Download Simulating the Impact of Climate Change on Rice

SEPTEMBER 2013
DEVKOTA ET AL.
2033
Simulating the Impact of Climate Change on Rice Phenology and Grain
Yield in Irrigated Drylands of Central Asia
K. P. DEVKOTA,*,1 A. M. MANSCHADI,# M. DEVKOTA,* J. P. A. LAMERS,* E. RUZIBAEV,@
O. EGAMBERDIEV,@ E. AMIRI,& AND P. L. G. VLEK*
* Center for Development Research (ZEF), University of Bonn, Bonn, Germany
Department of Crop Sciences, University of Natural Resources and Life Sciences Vienna, Tulln, Austria
@
‘‘Khorezm Project,’’ Center for Development Research (ZEF), University of Bonn/UNESCO, Urgench, Uzbekistan
&
Islamic Azad University of Lahijan, Lahijan, Iran
#
(Manuscript received 13 July 2012, in final form 7 April 2013)
ABSTRACT
Rice is the second major food crop in central Asia. Climate change may greatly affect the rice
production in the region. This study quantifies the effects of projected increases in temperature and
atmospheric CO2 concentration on the phenological development and grain yield of rice using the
‘‘ORYZA2000’’ simulation model. The model was parameterized and validated on the basis of datasets
from three field experiments with three widely cultivated rice varieties under various seeding dates in the
2008–09 growing seasons in the Khorezm region of Uzbekistan. The selected rice varieties represent
short-duration (SD), medium-duration (MD), and long-duration (LD) maturity types. The model was
linked with historical climate data (1970–99) and temperatures and CO2 concentrations projected by the
Intergovernmental Panel on Climate Change for the B1 and A1F1 scenarios for the period 2040–69 to
explore rice growth and yield formation at eight emergence dates from early May to mid-July. Simulation
results with historical daily weather data reveal a close relationship between seeding date and rice grain
yield. Optimal emergence dates were 25 June for SD, 5 June for MD, and 26 May for LD varieties. Under
both climate change scenarios, the seeding dates could be delayed by 10 days. Increased temperature and
CO2 concentration resulted in higher rice grain yields. However, seeding rice before and after the optimal
seeding dates reduced crop yield and yield stability significantly because of spikelet sterility induced by
both high and low temperatures. As the grain yield of SD varieties could be adversely affected by climate
change, rice breeding programs for central Asia should focus on developing appropriate heat-tolerant
MD and LD varieties.
1. Introduction
Climate change has become an important global issue.
Predictions for central Asia show that by the end of the
twenty-first century temperatures are likely to increase
by 38–48C, and the atmospheric CO2 concentration will
increase from the current 380 ppm to 485–1000 ppm.
Under such scenarios, crop yields are likely to decrease
by as much as 30% in the region even when the direct
1
Current affiliation: South Asia Regional Office, International
Maize and Wheat Improvement Center (CIMMYT), Kathmandu,
Nepal.
Corresponding author address: K. P. Devkota, International
Maize and Wheat Improvement Center (CIMMYT), P.O. Box
5168, Singh Durbar Plaza, Marga, Kathmandu, Nepal.
E-mail: [email protected]
DOI: 10.1175/JAMC-D-12-0182.1
Ó 2013 American Meteorological Society
positive physiological effects of increased CO2 are accounted for (Parry et al. 2007).
Rice growth, development, and yield formation are
very sensitive to temperature. Currently, most of the rice
production occurs in regions where temperatures are already above the optimum for crop growth (daytime
maximum 288C and nighttime minimum 228C) (Krishnan
et al. 2011). It is estimated that each 18C increase in the
daytime maximum–nighttime minimum temperatures
within the 288–218 to 348–278C range can decrease rice
yields by about 7%–8% (Baker et al. 1992).
In central Asia, rice is the second major food crop
and is grown on an area of 0.18 million ha (FAOSTAT
2010) in irrigated lowlands in the Amu Darya and Syr
Darya river basins. A number of studies have already
been conducted to examine the effect of increased
temperature and CO2 concentration on East and South
Asian rice cultivars (Wassmann et al. 2009; Krishnan
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JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY
et al. 2011), while far less attention has been devoted to
rice cultivars grown in central Asia. Rice is a C3 plant
and generally responds favorably to CO2 enrichment.
However, several studies have shown that high air
temperatures can reduce grain yield even under CO2
enrichment. Each 18C increase in the minimum temperature during the growing season could decrease
yields by 10%, whereas the effect of an increase in the
maximum temperature on crop yield is insignificant
(Peng et al. 2004); rice yield could decrease by (2%–6%)
8C21 with an average mean daily temperature of 268C
(Baker and Allen 1993; Matthews et al. 1995; Sheehy
et al. 2006). However, Krishnan et al. (2007) reported
that every 18C increase in temperature decreases rice
yield by 7.2% at the current CO2 concentration (380 ppm),
but increases in CO2 enrichment up to 700 ppm will lead
to an average yield increase of about 31% in India.
Similarly, Baker (2004) reported a 46%–71% increase in
rice yield at an ambient temperature of 288C with CO2
enrichment in U.S. cultivars. Furthermore, modeling
studies from Bangladesh (Karim et al. 1994), Japan
(Horie et al. 2000), China (Bachelet et al. 1995), and India
(Mall and Aggarwal 2002) reported country-specific variations in future rice production due to climate change;
the greatest decline in crop yields will likely occur between the latitudes 108 and 358N (Penning de Vries 1993;
Krishnan et al. 2011). CO2 enrichment is likely to increase
the photosynthetic rate, and thus biomass production,
which in turn may positively affect assimilated allocation
to reproductive organs (Wassmann et al. 2009). However,
the yield decline under increased temperature conditions
is the result of spikelet sterility due to the negative effect
on pollination processes (Krishnan et al. 2011).
