1. IMPACT OF AND ADAPTATION TO CLIMATE CHANGE ON COCONUT AND TEA INDUSTRY IN SRI LANKA (AS12) T S G Peiris 1, M A Wijeratne 2, C S Ranasinghe 1 A Aanadacoomaraswamy 2, M T N Fernando 1, A Jayakody 2 and J Ratnasiri 3 ( 1 Coconut Research Institute of Sri Lanka, Lunuwila, 2 Tea Research Institute of Sri Lanka, Talawekella and 3 SLAAS, Sri Lanka),

2. Outline Coconut Industry in a nutshell Climate Change in principal coconut growing regions Vulnerability & Adaptation - purely stat. model (preliminary) Tea Industry Climate change and adaptation – stat. /dynamic (preliminary)

3. World Situation for Coconut Mean annual production = 48 billion nuts Coconut extent = 11300 million hectares Productivity =4200 nuts/ha

4. Average contribution on the world production by the major coconut producing countries

5. Principal coconut growing regions in Sri Lanka

6. Temporal variability of Annual Coconut Production (ACP) Baseline mean

8. PATTERN OF UTILIZATION OF COCONUT NUTS

9. The periods of classical rainy seasons, particularly North east monsoon (NEM): Dec- Feb has significantly shifted over the years (p < 0.05). In all regions rainfall during January to March has significantly (p < 0.05) decreased. Tmax, Tmin and Tdif during January to March have significantly (p < 0.05) increased. Rate of increasing of Tmax > Tmin Summary of the historical climate data analysis (1932-2003)

10. Correlation between ACP and the quarterly rainfall in principal coconut growing areas - at one year lag RegionJFMAMJJASOND IL1 0.435***ns0.282*ns IL3 0.391**ns0.353**ns WL4 0.334**0.288* ns WL3 0.411**ns0.284*ns WL2 0.359**0.386**0.483***ns DL3 0.452***ns DL5 0.357**ns

11. Change in mean RF during Jan-Mar simulated from HadCM3 under three socio-economic scenarios SRES – B1 SRES – A2 SRES – A1FI

12. V & A Assessment (off line) V: Increasing rate of both Jan-Mar rainfall and Tmax are higher in wet zone indicating wet regions are more vulnerable to climate change than dry or intermediate regions. DL5 is not suitable for coconut plantation. A: Shifting coconut areas; Growing of shade trees. V: Pest damage on coconut would increase. A: More research on Integrated Pest Management (IPM). Needs to investigate more money. A: use of innovative methods.

13. V & A Assessment (off line) V: Problem for mixed farm models Coconut + Tea Coconut + pasture + cattle Buffalo farming Coconut + Tea Coconut + Intercrops

14. Impact on Production: Integrated statistical approach - Peiris et al. (2004) Yield = Climate Effect of the Previous Year + Technology Effect + Noise Effect

15. Integrated model Y t =  +exp (  +  *t) +  1 *RF_JFM WL3 t-1 +  2 *RF_JFM WL4 t-1 +  3 *RF_APJ WL2 t-1 -  4 *RF_APJ IL3 t-1 +  5 *RF_JAS WL4 t-1 (R 2 =.91, p < 0.001; all coefs. are sig.) 0 <  5 <  4 <  1 <  2 <  3 <1

16. Validation of the model (% error varies: [-10% to 10%] (r = 0.83, p< 0.0001)

17. Vulnerable climate indicators on Production At national Level : Jan – Mar rainfall ; Apr – Jun rainfall At regional Level : Jan - Mar rainfall; T MAX. and Intensity of rainfall At farm level : Rainfall during Jan – Feb TMAX, RHPM

18. Pattern of projected CO2 concentration Y = exp (  +  t)  = 0.00422 for B1 (R 2 =0.94 = AdjR 2 );  = 0.00822 for A2 (R 2 =0.99 = AdjR 2 );  = 0.00997 for A1FI (R 2 =0.94 = AdjR 2 )

19. Technology  CO2 increase Thus Yield at given SRES scenario = f(Climate effect) + f (CO2 effect at the same SRES scenario) + noise efect

20. Projected national coconut production (million nuts) based on two GCM’s combined with three SRES scenarios (a) CSIRO (b) HADCAM 3

21. Comparison of projected yield for 1995 GCMSRESDeparture in % CSIROA1FI13.8 A25.7 B1-6.9 HADCAM3A1FI13.2 A25.8 B1-4.9

22. Impact = Population x input per capita i.e. I (t) = P(t) x A(t) National impact – Only one aspect The increase in population and future climate change would affect the availability of nuts in future for industrial purposes

23. Demand for local consumption based on population projection There will be a shortage of nuts around 2040 under B1 scenario.

