Reduction in
Average
CO2
Carbon
Emissions
Accumulation
6.8
Zero Tillage
13. 9
7.2
Traditional
irrigation
8.1
1.8 Tons
CO2eq/year
0.9 Tons
CO2eq/year
3.2
-
Drip irrigation
55.8
5.0
3.2
Drip irrigation
51.8
0.4 Tons
CO2eq/year
16.8
Traditional
irrigation
0.3 Tons
CO2eq/year
-
Potato
Onion
16.8

31. Expanding the Analysis Using the Time
Dimension - Ghana
Financial support
can be removed
and alternatives
more profitable
that baseline

32. Expanding the Analysis Using the Time
Dimension - Mozambique
Minimum incentive (USD, per ha), present value at the each payment period
Column1
1st cycle
2nd cycle
3rd cycle
4th cycle
No-till
101
192
322
to add 500 kg ha-1 farm yard manure to each crop (38.5 kg N ha-1)
309
0
0
to add 60 kg N ha-1 chemical fertilizer nitrate
0
0
0
0
500 kg manure +60kg nitrate + no-till
0
0
0
0
332
0
0
0)
1000 kg manure +60kg nitrate + no-till;
566

33. Overall Lessons Learned:
Pilot Study and Cost Benefit Analysis of Alternate
Mitigation Practices
 Productivity growth, increased sustainable
production, reduction in GHG emissions, are
not mutually exclusive choices.
 Lack of credit, investments costs, and risk
aversion create a substantial barriers to the
adoption of mitigation practices.
 Compensation for mitigation services
facilitates the adoption of agronomic practices
that allow sustainable intensification.

34. Role of Uncertainty and Risk
 There is plenty of evidence that farmers are not
likely to be neutral to risk and actually tend to be
risk averse (Antle 1987; Chavas and Holt 1990;
Bar-Shira, Just, and Zilberman 1997; Hennessy
1998; Just and Pope 2002; Serra et al. 2006;
Yesuf and Bluffstone 2007)
 and that risk considerations affect input usage
and technology adoption (Just and Zilberman
1983; Feder, Just, and Zilberman 1985; Kebede
1992).
 Risk considerations should not be ignored in the
analysis of adoption of carbon sequestration
practices.

35. Implementation Challenges
 Implementation challenges
• costs involved in organizing farmers (aggregation
process)
• costs of empowering farmers with the necessary
knowledge
• costs of Monitoring, Reporting and Verification (MRV)
 Need for strong institutional support.
Institutions should include the potential of
various supply chains, producers of high value
export crops, non-governmental organizations
(NGOs), and farmer organizations as aggregators
and disseminators of management system
changes and measurement technologies

36. Your Opinion on Agriculture
and Mitigation Potential


37. Your Opinion on Agriculture
and Mitigation Potential


38. IFPRI’s Approach
Modeling Setting and Data
Pag
e
38

39. Technical Approach
 Combines and reconciles
• Limited spatial resolution of macro-level economic models that
operate through equilibrium-driven relationships at a subnational
or national level with
• Detailed models of biophysical processes at high spatial
resolution.
 Essential components are:
• a spatially-explicit model of land use choices which captures the
main drivers of land use change
• IMPACT model: a global partial equilibrium agriculture model that
allows policy and agricultural productivity investment simulations;
Output: spatially explicit country-level
results that are embedded in a framework
that enforces consistency with global
outcomes.

40. Satellite data
Parameter
estimates for
determinants of
land use change
Model of
Land Use
Choices
Ancillary data:
Ex. Soil type, climate, road
network, slope, population, l
ocal ag. statistics
Macroeconomic scenario:
Future commodity
prices and
rate of growth of
crop areas
IMPACT model
Ex. GDP and population growth
General Circulation Model
Model of
Land Use
Choices
Climate scenario:
Ex. Precipitation and
temperature
Baseline
Land use
change
Crop Model
Change in carbon stock
and GHG emissions
Policy Simulation
Policy scenario:
Ex. land use allocation
targets, infrastructure, adop
tion of low-emission
agronomic practices
Land use
change
Crop Model
Change in carbon
stock and GHG
emissions.
Economic trade-offs

41. Satellite data
Ancillary data:
Ex. Soil type, climate, road
network, slope, population,
local ag. statistics
Model of
Land Use
Choices
Parameter
estimates for
determinants of
land use change

