Download Climate Change Impact on Rice Based Farming Systems in

Climate Change Impact on Rice Based Farming Systems
in Sri Lanka and Adaptation Strategies
S.P. Nissanka, A.S. Karunaratne, W.M.W. Weerakoon, R.M. Herath, B.V.R. Punyawardena, P. Delpitiya, L. Zubair and J. Gunaratna
2. Rice based farming systems
1. Overview
The climate of Sri Lanka has been
changing and extreme weather events
have been in the rise. Rice is the
principal food crop and contributes
1.8% to the Sri Lanka GDP (2012).
Data on rice cultivation and
climate is available in the
Department of Agriculture.
Socio
Economic
and
cultivation information of 104
rice farmer families from
three rice based eco systems
(irrigated rice farming).
Rice is grown in wet, intermediate and
dry agro-climatic zones, where crops
are exposed to different degree of
climatic and soil variability.
The AgMIP - Sri Lanka project
investigated the climate change impact
on rice based farming systems and
adaptation strategies to ensure food
security under changing climate in
future.
Two growing seasons are;
(1) major-season (Maha:
Oct-Feb) and (2) minorseason (Yala: Apr-Sept). The
selected
famers
are
cultivating rice after rice as
the major cultivating crop.
Major rice growing regions in Sri Lanka, selected
Kurunegala district for the AgMIP homestretch, Farmer
communities of Rajanganaya, Nikaweratiya and Batalagoda
used for the study.
Farming system characterized with cultivation of rice in both seasons in
Major rice growing regions are located in the intermediate (RF: 1750-2250 mm/yr) and dry
(<1750mm/yr) agro-climatic regions of the country. Selected region of Kurunegala for the
study, is a representative of major rice growing agro-ecosystems. National Rice Research
and Development Institute (RRDI) of the Department of Agriculture is also located in the
same region.
highland and livestock (small scale) .
3. Climate Analysis
4. Model Calibration and Evaluation
130
34
Simulated days
32
0
Temperature ( C)
7
6
R
O
4
K
IE
3
30
28
120
Bg 300 CRVT
120
Bg 300 CRVT
110
Bg 357 CRVT
110
Bg 357 CRVT
100
Bg 358 CRVT
Detail Experiment
90
80
70
26
40
70
80
90
100
110
120
Average RF for historical vs average of 5 GCMs
20
Rainfall (mm/day)
15
10
5
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Simulated Yield (kg/ha)
RMSE - Major season: 1200 kg/ha
RMSE - Minor season: 1300 kg/ha
5000
4000
Bg 300
30
140
32
80
15
70
Clay %
16
21
25
37
12
26
Silt %
14
14
23
21
20
19
Stone %
71
65
52
43
68
55
Organic
carbon %
1.3
0.9
1.4
0.8
1.5
0.55
pH (H2O)
6.3
6.4
5.9
7.4
6.4
6.2
2000
1000
0
2000
3000
4000
5000
6000
7000
8000
Observed Yield(kg/ha)
0.5
0.4
0.3
0.2
0.8
8 days
7 days
Anthesis
4 days
5 days
Maturity
4 days
9 days
Anthesis
5 days
4 days
Maturity
7 days
5 days
DSSAT Genetic coefficients for calibrated varieties
P1
P2O
P2R
P5
G1
G2
G3
G4
Bg 300
407.9
134.7
396.2
12.6
50.7
.0260
0.95
1.10
22
24
14
16
Bg 357
382.0
122.5
393.8
11.5
55.5
.0250
0.95
1.23
1.42
1.48
1.42
1.42
1.4
1.5
Bg 358
435.6
72.5
500.1
10.7
50.3
.0190
0.69
1.10
Saturation
0.125
0.144
0.157
0.248
0.139
0.183
Bg 308
400.8
50.3
400.7
12.0
68.5
.0190
1.00
1.20
MPI-ESM-MR, changing climate
28
7.43
28.91
21.48
20.48
29.53
MIROC5, changing climate
31
7.31
24.75
17.43
16.87
29.27
HadGEM2-ES, changing climate
30
10.10
28.79
18.69
20.58
30.04
GFDL-ESM2M, changing
climate
31
7.94
25.51
17.57
17.76
29.30
CCSM4, changing climate
32
8.83
27.03
18.20
18.79
29.39
28
7.43
28.91
21.48
20.48
32.73
31
7.31
24.75
17.43
16.87
32.57
30
10.10
28.79
18.69
20.58
33.05
31
7.94
25.51
17.57
17.76
32.59
32
8.83
27.03
18.20
18.79
32.64
MPI-ESM-MR
200
0
25
50
75
100
0
25
50
75
Poverty rate with
Climate Change
HadGEM2-ES
50
75
200
0
0
100
25
50
75
-200
100
0
25
50
75
100
-200
-400
-400
CCSM4
400
0
25
-200
Percentage Gainers
GFDL-ESM2M
400
200
0
-400
-400
Percentage Gainers
Percentage Gainers
Percentage Gainers
Distribution of Relative Yields
MPI-ESM-MR
MIROC5
0
1000
2000
3000
4000
5000
6000
7000
8000
20
25
20
30
15
15
20
10
10
10
05
05
CCSM4
40
30
15
20
10
0.7
Yield (kg/ha)
GFDL-ESM2M
HadGEM2-ES
20
40
0.2
Yield (kg/ha)
Current poverty
rate
0
100
-200
Percentage Gainers
0.3
8000
Net Losses as a % of
mean net farm return
200
0
0
-200
0.4
0
Losses as a % of
mean net farm return
400
200
-400
APSIM
MIROC5
400
0.5
0
7000
Maturity
15
0.6
0.1
6000
DSSAT
0.7
0.1
5000
Observed
0.9
Probability Exceedance
Probability Exceedance
APSIM
4 days
% of Gainers
Frequency
Observed
0.6
Bg 358
5 days
Gain/Losses Diagrams
1
0.7
Bg 357
APSIM
Anthesis
Climate/Rap Scenario
Losses ($/farm)
1
0.8
RMSE
DSSAT
11
400
Minor season 2013 for 104 Framers in Kurunegala
