Economic of Climate change adaptation among Sweet Potato producers In Uganda.

John Ilukor, Bernard Bashaasha, Fred Bagamba

2011 February 26th

Introduction• Climate change threatens to intensify food

insecurity problems in Africa (Water insecurity, floods, drought, pest and diseases out break)

• Crop yields may fall by 10 to 20% by the year 2050 because of warming and drying (Jones and Thornton, 2003; Thornton et al., 2006).

• Uganda’s agricultural sector, which is the backbone of Uganda’s economy contributing 42% of the GDP, over 90% to exporting earnings and employing 80% of the population, is highly vulnerable.

Introduction (cont)Uganda’s vulnerability can be clearly seen based on macro level indicators • Weak institutional capacity, • Limited skills and equipment for disaster

management• Heavy dependence on rain fed agriculture,• limited financial resources and increasing

population.

Introduction (cont)The affects on agriculture in Uganda are experienced in two ways;

• First, there has been more erratic, unreliable rainfall during first rainy season in March to June, and this has been followed by drought affecting crop yields.

• Second, the rainfall especially, in the second rains,

is reported to be intense and destructive resulting into floods, landslides and soil erosion (Oxfam 2008)

Introduction (cont)• A graph showing means maximum monthly

temperatures in Soroti district

25.0

26.0

27.0

28.0

29.0

30.0

31.0

32.0

33.0

34.0

JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC Months

Mean monthly temperature C

Mean temperature 1961-1984 Mean monthly temp for 1991-2007

Introduction (cont)• A graph showing mean monthly rainfall

trends in Soroti district

0

50

100

150

200

250

JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC

Months

Rainfall in mm Mean monthly rainfall from 1992- 2007 Mean monthly rainfall from 1976- 1991 Mean monthly rainfall from 1961- 1975

Climate change and Sweet Potato

Temperature and rainfall changes influences out break of pest and diseases in sweet potato.

• Rising temperatures is increasing spread of sweet potato virus disease (SPVD) (Tairo et al., 2004, Claudia et al 2007)

• The Sweet potato virus disease can cause 65% to 72% reduction in yields from different cultivars (Gutiérrez et al, 2003).

• Results from NARO sweet potato programme indicate that the yield decline resulting from sweet potato virus ranges from 56 to 100%.

Motivation• New technologies have been developed to meet

climate change related challenges.

• These include cleaning of vines for viruses, pest and disease resistant varieties, tolerant to drought, tolerant to heat and nutrient depletion,

• These are varieties are NASPOT 1 (Gibson, 2005), and New Kawongo, Dimbuka-Bukulula, NK259L, NK103M (Mwanga, 2007)

• Cleaning of the planting material of the SPVD also

increase yields by over 56 percent in Uganda (Mukasa, et al 2006).

Motivation• Understanding what farms adopt, where ,and why? What

incentives are required to achieve a target adoption rate is necessary if we are minimize climate change effects

Modeling process: Minimum data Tradeoff Analysis Model (MD-TOA Model)

•Public stakeholders•Policy makers•Scientists

Indicators, tradeoffs and scenarios

Coordinated Disciplinary Research

Communicate results to stakeholders

A participatory process, not

a model

Methodology• Modeling Adoption Rates in Heterogeneous

Populations

• Farmers choose practices to max expected returns

• v (p, s, z) ($/ha)

• p = output & input prices, s = location, z = system 1, 2

• Farmers earn v (p, s, 1) for current system

• Farmers can adopt system 2 and earn

• v (p, s, 2) – TC – A

• where TC = transaction cost, A = other adoption costs

Methodology• The farmer will choose system 2 if

• v (p, s, 1) < v (p, s, 2) – TC – A

• The opportunity cost of switching from 1 to 2 is

• = v (p, s, 1) – v (p, s, 2) + TC + A

• adopt system 2 if < 0.

• Suppose Government or NGO wants to encourage adoption by providing incentive payment PAY (e.g., to reduce negative externalities of syst 1, or encourage positive externalities of syst 2)

• adopt system 2 if < PAY.

• Opportunity cost varies spatially, so at some sites farms adopt system 1 and at other sites adopt system 2

Analysis of Adaptation to CC

• Impacts of climate change: Productivity of traditional system declines more than resilient with new crops technology, e.g.,

• Pest Resistant variety vs traditional variety,

• Virus free vines + pest and disease resistant variety vs traditional variety

• PAY is amount needed to compensate for loss

• Adaptation is adoption of practices that are relatively less vulnerable under the changed climate

• Reduces loss due to climate change, or increases gains

Minimum Data Methods to Simulate Adoption Rates

(Antle and Valdivia, AJARE 2006)

•How to estimate the spatial distribution of opportunity cost of changing practices?