The climate in central Asia is continental and arid
(Kottek et al. 2006), with short, hot summers and long,
cold, dry winters. Under such climatic conditions, rice
cultivation is only possible for around 140 days during
the period May–October (Christmann et al. 2009). The
seeding time of rice, therefore, is very crucial, as the
flowering period with early seeding may coincide with
peak maximum temperatures, while late seeding may
result in low-temperature stress during grain filling
(Devkota 2011). Given the sensitivity of rice to temperature, optimizing the seeding date and using adapted
varieties will be of central importance for enhancing
yields under climate change scenarios (Matthews et al.
1997; Blanche and Linscombe 2009). Simulation studies
on various seeding dates with different growth-duration
rice varieties can contribute to identifying the optimal
seeding date and appropriate rice variety for specific
geographical regions under both current and predicted
climate change scenarios (Krishnan et al. 2011). This can
indirectly overcome the predicted adverse effects of
VOLUME 52
climate change on rice production in a particular region
and may contribute to the development of suitable
adaptation strategies through agronomic and plant
breeding practices (Matthews et al. 1995).
The rice simulation model ORYZA2000, version 2.13,
is capable of simulating phenology, growth, spikelet sterility, and grain yield of indica and japonica rice ecotypes in response to temperature, CO2, solar radiation,
and cultivar-specific genetic characteristics (Matthews
et al. 1997; Jing et al. 2007; Krishnan et al. 2007; Shen et al.
2011; Zhang and Tao 2013). It can simulate the response
of rice phenology to climate change and variability in
different climatic zones equally well or better than other
rice phenology models such as the Crop Estimation
through Resource and Environment Synthesis (CERES)
rice model, regional climate model (RCM), Beta model,
and Simulation Model for Rice–Weather Relationships
(SIMRIW) (Zhang and Tao 2013). The objective of
this study was to explore the potential effect of climate
change on rice phenology and grain yield in central Asia
by (i) parameterizing and validating the rice growth
model ORYZA2000 for local rice varieties and (ii) assessing the impact of climate change as projected by the
Intergovernmental Panel on Climate Change (IPCC)
Special Report on Emissions Scenarios (SRES) under
lowest future emission trajectory (SRES B1) and highest
future emission trajectory (SRES A1F1) (Parry et al.
2007) at different emergence dates.
2. Materials and methods
a. Study area
The field experiments were conducted in the 2008 and
2009 rice-growing seasons in the Urgench–Khorezm
region (418320 1200 N, 608400 4400 E) located in northwestern Uzbekistan on the left bank of the Amu Darya
River. The climate of the area is arid with a long-term
average annual rainfall of less than 100 mm. The soil at
the experimental site is an irrigated alluvial meadow
(Russian classification), that is, arenosol, gleyic, calcaric,
sodic [Food and Agriculture Organization (FAO) classification], sandy loam to loamy sand with high soil
salinity [2.7 dS m21, electrical conductivity of a saturated soil extract (ECe) 1:1 in 0–15-cm soil depth],
shallow (0.5–2 m) and saline (2–4 dS m21) groundwater
table, and low soil organic matter (0.4%–0.8%) (Table 1).
b. Field experiments
1) EXPERIMENTAL DESIGN AND TREATMENTS
Three field experiments (one in 2008 and two in 2009)
were conducted to evaluate phenology and growth of
a set of widely cultivated Uzbek rice varieties seeded
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DEVKOTA ET AL.
TABLE 1. Initial physical and chemical soil properties at the experimental site in Khorezm region, Uzbekistan.
Clay
Bulk
density (g cm23)
Soil
pH
ECe
(dS m21)
Soil organic
carbon (%)
Total
N (%)
Available
phosphorus
(mg kg21)
Exchangeable
potassium
(mg kg21)
19
18
12
8
8
1.35
1.41
1.42
1.52
1.57
5.57
5.56
5.57
5.69
5.78
2.7
2.1
2.0
1.8
3.0
0.36
0.30
0.26
0.23
0.19
0.05
0.05
0.04
0.03
0.03
27.9
25.9
21.9
19.2
17.6
98.5
95.0
89.3
81.4
76.8
Texture (%)
Depth
(cm)
Sand
Silt
0–10
10–20
20–30
30–60
60–90
23
33
26
29
49
58
49
62
63
43
at different dates. Rice varieties of short duration (SD;
Shoternboy-1, 85 days), medium duration (MD; Allanga-3,
105 days), and long duration (LD; Mustakillik, 125 days)
were evaluated. The first experiment (experiment I)
conducted in 2008 included evaluation of these varieties
in a randomized complete block design with eight replications in a 600-m2 plot at the Cotton Research Institute,
Urgench, Uzbekistan. Rice was seeded on 16 June in this
experiment, and the final yield and yield attributes were
recorded in a 7.5-m2 area in each plot. At a similar site,
a second experiment (experiment II) was conducted in
2009 to evaluate the three rice varieties seeded on 28 May
and 19 June in an unreplicated 30-m2 plot. For each variety, final yield and biomass were measured on three
subplots of 6 m2. In the same year (2009), another unreplicated experiment (experiment III) was conducted at
Urgench State University, Urgench, Uzbekistan (5 km
from the Cotton Research Institute), to evaluate the effect of six seeding dates starting on 5 May–15 July at 15–
20-day intervals. The plot size in this experiment was
20 m2, and final yield and biomass were measured in a
6-m2 plot for all seeding dates.
2) CROP ESTABLISHMENT AND MANAGEMENT
Field preparation, sowing, seed rate, and irrigation
water management were managed according to the
recommended practices in the region. In all experiments, the field was dry ploughed 3–4 times, leveled, and
irrigation water was applied. Pregerminated rice seeds
were then uniformly directly broadcast into the standing
water using the recommended seed rate of 80 kg ha21.
From seeding to 10 days of emergence, 1–2 cm of
standing water was maintained in the fields. After 10
days of emergence, similar to the farmers’ practice in the
region, 5–15 cm irrigation water was applied after the
disappearance of the standing water. In all experiments,
a fertilizer dose of 250 kg ha21 nitrogen (N), 120 kg ha21
phosphorus (P2O5), and 80 kg ha21 potash (K2O) was
applied. Phosphorus and K2O fertilizers and 50% of the
N were applied as a basal application during field
preparation. The remaining N was top-dressed in two
equal splits, that is, at panicle initiation and flowering.
