Economics of Climate Change
Adaptation in Sri Lanka: A
Ricardian Analysis
L.H.P.Gunaratne and Aruna Suriyaarachchi
Department of Agricultural Economics and Business Management
Faculty of Agriculture
University of Peradeniya
Sri Lanka
Outline
• Background
• The issue: climate change in Sri Lanka
• Objectives
• Approach
• Results and discussion
• Conclusions and implications
Climate Change
• Climate change has been defined as statistically significant variation in
the mean state of the climate or its variability, persisting for an
extended period.
• CC has been identified as one of the most important challenges for
global food security with the meeting of food demand for the
increasing population while sustaining the already stressed
environment.
• Climate change is real, and it has already led to significant impacts on
food security, water availability and human health in most parts of
the world.
Climate Change and Food Security
• Climate change will affect all four dimensions of food security:
•
•
•
•
food availability,
food accessibility
food utilization and
food systems stability (FAO framework on CC and food security)
• It further explains that the impact extended to human health, livelihood assets, food
production and distribution channels, as well as changing purchasing power and market
flows.
Impacts of climate change to Agriculture and Food
security
Vulnerable groups
• Effects of climate change spread to all the sectors.
• But, mostly farming communities and fishermen are more affected because
of over reliance on rain fed agriculture and other activities that are highly
weather- sensitive.
• Agriculture is the sector most affected by the climate change with
increasing vulnerability in the future (FAO).
• Tropical and island nations are in the priority list of the vulnerability to
climate change.
• Given that agriculture is the main livelihood of the majority, and the main
land and water use, and the importance of food security, the study on
impact on Agriculture is of paramount importance.
The case with Sri Lanka
• Sri Lanka is predominantly an agricultural country as evidenced by its
effective contribution to the GDP, export earnings, total employment
and land use.
• Agriculture plays a major in the livelihood of the farming communities,
although its contribution and direct labour force (10.8% to the GDP and
31% of the labour force, respectively) is diminishing. Therefore, study
on the effect of climate change on farm income is of paramount
importance.
Climate of Sri Lanka (at a glance)
• Sri Lanka lies in the equatorial and tropical zone
• Average annual temperature ranges from 28 to 32 Celsius. However, by
locations a low average of 16 °C is there inn Nuwara Eliya in the Central
Highlands.
• The mean annual rainfall varies between 900 mm - 5000 mm
• Three major climatic zones:
• Wet Zone: above 2500 mm rainfall
• Intermediate zone: 1750 – 2500 mm rainfall
• Dry Zone: less than 1750 mm rainfall
• Four climate seasons:
•
•
•
•
First inter-monsoon (March-April)
Southwest –monsoon season ( May – September) – Yala season
Second inter-monsoon Season (September – December)
Northeast – monsoon season (December – February) – Maha season
Wet Zone = > Annual Rainfall 2500
Intermediate Zone = Annual Rainfall 1750 - 2500
Dry Zone = < Annual Rainfall 1750
Climate zones of Sri Lanka
(Source: Punyawardena, 2007)
Climate change effect in Sri Lankan context …
• Over 19,900 cases of climate induced disease issues among the livestock are reported from 18 districts by
May 2014
• Over 1.8 million Sri Lankans are affected by drought since 2013
• Sri Lanka’s economic loss from floods alone -USD 1 billion for 10 years (Humanitarian Bulletin, Sri Lanka,
Issue 03 | Aug 2014).
• 87,281 ha of paddy lands were affected in Maha
2013/14
• Most agriculture ‐based livelihoods in the Dry
and Intermediate Zones were affected.
(Rapid Food Security Assessment in Districts Affected by
Erratic Weather Conditions in Sri Lanka: Preliminary findings
April 2014)
Objectives and Approaches
Objectives:
• To study the adaptation to climate change by farmers
• To investigate the effect of climate change on net revenue of
agriculture
• To identify the factors affecting the adaptation
Approaches adopted:
• Descriptive statistics
• Recardian model
• Ordered probit
Theoretical framework
1.Ricardian model
• Ricardian approach examines how climate in different places affects
the net rent or value of farmland instead of studying yields of specific
crops.
• Ricardian model corrects the bias and overestimation of damages arise
in the traditional production function by taking account of infinite
variety of substitutions , adaptations as climate changes.
• A simple model
Net revenue = ʄ (climate variables).
• Max NR = Pi ∗ Qi (R, E) − Ci (Qi , R, E)
• NR = f (E)
• Net revenue = ∫ (RF, RF2,T0…)
• RF
•
•
•
•
Annual RF
RF crop season (Total)
RF at planting / harvesting
Duration amount
• T0 – linear and Quadratic.
