1. 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

2. Outline Background The issue: climate change in Sri Lanka Objectives
Approach
Results and discussion
Conclusions and implications

3. 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.

4. 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.

5. Impacts of climate change to Agriculture and Food security

7. 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.

8. 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.

9. 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 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

10. Climate zones of Sri Lanka
Wet Zone = > Annual Rainfall 2500
Intermediate Zone = Annual Rainfall
Dry Zone = < Annual Rainfall 1750
Climate zones of Sri Lanka
(Source: Punyawardena, 2007)

11. 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)

12. Objectives and Approaches
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

13. 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).

14. 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

15. Data Data source: Sample size: 321 farmers in 40 agro-ecological zones
UNDP ADAPT ASIA Agriculture Survey
Natural Resource Management Centre
of the Department of Agriculture
Sample size: 321 farmers in 40
agro-ecological zones

16. RESULTS AND DISCUSSION

17. FARMER PROFILE

19. 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 44
238 74
Long term shifts in frequency of flooding
168
115 36
Long term shifts in frequency of pest and disease incidence 38
264 82

20. FARMERS’ AWARENESS – by climatic zone

22. Ricardian analysis (by seasons) Net Revenue Vs. Climatic Variables
Maha season
N= 145 R-sq= Adj R-sq=0.2378 NR
Maha T0
31.860C

23. NR=2683849-160300T+2515T2+1000P-2.06P2 Variable Observation Mean
Std. Dev.
Min
Max
Net Revenue
321
108,214.4
119,086.4
962.75
664,462.5 NR
Maha Precipitation
249.5mm
NR= T+2515T2+1000P-2.06P2

24. Yala season NR=432744+1063.85P-2.39P2 NR 222.39mm Nov precipitation
N= 145 R-sq= Adj R-sq=0.0204 NR
NR= P-2.39P2
Nov precipitation
222.39mm

25. 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)

26. Summary statistics used for Ricardian model estimation (comprehensive)
Variable
Mean
Std. Dev.
Min
Max
FIM_Temp
26.17
2.23
18.15
28.7
SWM_Temp
26.05
2.27
17.74
29.08
SIM_Temp
24.96
2.11
16.9
27.05
NEM_Temp
24.156
2.18
16.5
26.33
FIM_Temp_Sq
690.08
110.58
329.42
823.69
SWM_Temp_Sq
683.61
113.30
314.71
845.65
SIM_Temp_Sq
627.30
99.66
285.61
731.71
NEM_Temp_Sq
588.23
99.51
272.25
693.44
FIM_Prec
17.137
5.33
7.25
31.25
SWM_Prec
14.99
9.37
3.02
39.44
SIM_Prec
30.41
5.42
18.3
43.95
NEM_Prec
16.78
5.11
7.1
30.8
FIM_Prec_Sq
321.98
203.19
52.53
976.56
SWM_Prec_Sq
312.19
365.29
9.12
SIM_Prec_Sq
953.84
353.12
334.89
NEM_Prec_Sq
307.68
181.39
50.41
948.64

27. Variable
Estimate
Std. Error
FIM_Temp
-12,633***
4,821)
SWM_Temp
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
Constant
-13,839
R-squared
0.132

28. Model estimates Temperature: Precipitation
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 Celsius
NEM (Maha season) temperatures are at Celsius
(There is minimum net revenue at when temperature of the first inter- monsoon is )

29. ADAPTATION TO CLIMATE CHANGEWZ IM DZ
Changed planting dates
72%
84%
82%
Change crop types
57%
75%
65%
Use different crop varieties
63%
Made irrigation investment
49%
74%
Following all practices
22%
40%
At least three
55%
70%
71%
At least two
77%
83%
88%
At least one
85%
94%

30. Determinants of Adaptation measures (Multivariate probit analysis)
Variable
Change planting dates
Change crop types
Different crop varieties
Irrigation investment
Age of HH head +
Education HH
Selling distance
(-)
Household workers
Electricity
Notice climate
change
Yala T0
Land ownership

31. 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
(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.

32. Thank you