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

Climate zones of Sri Lanka 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 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: 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

RESULTS AND DISCUSSION

FARMER PROFILE

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

FARMERS’ AWARENESS – by climatic zone

Ricardian analysis (by seasons) Net Revenue Vs. Climatic Variables Maha season N= 145 R-sq=0.2696 Adj R-sq=0.2378 NR Maha T0 31.860C

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=2683849-160300T+2515T2+1000P-2.06P2

Yala season NR=432744+1063.85P-2.39P2 NR 222.39mm Nov precipitation N= 145 R-sq=0.0752 Adj R-sq=0.0204 NR NR=432744+1063.85P-2.39P2 Nov precipitation 222.39mm

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 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 1555.51 SIM_Prec_Sq 953.84 353.12 334.89 1931.60 NEM_Prec_Sq 307.68 181.39 50.41 948.64

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

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 27.67 Celsius NEM (Maha season) temperatures are at 25.67 Celsius (There is minimum net revenue at when temperature of the first inter- monsoon is 27.26.)

ADAPTATION TO CLIMATE CHANGE WZ 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%

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

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.

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