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

0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00

Indonesia

Philippines

India

Sri Lanka

Thailand

PNG

Vanuatu

Other Countries

% of contribution

Principal coconut growing regions in Sri Lanka

1750

1950

2150

2350

2550

2750

2950

3150

Nat

iona

l nut

pro

duct

ion

(mln

nut

s)Temporal variability of Annual

Coconut Production (ACP)

Baseline mean

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

65%1%

17%

13%4%

Freshnut- Culinary

Freshnut - Export

Desiccated Coconut

Coconut Oil

Other Kernal Products

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)

Correlation between ACP and the quarterly rainfall in principal coconut growing areas - at one year lag

Region

JFM AMJ JAS OND

IL1 0.435*** ns 0.282* ns

IL3 0.391** ns 0.353** ns

WL4 0.334** 0.288* 0.288* ns

WL3 0.411** ns 0.284* ns

WL2 0.359** 0.386** 0.483*** ns

DL3 0.452*** ns ns ns

DL5 0.357** ns ns ns

Change in mean RF during Jan-Mar simulated from HadCM3 under three socio-economic scenarios

SRES – B1 SRES – A2 SRES – A1FI

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 + Tea

Coconut + pasture + cattle Buffalo farming

Coconut + Tea

Coconut + 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_JFMWL4

t-1 + 3*RF_APJWL2t-1

- 4*RF_APJIL3t-1 + 5*RF_JASWL4

t-1

(R2 = .91, p < 0.001; all coefs. are sig.)

0 < 5 < 4 < 1 < 2 < 3 <1

1500

1700

1900

2100

2300

2500

2700

2900

3100

330019

51

1955

1959

1963

1967

1971

1975

1979

1983

1987

1991

1995

1999

Year

An

nu

al y

ield

(m

ln n

uts

)

ACTUAL

PREDICTED

Validation of the model

(% error varies: [-10% to 10%]

(r = 0.83, p< 0.0001)

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

200.0

400.0

600.0

800.0

1000.0

1991

1998

2005

2012

2019

2026

2033

2040

2047

2054

2061

2068

2075

2082

2089

2096

Year

CO

2 (p

pm

)

B1 A2 A1FI

Pattern of projected CO2 concentration

Y = exp ( +t)

= 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

Projected national coconut production (million nuts) based

on two GCM’s combined with three SRES scenarios

2000

4000

6000

8000

10000

Year

Pro

ject

ed A

CP

(m

ln n

uts

)

A1FI A2 B1

20003000400050006000700080009000

YearA1FI A2 B1

(a) CSIRO (b) HADCAM 3

Comparison of projected yield for 1995

GCM SRES Departure in %

CSIRO A1FI 13.8

A2 5.7

B1 -6.9

HADCAM3 A1FI 13.2

A2 5.8

B1 -4.9

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

Demand for local consumption based on population projection

1000

3000

5000

7000

9000

11000

1995 2010 2025 2040 2055 2070 2085 2100Year

Co

co

nu

t n

uts

in

mil

lio

n

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 Vul. indicator 2010 2025

Stake holder

DC industry, oil industry, coir industry etc

climate Jan – Mar RF

In regions

Socio-eco. GDP, nut price

Employment Women, men

Multivariate indicator -----?

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.

•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

WL

WM IU

WU

IM

Tea groping areas

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

1 2 3 4 5

S1

S30

500

1000

1500

2000

2500

Comparison of productivity between potential and drought years in different regions

IU WM IM WL

POTEN.

1991

1992

28%19% 14% 25%

26%

WU

Rainfall (mm) & Productivity (kg/ha/month)

AER Opt.RF (mm) M

WL 350±20 0.29±0.03

WM 417±49 0.36±0.06

IM 227±10 0.81±0.11

WU 223±38 0.55±0.07

IU 303±34 0.39±0.03

Opt.RF=Optimum Rainfall (mm/month)

M=Loss of yield (kg/ha/month/mm-RF)

0

20

40

60

80

100

120

140

160

180

200

0 200 400 600

RF

YP

H

Y= -508+63.7 T – 1.46 T2 (p<0.05)

Temperature & Monthly yield (kg/ha)

y = - 508 + 63.7x -1.46x2

0

50

100

150

200

250

300

350

400

450

10 15 20 25 30 35

Monthly Mean Temperature (oC)

Yie

ld (

kg/h

a/m

on

th)

22

Amarathunga et al,1999

CO2 vs Mean yield (WL)

Treatment Yield (kg/ha/yr)

Control-360ppm 4493 (100)

Enriched-600ppm 6175 (137)

0

20

40

60

80

100

120

1 8 15 22 29 36 43 50 57 64 71

Weeks

Yie

ld (

g/b

ush

/wee

k)

ENRICHED CONTROL

Total

BiomassTea yield-20%

HI

Rainfall

Moisture

Temperature Soil

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)

CO2

Yield (kg/ha/yr) CO2 RF Temp WL WM WU IU(ppm) (%) (oC) 370 0 0 2489 2217 2454 2651370 0 1 2282 2177 2651 2569370 0 2 2070 2117 2760 2469370 -10 0 2456 2161 2418 2591370 10 0 2482 2305 2480 2749435 0 0 2710 2695 3035 3080435 0 1 2502 2567 3235 2998

Yield prediction

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 YOUTHANK YOU

Acknowledgements

Indian Agric. Research Inst., India

IGCI, University of Waikato, NZ