Supplementary Material
Drought stress impacts of climate change on rainfed rice in South Asia
Tao Li*, Olivyn Angeles, Ando Radanielson,
Manuel Marcaida III, Emmali Manalo
(International Rice Research Institute, Los Baños, Philippines)
*: Corresponding author
Name: Tao Li, PhD, Scientist on crop modeling
Contact information:
International Rice Research Institute
Los Baños, Philippines
Tel: +63-2-580-5600, ext. 2732
Fax: +63-2-580-5699
Email: t.li@irri.org
Web: www.irri.org
This Supplementary Materials file includes:
Supplementary Text
Supplementary Figures S1 to S4
Supplementary Table S1
References
Supplementary Text
1. The performance of ORYZA2000 for multiple rice genotypes and environments
Table S1 and Figure S2 illustrate the evaluation results on the performance of ORYZA2000 in
estimating rice growth and grain yield. More than 90% of the simulations fell within the
confidence levels of actual measurements. ORYZA2000 simulations on both total above-ground
biomass (TAGB) and panicle biomass (as grain yield, GY) were highly correlated to field
measurements with coefficient of determination (R2) higher than 0.9 while the regression slope
and intercept were close to 1.0 and 0.0 (p<0.0001), respectively. The differences between
simulated and measured TAGB and GY were statistically insignificant (Student T-test Pt>>0.10).
ORYZA2000 efficiently represented the field measurements coming from different soil-climatic
conditions and agronomic practices. It was also reliable to estimate the irrigated and rainfed rice
grain yield for evaluating the impacts of drought stress caused by climate change on rainfed rice.
It is worthy to note that the model has ability to simulate the rice yields within the range of
uncertainties of the observed yield during its calibration and its validation. Variation in
simulation from crop models and climate models was reduced by increasing the number of
climate models and crop models in an ensemble (Challinor et al. 2009, Li et al., 2015). However,
the reduction in uncertainty due to ensemble size could be from several sources which
importance is not certain (Challinor et al. 2009, Smith and Stern 2011). It would be interesting to
see whether the similar trends can be achieved if using an ensemble model of simulation. That
will suggest that the simulation outputs will be generic and less sensitive to the crop model
hypothesis of representation of the rice cropping systems. From an early study, the uncertainties
of ORYZA2000 predictions on rice yield were consistent with other rice crop models and model
ensemble across environments (Li et al., 2015). Considering that this study focused more on the
relative changes of rice yields caused by drought stress associated with climate change,
ORYZA2000 was further confirmed to provide reliable predictions of rice yields for the goals of
the current study.
2. The determination of best sowing dates by ORYZA2000
The best sowing dates were the sowing dates used to achieve the highest attainable grain yield in
given weather and soil conditions for specific crop management and cultivar. In this study,
ORYZA2000 was used to estimate the attainable grain yields under rainfed condition for 24
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sowing dates per year starting on 1 January at 15-day interval. To evaluate the capability of
ORYZA2000 on the determination of the best sowing seasons of rainfed rice, additional
simulations were conducted using historical weather data from 1980 to 2008. The best sowing
dates were determined based on the attainable yields and variations among years (Fig. S3, see the
methods of determination of the best sowing dates in section 2.3.3 in main text). The best sowing
dates determined by ORYZA2000 were consistent with the reported sowing dates for rainfed rice
in eastern India.
3. Drought stress indicators as impact of climate change on rainfed rice
Using ORYZA2000, two sets of data were obtained to represent rice yields under potential
and rainfed conditions. No consistent relationships existed between potential and rainfed yields
across spatial and temporal scales due to varying soil-climatic conditions (Fig. S5).
The examples of randomly selected rice cells in Figure S5 illustrate that the gaps between
potential and rainfed yield varied across years and sowing dates. Considering the complex annual
and inter-annual (i.e. temporal) variations in potential and rainfed rice yield, the standard
difference (SDpa) calculated by Equation 3 (main text) was suitable for a statistical indicator of a
yield gap caused by drought stress across a temporal scale. It effectively represented the degree
of drought stress when different sowing dates were considered in specific rice areas.
However, the SDpa indicating the absolute gaps between potential and rainfed yields were
meaningless in representing the spatial variation on effects of drought stress on rice yield. The
equal gaps might not imply the same drought impacts due to the different potential yield (Fig.
S5). For instance, with the same yield gap, the drought impact is stronger under low potential
yield than under high potential yield. The variability in potential yield was the result of the
different climatic conditions such as radiation and air temperature, and interactions of the crop
with these climatic conditions. Therefore, the ratio of absolute yield gap to potential yield was
more relevant to represent the relative yield reduction caused by drought stress as calculated by
equation 4 (main text). This ratio, named RDI (Eqs. 3 and 4 in main text), not only represented
the effects of drought stress on rice yield across spatial scales but also imbedded the temporal
effects over years (Fig. 1 in main text).
