Simulating the Impact of Climate Change on Rice Phenology and GrainYield 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

positive physiological effects of increased CO2 are ac-

counted 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 al-

ready above the optimum for crop growth (daytime

maximum 288C and nighttimeminimum 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

1Current 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: k.devkota@cgiar.org

SEPTEMBER 2013 DEVKOTA ET AL . 2033

DOI: 10.1175/JAMC-D-12-0182.1

� 2013 American Meteorological Society

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 tem-

perature 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 (380ppm),

but increases in CO2 enrichment up to 700ppm 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 var-

iations in future rice production due to climate change;

the greatest decline in crop yields will likely occur be-

tween 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 tem-

perature, 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

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 ste-

rility, and grain yield of indica and japonica rice eco-

types 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) as-

sessing 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 (4183201200N, 6084004400E) located in northwest-

ern 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 100mm. The soil at

the experimental site is an irrigated alluvial meadow

(Russian classification), that is, arenosol, gleyic, calcaric,

sodic [Food and Agriculture Organization (FAO) clas-

sification], sandy loam to loamy sand with high soil

salinity [2.7 dSm21, electrical conductivity of a satu-

rated soil extract (ECe) 1:1 in 0–15-cm soil depth],

shallow (0.5–2m) and saline (2–4dSm21) groundwater

table, and low soil organicmatter (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

2034 JOURNAL OF APPL IED METEOROLOGY AND CL IMATOLOGY VOLUME 52

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 repli-

cations 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 28May

and 19 June in an unreplicated 30-m2 plot. For each va-

riety, final yield and biomass were measured on three

subplots of 6m2. In the same year (2009), another un-

replicated experiment (experiment III) was conducted at

Urgench State University, Urgench, Uzbekistan (5 km

from the Cotton Research Institute), to evaluate the ef-

fect of six seeding dates starting on 5 May–15 July at 15–

20-day intervals. The plot size in this experiment was

20m2, 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 experi-

ments, 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 re-

corded 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 cm3 50 cm area

at 15–20-day intervals for determination of biomass pro-

duction 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; m2m22).

Stem, green leaves, dead leaves, and panicles were sep-

arated and oven-dried separately for 72h 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 phe-

nological development is simulated based on daily ambi-

ent 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 (DVS5 0.4), (ii) photoperiod-

sensitive phase from DVS 5 0.4 until panicle initiation

(DVS5 0.65), (iii) panicle formation phase from panicle

initiation to 50% flowering (DVS 5 1.0), and (iv) grain-

filling phase from flowering to physiological maturity

TABLE 1. Initial physical and chemical soil properties at the experimental site in Khorezm region, Uzbekistan.

Depth

(cm)

Texture (%)Bulk

density (g cm23)

Soil

pH

ECe

(dSm21)

Soil organic

carbon (%)

Total

N (%)

Available

phosphorus

(mgkg21)

Exchangeable

potassium

(mgkg21)Sand Silt Clay

0–10 23 58 19 1.35 5.57 2.7 0.36 0.05 27.9 98.5

10–20 33 49 18 1.41 5.56 2.1 0.30 0.05 25.9 95.0

20–30 26 62 12 1.42 5.57 2.0 0.26 0.04 21.9 89.3

30–60 29 63 8 1.52 5.69 1.8 0.23 0.03 19.2 81.4

60–90 49 43 8 1.57 5.78 3.0 0.19 0.03 17.6 76.8

SEPTEMBER 2013 DEVKOTA ET AL . 2035

(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 [«;

kgCO2ha21 h21 (Jm22 s21)21]. The calculation of this

value uses the formula by Jansen (1990):

«5 «340

�12 exp(20:003 053CCO22 0:222)

12 exp(20:003 053 3402 0:222)

�, (1)

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 concen-

tration in the actual simulation environment.

Effect of temperature on grain formation and spikeletfertility.Rice grain yield is determined by carbohydrate pro-

duction (source size) during grain filling and the stor-

age 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 accu-

mulation from panicle initiation to 50% flowering

(G; kg dry matter ha21 day21) and spikelet formation

factor (Y; number per kilogram). The spikelet forma-

tion 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�(222Td) , (2)

where Td is the average temperature (corrected for

temperature increase caused by drought). The summa-

tion 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 12 (4:61 0:0543 SQ1:56t )/100. (3)

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):

Sh 5 1/f11 exp[0:853(Tm,a2 36:6)]g , (4)

where Tm,a is average daily maximum temperature over

the growing period (0.96 # DVS # 1.22) with elevated

and ambient CO2 concentrations.