24. Analysis of V&A - Multivariate time series approach : Res. Var - Yield Vul. groupVul. indicator20102025 Stake holder DC industry, oil industry, coir industry etc climateJan – Mar RF In regions Socio-eco.GDP, nut price EmploymentWomen, men Multivariate indicator -----?

26. National level Sri Lanka will need to import more substitute oil for coconut oil. This will have adverse socio- economic implications and national economy. Serious attention is required for a strategic policy on importation and probably to enhance cooperation in other coconut growing countries in the region.

27. Not use of multi level model for the analysis of V&A – multivariate time series /Ricardian model Lack of long-term data on most of the varaibles LIMITATION OF THE STUDY

28. WL WM IU WU IM Tea groping areas

29. Tea Growing Regions in Sri Lanka Major… U- Up country (>1200m amsl) T:10-27 o C M- Mid country (600-1200m amsl) T:19-30 o C L- Low country (<600m amsl) T:21-34 o C Agro-Ecological Regions…. Up country wet zone (WU 1-3) RF:1400->3175mm Mid country Wet zone (WM 1-3) RF:1250->3150mm Low country Wet zone (WL 1-2) RF:1900->2525mm Up country Intermediate zone (IU 1-3) RF:1150->2150mm Mid country Intermediate zone (IM 2) RF:1150->1400mm

30. Comparison of productivity between potential and drought years in different regions IU WM IM WL POTEN. 1991 1992 28% 19% 14% 25% 26% WU

31. Rainfall (mm) & Productivity (kg/ha/month) AEROpt.RF (mm)M WL350±200.29±0.03 WM417±490.36±0.06 IM227±100.81±0.11 WU223±380.55±0.07 IU303±340.39±0.03 Opt.RF=Optimum Rainfall (mm/month) M=Loss of yield (kg/ha/month/mm-RF)

32. Y= -508+63.7 T – 1.46 T 2 (p<0.05) Temperature & Monthly yield (kg/ha) 22 Amarathunga et al,1999

33. CO 2 vs Mean yield (WL) TreatmentYield (kg/ha/yr) Control-360ppm4493 (100) Enriched-600ppm6175 (137)

34. Total Biomass Tea yield-20% HI Rainfall Moisture TemperatureSoil Radiation Use Efficiency : RUE Harvest Index: HI Leaf Area Index: LAI B Density Retained-20% Respiration-60% Initial Biomass Development of a crop model RUE (0.3) LAI (5) CO 2

36. Yield (kg/ha/yr) CO2 RF Temp WL WM WU IU (ppm) (%) ( o C) 370 0 0 2489 2217 2454 2651 370 0 1 2282 2177 2651 2569 370 0 2 2070 2117 2760 2469 370 -10 0 2456 2161 2418 2591 370 10 0 2482 2305 2480 2749 435 0 0 2710 2695 3035 3080 435 0 1 2502 2567 3235 2998 Yield prediction

37. Crop improvement Drought tolerant cultivars

38. Soil Improvements Soil & soil moisture conservation Irrigation Soil Organic Carbon improvements

39. Crop environment Shade management Intercropping

40. CONCLUSIONS Expected climate change in Sri Lanka due to global climate change scenarios has significant impact on both coconut and tea industry. The climate change scenarios can help to identify the potential directions to the impacts and potential magnitudes of the overall effects. The magnitudes of changes should be looked with caution due to uncertainties in prediction process of climate. Impact of climate change on coconut production should be studied in other coconut producing countries as well.

41. THANK YOU Acknowledgements Indian Agric. Research Inst., India IGCI, University of Waikato, NZ