• Monthly
• seasonal
Data
• Data source:
• UNDP ADAPT ASIA Agriculture Survey
• Natural Resource Management Centre
of the Department of Agriculture
• Sample size: 321 farmers in 40
agro-ecological zones
RESULTS AND DISCUSSION
FARMER PROFILE
Age of respondent
Household size
0-5
5.-10
80
70
60
50
40
30
20
10
0
Percentage
WZ
IZ
DZ
80.77
85.23
83.93
19.23
14.76
16.07
WZ
IZ
DZ
25-45
18.3
21.2
35.2
45-65
61.5
70.2
55.5
65-80
20.2
8.6
9.3
Education
80
70
60
Percentage
90
80
70
60
50
40
30
20
10
0
50
40
30
20
10
0
WZ
IZ
DZ
Primary
27.6
27.6
45.5
Secondary
72.4
72.4
54.5
Primary Occupation
Tenure type
100
100%
90
90%
80
80%
70
60
50
40
Farmer
70%
Own land and rent to
others;
Officer
60%
Sharecropped land
Teacher
50%
Trader
30
Other
20
Own land and own use
Communal land
(traditional ownership)
40%
30%
Rented land
20%
10
10%
0
WZ
IZ
0%
DZ
Wetzone
Intermediate
System of farming
60%
Shifting cultivation
(With long fallow
period)
50%
40%
Continuous cropping
(no fallow period)
30%
20%
10%
0%
Wetzone Intermediate
Dryzone
Continuous cropping
with multiple rotations
(includes short fallow
period )
Dryzone
Borrowed land (Do not
pay for usage)
Farmer awareness
Aspect
No
Yes
Percentage
Long term shifts in temperature
26
295
92
Long term shifts in precipitation
15
283
88
Long term shifts in frequency of
drought
Long term shifts in frequency of
flooding
Long term shifts in frequency of pest
and disease incidence
44
238
74
168
115
36
38
264
82
FARMERS’ AWARENESS – by climatic zone
Long term shift in temperature
Long term shift in Precipitation
100%
100%
90%
90%
80%
80%
70%
70%
60%
Cooler
60%
Drier
50%
Warmer
50%
Wetter
40%
30%
unaware 40%
30%
No
20%
20%
10%
10%
0%
Unaware
no
0%
Wetzone
Intermediate
Dryzone
Wetzone
Intermediate
Dryzone
Long term shifts in pest and disease
incidence
Long term changes in frequency of
droughts
80%
120%
70%
100%
60%
80%
Frequency increased
50%
Frequency increased
60%
Frequency decreased
40%
Frequency decreased
30%
Unaware
20%
no
Unaware
40%
no
20%
10%
0%
0%
Wetzone
Intermediate
Dryzone
Wetzone
Intermediate
Dryzone
Long term shifts in frequency of
flooding
Past weather to predict next year’s
weather
70%
60%
50%
40%
Frequency increased
30%
Frequency decreased
20%
no
10%
0%
Wetzone
Intermediate
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Predicting years
Intermediate
Dryzone
Prediction Sources
80%
70%
60%
0-5
50%
6-10
40%
11-20
30%
20%
21-30
10%
31-40
0%
Intermediate
No
Wetzone
Dryzone
90%
Wetzone
Yes
Dryzone
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Rely on expert opinions
Rely on newspaper, TV
,Radio
Rely on both sources
Wetzone
Intermediate
Dryzone
Source
Ricardian analysis (by seasons)
800000
NR
600000
400000
0
200000
50
100
ID
150
df
5.5043e+11
1.4917e+12
6
138
9.1738e+10
1.0810e+10
Total
2.0421e+12
144
1.4182e+10
nr
Coef.
maha_t
maha_tsq
maha_p
maha_psq
oct_p
oct_psq
_cons
-160300.1
2515.349
1000.539
-2.062982
-1274.037
2.419141
2683849
Std. Err.
51152.87
911.632
321.4517
.6032669
611.9669
1.058829
678594.7
200
N= 145 R-sq=0.2696 Adj R-sq=0.2378
NR
0
31.860C
Maha T
MS
Model
Residual
Net Revenue Vs. Climatic Variables
 Maha season
0
SS
t
-3.13
2.76
3.11
-3.42
-2.08
2.28
3.96
Number of
F( 6,
1
Prob > F
R-squared
Adj R-squa
Root MSE
P>|t|
[95% Co
0.002
0.007
0.002
0.001
0.039
0.024
0.000
-261444.
712.775
364.931
-3.25582
-2484.08
.325514
134206
Variable
Observation
Net Revenue 321
Mean
Std. Dev.
Min
Max
108,214.4
119,086.4
962.75
664,462.5
NR
NR=2683849-160300T+2515T2+1000P-2.06P2
249.5mm
Maha Precipitation
NR
0
500000
1.0e+06
 Yala season
0
50
100
ID
150
Model
Residual
1.2120e+11
1.4914e+12
8
135
1.5150e+10
1.1047e+10
Total
1.6126e+12
143
1.1277e+10
NR
Coef.
prec_11
prec_12
prec_11sq
prec_12sq
yala_t
yala_tsq
yala_p
yala_psq
_cons
1063.852
-655.5775
-2.391821
2.11645
-30887.95
526.0468
181.0257
-.5486711
432744.9
Std. Err.
