Comparison of cost-benefit analyses for mitigation in different agroecosystems
Alex De Pinto
IFPRI
Rome
July, 2011

Challenges and Opportunities
Opportunities (besides CC mitigation)
•Help small poor farmers dealing with the effects of climate change
•Provide farmers with an additional source of income
•Food security and resilience
Challenges
•Uncertainty
•Identify “correct” set of incentives

Co-benefits of mitigation
Mitigation practices overlap considerably with sustainable use of resources
Positive correlation between soil C and crop yield. Some agricultural practices improve soil fertility and induce C sequestration
More efficient water use (reduces CO2from fuel/electricity): good for adaptation
Agricultural R&D, advisory services, and information systems

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. (2007a).)
Global mitigation potential in agriculture

We Can Do Better Now

The Example of Ghana
Province
Most Common Cropping system/rotation
Most Common Cropping system/rotation
Mitigation Options
Ashanti
Maize, cassava, 2 years fallow
No-burning/Manure/recommended amount of fertilizer
Brong Ahafo
Maize, cassava, 2 years fallow
Yam, 2 years fallow
No-burning/Manure/recommended amount of fertilizer
Central
Maize, cassava, 2 years fallow
No-burning/Manure/recommended amount of fertilizer
Eastern
Maize, cassava, 2 years fallow
Evolving into oil palm
No-burning/Manure/recommended amount of fertilizer
Greater Accra
Tomato, watermelon, maize
Tomato, watermelon, maize
Manure/recommended amount of fertilizer/no-till
Northern
Yam, maize, groundnuts, 1 year fallow
Manure/recommended amount of fertilizer
Upper East
Sorghum, groundnuts, maize, fallow
Millet, groundnuts, sorghum, fallow
Manure/recommended amount of fertilizer
Upper West
Sorghum, groundnuts, maize, fallow
Maize, groundnuts, sorghum, fallow
Manure/recommended amount of fertilizer
Volta
Maize, cassava, 2 years fallow
Yam, 2 years fallow, maize, cassava, 2 year fallow
No-burning/Manure/recommended amount of fertilizer
Western
Maize, cassava,
Evolving into cocoa

The Example of Ghana
Source: own simulations with DSSAT

SOC (kg[SOC]/ha/yr)
High : 150
Low : 1
Fertilizer, Manure Fertilizer, Manure, No-Till

SOC (kg[SOC]/ha/yr)
High : 150
Low : 1
Fertilizer, Manure, Residue management Fertilizer, Manure, Residue management No-Till

Page 10
GHANA
Price of CO2eq
$20
GCM Type, grwoth scenario, CO2 sequestered
Opt. Fertilizer
Opt. Fertilizer, Manure
Opt. Fertilizer, Manure , Residue management
CNRM-CM3, A2, Kg/yr
290862046.9
393020643.9
905733453.5
CSIRO-Mk3.0, A2, Kg/yr
288863610.3
378381542.6
876924355.8
ECHam5, A2, Kg/yr
308123656.7
401986671.4
938978094.2
MIROC3.2, A2, Kg/yr
462430229.8
544717157
1042135805
Low Scenario
Total mitigation potential: ton CO2eq/yr
1,060,129
1,388,660
3,218,312
Total mitigation potential US$/yr
$21,202,589
$27,773,205
$64,366,248
As % of Agricultural GDP
0.33%
0.43%
0.99%
High Scenario
Total mitigation potential: ton CO2eq/yr
1,697,119
1,999,112
3,824,638
Total mitigation potential US$/yr
$33,942,379
$39,982,239
$76,492,768
As % of Agricultural GDP
0.52%
0.62%
1.18%
Average
Total mitigation potential: ton CO2eq/yr
1,238,881
1,576,362
3,453,261
Total mitigation potential US$/yr
$24,777,630
$31,527,245
$69,065,211
As % of Agricultural GDP
0.38%
0.49%
1.07%
MOZAMBIQUE
Price of CO2eq
$20
GCM Type, grwoth scenario, CO2 sequestered
Opt. Fertilizer
Opt. Fertilizer, Manure
Opt. Fertilizer, Manure , Residue management
CNRM-CM3, A2, Kg/yr
130278112.9
284978331.9
639189652.3
CSIRO-Mk3.0, A2, Kg/yr
111638795.1
265190642.5
647180509.5
ECHam5, A2, Kg/yr
121155914.5
302669238.5
670911010.3
MIROC3.2, A2, Kg/yr
111587996.8
276227946.3
665151061.7
Low Scenario
Total mitigation potential: ton CO2eq/yr
409,528
973,250
2,345,826
Total mitigation potential US$/yr
$8,190,559
$19,464,993
$46,916,520
As % of Agricultural GDP
0.26%
0.62%
1.49%
High Scenario
Total mitigation potential: ton CO2eq/yr
478,121
1,110,796
2,462,243
Total mitigation potential US$/yr
$9,562,413
$22,215,922
$49,244,868
As % of Agricultural GDP
0.30%
0.71%
1.56%
Average
Total mitigation potential: ton CO2eq/yr
435,501
1,035,918
2,406,082
Total mitigation potential US$/yr
$8,710,026
$20,718,364
$48,121,631
As % of Agricultural GDP
0.28%
0.66%
1.53% We can get a sense for Agricultural contributionTo mitigation and magnitudeOf payments