The probability of crop failure over year-round sowing dates was represented by CFF (Fig. 2
in main text). Higher value meant greater risk for rainfed rice in a rice cell. CFF was determined
2
by the frequency of severe drought stress that caused crop failure in available rice-growing
seasons over years (Figs. 2 in main text and S5). For areas with low CFF values, a slight shift of
sowing date would create a chance for higher grain yield (Fig. S5c). CFF in this study referred to
an agronomic crop failure, meaning no grain yield (i.e. grain yield is 0 t/ha), which differed from
commercial crop failure (i.e. the market value of harvested grain could not compensate for the
cost for production). Although the latter is a more realistic indicator, it was difficult to use in this
study because of the varying costs of fertilizer, labor, fuel, pesticides, and other production
inputs.
Unlike RDI and CFF, MRGY and CVGY were two specific indicators of drought impacts in
the best sowing seasons of rainfed rice over years. In term of MRGY, the simulated maximum
rainfed yield was higher than 3 t/ha in more than 70% of the rice areas in South Asia. The
highest yield estimated was 9.4 t/ha under climate change (Figs. 3 in main text and S5). In
comparison with earlier results simulated by CERES-RICE, the experimental and farmer-site
measurements in the early 2000s (Aggarwal et al. 2008; Pathak et al. 2009) and the highest
recorded experimental yield of 7 to 7.2 t/ha under upland conditions (De Datta and Beachell
1972; Kawano et al. 1972; Schmitter et al. 2011), the simulated MRGY was slightly higher but
was consistent with field measurements due to timely and appropriate fertilizer application (Fig.
3 in main text). It could be argued that the values of MRGY did not represent annual rainfed rice
yield because multiple rainfed rice seasons might be available in a rice cell. The examples in
Figure S4 demonstrated that available rainfed rice seasons per year were possibly more than one
in some areas, although they were not the seasons with the most stable and/or highest yield.
Therefore, annual rainfed yield might be higher than what was presented in Figure 3 (main text).
This situation was true for some areas at the southern end and in coastal areas of South Asia.
About 5% of the rice-growing areas could have a total yield from two rainfed rice seasons of
more than 1.5 times that of the one best rainfed season. However, rainfed rice yield in these areas
with multiple seasons, could suffer higher inter-annual variation, implying a higher risk of large
yield loss. In general, the highest yield with the lowest yield variation was the best indicator of
local rice productivity, which could be identified with the indicators MRGY and CVGY.
Among the four indicators, RDI was the most independent of crop characteristics. It was a
relative expression of water-limited production to potential production. RDI was mainly subject
to local climatic-soil conditions. The other three indicators (MRGY, CVGY, and CFF) were
3
dependent on crop characteristics and strongly represent the interactions of the crop and its
environment. Across the temporal scale in a rice cell, the RDI and CFF represented the impacts
of drought stress on year-round rice production while CVGY and MRGY represented the impacts
in the best sowing season of rainfed rice. On a spatial scale, the variations in these four indicators
resulted from the spatial variation of drought stress caused by climate change. Because we used
the same virtual rice cultivar for all years and all rice cells, spatial variations of these indicators
mainly resulted from the variations in soil-climatic environments and also revealed the variations
in interactions of the rice crop and soil-climatic environments under rainfed conditions.
4. Changes of yield and the best sowing season of rainfed rice
In comparing the MRGY in the far to near future climate conditions, the large positive values
of ΔMRGY occurred in most rice areas in South Asia indicated the significant increase of rainfed
rice yield. Climate change could be beneficial to rainfed rice production in South Asia if the
rainfed rice season would be adjusted accordingly (Figs. 3 and 4 in main text, and S4), implying
that CO2 fertilization effects overcome the reduction caused by the increase in air temperature
(Baker et al. 1992; Matsui et al. 1997; Ainsworth and Ort 2010). This result was inconsistent
with early evaluations as that climate change may result in slight (~6%, Soora et al., 2013) or
significant (15-17%, Aggarwal and Mall, 2002) decreases of rainfed rice in India, or 'no effect
trends’ in rice yield (Knox et al., 2012).