The experimental fields were kept weed-free during the
entire crop-growing period through the combined use of
the postemergence herbicide Gulliver (Azimsulfuron
50 WG) at 25 g ha21 and hand weeding (performed on
two occasions). There was no visible nutrient or water
stress, and the crop was kept free of insect, pest, and
disease infestation; thus, the experiment was considered
as a potential production system (Bouman et al. 2001).
3) MEASUREMENTS
The phenological development of the rice was recorded in all experiments through visual observation
using the standard evaluation system for rice (IRRI
2002). For each seeding date of experiments II and III,
plant samples were collected from a 50 cm 3 50 cm area
at 15–20-day intervals for determination of biomass production and leaf area. The leaf area of green leaves was
measured with a leaf area meter (Li-Cor, Inc., LI-3100;
cm2) and converted to leaf area index (LAI; m2 m22).
Stem, green leaves, dead leaves, and panicles were separated and oven-dried separately for 72 h at 658C until
constant weight.
c. ORYZA2000 model
1) MODEL DESCRIPTION
ORYZA2000 simulates rice growth, development, and
water balance under potential production, water-limited,
and N-limited conditions (Bouman et al. 2001). The
model calculates the daily rate of biomass production
as a function of solar radiation, LAI, temperature, leaf N
content, and atmospheric CO2 concentration. The phenological development is simulated based on daily ambient temperature and photoperiod. The key development
stages (DVSs) for rice are emergence, panicle initiation,
flowering, and physiological maturity. Consequently, the
life cycle of rice is divided into four phenological phases:
(i) juvenile phase from emergence (DVS 5 0) to start of
photoperiod-sensitive phase (DVS 5 0.4), (ii) photoperiodsensitive phase from DVS 5 0.4 until panicle initiation
(DVS 5 0.65), (iii) panicle formation phase from panicle
initiation to 50% flowering (DVS 5 1.0), and (iv) grainfilling phase from flowering to physiological maturity
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(DVS 5 2.0). The duration of each of these four phases
is calculated based on a cultivar-specific development
rate constant, daily increment in heat units expressed in
degree-days, and photoperiod (Table 2).
The ORYZA2000 model also accounts for the effect
of CO2 enrichment by introducing a corrected coefficient
to the initial light-use efficiency of a single leaf [«;
kgCO2 ha21 h21 (J m22 s21)21]. The calculation of this
value uses the formula by Jansen (1990):
Rice spikelets are also sensitive to high temperature,
particularly at anthesis. Damage to the pollen occurs
when the temperature at flowering is above approximately
358C (Satake and Yoshida 1978). In ORYZA2000, the
fraction of fertile spikelets caused by high temperatures
(Sh, SF2) is calculated as (Horie 1993):
1 2 exp(20:003 05 3 CCO2 2 0:222)
, (1)
1 2 exp(20:003 05 3 340 2 0:222)
where Tm,a is average daily maximum temperature over
the growing period (0.96 # DVS # 1.22) with elevated
and ambient CO2 concentrations.
« 5 «340
where « is the value of CO2 effect, «340 represents the
reference effect value of CO2 with the concentration of
340 ppm (defined as 1), and CCO2 is the CO2 concentration in the actual simulation environment.
Effect of temperature on grain formation and spikelet
fertility.
Rice grain yield is determined by carbohydrate production (source size) during grain filling and the storage capacity of grains (sink size). Sink size is a function
of the number and maximum growth rate of spikelets.
The number of spikelets at flowering is calculated
from the total biomass accumulated from panicle
initiation until first flowering (Kropff et al. 1994). In
ORYZA2000, the rate of grain growth from panicle
initiation to 50% flowering is tracked, and the number
of spikelets formed (Si; number of spikelets per hectare
per day) is calculated as the product of biomass accumulation from panicle initiation to 50% flowering
(G; kg dry matter ha21 day21) and spikelet formation
factor (Y; number per kilogram). The spikelet formation factor Y is the slope of the relationship between
the effect of solar radiation, temperature, nitrogen,
competition, and water on spikelet formation. Spikelets
turn into grains during crop growth. However, some
spikelets can become sterile because of either too high or
too low temperatures and do not fill (Horie et al. 1992).
Sterility caused by cold temperatures is based on the
cooling degree-day (SQt) and is calculated as follows:
SQt 5 å(22 2 Td ) ,
(2)
where Td is the average temperature (corrected for
temperature increase caused by drought). The summation of SQt is done for the period of highest sensitivity of
the rice panicle to low temperatures (0.75 # DVS # 1.2).
The relation between the percentage sterility caused by
cold (Sc, SF1) and the sum of the cooling degree-day is
Sc 5 1 2 (4:6 1 0:054 3 SQ1:56
)/100.
t
(3)
Sh 5 1/f1 1 exp[0:853(Tm,a 2 36:6)]g,
(4)
2) MODEL PARAMETERIZATION
The model was parameterized for SD (Shoternboy-1,
85 days), MD (Allanga-3, 105 days), and LD (Mustakillik,
125 days) varieties starting with the standard crop parameters for cultivar IR72 and following the procedures
set out by Bouman et al. (2001). The data from two
seeding dates (from experiment II) were used for model
parameterization.
3) MODEL EVALUATION
Following model parameterization, the data for
phenology, biomass partitioning, and yield from experiments I and III (seven seeding dates) were used
for evaluating model performance. Following the
procedures set out by Bouman and van Laar (2006), a
combination of graphical presentation and various
statistical measures was used to evaluate the performance of ORYZA2000. The graphs of the simulated
and measured grain yield, biomass, green leaf dry
weight, dead leaf dry weight, and phenological stages
were compared. For the same variables, we computed
the slope a, intercept b, and coefficient of determination R2 of the linear regression between measured X and simulated Y values. The model was
also evaluated using the Student’s t test of means assuming unequal variance P(t*). The variation in
measured data is represented by mean standard deviation. The absolute root-mean-square errors (RMSEa)
and normalized root-mean-square errors (RMSEn) were
calculated as
1
RMSEa 5 å(Yi 2 Xi )2
n
0:5
(5)
and
RMSEn 5 100 3
h
i0:5
(1/n)å(Yi 2 Xi )2
åXi /n
,
(6)
SEPTEMBER 2013
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DEVKOTA ET AL.