We can construct spatially-explicit
mitigations costs per
ton of CO2eq
$ Ton CO2eq
$Cton.tif
<VALUE>
9.859458923 - 20
20.00000001 - 35
35.00000001 - 50
50.00000001 - 65
65.00000001 - 80
80.00000001 - 95
95.00000001 - 110
110.0000001 - 125
125.0000001 - 175
175.0000001 - 280
Mozambique
Fertilizer, Manure, Residue management No-Till
This map can be used with
other maps (e.g. poverty,
biodiversity) to identify
areas of intervention

# People < $1.25
125povMoz.tif
VALUE
0 - 108
108.0000001 - 252
252.0000001 - 454
454.0000001 - 764
764.0000001 - 1,369
1,369.000001 - 2,933
2,933.000001 - 6,820
6,820.000001 - 12,264
12,264.00001 - 23,025
23,025.00001 - 39,546
# People < $1.25
125povMoz.tif
VALUE
0 - 108
108.0000001 - 252
252.0000001 - 454
454.0000001 - 764
764.0000001 - 1,369
1,369.000001 - 2,933
2,933.000001 - 6,820
6,820.000001 - 12,264
12,264.00001 - 23,025
23,025.00001 - 39,546
Mozambique
Number of People who live with less than $1.25/day
$ Ton CO2eq
$Cton.tif
<VALUE>
9.859458923 - 20
20.00000001 - 35
35.00000001 - 50
50.00000001 - 65
65.00000001 - 80
80.00000001 - 95
95.00000001 - 110
110.0000001 - 125
125.0000001 - 175
175.0000001 - 280
Mozambique
Fertilizer, Manure, Residue management No-Till

CO2eq Sequestration Potential
cnra2_fmr
<VALUE>
11 - 20
20.00000001 - 30
30.00000001 - 40
40.00000001 - 50
50.00000001 - 60
60.00000001 - 70
70.00000001 - 135
# People < $1.25
125povMoz.tif
VALUE
0 - 108
108.0000001 - 252
252.0000001 - 454
454.0000001 - 764
764.0000001 - 1,369
1,369.000001 - 2,933
2,933.000001 - 6,820
6,820.000001 - 12,264
12,264.00001 - 23,025
23,025.00001 - 39,546
# People < $1.25
125povMoz.tif
VALUE
0 - 108
108.0000001 - 252
252.0000001 - 454
454.0000001 - 764
764.0000001 - 1,369
1,369.000001 - 2,933
2,933.000001 - 6,820
6,820.000001 - 12,264
12,264.00001 - 23,025
23,025.00001 - 39,546
Mozambique
Number of People who live with less than $1.25/day
Mozambique
CO2 Mitigation Potential
Fertilizer, Manure, Residue management No-Till

CBA for 5 Countries, 6 AEZ, 6 crop/cropping systems
Country
AEZ
Soil Texture
Crop
Morocco
Arid
Loam
Soft Wheat
Morocco
Arid
Loam
Potato
Morocco
Arid
Loam
Onion
Kenya
Arid
Clay
Maize
Kenya
Arid
Sand
Maize
Kenya
Semi-arid
Loam
Maize
Kenya
Semi-arid
Sand
Maize
Kenya
Semi-arid
Clay
Maize
Kenya
Temperate
Loam
Maize
Kenya
Humid
Loam
Maize
Ghana
Humid
Sandy/Clay/Loam
Maize/Cassava/Fallow
Mozambique
Semi-arid
Sandy/Loam
Maize/Cassava/Fallow
Mozambique
Semi-arid
Clay
Maize/Cassava/Fallow
Vietnam
Humid
Clay
Rice

