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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