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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