The Case of Kenya
Annual net profit per tCO2e from maize production in 4 AEZs of Kenya
Package 1
Package 2
Package 3
Package 4
RES
RES, FERT & MNR
RES, FERT, MNR, SWC & ROT
FRT, MNR, RES, SWC, ROT, & IRG
Annual net profit/tCO2e
Annual net profit/tCO2e
Annual net profit/tCO2e
Annual net profit/tCO2e
Arid
Clay
12.29
30.78
0
(-0.33)
0
(-53.02)
Arid
Sand
0
(-17.12)
14.19
0
(-9.64)
0
(-22.78)
Semi-arid
Loam
0
(-43.89)
0
(-23.36)
0
(-28.79)
0
(-53.40)
Semi-arid
Sand
0
(-41.26)
0
(-13.41)
0
(-13.18)
0
(-6.83)
Semi-arid
Clay
0
(-81.78)
0
(-55.26)
0
(-68.67)
0
(-73.02)
Temperate
Loam
0
(-3.27)
0
(-19.85)
0
(-23.20)
0
(-19.54)
Humid
Loam
N/A*
0
(-99.01)
0
(-96.93)
0
(-71.72)
*Applying only residues to loamy soils in the humid AEZ resulted in a loss in SOC over the 40-year period
Notes: RES=50% residues applied to soil, FERT=40kg N/ha, MNR=3t/ha/yr, SWC=soil water availability before planting is 30% of field capacity and small amount (2 mm/ha/10-day) of soil moisture is additionally available in the root zone throughout the growing season; ROT=rotation with dry beans every 4thyear; IRG=meet full crop water demand. Results are for an open pollinated variety maize.
Source: Bryan, E. et al. 2011

The Case of Morocco – 30 year analysis
NPV of Alternative Practices
Discount
Rate
4% 6% 8% 10%
Reduction in
CO2 Emissions
Soft
Wheat
Traditional 10, 880, 537 8, 068, 904 5, 910, 625 4, 234, 504
0.9 Tons
Zero Tillage 22, 363, 552 17, 588, 878 13, 906, 118 11, 030, 175 CO2eq/year
Potato
Traditional irrigation 14, 388, 214 10, 766, 683 8, 067, 484 6, 033, 598
0.3 Tons
CO2eq/year
Drip irrigation 88, 932, 314 70, 207, 148 55, 888, 428 44, 801, 899
Onion
Traditional irrigation 9, 455, 430 6, 920 634 5, 027, 825 3, 599, 420
0.4 Tons
CO2eq/year
Drip irrigation 84, 047, 807 65, 746, 343 51, 773, 878 40, 976, 765
S ource: Khalil Allali calculations

The Case of GhanaMaize-Cassava-FallowManure Applications of Various Levels

The Case of GhanaMaize-Cassava-FallowManure Applications of Various Levels

Maize-Cassava-FallowManure Applications of Various LevelsYield Variability Increases

Mean-Standard Deviation Utility Function
We follow Saha(1997) and we assume that farmers’ preferences can be represented by a mean-SD utility function
Changing change risk attitude
Under the assumption of risk aversion, decreasing (constant) [increasing] absolute risk aversion preferences require
Decreasing (constant) [increasing] relative risk aversion is denoted by

Maize-Cassava-FallowManure Applications of Various LevelsYield Variability Increases

Maize-Cassava-FallowManure Applications of Various LevelsDifferent Use of Inputs Manure + N

Maize-Cassava-FallowManure Applications of Various LevelsDifferent Soil Different Results

ψ
c
1/α
Effect of Payments on Investments on Soil Fertility

ψ
c
1/α
with carbon paymentswith carbon payments
Effect of Payments on Investments on Soil Fertility

Maize-Cassava-FallowPayments Are Not Required for an Indefinite Amount of Time -0 N

Maize-Cassava-FallowPayments Are Not Required for an Indefinite Amount of Time –60 Kg N

All These Results Are Predicated On
Knowledge / quantification of how different agronomic practices and different crops affect GHG emissions (DSSAT/Century, CropSys, EPIC, APSIM)
Capability of “reasonably” predict future land-use choices, crop choices, agronomic practices (surveys, models of land-use change)
Major obstacle: creating a baseline

Considerations
Risk-neutrality hides some of the complexities of implementing payment for environmental service schemes
Could save money proposing the “right practices” to the “right” farmers
Solution: create tiers of farmers?
Good targets are farmers whose actions are “highly” predictable
How do we account for the co-benefits?