TABLE 2. Parameters and values used for the parameterization the rice growth model ORYZA2000 for three Uzbek rice varieties.
Parameters
Shoternboy-1
Allanga-3
Mustakillik
0.002 106
0.000 758
0.000 883
0.002 528
0.30
0.001 101
0.000 758
0.000 735
0.002 636
0.289
0.000 801
0.000 758
0.000 639
0.002 860
0.20
21
Phenological development (8C day )
Development rate in juvenile phase
Development rate in photoperiod-sensitive phase
Development rate in panicle development phase
Development rate in reproductive phase
Fraction of carbohydrate allocated to the stems
where Yi and Xi are simulated and measured values,
respectively; Xi is the mean of all measured values; and n
is the number of measurements.
It is assumed that the model reproduces experimental
data best when a is close to 0, b is close to 1, R2 is close to
1, P(t*) is larger than 0.05, RMSEa is similar to the
standard errors of measured values, and RMSEn is
similar to the coefficient of variation of measured values.
4) CLIMATE CHANGE SCENARIO ANALYSIS
Historical data on rainfall, minimum and maximum
temperature, solar radiation, relative humidity, and vapor pressure (as required by ORYZA2000) were collected for a 29-yr period (1970–99) from the Urgench
airport (3 km from the experimental site). The projected
changes in surface air temperature under the SRES B1
and A1F1 scenarios for central Asia (Table 3) for 2040–
69 were collected from the IPCC Fourth Assessment
Report (Parry et al. 2007). The projected increase in
temperature was added to the daily minimum and
maximum temperatures, and two climate change scenarios were generated. The ambient CO2 concentration
of 340 ppm in historical data, 540 ppm in the B1 scenario,
and 960 ppm in the A1F1 scenario as projected by Parry
et al. (2007) were used in ORYZA2000 for scenario
analysis.
In the climate change simulations, ORYZA2000 was
used to simulate the impact of climate change on phenological development (days to flowering and physiological
maturity), grain yield, spikelet sterility factor due to low
temperature (Sc, SF1), and spikelet fertility factor due to
high temperature (Sh, SF2) in SD, MD, and LD rice varieties at eight emergence dates from early May to midJuly (6 May, 16 May, 26 May, 5 June, 15 June, 25 June,
5 July, and 15 July) over 29 years under current historical
weather data and for 2040–69 under the SRES B1 and
A1F1 scenarios. As the model is not capable of predicting
emergence dates, the simulation treatments were planned
with emergence dates instead of seeding dates.
3. Results
a. Parameterization and validation of ORYZA2000
The details seeding date, days to emergence, panicle
initiation, flowering, and physiological maturity were
recorded (Table 4). The goodness-of-fit parameters
(Tables 5, 6) show that the observed and simulated
phenological stages of all rice varieties at seeding
TABLE 3. Projected changes in surface air temperature (8C) for central Asia under SRES B1 (lowest future emission trajectory) and
SRES A1FI (highest future emission trajectory) scenarios for 2040–69 with respect to the baseline period 1971–2000 (derived from Parry
et al. 2007). Average day and night temperatures are averages from data measured in 30-min intervals from 2008 to 2010 at the experimental site. Here, ‘‘temp’’ indicates temperature.
Historical data
Month
Max temp
Min temp
Avg temp
Avg day temp
Avg night temp
SRES B1
scenario (8C)
SRES A1F1
scenario (8C)
January
February
March
April
May
June
July
August
September
October
November
December
1.5
4.8
11.9
22.0
28.8
34.2
35.7
33.4
27.8
19.7
11.1
4.0
26.7
25.0
0.8
9.1
14.7
19.4
21.2
18.4
12.1
5.4
0.3
23.9
22.6
20.1
6.4
15.6
21.8
26.8
28.5
25.9
19.9
12.5
5.7
0.0
20.2
4.7
11.6
13.1
24.7
30.4
31.4
28.5
22.0
15.5
8.7
0.9
22.4
2.3
8.6
10.1
19.7
24.4
25.5
22.6
15.9
9.6
4.9
21.4
2.60
2.60
2.58
2.58
2.58
3.12
3.12
3.12
2.74
2.74
2.74
2.60
3.93
3.93
3.71
3.71
3.71
4.42
4.42
4.42
3.96
3.96
3.96
3.93
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TABLE 4. Phenological development of three Uzbek rice varieties under various seeding dates and varietal evaluation experiments at the
Khorezm region of Uzbekistan during 2008–09. DAS stands for days after sowing.
