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IMPACT OF AND ADAPTATION TO CLIMATE CHANGE ON COCONUT AND TEA INDUSTRY IN SRI LANKA (AS12) T S G Peiris1, M A Wijeratne2, C S Ranasinghe1 A Aanadacoomaraswamy2, M T N Fernando1, A Jayakody2 and J Ratnasiri3 (1Coconut Research Institute of Sri Lanka, Lunuwila, 2Tea Research Institute of Sri Lanka, Talawekella and 3 SLAAS, Sri Lanka), 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) World Situation for Coconut Mean annual production = 48 billion nuts Coconut extent = 11300 million hectares Productivity =4200 nuts/ha Average contribution on the world production by the major coconut producing countries Other Countries Vanuatu PNG Thailand Sri Lanka India Philippines Indonesia 0.00 5.00 10.00 15.00 20.00 % of contribution 25.00 30.00 35.00 Principal coconut growing regions in Sri Lanka 19 50 19 53 19 56 19 59 19 62 19 65 19 68 19 71 19 74 19 77 19 80 19 83 19 86 19 89 19 92 19 95 19 98 20 01 20 04 National nut production (mln nuts) Temporal variability of Annual Coconut Production (ACP) 3150 2950 2750 2550 2350 2150 Baseline mean 1950 1750 Fresh Nut Consumption Export Desiccated Coconut Local Use Export Nuts Copra Nut Export Local Use Coconut Cream / Coconut milk Powder Export Oil Local Use PATTERN OF UTILIZATION OF COCONUT NUTS 4% 13% Freshnut- Culinary Freshnut - Export Desiccated Coconut 17% 1% 65% Coconut Oil Other Kernal Products Summary of the historical climate data analysis (1932-2003) The periods of classical rainy seasons, particularly North east monsoon (NEM): DecFeb 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 Correlation between ACP and the quarterly rainfall in principal coconut growing areas - at one year lag Region IL1 IL3 WL4 WL3 WL2 DL3 DL5 JFM AMJ JAS OND 0.435*** ns 0.282* ns 0.391** ns 0.353** ns 0.334** 0.288* 0.288* ns 0.411** ns 0.284* ns 0.359** 0.386** 0.483*** ns 0.452*** ns ns ns 0.357** ns ns ns Change in mean RF during Jan-Mar simulated from HadCM3 under three socio-economic scenarios SRES – A1FI SRES – A2 SRES – B1 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. V & A Assessment (off line) V: Problem for mixed farm models Coconut + pasture + cattle Coconut + Tea Buffalo farming CoconutCoconut + Tea + Intercrops Impact on Production: Integrated statistical approach - Peiris et al. (2004) Yield = Climate Effect of the Previous Year + Technology Effect + Noise Effect Integrated model Yt = +exp ( + *t) + 1*RF_JFMWL3t-1 + 2*RF_JFMWL4t-1 + 3*RF_APJWL2t-1 - 4*RF_APJIL3t-1 + 5*RF_JASWL4t-1 (R2 = .91, p < 0.001; all coefs. are sig.) 0 < 5 < 4 < 1 < 2 < 3 <1 (r = 0.83, p< 0.0001) 3300 3100 2900 2700 2500 2300 2100 1900 1700 1500 Year (% error varies: [-10% to 10%] 1999 1995 1991 1987 1983 1979 1975 1971 1967 1963 1959 1955 ACTUAL 1951 Annual yield (mln nuts) Validation of the model PREDICTED Vulnerable climate indicators on Production At national Level: Jan – Mar rainfall ; Apr – Jun rainfall At regional Level : Jan - Mar rainfall; TMAX. and Intensity of rainfall At farm level: Rainfall during Jan – Feb TMAX, RHPM Pattern of projected CO2 concentration CO2 (ppm) 1000.0 800.0 Y = exp ( +t) 600.0 400.0 2096 2089 2082 2075 2068 2061 2054 2047 2040 2033 2026 2019 2012 2005 1998 1991 200.0 Year B1 A2 A1FI = 0.00422 for B1 (R2 =0.94 = AdjR2); = 0.00822 for A2 (R2 =0.99 = AdjR2); = 0.00997 for A1FI (R2 =0.94 = AdjR2) Technology CO2 increase Thus Yield at given SRES scenario = f(Climate effect) + f (CO2 effect at the same SRES scenario) + noise efect A1FI