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 pa-

rameters 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 ex-

periments 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 perfor-

mance 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 deter-

mination R2 of the linear regression between mea-

sured X and simulated Y values. The model was

also evaluated using the Student’s t test of means as-

suming unequal variance P(t*). The variation in

measured data is represented by mean standard de-

viation. The absolute root-mean-square errors (RMSEa)

and normalized root-mean-square errors (RMSEn) were

calculated as

RMSEa 5

�1

n�(Yi 2Xi)

2

�0:5(5)

and

RMSEn5 1003

h(1/n)�(Yi 2Xi)

2i0:5

�Xi/n, (6)

2036 JOURNAL OF APPL IED METEOROLOGY AND CL IMATOLOGY VOLUME 52

where Yi and Xi are simulated and measured values,

respectively;Xi is themean 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 ofmeasured values.

4) CLIMATE CHANGE SCENARIO ANALYSIS

Historical data on rainfall, minimum and maximum

temperature, solar radiation, relative humidity, and va-

por pressure (as required by ORYZA2000) were col-

lected 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 sce-

narios 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 pheno-

logical 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 va-

rieties at eight emergence dates from early May to mid-

July (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 2. Parameters and values used for the parameterization the rice growth model ORYZA2000 for three Uzbek rice varieties.

Parameters Shoternboy-1 Allanga-3 Mustakillik

Phenological development (8C day21)

Development rate in juvenile phase 0.002 106 0.001 101 0.000 801

Development rate in photoperiod-sensitive phase 0.000 758 0.000 758 0.000 758

Development rate in panicle development phase 0.000 883 0.000 735 0.000 639

Development rate in reproductive phase 0.002 528 0.002 636 0.002 860

Fraction of carbohydrate allocated to the stems 0.30 0.289 0.20

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 exper-

imental site. Here, ‘‘temp’’ indicates temperature.

Month

Historical data SRES B1

scenario (8C)SRES A1F1

scenario (8C)Max temp Min temp Avg temp Avg day temp Avg night temp

January 1.5 26.7 22.6 20.2 22.4 2.60 3.93

February 4.8 25.0 20.1 4.7 2.3 2.60 3.93

March 11.9 0.8 6.4 11.6 8.6 2.58 3.71

April 22.0 9.1 15.6 13.1 10.1 2.58 3.71

May 28.8 14.7 21.8 24.7 19.7 2.58 3.71

June 34.2 19.4 26.8 30.4 24.4 3.12 4.42

July 35.7 21.2 28.5 31.4 25.5 3.12 4.42

August 33.4 18.4 25.9 28.5 22.6 3.12 4.42

September 27.8 12.1 19.9 22.0 15.9 2.74 3.96

October 19.7 5.4 12.5 15.5 9.6 2.74 3.96

November 11.1 0.3 5.7 8.7 4.9 2.74 3.96

December 4.0 23.9 0.0 0.9 21.4 2.60 3.93

SEPTEMBER 2013 DEVKOTA ET AL . 2037

dates and years matched well. Furthermore, the ob-

served 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 af-

fected under different seeding dates in the SD vari-

ety, 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, grainyield and spikelet sterility

1) RICE PHENOLOGY

Days to flowering varied among varieties and emer-

gence 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 sce-

narios, 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 sce-

nario relative to the historical data.

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 16 Jun 3 35 61 89 94

Allanga-3 16 Jun 3 47 78 116 124

Mustakillik 16 Jun 3 56 94 Not matured Not harvested

Expt II

Shoternboy-1 28 May 4 39 64 89 94

Allanga-3 28 May 4 50 79 124 133

Mustakillik 28 May 4 59 94 139 147

Shoternboy-1 19 Jun 3 36 60 91 96

Allanga-3 19 Jun 3 47 80 118 125

Mustakillik 19 Jun 3 56 97 Not matured Not harvested

Expt III

Shoternboy-1 5 May 13 44 73 97 102

Allanga-3 5 May 13 56 88 118 125

Mustakillik 5 May 13 72 100 130 138

Shoternboy-1 18 May 10 41 69 95 100

Allanga-3 18 May 10 54 86 117 123

Mustakillik 18 May 10 63 97 127 135

Shoternboy-1 1 Jun 5 39 64 90 95

Allanga-3 1 Jun 5 51 81 116 124

Mustakillik 1 Jun 5 38 95 126 134

Shoternboy-1 13 Jun 6 42 74 99 104

Allanga-3 13 Jun 6 57 92 121 128

Mustakillik 13 Jun 6 63 93 145 150

Shoternboy-1 6 Jul 8 41 76 105 111

Allanga-3 6 Jul 8 50 85 Not matured Not harvested

Mustakillik 6 Jul 8 58 119 Not matured Not harvested

Shoternboy-1 15 Jul 6 38 69 Not matured Not harvested

Allanga-3 15 Jul 6 52 104 Not matured Not harvested

Mustakillik 15 Jul 6 65 Not flowered Not matured Not harvested

2038 JOURNAL OF APPL IED METEOROLOGY AND CL IMATOLOGY VOLUME 52

In the SD variety, flowering was delayed under cli-

mate 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 18Cincrease in temperature with early emergence dates,

while under late (July) emergence conditions, flowering

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 flow-

ering stage (Table 7). However, the MD variety

emerging on 15 July did not flower in 37% of the years in

TABLE 5. Parameterization results forORYZA2000 simulations of crop growth variables over the entire growing season combined over

two seeding dates (28May 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.