Expt and variety
Sowing
date
Emergence
(DAS)
Panicle
initiation (DAS)
Flowering
(DAS)
Physiological
maturity (DAS)
Harvest
(DAS)
Expt I
Shoternboy-1
Allanga-3
Mustakillik
16 Jun
16 Jun
16 Jun
3
3
3
35
47
56
61
78
94
89
116
Not matured
94
124
Not harvested
Expt II
Shoternboy-1
Allanga-3
Mustakillik
28 May
28 May
28 May
4
4
4
39
50
59
64
79
94
89
124
139
94
133
147
19 Jun
19 Jun
19 Jun
3
3
3
36
47
56
60
80
97
91
118
Not matured
96
125
Not harvested
Shoternboy-1
Allanga-3
Mustakillik
Expt III
Shoternboy-1
Allanga-3
Mustakillik
5 May
5 May
5 May
13
13
13
44
56
72
73
88
100
97
118
130
102
125
138
Shoternboy-1
Allanga-3
Mustakillik
18 May
18 May
18 May
10
10
10
41
54
63
69
86
97
95
117
127
100
123
135
Shoternboy-1
Allanga-3
Mustakillik
1 Jun
1 Jun
1 Jun
5
5
5
39
51
38
64
81
95
90
116
126
95
124
134
Shoternboy-1
Allanga-3
Mustakillik
13 Jun
13 Jun
13 Jun
6
6
6
42
57
63
74
92
93
99
121
145
104
128
150
Shoternboy-1
Allanga-3
Mustakillik
6 Jul
6 Jul
6 Jul
8
8
8
41
50
58
76
85
119
105
Not matured
Not matured
111
Not harvested
Not harvested
Shoternboy-1
Allanga-3
Mustakillik
15 Jul
15 Jul
15 Jul
6
6
6
38
52
65
69
104
Not flowered
Not matured
Not matured
Not matured
Not harvested
Not harvested
Not harvested
dates and years matched well. Furthermore, the observed and simulated dates for panicle initiation,
flowering, and physiological maturity stages did not
differ by more than 4 days at all seeding dates and in
all rice varieties. Phenological stages were not affected under different seeding dates in the SD variety, while the MD and LD varieties did not reach
flowering and physiological maturity stages when
seeded in July.
The dynamics in biomass of green leaves, stems, dead
leaves, grain, and LAI (Figs. 1, 2) and periodic and
final grain yield and total aboveground biomass
(Fig. 3) were simulated quite well throughout the
growing season. The simulated LAI generally exceeded
the measured LAI in all varieties.
b. Impact of climate change on rice phenology, grain
yield and spikelet sterility
1) RICE PHENOLOGY
Days to flowering varied among varieties and emergence dates in both historical weather data and climate
change scenarios (Fig. 4). With historical weather data,
the simulated days to flowering at the farmers’ current
seeding date in the region (5–15 June emergence) were
59, 76, and 91 days after emergence in the SD, MD and
LD varieties, respectively. In the climate change scenarios, the predicted flowering dates for 5–15 June
emergence showed that flowering could be delayed by 4
days under the B1 and by 8 days under the A1F1 scenario relative to the historical data.
SEPTEMBER 2013
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DEVKOTA ET AL.
TABLE 5. Parameterization results for ORYZA2000 simulations of crop growth variables over the entire growing season combined over
two seeding dates (28 May and 19 Jun) and three rice varieties for 2009 (data from experiment II). Abbreviations: N, number of data pairs;
Xmean, mean of measured values; Xsd, standard deviations of measured values; Ymean, mean of simulated values; Ysd, standard deviations
of simulated values; SE, standard error of measured variables.
Ysd
P(t*)
a
b
R2
6400
6118
0.89
215.5
0.97
0.98
798
12
1104
2706
3814
2915
0.80
2529
1.0
0.96
588
14
781
3045
2694
2898
2643
0.82
255
0.97
0.97
424
14
476
32
1435
1268
1434
1255
0.98
41.3
0.97
0.96
246
17
224
16
1068
1205
968
1075
0.80
23.9
0.88
0.98
221
21
301
1.07
0.81
1.06
1.02
0.98
0.96
0.98
0.97
Crop variable
N
Xmean
Xsd
Ymean
Total crop biomass
(kg ha21)
Biomass of panicles
(kg ha21)
Biomass of stems
(kg ha21)
Biomass of green
leaves (kg ha21)
Biomass of dead
leaves (kg ha21)
LAI
Panicle initiation (days)
Flowering (days)
Physiological maturity
(days)
32
6599
6244
12
4096
32
32
9
9
7
2.4
44
79
106
2
11
14
15
2.6
45
77
104
2.1
9
15
16
In the SD variety, flowering was delayed under climate change scenarios relative to the historical data for
all eight emergence dates. However, in the MD and LD
varieties, flowering could be delayed by 1.5 days per 18C
increase in temperature with early emergence dates,
while under late (July) emergence conditions, flowering
0.64
0.87
0.79
0.79
0.06
8.9
7.3
5.03
RMSEa
0.43
2.73
2.60
3.38
RMSEn (%)
18
6
3
3
SE
0.34
3.7
4.7
5.7
was later in historical data than in the climate change
scenarios (Fig. 4).
Under both current and climate change scenarios at
all emergence dates, the SD variety reached the flowering stage (Table 7). However, the MD variety
emerging on 15 July did not flower in 37% of the years in
TABLE 6. Evaluation of ORYZA2000 simulations from data from experiment I (biomass and grain yield combined over eight replications and three varieties) and data from experiment III (combined over six seeding dates and three rice varieties over the entire growing
season in 2009).
Crop variable
Expt I
Total crop biomass
(kg ha21)
Biomass of panicles
(kg ha21)
Biomass of stems
(kg ha21)
Expt III
Total crop biomass
(kg ha21)
Biomass of panicles
(kg ha21)
Biomass of stems
(kg ha21)
Biomass of green
leaves (kg ha21)
Biomass of dead
leaves (kg ha21)
LAI
Panicle initiation
(days)
Flowering (days)
Physiological
maturity (days)
Ysd
P(t*)
a
b
R2
9215
1412
0.29
1265
0.90
0.87
637
7
300
960
3771
1005
0.28
969
0.88
0.71
614
18
196
5359
1313
5444
1410
0.83
630
0.89
0.70
772
14
268
75
7959
7010
7255
6186
0.66
630
0.83
0.89
2452
31
809
37
3764
2248
3902
2715
0.98
2200
1.14
0.95
696
18
380
75
3298
2602
3230
2596
0.80
211.5
0.98
0.97
450
14
300
75
1495
1138
1497
1164
0.84
211.9
0.99
0.94
269
18
131
41
1224
1120
1197
1117
0.89
1.20
0.98
0.96
211
17
175
2.3
10
0.31
0.62
0.06
4.4
1.16
0.91
0.95
0.94
0.68
2.13
27
5
0.22
2.1
14
17
0.76
0.65
0.98
0.97
0.97
0.97
2.29
2.53
3
6
3.2
4.4
N
Xmean
24
8823
1469
24
3464
24
75
21
19
15
2.5
46
78
110
Xsd
1.9
9
15
17
Ymean
2.9
44
77
107
22.7
2.3
RMSEa
RMSEn (%)
SE
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VOLUME 52
FIG. 1. (left) Measured and simulated biomass (kg ha21) of total aboveground biomass (AGB), stem, green leaves,
dead leaves, grain and (right) LAI of (a) SD (Shoternboy-1), (b) MD (Allanga-3), and (c) LD (Mustakillik) rice
varieties seeded on 28 May 2009 and grown under potential production system in Cotton Research Institute, Urgench, Uzbekistan (data from experiment II). Lines are simulated values, and dots are observed values.