A2 (a) CSIRO B1 A1FI Year A2 00 21 85 20 70 20 55 20 40 20 25 20 00 85 Year 21 70 20 55 20 20 40 20 25 20 10 20 95 2000 10 4000 20 6000 19 8000 95 9000 8000 7000 6000 5000 4000 3000 2000 10000 19 Projected ACP (mln nuts) Projected national coconut production (million nuts) based on two GCM’s combined with three SRES scenarios B1 (b) HADCAM 3 Comparison of projected yield for 1995 GCM CSIRO HADCAM3 SRES Departure in % A1FI 13.8 A2 5.7 B1 -6.9 A1FI 13.2 A2 5.8 B1 -4.9 National impact – Only one aspect The increase in population and future climate change would affect the availability of nuts in future for industrial purposes Impact = Population x input per capita i.e. I (t) = P(t) x A(t) Coconut nuts in million Demand for local consumption based on population projection 11000 9000 7000 5000 3000 1000 1995 2010 2025 2040 2055 2070 2085 2100 Year Lower rate (95 nuts/p/y) Upper liimit (105 nuts/p/y) There will be a shortage of nuts around 2040 under B1 scenario. Analysis of V&A - Multivariate time series approach : Res. Var - Yield Vul. group Stake holder Vul. indicator DC industry, oil industry, coir industry etc climate Jan – Mar RF In regions Socio-eco. GDP, nut price Employment Women, men Multivariate indicator -----? 2010 2025 National level • Sri Lanka will need to import more substitute oil for coconut oil. • This will have adverse socioeconomic 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. LIMITATION OF THE STUDY •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 Tea groping areas WU WM IU WL IM Tea Growing Regions in Sri Lanka Major… U- Up country (>1200m amsl) T:10-27 oC M- Mid country (600-1200m amsl) T:19-30 oC L- Low country (<600m amsl) T:21-34 oC 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 Comparison of productivity between potential and drought years in different regions 2500 2000 1500 1000 500 0 26% 1 WU 28% 19% 14% 2 IU 3 WM 4 IM 25% 5 WL POTEN. S3 1991 S1 1992 Rainfall (mm) & Productivity (kg/ha/month) Opt.RF=Optimum Rainfall (mm/month) M=Loss of yield (kg/ha/month/mm-RF) 200 180 160 YPH 140 120 100 80 AER WL WM Opt.RF (mm) M 350±20 0.29±0.03 417±49 0.36±0.06 IM WU IU 227±10 223±38 303±34 60 40 20 0 0 200 400 RF 600 0.81±0.11 0.55±0.07 0.39±0.03 Temperature & Monthly yield (kg/ha) Yield (kg/ha/month) Y= -508+63.7 T – 1.46 T2 (p<0.05) 450 400 350 300 250 y = - 508 + 63.7x -1.46x 2 200 150 100 50 0 10 15 20 22 25 30 35 Monthly Mean Temperature (o C) Amarathunga et al,1999 CO2 vs Mean yield (WL) Yield (g/bush/week) 120 100 80 60 40 20 0 1 8 15 22 29 36 43 50 57 64 71 Weeks ENRICHED CONTROL Treatment Yield (kg/ha/yr) Control-360ppm 4493 (100) Enriched-600ppm 6175 (137) Development of a crop model Respiration-60% Initial Biomass RUE (0.3) Total LAI (5) Biomass HI Tea yield-20% Retained-20% B Density Temperature CO2 Soil Rainfall Moisture Radiation Use Efficiency : RUE Harvest Index: HI Leaf Area Index: LAI Yield prediction CO2 (ppm) 370 370 370 370 370 435 435 RF (%) 0 0 0 -10 10 0 0 Temp (oC) 0 1 2 0 0 0 1 WL 2489 2282 2070 2456 2482 2710 2502 Yield (kg/ha/yr) WM WU 2217 2177 2117 2161 2305 2695 2567 2454 2651 2760 2418 2480 3035 3235 IU 2651 2569 2469 2591 2749 3080 2998 Crop improvement Drought tolerant cultivars Soil Improvements Soil & soil moisture conservation Irrigation Soil Organic Carbon improvements Crop environment Shade management Intercropping 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. THANK YOU Acknowledgements Indian Agric. Research Inst., India IGCI, University of Waikato, NZ