• Use “complete” data to estimate site-specific inherent-productivities (Inprods) and simulate site-specific land management decisions to construct spatial distribution of returns

• MD approach: estimate mean, variance, covariance of net returns distributions using available data

Need to know mean and variance of

= v (p, s, 1) – v (p, s, 2) + TC + A

MD approach: use available data to estimate mean and variance of

Mean: E () = E (v1 ) – E (v2 ) + TC + A

Suppose system 1 has one activity, then:

• E (v1 ) = p11 y11 – C11 is usually observed

• E (v2 ) = p21 y21 – C21 is estimated using In prods* and cost data:

• y21 = y11 {1+ (INP21 – INP11)/INP11}

* In prod = inherent productivity = expected yield at a site with “typical” management

• C21 is estimated using C11 and other information on changes in practices

• TC and A are estimated using available data, if relevant

Variance of returns:

• Observation: cost of production c y where is a constant and y is yield

Then v = py – c (p - ) y and CV of v is equal to CV of y

• Recall: = v (p, s, 1) – v (p, s, 2) + TC + A so we know 2

= 12 + 2

2 - 212

• Usually observe 12, can assume 1

2 22

• 12 difficult to observe. Can assume correlation is positive and high in most cases. If 1

2 22 = 2 then

2

22 - 212 2 = 22(1 – 12)

• Most systems involve multiple activities (crops, livestock). 1

2 and 22 depend on variances and covariance's of returns to

each activity. In the MD model, we assume all correlations between activities within system 1 are equal (1), and make the same assumption for system 2 (2).

• In general, incentive payments are calculated as

PAY = PES * ES

Where PES = $/unit of ES, ES = services / ha

• For adoption analysis, set ES = 1, then

PAY = PES ($/ha)

Conclusion: to implement MD approach we need:

• Mean yields for system 1

• Either mean yields for system 2, or Inprods for each activity in each system

• Output prices and cost of production for each activity

• Variances (or CVs) of returns (yields) for each system

• Correlation of returns to activities within each system (1 and 2)

• Correlation of returns between systems 1 and 2 (12)

Data for modeling Kabale applicationRegions Crop Activities Base system System 1 System 2

Flat

slopes   Cost/ha

Yields/

ha

Price/

kg Area/ha SD CV Weights

Drought

Resistant

Variety

(%)

Drought

Resistant

Variety +

Clean Vines

(%)

  Beans 289484 1414.4 725 109809.1 797.3 56.4 0.4 100 100

  Potatoes 301340 6670.8 325 4.3 4722.8 70.8 0.3 100 100

  Sweet- potatoes 128440 325 123.3 3.1 4070.8 56.4 0.2 130 169.2

  Sorghum 109809.1 2877.6 500 1.4 2874.9 99.9 0.1 100 100

Moderate

slopes Beans 125278.4 1708.4 725 1.4 1440.3 84.3 0.2 100 100

  Potatoes 328510 7561.5 325 2.6 4976.3 65.8 0.3 100 100

  Sweet- potatoes 0 6290.3 123.3 2.3 5825.4 92.6 0.3 130 169.2

  Sorghum 114608 3527.2 500 2.2 3337.8 94.6 0.3 100 100

Steep

slopes Beans 90985.8 2746.6 725 2.2 2877.5 105 0.2 100 100

  Potatoes 620175.8 7096.3 325 3 4712.4 66.4 0.3 100 100

  Sweet- potatoes 88920 5805.2 123.3 3.1 3297.7 56.8 0.3 130 169.2

  Sorghum 68295.5 1443.8 500 1.7 506.6 35.1 0.2 100 100

Source: Field Survey Data (May 2010)

Data for modeling Soroti application Regions

Crop

Activities Base system System 1 

  Cost/ha Yields/ha Price/kg Area/ha SD CV Weights

Drought

Resistant

Variety +

Clean Vines

(%)

 

Better-off

Sweet-

potatoes 171262.4 1602.6 200 7.9 1895.8 118.3 0.23 169.2 

Sorghum 68703.04 826.7 316.7 4.1 567.8 68.7 0.12 100  

Millet 159089 1139.1 316.3 3.5 2042 179.3 0.10 100  

Cassava 120836.7 560.9 440 12.4 423.8 75.6 0.37 100  

G/nuts 695344.1 1141.3 1000 3.5 2827.4 247.7 0.10 100  

Maize 128194 640.5 600 1.8 493.2 77 0.05 100  

Cowpeas 64489.23 278.3 900 0.6 75.6 27.2 0.02 100  

Worse-off

Sweet-

potatoes 241606.6 3287.2 200 4.94 3907 118.9 0.27 169.2 

Sorghum 132892.6 1467.7 316.7 3.1 2042 139.2 0.17 100  

Millet 401171.9 3987.3 316.3 1.7 11662.9 292.5 0.09 100  

Cassava 609988 3526.1 440 3.6 7761.36 220.1 0.2 100  

G/nuts 385070.9 4024.3 1000 2.7 11841.9 294.3 0.15 100  

Maize 109072.5 1729.96 600 1.5 2091.3 120.9 0.08 100  

Cowpeas 191558.7 485.2 900 0.7 227.3 46.9 0.04 100  

Source: Field Survey Data (March 2010)