the historical data and in 7% of the years under the B1
scenario. Similarly, the flowering date of the LD variety
was affected when emergence was after 5 July. Physiological maturity was also affected by the emergence date
in all varieties (Table 7). The SD variety emerging on
5 July did not physiologically mature in 19% of the years
in the historical data and in 7% of the years under the B1
scenario, while it was not affected under the A1F1 scenario. Likewise, the MD variety at 15 July emergence
and the LD variety at 5 July and at 15 July emergence
did not reach maturity in all years in any of the scenarios.
2) GRAIN YIELD
With the historical weather data, the simulated grain
yield was highest at 25 June emergence in the SD variety
(4.9 t ha21), at 5 June in the MD variety (7.1 t ha21), and at
26 May emergence in the LD variety (6.8 t ha21). For these
emergence dates, relative to historical data, grain yield
of the SD variety was increased by 9% in the B1 and by
27% in the A1F1 scenario. In the MD variety, grain yield
was not different between B1 and historical data, while
it was higher by 14% under A1F1. The simulated grain
SEPTEMBER 2013
DEVKOTA ET AL.
2041
FIG. 2. As in Fig. 1, but for (a) SD (Shoternboy-1), (b) MD (Allanga-3), and (c) LD (Mustakillik) rice varieties seeded
on 5 May 2009 at Urgench State University, Urgench, Uzbekistan (data from experiment III).
yields of the LD variety were 3% higher in the B1 and 16%
higher in the A1F1 scenario relative to historical data.
Overall, relative to historical data, rice yield could be
increased by 3% (185 kg ha21) under the B1 scenario and
by 18% (1122 kg ha21) under the A1F1 scenario, which
corresponds to an increase of about 187 kg 8C21 or
3% 8C21 in grain yield in 2040–69 relative to 1970–99
(Fig. 5). Under both climate change scenarios, grain yield
was significantly higher at 10–20 days after emergence
than at the abovementioned highest-yielding emergence
dates in historical data, that is, highest yields of SD rice at
5–15 July emergence, MD rice at 15–25 June emergence,
and LD rice at 5–15 June emergence.
Under earlier emergence than the highest-yielding
emergence date in the historical data, grain yield was
reduced by 4% under B1 and by 10% under A1F1 in SD
rice, by 14% under B1 and 9% under A1F1 in MD rice,
and by 6% under B1 and 5% under A1F1 scenario in LD
rice. Despite higher grain yield under later emergence
than at the highest-yielding emergence date in the historical data, rice plants in most years could not reach
maturity (not shown in Fig. 5). Under early emergence
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JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY
VOLUME 52
FIG. 3. Simulated vs measured values of (a) periodic and final grain yield and (b) total aboveground biomass
(kg ha21) of SD, MD, and LD rice varieties from experiments I and III. Solid lines are 1:1 relationship. Lines are
simulated values, and symbols are observed values.
dates in the climate change scenarios, grain yield was
more variable in the SD variety, while the LD variety
had consistently higher yields followed by the MD and
SD varieties.
3) SPIKELET STERILITY
In the historical weather data under best emergence
dates, that is, 15–25 June, the average spikelet sterility
for the MD rice variety was 10% and 5% due to high and
low temperatures, respectively (Fig. 6). For this emergence period, spikelet sterility due to high temperature
was higher under climate change scenarios (30% in B1
and 35% in A1F1) than in the historical data, while
spikelet sterility due to low temperature was at par in the
historical data and the climate change scenarios. The
spikelet fertility factor due to high temperature at
early emergence (compared to above-mentioned best
emergence dates) was significantly low under the climate change scenarios, where it was lowest under
A1F1 followed by B1. In contrast, spikelet sterility due
to low temperature at later emergence than the abovementioned best emergence date was highest in historical
data followed by the B1 and A1F1 scenarios. The same
trend was also observed in the SD and LD varieties (data
not shown). The grain filling process and spikelet fertility
due to high temperature on one of the early emergence
dates (16 May; yearday 136) in three rice varieties is shown
in Fig. 7. In all rice varieties, the spikelet fertility factor was
lowest in A1F1 followed by B1 and historical data.
4. Discussion
In the current version of ORYZA2000, v2.13,
development rates, partitioning factors, relative leaf
growth rate, specific leaf area, leaf death rate, and
fraction of stem reserve coefficients are genotype 3
environment 3 management (G 3 E 3 M) parameters,
and therefore, the model needs to be calibrated against
treatments for accurate simulation (Bouman et al.
2001). Also, the model does not produce yield components such as panicle density, which limits its applicability to diagnose the causes of different treatment
responses. Therefore, model improvements that incorporate development rates and other coefficients
into genetic parameters would be highly desirable
to enable application of the calibrated model across
a wider range of environmental and management
conditions.
The modeled biomass, leaf area index, and phenological development matched well with the observed
values (Figs. 1–3). The slightly lower measured LAI
than the simulated values could be due to a lower leaf
production (5–8 leaves) (Devkota 2011). In general,
rice varieties have 6–14 green leaves during flowering
(De Datta 1981). The results of the statistical evaluation of model parameterization (Table 5) and validation (Table 6) are comparable in terms of accuracy with
previous studies (Bouman and van Laar 2006; Belder
et al. 2007), where ORYZA2000 was evaluated in potential production and water- and nitrogen-limited
conditions, mostly in the humid, tropic region of Asia.
