De pinto a cost benefit analyses for mitigation

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 CO2 from

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 GhanaProvince 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


fertilizer

Eastern Maize, cassava, 2 years

fallow

Evolving into oil palm


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


Upper West Sorghum, groundnuts, maize,

fallow

Maize, groundnuts,

sorghum, fallow


Volta Maize, cassava, 2 years

fallow

Yam, 2 years fallow,

maize, cassava, 2 year

fallow


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, ManureOpt. 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




Average




MOZAMBIQUE

Price of CO2eq

$20 GCM Type, grwoth scenario, CO2 sequestered Opt. Fertilizer Opt. Fertilizer, ManureOpt. 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



High Scenario

Total mitigation potential: ton CO2eq/yr 478,121 1,110,796 2,462,243



Average

Total mitigation potential: ton CO2eq/yr 435,501 1,035,918 2,406,082



We can get a sense for

Agricultural contribution

To mitigation and magnitude

Of 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


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


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 4th

year; 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

CO2eq/year Zero Tillage 22, 363, 552 17, 588, 878 13, 906, 118 11, 030, 175

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

Source: Khalil Allali calculations

The Case of Ghana

Maize-Cassava-Fallow

Manure Applications of Various Levels

The Case of Ghana





Yield 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



Yield Variability Increases



Different Use of Inputs Manure + N



Different 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


Payments Are Not Required for an Indefinite

Amount of Time - 0 N


Payments 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?

Science

De pinto a cost benefit analyses for mitigation