Crop variable N Xmean Xsd Ymean Ysd P(t*) a b R2 RMSEa RMSEn (%) SE

Total crop biomass

(kg ha21)

32 6599 6244 6400 6118 0.89 215.5 0.97 0.98 798 12 1104

Biomass of panicles

(kg ha21)

12 4096 2706 3814 2915 0.80 2529 1.0 0.96 588 14 781

Biomass of stems

(kg ha21)

32 3045 2694 2898 2643 0.82 255 0.97 0.97 424 14 476

Biomass of green

leaves (kgha21)

32 1435 1268 1434 1255 0.98 41.3 0.97 0.96 246 17 224

Biomass of dead

leaves (kgha21)

16 1068 1205 968 1075 0.80 23.9 0.88 0.98 221 21 301

LAI 32 2.4 2 2.6 2.1 0.64 0.06 1.07 0.98 0.43 18 0.34

Panicle initiation (days) 9 44 11 45 9 0.87 8.9 0.81 0.96 2.73 6 3.7

Flowering (days) 9 79 14 77 15 0.79 7.3 1.06 0.98 2.60 3 4.7

Physiological maturity

(days)

7 106 15 104 16 0.79 5.03 1.02 0.97 3.38 3 5.7

TABLE 6. Evaluation of ORYZA2000 simulations from data from experiment I (biomass and grain yield combined over eight repli-

cations 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 N Xmean Xsd Ymean Ysd P(t*) a b R2 RMSEa RMSEn (%) SE

Expt I

Total crop biomass

(kg ha21)

24 8823 1469 9215 1412 0.29 1265 0.90 0.87 637 7 300

Biomass of panicles

(kg ha21)

24 3464 960 3771 1005 0.28 969 0.88 0.71 614 18 196

Biomass of stems

(kg ha21)

24 5359 1313 5444 1410 0.83 630 0.89 0.70 772 14 268

Expt III

Total crop biomass

(kg ha21)

75 7959 7010 7255 6186 0.66 630 0.83 0.89 2452 31 809

Biomass of panicles

(kg ha21)

37 3764 2248 3902 2715 0.98 2200 1.14 0.95 696 18 380

Biomass of stems

(kg ha21)

75 3298 2602 3230 2596 0.80 211.5 0.98 0.97 450 14 300

Biomass of green

leaves (kgha21)

75 1495 1138 1497 1164 0.84 211.9 0.99 0.94 269 18 131

Biomass of dead

leaves (kgha21)

41 1224 1120 1197 1117 0.89 1.20 0.98 0.96 211 17 175

LAI 75 2.5 1.9 2.9 2.3 0.31 0.06 1.16 0.95 0.68 27 0.22

Panicle initiation

(days)

21 46 9 44 10 0.62 4.4 0.91 0.94 2.13 5 2.1

Flowering (days) 19 78 15 77 14 0.76 22.7 0.98 0.97 2.29 3 3.2

Physiological

maturity (days)

15 110 17 107 17 0.65 2.3 0.97 0.97 2.53 6 4.4

SEPTEMBER 2013 DEVKOTA ET AL . 2039

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. Physio-

logical 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 sce-

nario. Likewise, the MD variety at 15 July emergence

and the LD variety at 5 July and at 15 July emergence

did not reachmaturity 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 theMDvariety (7.1 t ha21), and at

26May emergence in theLDvariety (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

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, Ur-

gench, Uzbekistan (data from experiment II). Lines are simulated values, and dots are observed values.

2040 JOURNAL OF APPL IED METEOROLOGY AND CL IMATOLOGY VOLUME 52

yields of theLDvarietywere 3%higher in theB1 and 16%

higher in the A1F1 scenario relative to historical data.

Overall, relative to historical data, rice yield could be

increased by 3% (185kgha21) under the B1 scenario and

by 18% (1122kgha21) 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%underA1F1 scenario in LD

rice. Despite higher grain yield under later emergence

than at the highest-yielding emergence date in the his-

torical data, rice plants in most years could not reach

maturity (not shown in Fig. 5). Under early emergence

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).