466.0662
372.3924
1.055526
1.02739
49672.03
889.2766
871.1063
1.906968
686890.2
t
2.28
-1.76
-2.27
2.06
-0.62
0.59
0.21
-0.29
0.63
200
N= 145 R-sq=0.0752 Adj R-sq=0.0204
NR
NR=432744+1063.85P-2.39P2
222.39mm
Nov precipitation
Prob > F
R-squared
Adj R-square
Root MSE
P>|t|
0.024
0.081
0.025
0.041
0.535
0.555
0.836
0.774
0.530
[95% Conf
142.1161
-1392.055
-4.479327
.0845894
-129123.9
-1232.669
-1541.755
-4.320067
-925712.5
Ricardian Analysis - comprehensive
• Considered temperature and precipitation values of four seasons, namely:
•
•
•
•
•
First inter-monsoon (FIM)
Second inter-monsoons (SIM)
South-west monsoons (SWM)
North-east monsoons (NEM), together with quadratic terms
Soil related variables.
• Model used:
NR = f (FIM temp, SWM Temp, SIM Temp, NEM Temp, FIM Prec, SWM Prec,
SIM Prec, NEM Prec, FIM temp Sq, SWM Temp Sq, SIM Temp Sq, NEM Temp
Sq, FIM Prec Sq, SWM Prec Sq, SIM Prec Sq, NEM Prec Sq, flat, steep, clay)
Summary statistics used for Ricardian model estimation
(comprehensive)
Variable
FIM_Temp
SWM_Temp
SIM_Temp
NEM_Temp
FIM_Temp_Sq
SWM_Temp_Sq
SIM_Temp_Sq
Mean
26.17
26.05
24.96
24.156
690.08
683.61
627.30
Std. Dev.
2.23
2.27
2.11
2.18
110.58
113.30
99.66
Min
18.15
17.74
16.9
16.5
329.42
314.71
285.61
NEM_Temp_Sq
FIM_Prec
SWM_Prec
SIM_Prec
NEM_Prec
FIM_Prec_Sq
SWM_Prec_Sq
SIM_Prec_Sq
NEM_Prec_Sq
588.23
17.137
14.99
30.41
16.78
321.98
312.19
953.84
307.68
99.51
5.33
9.37
5.42
5.11
203.19
365.29
353.12
181.39
272.25
7.25
3.02
18.3
7.1
52.53
9.12
334.89
50.41
Max
28.7
29.08
27.05
26.33
823.69
845.65
731.71
693.44
31.25
39.44
43.95
30.8
976.56
1555.51
1931.60
948.64
Variable
Estimate
Std. Error
-12,633***
4,821)
7,970**
3,555
SIM_Temp
-9,721
6,274
NEM_Temp
14,193***
3,759
FIM_Prec
48.56
93.41
SWM_Prec
44.76
38.31
SIM_Prec
-219.0**
87.44
NEM_Prec
41.44
52.27
FIM_Prec_Sq
-3.062
2.747
SWM_Prec_Sq
-0.481
0.749
SIM_Prec_Sq
3.708**
1.663
NEM_Prec_Sq
-0.351
1.358
FIM_Temp_Sq
231.7***
89.04
SWM_Temp_Sq
-144.0**
63.16
SIM_Temp_Sq
179.7
115.6
NEM_Temp_Sq
-276.5***
74..65
flat
-305.5***
114.5
steep
62.27
119.9
clay
106.8
98.79
FIM_Temp
SWM_Temp
Constant
-13,839
Model estimates
• Temperature:
• FIM: Temp linear, Temp quadratic
• SWM (Yala season): Temp linear, Temp quadratic
• NEM (Maha season): Temp linear, Temp quadratic
• Precipitation
• SIM: Rainfall linear, Rainfall quadratic
• Net revenues are at their maximums:
• SWM (Yala season) temperatures are 27.67 Celsius
• NEM (Maha season) temperatures are at 25.67 Celsius
• (There is minimum net revenue at when temperature of the first intermonsoon is 27.26.)
ADAPTATION TO CLIMATE CHANGE
Adaptation
WZ
IM
DZ
Changed planting dates
72%
84%
82%
57%
75%
65%
63%
75%
63%
49%
74%
65%
22%
40%
22%
55%
70%
71%
77%
83%
88%
85%
88%
94%
Change crop types
Use different crop varieties
Made irrigation investment
Following all practices
At least three
At least two
At least one
Determinants of Adaptation measures
(Multivariate probit analysis)
Variable
Change planting
dates
Age of HH head
+
Education HH
+
Change crop
types
+
Selling distance
Household
workers
Different crop
varieties
+
Irrigation
investment
+
(-)
+
Electricity
Notice climate
change
Yala T0
(-)
Land ownership
+
+
+
+
Conclusions and implications
• Climate change (changes in temperature regimes, shifts in rainfall
patterns) continue to affect agricultural productivity in Sri Lanka.
• Predicted climatic variation for Sri Lanka:
• Estimates: Annual average temperature rise 0.01 – 0.03 degree Celsius/ year
• Predictions: GCM Models: by 2080, temperature with increase in the range of
2.5 - 4.5 (C) A2 scenario; 2.5 – 3.25 (C) B2 scenario.
• The Ricardian model estimated could be used estimate the NR
changes due to anticipated climate change.
• Adaptation at present: old farmers, availability of family labour affect
adopting adaptation practices.
Thank you