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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...
Co-benefits of mitigation 
Mitigation practices overlap considerably with sustainable use of resources 
Positive correla...
Total technical mitigation potentials (all practices, all GHGs: MtCO2-eq/yr) for each region by 2030. 
Note: based on the ...
We Can Do Better Now
The Example of Ghana 
Province 
Most Common Cropping system/rotation 
Most Common Cropping system/rotation 
Mitigation Opt...
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...
Page 10 
GHANA 
Price of CO2eq 
$20 
GCM Type, grwoth scenario, CO2 sequestered 
Opt. Fertilizer 
Opt. Fertilizer, Manure ...
We can construct spatially-explicit 
mitigations costs per 
ton of CO2eq 
$ Ton CO2eq 
$Cton.tif 
<VALUE> 
9.859458923 - 2...
# People < $1.25 
125povMoz.tif 
VALUE 
0 - 108 
108.0000001 - 252 
252.0000001 - 454 
454.0000001 - 764 
764.0000001 - 1,...
CO2eq Sequestration Potential 
cnra2_fmr 
<VALUE> 
11 - 20 
20.00000001 - 30 
30.00000001 - 40 
40.00000001 - 50 
50.00000...
CBA for 5 Countries, 6 AEZ, 6 crop/cropping systems 
Country 
AEZ 
Soil Texture 
Crop 
Morocco 
Arid 
Loam 
Soft Wheat 
Mo...
The Case of Kenya 
Annual net profit per tCO2e from maize production in 4 AEZs of Kenya 
Package 1 
Package 2 
Package 3 
...
The Case of Morocco – 30 year analysis 
NPV of Alternative Practices 
Discount 
Rate 
4% 6% 8% 10% 
Reduction in 
CO2 Emis...
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...
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 ...
Considerations 
Risk-neutrality hides some of the complexities of implementing payment for environmental service schemes ...
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De pinto a cost benefit analyses for mitigation

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Presentation for Smallholder Mitigation: Mitigation Options and Incentive Mechanisms - Expert Workshop
7 - 8 July 2011


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De pinto a cost benefit analyses for mitigation

  1. 1. Comparison of cost-benefit analyses for mitigation in different agroecosystems Alex De Pinto IFPRI Rome July, 2011
  2. 2. 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
  3. 3. 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
  4. 4. 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
  5. 5. We Can Do Better Now
  6. 6. 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
  7. 7. The Example of Ghana Source: own simulations with DSSAT
  8. 8. SOC (kg[SOC]/ha/yr) High : 150 Low : 1 Fertilizer, Manure Fertilizer, Manure, No-Till
  9. 9. SOC (kg[SOC]/ha/yr) High : 150 Low : 1 Fertilizer, Manure, Residue management Fertilizer, Manure, Residue management No-Till
  10. 10. 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
  11. 11. 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
  12. 12. # 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
  13. 13. 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
  14. 14. 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
  15. 15. 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
  16. 16. 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
  17. 17. The Case of GhanaMaize-Cassava-FallowManure Applications of Various Levels
  18. 18. The Case of GhanaMaize-Cassava-FallowManure Applications of Various Levels
  19. 19. Maize-Cassava-FallowManure Applications of Various LevelsYield Variability Increases
  20. 20. 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
  21. 21. Maize-Cassava-FallowManure Applications of Various LevelsYield Variability Increases
  22. 22. Maize-Cassava-FallowManure Applications of Various LevelsDifferent Use of Inputs Manure + N
  23. 23. Maize-Cassava-FallowManure Applications of Various LevelsDifferent Soil Different Results
  24. 24. ψ c 1/α Effect of Payments on Investments on Soil Fertility
  25. 25. ψ c 1/α with carbon paymentswith carbon payments Effect of Payments on Investments on Soil Fertility
  26. 26. Maize-Cassava-FallowPayments Are Not Required for an Indefinite Amount of Time -0 N
  27. 27. Maize-Cassava-FallowPayments Are Not Required for an Indefinite Amount of Time –60 Kg N
  28. 28. 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
  29. 29. 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?

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