The positive effect of climate change on rainfed rice, hypothetically, applied when the best
sowing season of rainfed rice was utilized, which might change significantly in most areas in
South Asia (Fig. 4). The assessment in this study was based on analysis of modeling results. The
current farming practices consistent in the study area, and the suggested change of sowing season
might be different with the current sowing seasons of local farmers. In addition, assessment
results would not be the same if varieties with different drought tolerant traits or crop growth
duration were applied in different regions (Li et al. 2013). This study would like to demonstrate
that the adjustment on the sowing seasons of rainfed rice would be essential as the progress of
climate change. Reviews in climate change effect in South Asia also recommended that the shift
in planting dates would be the adaptive strategy at individual levels, and it would be more
helpful by accompaning with adoption of improved varieties and improved rice cultivation
technologies (Knox et al., 2012). Simulations study applied for Soya crop has also suggested that
4
shift in planting date would provide adaptation but could be limited by low temperature as
observed for the Gangetic plain (Mall et al., 2004). The variability of the best sowing season
(BSS) would be mainly attributed to the interaction among environmental factors.
Supplementary Figures:
b
a
c
900
1000
800
700
600
500
400
300
200
0
A1B
A2
1100
35
30
25
20
15
10
5
0
100
(%)
R10
Rain (mm)
d
e
f
40
20
0
-20
A1B
A2
60
70
60
50
40
30
20
10
0
-40
(%)
CR1020
Rain change (%)
Figure S1. Rainfall pattern of rice cultivated area in South Asia (colored cells) under SRES A1B
(a and d) and A2 (b and c). Each grid cell represents the 5×5 geographic arc-minute ground area.
Panels a and b present the average seasonal rainfall in the best rainfed rice season under these
two SRES scenarios in the near future (2015- 2044), while panels d and e show the relative
changes in seasonal rainfall in the best rainfed rice season from the near to far future (20452074). Panel c is the histogram distribution of seasonal rainfall and panel f is the histogram
distribution of the relative changes in rainfall for both SRES scenarios.
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Simulated Values
Simulated Values
22
20
18
16
14
12
10
8
6
4
2
0
a
0 2 4 6 8 10 12 14 16 18 20 22
Measured Values
12
11
10
9
8
7
6
5
4
3
2
1
0
b
0 1 2 3 4 5 6 7 8 9 10 11 12
Measured Values
Figure S2. The performance of ORYZA2000 in estimating total above-ground biomass and
grain yield (t/ha) using validation datasets from field experiments conducted at 32 locations in
Asia for a total of 91 rice genotypes (Castañeda et al. 2002, Sudhir-Yadav et al. 2011, and Li et
al. 2013). Stars on the map represent the locations of field experiments used in the analysis. The
graphs show the measured and simulated data pairs for total above-ground biomass (a) and grain
yield (b). Solid lines are the 1:1 relationship while the broken lines mark the range defined by the
coefficient of variation of measurements.
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Figure S3. The variation of simulated attainable (or maximum) rainfed grain yield (AGY) at
different sowing dates and the best sowing dates determined by ORYZA2000 using historical
weather data for the period from 1980 to 2008. The thick red arrows are the sowing dates
reported in literatures, the black circles indicate the regions considered, and the numbers in red
refers to references for this study (1. Mahmood et al., 2004; 2. Mandal and Shenoi, 1986; 3.
Misra et al., 1986).
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Figure S4: Spatial pattern of the coefficient of variation of maximum rainfed rice grain yield
(CVGY) in near future (2015-2044) and the change in CVGY (blue triangles) from the near to far
future (2045-2074) in SRES scenarios A1B (a) and A2 (b). Panel c is a histogram distribution of
the difference in CVGY between two A1B and A2 during the period from 2015 to 2074.
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Figure S5. Simulated grain yield (t/ha) under potential (blue) and water-limited (maximum
rainfed yield, red) conditions using 24 different sowing dates over a 10-year period. The code of
the sowing period represents the 24 sowing dates starting from 1 January at 15-day intervals.
Supplementary Table:
Table S1. Validation results of ORYZ2000 performance in simulating total above-ground
biomass (TAGB; t/ha) and grain yield (as panicle biomass, GY; t/ha). Data pairs of measured
and simulated values were composed from field experiments conducted in 32 locations of Asia
for a total of 91 rice genotypes.
Item
TAGB
GY
Data
pairs
639
315
Measurement
Simulation
Mean
5.55
3.16
Mean
5.74
3.08
sd
4.70
2.46
sd
4.85
2.54
Regression
b
0.99
0.99
a
0.22
-0.04
R2
0.93
0.91
Pt
RMSE
RMSEn
ME
0.49
0.71
1.327
0.762
0.239
0.241
0.92
0.90
Note: sd is standard deviation (t/ha); a, b, and R2are the intercept, slope, and coefficient of
determination; Pt is the probability of student T-test to accept that the simulation is the same as
the measurement; RMSE (t/ha) is the root mean square error between simulated and measured
values, while RMSEn is RMSE normalized by measured mean; and ME is the model efficiency in
representing the measurements.
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