Furthermore, the evaluation results of this study are
comparable with those reported for semiarid regions
by Amiri and Rezaei (2010). This suggests that the
model is able to simulate phenological development
and grain yield of rice accurately for arid climate conditions in central Asia. However, under the climate
change scenarios, the temperature during early May
(before 20 May) was higher than the threshold temperature required for emergence (208C), but as the current
version of ORYZA2000 starts simulation only after
emergence (Bouman et al. 2001), the required emergence days under climate change conditions could not be
simulated.
SEPTEMBER 2013
DEVKOTA ET AL.
FIG. 4. Simulated days to flowering of (a) SD, (b) MD, and (c) LD rice varieties seeded in 10-day intervals in
historical weather data and two climate change scenarios (B1 and A1F1) in 2040–69. Dotted lines inside the boxes
indicate means, and solid lines indicate medians. DAE stands for days after emergence.
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TABLE 7. Number of years in which simulated rice crops did not reach flowering and physiological maturity at different emergence dates
under historical weather data and two climate change scenarios. The NA stands for not affected; numbers in parentheses indicate percentage of the year.
Variety
Flowering
Short maturity (SD)
Medium maturity (MD)
Long maturity (LD)
Physiological maturity
Short maturity (SD)
Medium maturity (MD)
Long maturity (LD)
Emergence date
Historical weather data
SRES B1
SRES A1F1
All
15 Jul
5 Jul
15 Jul
NA
11 (37)
17 (57)
30 (100)
NA
2 (7)
6 (20)
20 (67)
NA
NA
3 (10)
16 (53)
5 Jul
15 Jul
15 Jun
25 Jun
5 Jul
15 Jul
5 Jun
15 Jun
25 Jun
5 Jul
15 Jul
6 (19)
26 (86)
3 (10)
18 (60)
30 (100)
30 (100)
4 (13)
24 (80)
30 (100)
30 (100)
30 (100)
2 (7)
10 (33)
NA
9 (30)
24 (80)
30 (100)
2 (7)
13 (43)
25 (83)
30 (100)
30 (100)
NA
7 (23)
NA
7 (23)
19 (63)
30 (100)
NA
10 (33)
21 (69)
30 (100)
30 (100)
The projected average temperature of the rice seasons
in 2040–69 would be raised by 2.98C under the B1 and
4.18C under the A1F1 scenarios in central Asia (Table 3).
This would potentially extend the length of the ricegrowing season by around 1 month. The historical data
for 1970–2009 show that, except for June to 10 August,
where the daily maximum temperature exceeds 358C in
70% of the days (50 days out of 70), the daily maximum
temperature remains lower than 358C in other days of
a year. As the study region has an arid climate, minimum
and night temperatures, which generally cause spikelet
sterility and yield reduction in rice (Peng et al. 2004), are
generally low (Table 3). Thus, the projected future climatic conditions (increased temperatures and CO2)
could provide greater opportunities for rice cultivation
and yield increases in the region.
The long duration of seeding to emergence (10–13
days; Table 4) before 20 May seeding could be related to
temperatures that were lower than required for emergence. In the 29-yr average (1970–99), the minimum
and average temperatures before 20 May were 148 and
208C, respectively (Table 3), and were thus lower than
the critical threshold temperatures required for rice
emergence (Yoshida 1981). Rice requires an average
temperature of more than 208C for emergence (Basnayake
et al. 2003).
Previous modeling studies have generally shown that
phenological development rate of rice would be accelerated and that the growing period would be shortened
in the future as a result of climatic warming (Chen et al.
2005; Karlsen et al. 2009). However, in agreement with
recent findings (Zhang and Tao 2013), our results reveal
increased growing duration and delayed flowering and
maturity under both B1 and A1F1 scenarios compared
to the historical data. The delayed flowering under the
climate change scenarios (Fig. 4) could be associated
with higher temperatures. For normal heading, rice requires a daily mean temperature of 218–308C (Krishnan
et al. 2011). Under the B1 and A1F1 scenarios, the
predicted mean daily temperature in June, July, and
August could be at or higher than the threshold level
(Table 3). The base, optimum, and maximum temperatures for rice are 88, 308, and 428C, respectively
(Gao et al. 1992). The development rate of rice increases
linearly above the base temperature to the optimum
temperature. Beyond the optimum temperature, the
development rate decreases linearly until a maximum
temperature is reached (Kiniry et al. 1991). Below the
base temperature or above the maximum temperature,
the development rate is zero. Furthermore, flowering is
longer at a mean temperature of 338C in comparison to
298C (Matthews et al. 1995). The delayed physiological
maturity of rice (Table 7) under late seeding conditions
with historical data could be related to low temperature. Generally, low temperature delays heading and
physiological maturity (Krishnan et al. 2011). For
physiological maturity, rice requires a minimum temperature of 128–188C and an optimum temperature of
308C (Yoshida 1981). Because of the extreme aridity
of the climate, even under the climate change scenarios
(increased temperature), the minimum and average
temperatures required for maturity during rice maturing
months (September, and October) are lower than the
threshold temperature for proper maturity (Table 3).
SEPTEMBER 2013
DEVKOTA ET AL.
FIG. 5. As in Fig. 4, but for simulated grain yields (kg ha21). Data are shown only for those years when rice physiologically
matured.
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VOLUME 52
FIG. 6. Simulated spikelet fertility factor due to (a) high temperature and (b) low temperature in historical data and under two climate
change scenarios for different emergence dates.
With the best seeding dates with historical data, unlike
many earlier findings (Baker and Allen 1993; Matthews
et al. 1995; Peng et al. 2004; Sheehy et al. 2006), our
findings show no yield reduction in rice under climate
change scenarios in central Asia (0%–9% and 14%–27%
yield increase under B1 and A1F1 scenarios, respectively). However, this is in agreement with observations
in India (Krishnan et al. 2007) and in the United States
(Baker 2004). To date, rice cultivation is mostly concentrated in tropical and subtropical regions. In such environments, temperatures are already above the optimum
for rice growth (288–228C), and high temperature is already one of the major environmental stresses limiting
rice productivity (Krishnan et al. 2011). Increasing CO2
SEPTEMBER 2013
DEVKOTA ET AL.
