Transcript Clinical services in livestock are characterized with externalities

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
34.0
33.0
32.0
Mean monthly temperature C
31.0
30.0
Mean temperature 1961-1984
Mean monthly temp for 1991-2007
29.0
28.0
27.0
26.0
25.0
JAN
FEB
MAR
APR
MAY
JUN
JUL
Months
AUG
SEPT
OCT
NOV
DEC
Introduction (cont)
• A graph showing mean monthly rainfall trends in
Soroti district
250
200
Rainfall in mm
150
100
50
0
JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC
Mean monthly rainfall from 19922007
Mean monthly rainfall from 19761991
Mean monthly rainfall from 19611975
Climate change and Sweet
Potato
Temperature and rainfall changes influences
break of pest and diseases in sweet potato.
out
• 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 inherentproductivities (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 + 22 - 212
•
Usually observe 12, can assume 12  22
•
12 difficult to observe. Can assume correlation is positive
and high in most cases. If 12  22 = 2 then
2  22 - 212  2 = 22(1 – 12)
•
Most systems involve multiple activities (crops, livestock). 12
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 application
Regions
Crop Activities
Base system
System 1
System 2
Drought
Flat
Yields/h Price/k
slopes
Cost/ha
a
g
Area/ha
SD
CV
Weights
Drought
Resistant
Resistant
Variety +
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
Beans
125278.4
1708.4
725
1.4
1440.3
84.3
0.2
100
100
328510
7561.5
325
2.6
4976.3
65.8
0.3
100
100
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
Beans
90985.8
2746.6
725
2.2
2877.5
105
0.2
100
100
620175.8
7096.3
325
3
4712.4
66.4
0.3
100
100
88920
5805.2
123.3
3.1
3297.7
56.8
0.3
130
169.2
68295.5
1443.8
500
1.7
506.6
35.1
0.2
100
100
Moderate
slopes
Potatoes
Sweet- potatoes
Steep
slopes
Potatoes
Sweet- potatoes
Sorghum
Source: Field Survey Data (May 2010)
application
Crop
Regions
Activities
Base system
System 1
Drought
Resistant
Variety +
Clean Vines
Cost/ha
Yields/ha
Price/kg
Area/ha
SD
CV
Weights
(%)
SweetBetter-off
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
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
64489.23
278.3
900
0.6
75.6
27.2
0.02
100
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
440
3.6
7761.36
220.1
0.2
100
Millet
Cowpeas
SweetWorse-off
Cassava
609988
3526.1
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
Results from Stakeholders
workshop
Farmers 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 migration
Note: 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.
• 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.
2000000
1500000
1000000
Payment to Adopt
•Adoption rate of planting pest
and disease resistant varieties
that are virus free is 65% without
compensation
Traditional
System Vs
Resistant Variety
and Virus free
Vines
500000
0
0
0.2
0.4
0.6
-500000
-1000000
-1500000
Adoption rate
rate 1.2
of
0.8 Adoption
1
Resistant varieties
with clean planting
material
Adoption rate of
resistant varieties
without clean
planting material
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%
Potential adoption of use clean, pest
and disease resistant varieties based
on Slope nature
2000000
1500000
•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 agroecological zones
Payment to Adopt
1000000
500000
0
0.2
0.4
0.6
0.8
1
-500000
Adoption rate of use of clean planting
material and Resistant variety on flat areas
-1000000
•Variations
in
adoption
is
depends on Competing uses
and
opportunity
cost
of
allocating
land
to
new
technology
0
-1500000
Adoption rate of clean planting material
and resistant variety
Adoption rate of clean planting material
and resistant variety steep slopes
1.2
Better off Vs
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
Adoption of the Practice of Cleaning
40000000 Sweet Potato Planting MateriaL
30000000
COMPENSATION PAY TO ADOPT
•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).
50000000
20000000
10000000
0
-0.2
-10000000
-20000000
-30000000
-40000000
•while those farmers less
endowed with land tend to
be risk averse and is hence
hesitant to take chances
with the new sweet potato
Adoption by better
off
-50000000
0
0.2
0.4
0.6
0.8
1
1.2
ADOPTION RATES
Adoption by the
worse off
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