Results from Stakeholders workshopFarmers experience• Unpredictable rainfall• Increased pest and disease• Declining soil fertility

Adaptation mechanism• Swamp cultivation• Disease and pest resistant

crop varieties• Mixed and multiple cropping• Short duration crops

(vegetables)• Water Harvesting• Flood and micro irrigation

Adaptation mechanism Cont

• Spraying for pest• Crop rotation and migrationNote: 1) Farmers noted that

only those with money and information can acquire some of technologies like resistant varieties

2) If provided under govt (NAADS), gainers are the politically powerful and the rich, even when the target is the poor.

Traditional System Vs Resistant

Variety and Virus free

Vines

•Adoption rate of planting pest

and disease resistant varieties

that are virus free is 65%

without compensation

• 57% of the households would

plant resistant varieties without

compensation.

•To raise adoption level by 20%

(from 65% to 85% and 57% to

80%), farmers should be

compensated by about 250,000

Uganda shillings per hectare

($110)

•These results indicate that

farmers are rational because

they do not adopt the

technology as long as benefits

do not exceed the costs.

0 0.2 0.4 0.6 0.8 1 1.2

-1500000

-1000000

-500000

0

500000

1000000

1500000

Adoption rate of Resistant varieties with clean plant-ing material

Adoption rate of resistant varieties without clean planting material

Adoption rate

Paym

en

t to

Adopt

Subsidy Vs No subsidy case

•63.8% will adopt virus free

planting material without

subsidy

• 65% adopt planting material

planting material if subsidy is

provided

•Results show small difference in

adoption rates implying that a

sweet potato vine subsidy would

achieve little in terms of

promoting the adoption of pest

and disease resistant virus free

planting materials.

•Subsidization in order to

increase adoption climate change

adaptation strategies is not

sustainable

Agro –ecological

zones• The adoption rate on flat

land is 65.3%

•The adoption rate on

moderate slopes is 60.7%

•The adoption rate on the

steep slopes is 64.4%

•The production of sweet

potatoes under new improved

sweet potato technologies

varies with the slope agro-

ecological zones

•Variations in adoption is

depends on Competing uses

and opportunity cost of

allocating land to new

technology

•

0 0.2 0.4 0.6 0.8 1 1.2

-1500000

-1000000

-500000

0

500000

1000000

1500000

2000000

Potential adoption of use clean, pest and disease re-sistant varieties based on

Slope nature

Adoption rate of use of clean planting material and Resistant variety on flat areasAdoption rate of clean planting ma-terial and resistant varietyAdoption rate of clean planting ma-terial and resistant variety steep slopes

Paym

ent

to A

dopt

Better off Vs Worse off

•The adoption potential for

those sweet potato farmers

with endowed with land

(better off) is 65.4% whereas

it is 53.85% for those farmers

less endowed with land (worse

off).

•This result implies that those

farmers endowed with land

have a stronger resource base

and better capacity to bear

the risks associated with the

new sweet potato technology

•while those farmers less

endowed with land tend to be

risk averse and is hence

hesitant to take chances with

the new sweet potato

technology.

0 0.2 0.4 0.6 0.8 1 1.2

-50000000

-40000000

-30000000

-20000000

-10000000

0

10000000

20000000

30000000

40000000

50000000

Adoption of the Practice of Cleaning Sweet Potato Planting

MateriaL

Adoption by better offAdoption by the worse offADOPTION RATES

CO

MP

EN

SA

TIO

N P

AY T

O A

DO

PT

Conclusion and Recommendation

• Households are adapting to climate change

• Some adaptation strategies are not affordable by some farmers.

• Subsidy provision is not sustainable in climate change adaptation.

• Opportunity cost of land is one of the critical determinants of sustainable adoption of improved agricultural technologies

• Adoption CC adaptation strategies varies base different agro-ecological zones

• Climate change policy needs to target particular households based agro-ecological zone or Poverty

• The institutional framework and systems should be strengthened to improve on accountability in the implementation of climate change adaptation strategies of a public nature

• Climate change policy should focus on reducing opportunity costs and transaction cost involved in adopting these CC adaptation strategies.

END