Major season 2012/2013 for 104 Framers in Kurunegala
DSSAT
130
Gains as a % of mean
net farm return
DSSAT and APSIM simulations for rice
DSSAT and APSIM simulations for rice
0.9
120
BD
(g/cm3)
MPI-ESM-MR, changing climate
& RAP
MIROC5, changing climate and
RAP
HadGEM2-ES, changing climate
and RAP
GFDL-ESM2M, changing
climate and RAP
CCSM4, changing climate and
RAP
3000
1000
110
TOA-MD Results
Major season
0
100
6. Economic Analysis
Minor season
6000
90
Phenology
Ranorawa Series
Depth cm)
CEC
(cmol/kg)
-5
80
Statistics of model calibration
Horizon Properties
8000
7000
70
Cultivar
Balalla,
Wariyapola,Maho
Series
Batalagoda Series
DSSAT Simulations for rice in Kurunegala 2012/2013
4000
60
Observed days
DSSAT Evaluation for rice 104 farmers in Kurunegala
3000
50
Soil Series
5. Multi-Model Comparison
2000
40
130
Observed days
c
v
60
De
No
p
ct
O
g
Se
l
Au
Ju
n
Ju
r
M
ay
50
Soil properties of different farmer locations
Daily meteorological data (Tmax,
Tmin, & RF) for the historical period
and base years (2012/13) for the
three locations obtained from the
Natural
Resource
Management
Centre of the Department of
Agriculture. Using RCP-8.5 scenario,
downscaling was done for 5 GCMs
(E, I, K, O, and R). SRAD was
generated using AgMERRA. Yield
simulation was done for 5 GCMs for
the mid century.
1000
70
50
Mean Temperature (oC)
0
80
60
24
32
Ap
31
b
30
M
ar
29
n
28
Fe
27
Detail Experiment
90
50
40
Ja
26
Bg 358 CRVT
100
60
40
2
APSIM Calibration for rice cultivars
RRDI Experiments and CRVT at RRDI and FCRDI
130
Simulated days
8
Rainfall (mm/day)
DSSAT Calibration for rice cultivars
RRDI Experiments and CRVT at RRDI and FCRDI
Average T for historical vs average of 5 GCMs
Historical vs 5 GCMs for mid century
5
Famer income other than rice
comes from home-garden,
chena, animal husbandry
and non-farm activities.
lowland (irrigated paddy fields), other seasonal and perennial crops in
0.8
0.9
1.0
1.1
0.7
0.8
0.9
1.0
1.1
1.2
10
05
0.6 0.7 0.8 0.9 1.0 1.1 1.2
0.8
0.9
1.0
1.1
0.7
0.8
0.9
1.0
1.1
1.2
Relative Yield
7. Climate Change Impact
8. Conclusions
Rice yield simulation for mid century without adaptation for two growing seasons
The base year RMSE for both seasons range around 1200-1300 kg/ha for observed
(major-season 4289 kg/ha; minor-season 3883 kg/ha) vs simulated using DSSAT (majorseason 4888 kg/ha; minor-season 4410kg/ha).
Compared to DSSAT simulated yield for historical period, a yield reduction of 14%, 12%,
22%, 12%, 17% for the major-season and 31%, 30%, 42%, 28%, 35% for minor-season for
five GCMs of CCS4, GFDL, HaD, MIROC, MPI, was observed.
Among the adaptation strategies explored (adjusting planting window and short-duration
variety), use of short-duration variety (Bg300) recovered yield losses significantly especially
in the minor-season where rainfall is relatively less and warmer. Delay in planting also
showed lower yield reduction.
Rice yield simulation for mid century with adaptation for two growing seasons:
Substitute with Bg300 with the respective farmer field managements intact
TOA-MD analyses revealed that the percentage looser is 68-72 and poverty level increase
from 17% to 33% due to climate change. With the adaptation strategy of using Bg 300
(short duration), overall losses could be reduced.
Overal results clearly suggest that the rice sector of Sri Lanka will be affected due
predicted climate change and the government of Sri Lanka needs to explore all possible
adaptation and mitigation measures to safeguard rice production to ensure food security in
the future. Finding of the AgMIP-Sri Lanka project will be used to aware the stakeholder
institutes and policy makers in the agriculture sector about the seriousness of the climate
change impacts on rice production.
AgMIP - Sri
Lanka
project team greatly
acknowledge
the continuous financial and
technical supports, and leadership and guidance
provided by the AgMIP Leadership, Resource
Personnel and Administrative Staff.
1
2
Partner organizations of: 1. University of Peradeniya
3
4
5
2. Foundation for Environment Climate Technology
Development Institute [FCRDI] & Rice Research and Development Institute [RRDI]) 4. Sugarcane Research Institute
of Sri Lanka 7. Rajarata University of Sri Lanka
6
7
3. Department of Agriculture (Field Crop Research &
5. University of Ruhuna 6. Sabaragamuwa University
All
Stakeholder
Institutes
and
Farmer
Communities for providing valuable suggestions
and information for RAPs, and very generous
support of all Partner Organizations of the project
and the hard work of Research Assistants are
greatly appreciated.