2047
FIG. 7. Simulated grain yield (kg ha21) and spikelet fertility factor due to high temperature in (a) SD, (b) MD,
and (c) LD rice varieties that emerged on 16 May (yearday 136) in historical data and two climate change
scenarios.
may influence rice yield positively by increasing the
amount of carbon available for photosynthesis and
negatively by increasing the air temperature due to the
greenhouse effect (Krishnan et al. 2011). However, in
contrast to other earlier findings, higher simulated
yields under climate change scenarios in our study indicate that an increase in photosynthesis due to enhanced
CO2 could surpass the negative effect of increased temperature on yield reduction in the higher latitudes zones
where the ambient temperature is at a low level (Table 3).
The critical air temperature for spikelet sterility could
be reduced by 18C at elevated CO2 because of low
transpiration cooling driven by stomata closure (Matsui
et al. 1997). Further, rice yields in the existing cropping
areas could be zero if climate predications are correct
(Matsui et al. 2001). As central Asia has arid climatic
conditions with extensive irrigation facilities, the higher
predicted rice yield under climate change conditions
could be due to optimal temperatures and ample solar
radiation throughout the growing season and sufficient
irrigation water supply. However, the projected climate
change could greatly affect the supply of water for irrigation in central Asia (Parry et al. 2007; Christmann
et al. 2009). The predicted higher temperatures due to
climate change will reduce yields under rain-fed conditions but may not have a strong effect under irrigation
conditions (Krishnan et al. 2011). Thus, the predicted
increase in grain yield of rice under climate change scenarios could depend on the future water supply situation
in the region.
With early rice seeding, the lower grain yield under
climate change scenarios was due to the fact that the
flowering and grain-filling stages coincided with the high
temperatures during the hot months (June and July;
Table 3), which resulted in a significant reduction in
spikelet fertility (Figs. 6, 7). Increased daily average
and maximum temperatures shorten the length of the
grain-filling phase (Bachelet et al. 1993) and reduce the
seed-setting rate in rice (Fu et al. 2008). Daily average
temperatures higher than 358C for more than 1 h during
flowering lead to a high spikelet sterility (Yoshida 1981;
Jagadish et al. 2007). Flowering in rice occurs over an
extended time period of 7–10 days (Yoshida 1981), and
the high temperatures during flowering could cause
significant spikelet sterility (Defeng and Shaokai 1995).
Furthermore, in ORYZA2000, respiration is modeled
explicitly as a function of temperature (Matthews et al.
1995; Bouman et al. 2001). Thus, yield reduction under
early emergence conditions could also be related to
higher respiration rates. Under late seeding conditions,
the decreased yields (Fig. 2) were due to slow development rate (Table 7), delayed and incomplete grain
filling, poor physiological maturity, and increased spikelet sterility due to cold temperatures (Fig. 6) as a result of
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JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY
the onset of the cold winter (Satake and Hayase 1970;
Farrell et al. 2001; Lee 2001).
Short-duration varieties had lower and more variable
grain yields than MD and LD varieties (Fig. 5). Thus,
our simulation results suggest that selection of appropriate seeding time and proper rice varieties are crucial
for adaptation to climate change. In the historical data,
the lowest grain yield variability and highest yield at 25
June emergence in SD (Fig. 5), at 5 June emergence in
MD, and at 26 May emergence in LD varieties compared to the other emergence dates suggest that these
are the best emergence dates for a consistently higher
yield. However, under both climate change scenarios,
consistently higher grain yield (Fig. 5) and phenological
development (Table 7, Fig. 4) at 5 July emergence in the
SD, 15–25 June in the MD, and 5–15 June in the LD rice
varieties indicates that the effect of increased temperature can be minimized through seeding 10 days later
than the dates in the historical data.
Similar findings on phenology and productivity with
respect to shifting the planting date of rice have been
reported (Zhang and Tao 2013). Similarly, comparatively higher grain yields of LD and MD varieties than of
SD varieties under the climate change scenarios (especially with early emergence conditions) suggests that the
adoption of LD and MD varieties could be an alternative adaptation strategy under climate change scenarios.
The use of LD varieties has also been suggested by
Matthews et al. (1997) for Southeast Asian countries.
Besides increased rice yield, climate change may have
other beneficial effects in central Asia. Increased temperatures in the region would increase the number of
frost-free days. In the historical weather data, the frostfree period is approximately from April to September,
while under climate change scenarios it may last 1 month
longer, that is, from mid-March to mid-October. This
could provide an opportunity for intensification of the
cropping system by allowing timely sowing of a second
crop.
5. Conclusions
ORYZA2000 is capable of simulating rice growth and
development under different seeding dates in arid, irrigated drylands of central Asia. Simulation studies with
the parameterized and evaluated model suggest that the
length of the rice-growing season and the crop grain
yield may increase under climate change scenarios because of more favorable temperature regimes. However,
selection and use of adapted rice varieties and optimal
seeding dates are crucial. The appropriate seeding dates
under the current climate conditions are 25 June for SD,
5 June for MD, and 26 May for LD varieties. Under
VOLUME 52
climate change scenarios, delaying rice seeding by 10
days is likely to result in comparatively higher rice
yields. As the SD varieties could be more negatively
affected by climate change, breeding programs should
focus on developing heat- and cold-tolerant MD and LD
rice varieties for the irrigated lowlands of central Asia.
Acknowledgments. This study was funded by the
German Ministry for Education and Research (BMBF;
project number 0339970A). Field work was conducted in
the ZEF/UNESCO project ‘‘Economic and Ecological
Restructuring of Land and Water Use in the Khorezm
Region (Uzbekistan): A Pilot Project in Development
Research.’’ Further comments and suggestions from two
anonymous reviewers substantially improved the quality
of this manuscript.
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