42. Macroeconomic scenario:
Ex. GDP and population growth
General Circulation Model
Climate scenario:
Ex. Precipitation and
temperature
IMPACT model
Future commodity
prices, yields, and
rate of growth of
crop areas

43. Satellite data
Ancillary data:
Ex. Soil type, climate, road
network, slope, population,
local ag. statistics
Macroeconomic scenario:
Ex. GDP and population growth
General Circulation Model
Climate scenario:
Ex. Precipitation and
temperature
Parameter
estimates for
determinants of
land use change
Model of
Land Use
Choices
Future commodity
prices and
rate of growth of
crop areas
IMPACT model
Model of
Land Use
Choices
Baseline
Land use
change
Crop Model
Change in carbon stock
and GHG emissions

44. Satellite data
Parameter
estimates for
determinants of
land use change
Model of
Land Use
Choices
Ancillary data:
Ex. Soil type, climate, road
network, slope, population,
local ag. statistics
Macroeconomic scenario:
Future commodity
prices and
rate of growth of
crop areas
IMPACT model
Ex. GDP and population growth
General Circulation Model
Model of
Land Use
Choices
Climate scenario:
Ex. Precipitation and
temperature
Baseline
Land use
change
Crop Model
Change in carbon stock
and GHG emissions
Policy Simulation
Policy scenario:
Ex. land use allocation
targets, infrastructure,
adoption of low-emission
agronomic practices
Land use
change
Crop Model
Change in carbon
stock and GHG
emissions.
Economic trade-offs

45. Baseline: Land cover – MODIS 2009

46. Climate change 2009 – 2030
Change in annual precipitation, 2009 to 2030
(CSIRO Climate Model)
Change in annual mean temperature, 2009 to
2030 (CSIRO Climate Model)

47. Assumptions in baseline scenario
Time period 2009 - 2030
 GDP – IMPACT medium variant
 Population – UN pop medium variant
 Exogenous productivity and area growth –
IMPACT standard
 Climate – CSIRO GCM, A1B scenario

48. Typical Modeling
Output
Pag
e
48

49. Baseline Scenario Price Changes
2009-2030
Price (USD/ton)
Yield (ton/ha)
a
b
Area
growth
(%) c
2009
Bean
Cassava
Cotton
Groundnuts
Maize
Irrigated Rice
Rainfed Rice
Soybean
Sugar cane
Sweet potato
Coffee
2030
Growth (% )
2009
2030
Growth (%)
523
577
10.16%
0.95
1.01
6.69%
8.67%
54
73
35.54%
16.80
20.40
21.40%
1.40%
898
1132
26.02%
1.33
1.57
17.78%
0.00%
512
577
12.73%
2.09
2.41
15.25%
-0.63%
53
74
39.01%
4.10
5.74
39.93%
1.73%
133
167
25.89%
5.19
5.90
13.69%
-1.71%
133
167
25.89%
2.87
3.27
13.69%
-1.71%
154
198
28.60%
1.46
1.41
-3.13%
-2.18%
13
17
27.32%
58.77
66.34
12.88%
43.68%
433
531
22.72%
8.26
12.25
48.33%
-3.33%
806
897
11.39%
2.08
2.22
6.69%
8.67%
Source: IMPACT.

50. Land Use Change 2009 - 2030
Baseline scenario
Land Use Category
2009 land area
(Million Hectares)
2030 land area
(Million Hectares)
Change in Area
2009 - 2030
(Million ha)
Cropland
5.05
5.14
0.10
Mosaic
6.52
6.72
0.20
Woody Savannas
6.22
5.29
-0.93
Forest
12.65
12.96
0.31
Shrub/Grassland
0.47
0.51
0.04
Other Land Uses
1.93
2.22
0.29
Total
32.84
32.84
0.00

51. Land Use 2030 – Baseline scenario
2009 area
2030 area
Change in Area
(ha)
Crops
(ha)
2009 – 2030
(ha)
Beans
187,200
203,437
16,237
Cassava
507,800
514,894
7,094
Cotton
9,600
9,600
0
Groundnuts
245,000
243,468
-1,532
Maize
1,089,200
1,108,073
18,873
Irrigated Rice
6,892,600
6,774,615
-117,985
Rainfed Rice
546,000
536,654
-9,346
Soybeans
131,900
129,018
-2,882
Sugarcane
265,600
381,612
116,012
Sweet Potato
146,400
141,524
-4,876
Coffee
538,400
585,100
46,700
Total
10,559,700
10,627,996
68,296
Pag
e
51