SEPTEMBER 2013 DEVKOTA ET AL . 2041

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 theMD rice variety was 10%and 5%due to high and

low temperatures, respectively (Fig. 6). For this emer-

gence 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 cli-

mate change scenarios, where it was lowest under

A1F1 followed by B1. In contrast, spikelet sterility due

to low temperature at later emergence than the above-

mentioned 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 (16May; 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 3environment3management (G3E3M) 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 com-

ponents such as panicle density, which limits its appli-

cability to diagnose the causes of different treatment

responses. Therefore, model improvements that in-

corporate 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 pheno-

logical 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 evalua-

tion of model parameterization (Table 5) and valida-

tion (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 po-

tential 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 con-

ditions in central Asia. However, under the climate

change scenarios, the temperature during early May

(before 20 May) was higher than the threshold temper-

ature required for emergence (208C), but as the current

version of ORYZA2000 starts simulation only after

emergence (Bouman et al. 2001), the required emer-

gence days under climate change conditions could not be

simulated.

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.

2042 JOURNAL OF APPL IED METEOROLOGY AND CL IMATOLOGY VOLUME 52

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.

SEPTEMBER 2013 DEVKOTA ET AL . 2043

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 rice-

growing 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 cli-

matic 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 20May seeding could be related to

temperatures that were lower than required for emer-

gence. In the 29-yr average (1970–99), the minimum

and average temperatures before 20 May were 148 and208C, respectively (Table 3), and were thus lower than

the critical threshold temperatures required for rice

emergence (Yoshida 1981). Rice requires an average

temperature ofmore than 208C for emergence (Basnayake

et al. 2003).

Previous modeling studies have generally shown that

phenological development rate of rice would be accel-

erated 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 re-

quires 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 tem-

peratures 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 tempera-

ture. Generally, low temperature delays heading and

physiological maturity (Krishnan et al. 2011). For

physiological maturity, rice requires a minimum tem-

perature 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).

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 per-

centage of the year.

Variety Emergence date Historical weather data SRES B1 SRES A1F1

Flowering

Short maturity (SD) All NA NA NA

Medium maturity (MD) 15 Jul 11 (37) 2 (7) NA

Long maturity (LD) 5 Jul 17 (57) 6 (20) 3 (10)

15 Jul 30 (100) 20 (67) 16 (53)

Physiological maturity

Short maturity (SD) 5 Jul 6 (19) 2 (7) NA

15 Jul 26 (86) 10 (33) 7 (23)

Medium maturity (MD) 15 Jun 3 (10) NA NA

25 Jun 18 (60) 9 (30) 7 (23)

5 Jul 30 (100) 24 (80) 19 (63)

15 Jul 30 (100) 30 (100) 30 (100)

Long maturity (LD) 5 Jun 4 (13) 2 (7) NA

15 Jun 24 (80) 13 (43) 10 (33)

25 Jun 30 (100) 25 (83) 21 (69)

5 Jul 30 (100) 30 (100) 30 (100)

15 Jul 30 (100) 30 (100) 30 (100)

2044 JOURNAL OF APPL IED METEOROLOGY AND CL IMATOLOGY VOLUME 52

FIG. 5. As in Fig. 4, but for simulated grain yields (kg ha21). Data are shown only for those years when rice physiologically

matured.

SEPTEMBER 2013 DEVKOTA ET AL . 2045

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, respec-

tively). 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 concen-

trated in tropical and subtropical regions. In such envi-

ronments, temperatures are already above the optimum

for rice growth (288–228C), and high temperature is al-

ready one of the major environmental stresses limiting

rice productivity (Krishnan et al. 2011). Increasing CO2

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.

2046 JOURNAL OF APPL IED METEOROLOGY AND CL IMATOLOGY VOLUME 52

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 in-

dicate that an increase in photosynthesis due to enhanced

CO2 could surpass the negative effect of increased tem-

perature 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 irri-

gation 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 condi-

tions 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 sce-

narios 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 de-

velopment rate (Table 7), delayed and incomplete grain

filling, poor physiological maturity, and increased spike-

let sterility due to cold temperatures (Fig. 6) as a result of

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.

SEPTEMBER 2013 DEVKOTA ET AL . 2047

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 appro-

priate 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 com-

pared 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 theMD, and 5–15 June in the LD rice

varieties indicates that the effect of increased tempera-

ture 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, compara-

tively higher grain yields of LD andMDvarieties than of

SD varieties under the climate change scenarios (espe-

cially with early emergence conditions) suggests that the

adoption of LD and MD varieties could be an alterna-

tive 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 tem-

peratures in the region would increase the number of

frost-free days. In the historical weather data, the frost-

free period is approximately from April to September,

while under climate change scenarios it may last 1month

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, irri-

gated 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 be-

cause ofmore 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

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-tolerantMD 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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