52. Land Use 2030 – Baseline Scenario
Land use conversion: Change in forested land.
Year 2009 – 2030
Land use conversion: Change in agricultural land.
Year 2009 – 2030

53. GHG Emissions and Carbon Stock
Below
ground
carbon
stock b
YES
Land Use
Category
Above
ground
carbon
stock a
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
SOC
c
CO2 d
N2O
d
CH4 d
Cropland
Mosaic
Woody
Savannas
Forest
Shrub/Grassl
and
Other Land
Uses

54. Page 54
ANALYSIS OF THE RESULTS
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

55. Carbon Stock – Changes 2009 - 2030
Land Use
Category
Soil
Organic
Carbon
2009
(Tg C)
Above
Ground
Biomass
2009
(Tg C)
Below
Ground
Biomass
2009
(Tg C)
Soil
Organic
Carbon
2030
(Tg C)
Above
Ground
Biomass
2030
(Tg C)
Below
Ground
Biomass
2030
(Tg C)
Net
Change in
Carbon
Stock
2009 2030
(Tg C)
Cropland
286.3
-
-
287.0
-
-
0.7
Mosaic
Woody
Savannas
Forest
294.0
246.2
135.6
29.1
33.5
15.0
298.9
208.8
148.0
25.7
36.4
13.9
20.2
-41.8
520.9
989.3
232.9
532.8
1,015.8
239.2
44.7
Shrub/Grassl
and
22.7
4.2
1.7
24.5
4.6
1.8
2.2
Other Land
Uses
Total
139.1
-
-
161.0
-
-
21.9
1,509.2
1,158.2
283.0
1,512.9
1,194.1
291.4
47.9

56. GHG Emissions – Changes 2009 - 2030
Per ha GHG
emission
2009 total GHG
emission
2030 total GHG
emission
(ton/ha)
(Tg CO2eq year-1)
(Tg CO2eq
year-1)
Beans
14.81
2.77
3.01
0.24
Cassava
14.87
7.55
7.64
0.09
Cotton
0.47
0.00
0.00
0
Groundnuts
10.62
2.60
2.58
-0.02
Maize
7.21
7.86
7.98
0.12
Irrigated Rice
21.21
146.17
143.82
-2.34
Rainfed Rice
21.37
11.67
11.47
-0.20
Soybeans
14.81
1.95
1.91
-0.04
Sugarcane
35.58
9.45
13.56
4.11
Sweet Potato
0.33
0.05
0.05
-0.002
Coffee
0.56
0.30
0.33
0.02
190.37
192.35
1.98
Crops
Total
Changes in
total GHG
emission
2009 - 2030
(Tg CO2eq)

57. Baseline Results
 Carbon stock increases at an annual rate of
0.08% while GHG emission increase at a rate
of 0.04% annually.
 Increase in GHG emissions deriving from
changes in crop production are
counterbalanced by an increase in carbon
stock, mainly due to increase in forested
areas.
Pag
e
57

58. Baseline Results
 Carbon stock increases at an annual rate of
0.08% while GHG emission increase at a rate
of 0.04% annually.
 Increase in GHG emissions deriving from
changes in crop production are
counterbalanced by an increase in carbon
stock, mainly due to increase in forested
areas.
Pag
e
58

59. Baseline Results - Economy
Crops
Change in Area
2009 – 2030
(ha)
2009 total
revenue
2030 total
revenue
(Million USD)
(Million USD)
Changes in
total revenue
(Million USD)
Beans
16,237
93.2
119.0
25.8
Cassava
7,094
460.9
769.1
308.2
Cotton
0
11.5
17.0
5.6
Groundnuts
-1,532
262.2
338.6
76.4
Maize
18,873
240.1
475.1
235.0
Irrigated Rice
-117,985
4,768.7
6,708.7
1,940.0
Rainfed Rice
-9,346
209.0
294.0
85.0
Soybeans
-2,882
29.7
36.2
6.5
Sugarcane
116,012
211.0
435.8
224.8
Sweet Potato
-4,876
523.7
921.5
397.8
Coffee
46,700
902.7
1,165.8
263.1
Total
68,296
7,712.7
11,280.8
3,568.2
Pag
e
59

60. Alternative Policies
 Objective: increase forested area
• Forest area would have to increase by an additional 1.6
million hectares compared to the baseline - forest area
increases by 310,000 hectares in the baseline projection
– with a net change in carbon stock equivalent to 510
million tons of CO2eq (the increase in forest carbon
stock of 800 million tons of CO2eq is counterbalanced a
by loss in stock from other land uses). The increase in
forested area causes a reduction in cropland area of
about 500,000 hectares with a resulting reduction in
annual GHG emissions from crop production estimated
to be 10.2 million tons of CO2eq and a reduction in
revenue from crop production of 670 million USD

61. Alternative Policies
 Rice area at 3.5 million hectares – Carbon stock from
all land uses increase by a total of 247 million tons of
CO2eq (an additional 71 million tons of CO2eq compared to
the baseline). Emissions from crop production decrease by
4.4 million tons of CO2eq a year by 2030 (baseline results
project a yearly increase of 1.7 million tons of CO2eq). The
reduction in rice area causes an estimated loss in revenue
equivalent to 154 million USD per year compared to the
baseline .
 Alternative wet and dry management for irrigated
rice – estimated reduction in GHG equivalent to 113 million
tons of CO2eq and a loss in revenue of 271 million USD.

62. Alternative Policies
 Use of compost manure - estimated reduction in GHG
equivalent to 22 million tons of CO2eq and a loss in
revenue of 530 million USD.
 Ammonium sulfate - estimated reduction in GHG
equivalent to 9 million tons of CO2eq and a gain in revenue
of 130 million USD.


63. Conclusion
This approach allows us to:
 Determine land use choices trends, pressure for
change in land uses and tension forest/
agriculture
 Simulate policy scenarios, their viability and the
role of market forces
 Simulate the long term effect on emissions and
sequestration trends of the identified policy
reforms in relation to global price changes and
trade policies
Pag
e
63

64. Conclusion
Next steps and important data needs:
 Include pasture and livestock in the analysis.
 Better inventory of existing carbon stock in
forests
 Include other crops: tea, cocoa, rubber
 Better prices at local markets
 Determine the policy and policy reforms
that Vietnam would like to explore and
simulate
Pag
e
64

65. Page 65
THANK YOU
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

66. Regulatory Markets
 UN-REDD and REDD+
• Reducing Emissions from Deforestation and Degradation
(REDD)
• REDD+ includes the role of conservation, sustainable
management of forests and enhancement of forest carbon
stocks.
• http://www.un-redd.org/
 Clean Development Mechanism (CDM)
• Exemples of Project accepted:
Biogas recovery and electricity generation from Palm Oil Mill Effluent
 Advanced swine manure treatment
 Methane capture and combustion from poultry manure treatment

• All GHG are considered, demanding and difficult to achieve
• http://cdm.unfccc.int/index.html

67. Voluntary Markets
 Gold Standard (non-profit organization)
• Examples:


Solar Cookers
Biodigesters
• In essence as demanding as CDM
• http://www.cdmgoldstandard.org/
 Voluntary Carbon Standards (VCS)
• Afforestation, Reforestation and Revegetation (ARR)
• Agricultural Land Management (ALM)
• Improved Forest Management (IFM)
• No approved methodology for agricultural land
management (ALM) yet
• http://www.v-c-s.org/
• Supposed to be more accessible than CDM, GS
Pag
e
67

68. Voluntary Markets
 Climate Action Reserve (CAR)
• No protocols yet (last time I checked)
• http://www.climateactionreserve.org/
Pag
e
68

69. Other Sources of Financing
• Nationally Appropriate Mitigation
Actions (NAMAs)
• Locally Appropriate Mitigation Actions
(LAMAs)
Pag
e
69

70. Concluding Remarks
 Farmers appear to have the potential to make a
meaningful contribution to climate change mitigation.
 Adoption of mitigation practices limited by lack of
credit, required investments and risk reducing
financial mechanisms.
 At the farm level, the distinction between adaptation
and mitigation goals becomes blurry.
 There already are in place organizations that could
facilitate the access to carbon markets. However, their
involvement could be costly given their limited
knowledge of the specifics involved in climate change
mitigation in agriculture.
 Functioning carbon market have the potential to help
poor smallholder farmers.